diff --git a/.clang-tidy b/.clang-tidy
index 3078beacc..952c0cca8 100644
--- a/.clang-tidy
+++ b/.clang-tidy
@@ -12,6 +12,7 @@ Checks: >
     -readability-implicit-bool-conversion,
     -readability-magic-numbers,
     -readability-uppercase-literal-suffix,
+    -readability-simplify-boolean-expr,
     clang-analyzer-*,
     -clang-analyzer-security.insecureAPI.DeprecatedOrUnsafeBufferHandling,
     performance-*,
diff --git a/.devops/full-cuda.Dockerfile b/.devops/full-cuda.Dockerfile
index 77a9ddc14..059fd2695 100644
--- a/.devops/full-cuda.Dockerfile
+++ b/.devops/full-cuda.Dockerfile
@@ -12,7 +12,7 @@ FROM ${BASE_CUDA_DEV_CONTAINER} as build
 ARG CUDA_DOCKER_ARCH=all
 
 RUN apt-get update && \
-    apt-get install -y build-essential python3 python3-pip git
+    apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev
 
 COPY requirements.txt   requirements.txt
 COPY requirements       requirements
@@ -26,8 +26,10 @@ COPY . .
 
 # Set nvcc architecture
 ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
-# Enable cuBLAS
-ENV LLAMA_CUBLAS=1
+# Enable CUDA
+ENV LLAMA_CUDA=1
+# Enable cURL
+ENV LLAMA_CURL=1
 
 RUN make
 
diff --git a/.devops/full-rocm.Dockerfile b/.devops/full-rocm.Dockerfile
index 8b9633dc4..6ecf3bcc7 100644
--- a/.devops/full-rocm.Dockerfile
+++ b/.devops/full-rocm.Dockerfile
@@ -40,6 +40,11 @@ ENV LLAMA_HIPBLAS=1
 ENV CC=/opt/rocm/llvm/bin/clang
 ENV CXX=/opt/rocm/llvm/bin/clang++
 
+# Enable cURL
+ENV LLAMA_CURL=1
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev
+
 RUN make
 
 ENTRYPOINT ["/app/.devops/tools.sh"]
diff --git a/.devops/full.Dockerfile b/.devops/full.Dockerfile
index cef1297d3..432fb5dad 100644
--- a/.devops/full.Dockerfile
+++ b/.devops/full.Dockerfile
@@ -3,7 +3,7 @@ ARG UBUNTU_VERSION=22.04
 FROM ubuntu:$UBUNTU_VERSION as build
 
 RUN apt-get update && \
-    apt-get install -y build-essential python3 python3-pip git
+    apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev
 
 COPY requirements.txt   requirements.txt
 COPY requirements       requirements
@@ -15,6 +15,9 @@ WORKDIR /app
 
 COPY . .
 
+ENV LLAMA_CURL=1
+
+
 RUN make
 
 ENV LC_ALL=C.utf8
diff --git a/.devops/llama-cpp-clblast.srpm.spec b/.devops/llama-cpp-clblast.srpm.spec
index 076f29695..774f63ddd 100644
--- a/.devops/llama-cpp-clblast.srpm.spec
+++ b/.devops/llama-cpp-clblast.srpm.spec
@@ -1,5 +1,5 @@
 # SRPM for building from source and packaging an RPM for RPM-based distros.
-# https://fedoraproject.org/wiki/How_to_create_an_RPM_package
+# https://docs.fedoraproject.org/en-US/quick-docs/creating-rpm-packages
 # Built and maintained by John Boero - boeroboy@gmail.com
 # In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal
 
diff --git a/.devops/llama-cpp-cublas.srpm.spec b/.devops/llama-cpp-cuda.srpm.spec
similarity index 79%
rename from .devops/llama-cpp-cublas.srpm.spec
rename to .devops/llama-cpp-cuda.srpm.spec
index f847ebb1e..ba9cb7cbb 100644
--- a/.devops/llama-cpp-cublas.srpm.spec
+++ b/.devops/llama-cpp-cuda.srpm.spec
@@ -1,5 +1,5 @@
 # SRPM for building from source and packaging an RPM for RPM-based distros.
-# https://fedoraproject.org/wiki/How_to_create_an_RPM_package
+# https://docs.fedoraproject.org/en-US/quick-docs/creating-rpm-packages
 # Built and maintained by John Boero - boeroboy@gmail.com
 # In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal
 
@@ -12,7 +12,7 @@
 # 4. OpenCL/CLBLAST support simply requires the ICD loader and basic opencl libraries.
 #    It is up to the user to install the correct vendor-specific support.
 
-Name:           llama.cpp-cublas
+Name:           llama.cpp-cuda
 Version:        %( date "+%%Y%%m%%d" )
 Release:        1%{?dist}
 Summary:        CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL)
@@ -32,16 +32,16 @@ CPU inference for Meta's Lllama2 models using default options.
 %setup -n llama.cpp-master
 
 %build
-make -j LLAMA_CUBLAS=1
+make -j LLAMA_CUDA=1
 
 %install
 mkdir -p %{buildroot}%{_bindir}/
-cp -p main %{buildroot}%{_bindir}/llamacppcublas
-cp -p server %{buildroot}%{_bindir}/llamacppcublasserver
-cp -p simple %{buildroot}%{_bindir}/llamacppcublassimple
+cp -p main %{buildroot}%{_bindir}/llamacppcuda
+cp -p server %{buildroot}%{_bindir}/llamacppcudaserver
+cp -p simple %{buildroot}%{_bindir}/llamacppcudasimple
 
 mkdir -p %{buildroot}/usr/lib/systemd/system
-%{__cat} <<EOF  > %{buildroot}/usr/lib/systemd/system/llamacublas.service
+%{__cat} <<EOF  > %{buildroot}/usr/lib/systemd/system/llamacuda.service
 [Unit]
 Description=Llama.cpp server, CPU only (no GPU support in this build).
 After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target
@@ -49,7 +49,7 @@ After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.t
 [Service]
 Type=simple
 EnvironmentFile=/etc/sysconfig/llama
-ExecStart=/usr/bin/llamacppcublasserver $LLAMA_ARGS
+ExecStart=/usr/bin/llamacppcudaserver $LLAMA_ARGS
 ExecReload=/bin/kill -s HUP $MAINPID
 Restart=never
 
@@ -67,10 +67,10 @@ rm -rf %{buildroot}
 rm -rf %{_builddir}/*
 
 %files
-%{_bindir}/llamacppcublas
-%{_bindir}/llamacppcublasserver
-%{_bindir}/llamacppcublassimple
-/usr/lib/systemd/system/llamacublas.service
+%{_bindir}/llamacppcuda
+%{_bindir}/llamacppcudaserver
+%{_bindir}/llamacppcudasimple
+/usr/lib/systemd/system/llamacuda.service
 %config /etc/sysconfig/llama
 
 %pre
diff --git a/.devops/llama-cpp.srpm.spec b/.devops/llama-cpp.srpm.spec
index 446213d69..1d9e4f425 100644
--- a/.devops/llama-cpp.srpm.spec
+++ b/.devops/llama-cpp.srpm.spec
@@ -1,5 +1,5 @@
 # SRPM for building from source and packaging an RPM for RPM-based distros.
-# https://fedoraproject.org/wiki/How_to_create_an_RPM_package
+# https://docs.fedoraproject.org/en-US/quick-docs/creating-rpm-packages
 # Built and maintained by John Boero - boeroboy@gmail.com
 # In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal
 
diff --git a/.devops/main-cuda.Dockerfile b/.devops/main-cuda.Dockerfile
index 2b7faf7c1..b937a4829 100644
--- a/.devops/main-cuda.Dockerfile
+++ b/.devops/main-cuda.Dockerfile
@@ -20,8 +20,8 @@ COPY . .
 
 # Set nvcc architecture
 ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
-# Enable cuBLAS
-ENV LLAMA_CUBLAS=1
+# Enable CUDA
+ENV LLAMA_CUDA=1
 
 RUN make
 
diff --git a/.devops/main-intel.Dockerfile b/.devops/main-intel.Dockerfile
index 572e5d8ea..274b91b71 100644
--- a/.devops/main-intel.Dockerfile
+++ b/.devops/main-intel.Dockerfile
@@ -10,14 +10,12 @@ WORKDIR /app
 
 COPY . .
 
-RUN mkdir build && \
-    cd build && \
-    if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
+RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
         echo "LLAMA_SYCL_F16 is set" && \
         export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \
     fi && \
-    cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \
-    cmake --build . --config Release --target main
+    cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \
+    cmake --build build --config Release --target main
 
 FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime
 
diff --git a/.devops/main-vulkan.Dockerfile b/.devops/main-vulkan.Dockerfile
index bca460365..6c2b2ed5b 100644
--- a/.devops/main-vulkan.Dockerfile
+++ b/.devops/main-vulkan.Dockerfile
@@ -14,10 +14,8 @@ RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key
 # Build it
 WORKDIR /app
 COPY . .
-RUN mkdir build && \
-    cd build && \
-    cmake .. -DLLAMA_VULKAN=1 && \
-    cmake --build . --config Release --target main
+RUN cmake -B build -DLLAMA_VULKAN=1 && \
+    cmake --build build --config Release --target main
 
 # Clean up
 WORKDIR /
diff --git a/.devops/nix/docker.nix b/.devops/nix/docker.nix
new file mode 100644
index 000000000..d607b4575
--- /dev/null
+++ b/.devops/nix/docker.nix
@@ -0,0 +1,37 @@
+{
+  lib,
+  dockerTools,
+  buildEnv,
+  llama-cpp,
+  interactive ? true,
+  coreutils,
+}:
+
+# A tar that can be fed into `docker load`:
+#
+# $ nix build .#llamaPackages.docker
+# $ docker load < result
+
+# For details and variations cf.
+# - https://nixos.org/manual/nixpkgs/unstable/#ssec-pkgs-dockerTools-buildLayeredImage
+# - https://discourse.nixos.org/t/a-faster-dockertools-buildimage-prototype/16922
+# - https://nixery.dev/
+
+# Approximate (compressed) sizes, at the time of writing, are:
+#
+# .#llamaPackages.docker: 125M;
+# .#llamaPackagesCuda.docker: 537M;
+# .#legacyPackages.aarch64-linux.llamaPackagesXavier.docker: 415M.
+
+dockerTools.buildLayeredImage {
+  name = llama-cpp.pname;
+  tag = "latest";
+
+  contents =
+    [ llama-cpp ]
+    ++ lib.optionals interactive [
+      coreutils
+      dockerTools.binSh
+      dockerTools.caCertificates
+    ];
+}
diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix
index ad23f7dd7..2c0ae4e2a 100644
--- a/.devops/nix/package.nix
+++ b/.devops/nix/package.nix
@@ -1,15 +1,17 @@
 {
   lib,
+  glibc,
   config,
   stdenv,
   mkShell,
+  runCommand,
   cmake,
   ninja,
   pkg-config,
   git,
   python3,
   mpi,
-  openblas, # TODO: Use the generic `blas` so users could switch between alternative implementations
+  blas,
   cudaPackages,
   darwin,
   rocmPackages,
@@ -22,7 +24,7 @@
     useOpenCL
     useRocm
     useVulkan
-  ],
+  ] && blas.meta.available,
   useCuda ? config.cudaSupport,
   useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin && !useOpenCL,
   useMpi ? false, # Increases the runtime closure size by ~700M
@@ -30,6 +32,12 @@
   useRocm ? config.rocmSupport,
   useVulkan ? false,
   llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake
+
+  # It's necessary to consistently use backendStdenv when building with CUDA support,
+  # otherwise we get libstdc++ errors downstream.
+  effectiveStdenv ? if useCuda then cudaPackages.backendStdenv else stdenv,
+  enableStatic ? effectiveStdenv.hostPlatform.isStatic,
+  precompileMetalShaders ? false
 }@inputs:
 
 let
@@ -41,10 +49,7 @@ let
     versionOlder
     ;
 
-  # It's necessary to consistently use backendStdenv when building with CUDA support,
-  # otherwise we get libstdc++ errors downstream.
   stdenv = throw "Use effectiveStdenv instead";
-  effectiveStdenv = if useCuda then cudaPackages.backendStdenv else inputs.stdenv;
 
   suffices =
     lib.optionals useBlas [ "BLAS" ]
@@ -62,10 +67,15 @@ let
     strings.optionalString (suffices != [ ])
       ", accelerated with ${strings.concatStringsSep ", " suffices}";
 
+  executableSuffix = effectiveStdenv.hostPlatform.extensions.executable;
+
   # TODO: package the Python in this repository in a Nix-like way.
   # It'd be nice to migrate to buildPythonPackage, as well as ensure this repo
   # is PEP 517-compatible, and ensure the correct .dist-info is generated.
   # https://peps.python.org/pep-0517/
+  #
+  # TODO: Package up each Python script or service appropriately, by making
+  # them into "entrypoints"
   llama-python = python3.withPackages (
     ps: [
       ps.numpy
@@ -84,6 +94,11 @@ let
     ]
   );
 
+  xcrunHost = runCommand "xcrunHost" {} ''
+    mkdir -p $out/bin
+    ln -s /usr/bin/xcrun $out/bin
+  '';
+
   # apple_sdk is supposed to choose sane defaults, no need to handle isAarch64
   # separately
   darwinBuildInputs =
@@ -147,13 +162,18 @@ effectiveStdenv.mkDerivation (
     postPatch = ''
       substituteInPlace ./ggml-metal.m \
         --replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";"
-
-      # TODO: Package up each Python script or service appropriately.
-      # If we were to migrate to buildPythonPackage and prepare the `pyproject.toml`,
-      # we could make those *.py into setuptools' entrypoints
-      substituteInPlace ./*.py --replace "/usr/bin/env python" "${llama-python}/bin/python"
+      substituteInPlace ./ggml-metal.m \
+        --replace '[bundle pathForResource:@"default" ofType:@"metallib"];' "@\"$out/bin/default.metallib\";"
     '';
 
+    # With PR#6015 https://github.com/ggerganov/llama.cpp/pull/6015,
+    # `default.metallib` may be compiled with Metal compiler from XCode
+    # and we need to escape sandbox on MacOS to access Metal compiler.
+    # `xcrun` is used find the path of the Metal compiler, which is varible
+    # and not on $PATH
+    # see https://github.com/ggerganov/llama.cpp/pull/6118 for discussion
+    __noChroot = effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders;
+
     nativeBuildInputs =
       [
         cmake
@@ -167,6 +187,11 @@ effectiveStdenv.mkDerivation (
         # TODO: Replace with autoAddDriverRunpath
         # once https://github.com/NixOS/nixpkgs/pull/275241 has been merged
         cudaPackages.autoAddOpenGLRunpathHook
+      ]
+      ++ optionals (effectiveStdenv.hostPlatform.isGnu && enableStatic) [
+        glibc.static
+      ] ++ optionals (effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders) [
+        xcrunHost
       ];
 
     buildInputs =
@@ -175,21 +200,23 @@ effectiveStdenv.mkDerivation (
       ++ optionals useMpi [ mpi ]
       ++ optionals useOpenCL [ clblast ]
       ++ optionals useRocm rocmBuildInputs
+      ++ optionals useBlas [ blas ]
       ++ optionals useVulkan vulkanBuildInputs;
 
     cmakeFlags =
       [
         (cmakeBool "LLAMA_NATIVE" false)
         (cmakeBool "LLAMA_BUILD_SERVER" true)
-        (cmakeBool "BUILD_SHARED_LIBS" true)
+        (cmakeBool "BUILD_SHARED_LIBS" (!enableStatic))
         (cmakeBool "CMAKE_SKIP_BUILD_RPATH" true)
         (cmakeBool "LLAMA_BLAS" useBlas)
         (cmakeBool "LLAMA_CLBLAST" useOpenCL)
-        (cmakeBool "LLAMA_CUBLAS" useCuda)
+        (cmakeBool "LLAMA_CUDA" useCuda)
         (cmakeBool "LLAMA_HIPBLAS" useRocm)
         (cmakeBool "LLAMA_METAL" useMetalKit)
         (cmakeBool "LLAMA_MPI" useMpi)
         (cmakeBool "LLAMA_VULKAN" useVulkan)
+        (cmakeBool "LLAMA_STATIC" enableStatic)
       ]
       ++ optionals useCuda [
         (
@@ -209,14 +236,16 @@ effectiveStdenv.mkDerivation (
         # Should likely use `rocmPackages.clr.gpuTargets`.
         "-DAMDGPU_TARGETS=gfx803;gfx900;gfx906:xnack-;gfx908:xnack-;gfx90a:xnack+;gfx90a:xnack-;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102"
       ]
-      ++ optionals useMetalKit [ (lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1") ]
-      ++ optionals useBlas [ (lib.cmakeFeature "LLAMA_BLAS_VENDOR" "OpenBLAS") ];
+      ++ optionals useMetalKit [
+        (lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1")
+        (cmakeBool "LLAMA_METAL_EMBED_LIBRARY" (!precompileMetalShaders))
+      ];
 
     # TODO(SomeoneSerge): It's better to add proper install targets at the CMake level,
     # if they haven't been added yet.
     postInstall = ''
-      mv $out/bin/main $out/bin/llama
-      mv $out/bin/server $out/bin/llama-server
+      mv $out/bin/main${executableSuffix} $out/bin/llama${executableSuffix}
+      mv $out/bin/server${executableSuffix} $out/bin/llama-server${executableSuffix}
       mkdir -p $out/include
       cp $src/llama.h $out/include/
     '';
@@ -255,11 +284,11 @@ effectiveStdenv.mkDerivation (
       # Configurations we don't want even the CI to evaluate. Results in the
       # "unsupported platform" messages. This is mostly a no-op, because
       # cudaPackages would've refused to evaluate anyway.
-      badPlatforms = optionals (useCuda || useOpenCL || useVulkan) lib.platforms.darwin;
+      badPlatforms = optionals (useCuda || useOpenCL) lib.platforms.darwin;
 
       # Configurations that are known to result in build failures. Can be
       # overridden by importing Nixpkgs with `allowBroken = true`.
-      broken = (useMetalKit && !effectiveStdenv.isDarwin) || (useVulkan && effectiveStdenv.isDarwin);
+      broken = (useMetalKit && !effectiveStdenv.isDarwin);
 
       description = "Inference of LLaMA model in pure C/C++${descriptionSuffix}";
       homepage = "https://github.com/ggerganov/llama.cpp/";
diff --git a/.devops/nix/scope.nix b/.devops/nix/scope.nix
index d295995a4..78530c9e8 100644
--- a/.devops/nix/scope.nix
+++ b/.devops/nix/scope.nix
@@ -12,5 +12,8 @@ lib.makeScope newScope (
   self: {
     inherit llamaVersion;
     llama-cpp = self.callPackage ./package.nix { };
+    docker = self.callPackage ./docker.nix { };
+    docker-min = self.callPackage ./docker.nix { interactive = false; };
+    sif = self.callPackage ./sif.nix { };
   }
 )
diff --git a/.devops/nix/sif.nix b/.devops/nix/sif.nix
new file mode 100644
index 000000000..7a5e1dd0f
--- /dev/null
+++ b/.devops/nix/sif.nix
@@ -0,0 +1,27 @@
+{
+  lib,
+  singularity-tools,
+  llama-cpp,
+  bashInteractive,
+  interactive ? false,
+}:
+
+let
+  optionalInt = cond: x: if cond then x else 0;
+in
+singularity-tools.buildImage rec {
+  inherit (llama-cpp) name;
+  contents = [ llama-cpp ] ++ lib.optionals interactive [ bashInteractive ];
+
+  # These are excessive (but safe) for most variants. Building singularity
+  # images requires superuser privileges, so we build them inside a VM in a
+  # writable image of pre-determined size.
+  #
+  # ROCm is currently affected by https://github.com/NixOS/nixpkgs/issues/276846
+  #
+  # Expected image sizes:
+  # - cpu/blas: 150M,
+  # - cuda, all gencodes: 560M,
+  diskSize = 4096 + optionalInt llama-cpp.useRocm 16384;
+  memSize = diskSize;
+}
diff --git a/.devops/server-cuda.Dockerfile b/.devops/server-cuda.Dockerfile
index 4f83904bc..59a52ba21 100644
--- a/.devops/server-cuda.Dockerfile
+++ b/.devops/server-cuda.Dockerfile
@@ -12,7 +12,7 @@ FROM ${BASE_CUDA_DEV_CONTAINER} as build
 ARG CUDA_DOCKER_ARCH=all
 
 RUN apt-get update && \
-    apt-get install -y build-essential git
+    apt-get install -y build-essential git libcurl4-openssl-dev
 
 WORKDIR /app
 
@@ -20,13 +20,18 @@ COPY . .
 
 # Set nvcc architecture
 ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
-# Enable cuBLAS
-ENV LLAMA_CUBLAS=1
+# Enable CUDA
+ENV LLAMA_CUDA=1
+# Enable cURL
+ENV LLAMA_CURL=1
 
 RUN make
 
 FROM ${BASE_CUDA_RUN_CONTAINER} as runtime
 
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev
+
 COPY --from=build /app/server /server
 
 ENTRYPOINT [ "/server" ]
diff --git a/.devops/server-intel.Dockerfile b/.devops/server-intel.Dockerfile
index 312f2df80..a8e451fa9 100644
--- a/.devops/server-intel.Dockerfile
+++ b/.devops/server-intel.Dockerfile
@@ -4,23 +4,24 @@ FROM intel/oneapi-basekit:$ONEAPI_VERSION as build
 
 ARG LLAMA_SYCL_F16=OFF
 RUN apt-get update && \
-    apt-get install -y git
+    apt-get install -y git libcurl4-openssl-dev
 
 WORKDIR /app
 
 COPY . .
 
-RUN mkdir build && \
-    cd build && \
-    if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
+RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
         echo "LLAMA_SYCL_F16 is set" && \
         export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \
     fi && \
-    cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \
-    cmake --build . --config Release --target server
+    cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
+    cmake --build build --config Release --target server
 
 FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime
 
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev
+
 COPY --from=build /app/build/bin/server /server
 
 ENV LC_ALL=C.utf8
diff --git a/.devops/server-rocm.Dockerfile b/.devops/server-rocm.Dockerfile
index e9a31647c..c02a31dd8 100644
--- a/.devops/server-rocm.Dockerfile
+++ b/.devops/server-rocm.Dockerfile
@@ -40,6 +40,11 @@ ENV LLAMA_HIPBLAS=1
 ENV CC=/opt/rocm/llvm/bin/clang
 ENV CXX=/opt/rocm/llvm/bin/clang++
 
+# Enable cURL
+ENV LLAMA_CURL=1
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev
+
 RUN make
 
 ENTRYPOINT [ "/app/server" ]
diff --git a/.devops/server-vulkan.Dockerfile b/.devops/server-vulkan.Dockerfile
index e0add6fc3..6e757e171 100644
--- a/.devops/server-vulkan.Dockerfile
+++ b/.devops/server-vulkan.Dockerfile
@@ -11,13 +11,15 @@ RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key
     apt update -y && \
     apt-get install -y vulkan-sdk
 
+# Install cURL
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev
+
 # Build it
 WORKDIR /app
 COPY . .
-RUN mkdir build && \
-    cd build && \
-    cmake .. -DLLAMA_VULKAN=1 && \
-    cmake --build . --config Release --target server
+RUN cmake -B build -DLLAMA_VULKAN=1 -DLLAMA_CURL=1 && \
+    cmake --build build --config Release --target server
 
 # Clean up
 WORKDIR /
diff --git a/.devops/server.Dockerfile b/.devops/server.Dockerfile
index 134588fe2..be964e0e8 100644
--- a/.devops/server.Dockerfile
+++ b/.devops/server.Dockerfile
@@ -3,16 +3,21 @@ ARG UBUNTU_VERSION=22.04
 FROM ubuntu:$UBUNTU_VERSION as build
 
 RUN apt-get update && \
-    apt-get install -y build-essential git
+    apt-get install -y build-essential git libcurl4-openssl-dev
 
 WORKDIR /app
 
 COPY . .
 
+ENV LLAMA_CURL=1
+
 RUN make
 
 FROM ubuntu:$UBUNTU_VERSION as runtime
 
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev
+
 COPY --from=build /app/server /server
 
 ENV LC_ALL=C.utf8
diff --git a/.flake8 b/.flake8
index 18fba2c15..d64c2564a 100644
--- a/.flake8
+++ b/.flake8
@@ -1,3 +1,17 @@
 [flake8]
 max-line-length = 125
-ignore = W503
+ignore = E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503
+exclude =
+    # Do not traverse examples
+    examples,
+    # Do not include package initializers
+    __init__.py,
+    # No need to traverse our git directory
+    .git,
+    # There's no value in checking cache directories
+    __pycache__,
+    # No need to include the build path
+    build,
+    # This contains builds that we don't want to check
+    dist  # This is generated with `python build .` for package releases
+# max-complexity = 10
diff --git a/.github/ISSUE_TEMPLATE/bug.md b/.github/ISSUE_TEMPLATE/bug.md
index ce69e6395..49812832c 100644
--- a/.github/ISSUE_TEMPLATE/bug.md
+++ b/.github/ISSUE_TEMPLATE/bug.md
@@ -7,3 +7,5 @@ assignees: ''
 ---
 
 Please include information about your system, the steps to reproduce the bug, and the version of llama.cpp that you are using. If possible, please provide a minimal code example that reproduces the bug.
+
+If the bug concerns the server, please try to reproduce it first using the [server test scenario framework](https://github.com/ggerganov/llama.cpp/tree/master/examples/server/tests).
diff --git a/.github/workflows/bench.yml b/.github/workflows/bench.yml
new file mode 100644
index 000000000..de0d994c8
--- /dev/null
+++ b/.github/workflows/bench.yml
@@ -0,0 +1,310 @@
+# Benchmark
+name: Benchmark
+
+on:
+  workflow_dispatch:
+    inputs:
+      gpu-series:
+        description: 'Azure GPU series to run with'
+        required: true
+        type: choice
+        options:
+          - Standard_NC4as_T4_v3
+          - Standard_NC24ads_A100_v4
+          - Standard_NC80adis_H100_v5
+      sha:
+        description: 'Commit SHA1 to build'
+        required: false
+        type: string
+      duration:
+        description: 'Duration of the bench'
+        type: string
+        default: 10m
+
+  push:
+    branches:
+      - master
+    paths: ['llama.cpp', 'ggml.c', 'ggml-backend.c', 'ggml-quants.c', '**/*.cu', 'examples/server/*.h*', 'examples/server/*.cpp']
+  pull_request_target:
+    types: [opened, synchronize, reopened]
+    paths: ['llama.cpp', 'ggml.c', 'ggml-backend.c', 'ggml-quants.c', '**/*.cu', 'examples/server/*.h*', 'examples/server/*.cpp']
+  schedule:
+    -  cron: '04 2 * * *'
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}-${{ github.event.inputs.sha }}
+  cancel-in-progress: true
+
+jobs:
+  bench-server-baseline:
+    runs-on: Standard_NC4as_T4_v3
+    env:
+      RUNNER_LABEL: Standard_NC4as_T4_v3 # FIXME Do not find a way to not duplicate it
+      N_USERS: 8
+      DURATION: 10m
+
+    strategy:
+      matrix:
+        model: [phi-2]
+        ftype: [q4_0, q8_0, f16]
+        include:
+          - model: phi-2
+            ftype: q4_0
+            pr_comment_enabled: "true"
+
+    if: |
+      inputs.gpu-series == 'Standard_NC4as_T4_v3'
+      || (
+        github.event_name == 'schedule'
+        && github.ref_name == 'master'
+        && github.repository_owner == 'ggerganov'
+      )
+      || github.event_name == 'pull_request_target'
+      || (
+        github.event_name == 'push'
+        && github.event.ref == 'refs/heads/master'
+        && github.repository_owner == 'ggerganov'
+      )
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+
+      - name: Install python env
+        id: pipenv
+        run: |
+          cd examples/server/bench
+          python3 -m venv venv
+          source venv/bin/activate
+          pip install -r requirements.txt
+
+      - name: Prometheus
+        id: install_prometheus
+        run: |
+          wget --quiet https://github.com/prometheus/prometheus/releases/download/v2.51.0/prometheus-2.51.0.linux-amd64.tar.gz
+          tar xzf prometheus*.tar.gz --strip-components=1
+          ./prometheus --config.file=examples/server/bench/prometheus.yml &
+          while ! nc -z localhost 9090; do
+            sleep 0.1
+          done
+
+      - name: Set up Go
+        uses: actions/setup-go@v5
+        with:
+          go-version: '1.21'
+
+      - name: Install k6 and xk6-sse
+        id: k6_installation
+        run: |
+          cd examples/server/bench
+          go install go.k6.io/xk6/cmd/xk6@latest
+          xk6 build master \
+              --with github.com/phymbert/xk6-sse
+
+      - name: Build
+        id: cmake_build
+        run: |
+          set -eux
+          cmake -B build \
+              -DLLAMA_NATIVE=OFF \
+              -DLLAMA_BUILD_SERVER=ON \
+              -DLLAMA_CURL=ON \
+              -DLLAMA_CUBLAS=ON \
+              -DCUDAToolkit_ROOT=/usr/local/cuda \
+              -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc \
+              -DCMAKE_CUDA_ARCHITECTURES=75 \
+              -DLLAMA_FATAL_WARNINGS=OFF \
+              -DLLAMA_ALL_WARNINGS=OFF \
+              -DCMAKE_BUILD_TYPE=Release;
+          cmake --build build --config Release -j $(nproc) --target server
+
+      - name: Download the dataset
+        id: download_dataset
+        run: |
+          cd examples/server/bench
+          wget --quiet https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+
+      - name: Server bench
+        id: server_bench
+        run: |
+          set -eux
+
+          cd examples/server/bench
+          source venv/bin/activate
+          python bench.py \
+              --runner-label ${{ env.RUNNER_LABEL }} \
+              --name ${{ github.job }} \
+              --branch ${{ github.head_ref || github.ref_name }} \
+              --commit ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha }} \
+              --scenario script.js \
+              --duration ${{ github.event.inputs.duration || env.DURATION }} \
+              --hf-repo ggml-org/models	 \
+              --hf-file ${{ matrix.model }}/ggml-model-${{ matrix.ftype }}.gguf \
+              --model-path-prefix /models \
+              --parallel ${{ env.N_USERS }} \
+              -ngl 33 \
+              --batch-size 2048 \
+              --ubatch-size	256 \
+              --ctx-size 16384 \
+              --n-prompts 1000 \
+              --max-prompt-tokens 1024 \
+              --max-tokens 2048
+
+          cat results.github.env >> $GITHUB_ENV
+
+          # Remove dataset as we do not want it in the artefact
+          rm ShareGPT_V3_unfiltered_cleaned_split.json
+
+      - uses: actions/upload-artifact@v4
+        with:
+          name: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }}
+          compression-level: 9
+          path: |
+            examples/server/bench/*.jpg
+            examples/server/bench/*.json
+            examples/server/bench/*.log
+
+      - name: Commit status
+        uses: Sibz/github-status-action@v1
+        with:
+          authToken: ${{secrets.GITHUB_TOKEN}}
+          sha: ${{ inputs.sha || github.event.pull_request.head.sha || github.sha }}
+          context: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }}
+          description: |
+            ${{ env.BENCH_RESULTS }}
+          state: 'success'
+
+      - name: Upload benchmark images
+        uses: devicons/public-upload-to-imgur@v2.2.2
+        continue-on-error: true # Important as it looks unstable: 503
+        id: imgur_step
+        with:
+          client_id: ${{secrets.IMGUR_CLIENT_ID}}
+          path: |
+            examples/server/bench/prompt_tokens_seconds.jpg
+            examples/server/bench/predicted_tokens_seconds.jpg
+            examples/server/bench/kv_cache_usage_ratio.jpg
+            examples/server/bench/requests_processing.jpg
+
+      - name: Extract mermaid
+        id: set_mermaid
+        run: |
+          set -eux
+
+          cd examples/server/bench
+          PROMPT_TOKENS_SECONDS=$(cat prompt_tokens_seconds.mermaid)
+          echo "PROMPT_TOKENS_SECONDS<<EOF" >> $GITHUB_ENV
+          echo "$PROMPT_TOKENS_SECONDS" >> $GITHUB_ENV
+          echo "EOF" >> $GITHUB_ENV
+
+          PREDICTED_TOKENS_SECONDS=$(cat predicted_tokens_seconds.mermaid)
+          echo "PREDICTED_TOKENS_SECONDS<<EOF" >> $GITHUB_ENV
+          echo "$PREDICTED_TOKENS_SECONDS" >> $GITHUB_ENV
+          echo "EOF" >> $GITHUB_ENV
+
+          KV_CACHE_USAGE_RATIO=$(cat kv_cache_usage_ratio.mermaid)
+          echo "KV_CACHE_USAGE_RATIO<<EOF" >> $GITHUB_ENV
+          echo "$KV_CACHE_USAGE_RATIO" >> $GITHUB_ENV
+          echo "EOF" >> $GITHUB_ENV
+
+          REQUESTS_PROCESSING=$(cat requests_processing.mermaid)
+          echo "REQUESTS_PROCESSING<<EOF" >> $GITHUB_ENV
+          echo "$REQUESTS_PROCESSING" >> $GITHUB_ENV
+          echo "EOF" >> $GITHUB_ENV
+
+      - name: Extract image url
+        id: extract_image_url
+        continue-on-error: true
+        run: |
+          set -eux
+
+          echo "IMAGE_O=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[0] }}" >> $GITHUB_ENV
+          echo "IMAGE_1=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[1] }}" >> $GITHUB_ENV
+          echo "IMAGE_2=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[2] }}" >> $GITHUB_ENV
+          echo "IMAGE_3=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[3] }}" >> $GITHUB_ENV
+
+      - name: Comment PR
+        uses: mshick/add-pr-comment@v2
+        id: comment_pr
+        if: ${{ github.event.pull_request != '' && matrix.pr_comment_enabled == 'true' }}
+        with:
+          message-id: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }}
+          message: |
+            <p align="center">
+
+            📈 **llama.cpp server** for _${{ github.job }}_ on _${{ env.RUNNER_LABEL }}_ for `${{ matrix.model }}`-`${{ matrix.ftype }}`: **${{ env.BENCH_ITERATIONS}} iterations** 🚀
+
+            </p>
+
+            <details>
+
+            <summary>Expand details for performance related PR only</summary>
+
+            - Concurrent users: ${{ env.N_USERS }}, duration: ${{ github.event.inputs.duration || env.DURATION }}
+            - HTTP request          : avg=${{ env.HTTP_REQ_DURATION_AVG }}ms        p(95)=${{ env.HTTP_REQ_DURATION_P_95_ }}ms fails=${{ env.HTTP_REQ_FAILED_PASSES }}, finish reason: stop=${{ env.LLAMACPP_COMPLETIONS_STOP_RATE_PASSES }} truncated=${{ env.LLAMACPP_COMPLETIONS_TRUNCATED_RATE_PASSES }}
+            - Prompt processing (pp): avg=${{ env.LLAMACPP_PROMPT_PROCESSING_SECOND_AVG }}tk/s p(95)=${{ env.LLAMACPP_PROMPT_PROCESSING_SECOND_P_95_ }}tk/s
+            - Token generation  (tg): avg=${{ env.LLAMACPP_TOKENS_SECOND_AVG }}tk/s p(95)=${{ env.LLAMACPP_TOKENS_SECOND_P_95_ }}tk/s
+            - ${{ env.BENCH_GRAPH_XLABEL }}
+
+
+            <p align="center">
+
+            <img width="100%" height="100%" src="${{ env.IMAGE_O }}" alt="prompt_tokens_seconds" />
+
+            <details>
+
+            <summary>More</summary>
+
+            ```mermaid
+            ${{ env.PROMPT_TOKENS_SECONDS }}
+            ```
+
+            </details>
+
+            <img width="100%" height="100%" src="${{ env.IMAGE_1 }}" alt="predicted_tokens_seconds"/>
+
+            <details>
+                <summary>More</summary>
+
+            ```mermaid
+            ${{ env.PREDICTED_TOKENS_SECONDS }}
+            ```
+
+            </details>
+
+            </p>
+
+            <details>
+
+            <summary>Details</summary>
+
+            <p align="center">
+
+            <img width="100%" height="100%" src="${{ env.IMAGE_2 }}" alt="kv_cache_usage_ratio" />
+
+            <details>
+                <summary>More</summary>
+
+            ```mermaid
+            ${{ env.KV_CACHE_USAGE_RATIO }}
+            ```
+
+            </details>
+
+            <img width="100%" height="100%" src="${{ env.IMAGE_3 }}" alt="requests_processing"/>
+
+            <details>
+                <summary>More</summary>
+
+            ```mermaid
+            ${{ env.REQUESTS_PROCESSING }}
+            ```
+
+            </details>
+
+            </p>
+            </details>
+            </details>
diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 03d76d455..8c338534d 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -15,19 +15,144 @@ on:
     types: [opened, synchronize, reopened]
     paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m']
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
 env:
   BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
   GGML_NLOOP: 3
   GGML_N_THREADS: 1
 
 jobs:
-  ubuntu-focal-make:
-    runs-on: ubuntu-20.04
+  macOS-latest-cmake-arm64:
+    runs-on: macos-14
 
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Dependencies
+        id: depends
+        continue-on-error: true
+        run: |
+          brew update
+
+      - name: Build
+        id: cmake_build
+        run: |
+          sysctl -a
+          mkdir build
+          cd build
+          cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL_EMBED_LIBRARY=ON -DLLAMA_CURL=ON ..
+          cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
+
+      - name: Test
+        id: cmake_test
+        run: |
+          cd build
+          ctest -L 'main|curl' --verbose --timeout 900
+
+      - name: Determine tag name
+        id: tag
+        shell: bash
+        run: |
+          BUILD_NUMBER="$(git rev-list --count HEAD)"
+          SHORT_HASH="$(git rev-parse --short=7 HEAD)"
+          if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then
+            echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT
+          else
+            SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-')
+            echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT
+          fi
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          cp LICENSE ./build/bin/
+          zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip ./build/bin/*
+
+      - name: Upload artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip
+          name: llama-bin-macos-arm64.zip
+
+  macOS-latest-cmake-x64:
+    runs-on: macos-latest
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Dependencies
+        id: depends
+        continue-on-error: true
+        run: |
+          brew update
+
+      - name: Build
+        id: cmake_build
+        run: |
+          sysctl -a
+          mkdir build
+          cd build
+          # Metal is disabled due to intermittent failures with Github runners not having a GPU:
+          # https://github.com/ggerganov/llama.cpp/actions/runs/8635935781/job/23674807267#step:5:2313
+          cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL=OFF -DLLAMA_CURL=ON ..
+          cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
+
+      - name: Test
+        id: cmake_test
+        run: |
+          cd build
+          ctest -L main --verbose --timeout 900
+
+      - name: Determine tag name
+        id: tag
+        shell: bash
+        run: |
+          BUILD_NUMBER="$(git rev-list --count HEAD)"
+          SHORT_HASH="$(git rev-parse --short=7 HEAD)"
+          if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then
+            echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT
+          else
+            SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-')
+            echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT
+          fi
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          cp LICENSE ./build/bin/
+          zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip ./build/bin/*
+
+      - name: Upload artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip
+          name: llama-bin-macos-x64.zip
+
+  ubuntu-focal-make:
+    runs-on: ubuntu-20.04
+    env:
+      LLAMA_NODE_AVAILABLE: true
+      LLAMA_PYTHON_AVAILABLE: true
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
 
       - name: Dependencies
         id: depends
@@ -35,6 +160,14 @@ jobs:
           sudo apt-get update
           sudo apt-get install build-essential gcc-8
 
+      - uses: actions/setup-node@v4
+        with:
+          node-version: "20"
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
       - name: Build
         id: make_build
         env:
@@ -48,68 +181,130 @@ jobs:
           CC=gcc-8 make tests -j $(nproc)
           make test -j $(nproc)
 
+  ubuntu-focal-make-curl:
+    runs-on: ubuntu-20.04
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+
+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt-get update
+          sudo apt-get install build-essential gcc-8 libcurl4-openssl-dev
+
+      - name: Build
+        id: make_build
+        env:
+          LLAMA_FATAL_WARNINGS: 1
+          LLAMA_CURL: 1
+        run: |
+          CC=gcc-8 make -j $(nproc)
+
   ubuntu-latest-cmake:
     runs-on: ubuntu-latest
 
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
 
       - name: Dependencies
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential
+          sudo apt-get install build-essential libcurl4-openssl-dev
 
       - name: Build
         id: cmake_build
         run: |
           mkdir build
           cd build
-          cmake .. -DLLAMA_FATAL_WARNINGS=ON
+          cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=ON
           cmake --build . --config Release -j $(nproc)
 
       - name: Test
         id: cmake_test
         run: |
           cd build
-          ctest -L main --verbose --timeout 900
+          ctest -L 'main|curl' --verbose --timeout 900
 
-  ubuntu-latest-cmake-sanitizer:
-    runs-on: ubuntu-latest
-
-    continue-on-error: true
-
-    strategy:
-      matrix:
-        sanitizer: [ADDRESS, THREAD, UNDEFINED]
-        build_type: [Debug, Release]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v3
-
-      - name: Dependencies
-        id: depends
-        run: |
-          sudo apt-get update
-          sudo apt-get install build-essential
-
-      - name: Build
-        id: cmake_build
-        run: |
-          mkdir build
-          cd build
-          cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
-          cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
-
-      - name: Test
-        id: cmake_test
+      - name: Test llama2c conversion
+        id: llama2c_test
         run: |
           cd build
-          ctest -L main --verbose --timeout 900
+          echo "Fetch tokenizer"
+          wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/tok512.bin
+          echo "Fetch llama2c model"
+          wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/stories260K.bin
+          ./bin/convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf
+          ./bin/main -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256
+
+      - name: Determine tag name
+        id: tag
+        shell: bash
+        run: |
+          BUILD_NUMBER="$(git rev-list --count HEAD)"
+          SHORT_HASH="$(git rev-parse --short=7 HEAD)"
+          if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then
+            echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT
+          else
+            SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-')
+            echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT
+          fi
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          cp LICENSE ./build/bin/
+          zip -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-x64.zip ./build/bin/*
+
+      - name: Upload artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-x64.zip
+          name: llama-bin-ubuntu-x64.zip
+
+#  ubuntu-latest-cmake-sanitizer:
+#    runs-on: ubuntu-latest
+#
+#    continue-on-error: true
+#
+#    strategy:
+#      matrix:
+#        sanitizer: [ADDRESS, THREAD, UNDEFINED]
+#        build_type: [Debug, Release]
+#
+#    steps:
+#      - name: Clone
+#        id: checkout
+#        uses: actions/checkout@v4
+#
+#      - name: Dependencies
+#        id: depends
+#        run: |
+#          sudo apt-get update
+#          sudo apt-get install build-essential
+#
+#      - name: Build
+#        id: cmake_build
+#        run: |
+#          mkdir build
+#          cd build
+#          cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
+#          cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
+#
+#      - name: Test
+#        id: cmake_test
+#        run: |
+#          cd build
+#          ctest -L main --verbose --timeout 900
 
   ubuntu-latest-cmake-mpi:
     runs-on: ubuntu-latest
@@ -123,7 +318,7 @@ jobs:
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Dependencies
         id: depends
@@ -145,6 +340,28 @@ jobs:
           cd build
           ctest -L main --verbose
 
+  ubuntu-22-cmake-vulkan:
+    runs-on: ubuntu-22.04
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+
+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt-get update
+          sudo apt-get install build-essential libvulkan-dev
+
+      - name: Build
+        id: cmake_build
+        run: |
+          mkdir build
+          cd build
+          cmake -DLLAMA_VULKAN=ON ..
+          cmake --build . --config Release -j $(nproc)
+
   ubuntu-22-cmake-sycl:
     runs-on: ubuntu-22.04
 
@@ -175,7 +392,7 @@ jobs:
 
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Build
         id: cmake_build
@@ -216,7 +433,7 @@ jobs:
 
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Build
         id: cmake_build
@@ -236,7 +453,7 @@ jobs:
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Dependencies
         id: depends
@@ -267,7 +484,7 @@ jobs:
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Dependencies
         id: depends
@@ -311,6 +528,7 @@ jobs:
           mkdir build
           cd build
           cmake -G Xcode .. \
+            -DLLAMA_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
             -DLLAMA_BUILD_SERVER=OFF \
@@ -339,6 +557,7 @@ jobs:
           mkdir build
           cd build
           cmake -G Xcode .. \
+            -DLLAMA_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
             -DLLAMA_BUILD_SERVER=OFF \
@@ -374,6 +593,63 @@ jobs:
         run: |
             make swift
 
+  windows-msys2:
+    runs-on: windows-latest
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - { sys: UCRT64,  env: ucrt-x86_64,  build: Release }
+          - { sys: CLANG64, env: clang-x86_64, build: Release }
+
+    steps:
+      - name: Clone
+        uses: actions/checkout@v4
+
+      - name: Setup ${{ matrix.sys }}
+        uses: msys2/setup-msys2@v2
+        with:
+          update: true
+          msystem: ${{matrix.sys}}
+          install: >-
+            base-devel
+            mingw-w64-${{matrix.env}}-toolchain
+            mingw-w64-${{matrix.env}}-cmake
+            mingw-w64-${{matrix.env}}-openblas
+
+      - name: Build using make
+        shell: msys2 {0}
+        run: |
+            make -j $(nproc)
+
+      - name: Clean after building using make
+        shell: msys2 {0}
+        run: |
+            make clean
+
+      - name: Build using make w/ OpenBLAS
+        shell: msys2 {0}
+        run: |
+            make LLAMA_OPENBLAS=1 -j $(nproc)
+
+      - name: Build using CMake
+        shell: msys2 {0}
+        run: |
+            cmake -B build
+            cmake --build build --config ${{ matrix.build }} -j $(nproc)
+
+      - name: Clean after building using CMake
+        shell: msys2 {0}
+        run: |
+            rm -rf build
+
+      - name: Build using CMake w/ OpenBLAS
+        shell: msys2 {0}
+        run: |
+            cmake -B build -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS
+            cmake --build build --config ${{ matrix.build }} -j $(nproc)
+
   windows-latest-cmake:
     runs-on: windows-latest
 
@@ -403,11 +679,13 @@ jobs:
             defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON'
           - build: 'vulkan'
             defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_VULKAN=ON -DBUILD_SHARED_LIBS=ON'
+          - build: 'arm64'
+            defines: '-A ARM64 -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
 
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
         with:
           fetch-depth: 0
 
@@ -498,7 +776,7 @@ jobs:
       - name: Test
         id: cmake_test
         # not all machines have native AVX-512
-        if: ${{ matrix.build != 'clblast' && matrix.build != 'kompute' && matrix.build != 'vulkan' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }}
+        if: ${{ matrix.build != 'arm64' && matrix.build != 'clblast' && matrix.build != 'kompute' && matrix.build != 'vulkan' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }}
         run: |
           cd build
           ctest -L main -C Release --verbose --timeout 900
@@ -537,23 +815,23 @@ jobs:
 
       - name: Upload artifacts
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
-          path: |
-            llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-x64.zip
+          path: llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-x64.zip
+          name: llama-bin-win-${{ matrix.build }}-x64.zip
 
-  windows-latest-cmake-cublas:
+  windows-latest-cmake-cuda:
     runs-on: windows-latest
 
     strategy:
       matrix:
         cuda: ['12.2.0', '11.7.1']
-        build: ['cublas']
+        build: ['cuda']
 
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
         with:
           fetch-depth: 0
 
@@ -569,7 +847,7 @@ jobs:
         run: |
           mkdir build
           cd build
-          cmake .. -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CUBLAS=ON -DBUILD_SHARED_LIBS=ON
+          cmake .. -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CUDA=ON -DBUILD_SHARED_LIBS=ON
           cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS}
 
       - name: Determine tag name
@@ -593,10 +871,10 @@ jobs:
 
       - name: Upload artifacts
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
-          path: |
-            llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-cu${{ matrix.cuda }}-x64.zip
+          path: llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-cu${{ matrix.cuda }}-x64.zip
+          name: llama-bin-win-cu${{ matrix.cuda }}-x64.zip
 
       - name: Copy and pack Cuda runtime
         run: |
@@ -607,26 +885,26 @@ jobs:
 
       - name: Upload Cuda runtime
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
-          path: |
-            cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip
+          path: cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip
+          name: cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip
 
   windows-latest-cmake-sycl:
     runs-on: windows-latest
+
     defaults:
       run:
         shell: bash
 
     env:
-      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/62641e01-1e8d-4ace-91d6-ae03f7f8a71f/w_BaseKit_p_2024.0.0.49563_offline.exe
+      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/7dff44ba-e3af-4448-841c-0d616c8da6e7/w_BaseKit_p_2024.1.0.595_offline.exe
       WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel
-
-
+      ONEAPI_ROOT: "C:/Program Files (x86)/Intel/oneAPI"
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
         with:
           fetch-depth: 0
 
@@ -637,12 +915,49 @@ jobs:
         id: cmake_build
         run:  examples/sycl/win-build-sycl.bat
 
+      - name: Determine tag name
+        id: tag
+        shell: bash
+        run: |
+          BUILD_NUMBER="$(git rev-list --count HEAD)"
+          SHORT_HASH="$(git rev-parse --short=7 HEAD)"
+          if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then
+            echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT
+          else
+            SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-')
+            echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT
+          fi
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          echo "cp oneAPI running time dll files in ${{ env.ONEAPI_ROOT }} to ./build/bin"
+          cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_sycl_blas.4.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_core.2.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_tbb_thread.2.dll" ./build/bin
+
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/pi_win_proxy_loader.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/pi_level_zero.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/sycl7.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/svml_dispmd.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libmmd.dll" ./build/bin
+          echo "cp oneAPI running time dll files to ./build/bin done"
+          7z a llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip ./build/bin/*
+
+      - name: Upload artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip
+          name: llama-bin-win-sycl-x64.zip
+
   ios-xcode-build:
     runs-on: macos-latest
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Build Xcode project
         run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' build
@@ -652,7 +967,7 @@ jobs:
 
     steps:
       - name: Clone
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Set up JDK
         uses: actions/setup-java@v3
@@ -669,14 +984,13 @@ jobs:
         run: |
           cd examples/llama.android
 
-          # Skip armeabi-v7a for now (https://github.com/llvm/llvm-project/issues/65820).
-          ./gradlew build --no-daemon -Pskip-armeabi-v7a
+          ./gradlew build --no-daemon
 
 #  freeBSD-latest:
 #    runs-on: macos-12
 #    steps:
 #    - name: Clone
-#      uses: actions/checkout@v3
+#      uses: actions/checkout@v4
 #
 #    - name: Build
 #      uses: cross-platform-actions/action@v0.19.0
@@ -700,12 +1014,14 @@ jobs:
       - macOS-latest-make
       - macOS-latest-cmake
       - windows-latest-cmake
-      - windows-latest-cmake-cublas
+      - windows-latest-cmake-cuda
+      - macOS-latest-cmake-arm64
+      - macOS-latest-cmake-x64
 
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
         with:
           fetch-depth: 0
 
@@ -724,7 +1040,13 @@ jobs:
 
       - name: Download artifacts
         id: download-artifact
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
+        with:
+          path: ./artifact
+
+      - name: Move artifacts
+        id: move_artifacts
+        run: mkdir -p ./artifact/release && mv ./artifact/*/*.zip ./artifact/release
 
       - name: Create release
         id: create_release
@@ -743,7 +1065,7 @@ jobs:
             const path = require('path');
             const fs = require('fs');
             const release_id = '${{ steps.create_release.outputs.id }}';
-            for (let file of await fs.readdirSync('./artifact')) {
+            for (let file of await fs.readdirSync('./artifact/release')) {
               if (path.extname(file) === '.zip') {
                 console.log('uploadReleaseAsset', file);
                 await github.repos.uploadReleaseAsset({
@@ -751,7 +1073,7 @@ jobs:
                   repo: context.repo.repo,
                   release_id: release_id,
                   name: file,
-                  data: await fs.readFileSync(`./artifact/${file}`)
+                  data: await fs.readFileSync(`./artifact/release/${file}`)
                 });
               }
             }
@@ -765,7 +1087,7 @@ jobs:
 #
 #    steps:
 #      - name: Clone
-#        uses: actions/checkout@v3
+#        uses: actions/checkout@v4
 #
 #      - name: Dependencies
 #        run: |
@@ -789,7 +1111,7 @@ jobs:
 #
 #    steps:
 #      - name: Clone
-#        uses: actions/checkout@v3
+#        uses: actions/checkout@v4
 #
 #      - name: Dependencies
 #        run: |
@@ -813,7 +1135,7 @@ jobs:
 #
 #    steps:
 #      - name: Clone
-#        uses: actions/checkout@v3
+#        uses: actions/checkout@v4
 #
 #      - name: Dependencies
 #        run: |
@@ -843,7 +1165,7 @@ jobs:
 #
 #    steps:
 #      - name: Clone
-#        uses: actions/checkout@v3
+#        uses: actions/checkout@v4
 #
 #      - name: Add msbuild to PATH
 #        uses: microsoft/setup-msbuild@v1
@@ -859,7 +1181,7 @@ jobs:
 #          msbuild ALL_BUILD.vcxproj -t:build -p:configuration=${{ matrix.build }} -p:platform=${{ matrix.arch }}
 #
 #      - name: Upload binaries
-#        uses: actions/upload-artifact@v1
+#        uses: actions/upload-artifact@v4
 #        with:
 #          name: llama-bin-${{ matrix.arch }}
 #          path: build/bin/${{ matrix.build }}
@@ -882,7 +1204,7 @@ jobs:
 #
 #    steps:
 #      - name: Clone
-#        uses: actions/checkout@v3
+#        uses: actions/checkout@v4
 #
 #      - name: Add msbuild to PATH
 #        uses: microsoft/setup-msbuild@v1
@@ -914,7 +1236,7 @@ jobs:
 #
 #      - name: Upload binaries
 #        if: matrix.blas == 'ON'
-#        uses: actions/upload-artifact@v1
+#        uses: actions/upload-artifact@v4
 #        with:
 #          name: llama-blas-bin-${{ matrix.arch }}
 #          path: build/bin/${{ matrix.build }}
@@ -928,7 +1250,7 @@ jobs:
 #
 #    steps:
 #      - name: Clone
-#        uses: actions/checkout@v3
+#        uses: actions/checkout@v4
 #
 #      - name: Dependencies
 #        run: |
diff --git a/.github/workflows/close-issue.yml b/.github/workflows/close-issue.yml
new file mode 100644
index 000000000..69c9f4f69
--- /dev/null
+++ b/.github/workflows/close-issue.yml
@@ -0,0 +1,23 @@
+name: Close inactive issues
+on:
+  schedule:
+    - cron: "42 0 * * *"
+
+jobs:
+  close-issues:
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+      pull-requests: write
+    steps:
+      - uses: actions/stale@v5
+        with:
+          exempt-issue-labels: "refactor,help wanted,good first issue,research,bug"
+          days-before-issue-stale: 30
+          days-before-issue-close: 14
+          stale-issue-label: "stale"
+          close-issue-message: "This issue was closed because it has been inactive for 14 days since being marked as stale."
+          days-before-pr-stale: -1
+          days-before-pr-close: -1
+          operations-per-run: 10000
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
diff --git a/.github/workflows/code-coverage.yml b/.github/workflows/code-coverage.yml
index 392db8a08..f12c558f8 100644
--- a/.github/workflows/code-coverage.yml
+++ b/.github/workflows/code-coverage.yml
@@ -5,12 +5,16 @@ env:
   GGML_NLOOP: 3
   GGML_N_THREADS: 1
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
 jobs:
   run:
     runs-on: ubuntu-20.04
     steps:
       - name: Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Dependencies
         run: |
diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 94f9161fc..9b03d19bc 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -15,6 +15,10 @@ on:
     branches:
       - master
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
 jobs:
   push_to_registry:
     name: Push Docker image to Docker Hub
@@ -42,7 +46,7 @@ jobs:
           - { tag: "server-intel", dockerfile: ".devops/server-intel.Dockerfile", platforms: "linux/amd64" }
     steps:
       - name: Check out the repo
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Set up QEMU
         uses: docker/setup-qemu-action@v2
@@ -87,6 +91,12 @@ jobs:
             echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT
           fi
 
+      - name: Downcase github.repository_owner
+        run: |
+          echo "repository_owner_lowercase=${GITHUB_REPOSITORY_OWNER@L}" >> $GITHUB_ENV
+        env:
+          GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}'
+
       - name: Build and push Docker image (versioned)
         if: github.event_name == 'push'
         uses: docker/build-push-action@v4
@@ -94,7 +104,7 @@ jobs:
           context: .
           push: true
           platforms: ${{ matrix.config.platforms }}
-          tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}-${{ env.COMMIT_SHA }}"
+          tags: "ghcr.io/${{ env.repository_owner_lowercase }}/llama.cpp:${{ matrix.config.tag }}-${{ env.COMMIT_SHA }}"
           file: ${{ matrix.config.dockerfile }}
 
       - name: Build and push Docker image (tagged)
@@ -103,5 +113,5 @@ jobs:
           context: .
           push: ${{ github.event_name == 'push' }}
           platforms: ${{ matrix.config.platforms }}
-          tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }},ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}-${{ steps.tag.outputs.name }}"
+          tags: "ghcr.io/${{ env.repository_owner_lowercase }}/llama.cpp:${{ matrix.config.tag }},ghcr.io/${{ env.repository_owner_lowercase }}/llama.cpp:${{ matrix.config.tag }}-${{ steps.tag.outputs.name }}"
           file: ${{ matrix.config.dockerfile }}
diff --git a/.github/workflows/editorconfig.yml b/.github/workflows/editorconfig.yml
index 0e0993cd4..ae86e9927 100644
--- a/.github/workflows/editorconfig.yml
+++ b/.github/workflows/editorconfig.yml
@@ -14,10 +14,14 @@ on:
     branches:
       - master
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
 jobs:
   editorconfig:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - uses: editorconfig-checker/action-editorconfig-checker@main
       - run: editorconfig-checker
diff --git a/.github/workflows/gguf-publish.yml b/.github/workflows/gguf-publish.yml
index 57db17512..3ca4d3058 100644
--- a/.github/workflows/gguf-publish.yml
+++ b/.github/workflows/gguf-publish.yml
@@ -24,9 +24,9 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - name: Set up Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v5
       with:
         python-version: '3.9.x'
     - name: Install dependencies
diff --git a/.github/workflows/nix-ci-aarch64.yml b/.github/workflows/nix-ci-aarch64.yml
index 0c6cf5f09..4aa4b2379 100644
--- a/.github/workflows/nix-ci-aarch64.yml
+++ b/.github/workflows/nix-ci-aarch64.yml
@@ -17,9 +17,12 @@ on:
     types: [opened, synchronize, reopened]
     paths: ['**/*.nix', 'flake.lock']
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
 jobs:
   nix-build-aarch64:
-    if: ${{ vars.CACHIX_NAME != '' }}
     runs-on: ubuntu-latest
     steps:
     - name: Checkout repository
@@ -37,8 +40,8 @@ jobs:
         extra-conf: |
           extra-platforms = aarch64-linux
           extra-system-features = nixos-test kvm
-          extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org
-          extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
+          extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org
+          extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
     - uses: DeterminateSystems/magic-nix-cache-action@v2
       with:
         upstream-cache: https://${{ matrix.cachixName }}.cachix.org
@@ -46,7 +49,7 @@ jobs:
       uses: cachix/cachix-action@v13
       with:
         authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}'
-        name: ${{ vars.CACHIX_NAME }}
+        name: llama-cpp
     - name: Show all output paths
       run: >
           nix run github:nix-community/nix-eval-jobs
diff --git a/.github/workflows/nix-ci.yml b/.github/workflows/nix-ci.yml
index d19c7a576..8955f38d0 100644
--- a/.github/workflows/nix-ci.yml
+++ b/.github/workflows/nix-ci.yml
@@ -8,6 +8,10 @@ on:
   pull_request:
     types: [opened, synchronize, reopened]
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
 jobs:
   nix-eval:
     strategy:
@@ -23,8 +27,8 @@ jobs:
       with:
         github-token: ${{ secrets.GITHUB_TOKEN }}
         extra-conf: |
-          extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org
-          extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
+          extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org
+          extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
     - uses: DeterminateSystems/magic-nix-cache-action@v2
       with:
         upstream-cache: https://${{ matrix.cachixName }}.cachix.org
@@ -37,7 +41,6 @@ jobs:
           --flake
           ".#packages.$(nix eval --raw --impure --expr builtins.currentSystem)"
   nix-build:
-    if: ${{ vars.CACHIX_NAME != '' }}
     strategy:
       fail-fast: false
       matrix:
@@ -51,8 +54,8 @@ jobs:
       with:
         github-token: ${{ secrets.GITHUB_TOKEN }}
         extra-conf: |
-          extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org
-          extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
+          extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org
+          extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
     - uses: DeterminateSystems/magic-nix-cache-action@v2
       with:
         upstream-cache: https://${{ matrix.cachixName }}.cachix.org
@@ -60,7 +63,7 @@ jobs:
       uses: cachix/cachix-action@v13
       with:
         authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}'
-        name: ${{ vars.CACHIX_NAME }}
+        name: llama-cpp
     - name: Build
       run: >
           nix run github:Mic92/nix-fast-build
diff --git a/.github/workflows/python-check-requirements.yml b/.github/workflows/python-check-requirements.yml
index 92e1108b3..4e0374fc6 100644
--- a/.github/workflows/python-check-requirements.yml
+++ b/.github/workflows/python-check-requirements.yml
@@ -3,27 +3,33 @@ name: Python check requirements.txt
 on:
   push:
     paths:
+      - '.github/workflows/python-check-requirements.yml'
       - 'scripts/check-requirements.sh'
       - 'convert*.py'
       - 'requirements.txt'
       - 'requirements/*.txt'
   pull_request:
     paths:
+      - '.github/workflows/python-check-requirements.yml'
       - 'scripts/check-requirements.sh'
       - 'convert*.py'
       - 'requirements.txt'
       - 'requirements/*.txt'
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
 jobs:
   python-check-requirements:
     runs-on: ubuntu-latest
     name: check-requirements
     steps:
       - name: Check out source repository
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
       - name: Set up Python environment
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: "3.11"
       - name: Run check-requirements.sh script
-        run:  bash scripts/check-requirements.sh nocleanup
+        run:  bash scripts/check-requirements.sh
diff --git a/.github/workflows/python-lint.yml b/.github/workflows/python-lint.yml
index ea0a05ea1..a8d46f31d 100644
--- a/.github/workflows/python-lint.yml
+++ b/.github/workflows/python-lint.yml
@@ -2,19 +2,22 @@ name: flake8 Lint
 
 on: [push, pull_request]
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
 jobs:
   flake8-lint:
     runs-on: ubuntu-latest
     name: Lint
     steps:
       - name: Check out source repository
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
       - name: Set up Python environment
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: "3.11"
       - name: flake8 Lint
         uses: py-actions/flake8@v2
         with:
-            ignore: "E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503"
-            exclude: "examples/*,examples/*/**,*/**/__init__.py"
+            plugins: "flake8-no-print"
diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml
new file mode 100644
index 000000000..afac89c5b
--- /dev/null
+++ b/.github/workflows/server.yml
@@ -0,0 +1,175 @@
+# Server build and tests
+name: Server
+
+on:
+  workflow_dispatch: # allows manual triggering
+    inputs:
+      sha:
+        description: 'Commit SHA1 to build'
+        required: false
+        type: string
+      slow_tests:
+        description: 'Run slow tests'
+        required: true
+        type: boolean
+  push:
+    branches:
+      - master
+    paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
+  pull_request_target:
+    types: [opened, synchronize, reopened]
+    paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
+  schedule:
+    -  cron: '2 4 * * *'
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  server:
+    runs-on: ubuntu-latest
+
+    strategy:
+      matrix:
+        # TODO: temporary disabled due to linux kernel issues
+        #sanitizer: [ADDRESS, THREAD, UNDEFINED]
+        sanitizer: [UNDEFINED]
+        build_type: [Debug]
+        include:
+          - build_type: Release
+            sanitizer: ""
+      fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
+
+    steps:
+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt-get update
+          sudo apt-get -y install \
+            build-essential \
+            xxd \
+            git \
+            cmake \
+            curl \
+            wget \
+            language-pack-en \
+            libcurl4-openssl-dev
+
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+
+      - name: Python setup
+        id: setup_python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+
+      - name: Tests dependencies
+        id: test_dependencies
+        run: |
+          pip install -r examples/server/tests/requirements.txt
+
+      - name: Verify server deps
+        id: verify_server_deps
+        run: |
+          git config --global --add safe.directory $(realpath .)
+          cd examples/server
+          git ls-files --others --modified
+          git status
+          ./deps.sh
+          git status
+          not_ignored_files="$(git ls-files --others --modified)"
+          echo "Modified files: ${not_ignored_files}"
+          if [ -n "${not_ignored_files}" ]; then
+            echo "Repository is dirty or server deps are not built as expected"
+            echo "${not_ignored_files}"
+            exit 1
+          fi
+
+      - name: Build
+        id: cmake_build
+        run: |
+          cmake -B build \
+              -DLLAMA_NATIVE=OFF \
+              -DLLAMA_BUILD_SERVER=ON \
+              -DLLAMA_CURL=ON \
+              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
+              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
+          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target server
+
+
+      - name: Tests
+        id: server_integration_tests
+        if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }}
+        run: |
+          cd examples/server/tests
+          PORT=8888 ./tests.sh
+
+      - name: Slow tests
+        id: server_integration_tests_slow
+        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
+        run: |
+          cd examples/server/tests
+          PORT=8888 ./tests.sh --stop --no-skipped --no-capture --tags slow
+
+
+  server-windows:
+    runs-on: windows-latest
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+
+      - name: libCURL
+        id: get_libcurl
+        env:
+          CURL_VERSION: 8.6.0_6
+        run: |
+          curl.exe -o $env:RUNNER_TEMP/curl.zip -L "https://curl.se/windows/dl-${env:CURL_VERSION}/curl-${env:CURL_VERSION}-win64-mingw.zip"
+          mkdir $env:RUNNER_TEMP/libcurl
+          tar.exe -xvf $env:RUNNER_TEMP/curl.zip --strip-components=1 -C $env:RUNNER_TEMP/libcurl
+
+      - name: Build
+        id: cmake_build
+        run: |
+          cmake -B build -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include"
+          cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target server
+
+      - name: Python setup
+        id: setup_python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+
+      - name: Tests dependencies
+        id: test_dependencies
+        run: |
+          pip install -r examples/server/tests/requirements.txt
+
+      - name: Copy Libcurl
+        id: prepare_libcurl
+        run: |
+          cp $env:RUNNER_TEMP/libcurl/bin/libcurl-x64.dll ./build/bin/Release/libcurl-x64.dll
+
+      - name: Tests
+        id: server_integration_tests
+        if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }}
+        run: |
+          cd examples/server/tests
+          behave.exe --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp
+
+      - name: Slow tests
+        id: server_integration_tests_slow
+        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
+        run: |
+          cd examples/server/tests
+          behave.exe --stop --no-skipped --no-capture --tags slow
diff --git a/.github/workflows/tidy-post.yml b/.github/workflows/tidy-post.yml
deleted file mode 100644
index 03652760c..000000000
--- a/.github/workflows/tidy-post.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-name: clang-tidy review post comments
-
-on:
-  workflow_dispatch:
-    workflows: ["clang-tidy-review"]
-    types:
-      - completed
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-
-    steps:
-      - uses: ZedThree/clang-tidy-review/post@v0.13.0
-        # lgtm_comment_body, max_comments, and annotations need to be set on the posting workflow in a split setup
-        with:
-          # adjust options as necessary
-          lgtm_comment_body: ''
-          annotations: false
-          max_comments: 25
diff --git a/.github/workflows/tidy-review.yml b/.github/workflows/tidy-review.yml
deleted file mode 100644
index a4bc8d976..000000000
--- a/.github/workflows/tidy-review.yml
+++ /dev/null
@@ -1,23 +0,0 @@
-name: clang-tidy-review
-
-on:
-  pull_request:
-    branches:
-      - master
-
-jobs:
-  clang-tidy-review:
-    runs-on: ubuntu-latest
-
-    steps:
-    - uses: actions/checkout@v3
-
-    - uses: ZedThree/clang-tidy-review@v0.13.0
-      id: review
-      with:
-        lgtm_comment_body: ''
-        build_dir: build
-        cmake_command: cmake . -B build -DCMAKE_EXPORT_COMPILE_COMMANDS=on
-        split_workflow: true
-
-    - uses: ZedThree/clang-tidy-review/upload@v0.13.0
diff --git a/.github/workflows/zig-build.yml b/.github/workflows/zig-build.yml
index 68a698ab9..747c35cc0 100644
--- a/.github/workflows/zig-build.yml
+++ b/.github/workflows/zig-build.yml
@@ -6,6 +6,10 @@ on:
     branches:
       - master
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
 jobs:
   build:
     strategy:
@@ -14,7 +18,7 @@ jobs:
         runs-on: [ubuntu-latest, macos-latest, windows-latest]
     runs-on: ${{ matrix.runs-on }}
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           submodules: recursive
           fetch-depth: 0
diff --git a/.gitignore b/.gitignore
index 62b6b8b1a..50ae0973a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,6 +2,7 @@
 *.a
 *.so
 *.gguf
+*.gguf.json
 *.bin
 *.exe
 *.dll
@@ -11,7 +12,10 @@
 *.gcda
 *.dot
 *.bat
+*.tmp
 *.metallib
+*.etag
+*.lastModified
 .DS_Store
 .build/
 .cache/
@@ -25,10 +29,13 @@
 .vscode/
 .idea/
 
+ggml-metal-embed.metal
+
 lcov-report/
 gcovr-report/
 
 build*
+!build.zig
 cmake-build-*
 out/
 tmp/
@@ -43,8 +50,11 @@ models-mnt
 /convert-llama2c-to-ggml
 /embd-input-test
 /embedding
+/eval-callback
 /gguf
 /gguf-llama-simple
+/gguf-split
+/gritlm
 /imatrix
 /infill
 /libllama.so
@@ -52,6 +62,9 @@ models-mnt
 /llava-cli
 /lookahead
 /lookup
+/lookup-create
+/lookup-merge
+/lookup-stats
 /main
 /metal
 /passkey
@@ -67,6 +80,7 @@ models-mnt
 /batched-bench
 /export-lora
 /finetune
+/retrieval
 /speculative
 /parallel
 /train-text-from-scratch
@@ -88,7 +102,25 @@ qnt-*.txt
 perf-*.txt
 
 examples/jeopardy/results.txt
+examples/server/*.html.hpp
+examples/server/*.js.hpp
+examples/server/*.mjs.hpp
 
 poetry.lock
 poetry.toml
 nppBackup
+
+# Test binaries
+/tests/test-grammar-parser
+/tests/test-llama-grammar
+/tests/test-double-float
+/tests/test-grad0
+/tests/test-opt
+/tests/test-quantize-fns
+/tests/test-quantize-perf
+/tests/test-sampling
+/tests/test-tokenizer-0
+/tests/test-tokenizer-1-spm
+/tests/test-tokenizer-1-bpe
+/tests/test-rope
+/tests/test-backend-ops
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 65796fe2e..91d791628 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -3,13 +3,14 @@
 exclude: prompts/.*.txt
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v3.2.0
+  rev: v4.6.0
   hooks:
   - id: trailing-whitespace
   - id: end-of-file-fixer
   - id: check-yaml
   - id: check-added-large-files
 - repo: https://github.com/PyCQA/flake8
-  rev: 6.0.0
+  rev: 7.0.0
   hooks:
   -   id: flake8
+      additional_dependencies: [flake8-no-print]
diff --git a/AUTHORS b/AUTHORS
new file mode 100644
index 000000000..b029f13da
--- /dev/null
+++ b/AUTHORS
@@ -0,0 +1,655 @@
+# date: Tue Apr  9 09:17:14 EEST 2024
+# this file is auto-generated by scripts/gen-authors.sh
+
+0cc4m <picard12@live.de>
+0xspringtime <110655352+0xspringtime@users.noreply.github.com>
+2f38b454 <dxf@protonmail.com>
+3ooabkhxtn <31479382+3ooabkhxtn@users.noreply.github.com>
+44670 <44670@users.noreply.github.com>
+AN Long <aisk@users.noreply.github.com>
+AT <manyoso@users.noreply.github.com>
+Aarni Koskela <akx@iki.fi>
+Aaron Miller <apage43@ninjawhale.com>
+Aaryaman Vasishta <aaryaman.vasishta@amd.com>
+Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>
+Abhishek Gopinath K <31348521+overtunned@users.noreply.github.com>
+Adithya Balaji <adithya.b94@gmail.com>
+AdithyanI <adithyan.i4internet@gmail.com>
+Adrian <smith.adriane@gmail.com>
+Adrian Hesketh <a-h@users.noreply.github.com>
+AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com>
+Aisuko <urakiny@gmail.com>
+Alberto <57916483+albbus-stack@users.noreply.github.com>
+Alex <awhill19@icloud.com>
+Alex Azarov <alex@azarov.by>
+Alex Azarov <alexander.azarov@mapbox.com>
+Alex Klinkhamer <from.github.com.917@grencez.dev>
+Alex Klinkhamer <git@grencez.dev>
+Alex Nguyen <tiendung@users.noreply.github.com>
+Alex Petenchea <alex.petenchea@gmail.com>
+Alex Renda <alexrenda@users.noreply.github.com>
+Alex von Gluck IV <kallisti5@unixzen.com>
+Alexey Parfenov <zxed@alkatrazstudio.net>
+Ali Chraghi <63465728+alichraghi@users.noreply.github.com>
+Ali Nehzat <ali.nehzat@thanks.dev>
+Ali Tariq <ali.tariq@10xengineers.ai>
+Alon <alonfaraj@gmail.com>
+AlpinDale <52078762+AlpinDale@users.noreply.github.com>
+AmirAli Mirian <37371367+amiralimi@users.noreply.github.com>
+Ananta Bastola <anantarajbastola@gmail.com>
+Anas Ahouzi <112881240+aahouzi@users.noreply.github.com>
+András Salamon <ott2@users.noreply.github.com>
+Andrei <abetlen@gmail.com>
+Andrew Canis <andrew.canis@gmail.com>
+Andrew Duffy <a10y@users.noreply.github.com>
+Andrew Godfrey <AndrewGodfrey@users.noreply.github.com>
+Arik Poznanski <arikpoz@users.noreply.github.com>
+Artem <guinmoon@gmail.com>
+Artyom Lebedev <vagran.ast@gmail.com>
+Asbjørn Olling <asbjornolling@gmail.com>
+Ásgeir Bjarni Ingvarsson <asgeir@fundinn.org>
+Ashok Gelal <401055+ashokgelal@users.noreply.github.com>
+Ashraful Islam <ashraful.meche@gmail.com>
+Atsushi Tatsuma <yoshoku@outlook.com>
+Austin <77757836+teleprint-me@users.noreply.github.com>
+AustinMroz <austinmroz@utexas.edu>
+BADR <contact@pythops.com>
+Bach Le <bach@bullno1.com>
+Bailey Chittle <39804642+bachittle@users.noreply.github.com>
+BarfingLemurs <128182951+BarfingLemurs@users.noreply.github.com>
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diff --git a/CMakeLists.txt b/CMakeLists.txt
index 168b133f4..1c3b5c8e4 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -43,6 +43,8 @@ else()
     set(LLAMA_METAL_DEFAULT OFF)
 endif()
 
+set(LLAMA_LLAMAFILE_DEFAULT ON)
+
 # general
 option(BUILD_SHARED_LIBS                "build shared libraries"                                OFF)
 option(LLAMA_STATIC                     "llama: static link libraries"                          OFF)
@@ -88,9 +90,10 @@ endif()
 # 3rd party libs
 option(LLAMA_ACCELERATE                      "llama: enable Accelerate framework"               ON)
 option(LLAMA_BLAS                            "llama: use BLAS"                                  OFF)
+option(LLAMA_LLAMAFILE                       "llama: use llamafile SGEMM"                       ${LLAMA_LLAMAFILE_DEFAULT})
 set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor")
-option(LLAMA_CUBLAS                          "llama: use CUDA"                                  OFF)
-#option(LLAMA_CUDA_CUBLAS                     "llama: use cuBLAS for prompt processing"          OFF)
+option(LLAMA_CUDA                            "llama: use CUDA"                                  OFF)
+option(LLAMA_CUBLAS                          "llama: use CUDA (deprecated, use LLAMA_CUDA)"     OFF)
 option(LLAMA_CUDA_FORCE_DMMV                 "llama: use dmmv instead of mmvq CUDA kernels"     OFF)
 option(LLAMA_CUDA_FORCE_MMQ                  "llama: use mmq kernels instead of cuBLAS"         OFF)
 set(LLAMA_CUDA_DMMV_X      "32" CACHE STRING "llama: x stride for dmmv CUDA kernels")
@@ -99,6 +102,10 @@ option(LLAMA_CUDA_F16                        "llama: use 16 bit floats for some
 set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
 set(LLAMA_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING
                                              "llama: max. batch size for using peer access")
+option(LLAMA_CUDA_NO_PEER_COPY               "llama: do not use peer to peer copies"            OFF)
+option(LLAMA_CUDA_NO_VMM                     "llama: do not try to use CUDA VMM"                OFF)
+
+option(LLAMA_CURL                            "llama: use libcurl to download model from an URL" OFF)
 option(LLAMA_HIPBLAS                         "llama: use hipBLAS"                               OFF)
 option(LLAMA_HIP_UMA                         "llama: use HIP unified memory architecture"       OFF)
 option(LLAMA_CLBLAST                         "llama: use CLBlast"                               OFF)
@@ -110,12 +117,18 @@ option(LLAMA_VULKAN_RUN_TESTS                "llama: run Vulkan tests"
 option(LLAMA_METAL                           "llama: use Metal"                                 ${LLAMA_METAL_DEFAULT})
 option(LLAMA_METAL_NDEBUG                    "llama: disable Metal debugging"                   OFF)
 option(LLAMA_METAL_SHADER_DEBUG              "llama: compile Metal with -fno-fast-math"         OFF)
+option(LLAMA_METAL_EMBED_LIBRARY             "llama: embed Metal library"                       OFF)
+set(LLAMA_METAL_MACOSX_VERSION_MIN "" CACHE STRING
+                                             "llama: metal minimum macOS version")
+set(LLAMA_METAL_STD "" CACHE STRING          "llama: metal standard version (-std flag)")
 option(LLAMA_KOMPUTE                         "llama: use Kompute"                               OFF)
 option(LLAMA_MPI                             "llama: use MPI"                                   OFF)
 option(LLAMA_QKK_64                          "llama: use super-block size of 64 for k-quants"   OFF)
 option(LLAMA_SYCL                            "llama: use SYCL"                                  OFF)
 option(LLAMA_SYCL_F16                        "llama: use 16 bit floats for sycl calculations"   OFF)
+set(LLAMA_SYCL_TARGET   "INTEL" CACHE STRING "llama: sycl target device")
 option(LLAMA_CPU_HBM                         "llama: use memkind for CPU HBM"                   OFF)
+set(LLAMA_SCHED_MAX_COPIES  "4" CACHE STRING "llama: max input copies for pipeline parallelism")
 
 option(LLAMA_BUILD_TESTS                     "llama: build tests"    ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_EXAMPLES                  "llama: build examples" ${LLAMA_STANDALONE})
@@ -145,6 +158,8 @@ set(THREADS_PREFER_PTHREAD_FLAG ON)
 find_package(Threads REQUIRED)
 include(CheckCXXCompilerFlag)
 
+add_compile_definitions(GGML_SCHED_MAX_COPIES=${LLAMA_SCHED_MAX_COPIES})
+
 # enable libstdc++ assertions for debug builds
 if (CMAKE_SYSTEM_NAME MATCHES "Linux")
     add_compile_definitions($<$<CONFIG:Debug>:_GLIBCXX_ASSERTIONS>)
@@ -195,40 +210,80 @@ if (LLAMA_METAL)
         add_compile_definitions(GGML_METAL_NDEBUG)
     endif()
 
-    # get full path to the file
-    #add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/")
-
-    # copy ggml-metal.metal to bin directory
+    # copy ggml-common.h and ggml-metal.metal to bin directory
+    configure_file(ggml-common.h    ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h    COPYONLY)
     configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY)
 
-    if (LLAMA_METAL_SHADER_DEBUG)
-        # custom command to do the following:
-        #   xcrun -sdk macosx metal    -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
-        #   xcrun -sdk macosx metallib                   ggml-metal.air   -o default.metallib
-        #
-        # note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works
-        #       disabling fast math is needed in order to pass tests/test-backend-ops
-        # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1
-        # note: unfortunately, we have to call it default.metallib instead of ggml.metallib
-        #       ref: https://github.com/ggerganov/whisper.cpp/issues/1720
-        set(XC_FLAGS -fno-fast-math -fno-inline -g)
-        if (LLAMA_QKK_64)
-            set(XC_FLAGS ${XC_FLAGS} -DQK_K=64)
+    if (LLAMA_METAL_EMBED_LIBRARY)
+        enable_language(ASM)
+        add_compile_definitions(GGML_METAL_EMBED_LIBRARY)
+
+        set(METALLIB_COMMON "${CMAKE_CURRENT_SOURCE_DIR}/ggml-common.h")
+        set(METALLIB_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal")
+
+        file(MAKE_DIRECTORY "${CMAKE_BINARY_DIR}/autogenerated")
+
+        # merge ggml-common.h and ggml-metal.metal into a single file
+        set(METALLIB_EMBED_ASM    "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.s")
+        set(METALLIB_SOURCE_EMBED "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.metal")
+
+        add_custom_command(
+            OUTPUT ${METALLIB_EMBED_ASM}
+            COMMAND echo "Embedding Metal library"
+            COMMAND sed -e '/\#include \"ggml-common.h\"/r ${METALLIB_COMMON}' -e '/\#include \"ggml-common.h\"/d' < ${METALLIB_SOURCE} > ${METALLIB_SOURCE_EMBED}
+            COMMAND echo ".section __DATA,__ggml_metallib"          >  ${METALLIB_EMBED_ASM}
+            COMMAND echo ".globl _ggml_metallib_start"              >> ${METALLIB_EMBED_ASM}
+            COMMAND echo "_ggml_metallib_start:"                    >> ${METALLIB_EMBED_ASM}
+            COMMAND echo ".incbin \\\"${METALLIB_SOURCE_EMBED}\\\"" >> ${METALLIB_EMBED_ASM}
+            COMMAND echo ".globl _ggml_metallib_end"                >> ${METALLIB_EMBED_ASM}
+            COMMAND echo "_ggml_metallib_end:"                      >> ${METALLIB_EMBED_ASM}
+            DEPENDS ggml-metal.metal ggml-common.h
+            COMMENT "Generate assembly for embedded Metal library"
+        )
+
+        set(GGML_SOURCES_METAL ${GGML_SOURCES_METAL} ${METALLIB_EMBED_ASM})
+    else()
+        if (LLAMA_METAL_SHADER_DEBUG)
+            # custom command to do the following:
+            #   xcrun -sdk macosx metal    -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
+            #   xcrun -sdk macosx metallib                   ggml-metal.air   -o default.metallib
+            #
+            # note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works
+            #       disabling fast math is needed in order to pass tests/test-backend-ops
+            # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1
+            # note: unfortunately, we have to call it default.metallib instead of ggml.metallib
+            #       ref: https://github.com/ggerganov/whisper.cpp/issues/1720
+            set(XC_FLAGS -fno-fast-math -fno-inline -g)
+        else()
+            set(XC_FLAGS -O3)
+        endif()
+
+        # Append macOS metal versioning flags
+        if (LLAMA_METAL_MACOSX_VERSION_MIN)
+            message(STATUS "Adding -mmacosx-version-min=${LLAMA_METAL_MACOSX_VERSION_MIN} flag to metal compilation")
+            list(APPEND XC_FLAGS -mmacosx-version-min=${LLAMA_METAL_MACOSX_VERSION_MIN})
+        endif()
+        if (LLAMA_METAL_STD)
+            message(STATUS "Adding -std=${LLAMA_METAL_STD} flag to metal compilation")
+            list(APPEND XC_FLAGS -std=${LLAMA_METAL_STD})
         endif()
 
         add_custom_command(
             OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
             COMMAND xcrun -sdk macosx metal    ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
             COMMAND xcrun -sdk macosx metallib                ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air   -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
-            DEPENDS ggml-metal.metal
+            COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
+            COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h
+            COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal
+            DEPENDS ggml-metal.metal ggml-common.h
             COMMENT "Compiling Metal kernels"
-        )
+            )
 
         add_custom_target(
             ggml-metal ALL
             DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
-        )
-    endif()
+            )
+    endif() # LLAMA_METAL_EMBED_LIBRARY
 
     set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}
         ${FOUNDATION_LIBRARY}
@@ -236,11 +291,12 @@ if (LLAMA_METAL)
         ${METALKIT_FRAMEWORK}
         )
 endif()
+
 if (LLAMA_BLAS)
     if (LLAMA_STATIC)
         set(BLA_STATIC ON)
     endif()
-    if ($(CMAKE_VERSION) VERSION_GREATER_EQUAL 3.22)
+    if (CMAKE_VERSION VERSION_GREATER_EQUAL 3.22)
         set(BLA_SIZEOF_INTEGER 8)
     endif()
 
@@ -318,29 +374,47 @@ if (LLAMA_BLAS)
     endif()
 endif()
 
+if (LLAMA_LLAMAFILE)
+    add_compile_definitions(GGML_USE_LLAMAFILE)
+
+    set(GGML_HEADERS_LLAMAFILE sgemm.h)
+    set(GGML_SOURCES_LLAMAFILE sgemm.cpp)
+endif()
+
 if (LLAMA_QKK_64)
     add_compile_definitions(GGML_QKK_64)
 endif()
 
 if (LLAMA_CUBLAS)
+    message(WARNING "LLAMA_CUBLAS is deprecated and will be removed in the future.\nUse LLAMA_CUDA instead")
+    set(LLAMA_CUDA ON)
+endif()
+
+if (LLAMA_CUDA)
     cmake_minimum_required(VERSION 3.17)
 
     find_package(CUDAToolkit)
     if (CUDAToolkit_FOUND)
-        message(STATUS "cuBLAS found")
+        message(STATUS "CUDA found")
 
         enable_language(CUDA)
 
         set(GGML_HEADERS_CUDA ggml-cuda.h)
-        set(GGML_SOURCES_CUDA ggml-cuda.cu)
 
-        add_compile_definitions(GGML_USE_CUBLAS)
+        file(GLOB GGML_SOURCES_CUDA "ggml-cuda/*.cu")
+        list(APPEND GGML_SOURCES_CUDA "ggml-cuda.cu")
+
+        add_compile_definitions(GGML_USE_CUDA)
+        add_compile_definitions(GGML_CUDA_USE_GRAPHS)
         if (LLAMA_CUDA_FORCE_DMMV)
             add_compile_definitions(GGML_CUDA_FORCE_DMMV)
         endif()
         if (LLAMA_CUDA_FORCE_MMQ)
             add_compile_definitions(GGML_CUDA_FORCE_MMQ)
         endif()
+        if (LLAMA_CUDA_NO_VMM)
+            add_compile_definitions(GGML_CUDA_NO_VMM)
+        endif()
         add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
         add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
         if (DEFINED LLAMA_CUDA_DMMV_Y)
@@ -351,10 +425,13 @@ if (LLAMA_CUBLAS)
         endif()
         add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
         add_compile_definitions(GGML_CUDA_PEER_MAX_BATCH_SIZE=${LLAMA_CUDA_PEER_MAX_BATCH_SIZE})
+        if (LLAMA_CUDA_NO_PEER_COPY)
+            add_compile_definitions(GGML_CUDA_NO_PEER_COPY)
+        endif()
 
         if (LLAMA_STATIC)
             if (WIN32)
-                # As of 12.3.1 CUDA Tookit for Windows does not offer a static cublas library
+                # As of 12.3.1 CUDA Toolkit for Windows does not offer a static cublas library
                 set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas CUDA::cublasLt)
             else ()
                 set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
@@ -363,7 +440,11 @@ if (LLAMA_CUBLAS)
             set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt)
         endif()
 
-        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cuda_driver)
+        if (LLAMA_CUDA_NO_VMM)
+            # No VMM requested, no need to link directly with the cuda driver lib (libcuda.so)
+        else()
+            set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cuda_driver) # required by cuDeviceGetAttribute(), cuMemGetAllocationGranularity(...), ...
+        endif()
 
     if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
         # 52 == lowest CUDA 12 standard
@@ -380,7 +461,7 @@ if (LLAMA_CUBLAS)
     message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}")
 
     else()
-        message(WARNING "cuBLAS not found")
+        message(WARNING "CUDA not found")
     endif()
 endif()
 
@@ -479,9 +560,11 @@ if (LLAMA_HIPBLAS)
     message(STATUS "HIP and hipBLAS found")
 
     set(GGML_HEADERS_ROCM ggml-cuda.h)
-    set(GGML_SOURCES_ROCM ggml-cuda.cu)
 
-    add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUBLAS)
+    file(GLOB GGML_SOURCES_ROCM "ggml-cuda/*.cu")
+    list(APPEND GGML_SOURCES_ROCM "ggml-cuda.cu")
+
+    add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUDA)
 
     if (LLAMA_HIP_UMA)
         add_compile_definitions(GGML_HIP_UMA)
@@ -495,11 +578,15 @@ if (LLAMA_HIPBLAS)
         add_compile_definitions(GGML_CUDA_FORCE_MMQ)
     endif()
 
+    if (LLAMA_CUDA_NO_PEER_COPY)
+        add_compile_definitions(GGML_CUDA_NO_PEER_COPY)
+    endif()
+
     add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
     add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
     add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
 
-    set_source_files_properties(ggml-cuda.cu PROPERTIES LANGUAGE CXX)
+    set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE CXX)
 
     if (LLAMA_STATIC)
         message(FATAL_ERROR "Static linking not supported for HIP/ROCm")
@@ -509,6 +596,10 @@ if (LLAMA_HIPBLAS)
 endif()
 
 if (LLAMA_SYCL)
+    if (NOT LLAMA_SYCL_TARGET MATCHES "^(INTEL|NVIDIA)$")
+        message(FATAL_ERROR "Invalid backend chosen, supported options are INTEL or NVIDIA")
+    endif()
+
     if ( NOT DEFINED ENV{ONEAPI_ROOT})
         message(FATAL_ERROR "Not detect ENV {ONEAPI_ROOT}, please install oneAPI & source it, like: source /opt/intel/oneapi/setvars.sh")
     endif()
@@ -530,6 +621,9 @@ if (LLAMA_SYCL)
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing")
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3")
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl -L${MKLROOT}/lib")
+    if (LLAMA_SYCL_TARGET STREQUAL "NVIDIA")
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda")
+    endif()
 
     set(GGML_HEADERS_SYCL ggml-sycl.h)
     set(GGML_SOURCES_SYCL ggml-sycl.cpp)
@@ -537,7 +631,11 @@ if (LLAMA_SYCL)
     if (WIN32)
         set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl sycl7 OpenCL mkl_sycl_blas_dll.lib mkl_intel_ilp64_dll.lib mkl_sequential_dll.lib mkl_core_dll.lib)
     else()
-        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
+        if (LLAMA_SYCL_TARGET STREQUAL "INTEL")
+            set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
+        elseif (LLAMA_SYCL_TARGET STREQUAL "NVIDIA")
+            set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl pthread m dl onemkl)
+        endif()
     endif()
 endif()
 
@@ -771,7 +869,7 @@ endif()
 
 set(CUDA_CXX_FLAGS "")
 
-if (LLAMA_CUBLAS)
+if (LLAMA_CUDA)
     set(CUDA_FLAGS -use_fast_math)
 
     if (LLAMA_FATAL_WARNINGS)
@@ -912,10 +1010,16 @@ if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR CMAKE_GENERATOR_PLATFORM_LWR STR
             list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access)
         endif()
         if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv7")
-            # Raspberry Pi 2
-            list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations)
+            if ("${CMAKE_SYSTEM_NAME}" STREQUAL "Android")
+                # Android armeabi-v7a
+                list(APPEND ARCH_FLAGS -mfpu=neon-vfpv4 -mno-unaligned-access -funsafe-math-optimizations)
+            else()
+                # Raspberry Pi 2
+                list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations)
+            endif()
         endif()
         if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv8")
+            # Android arm64-v8a
             # Raspberry Pi 3, 4, Zero 2 (32-bit)
             list(APPEND ARCH_FLAGS -mno-unaligned-access)
         endif()
@@ -990,7 +1094,7 @@ endif()
 add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:${ARCH_FLAGS}>")
 add_compile_options("$<$<COMPILE_LANGUAGE:C>:${ARCH_FLAGS}>")
 
-if (LLAMA_CUBLAS)
+if (LLAMA_CUDA)
     list(APPEND CUDA_CXX_FLAGS ${ARCH_FLAGS})
     list(JOIN CUDA_CXX_FLAGS " " CUDA_CXX_FLAGS_JOINED)  # pass host compiler flags as a single argument
     if (NOT CUDA_CXX_FLAGS_JOINED STREQUAL "")
@@ -1068,15 +1172,16 @@ add_library(ggml OBJECT
             ggml-backend.h
             ggml-quants.c
             ggml-quants.h
-            ${GGML_SOURCES_CUDA}    ${GGML_HEADERS_CUDA}
-            ${GGML_SOURCES_OPENCL}  ${GGML_HEADERS_OPENCL}
-            ${GGML_SOURCES_METAL}   ${GGML_HEADERS_METAL}
-            ${GGML_SOURCES_MPI}     ${GGML_HEADERS_MPI}
-            ${GGML_SOURCES_EXTRA}   ${GGML_HEADERS_EXTRA}
-            ${GGML_SOURCES_SYCL}    ${GGML_HEADERS_SYCL}
-            ${GGML_SOURCES_KOMPUTE} ${GGML_HEADERS_KOMPUTE}
-            ${GGML_SOURCES_VULKAN}  ${GGML_HEADERS_VULKAN}
-            ${GGML_SOURCES_ROCM}    ${GGML_HEADERS_ROCM}
+            ${GGML_SOURCES_CUDA}      ${GGML_HEADERS_CUDA}
+            ${GGML_SOURCES_OPENCL}    ${GGML_HEADERS_OPENCL}
+            ${GGML_SOURCES_METAL}     ${GGML_HEADERS_METAL}
+            ${GGML_SOURCES_MPI}       ${GGML_HEADERS_MPI}
+            ${GGML_SOURCES_EXTRA}     ${GGML_HEADERS_EXTRA}
+            ${GGML_SOURCES_SYCL}      ${GGML_HEADERS_SYCL}
+            ${GGML_SOURCES_KOMPUTE}   ${GGML_HEADERS_KOMPUTE}
+            ${GGML_SOURCES_VULKAN}    ${GGML_HEADERS_VULKAN}
+            ${GGML_SOURCES_ROCM}      ${GGML_HEADERS_ROCM}
+            ${GGML_SOURCES_LLAMAFILE} ${GGML_HEADERS_LLAMAFILE}
             )
 
 target_include_directories(ggml PUBLIC . ${LLAMA_EXTRA_INCLUDES})
@@ -1098,6 +1203,9 @@ endif()
 add_library(llama
             llama.cpp
             llama.h
+            unicode.h
+            unicode.cpp
+            unicode-data.cpp
             )
 
 target_include_directories(llama PUBLIC .)
@@ -1173,17 +1281,6 @@ install(
         WORLD_READ
         WORLD_EXECUTE
     DESTINATION ${CMAKE_INSTALL_BINDIR})
-install(
-    FILES convert-lora-to-ggml.py
-    PERMISSIONS
-        OWNER_READ
-        OWNER_WRITE
-        OWNER_EXECUTE
-        GROUP_READ
-        GROUP_EXECUTE
-        WORLD_READ
-        WORLD_EXECUTE
-    DESTINATION ${CMAKE_INSTALL_BINDIR})
 if (LLAMA_METAL)
     install(
         FILES ggml-metal.metal
@@ -1193,6 +1290,12 @@ if (LLAMA_METAL)
             GROUP_READ
             WORLD_READ
         DESTINATION ${CMAKE_INSTALL_BINDIR})
+    if (NOT LLAMA_METAL_EMBED_LIBRARY)
+        install(
+            FILES ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
+            DESTINATION ${CMAKE_INSTALL_BINDIR}
+        )
+    endif()
 endif()
 
 #
diff --git a/LICENSE b/LICENSE
index 76f67efdc..acb96ce78 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2023 Georgi Gerganov
+Copyright (c) 2023-2024 The ggml authors
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/Makefile b/Makefile
index db5df1b32..3fa56d13a 100644
--- a/Makefile
+++ b/Makefile
@@ -1,15 +1,28 @@
 # Define the default target now so that it is always the first target
 BUILD_TARGETS = \
 	main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
-	simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search  \
-	speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey tests/test-c.o
+	simple batched batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli baby-llama beam-search  \
+	retrieval speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm tests/test-c.o
 
 # Binaries only useful for tests
 TEST_TARGETS = \
-	tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt \
-	tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama          \
-	tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe tests/test-rope      \
-	tests/test-backend-ops tests/test-model-load-cancel tests/test-autorelease
+	tests/test-autorelease \
+	tests/test-backend-ops \
+	tests/test-double-float \
+	tests/test-grad0 \
+	tests/test-grammar-integration \
+	tests/test-grammar-parser \
+	tests/test-json-schema-to-grammar \
+	tests/test-llama-grammar \
+	tests/test-model-load-cancel \
+	tests/test-opt \
+	tests/test-quantize-fns \
+	tests/test-quantize-perf \
+	tests/test-rope \
+	tests/test-sampling \
+	tests/test-tokenizer-0 \
+	tests/test-tokenizer-1-bpe \
+	tests/test-tokenizer-1-spm
 
 # Code coverage output files
 COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report
@@ -26,6 +39,17 @@ ifndef UNAME_M
 UNAME_M := $(shell uname -m)
 endif
 
+# In GNU make default CXX is g++ instead of c++.  Let's fix that so that users
+# of non-gcc compilers don't have to provide g++ alias or wrapper.
+DEFCC  := cc
+DEFCXX := c++
+ifeq ($(origin CC),default)
+CC  := $(DEFCC)
+endif
+ifeq ($(origin CXX),default)
+CXX := $(DEFCXX)
+endif
+
 # Mac OS + Arm can report x86_64
 # ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789
 ifeq ($(UNAME_S),Darwin)
@@ -48,11 +72,16 @@ default: $(BUILD_TARGETS)
 test: $(TEST_TARGETS)
 	@failures=0; \
 	for test_target in $(TEST_TARGETS); do \
-		if [ "$$test_target" = "tests/test-tokenizer-0-llama" ]; then \
-			./$$test_target $(CURDIR)/models/ggml-vocab-llama.gguf; \
-		elif [ "$$test_target" = "tests/test-tokenizer-0-falcon" ]; then \
+		if [ "$$test_target" = "tests/test-tokenizer-0" ]; then \
+			./$$test_target $(CURDIR)/models/ggml-vocab-llama-spm.gguf; \
+			./$$test_target $(CURDIR)/models/ggml-vocab-llama-bpe.gguf; \
+			./$$test_target $(CURDIR)/models/ggml-vocab-phi-3.gguf; \
 			./$$test_target $(CURDIR)/models/ggml-vocab-falcon.gguf; \
-		elif [ "$$test_target" = "tests/test-tokenizer-1-llama" ]; then \
+			./$$test_target $(CURDIR)/models/ggml-vocab-bert-bge.gguf; \
+			./$$test_target $(CURDIR)/models/ggml-vocab-starcoder.gguf; \
+			./$$test_target $(CURDIR)/models/ggml-vocab-gpt-2.gguf; \
+			./$$test_target $(CURDIR)/models/ggml-vocab-refact.gguf; \
+		elif [ "$$test_target" = "tests/test-tokenizer-1-spm" ]; then \
 			continue; \
 		elif [ "$$test_target" = "tests/test-tokenizer-1-bpe" ]; then \
 			continue; \
@@ -167,13 +196,17 @@ ifeq ($(UNAME_S),OpenBSD)
 	MK_CPPFLAGS += -D_BSD_SOURCE
 endif
 
+ifdef LLAMA_SCHED_MAX_COPIES
+	MK_CPPFLAGS += -DGGML_SCHED_MAX_COPIES=$(LLAMA_SCHED_MAX_COPIES)
+endif
+
 ifdef LLAMA_DEBUG
 	MK_CFLAGS   += -O0 -g
 	MK_CXXFLAGS += -O0 -g
 	MK_LDFLAGS  += -g
 
 	ifeq ($(UNAME_S),Linux)
-		MK_CXXFLAGS += -Wp,-D_GLIBCXX_ASSERTIONS
+		MK_CPPFLAGS += -D_GLIBCXX_ASSERTIONS
 	endif
 else
 	MK_CPPFLAGS += -DNDEBUG
@@ -201,6 +234,10 @@ ifdef LLAMA_SERVER_VERBOSE
 	MK_CPPFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE)
 endif
 
+ifdef LLAMA_SERVER_SSL
+	MK_CPPFLAGS += -DCPPHTTPLIB_OPENSSL_SUPPORT
+	MK_LDFLAGS += -lssl -lcrypto
+endif
 
 ifdef LLAMA_CODE_COVERAGE
 	MK_CXXFLAGS += -fprofile-arcs -ftest-coverage -dumpbase ''
@@ -375,15 +412,31 @@ ifdef LLAMA_OPENBLAS
 	MK_LDFLAGS  += $(shell pkg-config --libs openblas)
 endif # LLAMA_OPENBLAS
 
+ifndef LLAMA_NO_LLAMAFILE
+	MK_CPPFLAGS += -DGGML_USE_LLAMAFILE
+	OBJS        += sgemm.o
+endif
+
 ifdef LLAMA_BLIS
 	MK_CPPFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/blis -I/usr/include/blis
 	MK_LDFLAGS  += -lblis -L/usr/local/lib
 endif # LLAMA_BLIS
 
 ifdef LLAMA_CUBLAS
-	MK_CPPFLAGS  += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include -I/usr/local/cuda/targets/aarch64-linux/include
-	MK_LDFLAGS   += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib -L/usr/local/cuda/targets/aarch64-linux/lib -L/usr/lib/wsl/lib
+# LLAMA_CUBLAS is deprecated and will be removed in the future
+	LLAMA_CUDA := 1
+endif
+
+ifdef LLAMA_CUDA
+	ifneq ('', '$(wildcard /opt/cuda)')
+		CUDA_PATH ?= /opt/cuda
+	else
+		CUDA_PATH ?= /usr/local/cuda
+	endif
+	MK_CPPFLAGS  += -DGGML_USE_CUDA -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include -DGGML_CUDA_USE_GRAPHS
+	MK_LDFLAGS   += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L/usr/lib/wsl/lib
 	OBJS         += ggml-cuda.o
+	OBJS         += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu))
 	MK_NVCCFLAGS += -use_fast_math
 ifdef LLAMA_FATAL_WARNINGS
 	MK_NVCCFLAGS += -Werror all-warnings
@@ -438,22 +491,31 @@ ifdef LLAMA_CUDA_PEER_MAX_BATCH_SIZE
 else
 	MK_NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128
 endif # LLAMA_CUDA_PEER_MAX_BATCH_SIZE
-#ifdef LLAMA_CUDA_CUBLAS
-#	MK_NVCCFLAGS += -DGGML_CUDA_CUBLAS
-#endif # LLAMA_CUDA_CUBLAS
+ifdef LLAMA_CUDA_NO_PEER_COPY
+	MK_NVCCFLAGS += -DGGML_CUDA_NO_PEER_COPY
+endif # LLAMA_CUDA_NO_PEER_COPY
 ifdef LLAMA_CUDA_CCBIN
 	MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
 endif
-ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
+
 ifdef JETSON_EOL_MODULE_DETECT
-	$(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
+define NVCC_COMPILE
+	$(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUDA -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
+endef # NVCC_COMPILE
 else
+define NVCC_COMPILE
 	$(NVCC) $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
+endef # NVCC_COMPILE
 endif # JETSON_EOL_MODULE_DETECT
-endif # LLAMA_CUBLAS
+
+ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/common.cuh
+	$(NVCC_COMPILE)
+
+ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh)
+	$(NVCC_COMPILE)
+endif # LLAMA_CUDA
 
 ifdef LLAMA_CLBLAST
-
 	MK_CPPFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags-only-I clblast OpenCL)
 	MK_CFLAGS   += $(shell pkg-config --cflags-only-other clblast OpenCL)
 	MK_CXXFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL)
@@ -496,7 +558,6 @@ ggml-vulkan.o: ggml-vulkan.cpp ggml-vulkan.h
 endif # LLAMA_VULKAN
 
 ifdef LLAMA_HIPBLAS
-
 	ifeq ($(wildcard /opt/rocm),)
 		ROCM_PATH	?= /usr
 		GPU_TARGETS ?= $(shell $(shell which amdgpu-arch))
@@ -508,7 +569,7 @@ ifdef LLAMA_HIPBLAS
 	LLAMA_CUDA_DMMV_X       ?= 32
 	LLAMA_CUDA_MMV_Y        ?= 1
 	LLAMA_CUDA_KQUANTS_ITER ?= 2
-	MK_CPPFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS
+	MK_CPPFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUDA
 ifdef LLAMA_HIP_UMA
 	MK_CPPFLAGS += -DGGML_HIP_UMA
 endif # LLAMA_HIP_UMA
@@ -521,9 +582,18 @@ endif # LLAMA_HIP_UMA
 ifdef LLAMA_CUDA_FORCE_DMMV
 	HIPFLAGS 	+= -DGGML_CUDA_FORCE_DMMV
 endif # LLAMA_CUDA_FORCE_DMMV
+ifdef LLAMA_CUDA_NO_PEER_COPY
+	HIPFLAGS 	+= -DGGML_CUDA_NO_PEER_COPY
+endif # LLAMA_CUDA_NO_PEER_COPY
 	OBJS        += ggml-cuda.o
-ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
+	OBJS        += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu))
+
+ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh)
 	$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
+
+ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/common.cuh
+	$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
+
 endif # LLAMA_HIPBLAS
 
 ifdef LLAMA_METAL
@@ -533,11 +603,30 @@ ifdef LLAMA_METAL
 ifdef LLAMA_METAL_NDEBUG
 	MK_CPPFLAGS += -DGGML_METAL_NDEBUG
 endif
+ifdef LLAMA_METAL_EMBED_LIBRARY
+	MK_CPPFLAGS += -DGGML_METAL_EMBED_LIBRARY
+	OBJS        += ggml-metal-embed.o
+endif
 endif # LLAMA_METAL
 
 ifdef LLAMA_METAL
-ggml-metal.o: ggml-metal.m ggml-metal.h
+ggml-metal.o: ggml-metal.m ggml-metal.h ggml.h
 	$(CC) $(CFLAGS) -c $< -o $@
+
+ifdef LLAMA_METAL_EMBED_LIBRARY
+ggml-metal-embed.o: ggml-metal.metal ggml-common.h
+	@echo "Embedding Metal library"
+	@sed -e '/#include "ggml-common.h"/r ggml-common.h' -e '/#include "ggml-common.h"/d' < ggml-metal.metal > ggml-metal-embed.metal
+	$(eval TEMP_ASSEMBLY=$(shell mktemp))
+	@echo ".section __DATA, __ggml_metallib"   >  $(TEMP_ASSEMBLY)
+	@echo ".globl _ggml_metallib_start"        >> $(TEMP_ASSEMBLY)
+	@echo "_ggml_metallib_start:"              >> $(TEMP_ASSEMBLY)
+	@echo ".incbin \"ggml-metal-embed.metal\"" >> $(TEMP_ASSEMBLY)
+	@echo ".globl _ggml_metallib_end"          >> $(TEMP_ASSEMBLY)
+	@echo "_ggml_metallib_end:"                >> $(TEMP_ASSEMBLY)
+	@$(AS) $(TEMP_ASSEMBLY) -o $@
+	@rm -f ${TEMP_ASSEMBLY}
+endif
 endif # LLAMA_METAL
 
 ifdef LLAMA_MPI
@@ -545,6 +634,11 @@ ggml-mpi.o: ggml-mpi.c ggml-mpi.h
 	$(CC) $(CFLAGS) -c $< -o $@
 endif # LLAMA_MPI
 
+ifndef LLAMA_NO_LLAMAFILE
+sgemm.o: sgemm.cpp sgemm.h ggml.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+endif
+
 GF_CC := $(CC)
 include scripts/get-flags.mk
 
@@ -557,12 +651,17 @@ override NVCCFLAGS := $(MK_NVCCFLAGS) $(NVCCFLAGS)
 override LDFLAGS   := $(MK_LDFLAGS) $(LDFLAGS)
 
 # identify CUDA host compiler
-ifdef LLAMA_CUBLAS
+ifdef LLAMA_CUDA
 GF_CC := $(NVCC) $(NVCCFLAGS) 2>/dev/null .c -Xcompiler
 include scripts/get-flags.mk
 CUDA_CXXFLAGS := $(BASE_CXXFLAGS) $(GF_CXXFLAGS) -Wno-pedantic
 endif
 
+ifdef LLAMA_CURL
+override CXXFLAGS := $(CXXFLAGS) -DLLAMA_USE_CURL
+override LDFLAGS  := $(LDFLAGS) -lcurl
+endif
+
 #
 # Print build information
 #
@@ -577,19 +676,26 @@ $(info I NVCCFLAGS: $(NVCCFLAGS))
 $(info I LDFLAGS:   $(LDFLAGS))
 $(info I CC:        $(shell $(CC)   --version | head -n 1))
 $(info I CXX:       $(shell $(CXX)  --version | head -n 1))
-ifdef LLAMA_CUBLAS
+ifdef LLAMA_CUDA
 $(info I NVCC:      $(shell $(NVCC) --version | tail -n 1))
-CUDA_VERSION := $(shell nvcc --version | grep -oP 'release (\K[0-9]+\.[0-9])')
+CUDA_VERSION := $(shell $(NVCC) --version | grep -oP 'release (\K[0-9]+\.[0-9])')
 ifeq ($(shell awk -v "v=$(CUDA_VERSION)" 'BEGIN { print (v < 11.7) }'),1)
 ifndef CUDA_DOCKER_ARCH
 ifndef CUDA_POWER_ARCH
-$(error I ERROR: For CUDA versions < 11.7 a target CUDA architecture must be explicitly provided via CUDA_DOCKER_ARCH)
+$(error I ERROR: For CUDA versions < 11.7 a target CUDA architecture must be explicitly provided via environment variable CUDA_DOCKER_ARCH, e.g. by running "export CUDA_DOCKER_ARCH=compute_XX" on Unix-like systems, where XX is the minimum compute capability that the code needs to run on. A list with compute capabilities can be found here: https://developer.nvidia.com/cuda-gpus )
 endif # CUDA_POWER_ARCH
 endif # CUDA_DOCKER_ARCH
 endif # eq ($(shell echo "$(CUDA_VERSION) < 11.7" | bc),1)
-endif # LLAMA_CUBLAS
+endif # LLAMA_CUDA
 $(info )
 
+ifdef LLAMA_CUBLAS
+$(info !!!!)
+$(info LLAMA_CUBLAS is deprecated and will be removed in the future. Use LLAMA_CUDA instead.)
+$(info !!!!)
+$(info )
+endif
+
 #
 # Build library
 #
@@ -603,16 +709,22 @@ ggml-alloc.o: ggml-alloc.c ggml.h ggml-alloc.h
 ggml-backend.o: ggml-backend.c ggml.h ggml-backend.h
 	$(CC)  $(CFLAGS)   -c $< -o $@
 
-ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h
+ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h ggml-common.h
 	$(CC) $(CFLAGS)    -c $< -o $@
 
-OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o
-
-llama.o: llama.cpp ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
+unicode.o: unicode.cpp unicode.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-COMMON_H_DEPS = common/common.h common/sampling.h common/log.h
-COMMON_DEPS   = common.o sampling.o grammar-parser.o build-info.o
+unicode-data.o: unicode-data.cpp unicode-data.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+
+OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o unicode-data.o
+
+llama.o: llama.cpp unicode.h ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+
+COMMON_H_DEPS = common/common.h common/sampling.h common/log.h llama.h
+COMMON_DEPS   = common.o sampling.o grammar-parser.o build-info.o json-schema-to-grammar.o
 
 common.o: common/common.cpp $(COMMON_H_DEPS)
 	$(CXX) $(CXXFLAGS) -c $< -o $@
@@ -626,9 +738,15 @@ console.o: common/console.cpp common/console.h
 grammar-parser.o: common/grammar-parser.cpp common/grammar-parser.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
+json-schema-to-grammar.o: common/json-schema-to-grammar.cpp common/json-schema-to-grammar.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+
 train.o: common/train.cpp common/train.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
+ngram-cache.o: common/ngram-cache.cpp common/ngram-cache.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+
 libllama.so: llama.o ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
 
@@ -636,7 +754,8 @@ libllama.a: llama.o ggml.o $(OBJS) $(COMMON_DEPS)
 	ar rcs libllama.a llama.o ggml.o $(OBJS) $(COMMON_DEPS)
 
 clean:
-	rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS)
+	rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult lookup-create lookup-merge lookup-stats common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS)
+	rm -vrf ggml-cuda/*.o
 	find examples pocs -type f -name "*.o" -delete
 
 #
@@ -673,11 +792,11 @@ batched: examples/batched/batched.cpp                         ggml.o llama.o $(C
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-batched-bench: examples/batched-bench/batched-bench.cpp       build-info.o ggml.o llama.o common.o $(OBJS)
+batched-bench: examples/batched-bench/batched-bench.cpp       build-info.o ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-quantize: examples/quantize/quantize.cpp                      build-info.o ggml.o llama.o $(OBJS)
+quantize: examples/quantize/quantize.cpp                      ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
@@ -697,19 +816,39 @@ embedding: examples/embedding/embedding.cpp                   ggml.o llama.o $(C
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
+gritlm: examples/gritlm/gritlm.cpp                         ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
 save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-server: examples/server/server.cpp examples/server/oai.hpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
+server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/server/json-schema-to-grammar.mjs.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual
-	$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h %.hpp $< examples/llava/clip.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) -o $@ $(LDFLAGS) $(LWINSOCK2)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
+
+# Portable equivalent of `cd examples/server/public && xxd -i $(notdir $<) ../$(notdir $<).hpp`:
+examples/server/%.hpp: examples/server/public/% Makefile
+	@( export NAME=$(subst .,_,$(subst -,_,$(notdir $<))) && \
+		echo "unsigned char $${NAME}[] = {" && \
+		cat $< | od -v -t x1 -An | sed -E 's/([0-9a-fA-F]+)/0x\1, /g' && \
+		echo "};" && \
+		echo "unsigned int $${NAME}_len = $(shell cat $< | wc -c );" \
+	) > $@
 
 gguf: examples/gguf/gguf.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
+gguf-split: examples/gguf-split/gguf-split.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
+eval-callback: examples/eval-callback/eval-callback.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
 train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
@@ -747,6 +886,10 @@ export-lora: examples/export-lora/export-lora.cpp ggml.o common/common.h $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
+retrieval: examples/retrieval/retrieval.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
 speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
@@ -759,11 +902,21 @@ lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-lookup: examples/lookup/lookup.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+lookup: examples/lookup/lookup.cpp ggml.o llama.o ngram-cache.o $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c examples/lookup/lookup-create.cpp -o $(call GET_OBJ_FILE, examples/lookup/lookup-create.cpp)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, examples/lookup/lookup-create.cpp) -o lookup-create $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c examples/lookup/lookup-merge.cpp -o $(call GET_OBJ_FILE, examples/lookup/lookup-merge.cpp)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, examples/lookup/lookup-merge.cpp) -o lookup-merge $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c examples/lookup/lookup-stats.cpp -o $(call GET_OBJ_FILE, examples/lookup/lookup-stats.cpp)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, examples/lookup/lookup-stats.cpp) -o lookup-stats $(LDFLAGS)
+
+passkey: examples/passkey/passkey.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-passkey: examples/passkey/passkey.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+gbnf-validator: examples/gbnf-validator/gbnf-validator.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
@@ -814,10 +967,18 @@ tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o grammar-
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
+tests/test-grammar-integration: tests/test-grammar-integration.cpp ggml.o llama.o grammar-parser.o $(OBJS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
 tests/test-double-float: tests/test-double-float.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
+tests/test-json-schema-to-grammar: tests/test-json-schema-to-grammar.cpp json-schema-to-grammar.o ggml.o llama.o grammar-parser.o $(OBJS)
+	$(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
 tests/test-grad0: tests/test-grad0.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
@@ -838,11 +999,7 @@ tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
+tests/test-tokenizer-0: tests/test-tokenizer-0.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
@@ -850,7 +1007,7 @@ tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMM
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
+tests/test-tokenizer-1-spm: tests/test-tokenizer-1-spm.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
diff --git a/Package.swift b/Package.swift
index b24c9204a..183e64757 100644
--- a/Package.swift
+++ b/Package.swift
@@ -2,6 +2,45 @@
 
 import PackageDescription
 
+var sources = [
+    "ggml.c",
+    "sgemm.cpp",
+    "llama.cpp",
+    "unicode.cpp",
+    "unicode-data.cpp",
+    "ggml-alloc.c",
+    "ggml-backend.c",
+    "ggml-quants.c",
+]
+
+var resources: [Resource] = []
+var linkerSettings: [LinkerSetting] = []
+var cSettings: [CSetting] =  [
+    .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]),
+    .unsafeFlags(["-fno-objc-arc"]),
+    // NOTE: NEW_LAPACK will required iOS version 16.4+
+    // We should consider add this in the future when we drop support for iOS 14
+    // (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc)
+    // .define("ACCELERATE_NEW_LAPACK"),
+    // .define("ACCELERATE_LAPACK_ILP64")
+]
+
+#if canImport(Darwin)
+sources.append("ggml-metal.m")
+resources.append(.process("ggml-metal.metal"))
+linkerSettings.append(.linkedFramework("Accelerate"))
+cSettings.append(
+    contentsOf: [
+        .define("GGML_USE_ACCELERATE"),
+        .define("GGML_USE_METAL")
+    ]
+)
+#endif
+
+#if os(Linux)
+    cSettings.append(.define("_GNU_SOURCE"))
+#endif
+
 let package = Package(
     name: "llama",
     platforms: [
@@ -28,32 +67,11 @@ let package = Package(
                "ggml-cuda.h",
                "Makefile"
             ],
-            sources: [
-                "ggml.c",
-                "llama.cpp",
-                "ggml-alloc.c",
-                "ggml-backend.c",
-                "ggml-quants.c",
-                "ggml-metal.m",
-            ],
-            resources: [
-                .process("ggml-metal.metal")
-            ],
+            sources: sources,
+            resources: resources,
             publicHeadersPath: "spm-headers",
-            cSettings: [
-                .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]),
-                .define("GGML_USE_ACCELERATE"),
-                .unsafeFlags(["-fno-objc-arc"]),
-                .define("GGML_USE_METAL"),
-                // NOTE: NEW_LAPACK will required iOS version 16.4+
-                // We should consider add this in the future when we drop support for iOS 14
-                // (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc)
-                // .define("ACCELERATE_NEW_LAPACK"),
-                // .define("ACCELERATE_LAPACK_ILP64")
-            ],
-            linkerSettings: [
-                .linkedFramework("Accelerate")
-            ]
+            cSettings: cSettings,
+            linkerSettings: linkerSettings
         )
     ],
     cxxLanguageStandard: .cxx11
diff --git a/README-sycl.md b/README-sycl.md
index dd5bf9dea..cfa248a95 100644
--- a/README-sycl.md
+++ b/README-sycl.md
@@ -1,303 +1,388 @@
 # llama.cpp for SYCL
 
 - [Background](#background)
+- [News](#news)
 - [OS](#os)
-- [Intel GPU](#intel-gpu)
+- [Hardware](#hardware)
 - [Docker](#docker)
 - [Linux](#linux)
 - [Windows](#windows)
 - [Environment Variable](#environment-variable)
-- [Known Issue](#known-issue)
-- [Q&A](#q&a)
-- [Todo](#todo)
+- [Known Issue](#known-issues)
+- [Q&A](#qa)
+- [TODO](#todo)
 
 ## Background
 
-SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators—such as CPUs, GPUs, and FPGAs. It is a single-source embedded domain-specific language based on pure C++17.
+**SYCL** is a high-level parallel programming model designed to improve developers productivity writing code across various hardware accelerators such as CPUs, GPUs, and FPGAs. It is a single-source language designed for heterogeneous computing and based on standard C++17.
 
-oneAPI is a specification that is open and standards-based, supporting multiple architecture types including but not limited to GPU, CPU, and FPGA. The spec has both direct programming and API-based programming paradigms.
+**oneAPI** is an open ecosystem and a standard-based specification, supporting multiple architectures including but not limited to intel CPUs, GPUs and FPGAs. The key components of the oneAPI ecosystem include:
 
-Intel uses the SYCL as direct programming language to support CPU, GPUs and FPGAs.
+- **DPCPP** *(Data Parallel C++)*: The primary oneAPI SYCL implementation, which includes the icpx/icx Compilers.
+- **oneAPI Libraries**: A set of highly optimized libraries targeting multiple domains *(e.g. oneMKL - Math Kernel Library)*.
+- **oneAPI LevelZero**: A high performance low level interface for fine-grained control over intel iGPUs and dGPUs.
+- **Nvidia & AMD Plugins**: These are plugins extending oneAPI's DPCPP support to SYCL on Nvidia and AMD GPU targets.
 
-To avoid to re-invent the wheel, this code refer other code paths in llama.cpp (like OpenBLAS, cuBLAS, CLBlast). We use a open-source tool [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) migrate to SYCL.
+### Llama.cpp + SYCL
 
-The llama.cpp for SYCL is used to support Intel GPUs.
+The llama.cpp SYCL backend is designed to support **Intel GPU** firstly. Based on the cross-platform feature of SYCL, it could support other vendor GPUs: Nvidia GPU (*AMD GPU coming*).
 
-For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building).
+When targeting **Intel CPU**, it is recommended to use llama.cpp for [Intel oneMKL](README.md#intel-onemkl) backend.
+
+It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, cuBLAS, CLBlast etc..*. In beginning work, the oneAPI's [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) open-source migration tool (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) was used for this purpose.
+
+## News
+
+- 2024.4
+  - Support data types: GGML_TYPE_IQ4_NL, GGML_TYPE_IQ4_XS, GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ3_S, GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M.
+
+- 2024.3
+  - Release binary files of Windows.
+  - A blog is published: **Run LLM on all Intel GPUs Using llama.cpp**: [intel.com](https://www.intel.com/content/www/us/en/developer/articles/technical/run-llm-on-all-gpus-using-llama-cpp-artical.html) or [medium.com](https://medium.com/@jianyu_neo/run-llm-on-all-intel-gpus-using-llama-cpp-fd2e2dcbd9bd).
+  - New base line is ready: [tag b2437](https://github.com/ggerganov/llama.cpp/tree/b2437).
+  - Support multiple cards: **--split-mode**: [none|layer]; not support [row], it's on developing.
+  - Support to assign main GPU by **--main-gpu**, replace $GGML_SYCL_DEVICE.
+  - Support detecting all GPUs with level-zero and same top **Max compute units**.
+  - Support OPs
+    - hardsigmoid
+    - hardswish
+    - pool2d
+
+- 2024.1
+  - Create SYCL backend for Intel GPU.
+  - Support Windows build
 
 ## OS
 
-|OS|Status|Verified|
-|-|-|-|
-|Linux|Support|Ubuntu 22.04, Fedora Silverblue 39|
-|Windows|Support|Windows 11|
+| OS      | Status  | Verified                           |
+|---------|---------|------------------------------------|
+| Linux   | Support | Ubuntu 22.04, Fedora Silverblue 39 |
+| Windows | Support | Windows 11                         |
 
 
-## Intel GPU
+## Hardware
 
-### Verified
+### Intel GPU
 
-|Intel GPU| Status | Verified Model|
-|-|-|-|
-|Intel Data Center Max Series| Support| Max 1550|
-|Intel Data Center Flex Series| Support| Flex 170|
-|Intel Arc Series| Support| Arc 770, 730M|
-|Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake|
-|Intel iGPU| Support| iGPU in i5-1250P, i7-1260P, i7-1165G7|
+**Verified devices**
 
-Note: If the EUs (Execution Unit) in iGPU is less than 80, the inference speed will be too slow to use.
+| Intel GPU                     | Status  | Verified Model                        |
+|-------------------------------|---------|---------------------------------------|
+| Intel Data Center Max Series  | Support | Max 1550, 1100                        |
+| Intel Data Center Flex Series | Support | Flex 170                              |
+| Intel Arc Series              | Support | Arc 770, 730M                         |
+| Intel built-in Arc GPU        | Support | built-in Arc GPU in Meteor Lake       |
+| Intel iGPU                    | Support | iGPU in i5-1250P, i7-1260P, i7-1165G7 |
 
-### Memory
+*Notes:*
 
-The memory is a limitation to run LLM on GPUs.
+- **Memory**
+  - The device memory is a limitation when running a large model. The loaded model size, *`llm_load_tensors: buffer_size`*, is displayed in the log when running `./bin/main`.
 
-When run llama.cpp, there is print log to show the applied memory on GPU. You could know how much memory to be used in your case. Like `llm_load_tensors:            buffer size =  3577.56 MiB`.
+  - Please make sure the GPU shared memory from the host is large enough to account for the model's size. For e.g. the *llama-2-7b.Q4_0* requires at least 8.0GB for integrated GPU and 4.0GB for discrete GPU.
 
-For iGPU, please make sure the shared memory from host memory is enough. For llama-2-7b.Q4_0, recommend the host memory is 8GB+.
+- **Execution Unit (EU)**
+  - If the iGPU has less than 80 EUs, the inference speed will likely be too slow for practical use.
 
-For dGPU, please make sure the device memory is enough. For llama-2-7b.Q4_0, recommend the device memory is 4GB+.
+### Other Vendor GPU
+
+**Verified devices**
+
+| Nvidia GPU               | Status  | Verified Model |
+|--------------------------|---------|----------------|
+| Ampere Series            | Support | A100, A4000    |
+| Ampere Series *(Mobile)* | Support | RTX 40 Series  |
 
 ## Docker
+The docker build option is currently limited to *intel GPU* targets.
 
-Note:
-- Only docker on Linux is tested. Docker on WSL may not work.
-- You may need to install Intel GPU driver on the host machine (See the [Linux](#linux) section to know how to do that)
-
-### Build the image
-
-You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference.
-
-
+### Build image
 ```sh
-# For F16:
-#docker build -t llama-cpp-sycl --build-arg="LLAMA_SYCL_F16=ON" -f .devops/main-intel.Dockerfile .
-
-# Or, for F32:
-docker build -t llama-cpp-sycl -f .devops/main-intel.Dockerfile .
-
-# Note: you can also use the ".devops/main-server.Dockerfile", which compiles the "server" example
+# Using FP16
+docker build -t llama-cpp-sycl --build-arg="LLAMA_SYCL_F16=ON" -f .devops/main-intel.Dockerfile .
 ```
 
-### Run
+*Notes*:
+
+To build in default FP32 *(Slower than FP16 alternative)*, you can remove the `--build-arg="LLAMA_SYCL_F16=ON"` argument from the previous command.
+
+You can also use the `.devops/server-intel.Dockerfile`, which builds the *"server"* alternative.
+
+### Run container
 
 ```sh
-# Firstly, find all the DRI cards:
+# First, find all the DRI cards
 ls -la /dev/dri
-# Then, pick the card that you want to use.
-
-# For example with "/dev/dri/card1"
+# Then, pick the card that you want to use (here for e.g. /dev/dri/card1).
 docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-sycl -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
 ```
 
+*Notes:*
+- Docker has been tested successfully on native Linux. WSL support has not been verified yet.
+- You may need to install Intel GPU driver on the **host** machine *(Please refer to the [Linux configuration](#linux) for details)*.
+
 ## Linux
 
-### Setup Environment
+### I. Setup Environment
 
-1. Install Intel GPU driver.
+1. **Install GPU drivers**
 
-a. Please install Intel GPU driver by official guide: [Install GPU Drivers](https://dgpu-docs.intel.com/driver/installation.html).
+  - **Intel GPU**
 
-Note: for iGPU, please install the client GPU driver.
+Intel data center GPUs drivers installation guide and download page can be found here: [Get intel dGPU Drivers](https://dgpu-docs.intel.com/driver/installation.html#ubuntu-install-steps).
 
-b. Add user to group: video, render.
+*Note*: for client GPUs *(iGPU & Arc A-Series)*, please refer to the [client iGPU driver installation](https://dgpu-docs.intel.com/driver/client/overview.html).
+
+Once installed, add the user(s) to the `video` and `render` groups.
 
 ```sh
-sudo usermod -aG render username
-sudo usermod -aG video username
+sudo usermod -aG render $USER
+sudo usermod -aG video $USER
 ```
 
-Note: re-login to enable it.
+*Note*: logout/re-login for the changes to take effect.
 
-c. Check
+Verify installation through `clinfo`:
 
 ```sh
 sudo apt install clinfo
 sudo clinfo -l
 ```
 
-Output (example):
+Sample output:
 
-```
+```sh
 Platform #0: Intel(R) OpenCL Graphics
  `-- Device #0: Intel(R) Arc(TM) A770 Graphics
 
-
 Platform #0: Intel(R) OpenCL HD Graphics
  `-- Device #0: Intel(R) Iris(R) Xe Graphics [0x9a49]
 ```
 
-2. Install Intel® oneAPI Base toolkit.
+- **Nvidia GPU**
 
-a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html).
+In order to target Nvidia GPUs through SYCL, please make sure the CUDA/CUBLAS native requirements *-found [here](README.md#cuda)-* are installed.
 
-Recommend to install to default folder: **/opt/intel/oneapi**.
+2. **Install Intel® oneAPI Base toolkit**
 
-Following guide use the default folder as example. If you use other folder, please modify the following guide info with your folder.
+- **For Intel GPU**
 
-b. Check
+The base toolkit can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page.
+
+Please follow the instructions for downloading and installing the Toolkit for Linux, and preferably keep the default installation values unchanged, notably the installation path *(`/opt/intel/oneapi` by default)*.
+
+Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable.
+
+Upon a successful installation, SYCL is enabled for the available intel devices, along with relevant libraries such as oneAPI MKL for intel GPUs.
+
+- **Adding support to Nvidia GPUs**
+
+**oneAPI Plugin**: In order to enable SYCL support on Nvidia GPUs, please install the [Codeplay oneAPI Plugin for Nvidia GPUs](https://developer.codeplay.com/products/oneapi/nvidia/download). User should also make sure the plugin version matches the installed base toolkit one *(previous step)* for a seamless "oneAPI on Nvidia GPU" setup.
+
+
+**oneMKL for cuBlas**: The current oneMKL releases *(shipped with the oneAPI base-toolkit)* do not contain the cuBLAS backend. A build from source of the upstream [oneMKL](https://github.com/oneapi-src/oneMKL) with the *cuBLAS* backend enabled is thus required to run it on Nvidia GPUs.
 
 ```sh
-source /opt/intel/oneapi/setvars.sh
+git clone https://github.com/oneapi-src/oneMKL
+cd oneMKL
+cmake -B buildWithCublas -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_CUBLAS_BACKEND=ON -DTARGET_DOMAINS=blas
+cmake --build buildWithCublas --config Release
+```
 
+
+3. **Verify installation and environment**
+
+In order to check the available SYCL devices on the machine, please use the `sycl-ls` command.
+```sh
+source /opt/intel/oneapi/setvars.sh
 sycl-ls
 ```
 
-There should be one or more level-zero devices. Please confirm that at least one GPU is present, like **[ext_oneapi_level_zero:gpu:0]**.
+- **Intel GPU**
+
+When targeting an intel GPU, the user should expect one or more level-zero devices among the available SYCL devices. Please make sure that at least one GPU is present, for instance [`ext_oneapi_level_zero:gpu:0`] in the sample output below:
 
-Output (example):
 ```
 [opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2  [2023.16.10.0.17_160000]
 [opencl:cpu:1] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i7-13700K OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000]
 [opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics OpenCL 3.0 NEO  [23.30.26918.50]
 [ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Arc(TM) A770 Graphics 1.3 [1.3.26918]
-
 ```
 
-2. Build locally:
+- **Nvidia GPU**
 
-Note:
-- You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference.
-- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only.
+Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA device [`ext_oneapi_cuda:gpu`] as bellow:
+```
+[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2  [2023.16.12.0.12_195853.xmain-hotfix]
+[opencl:cpu:1] Intel(R) OpenCL, Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz OpenCL 3.0 (Build 0) [2023.16.12.0.12_195853.xmain-hotfix]
+[ext_oneapi_cuda:gpu:0] NVIDIA CUDA BACKEND, NVIDIA A100-PCIE-40GB 8.0 [CUDA 12.2]
+```
 
+### II. Build llama.cpp
+
+#### Intel GPU
 ```sh
-mkdir -p build
-cd build
+# Export relevant ENV variables
 source /opt/intel/oneapi/setvars.sh
 
-# For FP16:
-#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
+# Build LLAMA with MKL BLAS acceleration for intel GPU
 
-# Or, for FP32:
-cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
+# Option 1: Use FP32 (recommended for better performance in most cases)
+cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
 
-# Build example/main only
-#cmake --build . --config Release --target main
+# Option 2: Use FP16
+cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
 
-# Or, build all binary
-cmake --build . --config Release -v
-
-cd ..
+# build all binary
+cmake --build build --config Release -j -v
 ```
 
-or
-
+#### Nvidia GPU
 ```sh
-./examples/sycl/build.sh
+# Export relevant ENV variables
+export LD_LIBRARY_PATH=/path/to/oneMKL/buildWithCublas/lib:$LD_LIBRARY_PATH
+export LIBRARY_PATH=/path/to/oneMKL/buildWithCublas/lib:$LIBRARY_PATH
+export CPLUS_INCLUDE_DIR=/path/to/oneMKL/buildWithCublas/include:$CPLUS_INCLUDE_DIR
+export CPLUS_INCLUDE_DIR=/path/to/oneMKL/include:$CPLUS_INCLUDE_DIR
+
+# Build LLAMA with Nvidia BLAS acceleration through SYCL
+
+# Option 1: Use FP32 (recommended for better performance in most cases)
+cmake -B build -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
+
+# Option 2: Use FP16
+cmake -B build -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
+
+# build all binary
+cmake --build build --config Release -j -v
+
 ```
 
-### Run
+### III. Run the inference
 
-1. Put model file to folder **models**
+1. Retrieve and prepare model
 
-You could download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) as example.
+You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example.
 
 2. Enable oneAPI running environment
 
-```
+```sh
 source /opt/intel/oneapi/setvars.sh
 ```
 
-3. List device ID
+3. List devices information
 
-Run without parameter:
+Similar to the native `sycl-ls`, available SYCL devices can be queried as follow:
 
 ```sh
 ./build/bin/ls-sycl-device
-
-# or running the "main" executable and look at the output log:
-
-./build/bin/main
+```
+A example of such log in a system with 1 *intel CPU* and 1 *intel GPU* can look like the following:
+```
+found 6 SYCL devices:
+|  |                  |                                             |Compute   |Max compute|Max work|Max sub|               |
+|ID|       Device Type|                                         Name|capability|units      |group   |group  |Global mem size|
+|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|
+| 0|[level_zero:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       1.3|        512|    1024|     32|    16225243136|
+| 1|[level_zero:gpu:1]|                    Intel(R) UHD Graphics 770|       1.3|         32|     512|     32|    53651849216|
+| 2|    [opencl:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       3.0|        512|    1024|     32|    16225243136|
+| 3|    [opencl:gpu:1]|                    Intel(R) UHD Graphics 770|       3.0|         32|     512|     32|    53651849216|
+| 4|    [opencl:cpu:0]|         13th Gen Intel(R) Core(TM) i7-13700K|       3.0|         24|    8192|     64|    67064815616|
+| 5|    [opencl:acc:0]|               Intel(R) FPGA Emulation Device|       1.2|         24|67108864|     64|    67064815616|
 ```
 
-Check the ID in startup log, like:
+| Attribute              | Note                                                        |
+|------------------------|-------------------------------------------------------------|
+| compute capability 1.3 | Level-zero driver/runtime, recommended                      |
+| compute capability 3.0 | OpenCL driver/runtime, slower than level-zero in most cases |
 
-```
-found 4 SYCL devices:
-  Device 0: Intel(R) Arc(TM) A770 Graphics,	compute capability 1.3,
-    max compute_units 512,	max work group size 1024,	max sub group size 32,	global mem size 16225243136
-  Device 1: Intel(R) FPGA Emulation Device,	compute capability 1.2,
-    max compute_units 24,	max work group size 67108864,	max sub group size 64,	global mem size 67065057280
-  Device 2: 13th Gen Intel(R) Core(TM) i7-13700K,	compute capability 3.0,
-    max compute_units 24,	max work group size 8192,	max sub group size 64,	global mem size 67065057280
-  Device 3: Intel(R) Arc(TM) A770 Graphics,	compute capability 3.0,
-    max compute_units 512,	max work group size 1024,	max sub group size 32,	global mem size 16225243136
+4. Launch inference
 
-```
+There are two device selection modes:
 
-|Attribute|Note|
-|-|-|
-|compute capability 1.3|Level-zero running time, recommended |
-|compute capability 3.0|OpenCL running time, slower than level-zero in most cases|
+- Single device: Use one device target specified by the user.
+- Multiple devices: Automatically select the devices with the same largest Max compute-units.
 
-4. Set device ID and execute llama.cpp
+| Device selection | Parameter                              |
+|------------------|----------------------------------------|
+| Single device    | --split-mode none --main-gpu DEVICE_ID |
+| Multiple devices | --split-mode layer (default)           |
 
-Set device ID = 0 by **GGML_SYCL_DEVICE=0**
+Examples:
+
+- Use device 0:
 
 ```sh
-GGML_SYCL_DEVICE=0 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
+ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm none -mg 0
 ```
 or run by script:
 
+```sh
+./examples/sycl/run_llama2.sh 0
+```
+
+- Use multiple devices:
+
+```sh
+ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm layer
+```
+
+Otherwise, you can run the script:
+
 ```sh
 ./examples/sycl/run_llama2.sh
 ```
 
-Note:
+*Notes:*
 
-- By default, mmap is used to read model file. In some cases, it leads to the hang issue. Recommend to use parameter **--no-mmap** to disable mmap() to skip this issue.
+- Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow:
 
-
-5. Check the device ID in output
-
-Like:
+```sh
+detect 1 SYCL GPUs: [0] with top Max compute units:512
 ```
-Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device
+Or
+```sh
+use 1 SYCL GPUs: [0] with Max compute units:512
 ```
 
 ## Windows
 
-### Setup Environment
+### I. Setup Environment
 
-1. Install Intel GPU driver.
+1. Install GPU driver
 
-Please install Intel GPU driver by official guide: [Install GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html).
+Intel GPU drivers instructions guide and download page can be found here: [Get intel GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html).
 
-Note: **The driver is mandatory for compute function**.
+2. Install Visual Studio
 
-2. Install Visual Studio.
+If you already have a recent version of Microsoft Visual Studio, you can skip this step. Otherwise, please refer to the official download page for [Microsoft Visual Studio](https://visualstudio.microsoft.com/).
 
-Please install [Visual Studio](https://visualstudio.microsoft.com/) which impact oneAPI environment enabling in Windows.
+3. Install Intel® oneAPI Base toolkit
 
-3. Install Intel® oneAPI Base toolkit.
+The base toolkit can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page.
 
-a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html).
+Please follow the instructions for downloading and installing the Toolkit for Windows, and preferably keep the default installation values unchanged, notably the installation path *(`C:\Program Files (x86)\Intel\oneAPI` by default)*.
 
-Recommend to install to default folder: **C:\Program Files (x86)\Intel\oneAPI**.
-
-Following guide uses the default folder as example. If you use other folder, please modify the following guide info with your folder.
+Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable.
 
 b. Enable oneAPI running environment:
 
-- In Search, input 'oneAPI'.
+- Type "oneAPI" in the search bar, then open the `Intel oneAPI command prompt for Intel 64 for Visual Studio 2022` App.
 
-Search & open "Intel oneAPI command prompt for Intel 64 for Visual Studio 2022"
-
-- In Run:
-
-In CMD:
+- On the command prompt, enable the runtime environment with the following:
 ```
 "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
 ```
 
-c. Check GPU
+c. Verify installation
 
-In oneAPI command line:
+In the oneAPI command line, run the following to print the available SYCL devices:
 
 ```
 sycl-ls
 ```
 
-There should be one or more level-zero devices. Please confirm that at least one GPU is present, like **[ext_oneapi_level_zero:gpu:0]**.
+There should be one or more *level-zero* GPU devices displayed as **[ext_oneapi_level_zero:gpu]**. Below is example of such output detecting an *intel Iris Xe* GPU as a Level-zero SYCL device:
 
 Output (example):
 ```
@@ -307,113 +392,111 @@ Output (example):
 [ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044]
 ```
 
-4. Install cmake & make
+4. Install build tools
 
 a. Download & install cmake for Windows: https://cmake.org/download/
 
 b. Download & install mingw-w64 make for Windows provided by w64devkit
 
-- Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
+- Download the 1.19.0 version of [w64devkit](https://github.com/skeeto/w64devkit/releases/download/v1.19.0/w64devkit-1.19.0.zip).
 
 - Extract `w64devkit` on your pc.
 
-- Add the **bin** folder path in the Windows system PATH environment, like `C:\xxx\w64devkit\bin\`.
+- Add the **bin** folder path in the Windows system PATH environment (for e.g. `C:\xxx\w64devkit\bin\`).
 
-### Build locally:
+### II. Build llama.cpp
 
-In oneAPI command line window:
+On the oneAPI command line window, step into the llama.cpp main directory and run the following:
 
 ```
-mkdir -p build
-cd build
 @call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
 
-::  for FP16
-::  faster for long-prompt inference
-::  cmake -G "MinGW Makefiles" ..  -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
+# Option 1: Use FP32 (recommended for better performance in most cases)
+cmake -B build -G "MinGW Makefiles" -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release
 
-::  for FP32
-cmake -G "MinGW Makefiles" ..  -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release
+# Option 2: Or FP16
+cmake -B build -G "MinGW Makefiles" -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
 
-
-::  build example/main only
-::  make main
-
-::  build all binary
-make -j
-cd ..
+cmake --build build --config Release -j
 ```
 
-or
-
-```
+Otherwise, run the `win-build-sycl.bat` wrapper which encapsulates the former instructions:
+```sh
 .\examples\sycl\win-build-sycl.bat
 ```
 
-Note:
+*Notes:*
 
-- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only.
+- By default, calling `make` will build all target binary files. In case of a minimal experimental setup, the user can build the inference executable only through `make main`.
 
-### Run
+### III. Run the inference
 
-1. Put model file to folder **models**
+1. Retrieve and prepare model
 
-You could download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) as example.
+You can refer to the general [*Prepare and Quantize*](README#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example.
 
 2. Enable oneAPI running environment
 
-- In Search, input 'oneAPI'.
-
-Search & open "Intel oneAPI command prompt for Intel 64 for Visual Studio 2022"
-
-- In Run:
-
-In CMD:
+On the oneAPI command line window, run the following and step into the llama.cpp directory:
 ```
 "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
 ```
 
-3. List device ID
+3. List devices information
 
-Run without parameter:
+Similar to the native `sycl-ls`, available SYCL devices can be queried as follow:
 
 ```
 build\bin\ls-sycl-device.exe
-
-or
-
-build\bin\main.exe
 ```
 
-Check the ID in startup log, like:
-
+The output of this command in a system with 1 *intel CPU* and 1 *intel GPU* would look like the following:
 ```
-found 4 SYCL devices:
-  Device 0: Intel(R) Arc(TM) A770 Graphics,	compute capability 1.3,
-    max compute_units 512,	max work group size 1024,	max sub group size 32,	global mem size 16225243136
-  Device 1: Intel(R) FPGA Emulation Device,	compute capability 1.2,
-    max compute_units 24,	max work group size 67108864,	max sub group size 64,	global mem size 67065057280
-  Device 2: 13th Gen Intel(R) Core(TM) i7-13700K,	compute capability 3.0,
-    max compute_units 24,	max work group size 8192,	max sub group size 64,	global mem size 67065057280
-  Device 3: Intel(R) Arc(TM) A770 Graphics,	compute capability 3.0,
-    max compute_units 512,	max work group size 1024,	max sub group size 32,	global mem size 16225243136
+found 6 SYCL devices:
+|  |                  |                                             |Compute   |Max compute|Max work|Max sub|               |
+|ID|       Device Type|                                         Name|capability|units      |group   |group  |Global mem size|
+|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|
+| 0|[level_zero:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       1.3|        512|    1024|     32|    16225243136|
+| 1|[level_zero:gpu:1]|                    Intel(R) UHD Graphics 770|       1.3|         32|     512|     32|    53651849216|
+| 2|    [opencl:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       3.0|        512|    1024|     32|    16225243136|
+| 3|    [opencl:gpu:1]|                    Intel(R) UHD Graphics 770|       3.0|         32|     512|     32|    53651849216|
+| 4|    [opencl:cpu:0]|         13th Gen Intel(R) Core(TM) i7-13700K|       3.0|         24|    8192|     64|    67064815616|
+| 5|    [opencl:acc:0]|               Intel(R) FPGA Emulation Device|       1.2|         24|67108864|     64|    67064815616|
 
 ```
 
-|Attribute|Note|
-|-|-|
-|compute capability 1.3|Level-zero running time, recommended |
-|compute capability 3.0|OpenCL running time, slower than level-zero in most cases|
+| Attribute              | Note                                                      |
+|------------------------|-----------------------------------------------------------|
+| compute capability 1.3 | Level-zero running time, recommended                      |
+| compute capability 3.0 | OpenCL running time, slower than level-zero in most cases |
 
-4. Set device ID and execute llama.cpp
 
-Set device ID = 0 by **set GGML_SYCL_DEVICE=0**
+4. Launch inference
+
+There are two device selection modes:
+
+- Single device: Use one device assigned by user.
+- Multiple devices: Automatically choose the devices with the same biggest Max compute units.
+
+| Device selection | Parameter                              |
+|------------------|----------------------------------------|
+| Single device    | --split-mode none --main-gpu DEVICE_ID |
+| Multiple devices | --split-mode layer (default)           |
+
+Examples:
+
+- Use device 0:
 
 ```
-set GGML_SYCL_DEVICE=0
-build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0
+build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm none -mg 0
 ```
-or run by script:
+
+- Use multiple devices:
+
+```
+build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm layer
+```
+Otherwise, run the following wrapper script:
 
 ```
 .\examples\sycl\win-run-llama2.bat
@@ -421,74 +504,65 @@ or run by script:
 
 Note:
 
-- By default, mmap is used to read model file. In some cases, it leads to the hang issue. Recommend to use parameter **--no-mmap** to disable mmap() to skip this issue.
+- Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow:
 
-
-5. Check the device ID in output
-
-Like:
+```sh
+detect 1 SYCL GPUs: [0] with top Max compute units:512
 ```
-Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device
+Or
+```sh
+use 1 SYCL GPUs: [0] with Max compute units:512
 ```
 
 ## Environment Variable
 
 #### Build
 
-|Name|Value|Function|
-|-|-|-|
-|LLAMA_SYCL|ON (mandatory)|Enable build with SYCL code path. <br>For FP32/FP16, LLAMA_SYCL=ON is mandatory.|
-|LLAMA_SYCL_F16|ON (optional)|Enable FP16 build with SYCL code path. Faster for long-prompt inference. <br>For FP32, not set it.|
-|CMAKE_C_COMPILER|icx|Use icx compiler for SYCL code path|
-|CMAKE_CXX_COMPILER|icpx (Linux), icx (Windows)|use icpx/icx for SYCL code path|
+| Name               | Value                             | Function                                    |
+|--------------------|-----------------------------------|---------------------------------------------|
+| LLAMA_SYCL         | ON (mandatory)                    | Enable build with SYCL code path.           |
+| LLAMA_SYCL_TARGET  | INTEL *(default)* \| NVIDIA       | Set the SYCL target device type.            |
+| LLAMA_SYCL_F16     | OFF *(default)* \|ON *(optional)* | Enable FP16 build with SYCL code path.      |
+| CMAKE_C_COMPILER   | icx                               | Set *icx* compiler for SYCL code path.      |
+| CMAKE_CXX_COMPILER | icpx *(Linux)*, icx *(Windows)*   | Set `icpx/icx` compiler for SYCL code path. |
 
-#### Running
+#### Runtime
 
+| Name              | Value            | Function                                                                                                                  |
+|-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------|
+| GGML_SYCL_DEBUG   | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG                                                                             |
+| ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer |
 
-|Name|Value|Function|
-|-|-|-|
-|GGML_SYCL_DEVICE|0 (default) or 1|Set the device id used. Check the device ids by default running output|
-|GGML_SYCL_DEBUG|0 (default) or 1|Enable log function by macro: GGML_SYCL_DEBUG|
+## Known Issues
 
-## Known Issue
-
-- Hang during startup
-
-  llama.cpp use mmap as default way to read model file and copy to GPU. In some system, memcpy will be abnormal and block.
-
-  Solution: add **--no-mmap** or **--mmap 0**.
+- `Split-mode:[row]` is not supported.
 
 ## Q&A
 
 - Error:  `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`.
 
-  Miss to enable oneAPI running environment.
+  - Potential cause: Unavailable oneAPI installation or not set ENV variables.
+  - Solution: Install *oneAPI base toolkit* and enable its ENV through: `source /opt/intel/oneapi/setvars.sh`.
 
-  Install oneAPI base toolkit and enable it by: `source /opt/intel/oneapi/setvars.sh`.
+- General compiler error:
 
-- In Windows, no result, not error.
+  - Remove **build** folder or try a clean-build.
 
-  Miss to enable oneAPI running environment.
+- I can **not** see `[ext_oneapi_level_zero:gpu]` afer installing the GPU driver on Linux.
 
-- Meet compile error.
+  Please double-check with `sudo sycl-ls`.
 
-  Remove folder **build** and try again.
-
-- I can **not** see **[ext_oneapi_level_zero:gpu:0]** afer install GPU driver in Linux.
-
-  Please run **sudo sycl-ls**.
-
-  If you see it in result, please add video/render group to your ID:
+  If it's present in the list, please add video/render group to your user then **logout/login** or restart your system:
 
   ```
-  sudo usermod -aG render username
-  sudo usermod -aG video username
+  sudo usermod -aG render $USER
+  sudo usermod -aG video $USER
   ```
+  Otherwise, please double-check the GPU driver installation steps.
 
-  Then **relogin**.
+### **GitHub contribution**:
+Please add the **[SYCL]** prefix/tag in issues/PRs titles to help the SYCL-team check/address them without delay.
 
-  If you do not see it, please check the installation GPU steps again.
+## TODO
 
-## Todo
-
-- Support multiple cards.
+- Support row layer split for multiple card runs.
diff --git a/README.md b/README.md
index 70866e249..9f2f8df64 100644
--- a/README.md
+++ b/README.md
@@ -2,22 +2,33 @@
 
 ![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png)
 
-[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
+[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg?branch=master&event=schedule)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml)
 
 [Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml)
 
 Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++
 
+### Recent API changes
+
+- [2024 Apr 21] `llama_token_to_piece` can now optionally render special tokens https://github.com/ggerganov/llama.cpp/pull/6807
+- [2024 Apr 4] State and session file functions reorganized under `llama_state_*` https://github.com/ggerganov/llama.cpp/pull/6341
+- [2024 Mar 26] Logits and embeddings API updated for compactness https://github.com/ggerganov/llama.cpp/pull/6122
+- [2024 Mar 13] Add `llama_synchronize()` + `llama_context_params.n_ubatch` https://github.com/ggerganov/llama.cpp/pull/6017
+- [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_seq_max()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328
+- [2024 Mar 4] Embeddings API updated https://github.com/ggerganov/llama.cpp/pull/5796
+- [2024 Mar 3] `struct llama_context_params` https://github.com/ggerganov/llama.cpp/pull/5849
+
 ### Hot topics
 
-- Remove LLAMA_MAX_DEVICES and LLAMA_SUPPORTS_GPU_OFFLOAD: https://github.com/ggerganov/llama.cpp/pull/5240
-- Incoming backends: https://github.com/ggerganov/llama.cpp/discussions/5138
-  - [SYCL backend](README-sycl.md) is ready (1/28/2024), support Linux/Windows in Intel GPUs (iGPU, Arc/Flex/Max series)
-- New SOTA quantized models, including pure 2-bits: https://huggingface.co/ikawrakow
-- Collecting Apple Silicon performance stats:
-  - M-series: https://github.com/ggerganov/llama.cpp/discussions/4167
-  - A-series: https://github.com/ggerganov/llama.cpp/discussions/4508
-- Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216
+- **Initial Flash-Attention support: https://github.com/ggerganov/llama.cpp/pull/5021**
+- BPE pre-tokenization support has been added: https://github.com/ggerganov/llama.cpp/pull/6920
+- MoE memory layout has been updated - reconvert models for `mmap` support and regenerate `imatrix` https://github.com/ggerganov/llama.cpp/pull/6387
+- Model sharding instructions using `gguf-split` https://github.com/ggerganov/llama.cpp/discussions/6404
+- Fix major bug in Metal batched inference https://github.com/ggerganov/llama.cpp/pull/6225
+- Multi-GPU pipeline parallelism support https://github.com/ggerganov/llama.cpp/pull/6017
+- Looking for contributions to add Deepseek support: https://github.com/ggerganov/llama.cpp/issues/5981
+- Quantization blind testing: https://github.com/ggerganov/llama.cpp/discussions/5962
+- Initial Mamba support has been added: https://github.com/ggerganov/llama.cpp/pull/5328
 
 ----
 
@@ -84,9 +95,11 @@ Typically finetunes of the base models below are supported as well.
 
 - [X] LLaMA 🦙
 - [x] LLaMA 2 🦙🦙
+- [x] LLaMA 3 🦙🦙🦙
 - [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1)
 - [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
-- [X] Falcon
+- [x] [DBRX](https://huggingface.co/databricks/dbrx-instruct)
+- [X] [Falcon](https://huggingface.co/models?search=tiiuae/falcon)
 - [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)
 - [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
 - [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)
@@ -107,16 +120,31 @@ Typically finetunes of the base models below are supported as well.
 - [x] [Orion 14B](https://github.com/ggerganov/llama.cpp/pull/5118)
 - [x] [InternLM2](https://huggingface.co/models?search=internlm2)
 - [x] [CodeShell](https://github.com/WisdomShell/codeshell)
+- [x] [Gemma](https://ai.google.dev/gemma)
+- [x] [Mamba](https://github.com/state-spaces/mamba)
+- [x] [Grok-1](https://huggingface.co/keyfan/grok-1-hf)
+- [x] [Xverse](https://huggingface.co/models?search=xverse)
+- [x] [Command-R models](https://huggingface.co/models?search=CohereForAI/c4ai-command-r)
+- [x] [SEA-LION](https://huggingface.co/models?search=sea-lion)
+- [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B)
+- [x] [OLMo](https://allenai.org/olmo)
+
+(instructions for supporting more models: [HOWTO-add-model.md](./docs/HOWTO-add-model.md))
 
 **Multimodal models:**
 
-- [x] [LLaVA 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e)
+- [x] [LLaVA 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e), [LLaVA 1.6 models](https://huggingface.co/collections/liuhaotian/llava-16-65b9e40155f60fd046a5ccf2)
 - [x] [BakLLaVA](https://huggingface.co/models?search=SkunkworksAI/Bakllava)
 - [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5)
 - [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V)
 - [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM)
 - [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL)
+- [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM)
+- [x] [Moondream](https://huggingface.co/vikhyatk/moondream2)
 
+**HTTP server**
+
+[llama.cpp web server](./examples/server) is a lightweight [OpenAI API](https://github.com/openai/openai-openapi) compatible HTTP server that can be used to serve local models and easily connect them to existing clients.
 
 **Bindings:**
 
@@ -125,7 +153,9 @@ Typically finetunes of the base models below are supported as well.
 - Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp)
 - JS/TS (llama.cpp server client): [lgrammel/modelfusion](https://modelfusion.dev/integration/model-provider/llamacpp)
 - JavaScript/Wasm (works in browser): [tangledgroup/llama-cpp-wasm](https://github.com/tangledgroup/llama-cpp-wasm)
+- Typescript/Wasm (nicer API, available on npm): [ngxson/wllama](https://github.com/ngxson/wllama)
 - Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb)
+- Rust (more features): [edgenai/llama_cpp-rs](https://github.com/edgenai/llama_cpp-rs)
 - Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
 - Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs)
 - C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
@@ -135,6 +165,7 @@ Typically finetunes of the base models below are supported as well.
 - Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp)
 - Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig)
 - Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart)
+- PHP (API bindings and features built on top of llama.cpp): [distantmagic/resonance](https://github.com/distantmagic/resonance) [(more info)](https://github.com/ggerganov/llama.cpp/pull/6326)
 
 **UI:**
 
@@ -145,6 +176,8 @@ Unless otherwise noted these projects are open-source with permissive licensing:
 - [nat/openplayground](https://github.com/nat/openplayground)
 - [Faraday](https://faraday.dev/) (proprietary)
 - [LMStudio](https://lmstudio.ai/) (proprietary)
+- [Layla](https://play.google.com/store/apps/details?id=com.laylalite) (proprietary)
+- [LocalAI](https://github.com/mudler/LocalAI) (MIT)
 - [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL)
 - [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile)
 - [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all)
@@ -154,8 +187,20 @@ Unless otherwise noted these projects are open-source with permissive licensing:
 - [cztomsik/ava](https://github.com/cztomsik/ava) (MIT)
 - [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal)
 - [pythops/tenere](https://github.com/pythops/tenere) (AGPL)
+- [RecurseChat](https://recurse.chat/) (proprietary)
 - [semperai/amica](https://github.com/semperai/amica)
 - [withcatai/catai](https://github.com/withcatai/catai)
+- [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT)
+- [Msty](https://msty.app) (proprietary)
+- [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT)
+- [KanTV](https://github.com/zhouwg/kantv?tab=readme-ov-file)(Apachev2.0 or later)
+- [Dot](https://github.com/alexpinel/Dot) (GPL)
+- [MindMac](https://mindmac.app) (proprietary)
+- [KodiBot](https://github.com/firatkiral/kodibot) (GPL)
+- [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT)
+- [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT)
+
+*(to have a project listed here, it should clearly state that it depends on `llama.cpp`)*
 
 ---
 
@@ -265,6 +310,8 @@ In order to build llama.cpp you have three different options.
       make
       ```
 
+      **Note**: for `Debug` builds, run `make LLAMA_DEBUG=1`
+
   - On Windows:
 
     1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
@@ -279,12 +326,26 @@ In order to build llama.cpp you have three different options.
 - Using `CMake`:
 
     ```bash
-    mkdir build
-    cd build
-    cmake ..
-    cmake --build . --config Release
+    cmake -B build
+    cmake --build build --config Release
     ```
 
+    **Note**: for `Debug` builds, there are two cases:
+
+    - Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag):
+
+      ```bash
+      cmake -B build -DCMAKE_BUILD_TYPE=Debug
+      cmake --build build
+      ```
+
+    - Multi-config generators (`-G` param set to Visual Studio, XCode...):
+
+      ```bash
+      cmake -B build -G "Xcode"
+      cmake --build build --config Debug
+      ```
+
 - Using `Zig` (version 0.11 or later):
 
     Building for optimization levels and CPU features can be accomplished using standard build arguments, for example AVX2, FMA, F16C,
@@ -396,10 +457,8 @@ Building the program with BLAS support may lead to some performance improvements
   - Using `CMake` on Linux:
 
       ```bash
-      mkdir build
-      cd build
-      cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS
-      cmake --build . --config Release
+      cmake -B build -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS
+      cmake --build build --config Release
       ```
 
 - #### BLIS
@@ -419,11 +478,9 @@ Building the program with BLAS support may lead to some performance improvements
   - Using manual oneAPI installation:
     By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps:
       ```bash
-      mkdir build
-      cd build
       source /opt/intel/oneapi/setvars.sh # You can skip this step if  in oneapi-basekit docker image, only required for manual installation
-      cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON
-      cmake --build . --config Release
+      cmake -B build -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON
+      cmake --build build --config Release
       ```
 
   - Using oneAPI docker image:
@@ -431,44 +488,39 @@ Building the program with BLAS support may lead to some performance improvements
 
   Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information.
 
-- #### cuBLAS
+- #### CUDA
 
-  This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).
+  This provides GPU acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).
 
   For Jetson user, if you have Jetson Orin, you can try this: [Offical Support](https://www.jetson-ai-lab.com/tutorial_text-generation.html). If you are using an old model(nano/TX2), need some additional operations before compiling.
 
   - Using `make`:
     ```bash
-    make LLAMA_CUBLAS=1
+    make LLAMA_CUDA=1
     ```
   - Using `CMake`:
 
     ```bash
-    mkdir build
-    cd build
-    cmake .. -DLLAMA_CUBLAS=ON
-    cmake --build . --config Release
+    cmake -B build -DLLAMA_CUDA=ON
+    cmake --build build --config Release
     ```
 
   The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance:
 
-<!---
-  | LLAMA_CUDA_CUBLAS       | Boolean                |   false | Use cuBLAS instead of custom CUDA kernels for prompt processing. Faster for all quantization formats except for q4_0 and q8_0, especially for k-quants. Increases VRAM usage (700 MiB for 7b, 970 MiB for 13b, 1430 MiB for 33b). |
---->
-  | Option                         | Legal values           | Default | Description |
-  |--------------------------------|------------------------|---------|-------------|
-  | LLAMA_CUDA_FORCE_DMMV          | Boolean                |   false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. |
-  | LLAMA_CUDA_DMMV_X              | Positive integer >= 32 |      32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
-  | LLAMA_CUDA_MMV_Y               | Positive integer       |       1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. |
-  | LLAMA_CUDA_F16                 | Boolean                |   false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. |
-  | LLAMA_CUDA_KQUANTS_ITER        | 1 or 2                 |       2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
-  | LLAMA_CUDA_PEER_MAX_BATCH_SIZE | Positive integer       |     128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. |
+  | Option                         | Legal values           | Default | Description                                                                                                                                                                                                                                                                             |
+  |--------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | LLAMA_CUDA_FORCE_DMMV          | Boolean                | false   | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. |
+  | LLAMA_CUDA_DMMV_X              | Positive integer >= 32 | 32      | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants.                                         |
+  | LLAMA_CUDA_MMV_Y               | Positive integer       | 1       | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended.                                                                                                                                         |
+  | LLAMA_CUDA_F16                 | Boolean                | false   | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs.                                                           |
+  | LLAMA_CUDA_KQUANTS_ITER        | 1 or 2                 | 2       | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs.                                                                                                                     |
+  | LLAMA_CUDA_PEER_MAX_BATCH_SIZE | Positive integer       | 128     | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial.                                                                         |
 
 - #### hipBLAS
 
   This provides BLAS acceleration on HIP-supported AMD GPUs.
   Make sure to have ROCm installed.
-  You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/en/latest/deploy/linux/quick_start.html).
+  You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html#rocm-install-quick).
 
   - Using `make`:
     ```bash
@@ -477,15 +529,15 @@ Building the program with BLAS support may lead to some performance improvements
   - Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU):
     ```bash
     CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ \
-        cmake -H. -Bbuild -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
-        && cmake --build build -- -j 16
+        cmake -B build -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
+        && cmake --build build --config Release -- -j 16
     ```
     On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DLLAMA_HIP_UMA=ON"`.
     However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs).
 
   - Using `make` (example for target gfx1030, build with 16 CPU threads):
     ```bash
-    make -j16 LLAMA_HIPBLAS=1 LLAMA_HIP_UMA=1 AMDGPU_TARGETS=gxf1030
+    make -j16 LLAMA_HIPBLAS=1 LLAMA_HIP_UMA=1 AMDGPU_TARGETS=gfx1030
     ```
 
   - Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU):
@@ -493,7 +545,7 @@ Building the program with BLAS support may lead to some performance improvements
     set PATH=%HIP_PATH%\bin;%PATH%
     mkdir build
     cd build
-    cmake -G Ninja -DAMDGPU_TARGETS=gfx1100 -DLLAMA_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ ..
+    cmake -G Ninja -DAMDGPU_TARGETS=gfx1100 -DLLAMA_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release ..
     cmake --build .
     ```
     Make sure that `AMDGPU_TARGETS` is set to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors)
@@ -504,18 +556,18 @@ Building the program with BLAS support may lead to some performance improvements
   If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3.
   The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above):
 
-  | Option                  | Legal values           | Default | Description |
-  |-------------------------|------------------------|---------|-------------|
-  | LLAMA_CUDA_DMMV_X       | Positive integer >= 32 |      32 | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
-  | LLAMA_CUDA_MMV_Y        | Positive integer       |       1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
-  | LLAMA_CUDA_KQUANTS_ITER | 1 or 2                 |       2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
+  | Option                  | Legal values           | Default | Description                                                                                                                                                                                                                                    |
+  |-------------------------|------------------------|---------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | LLAMA_CUDA_DMMV_X       | Positive integer >= 32 | 32      | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
+  | LLAMA_CUDA_MMV_Y        | Positive integer       | 1       | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants.                                                                       |
+  | LLAMA_CUDA_KQUANTS_ITER | 1 or 2                 | 2       | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs.                                                                             |
 
 - #### CLBlast
 
   OpenCL acceleration is provided by the matrix multiplication kernels from the [CLBlast](https://github.com/CNugteren/CLBlast) project and custom kernels for ggml that can generate tokens on the GPU.
 
   You will need the [OpenCL SDK](https://github.com/KhronosGroup/OpenCL-SDK).
-    - For Ubuntu or Debian, the packages `opencl-headers`, `ocl-icd` may be needed.
+    - For Ubuntu, Debian, and Fedora the packages `opencl-headers`, `ocl-icd` may be needed.
 
     - For Windows, a pre-built SDK is available on the [OpenCL Releases](https://github.com/KhronosGroup/OpenCL-SDK/releases) page.
 
@@ -524,15 +576,14 @@ Building the program with BLAS support may lead to some performance improvements
 
         ```sh
         git clone --recurse-submodules https://github.com/KhronosGroup/OpenCL-SDK.git
-        mkdir OpenCL-SDK/build
-        cd OpenCL-SDK/build
-        cmake .. -DBUILD_DOCS=OFF \
+        cd OpenCL-SDK
+        cmake -B build -DBUILD_DOCS=OFF \
           -DBUILD_EXAMPLES=OFF \
           -DBUILD_TESTING=OFF \
           -DOPENCL_SDK_BUILD_SAMPLES=OFF \
           -DOPENCL_SDK_TEST_SAMPLES=OFF
-        cmake --build . --config Release
-        cmake --install . --prefix /some/path
+        cmake --build build
+        cmake --install build --prefix /some/path
         ```
       </details>
 
@@ -540,6 +591,12 @@ Building the program with BLAS support may lead to some performance improvements
 
   Pre-built CLBlast binaries may be found on the [CLBlast Releases](https://github.com/CNugteren/CLBlast/releases) page. For Unix variants, it may also be found in your operating system's packages.
 
+  Linux packaging:
+  Fedora Linux:
+  ```bash
+  sudo dnf install clblast
+  ```
+
   Alternatively, they may be built from source.
 
   - <details>
@@ -548,23 +605,23 @@ Building the program with BLAS support may lead to some performance improvements
       ```cmd
       set OPENCL_SDK_ROOT="C:/OpenCL-SDK-v2023.04.17-Win-x64"
       git clone https://github.com/CNugteren/CLBlast.git
-      mkdir CLBlast\build
-      cd CLBlast\build
-      cmake .. -DBUILD_SHARED_LIBS=OFF -DOVERRIDE_MSVC_FLAGS_TO_MT=OFF -DTUNERS=OFF -DOPENCL_ROOT=%OPENCL_SDK_ROOT% -G "Visual Studio 17 2022" -A x64
-      cmake --build . --config Release
-      cmake --install . --prefix C:/CLBlast
+      cd CLBlast
+      cmake -B build -DBUILD_SHARED_LIBS=OFF -DOVERRIDE_MSVC_FLAGS_TO_MT=OFF -DTUNERS=OFF -DOPENCL_ROOT=%OPENCL_SDK_ROOT% -G "Visual Studio 17 2022" -A x64
+      cmake --build build --config Release
+      cmake --install build --prefix C:/CLBlast
       ```
 
+      (note: `--config Release` at build time is the default and only relevant for Visual Studio builds - or multi-config Ninja builds)
+
   - <details>
     <summary>Unix:</summary>
 
       ```sh
       git clone https://github.com/CNugteren/CLBlast.git
-      mkdir CLBlast/build
-      cd CLBlast/build
-      cmake .. -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF
-      cmake --build . --config Release
-      cmake --install . --prefix /some/path
+      cd CLBlast
+      cmake -B build -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF
+      cmake --build build --config Release
+      cmake --install build --prefix /some/path
       ```
 
       Where `/some/path` is where the built library will be installed (default is `/usr/local`).
@@ -578,21 +635,17 @@ Building the program with BLAS support may lead to some performance improvements
     ```
   - CMake (Unix):
     ```sh
-    mkdir build
-    cd build
-    cmake .. -DLLAMA_CLBLAST=ON -DCLBlast_DIR=/some/path
-    cmake --build . --config Release
+    cmake -B build -DLLAMA_CLBLAST=ON -DCLBlast_DIR=/some/path
+    cmake --build build --config Release
     ```
   - CMake (Windows):
     ```cmd
     set CL_BLAST_CMAKE_PKG="C:/CLBlast/lib/cmake/CLBlast"
     git clone https://github.com/ggerganov/llama.cpp
     cd llama.cpp
-    mkdir build
-    cd build
-    cmake .. -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64
-    cmake --build . --config Release
-    cmake --install . --prefix C:/LlamaCPP
+    cmake -B build -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64
+    cmake --build build --config Release
+    cmake --install build --prefix C:/LlamaCPP
     ```
 
   ##### Running Llama with CLBlast
@@ -648,10 +701,8 @@ Building the program with BLAS support may lead to some performance improvements
   Then, build llama.cpp using the cmake command below:
 
   ```bash
-  mkdir -p build
-  cd build
-  cmake .. -DLLAMA_VULKAN=1
-  cmake --build . --config Release
+  cmake -B build -DLLAMA_VULKAN=1
+  cmake --build build --config Release
   # Test the output binary (with "-ngl 33" to offload all layers to GPU)
   ./bin/main -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4
 
@@ -663,6 +714,8 @@ Building the program with BLAS support may lead to some performance improvements
 
 To obtain the official LLaMA 2 weights please see the <a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a> section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face.
 
+Note: `convert.py` does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face.
+
 ```bash
 # obtain the official LLaMA model weights and place them in ./models
 ls ./models
@@ -716,11 +769,11 @@ From the unzipped folder, open a terminal/cmd window here and place a pre-conver
 As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same.
 
 | Model | Original size | Quantized size (Q4_0) |
-|------:|--------------:|-----------------------:|
-|    7B |         13 GB |                 3.9 GB |
-|   13B |         24 GB |                 7.8 GB |
-|   30B |         60 GB |                19.5 GB |
-|   65B |        120 GB |                38.5 GB |
+|------:|--------------:|----------------------:|
+|    7B |         13 GB |                3.9 GB |
+|   13B |         24 GB |                7.8 GB |
+|   30B |         60 GB |               19.5 GB |
+|   65B |        120 GB |               38.5 GB |
 
 ### Quantization
 
@@ -728,7 +781,7 @@ Several quantization methods are supported. They differ in the resulting model d
 
 *(outdated)*
 
-| Model | Measure      | F16    | Q4_0   | Q4_1   | Q5_0   | Q5_1   | Q8_0   |
+| Model | Measure      |    F16 |   Q4_0 |   Q4_1 |   Q5_0 |   Q5_1 |   Q8_0 |
 |------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:|
 |    7B | perplexity   | 5.9066 | 6.1565 | 6.0912 | 5.9862 | 5.9481 | 5.9070 |
 |    7B | file size    |  13.0G |   3.5G |   3.9G |   4.3G |   4.7G |   6.7G |
@@ -781,7 +834,7 @@ And after 4.45 hours, you will have the final perplexity.
 ### Interactive mode
 
 If you want a more ChatGPT-like experience, you can run in interactive mode by passing `-i` as a parameter.
-In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMa emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`.
+In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMA emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`.
 
 Here is an example of a few-shot interaction, invoked with the command
 
@@ -845,7 +898,7 @@ Sample run:
 ```
 == Running in interactive mode. ==
  - Press Ctrl+C to interject at any time.
- - Press Return to return control to LLaMa.
+ - Press Return to return control to LLaMA.
  - If you want to submit another line, end your input in '\'.
 
  Below is an instruction that describes a task. Write a response that appropriately completes the request.
@@ -884,14 +937,25 @@ If your issue is with model generation quality, then please at least scan the fo
 
 ### Android
 
-#### Building the Project using Android NDK
-You can easily run `llama.cpp` on Android device with [termux](https://termux.dev/).
+#### Build on Android using Termux
+[Termux](https://github.com/termux/termux-app#installation) is a method to execute `llama.cpp` on an Android device (no root required).
+```
+apt update && apt upgrade -y
+apt install git make cmake
+```
 
-First, install the essential packages for termux:
+It's recommended to move your model inside the `~/` directory for best performance:
 ```
-pkg install clang wget git cmake
+cd storage/downloads
+mv model.gguf ~/
 ```
-Second, obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake:
+
+[Get the code](https://github.com/ggerganov/llama.cpp#get-the-code) & [follow the Linux build instructions](https://github.com/ggerganov/llama.cpp#build) to build `llama.cpp`.
+
+#### Building the Project using Android NDK
+Obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake.
+
+Execute the following commands on your computer to avoid downloading the NDK to your mobile. Alternatively, you can also do this in Termux:
 ```
 $ mkdir build-android
 $ cd build-android
@@ -899,54 +963,34 @@ $ export NDK=<your_ndk_directory>
 $ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod ..
 $ make
 ```
-Install [termux](https://termux.dev/) on your device and run `termux-setup-storage` to get access to your SD card.
-Finally, copy the `llama` binary and the model files to your device storage. Here is a demo of an interactive session running on Pixel 5 phone:
+
+Install [termux](https://github.com/termux/termux-app#installation) on your device and run `termux-setup-storage` to get access to your SD card (if Android 11+ then run the command twice).
+
+Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission:
+
+(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`)
+```
+$cp -r /sdcard/llama.cpp/bin /data/data/com.termux/files/home/
+$cd /data/data/com.termux/files/home/bin
+$chmod +x ./*
+```
+
+Download model [llama-2-7b-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/blob/main/llama-2-7b-chat.Q4_K_M.gguf), and push it to `/sdcard/llama.cpp/`, then move it to `/data/data/com.termux/files/home/model/`
+
+```
+$mv /sdcard/llama.cpp/llama-2-7b-chat.Q4_K_M.gguf /data/data/com.termux/files/home/model/
+```
+
+Now, you can start chatting:
+```
+$cd /data/data/com.termux/files/home/bin
+$./main -m ../model/llama-2-7b-chat.Q4_K_M.gguf -n 128 -cml
+```
+
+Here's a demo of an interactive session running on Pixel 5 phone:
 
 https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
 
-#### Building the Project using Termux (F-Droid)
-Termux from F-Droid offers an alternative route to execute the project on an Android device. This method empowers you to construct the project right from within the terminal, negating the requirement for a rooted device or SD Card.
-
-Outlined below are the directives for installing the project using OpenBLAS and CLBlast. This combination is specifically designed to deliver peak performance on recent devices that feature a GPU.
-
-If you opt to utilize OpenBLAS, you'll need to install the corresponding package.
-```
-apt install libopenblas
-```
-
-Subsequently, if you decide to incorporate CLBlast, you'll first need to install the requisite OpenCL packages:
-```
-apt install ocl-icd opencl-headers opencl-clhpp clinfo
-```
-
-In order to compile CLBlast, you'll need to first clone the respective Git repository, which can be found at this URL: https://github.com/CNugteren/CLBlast. Alongside this, clone this repository into your home directory. Once this is done, navigate to the CLBlast folder and execute the commands detailed below:
-```
-cmake .
-make
-cp libclblast.so* $PREFIX/lib
-cp ./include/clblast.h ../llama.cpp
-```
-
-Following the previous steps, navigate to the LlamaCpp directory. To compile it with OpenBLAS and CLBlast, execute the command provided below:
-```
-cp /data/data/com.termux/files/usr/include/openblas/cblas.h .
-cp /data/data/com.termux/files/usr/include/openblas/openblas_config.h .
-make LLAMA_CLBLAST=1 //(sometimes you need to run this command twice)
-```
-
-Upon completion of the aforementioned steps, you will have successfully compiled the project. To run it using CLBlast, a slight adjustment is required: a command must be issued to direct the operations towards your device's physical GPU, rather than the virtual one. The necessary command is detailed below:
-```
-GGML_OPENCL_PLATFORM=0
-GGML_OPENCL_DEVICE=0
-export LD_LIBRARY_PATH=/vendor/lib64:$LD_LIBRARY_PATH
-```
-
-(Note: some Android devices, like the Zenfone 8, need the following command instead - "export LD_LIBRARY_PATH=/system/vendor/lib64:$LD_LIBRARY_PATH". Source: https://www.reddit.com/r/termux/comments/kc3ynp/opencl_working_in_termux_more_in_comments/ )
-
-For easy and swift re-execution, consider documenting this final part in a .sh script file. This will enable you to rerun the process with minimal hassle.
-
-Place your desired model into the `~/llama.cpp/models/` directory and execute the `./main (...)` script.
-
 ### Docker
 
 #### Prerequisites
@@ -1052,7 +1096,9 @@ docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m
 - Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a`
 - See [good first issues](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions
 - Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices
-- Matrix multiplication is unconventional: [`z = ggml_mul_mat(ctx, x, y)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means `zT = x @ yT`
+- Matrix multiplication is unconventional: [`C = ggml_mul_mat(ctx, A, B)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means $C^T = A B^T \Leftrightarrow C = B A^T.$
+
+![matmul](media/matmul.png)
 
 ### Docs
 
diff --git a/SECURITY.md b/SECURITY.md
new file mode 100644
index 000000000..f4322c6ee
--- /dev/null
+++ b/SECURITY.md
@@ -0,0 +1,67 @@
+# Security Policy
+
+ - [**Using llama.cpp securely**](#using-llamacpp-securely)
+   - [Untrusted models](#untrusted-models)
+   - [Untrusted inputs](#untrusted-inputs)
+   - [Data privacy](#data-privacy)
+   - [Untrusted environments or networks](#untrusted-environments-or-networks)
+   - [Multi-Tenant environments](#multi-tenant-environments)
+ - [**Reporting a vulnerability**](#reporting-a-vulnerability)
+
+## Using llama.cpp securely
+
+### Untrusted models
+Be careful when running untrusted models. This classification includes models created by unknown developers or utilizing data obtained from unknown sources.
+
+*Always execute untrusted models within a secure, isolated environment such as a sandbox* (e.g., containers, virtual machines). This helps protect your system from potentially malicious code.
+
+> [!NOTE]
+> The trustworthiness of a model is not binary. You must always determine the proper level of caution depending on the specific model and how it matches your use case and risk tolerance.
+
+### Untrusted inputs
+
+Some models accept various input formats (text, images, audio, etc.). The libraries converting these inputs have varying security levels, so it's crucial to isolate the model and carefully pre-process inputs to mitigate script injection risks.
+
+For maximum security when handling untrusted inputs, you may need to employ the following:
+
+* Sandboxing: Isolate the environment where the inference happens.
+* Pre-analysis: Check how the model performs by default when exposed to prompt injection (e.g. using [fuzzing for prompt injection](https://github.com/FonduAI/awesome-prompt-injection?tab=readme-ov-file#tools)). This will give you leads on how hard you will have to work on the next topics.
+* Updates: Keep both LLaMA C++ and your libraries updated with the latest security patches.
+* Input Sanitation: Before feeding data to the model, sanitize inputs rigorously. This involves techniques such as:
+    * Validation: Enforce strict rules on allowed characters and data types.
+    * Filtering: Remove potentially malicious scripts or code fragments.
+    * Encoding: Convert special characters into safe representations.
+    * Verification: Run tooling that identifies potential script injections (e.g. [models that detect prompt injection attempts](https://python.langchain.com/docs/guides/safety/hugging_face_prompt_injection)).
+
+### Data privacy
+
+To protect sensitive data from potential leaks or unauthorized access, it is crucial to sandbox the model execution. This means running the model in a secure, isolated environment, which helps mitigate many attack vectors.
+
+### Untrusted environments or networks
+
+If you can't run your models in a secure and isolated environment or if it must be exposed to an untrusted network, make sure to take the following security precautions:
+* Confirm the hash of any downloaded artifact (e.g. pre-trained model weights) matches a known-good value
+* Encrypt your data if sending it over the network.
+
+### Multi-Tenant environments
+
+If you intend to run multiple models in parallel with shared memory, it is your responsibility to ensure the models do not interact or access each other's data. The primary areas of concern are tenant isolation, resource allocation, model sharing and hardware attacks.
+
+1. Tenant Isolation: Models should run separately with strong isolation methods to prevent unwanted data access. Separating networks is crucial for isolation, as it prevents unauthorized access to data or models and malicious users from sending graphs to execute under another tenant's identity.
+
+2. Resource Allocation: A denial of service caused by one model can impact the overall system health. Implement safeguards like rate limits, access controls, and health monitoring.
+
+3. Model Sharing: In a multitenant model sharing design, tenants and users must understand the security risks of running code provided by others. Since there are no reliable methods to detect malicious models, sandboxing the model execution is the recommended approach to mitigate the risk.
+
+4. Hardware Attacks: GPUs or TPUs can also be attacked. [Researches](https://scholar.google.com/scholar?q=gpu+side+channel) has shown that side channel attacks on GPUs are possible, which can make data leak from other models or processes running on the same system at the same time.
+
+## Reporting a vulnerability
+
+Beware that none of the topics under [Using llama.cpp securely](#using-llamacpp-securely) are considered vulnerabilities of LLaMA C++.
+
+<!-- normal version -->
+However, If you have discovered a security vulnerability in this project, please report it privately. **Do not disclose it as a public issue.** This gives us time to work with you to fix the issue before public exposure, reducing the chance that the exploit will be used before a patch is released.
+
+Please disclose it as a private [security advisory](https://github.com/ggerganov/llama.cpp/security/advisories/new).
+
+A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure.
diff --git a/awq-py/README.md b/awq-py/README.md
deleted file mode 100644
index 16e68d027..000000000
--- a/awq-py/README.md
+++ /dev/null
@@ -1,116 +0,0 @@
-# AWQ: Activation-aware Weight Quantization for LLM - version apply to llamacpp
-[[Paper](https://arxiv.org/abs/2306.00978)][[Original Repo](https://github.com/mit-han-lab/llm-awq)][[Easy-to-use Repo](https://github.com/casper-hansen/AutoAWQ)]
-
-**Supported models:**
-
-- [X] LLaMA
-- [x] LLaMA 2
-- [X] MPT
-- [X] Mistral AI v0.1
-- [ ] Bloom
-- [ ] Mixtral MoE
-
-**TODO:**
-- [x] Update version work with both MPT and MPT-AWQ model
-- [ ] Add OPT model
-- [ ] Add Bloom model
-- [ ] Add Mixtral MoE
-- [ ] Support w3, w2
-
-
-## Contents
-
-- [Install](##Install)
-- [Convert](##Convert)
-- [Quantize](##Quantize)
-- [Test](##Test)
-- [Benchmark](##Benchmark)
-- [Results](##Results)
-
-## Install
-Install requirements
-```bash
-pip install -r requirements.txt
-```
-Get the pre-computed AWQ search results for multiple model families, including LLaMA, LLaMA2, MPT, OPT
-```bash
-git clone https://huggingface.co/datasets/mit-han-lab/awq-model-zoo awq_cache
-```
-
-## Convert
-Example for llama model
-```bash
-# For llama7b and llama2 models
-python convert.py models/llama-7b/ --awq-path awq_cache/llama-7b-w4-g128.pt --outfile models/llama_7b_fp16.gguf
-# For mistral and mpt models
-python convert-hf-to-gguf.py models/mpt-7b/ --awq-path awq_cache/mpt-7b-w4-g128.pt --outfile models/mpt_7b_fp16.gguf
-```
-
-## Quantize
-```bash
-# We only benchmark and confirm the results on q4_0, q4_1, and q2_k types.
-./quantize models/llama_7b_fp16.gguf models/llama_7b_q4_0.gguf q4_0
-```
-
-## Test
-```bash
-# For all models.
-./build/bin/main -m models/llama_7b_q4_0.gguf -n 128 --prompt "Once upon a time"
-```
-
-## Benchmark
-The perplexity measurements in table above are done against the `wikitext2` test dataset (https://paperswithcode.com/dataset/wikitext-2), with context length of 512.
-```bash
-# For llama and llama2, and mistral models.
-./perplexity -m models/llama_7b_q4_0.gguf -f datasets/wikitext-2-raw/wiki.test.raw
-```
-
-## Results
-Results are run on OpenBLAS (CPU) and CuBLAS (GPU) for fair comparison
-We use three types of llamacpp quantization methods to work with our version, including q4_0, q4_1, and q2_k
-
-### Llama 7B (Build with OpenBLAS)
-
-| Model      | Measure      | F16    | Q4_0   | Q4_1   | Q2_K   |
-|-----------:|--------------|-------:|-------:|-------:|-------:|
-|Llama 7B    | perplexity   | 5.9066 | 6.1214 | 6.0643 | 6.5808 |
-|Llama 7B    | file size    |  12.9G  |   3.5G |   3.9G |   2.7G |
-|Llama 7B    | bits/weight  |   16.0 |    4.5 |    5.0 |    2.6 |
-|AWQ-LLama 7B| perplexity   | 5.9175 | 6.0252 | 5.9987 | 6.3692 |
-|AWQ-LLama 7B| file size    |  12.9G  |   3.5G |   3.9G |   2.7G |
-|AWQ-LLama 7B| bits/weight  |   16.0 |    4.5 |    5.0 |    2.6 |
-
-
-### Llama2 7B (Build with CuBLAS)
-
-| Model       | Measure      | F16    | Q4_0   | Q4_1   | Q2_K   |
-|------------:|--------------|-------:|-------:|-------:|-------:|
-|Llama2 7B    | perplexity   | 5.8664 | 6.0260 | 6.0656 | 6.4496 |
-|Llama2 7B    | file size    |  12.9G  |   3.5G |   3.9G |   2.7G |
-|Llama2 7B    | bits/weight  |   16.0 |    4.5 |    5.0 |    2.6 |
-|AWQ-LLama2 7B| perplexity   | 5.8801 | 6.0054 | 5.9849 | 6.3650 |
-|AWQ-LLama2 7B| file size    |  12.9G  |   3.5G |   3.9G |   2.7G |
-|AWQ-LLama2 7B| bits/weight  |   16.0 |    4.5 |    5.0 |    2.6 |
-
-
-### Mistral 7B v0.1 (Build with CuBLAS)
-
-| Model        | Measure      | F16    | Q4_0   | Q4_1   | Q2_K   |
-|-------------:|--------------|-------:|-------:|-------:|-------:|
-|Mistral 7B    | perplexity   | 5.6931 | 5.8202 | 5.8268 | 6.1645 |
-|Mistral 7B    | file size     |  14.5G |   4.1G |   4.5G |   3.1G |
-|Mistral 7B    | bits/weight  |   16.0 |    4.5 |    5.0 |    2.6 |
-|AWQ-Mistral 7B| perplexity   | 5.6934 | 5.8020 | 5.7691 | 6.0426 |
-|AWQ-Mistral 7B| file size     |  14.5G |   4.1G |   4.5G |   3.1G |
-|AWQ-Mistral 7B| bits/weight  |   16.0 |    4.5 |    5.0 |    2.6 |
-
-### MPT 7B (Build with OpenBLAS)
-
-| Model    | Measure      | F16    | Q4_0   | Q4_1   | Q2_K    |
-|---------:|--------------|-------:|-------:|-------:|--------:|
-|MPT 7B    | perplexity   | 8.4369 | 8.7956 | 8.6265 | 11.4913 |
-|MPT 7B    | file size    |  13.7G  |   3.9G |   4.3G |   2.8G  |
-|MPT 7B    | bits/weight  |   16.0 |    4.5 |    5.0 |    2.6  |
-|AWQ-MPT 7B| perplexity   | 8.4944 | 8.7053 |  8.6750 | 10.2873|
-|AWQ-MPT 7B| file size    |  13.7G  |   3.9G |   4.3G |   2.8G  |
-|AWQ-MPT 7B| bits/weight  |   16.0 |    4.5 |    5.0 |    2.6  |
diff --git a/awq-py/awq/apply_awq.py b/awq-py/awq/apply_awq.py
deleted file mode 100644
index 11132c5d2..000000000
--- a/awq-py/awq/apply_awq.py
+++ /dev/null
@@ -1,254 +0,0 @@
-"""
-Implements the AWQ for llama.cpp use cases.
-Original paper: https://arxiv.org/abs/2306.00978
-
-This code is based on versions of the AWQ implementation found in the following repositories:
-* https://github.com/mit-han-lab/llm-awq
-* https://github.com/casper-hansen/AutoAWQ
-"""
-
-import os
-import torch
-import torch.nn as nn
-
-from transformers import AutoModelForCausalLM, AutoConfig
-from transformers.models.bloom.modeling_bloom import BloomGelu
-from transformers.models.llama.modeling_llama import LlamaRMSNorm
-from transformers.activations import GELUActivation
-
-
-class ScaledActivation(nn.Module):
-    """
-    ScaledActivation module wraps an existing activation function and applies a
-    scale factor to its output.
-
-    Args:
-        module (nn.Module): The activation function to be scaled.
-        scales (torch.Tensor): A tensor of size (num_features,) containing the initial
-            scale factors for each feature.
-
-    Returns:
-        torch.Tensor: The scaled output of the activation function.
-    """
-
-    def __init__(self, module, scales):
-        super().__init__()
-        self.act = module
-        self.scales = nn.Parameter(scales.data)
-
-    def forward(self, x):
-        return self.act(x) / self.scales.view(1, 1, -1).to(x.device)
-
-
-def set_op_by_name(layer, name, new_module):
-    """
-    Set the new module for given module's name.
-
-    Args:
-        layer (nn.Module): The layer in which to replace the submodule.
-        name (str): The path to the submodule to be replaced, using dot notation
-            to access nested modules.
-        new_module (nn.Module): The new module to replace the existing one.
-    """
-    levels = name.split(".")
-    if len(levels) > 1:
-        mod_ = layer
-        for l_idx in range(len(levels) - 1):
-            if levels[l_idx].isdigit():
-                mod_ = mod_[int(levels[l_idx])]
-            else:
-                mod_ = getattr(mod_, levels[l_idx])
-        setattr(mod_, levels[-1], new_module)
-    else:
-        setattr(layer, name, new_module)
-
-
-def get_op_by_name(module, op_name):
-    """
-    Retrieves a submodule within a given layer based on its name.
-
-    Args:
-        module (nn.Module): The layer containing the submodule to find.
-        op_name (str): The name of the submodule.
-
-    Returns:
-        nn.Module: The requested submodule found within the given layer.
-
-    Raises:
-        ValueError: If the specified submodule cannot be found within the layer.
-    """
-    for name, m in module.named_modules():
-        if name == op_name:
-            return m
-    raise ValueError(f"Cannot find op {op_name} in module {module}")
-
-
-@torch.no_grad()
-def scale_ln_fcs(ln, fcs, scales):
-    """
-    Scales the weights of a LayerNorm and a list of fully-connected layers proportionally.
-
-    Args:
-        ln (nn.LayerNorm): The LayerNorm module to be scaled.
-        fcs (List[nn.Linear]): A list of fully-connected layers to be scaled.
-        scales (torch.Tensor): A 1D tensor of size (num_features,).
-    """
-
-    if not isinstance(fcs, list):
-        fcs = [fcs]
-
-    scales = scales.to(ln.weight.device)
-
-    ln.weight.div_(scales)
-    if hasattr(ln, "bias") and ln.bias is not None:
-        ln.bias.div_(scales)
-
-    for fc in fcs:
-        fc.weight.mul_(scales.view(1, -1))
-
-    for p in ln.parameters():
-        assert torch.isnan(p).sum() == 0
-    for fc in fcs:
-        for p in fc.parameters():
-            assert torch.isnan(p).sum() == 0
-
-
-@torch.no_grad()
-def scale_fc_fc(fc1, fc2, scales):
-    """
-    Scales the weights of two fully-connected layers in a specific pattern.
-
-    Args:
-        fc1 (nn.Linear): The first fully-connected layer to be scaled.
-        fc2 (nn.Linear): The second fully-connected layer to be scaled.
-        scales (torch.Tensor): A 1D tensor of size (num_features,).
-    """
-    assert isinstance(fc1, nn.Linear)
-    assert isinstance(fc2, nn.Linear)
-
-    scales = scales.to(fc1.weight.device)
-
-    fc1.weight[-scales.size(0):].div_(scales.view(-1, 1))
-    if fc1.bias is not None:
-        fc1.bias.div_(scales.view(-1))
-
-    fc2.weight.mul_(scales.view(1, -1))
-
-    for p in fc1.parameters():
-        assert torch.isnan(p).sum() == 0
-    for p in fc2.parameters():
-        assert torch.isnan(p).sum() == 0
-
-
-@torch.no_grad()
-def scale_gelu_fc(gelu, fc, scales):
-    """
-    Scales the weight of a GELU activation and a fully-connected layer proportionally.
-
-    Args:
-        gelu (Union[nn.GELU, BloomGelu, GELUActivation]): The GELU activation module to be scaled.
-        fc (nn.Linear): The fully-connected layer to be scaled.
-        scales (torch.Tensor): A 1D tensor of size (num_features,).
-
-    Raises:
-        TypeError: If the `gelu` module is not of type `nn.GELU`, `BloomGelu`, or `GELUActivation`.
-        TypeError: If the `fc` module is not of type `nn.Linear`.
-    """
-    assert isinstance(gelu, (nn.GELU, BloomGelu, GELUActivation))
-    assert isinstance(fc, nn.Linear)
-
-    fc.weight.mul_(scales.view(1, -1).to(fc.weight.device))
-
-    for p in fc.parameters():
-        assert torch.isnan(p).sum() == 0
-
-
-def apply_scale(module, scales_list, input_feat_dict=None):
-    """
-    Applies different scaling strategies to layers based on their type and hierarchy within a given module.
-
-    Args:
-        module (nn.Module): The module containing the layers to be scaled.
-        scales_list (List[Tuple[str, List[str], torch.Tensor]]): A list of tuples containing:
-            * prev_op_name (str): The name of the preceding operation or module,
-                relative to which the layers to be scaled are located.
-            * layer_names (List[str]): A list of names of the layers to be scaled, relative to the preceding operation.
-            * scales (torch.Tensor): A 1D tensor of size (num_features,) containing the scaling factors for each feature.
-        input_feat_dict (Optional[Dict[str, torch.Tensor]]): A dictionary mapping layer names to their corresponding
-            input features (optional).
-    """
-    for prev_op_name, layer_names, scales in scales_list:
-        prev_op = get_op_by_name(module, prev_op_name)
-        layers = [get_op_by_name(module, name) for name in layer_names]
-
-        prev_op.cuda()
-        for layer in layers:
-            layer.cuda()
-        scales.cuda()
-
-        if isinstance(prev_op, nn.Linear):
-            assert len(layers) == 1
-            scale_fc_fc(prev_op, layers[0], scales)
-        elif isinstance(prev_op, (nn.LayerNorm, LlamaRMSNorm)) or "rmsnorm" in str(prev_op.__class__).lower():
-            scale_ln_fcs(prev_op, layers, scales)
-        elif isinstance(prev_op, (nn.GELU, BloomGelu, GELUActivation)):
-            new_module = ScaledActivation(prev_op, scales)
-            set_op_by_name(module, prev_op_name, new_module)
-            scale_gelu_fc(prev_op, layers[0], scales)
-        else:
-            raise NotImplementedError(f"prev_op {type(prev_op)} not supported yet!")
-
-        # apply the scaling to input feat if given; prepare it for clipping
-        if input_feat_dict is not None:
-            for layer_name in layer_names:
-                inp = input_feat_dict[layer_name]
-                inp.div_(scales.view(1, -1).to(inp.device))
-
-        prev_op.cpu()
-        for layer in layers:
-            layer.cpu()
-        scales.cpu()
-
-
-@torch.no_grad()
-def apply_clip(module, clip_list):
-    """
-    Applies element-wise clipping to the weight of a specific layer within a given module.
-
-    Args:
-        module (nn.Module): The module containing the layer to be clipped.
-        clip_list (List[Tuple[str, torch.Tensor]]): A list of tuples containing:
-            * name (str): The name of the layer to be clipped, relative to the root of the module.
-            * max_val (torch.Tensor): A 1D or 2D tensor defining the upper bound for each element of the layer's weight.
-    """
-    for name, max_val in clip_list:
-        layer = get_op_by_name(module, name)
-        layer.cuda()
-        max_val = max_val.to(layer.weight.device)
-        org_shape = layer.weight.shape
-        layer.weight.data = layer.weight.data.reshape(*max_val.shape[:2], -1)
-        layer.weight.data = torch.clamp(layer.weight.data, -max_val, max_val)
-        layer.weight.data = layer.weight.data.reshape(org_shape)
-        layer.cpu()
-
-
-def add_scale_weights(model_path, scale_path, tmp_path):
-    """
-    Adds pre-computed Activation Weight Quantization (AWQ) results to a model,
-    including scaling factors and clipping bounds.
-
-    Args:
-        model_path (str): Path to the pre-trained model to be equipped with AWQ.
-        scale_path (str): Path to the AWQ scale factors (.pt file).
-        tmp_path (str): Path to the temporary directory where the equipped model will be saved.
-    """
-    config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
-    model = AutoModelForCausalLM.from_pretrained(
-        model_path, config=config, trust_remote_code=True
-    )
-    model.eval()
-    awq_results = torch.load(str(scale_path), map_location="cpu")
-    apply_scale(model, awq_results["scale"])
-    apply_clip(model, awq_results["clip"])
-    model.save_pretrained(str(tmp_path))
-    os.system(f"cp {str(model_path)}/tokenizer* {str(tmp_path)}")
diff --git a/awq-py/requirements.txt b/awq-py/requirements.txt
deleted file mode 100644
index 991896116..000000000
--- a/awq-py/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-torch>=2.1.1
-transformers>=4.32.0
diff --git a/build.zig b/build.zig
index 699738f3d..96783574f 100644
--- a/build.zig
+++ b/build.zig
@@ -112,27 +112,61 @@ pub fn build(b: *std.build.Builder) !void {
     make.enable_lto = b.option(bool, "lto", "Enable LTO optimization, (default: false)") orelse false;
 
     const ggml = make.obj("ggml", "ggml.c");
+    const sgemm = make.obj("sgemm", "sgemm.cpp");
     const ggml_alloc = make.obj("ggml-alloc", "ggml-alloc.c");
     const ggml_backend = make.obj("ggml-backend", "ggml-backend.c");
     const ggml_quants = make.obj("ggml-quants", "ggml-quants.c");
+    const unicode = make.obj("unicode", "unicode.cpp");
+    const unicode_data = make.obj("unicode-data", "unicode-data.cpp");
     const llama = make.obj("llama", "llama.cpp");
     const buildinfo = make.obj("common", "common/build-info.cpp");
     const common = make.obj("common", "common/common.cpp");
     const console = make.obj("console", "common/console.cpp");
     const sampling = make.obj("sampling", "common/sampling.cpp");
     const grammar_parser = make.obj("grammar-parser", "common/grammar-parser.cpp");
+    const json_schema_to_grammar = make.obj("json-schema-to-grammar", "common/json-schema-to-grammar.cpp");
     const train = make.obj("train", "common/train.cpp");
     const clip = make.obj("clip", "examples/llava/clip.cpp");
+    const llava = make.obj("llava", "examples/llava/llava.cpp");
 
-    _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, console, grammar_parser });
-    _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });
-    _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });
-    _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });
-    _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train });
-    _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train });
+    _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, sampling, console, grammar_parser });
+    _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo });
+    _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo });
+    _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo });
+    _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, train });
+    _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, train });
 
-    const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, grammar_parser, clip });
+    const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, sampling, grammar_parser, clip, llava });
     if (server.target.isWindows()) {
         server.linkSystemLibrary("ws2_32");
     }
+
+    const server_assets = [_][]const u8{ "index.html", "index.js", "completion.js", "json-schema-to-grammar.mjs" };
+    for (server_assets) |asset| {
+        const input_path = b.fmt("examples/server/public/{s}", .{asset});
+        const output_path = b.fmt("examples/server/{s}.hpp", .{asset});
+
+        // Portable equivalent of `b.addSystemCommand(&.{ "xxd", "-n", asset, "-i", input_path, output_path }) })`:
+
+        const input = try std.fs.cwd().readFileAlloc(b.allocator, input_path, std.math.maxInt(usize));
+        defer b.allocator.free(input);
+
+        var buf = std.ArrayList(u8).init(b.allocator);
+        defer buf.deinit();
+
+        for (input) |byte| {
+            try std.fmt.format(buf.writer(), "0x{X:0>2}, ", .{byte});
+        }
+
+        var name = try std.mem.replaceOwned(u8, b.allocator, asset, "-", "_");
+        defer b.allocator.free(name);
+        std.mem.replaceScalar(u8, name, '.', '_');
+
+        try std.fs.cwd().writeFile(output_path, b.fmt(
+            "unsigned char {s}[] = {{{s}}};\nunsigned int {s}_len = {d};\n",
+            .{ name, buf.items, name, input.len },
+        ));
+
+        std.debug.print("Dumped hex of \"{s}\" ({s}) to {s}\n", .{ input_path, name, output_path });
+    }
 }
diff --git a/ci/run.sh b/ci/run.sh
index f3a29c2e9..d5972480b 100755
--- a/ci/run.sh
+++ b/ci/run.sh
@@ -40,12 +40,13 @@ if [ ! -z ${GG_BUILD_METAL} ]; then
 fi
 
 if [ ! -z ${GG_BUILD_CUDA} ]; then
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_CUBLAS=1"
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_CUDA=1"
 fi
 
 if [ ! -z ${GG_BUILD_SYCL} ]; then
     if [ -z ${ONEAPI_ROOT} ]; then
-        echo "Not detected ONEAPI_ROOT, please install oneAPI base toolkit and enable it by:\n source /opt/intel/oneapi/setvars.sh"
+        echo "Not detected ONEAPI_ROOT, please install oneAPI base toolkit and enable it by:"
+        echo "source /opt/intel/oneapi/setvars.sh"
         exit 1
     fi
 
@@ -152,6 +153,54 @@ function gg_sum_ctest_release {
     gg_printf '```\n'
 }
 
+# test_scripts_debug
+
+function gg_run_test_scripts_debug {
+    cd ${SRC}
+
+    set -e
+
+    (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
+    (cd ./examples/quantize   && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
+
+    set +e
+}
+
+function gg_sum_test_scripts_debug {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Runs test scripts in debug mode\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '```\n'
+    gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)"
+    gg_printf '```\n'
+    gg_printf '\n'
+}
+
+# test_scripts_release
+
+function gg_run_test_scripts_release {
+    cd ${SRC}
+
+    set -e
+
+    (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
+    (cd ./examples/quantize   && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
+
+    set +e
+}
+
+function gg_sum_test_scripts_release {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Runs test scripts in release mode\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '```\n'
+    gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)"
+    gg_printf '```\n'
+    gg_printf '\n'
+}
+
 function gg_get_model {
     local gguf_3b="$MNT/models/open-llama/3B-v2/ggml-model-f16.gguf"
     local gguf_7b="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf"
@@ -272,21 +321,22 @@ function gg_run_open_llama_3b_v2 {
     (time ./bin/main --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
     (time ./bin/main --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
-    (time ./bin/perplexity --model ${model_f16}  -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+    (time ./bin/perplexity --model ${model_f16}  -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
-    (time ./bin/imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
+    (time ./bin/imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
 
-    (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/save-load-state     --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/save-load-state -fa --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
 
     function check_ppl {
         qnt="$1"
@@ -315,47 +365,6 @@ function gg_run_open_llama_3b_v2 {
 
     cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log
 
-    # lora
-    function compare_ppl {
-        qnt="$1"
-        ppl1=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
-        ppl2=$(echo "$3" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
-
-        if [ $(echo "$ppl1 < $ppl2" | bc) -eq 1 ]; then
-            printf '  - %s @ %s (FAIL: %s > %s)\n' "$qnt" "$ppl" "$ppl1" "$ppl2"
-            return 20
-        fi
-
-        printf '  - %s @ %s %s OK\n' "$qnt" "$ppl1" "$ppl2"
-        return 0
-    }
-
-    path_lora="../models-mnt/open-llama/3B-v2/lora"
-    path_shakespeare="../models-mnt/shakespeare"
-
-    shakespeare="${path_shakespeare}/shakespeare.txt"
-    lora_shakespeare="${path_lora}/ggml-adapter-model.bin"
-
-    gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/adapter_config.json
-    gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/adapter_model.bin
-    gg_wget ${path_shakespeare} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/shakespeare.txt
-
-    python3 ../convert-lora-to-ggml.py ${path_lora}
-
-    # f16
-    (time ./bin/perplexity --model ${model_f16} -f ${shakespeare}                            -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log
-    (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log
-    compare_ppl "f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-f16.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
-
-    # q8_0
-    (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare}                            -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log
-    (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log
-    compare_ppl "q8_0 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
-
-    # q8_0 + f16 lora-base
-    (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log
-    compare_ppl "q8_0 / f16 base shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
-
     set +e
 }
 
@@ -366,7 +375,6 @@ function gg_sum_open_llama_3b_v2 {
     gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
     gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
     gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)"
-    gg_printf '- lora:\n%s\n' "$(cat $OUT/${ci}-lora-ppl.log)"
     gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
     gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
     gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
@@ -379,11 +387,6 @@ function gg_sum_open_llama_3b_v2 {
     gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
     gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
     gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)"
-    gg_printf '- shakespeare (f16):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-f16.log)"
-    gg_printf '- shakespeare (f16 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)"
-    gg_printf '- shakespeare (q8_0):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)"
-    gg_printf '- shakespeare (q8_0 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log)"
-    gg_printf '- shakespeare (q8_0 / f16 base lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log)"
 }
 
 # open_llama_7b_v2
@@ -411,8 +414,8 @@ function gg_run_open_llama_7b_v2 {
 
     set -e
 
-    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUBLAS=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j                                                             ) 2>&1 | tee -a $OUT/${ci}-make.log
+    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j                                                           ) 2>&1 | tee -a $OUT/${ci}-make.log
 
     python3 ../convert.py ${path_models}
 
@@ -467,7 +470,10 @@ function gg_run_open_llama_7b_v2 {
 
     (time ./bin/imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
 
-    (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/save-load-state     -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/save-load-state     -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
 
     function check_ppl {
         qnt="$1"
@@ -496,48 +502,6 @@ function gg_run_open_llama_7b_v2 {
 
     cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log
 
-    # lora
-    function compare_ppl {
-        qnt="$1"
-        ppl1=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
-        ppl2=$(echo "$3" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
-
-        if [ $(echo "$ppl1 < $ppl2" | bc) -eq 1 ]; then
-            printf '  - %s @ %s (FAIL: %s > %s)\n' "$qnt" "$ppl" "$ppl1" "$ppl2"
-            return 20
-        fi
-
-        printf '  - %s @ %s %s OK\n' "$qnt" "$ppl1" "$ppl2"
-        return 0
-    }
-
-    path_lora="../models-mnt/open-llama/7B-v2/lora"
-    path_shakespeare="../models-mnt/shakespeare"
-
-    shakespeare="${path_shakespeare}/shakespeare.txt"
-    lora_shakespeare="${path_lora}/ggml-adapter-model.bin"
-
-    gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/adapter_config.json
-    gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/adapter_model.bin
-    gg_wget ${path_shakespeare} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/shakespeare.txt
-
-    python3 ../convert-lora-to-ggml.py ${path_lora}
-
-    # f16
-    (time ./bin/perplexity --model ${model_f16} -f ${shakespeare}                            -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log
-    (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log
-    compare_ppl "f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-f16.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
-
-    # currently not supported by the CUDA backend
-    # q8_0
-    #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare}                            -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log
-    #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log
-    #compare_ppl "q8_0 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
-
-    # q8_0 + f16 lora-base
-    #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log
-    #compare_ppl "q8_0 / f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
-
     set +e
 }
 
@@ -548,7 +512,6 @@ function gg_sum_open_llama_7b_v2 {
     gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
     gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
     gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)"
-    gg_printf '- lora:\n%s\n' "$(cat $OUT/${ci}-lora-ppl.log)"
     gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
     gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
     gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
@@ -561,11 +524,6 @@ function gg_sum_open_llama_7b_v2 {
     gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
     gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
     gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)"
-    gg_printf '- shakespeare (f16):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-f16.log)"
-    gg_printf '- shakespeare (f16 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)"
-    #gg_printf '- shakespeare (q8_0):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)"
-    #gg_printf '- shakespeare (q8_0 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log)"
-    #gg_printf '- shakespeare (q8_0 / f16 base lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log)"
 }
 
 # bge-small
@@ -574,7 +532,7 @@ function gg_run_embd_bge_small {
     cd ${SRC}
 
     gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/config.json
-    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/resolve/main/tokenizer.model
+    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/tokenizer.json
     gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/tokenizer_config.json
     gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/special_tokens_map.json
     gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/resolve/main/pytorch_model.bin
@@ -641,6 +599,11 @@ test $ret -eq 0 && gg_run ctest_release
 if [ -z ${GG_BUILD_LOW_PERF} ]; then
     test $ret -eq 0 && gg_run embd_bge_small
 
+    if [ -z ${GG_BUILD_CLOUD} ] || [ ${GG_BUILD_EXTRA_TESTS_0} ]; then
+        test $ret -eq 0 && gg_run test_scripts_debug
+        test $ret -eq 0 && gg_run test_scripts_release
+    fi
+
     if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then
         if [ -z ${GG_BUILD_CUDA} ]; then
             test $ret -eq 0 && gg_run open_llama_3b_v2
diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt
index f79acfef1..0ec8d6d8d 100644
--- a/common/CMakeLists.txt
+++ b/common/CMakeLists.txt
@@ -19,7 +19,12 @@ if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git")
         endif()
     endif()
 
-    set(GIT_INDEX "${GIT_DIR}/index")
+    if(EXISTS "${GIT_DIR}/index")
+        set(GIT_INDEX "${GIT_DIR}/index")
+    else()
+        message(WARNING "Git index not found in git repository.")
+        set(GIT_INDEX "")
+    endif()
 else()
     message(WARNING "Git repository not found; to enable automatic generation of build info, make sure Git is installed and the project is a Git repository.")
     set(GIT_INDEX "")
@@ -42,7 +47,6 @@ if (BUILD_SHARED_LIBS)
     set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
 endif()
 
-
 set(TARGET common)
 
 add_library(${TARGET} STATIC
@@ -55,14 +59,29 @@ add_library(${TARGET} STATIC
     console.cpp
     grammar-parser.h
     grammar-parser.cpp
+    json.hpp
+    json-schema-to-grammar.cpp
     train.h
     train.cpp
+    ngram-cache.h
+    ngram-cache.cpp
     )
 
 if (BUILD_SHARED_LIBS)
     set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
 endif()
 
+set(LLAMA_COMMON_EXTRA_LIBS build_info)
+
+# Use curl to download model url
+if (LLAMA_CURL)
+    find_package(CURL REQUIRED)
+    add_definitions(-DLLAMA_USE_CURL)
+    include_directories(${CURL_INCLUDE_DIRS})
+    find_library(CURL_LIBRARY curl REQUIRED)
+    set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARY})
+endif ()
+
 target_include_directories(${TARGET} PUBLIC .)
 target_compile_features(${TARGET} PUBLIC cxx_std_11)
-target_link_libraries(${TARGET} PRIVATE build_info PUBLIC llama)
+target_link_libraries(${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama)
diff --git a/common/common.cpp b/common/common.cpp
index 10ef11829..ba1ecf0e5 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1,4 +1,8 @@
 #include "common.h"
+// Change JSON_ASSERT from assert() to GGML_ASSERT:
+#define JSON_ASSERT GGML_ASSERT
+#include "json.hpp"
+#include "json-schema-to-grammar.h"
 #include "llama.h"
 
 #include <algorithm>
@@ -16,6 +20,7 @@
 #include <unordered_set>
 #include <vector>
 #include <cinttypes>
+#include <codecvt>
 
 #if defined(__APPLE__) && defined(__MACH__)
 #include <sys/types.h>
@@ -27,7 +32,6 @@
 #ifndef NOMINMAX
 #   define NOMINMAX
 #endif
-#include <codecvt>
 #include <locale>
 #include <windows.h>
 #include <fcntl.h>
@@ -37,25 +41,44 @@
 #include <sys/stat.h>
 #include <unistd.h>
 #endif
+#if defined(LLAMA_USE_CURL)
+#include <curl/curl.h>
+#include <curl/easy.h>
+#include <thread>
+#include <future>
+#endif
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
-#if (defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL))
-#define GGML_USE_CUBLAS_SYCL
+#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL))
+#define GGML_USE_CUDA_SYCL
 #endif
 
-#if (defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN)
-#define GGML_USE_CUBLAS_SYCL_VULKAN
+#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN)
+#define GGML_USE_CUDA_SYCL_VULKAN
 #endif
 
+#if defined(LLAMA_USE_CURL)
+#ifdef __linux__
+#include <linux/limits.h>
+#elif defined(_WIN32)
+#define PATH_MAX MAX_PATH
+#else
+#include <sys/syslimits.h>
+#endif
+#define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
+#endif // LLAMA_USE_CURL
+
+using json = nlohmann::ordered_json;
+
 int32_t get_num_physical_cores() {
 #ifdef __linux__
     // enumerate the set of thread siblings, num entries is num cores
     std::unordered_set<std::string> siblings;
     for (uint32_t cpu=0; cpu < UINT32_MAX; ++cpu) {
-        std::ifstream thread_siblings("/sys/devices/system/cpu"
+        std::ifstream thread_siblings("/sys/devices/system/cpu/cpu"
             + std::to_string(cpu) + "/topology/thread_siblings");
         if (!thread_siblings.is_open()) {
             break; // no more cpus
@@ -86,7 +109,80 @@ int32_t get_num_physical_cores() {
     return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
 }
 
-void process_escapes(std::string& input) {
+#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
+#include <pthread.h>
+
+static void cpuid(unsigned leaf, unsigned subleaf,
+                  unsigned *eax, unsigned *ebx, unsigned *ecx, unsigned *edx) {
+    __asm__("movq\t%%rbx,%%rsi\n\t"
+            "cpuid\n\t"
+            "xchgq\t%%rbx,%%rsi"
+            : "=a"(*eax), "=S"(*ebx), "=c"(*ecx), "=d"(*edx)
+            : "0"(leaf), "2"(subleaf));
+}
+
+static int pin_cpu(int cpu) {
+    cpu_set_t mask;
+    CPU_ZERO(&mask);
+    CPU_SET(cpu, &mask);
+    return pthread_setaffinity_np(pthread_self(), sizeof(mask), &mask);
+}
+
+static bool is_hybrid_cpu(void) {
+    unsigned eax, ebx, ecx, edx;
+    cpuid(7, 0, &eax, &ebx, &ecx, &edx);
+    return !!(edx & (1u << 15));
+}
+
+static bool is_running_on_efficiency_core(void) {
+    unsigned eax, ebx, ecx, edx;
+    cpuid(0x1a, 0, &eax, &ebx, &ecx, &edx);
+    int intel_atom = 0x20;
+    int core_type = (eax & 0xff000000u) >> 24;
+    return core_type == intel_atom;
+}
+
+static int count_math_cpus(int cpu_count) {
+    int result = 0;
+    for (int cpu = 0; cpu < cpu_count; ++cpu) {
+        if (pin_cpu(cpu)) {
+            return -1;
+        }
+        if (is_running_on_efficiency_core()) {
+            continue; // efficiency cores harm lockstep threading
+        }
+        ++cpu; // hyperthreading isn't useful for linear algebra
+        ++result;
+    }
+    return result;
+}
+
+#endif // __x86_64__ && __linux__
+
+/**
+ * Returns number of CPUs on system that are useful for math.
+ */
+int get_math_cpu_count() {
+#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
+    int cpu_count = sysconf(_SC_NPROCESSORS_ONLN);
+    if (cpu_count < 1) {
+        return get_num_physical_cores();
+    }
+    if (is_hybrid_cpu()) {
+        cpu_set_t affinity;
+        if (!pthread_getaffinity_np(pthread_self(), sizeof(affinity), &affinity)) {
+            int result = count_math_cpus(cpu_count);
+            pthread_setaffinity_np(pthread_self(), sizeof(affinity), &affinity);
+            if (result > 0) {
+                return result;
+            }
+        }
+    }
+#endif
+    return get_num_physical_cores();
+}
+
+void process_escapes(std::string & input) {
     std::size_t input_len = input.length();
     std::size_t output_idx = 0;
 
@@ -139,6 +235,1131 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
     return result;
 }
 
+bool parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
+    const char * sep = strchr(data, '=');
+    if (sep == nullptr || sep - data >= 128) {
+        fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
+        return false;
+    }
+    llama_model_kv_override kvo;
+    std::strncpy(kvo.key, data, sep - data);
+    kvo.key[sep - data] = 0;
+    sep++;
+    if (strncmp(sep, "int:", 4) == 0) {
+        sep += 4;
+        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
+        kvo.val_i64 = std::atol(sep);
+    } else if (strncmp(sep, "float:", 6) == 0) {
+        sep += 6;
+        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
+        kvo.val_f64 = std::atof(sep);
+    } else if (strncmp(sep, "bool:", 5) == 0) {
+        sep += 5;
+        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
+        if (std::strcmp(sep, "true") == 0) {
+            kvo.val_bool = true;
+        } else if (std::strcmp(sep, "false") == 0) {
+            kvo.val_bool = false;
+        } else {
+            fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
+            return false;
+        }
+    } else if (strncmp(sep, "str:", 4) == 0) {
+        sep += 4;
+        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
+        if (strlen(sep) > 127) {
+            fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
+            return false;
+        }
+        strncpy(kvo.val_str, sep, 127);
+        kvo.val_str[127] = '\0';
+    } else {
+        fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
+        return false;
+    }
+    overrides.emplace_back(std::move(kvo));
+    return true;
+}
+
+bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) {
+    llama_sampling_params & sparams = params.sparams;
+
+    if (arg == "-s" || arg == "--seed") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        // This is temporary, in the future the samplign state will be moved fully to llama_sampling_context.
+        params.seed = std::stoul(argv[i]);
+        sparams.seed = std::stoul(argv[i]);
+        return true;
+    }
+    if (arg == "-t" || arg == "--threads") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_threads = std::stoi(argv[i]);
+        if (params.n_threads <= 0) {
+            params.n_threads = std::thread::hardware_concurrency();
+        }
+        return true;
+    }
+    if (arg == "-tb" || arg == "--threads-batch") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_threads_batch = std::stoi(argv[i]);
+        if (params.n_threads_batch <= 0) {
+            params.n_threads_batch = std::thread::hardware_concurrency();
+        }
+        return true;
+    }
+    if (arg == "-td" || arg == "--threads-draft") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_threads_draft = std::stoi(argv[i]);
+        if (params.n_threads_draft <= 0) {
+            params.n_threads_draft = std::thread::hardware_concurrency();
+        }
+        return true;
+    }
+    if (arg == "-tbd" || arg == "--threads-batch-draft") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_threads_batch_draft = std::stoi(argv[i]);
+        if (params.n_threads_batch_draft <= 0) {
+            params.n_threads_batch_draft = std::thread::hardware_concurrency();
+        }
+        return true;
+    }
+    if (arg == "-p" || arg == "--prompt") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.prompt = argv[i];
+        return true;
+    }
+    if (arg == "-e" || arg == "--escape") {
+        params.escape = true;
+        return true;
+    }
+    if (arg == "--prompt-cache") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.path_prompt_cache = argv[i];
+        return true;
+    }
+    if (arg == "--prompt-cache-all") {
+        params.prompt_cache_all = true;
+        return true;
+    }
+    if (arg == "--prompt-cache-ro") {
+        params.prompt_cache_ro = true;
+        return true;
+    }
+    if (arg == "-bf" || arg == "--binary-file") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        std::ifstream file(argv[i], std::ios::binary);
+        if (!file) {
+            fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
+            invalid_param = true;
+            return true;
+        }
+        // store the external file name in params
+        params.prompt_file = argv[i];
+        std::ostringstream ss;
+        ss << file.rdbuf();
+        params.prompt = ss.str();
+        fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), argv[i]);
+        return true;
+    }
+    if (arg == "-f" || arg == "--file") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        std::ifstream file(argv[i]);
+        if (!file) {
+            fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
+            invalid_param = true;
+            return true;
+        }
+        // store the external file name in params
+        params.prompt_file = argv[i];
+        std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
+        if (!params.prompt.empty() && params.prompt.back() == '\n') {
+            params.prompt.pop_back();
+        }
+        return true;
+    }
+    if (arg == "-n" || arg == "--n-predict") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_predict = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--top-k") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.top_k = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "-c" || arg == "--ctx-size") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_ctx = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--grp-attn-n" || arg == "-gan") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.grp_attn_n = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--grp-attn-w" || arg == "-gaw") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.grp_attn_w = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--rope-freq-base") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.rope_freq_base = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--rope-freq-scale") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.rope_freq_scale = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--rope-scaling") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        std::string value(argv[i]);
+        /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
+        else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
+        else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
+        else { invalid_param = true; }
+        return true;
+    }
+    if (arg == "--rope-scale") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.rope_freq_scale = 1.0f / std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--yarn-orig-ctx") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.yarn_orig_ctx = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--yarn-ext-factor") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.yarn_ext_factor = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--yarn-attn-factor") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.yarn_attn_factor = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--yarn-beta-fast") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.yarn_beta_fast = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--yarn-beta-slow") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.yarn_beta_slow = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--pooling") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        std::string value(argv[i]);
+        /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
+        else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
+        else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
+        else { invalid_param = true; }
+        return true;
+    }
+    if (arg == "--defrag-thold" || arg == "-dt") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.defrag_thold = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--samplers") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        const auto sampler_names = string_split(argv[i], ';');
+        sparams.samplers_sequence = sampler_types_from_names(sampler_names, true);
+        return true;
+    }
+    if (arg == "--sampling-seq") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.samplers_sequence = sampler_types_from_chars(argv[i]);
+        return true;
+    }
+    if (arg == "--top-p") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.top_p = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--min-p") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.min_p = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--temp") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.temp = std::stof(argv[i]);
+        sparams.temp = std::max(sparams.temp, 0.0f);
+        return true;
+    }
+    if (arg == "--tfs") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.tfs_z = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--typical") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.typical_p = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--repeat-last-n") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.penalty_last_n = std::stoi(argv[i]);
+        sparams.n_prev = std::max(sparams.n_prev, sparams.penalty_last_n);
+        return true;
+    }
+    if (arg == "--repeat-penalty") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.penalty_repeat = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--frequency-penalty") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.penalty_freq = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--presence-penalty") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.penalty_present = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--dynatemp-range") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.dynatemp_range = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--dynatemp-exp") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.dynatemp_exponent = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--mirostat") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.mirostat = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--mirostat-lr") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.mirostat_eta = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--mirostat-ent") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.mirostat_tau = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--cfg-negative-prompt") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.cfg_negative_prompt = argv[i];
+        return true;
+    }
+    if (arg == "--cfg-negative-prompt-file") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        std::ifstream file(argv[i]);
+        if (!file) {
+            fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
+            invalid_param = true;
+            return true;
+        }
+        std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(sparams.cfg_negative_prompt));
+        if (!sparams.cfg_negative_prompt.empty() && sparams.cfg_negative_prompt.back() == '\n') {
+            sparams.cfg_negative_prompt.pop_back();
+        }
+        return true;
+    }
+    if (arg == "--cfg-scale") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.cfg_scale = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "-b" || arg == "--batch-size") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_batch = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "-ub" || arg == "--ubatch-size") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_ubatch = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--keep") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_keep = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--draft") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_draft = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--chunks") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_chunks = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "-np" || arg == "--parallel") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_parallel = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "-ns" || arg == "--sequences") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_sequences = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--p-split" || arg == "-ps") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.p_split = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "-m" || arg == "--model") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.model = argv[i];
+        return true;
+    }
+    if (arg == "-md" || arg == "--model-draft") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.model_draft = argv[i];
+        return true;
+    }
+    if (arg == "-a" || arg == "--alias") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.model_alias = argv[i];
+        return true;
+    }
+    if (arg == "-mu" || arg == "--model-url") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.model_url = argv[i];
+        return true;
+    }
+    if (arg == "-hfr" || arg == "--hf-repo") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.hf_repo = argv[i];
+        return true;
+    }
+    if (arg == "-hff" || arg == "--hf-file") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.hf_file = argv[i];
+        return true;
+    }
+    if (arg == "--lora") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.lora_adapter.emplace_back(argv[i], 1.0f);
+        params.use_mmap = false;
+        return true;
+    }
+    if (arg == "--lora-scaled") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        const char* lora_adapter = argv[i];
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i]));
+        params.use_mmap = false;
+        return true;
+    }
+    if (arg == "--lora-base") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.lora_base = argv[i];
+        return true;
+    }
+    if (arg == "--control-vector") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.control_vectors.push_back({ 1.0f, argv[i], });
+        return true;
+    }
+    if (arg == "--control-vector-scaled") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        const char* fname = argv[i];
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.control_vectors.push_back({ std::stof(argv[i]), fname, });
+        return true;
+    }
+    if (arg == "--control-vector-layer-range") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.control_vector_layer_start = std::stoi(argv[i]);
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.control_vector_layer_end = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--mmproj") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.mmproj = argv[i];
+        return true;
+    }
+    if (arg == "--image") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.image.emplace_back(argv[i]);
+        return true;
+    }
+    if (arg == "-i" || arg == "--interactive") {
+        params.interactive = true;
+        return true;
+    }
+    if (arg == "--interactive-specials") {
+        params.interactive_specials = true;
+        return true;
+    }
+    if (arg == "--embedding") {
+        params.embedding = true;
+        return true;
+    }
+    if (arg == "--interactive-first") {
+        params.interactive_first = true;
+        return true;
+    }
+    if (arg == "-ins" || arg == "--instruct") {
+        params.instruct = true;
+        return true;
+    }
+    if (arg == "-cnv" || arg == "--conversation") {
+        params.conversation = true;
+        return true;
+    }
+    if (arg == "-cml" || arg == "--chatml") {
+        params.chatml = true;
+        return true;
+    }
+    if (arg == "--infill") {
+        params.infill = true;
+        return true;
+    }
+    if (arg == "-dkvc" || arg == "--dump-kv-cache") {
+        params.dump_kv_cache = true;
+        return true;
+    }
+    if (arg == "-nkvo" || arg == "--no-kv-offload") {
+        params.no_kv_offload = true;
+        return true;
+    }
+    if (arg == "-ctk" || arg == "--cache-type-k") {
+        params.cache_type_k = argv[++i];
+        return true;
+    }
+    if (arg == "-ctv" || arg == "--cache-type-v") {
+        params.cache_type_v = argv[++i];
+        return true;
+    }
+    if (arg == "--multiline-input") {
+        params.multiline_input = true;
+        return true;
+    }
+    if (arg == "--simple-io") {
+        params.simple_io = true;
+        return true;
+    }
+    if (arg == "-cb" || arg == "--cont-batching") {
+        params.cont_batching = true;
+        return true;
+    }
+    if (arg == "-fa" || arg == "--flash-attn") {
+        params.flash_attn = true;
+        return true;
+    }
+    if (arg == "--color") {
+        params.use_color = true;
+        return true;
+    }
+    if (arg == "--mlock") {
+        params.use_mlock = true;
+        return true;
+    }
+    if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_gpu_layers = std::stoi(argv[i]);
+        if (!llama_supports_gpu_offload()) {
+            fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n");
+            fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
+        }
+        return true;
+    }
+    if (arg == "--gpu-layers-draft" || arg == "-ngld" || arg == "--n-gpu-layers-draft") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_gpu_layers_draft = std::stoi(argv[i]);
+        if (!llama_supports_gpu_offload()) {
+            fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n");
+            fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
+        }
+        return true;
+    }
+    if (arg == "--main-gpu" || arg == "-mg") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.main_gpu = std::stoi(argv[i]);
+#ifndef GGML_USE_CUDA_SYCL
+        fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL. Setting the main GPU has no effect.\n");
+#endif // GGML_USE_CUDA_SYCL
+        return true;
+    }
+    if (arg == "--split-mode" || arg == "-sm") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        std::string arg_next = argv[i];
+        if (arg_next == "none") {
+            params.split_mode = LLAMA_SPLIT_MODE_NONE;
+        }
+        else if (arg_next == "layer") {
+            params.split_mode = LLAMA_SPLIT_MODE_LAYER;
+        }
+        else if (arg_next == "row") {
+#ifdef GGML_USE_SYCL
+            fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n");
+            exit(1);
+#endif // GGML_USE_SYCL
+            params.split_mode = LLAMA_SPLIT_MODE_ROW;
+        }
+        else {
+            invalid_param = true;
+            return true;
+        }
+#ifndef GGML_USE_CUDA_SYCL
+        fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL. Setting the split mode has no effect.\n");
+#endif // GGML_USE_CUDA_SYCL
+        return true;
+    }
+    if (arg == "--tensor-split" || arg == "-ts") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        std::string arg_next = argv[i];
+
+        // split string by , and /
+        const std::regex regex{ R"([,/]+)" };
+        std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 };
+        std::vector<std::string> split_arg{ it, {} };
+        if (split_arg.size() >= llama_max_devices()) {
+            invalid_param = true;
+            return true;
+        }
+        for (size_t i = 0; i < llama_max_devices(); ++i) {
+            if (i < split_arg.size()) {
+                params.tensor_split[i] = std::stof(split_arg[i]);
+            }
+            else {
+                params.tensor_split[i] = 0.0f;
+            }
+        }
+#ifndef GGML_USE_CUDA_SYCL_VULKAN
+        fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting a tensor split has no effect.\n");
+#endif // GGML_USE_CUDA_SYCL_VULKAN
+        return true;
+    }
+    if (arg == "--no-mmap") {
+        params.use_mmap = false;
+        return true;
+    }
+    if (arg == "--numa") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        std::string value(argv[i]);
+        /**/ if (value == "distribute" || value == "") { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
+        else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
+        else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
+        else { invalid_param = true; }
+        return true;
+    }
+    if (arg == "--verbose-prompt") {
+        params.verbose_prompt = true;
+        return true;
+    }
+    if (arg == "--no-display-prompt") {
+        params.display_prompt = false;
+        return true;
+    }
+    if (arg == "-r" || arg == "--reverse-prompt") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.antiprompt.emplace_back(argv[i]);
+        return true;
+    }
+    if (arg == "-ld" || arg == "--logdir") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.logdir = argv[i];
+
+        if (params.logdir.back() != DIRECTORY_SEPARATOR) {
+            params.logdir += DIRECTORY_SEPARATOR;
+        }
+        return true;
+    }
+    if (arg == "-lcs" || arg == "--lookup-cache-static") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.lookup_cache_static = argv[i];
+        return true;
+    }
+    if (arg == "-lcd" || arg == "--lookup-cache-dynamic") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.lookup_cache_dynamic = argv[i];
+        return true;
+    }
+    if (arg == "--save-all-logits" || arg == "--kl-divergence-base") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.logits_file = argv[i];
+        return true;
+    }
+    if (arg == "--perplexity" || arg == "--all-logits") {
+        params.logits_all = true;
+        return true;
+    }
+    if (arg == "--ppl-stride") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.ppl_stride = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "-ptc" || arg == "--print-token-count") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_print = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--check-tensors") {
+        params.check_tensors = true;
+        return true;
+    }
+    if (arg == "--ppl-output-type") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.ppl_output_type = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--hellaswag") {
+        params.hellaswag = true;
+        return true;
+    }
+    if (arg == "--hellaswag-tasks") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.hellaswag_tasks = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--winogrande") {
+        params.winogrande = true;
+        return true;
+    }
+    if (arg == "--winogrande-tasks") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.winogrande_tasks = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--multiple-choice") {
+        params.multiple_choice = true;
+        return true;
+    }
+    if (arg == "--multiple-choice-tasks") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.multiple_choice_tasks = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--kl-divergence") {
+        params.kl_divergence = true;
+        return true;
+    }
+    if (arg == "--ignore-eos") {
+        params.ignore_eos = true;
+        return true;
+    }
+    if (arg == "--penalize-nl") {
+        sparams.penalize_nl = true;
+        return true;
+    }
+    if (arg == "-l" || arg == "--logit-bias") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        std::stringstream ss(argv[i]);
+        llama_token key;
+        char sign;
+        std::string value_str;
+        try {
+            if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
+                sparams.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
+            }
+            else {
+                throw std::exception();
+            }
+        }
+        catch (const std::exception&) {
+            invalid_param = true;
+            return true;
+        }
+        return true;
+    }
+    if (arg == "-h" || arg == "--help") {
+        gpt_print_usage(argc, argv, gpt_params());
+        exit(0);
+    }
+    if (arg == "--version") {
+        fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
+        fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
+        exit(0);
+    }
+    if (arg == "--random-prompt") {
+        params.random_prompt = true;
+        return true;
+    }
+    if (arg == "--in-prefix-bos") {
+        params.input_prefix_bos = true;
+        return true;
+    }
+    if (arg == "--in-prefix") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.input_prefix = argv[i];
+        return true;
+    }
+    if (arg == "--in-suffix") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.input_suffix = argv[i];
+        return true;
+    }
+    if (arg == "--grammar") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.grammar = argv[i];
+        return true;
+    }
+    if (arg == "--grammar-file") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        std::ifstream file(argv[i]);
+        if (!file) {
+            fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
+            invalid_param = true;
+            return true;
+        }
+        std::copy(
+            std::istreambuf_iterator<char>(file),
+            std::istreambuf_iterator<char>(),
+            std::back_inserter(sparams.grammar)
+        );
+        return true;
+    }
+    if (arg == "-j" || arg == "--json-schema") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.grammar = json_schema_to_grammar(json::parse(argv[i]));
+        return true;
+    }
+    if (arg == "--override-kv") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        if (!parse_kv_override(argv[i], params.kv_overrides)) {
+            fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
+            invalid_param = true;
+            return true;
+        }
+        return true;
+    }
+#ifndef LOG_DISABLE_LOGS
+    // Parse args for logging parameters
+    if (log_param_single_parse(argv[i])) {
+        // Do nothing, log_param_single_parse automatically does it's thing
+        //  and returns if a match was found and parsed.
+        return true;
+    }
+    if (log_param_pair_parse( /*check_but_dont_parse*/ true, argv[i])) {
+        // We have a matching known parameter requiring an argument,
+        //  now we need to check if there is anything after this argv
+        //  and flag invalid_param or parse it.
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        if (!log_param_pair_parse( /*check_but_dont_parse*/ false, argv[i - 1], argv[i])) {
+            invalid_param = true;
+            return true;
+        }
+        return true;
+    }
+    // End of Parse args for logging parameters
+#endif // LOG_DISABLE_LOGS
+
+    return false;
+}
+
+void gpt_params_handle_model_default(gpt_params & params) {
+    if (!params.hf_repo.empty()) {
+        // short-hand to avoid specifying --hf-file -> default it to --model
+        if (params.hf_file.empty()) {
+            if (params.model.empty()) {
+                throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
+            }
+            params.hf_file = params.model;
+        } else if (params.model.empty()) {
+            params.model = "models/" + string_split(params.hf_file, '/').back();
+        }
+    } else if (!params.model_url.empty()) {
+        if (params.model.empty()) {
+            auto f = string_split(params.model_url, '#').front();
+            f = string_split(f, '?').front();
+            f = string_split(f, '/').back();
+            params.model =  "models/" + f;
+        }
+    } else if (params.model.empty()) {
+        params.model = DEFAULT_MODEL_PATH;
+    }
+}
+
 bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
     bool invalid_param = false;
     std::string arg;
@@ -150,743 +1371,14 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
         if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
             std::replace(arg.begin(), arg.end(), '_', '-');
         }
-
-        if (arg == "-s" || arg == "--seed") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.seed = std::stoul(argv[i]);
-        } else if (arg == "-t" || arg == "--threads") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_threads = std::stoi(argv[i]);
-            if (params.n_threads <= 0) {
-                params.n_threads = std::thread::hardware_concurrency();
-            }
-        } else if (arg == "-tb" || arg == "--threads-batch") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_threads_batch = std::stoi(argv[i]);
-            if (params.n_threads_batch <= 0) {
-                params.n_threads_batch = std::thread::hardware_concurrency();
-            }
-        } else if (arg == "-td" || arg == "--threads-draft") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_threads_draft = std::stoi(argv[i]);
-            if (params.n_threads_draft <= 0) {
-                params.n_threads_draft = std::thread::hardware_concurrency();
-            }
-        } else if (arg == "-tbd" || arg == "--threads-batch-draft") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_threads_batch_draft = std::stoi(argv[i]);
-            if (params.n_threads_batch_draft <= 0) {
-                params.n_threads_batch_draft = std::thread::hardware_concurrency();
-            }
-        } else if (arg == "-p" || arg == "--prompt") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.prompt = argv[i];
-        } else if (arg == "-e" || arg == "--escape") {
-            params.escape = true;
-        } else if (arg == "--prompt-cache") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.path_prompt_cache = argv[i];
-        } else if (arg == "--prompt-cache-all") {
-            params.prompt_cache_all = true;
-        } else if (arg == "--prompt-cache-ro") {
-            params.prompt_cache_ro = true;
-        } else if (arg == "-bf" || arg == "--binary-file") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            std::ifstream file(argv[i], std::ios::binary);
-            if (!file) {
-                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
-                invalid_param = true;
-                break;
-            }
-            // store the external file name in params
-            params.prompt_file = argv[i];
-            std::ostringstream ss;
-            ss << file.rdbuf();
-            params.prompt = ss.str();
-            fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), argv[i]);
-        } else if (arg == "-f" || arg == "--file") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            std::ifstream file(argv[i]);
-            if (!file) {
-                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
-                invalid_param = true;
-                break;
-            }
-            // store the external file name in params
-            params.prompt_file = argv[i];
-            std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
-            if (!params.prompt.empty() && params.prompt.back() == '\n') {
-                params.prompt.pop_back();
-            }
-        } else if (arg == "-n" || arg == "--n-predict") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_predict = std::stoi(argv[i]);
-        } else if (arg == "--top-k") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.top_k = std::stoi(argv[i]);
-        } else if (arg == "-c" || arg == "--ctx-size") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_ctx = std::stoi(argv[i]);
-        } else if (arg == "--grp-attn-n" || arg == "-gan") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-
-            params.grp_attn_n = std::stoi(argv[i]);
-        } else if (arg == "--grp-attn-w" || arg == "-gaw") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-
-            params.grp_attn_w = std::stoi(argv[i]);
-        } else if (arg == "--rope-freq-base") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.rope_freq_base = std::stof(argv[i]);
-        } else if (arg == "--rope-freq-scale") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.rope_freq_scale = std::stof(argv[i]);
-        } else if (arg == "--rope-scaling") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            std::string value(argv[i]);
-            /**/ if (value == "none")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; }
-            else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; }
-            else if (value == "yarn")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; }
-            else { invalid_param = true; break; }
-        } else if (arg == "--rope-scale") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.rope_freq_scale = 1.0f/std::stof(argv[i]);
-        } else if (arg == "--yarn-orig-ctx") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.yarn_orig_ctx = std::stoi(argv[i]);
-        } else if (arg == "--yarn-ext-factor") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.yarn_ext_factor = std::stof(argv[i]);
-        } else if (arg == "--yarn-attn-factor") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.yarn_attn_factor = std::stof(argv[i]);
-        } else if (arg == "--yarn-beta-fast") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.yarn_beta_fast = std::stof(argv[i]);
-        } else if (arg == "--yarn-beta-slow") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.yarn_beta_slow = std::stof(argv[i]);
-        } else if (arg == "--samplers") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            const auto sampler_names = string_split(argv[i], ';');
-            sparams.samplers_sequence = sampler_types_from_names(sampler_names, true);
-        } else if (arg == "--sampling-seq") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.samplers_sequence = sampler_types_from_chars(argv[i]);
-        } else if (arg == "--top-p") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.top_p = std::stof(argv[i]);
-        } else if (arg == "--min-p") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.min_p = std::stof(argv[i]);
-        } else if (arg == "--temp") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.temp = std::stof(argv[i]);
-            sparams.temp = std::max(sparams.temp, 0.0f);
-        } else if (arg == "--tfs") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.tfs_z = std::stof(argv[i]);
-        } else if (arg == "--typical") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.typical_p = std::stof(argv[i]);
-        } else if (arg == "--repeat-last-n") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.penalty_last_n = std::stoi(argv[i]);
-            sparams.n_prev = std::max(sparams.n_prev, sparams.penalty_last_n);
-        } else if (arg == "--repeat-penalty") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.penalty_repeat = std::stof(argv[i]);
-        } else if (arg == "--frequency-penalty") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.penalty_freq = std::stof(argv[i]);
-        } else if (arg == "--presence-penalty") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.penalty_present = std::stof(argv[i]);
-        } else if (arg == "--dynatemp-range") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.dynatemp_range = std::stof(argv[i]);
-        } else if (arg == "--dynatemp-exp") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.dynatemp_exponent = std::stof(argv[i]);
-        } else if (arg == "--mirostat") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.mirostat = std::stoi(argv[i]);
-        } else if (arg == "--mirostat-lr") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.mirostat_eta = std::stof(argv[i]);
-        } else if (arg == "--mirostat-ent") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.mirostat_tau = std::stof(argv[i]);
-        } else if (arg == "--cfg-negative-prompt") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.cfg_negative_prompt = argv[i];
-        } else if (arg == "--cfg-negative-prompt-file") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            std::ifstream file(argv[i]);
-            if (!file) {
-                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
-                invalid_param = true;
-                break;
-            }
-            std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(sparams.cfg_negative_prompt));
-            if (!sparams.cfg_negative_prompt.empty() && sparams.cfg_negative_prompt.back() == '\n') {
-                sparams.cfg_negative_prompt.pop_back();
-            }
-        } else if (arg == "--cfg-scale") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.cfg_scale = std::stof(argv[i]);
-        } else if (arg == "-b" || arg == "--batch-size") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_batch = std::stoi(argv[i]);
-        } else if (arg == "--keep") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_keep = std::stoi(argv[i]);
-        } else if (arg == "--draft") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_draft = std::stoi(argv[i]);
-        } else if (arg == "--chunks") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_chunks = std::stoi(argv[i]);
-        } else if (arg == "-np" || arg == "--parallel") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_parallel = std::stoi(argv[i]);
-        } else if (arg == "-ns" || arg == "--sequences") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_sequences = std::stoi(argv[i]);
-        } else if (arg == "--p-accept" || arg == "-pa") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.p_accept = std::stof(argv[i]);
-        } else if (arg == "--p-split" || arg == "-ps") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.p_split = std::stof(argv[i]);
-        } else if (arg == "-m" || arg == "--model") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.model = argv[i];
-        } else if (arg == "-md" || arg == "--model-draft") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.model_draft = argv[i];
-        } else if (arg == "-a" || arg == "--alias") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.model_alias = argv[i];
-        } else if (arg == "--lora") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.lora_adapter.emplace_back(argv[i], 1.0f);
-            params.use_mmap = false;
-        } else if (arg == "--lora-scaled") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            const char * lora_adapter = argv[i];
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i]));
-            params.use_mmap = false;
-        } else if (arg == "--lora-base") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.lora_base = argv[i];
-        } else if (arg == "--mmproj") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.mmproj = argv[i];
-        } else if (arg == "--image") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.image = argv[i];
-        } else if (arg == "-i" || arg == "--interactive") {
-            params.interactive = true;
-        } else if (arg == "--embedding") {
-            params.embedding = true;
-        } else if (arg == "--interactive-first") {
-            params.interactive_first = true;
-        } else if (arg == "-ins" || arg == "--instruct") {
-            params.instruct = true;
-        } else if (arg == "-cml" || arg == "--chatml") {
-            params.chatml = true;
-        } else if (arg == "--infill") {
-            params.infill = true;
-        } else if (arg == "-dkvc" || arg == "--dump-kv-cache") {
-            params.dump_kv_cache = true;
-        } else if (arg == "-nkvo" || arg == "--no-kv-offload") {
-            params.no_kv_offload = true;
-        } else if (arg == "-ctk" || arg == "--cache-type-k") {
-            params.cache_type_k = argv[++i];
-        } else if (arg == "-ctv" || arg == "--cache-type-v") {
-            params.cache_type_v = argv[++i];
-        } else if (arg == "--multiline-input") {
-            params.multiline_input = true;
-        } else if (arg == "--simple-io") {
-            params.simple_io = true;
-        } else if (arg == "-cb" || arg == "--cont-batching") {
-            params.cont_batching = true;
-        } else if (arg == "--color") {
-            params.use_color = true;
-        } else if (arg == "--mlock") {
-            params.use_mlock = true;
-        } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_gpu_layers = std::stoi(argv[i]);
-            if (!llama_supports_gpu_offload()) {
-                fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n");
-                fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
-            }
-        } else if (arg == "--gpu-layers-draft" || arg == "-ngld" || arg == "--n-gpu-layers-draft") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_gpu_layers_draft = std::stoi(argv[i]);
-            if (!llama_supports_gpu_offload()) {
-                fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n");
-                fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
-            }
-        } else if (arg == "--main-gpu" || arg == "-mg") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.main_gpu = std::stoi(argv[i]);
-#ifndef GGML_USE_CUBLAS_SYCL
-            fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting the main GPU has no effect.\n");
-#endif // GGML_USE_CUBLAS_SYCL
-        } else if (arg == "--split-mode" || arg == "-sm") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            std::string arg_next = argv[i];
-            if (arg_next == "none") {
-                params.split_mode = LLAMA_SPLIT_NONE;
-            } else if (arg_next == "layer") {
-                params.split_mode = LLAMA_SPLIT_LAYER;
-            } else if (arg_next == "row") {
-                params.split_mode = LLAMA_SPLIT_ROW;
-            } else {
-                invalid_param = true;
-                break;
-            }
-#ifndef GGML_USE_CUBLAS_SYCL
-            fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting the split mode has no effect.\n");
-#endif // GGML_USE_CUBLAS_SYCL
-
-        } else if (arg == "--tensor-split" || arg == "-ts") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            std::string arg_next = argv[i];
-
-            // split string by , and /
-            const std::regex regex{R"([,/]+)"};
-            std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
-            std::vector<std::string> split_arg{it, {}};
-            if (split_arg.size() >= llama_max_devices()) {
-                invalid_param = true;
-                break;
-            }
-            for (size_t i = 0; i < llama_max_devices(); ++i) {
-                if (i < split_arg.size()) {
-                    params.tensor_split[i] = std::stof(split_arg[i]);
-                } else {
-                    params.tensor_split[i] = 0.0f;
-                }
-            }
-#ifndef GGML_USE_CUBLAS_SYCL_VULKAN
-            fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL/Vulkan. Setting a tensor split has no effect.\n");
-#endif // GGML_USE_CUBLAS_SYCL
-        } else if (arg == "--no-mmap") {
-            params.use_mmap = false;
-        } else if (arg == "--numa") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            std::string value(argv[i]);
-            /**/ if (value == "distribute" || value == "") { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
-            else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
-            else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
-            else { invalid_param = true; break; }
-        } else if (arg == "--verbose-prompt") {
-            params.verbose_prompt = true;
-        } else if (arg == "--no-display-prompt") {
-            params.display_prompt = false;
-        } else if (arg == "-r" || arg == "--reverse-prompt") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.antiprompt.emplace_back(argv[i]);
-        } else if (arg == "-ld" || arg == "--logdir") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.logdir = argv[i];
-
-            if (params.logdir.back() != DIRECTORY_SEPARATOR) {
-                params.logdir += DIRECTORY_SEPARATOR;
-            }
-        } else if (arg == "--save-all-logits" || arg == "--kl-divergence-base") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.logits_file = argv[i];
-        } else if (arg == "--perplexity" || arg == "--all-logits") {
-            params.logits_all = true;
-        } else if (arg == "--ppl-stride") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.ppl_stride = std::stoi(argv[i]);
-        } else if (arg == "-ptc" || arg == "--print-token-count") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_print = std::stoi(argv[i]);
-        } else if (arg == "--ppl-output-type") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.ppl_output_type = std::stoi(argv[i]);
-        } else if (arg == "--hellaswag") {
-            params.hellaswag = true;
-        } else if (arg == "--hellaswag-tasks") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.hellaswag_tasks = std::stoi(argv[i]);
-        } else if (arg == "--winogrande") {
-            params.winogrande = true;
-        } else if (arg == "--winogrande-tasks") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.winogrande_tasks = std::stoi(argv[i]);
-        } else if (arg == "--multiple-choice") {
-            params.multiple_choice = true;
-        } else if (arg == "--multiple-choice-tasks") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.multiple_choice_tasks = std::stoi(argv[i]);
-        } else if (arg == "--kl-divergence") {
-            params.kl_divergence = true;
-        } else if (arg == "--ignore-eos") {
-            params.ignore_eos = true;
-        } else if (arg == "--no-penalize-nl") {
-            sparams.penalize_nl = false;
-        } else if (arg == "-l" || arg == "--logit-bias") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            std::stringstream ss(argv[i]);
-            llama_token key;
-            char sign;
-            std::string value_str;
-            try {
-                if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
-                    sparams.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
-                } else {
-                    throw std::exception();
-                }
-            } catch (const std::exception&) {
-                invalid_param = true;
-                break;
-            }
-        } else if (arg == "-h" || arg == "--help") {
-            return false;
-
-        } else if (arg == "--version") {
-            fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
-            fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
-            exit(0);
-        } else if (arg == "--random-prompt") {
-            params.random_prompt = true;
-        } else if (arg == "--in-prefix-bos") {
-            params.input_prefix_bos = true;
-        } else if (arg == "--in-prefix") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.input_prefix = argv[i];
-        } else if (arg == "--in-suffix") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.input_suffix = argv[i];
-        } else if (arg == "--grammar") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.grammar = argv[i];
-        } else if (arg == "--grammar-file") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            std::ifstream file(argv[i]);
-            if (!file) {
-                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
-                invalid_param = true;
-                break;
-            }
-            std::copy(
-                std::istreambuf_iterator<char>(file),
-                std::istreambuf_iterator<char>(),
-                std::back_inserter(sparams.grammar)
-            );
-        } else if (arg == "--override-kv") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            char * sep = strchr(argv[i], '=');
-            if (sep == nullptr || sep - argv[i] >= 128) {
-                fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
-                invalid_param = true;
-                break;
-            }
-            struct llama_model_kv_override kvo;
-            std::strncpy(kvo.key, argv[i], sep - argv[i]);
-            kvo.key[sep - argv[i]] = 0;
-            sep++;
-            if (strncmp(sep, "int:", 4) == 0) {
-                sep += 4;
-                kvo.tag = LLAMA_KV_OVERRIDE_INT;
-                kvo.int_value = std::atol(sep);
-            } else if (strncmp(sep, "float:", 6) == 0) {
-                sep += 6;
-                kvo.tag = LLAMA_KV_OVERRIDE_FLOAT;
-                kvo.float_value = std::atof(sep);
-            } else if (strncmp(sep, "bool:", 5) == 0) {
-                sep += 5;
-                kvo.tag = LLAMA_KV_OVERRIDE_BOOL;
-                if (std::strcmp(sep, "true") == 0) {
-                    kvo.bool_value = true;
-                } else if (std::strcmp(sep, "false") == 0) {
-                    kvo.bool_value = false;
-                } else {
-                    fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
-                    invalid_param = true;
-                    break;
-                }
-            } else {
-                fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
-                invalid_param = true;
-                break;
-            }
-            params.kv_overrides.push_back(kvo);
-#ifndef LOG_DISABLE_LOGS
-        // Parse args for logging parameters
-        } else if ( log_param_single_parse( argv[i] ) ) {
-            // Do nothing, log_param_single_parse automatically does it's thing
-            //  and returns if a match was found and parsed.
-        } else if ( log_param_pair_parse( /*check_but_dont_parse*/ true, argv[i] ) ) {
-            // We have a matching known parameter requiring an argument,
-            //  now we need to check if there is anything after this argv
-            //  and flag invalid_param or parse it.
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            if( !log_param_pair_parse( /*check_but_dont_parse*/ false, argv[i-1], argv[i]) ) {
-                invalid_param = true;
-                break;
-            }
-        // End of Parse args for logging parameters
-#endif // LOG_DISABLE_LOGS
-        } else {
+        if (!gpt_params_find_arg(argc, argv, arg, params, i, invalid_param)) {
             throw std::invalid_argument("error: unknown argument: " + arg);
         }
+        if (invalid_param) {
+            throw std::invalid_argument("error: invalid parameter for argument: " + arg);
+        }
     }
-    if (invalid_param) {
-        throw std::invalid_argument("error: invalid parameter for argument: " + arg);
-    }
+
     if (params.prompt_cache_all &&
             (params.interactive || params.interactive_first ||
              params.instruct)) {
@@ -894,6 +1386,8 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
         throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
     }
 
+    gpt_params_handle_model_default(params);
+
     if (params.escape) {
         process_escapes(params.prompt);
         process_escapes(params.input_prefix);
@@ -930,7 +1424,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  -h, --help            show this help message and exit\n");
     printf("  --version             show version and build info\n");
     printf("  -i, --interactive     run in interactive mode\n");
+    printf("  --interactive-specials allow special tokens in user text, in interactive mode\n");
     printf("  --interactive-first   run in interactive mode and wait for input right away\n");
+    printf("  -cnv, --conversation  run in conversation mode (does not print special tokens and suffix/prefix)\n");
     printf("  -ins, --instruct      run in instruction mode (use with Alpaca models)\n");
     printf("  -cml, --chatml        run in chatml mode (use with ChatML-compatible models)\n");
     printf("  --multiline-input     allows you to write or paste multiple lines without ending each in '\\'\n");
@@ -963,7 +1459,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("                        binary file containing multiple choice tasks.\n");
     printf("  -n N, --n-predict N   number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
     printf("  -c N, --ctx-size N    size of the prompt context (default: %d, 0 = loaded from model)\n", params.n_ctx);
-    printf("  -b N, --batch-size N  batch size for prompt processing (default: %d)\n", params.n_batch);
+    printf("  -b N, --batch-size N  logical maximum batch size (default: %d)\n", params.n_batch);
+    printf("  -ub N, --ubatch-size N\n");
+    printf("                        physical maximum batch size (default: %d)\n", params.n_ubatch);
     printf("  --samplers            samplers that will be used for generation in the order, separated by \';\'\n");
     printf("                        (default: %s)\n", sampler_type_names.c_str());
     printf("  --sampling-seq        simplified sequence for samplers that will be used (default: %s)\n", sampler_type_chars.c_str());
@@ -989,6 +1487,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("                        or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n");
     printf("  --grammar GRAMMAR     BNF-like grammar to constrain generations (see samples in grammars/ dir)\n");
     printf("  --grammar-file FNAME  file to read grammar from\n");
+    printf("  -j SCHEMA, --json-schema SCHEMA\n");
+    printf("                        JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object.\n");
+    printf("                        For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead\n");
     printf("  --cfg-negative-prompt PROMPT\n");
     printf("                        negative prompt to use for guidance. (default: empty)\n");
     printf("  --cfg-negative-prompt-file FNAME\n");
@@ -1004,10 +1505,14 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  --yarn-attn-factor N  YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n");
     printf("  --yarn-beta-slow N    YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow);
     printf("  --yarn-beta-fast N    YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast);
+    printf("  --pooling {none,mean,cls}\n");
+    printf("                        pooling type for embeddings, use model default if unspecified\n");
+    printf("  -dt N, --defrag-thold N\n");
+    printf("                        KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold);
     printf("  --ignore-eos          ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
-    printf("  --no-penalize-nl      do not penalize newline token\n");
+    printf("  --penalize-nl         penalize newline tokens\n");
     printf("  --temp N              temperature (default: %.1f)\n", (double)sparams.temp);
-    printf("  --logits-all          return logits for all tokens in the batch (default: disabled)\n");
+    printf("  --all-logits          return logits for all tokens in the batch (default: disabled)\n");
     printf("  --hellaswag           compute HellaSwag score over random tasks from datafile supplied with -f\n");
     printf("  --hellaswag-tasks N   number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
     printf("  --winogrande          compute Winogrande score over random tasks from datafile supplied with -f\n");
@@ -1020,11 +1525,11 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  --chunks N            max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
     printf("  -np N, --parallel N   number of parallel sequences to decode (default: %d)\n", params.n_parallel);
     printf("  -ns N, --sequences N  number of sequences to decode (default: %d)\n", params.n_sequences);
-    printf("  -pa N, --p-accept N   speculative decoding accept probability (default: %.1f)\n", (double)params.p_accept);
     printf("  -ps N, --p-split N    speculative decoding split probability (default: %.1f)\n", (double)params.p_split);
     printf("  -cb, --cont-batching  enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
+    printf("  -fa, --flash-attn     enable Flash Attention (default: %s)\n", params.flash_attn ? "enabled" : "disabled");
     printf("  --mmproj MMPROJ_FILE  path to a multimodal projector file for LLaVA. see examples/llava/README.md\n");
-    printf("  --image IMAGE_FILE    path to an image file. use with multimodal models\n");
+    printf("  --image IMAGE_FILE    path to an image file. use with multimodal models. Specify multiple times for batching\n");
     if (llama_supports_mlock()) {
         printf("  --mlock               force system to keep model in RAM rather than swapping or compressing\n");
     }
@@ -1070,17 +1575,34 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  --lora FNAME          apply LoRA adapter (implies --no-mmap)\n");
     printf("  --lora-scaled FNAME S apply LoRA adapter with user defined scaling S (implies --no-mmap)\n");
     printf("  --lora-base FNAME     optional model to use as a base for the layers modified by the LoRA adapter\n");
+    printf("  --control-vector FNAME\n");
+    printf("                        add a control vector\n");
+    printf("  --control-vector-scaled FNAME S\n");
+    printf("                        add a control vector with user defined scaling S\n");
+    printf("  --control-vector-layer-range START END\n");
+    printf("                        layer range to apply the control vector(s) to, start and end inclusive\n");
     printf("  -m FNAME, --model FNAME\n");
-    printf("                        model path (default: %s)\n", params.model.c_str());
+    printf("                        model path (default: models/$filename with filename from --hf-file or --model-url if set, otherwise %s)\n", DEFAULT_MODEL_PATH);
     printf("  -md FNAME, --model-draft FNAME\n");
-    printf("                        draft model for speculative decoding\n");
+    printf("                        draft model for speculative decoding (default: unused)\n");
+    printf("  -mu MODEL_URL, --model-url MODEL_URL\n");
+    printf("                        model download url (default: unused)\n");
+    printf("  -hfr REPO, --hf-repo REPO\n");
+    printf("                        Hugging Face model repository (default: unused)\n");
+    printf("  -hff FILE, --hf-file FILE\n");
+    printf("                        Hugging Face model file (default: unused)\n");
     printf("  -ld LOGDIR, --logdir LOGDIR\n");
     printf("                        path under which to save YAML logs (no logging if unset)\n");
+    printf("  -lcs FNAME, --lookup-cache-static FNAME\n");
+    printf("                        path to static lookup cache to use for lookup decoding (not updated by generation)\n");
+    printf("  -lcd FNAME, --lookup-cache-dynamic FNAME\n");
+    printf("                        path to dynamic lookup cache to use for lookup decoding (updated by generation)\n");
     printf("  --override-kv KEY=TYPE:VALUE\n");
     printf("                        advanced option to override model metadata by key. may be specified multiple times.\n");
-    printf("                        types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
+    printf("                        types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
     printf("  -ptc N, --print-token-count N\n");
     printf("                        print token count every N tokens (default: %d)\n", params.n_print);
+    printf("  --check-tensors       check model tensor data for invalid values\n");
     printf("\n");
 #ifndef LOG_DISABLE_LOGS
     log_print_usage();
@@ -1117,6 +1639,77 @@ std::string gpt_random_prompt(std::mt19937 & rng) {
     GGML_UNREACHABLE();
 }
 
+// Validate if a filename is safe to use
+// To validate a full path, split the path by the OS-specific path separator, and validate each part with this function
+bool validate_file_name(const std::string & filename) {
+    if (!filename.length()) {
+        // Empty filename invalid
+        return false;
+    }
+    if (filename.length() > 255) {
+        // Limit at common largest possible filename on Linux filesystems
+        // to avoid unnecessary further validation
+        // (On systems with smaller limits it will be caught by the OS)
+        return false;
+    }
+
+    std::u32string filename_utf32;
+    try {
+        std::wstring_convert<std::codecvt_utf8<char32_t>, char32_t> converter;
+        filename_utf32 = converter.from_bytes(filename);
+
+        // If the reverse conversion mismatches, it means overlong UTF-8 sequences were used,
+        // or invalid encodings were encountered. Reject such attempts
+        std::string filename_reencoded = converter.to_bytes(filename_utf32);
+        if (filename_reencoded != filename) {
+            return false;
+        }
+    } catch (const std::exception &) {
+        return false;
+    }
+
+    // Check for forbidden codepoints:
+    // - Control characters
+    // - Unicode equivalents of illegal characters
+    // - UTF-16 surrogate pairs
+    // - UTF-8 replacement character
+    // - Byte order mark (BOM)
+    // - Illegal characters: / \ : * ? " < > |
+    for (char32_t c : filename_utf32) {
+        if (c <= 0x1F // Control characters (C0)
+            || c == 0x7F // Control characters (DEL)
+            || (c >= 0x80 && c <= 0x9F) // Control characters (C1)
+            || c == 0xFF0E // Fullwidth Full Stop (period equivalent)
+            || c == 0x2215 // Division Slash (forward slash equivalent)
+            || c == 0x2216 // Set Minus (backslash equivalent)
+            || (c >= 0xD800 && c <= 0xDFFF) // UTF-16 surrogate pairs
+            || c == 0xFFFD // Replacement Character (UTF-8)
+            || c == 0xFEFF // Byte Order Mark (BOM)
+            || c == '/' || c == '\\' || c == ':' || c == '*' // Illegal characters
+            || c == '?' || c == '"' || c == '<' || c == '>' || c == '|') {
+            return false;
+        }
+    }
+
+    // Reject any leading or trailing ' ', or any trailing '.', these are stripped on Windows and will cause a different filename
+    // Unicode and other whitespace is not affected, only 0x20 space
+    if (filename.front() == ' ' || filename.back() == ' ' || filename.back() == '.') {
+        return false;
+    }
+
+    // Reject any ".." (currently stricter than necessary, it should be fine to just check for == ".." instead)
+    if (filename.find("..") != std::string::npos) {
+        return false;
+    }
+
+    // Reject "."
+    if (filename == ".") {
+        return false;
+    }
+
+    return true;
+}
+
 //
 // String utils
 //
@@ -1134,6 +1727,18 @@ std::vector<std::string> string_split(std::string input, char separator) {
     return parts;
 }
 
+std::string string_strip(const std::string & str) {
+    size_t start = 0;
+    size_t end = str.size();
+    while (start < end && std::isspace(str[start])) {
+        start++;
+    }
+    while (end > start && std::isspace(str[end - 1])) {
+        end--;
+    }
+    return str.substr(start, end - start);
+}
+
 std::vector<llama_sampler_type> sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names) {
     std::unordered_map<std::string, llama_sampler_type> sampler_canonical_name_map {
         {"top_k",       llama_sampler_type::TOP_K},
@@ -1230,6 +1835,7 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params &
     mparams.tensor_split    = params.tensor_split;
     mparams.use_mmap        = params.use_mmap;
     mparams.use_mlock       = params.use_mlock;
+    mparams.check_tensors   = params.check_tensors;
     if (params.kv_overrides.empty()) {
         mparams.kv_overrides = NULL;
     } else {
@@ -1256,6 +1862,9 @@ static ggml_type kv_cache_type_from_str(const std::string & s) {
     if (s == "q4_1") {
         return GGML_TYPE_Q4_1;
     }
+    if (s == "iq4_nl") {
+        return GGML_TYPE_IQ4_NL;
+    }
     if (s == "q5_0") {
         return GGML_TYPE_Q5_0;
     }
@@ -1270,13 +1879,14 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
     auto cparams = llama_context_default_params();
 
     cparams.n_ctx             = params.n_ctx;
+    cparams.n_seq_max         = params.n_parallel;
     cparams.n_batch           = params.n_batch;
+    cparams.n_ubatch          = params.n_ubatch;
     cparams.n_threads         = params.n_threads;
     cparams.n_threads_batch   = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
-    cparams.mul_mat_q         = params.mul_mat_q;
     cparams.seed              = params.seed;
     cparams.logits_all        = params.logits_all;
-    cparams.embedding         = params.embedding;
+    cparams.embeddings        = params.embedding;
     cparams.rope_scaling_type = params.rope_scaling_type;
     cparams.rope_freq_base    = params.rope_freq_base;
     cparams.rope_freq_scale   = params.rope_freq_scale;
@@ -1285,7 +1895,12 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
     cparams.yarn_beta_fast    = params.yarn_beta_fast;
     cparams.yarn_beta_slow    = params.yarn_beta_slow;
     cparams.yarn_orig_ctx     = params.yarn_orig_ctx;
+    cparams.pooling_type      = params.pooling_type;
+    cparams.defrag_thold      = params.defrag_thold;
+    cparams.cb_eval           = params.cb_eval;
+    cparams.cb_eval_user_data = params.cb_eval_user_data;
     cparams.offload_kqv       = !params.no_kv_offload;
+    cparams.flash_attn        = params.flash_attn;
 
     cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
     cparams.type_v = kv_cache_type_from_str(params.cache_type_v);
@@ -1314,10 +1929,349 @@ void llama_batch_add(
     batch.n_tokens++;
 }
 
+#ifdef LLAMA_USE_CURL
+
+static bool starts_with(const std::string & str, const std::string & prefix) {
+    // While we wait for C++20's std::string::starts_with...
+    return str.rfind(prefix, 0) == 0;
+}
+
+static bool llama_download_file(const std::string & url, const std::string & path) {
+
+    // Initialize libcurl
+    std::unique_ptr<CURL, decltype(&curl_easy_cleanup)> curl(curl_easy_init(), &curl_easy_cleanup);
+    if (!curl) {
+        fprintf(stderr, "%s: error initializing libcurl\n", __func__);
+        return false;
+    }
+
+    bool force_download = false;
+
+    // Set the URL, allow to follow http redirection
+    curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
+    curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);
+
+#if defined(_WIN32)
+    // CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of
+    //   operating system. Currently implemented under MS-Windows.
+    curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
+#endif
+
+    // Check if the file already exists locally
+    struct stat model_file_info;
+    auto file_exists = (stat(path.c_str(), &model_file_info) == 0);
+
+    // If the file exists, check its JSON metadata companion file.
+    std::string metadata_path = path + ".json";
+    nlohmann::json metadata;
+    std::string etag;
+    std::string last_modified;
+
+    if (file_exists) {
+        // Try and read the JSON metadata file (note: stream autoclosed upon exiting this block).
+        std::ifstream metadata_in(metadata_path);
+        if (metadata_in.good()) {
+            try {
+                metadata_in >> metadata;
+                fprintf(stderr, "%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
+                if (metadata.contains("url") && metadata.at("url").is_string()) {
+                    auto previous_url = metadata.at("url").get<std::string>();
+                    if (previous_url != url) {
+                        fprintf(stderr, "%s: Model URL mismatch: %s != %s\n", __func__, url.c_str(), previous_url.c_str());
+                        return false;
+                    }
+                }
+                if (metadata.contains("etag") && metadata.at("etag").is_string()) {
+                    etag = metadata.at("etag");
+                }
+                if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) {
+                    last_modified = metadata.at("lastModified");
+                }
+            } catch (const nlohmann::json::exception & e) {
+                fprintf(stderr, "%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
+                return false;
+            }
+        }
+    } else {
+        fprintf(stderr, "%s: no previous model file found %s\n", __func__, path.c_str());
+    }
+
+    // Send a HEAD request to retrieve the etag and last-modified headers
+    struct llama_load_model_from_url_headers {
+        std::string etag;
+        std::string last_modified;
+    };
+    llama_load_model_from_url_headers headers;
+    {
+        typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
+        auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
+            llama_load_model_from_url_headers *headers = (llama_load_model_from_url_headers *) userdata;
+
+            static std::regex header_regex("([^:]+): (.*)\r\n");
+            static std::regex etag_regex("ETag", std::regex_constants::icase);
+            static std::regex last_modified_regex("Last-Modified", std::regex_constants::icase);
+
+            std::string header(buffer, n_items);
+            std::smatch match;
+            if (std::regex_match(header, match, header_regex)) {
+                const std::string & key = match[1];
+                const std::string & value = match[2];
+                if (std::regex_match(key, match, etag_regex)) {
+                    headers->etag = value;
+                } else if (std::regex_match(key, match, last_modified_regex)) {
+                    headers->last_modified = value;
+                }
+            }
+            return n_items;
+        };
+
+        curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 1L); // will trigger the HEAD verb
+        curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress
+        curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
+        curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
+
+        CURLcode res = curl_easy_perform(curl.get());
+        if (res != CURLE_OK) {
+            fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
+            return false;
+        }
+
+        long http_code = 0;
+        curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
+        if (http_code != 200) {
+            // HEAD not supported, we don't know if the file has changed
+            // force trigger downloading
+            force_download = true;
+            fprintf(stderr, "%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
+        }
+    }
+
+    bool should_download = !file_exists || force_download;
+    if (!should_download) {
+        if (!etag.empty() && etag != headers.etag) {
+            fprintf(stderr, "%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(), headers.etag.c_str());
+            should_download = true;
+        } else if (!last_modified.empty() && last_modified != headers.last_modified) {
+            fprintf(stderr, "%s: Last-Modified header is different (%s != %s): triggering a new download\n", __func__, last_modified.c_str(), headers.last_modified.c_str());
+            should_download = true;
+        }
+    }
+    if (should_download) {
+        std::string path_temporary = path + ".downloadInProgress";
+        if (file_exists) {
+            fprintf(stderr, "%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
+            if (remove(path.c_str()) != 0) {
+                fprintf(stderr, "%s: unable to delete file: %s\n", __func__, path.c_str());
+                return false;
+            }
+        }
+
+        // Set the output file
+        std::unique_ptr<FILE, decltype(&fclose)> outfile(fopen(path_temporary.c_str(), "wb"), fclose);
+        if (!outfile) {
+            fprintf(stderr, "%s: error opening local file for writing: %s\n", __func__, path.c_str());
+            return false;
+        }
+
+        typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * data, size_t size, size_t nmemb, void * fd);
+        auto write_callback = [](void * data, size_t size, size_t nmemb, void * fd) -> size_t {
+            return fwrite(data, size, nmemb, (FILE *)fd);
+        };
+        curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 0L);
+        curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
+        curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, outfile.get());
+
+        //  display download progress
+        curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 0L);
+
+        // helper function to hide password in URL
+        auto llama_download_hide_password_in_url = [](const std::string & url) -> std::string {
+            std::size_t protocol_pos = url.find("://");
+            if (protocol_pos == std::string::npos) {
+                return url;  // Malformed URL
+            }
+
+            std::size_t at_pos = url.find('@', protocol_pos + 3);
+            if (at_pos == std::string::npos) {
+                return url;  // No password in URL
+            }
+
+            return url.substr(0, protocol_pos + 3) + "********" + url.substr(at_pos);
+        };
+
+        // start the download
+        fprintf(stderr, "%s: downloading from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
+                llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
+        auto res = curl_easy_perform(curl.get());
+        if (res != CURLE_OK) {
+            fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
+            return false;
+        }
+
+        long http_code = 0;
+        curl_easy_getinfo (curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
+        if (http_code < 200 || http_code >= 400) {
+            fprintf(stderr, "%s: invalid http status code received: %ld\n", __func__, http_code);
+            return false;
+        }
+
+        // Causes file to be closed explicitly here before we rename it.
+        outfile.reset();
+
+        // Write the updated JSON metadata file.
+        metadata.update({
+            {"url", url},
+            {"etag", headers.etag},
+            {"lastModified", headers.last_modified}
+        });
+        std::ofstream(metadata_path) << metadata.dump(4);
+        fprintf(stderr, "%s: file metadata saved: %s\n", __func__, metadata_path.c_str());
+
+        if (rename(path_temporary.c_str(), path.c_str()) != 0) {
+            fprintf(stderr, "%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
+            return false;
+        }
+    }
+
+    return true;
+}
+
+struct llama_model * llama_load_model_from_url(
+        const char * model_url,
+        const char * path_model,
+        const struct llama_model_params & params) {
+    // Basic validation of the model_url
+    if (!model_url || strlen(model_url) == 0) {
+        fprintf(stderr, "%s: invalid model_url\n", __func__);
+        return NULL;
+    }
+
+    if (!llama_download_file(model_url, path_model)) {
+        return NULL;
+    }
+
+    // check for additional GGUFs split to download
+    int n_split = 0;
+    {
+        struct gguf_init_params gguf_params = {
+            /*.no_alloc = */ true,
+            /*.ctx      = */ NULL,
+        };
+        auto * ctx_gguf = gguf_init_from_file(path_model, gguf_params);
+        if (!ctx_gguf) {
+            fprintf(stderr, "\n%s:  failed to load input GGUF from %s\n", __func__, path_model);
+            return NULL;
+        }
+
+        auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT);
+        if (key_n_split >= 0) {
+            n_split = gguf_get_val_u16(ctx_gguf, key_n_split);
+        }
+
+        gguf_free(ctx_gguf);
+    }
+
+    if (n_split > 1) {
+        char split_prefix[PATH_MAX] = {0};
+        char split_url_prefix[LLAMA_CURL_MAX_URL_LENGTH] = {0};
+
+        // Verify the first split file format
+        // and extract split URL and PATH prefixes
+        {
+            if (!llama_split_prefix(split_prefix, sizeof(split_prefix), path_model, 0, n_split)) {
+                fprintf(stderr, "\n%s: unexpected model file name: %s"
+                                " n_split=%d\n", __func__, path_model, n_split);
+                return NULL;
+            }
+
+            if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model_url, 0, n_split)) {
+                fprintf(stderr, "\n%s: unexpected model url: %s"
+                                " n_split=%d\n", __func__, model_url, n_split);
+                return NULL;
+            }
+        }
+
+        // Prepare download in parallel
+        std::vector<std::future<bool>> futures_download;
+        for (int idx = 1; idx < n_split; idx++) {
+            futures_download.push_back(std::async(std::launch::async, [&split_prefix, &split_url_prefix, &n_split](int download_idx) -> bool {
+                char split_path[PATH_MAX] = {0};
+                llama_split_path(split_path, sizeof(split_path), split_prefix, download_idx, n_split);
+
+                char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0};
+                llama_split_path(split_url, sizeof(split_url), split_url_prefix, download_idx, n_split);
+
+                return llama_download_file(split_url, split_path);
+            }, idx));
+        }
+
+        // Wait for all downloads to complete
+        for (auto & f : futures_download) {
+            if (!f.get()) {
+                return NULL;
+            }
+        }
+    }
+
+    return llama_load_model_from_file(path_model, params);
+}
+
+struct llama_model * llama_load_model_from_hf(
+        const char * repo,
+        const char * model,
+        const char * path_model,
+        const struct llama_model_params & params) {
+    // construct hugging face model url:
+    //
+    //  --repo ggml-org/models --file tinyllama-1.1b/ggml-model-f16.gguf
+    //    https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf
+    //
+    //  --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf
+    //    https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf
+    //
+
+    std::string model_url = "https://huggingface.co/";
+    model_url += repo;
+    model_url += "/resolve/main/";
+    model_url += model;
+
+    return llama_load_model_from_url(model_url.c_str(), path_model, params);
+}
+
+#else
+
+struct llama_model * llama_load_model_from_url(
+        const char * /*model_url*/,
+        const char * /*path_model*/,
+        const struct llama_model_params & /*params*/) {
+    fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__);
+    return nullptr;
+}
+
+struct llama_model * llama_load_model_from_hf(
+        const char * /*repo*/,
+        const char * /*model*/,
+        const char * /*path_model*/,
+        const struct llama_model_params & /*params*/) {
+    fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__);
+    return nullptr;
+}
+
+#endif // LLAMA_USE_CURL
+
 std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(gpt_params & params) {
     auto mparams = llama_model_params_from_gpt_params(params);
 
-    llama_model * model  = llama_load_model_from_file(params.model.c_str(), mparams);
+    llama_model * model = nullptr;
+
+    if (!params.hf_repo.empty() && !params.hf_file.empty()) {
+        model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), mparams);
+    } else if (!params.model_url.empty()) {
+        model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), mparams);
+    } else {
+        model = llama_load_model_from_file(params.model.c_str(), mparams);
+    }
+
     if (model == NULL) {
         fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
         return std::make_tuple(nullptr, nullptr);
@@ -1332,8 +2286,32 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
         return std::make_tuple(nullptr, nullptr);
     }
 
+    if (!params.control_vectors.empty()) {
+        if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
+        if (params.control_vector_layer_end   <= 0) params.control_vector_layer_end   = llama_n_layer(model);
+
+        const auto cvec = llama_control_vector_load(params.control_vectors);
+        if (cvec.n_embd == -1) {
+            llama_free(lctx);
+            llama_free_model(model);
+            return std::make_tuple(nullptr, nullptr);
+        }
+
+        int err = llama_control_vector_apply(lctx,
+                                             cvec.data.data(),
+                                             cvec.data.size(),
+                                             cvec.n_embd,
+                                             params.control_vector_layer_start,
+                                             params.control_vector_layer_end);
+        if (err) {
+            llama_free(lctx);
+            llama_free_model(model);
+            return std::make_tuple(nullptr, nullptr);
+        }
+    }
+
     for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) {
-        const std::string& lora_adapter = std::get<0>(params.lora_adapter[i]);
+        const std::string & lora_adapter = std::get<0>(params.lora_adapter[i]);
         float lora_scale = std::get<1>(params.lora_adapter[i]);
         int err = llama_model_apply_lora_from_file(model,
                                              lora_adapter.c_str(),
@@ -1354,12 +2332,13 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
         params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
     }
 
-    {
+    if (params.warmup) {
         LOG("warming up the model with an empty run\n");
 
         std::vector<llama_token> tmp = { llama_token_bos(model), llama_token_eos(model), };
         llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
         llama_kv_cache_clear(lctx);
+        llama_synchronize(lctx);
         llama_reset_timings(lctx);
     }
 
@@ -1373,23 +2352,23 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
 std::vector<llama_token> llama_tokenize(
   const struct llama_context * ctx,
            const std::string & text,
-                        bool   add_bos,
-                        bool   special) {
-    return llama_tokenize(llama_get_model(ctx), text, add_bos, special);
+                        bool   add_special,
+                        bool   parse_special) {
+    return llama_tokenize(llama_get_model(ctx), text, add_special, parse_special);
 }
 
 std::vector<llama_token> llama_tokenize(
     const struct llama_model * model,
            const std::string & text,
-                        bool   add_bos,
-                        bool   special) {
+                        bool   add_special,
+                        bool   parse_special) {
     // upper limit for the number of tokens
-    int n_tokens = text.length() + add_bos;
+    int n_tokens = text.length() + 2 * add_special;
     std::vector<llama_token> result(n_tokens);
-    n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos, special);
+    n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
     if (n_tokens < 0) {
         result.resize(-n_tokens);
-        int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos, special);
+        int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
         GGML_ASSERT(check == -n_tokens);
     } else {
         result.resize(n_tokens);
@@ -1397,12 +2376,12 @@ std::vector<llama_token> llama_tokenize(
     return result;
 }
 
-std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
+std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
     std::vector<char> result(8, 0);
-    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
+    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
     if (n_tokens < 0) {
         result.resize(-n_tokens);
-        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
+        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
         GGML_ASSERT(check == -n_tokens);
     } else {
         result.resize(n_tokens);
@@ -1614,7 +2593,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "cpu_has_avx512: %s\n",      ggml_cpu_has_avx512()      ? "true" : "false");
     fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false");
     fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false");
-    fprintf(stream, "cpu_has_cublas: %s\n",      ggml_cpu_has_cublas()      ? "true" : "false");
+    fprintf(stream, "cpu_has_cuda: %s\n",        ggml_cpu_has_cuda()        ? "true" : "false");
     fprintf(stream, "cpu_has_vulkan: %s\n",      ggml_cpu_has_vulkan()      ? "true" : "false");
     fprintf(stream, "cpu_has_clblast: %s\n",     ggml_cpu_has_clblast()     ? "true" : "false");
     fprintf(stream, "cpu_has_kompute: %s\n",     ggml_cpu_has_kompute()     ? "true" : "false");
@@ -1676,6 +2655,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     dump_string_yaml_multiline(stream, "in_suffix", params.input_prefix.c_str());
     fprintf(stream, "instruct: %s # default: false\n", params.instruct ? "true" : "false");
     fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false");
+    fprintf(stream, "interactive_specials: %s # default: false\n", params.interactive_specials ? "true" : "false");
     fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false");
     fprintf(stream, "keep: %d # default: 0\n", params.n_keep);
     fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str());
@@ -1709,15 +2689,14 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau);
     fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta);
     fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
-    fprintf(stream, "model: %s # default: models/7B/ggml-model.bin\n", params.model.c_str());
+    fprintf(stream, "model: %s # default: %s\n", params.model.c_str(), DEFAULT_MODEL_PATH);
     fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str());
     fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
     fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers);
     fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
     fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs);
     fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
-    fprintf(stream, "no_mul_mat_q: %s # default: false\n", !params.mul_mat_q ? "true" : "false");
-    fprintf(stream, "no_penalize_nl: %s # default: false\n", !sparams.penalize_nl ? "true" : "false");
+    fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false");
     fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
     fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
     fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present);
@@ -1745,6 +2724,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "seed: %u # default: -1 (random seed)\n", params.seed);
     fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
     fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false");
+    fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "true" : "false");
     fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);
 
     const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices());
@@ -1768,17 +2748,17 @@ void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size) {
     static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+";
 
     printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d",
-        view.n_cells, view.n_max_seq, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
+        view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
 
     llama_kv_cache_view_cell * c_curr = view.cells;
     llama_seq_id * cs_curr = view.cells_sequences;
 
-    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) {
+    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
         if (i % row_size == 0) {
             printf("\n%5d: ", i);
         }
         int seq_count = 0;
-        for (int j = 0; j < view.n_max_seq; j++) {
+        for (int j = 0; j < view.n_seq_max; j++) {
             if (cs_curr[j] >= 0) { seq_count++; }
         }
         putchar(slot_chars[std::min(sizeof(slot_chars) - 2, size_t(seq_count))]);
@@ -1791,14 +2771,14 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) {
     static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
 
     printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n",
-        view.n_cells, view.n_max_seq, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
+        view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
 
     std::unordered_map<llama_seq_id, size_t> seqs;
     llama_kv_cache_view_cell * c_curr = view.cells;
     llama_seq_id * cs_curr = view.cells_sequences;
 
-    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) {
-        for (int j = 0; j < view.n_max_seq; j++) {
+    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
+        for (int j = 0; j < view.n_seq_max; j++) {
             if (cs_curr[j] < 0) { continue; }
             if (seqs.find(cs_curr[j]) == seqs.end()) {
                 if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
@@ -1817,11 +2797,11 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) {
 
     c_curr = view.cells;
     cs_curr = view.cells_sequences;
-    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) {
+    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
         if (i % row_size == 0) {
             printf("\n%5d: ", i);
         }
-        for (int j = 0; j < view.n_max_seq; j++) {
+        for (int j = 0; j < view.n_seq_max; j++) {
             if (cs_curr[j] >= 0) {
                 const auto & it = seqs.find(cs_curr[j]);
                 putchar(it != seqs.end() ? int(slot_chars[it->second]) : '+');
@@ -1834,3 +2814,188 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) {
 
     printf("\n=== Done dumping\n");
 }
+
+void llama_embd_normalize(const float * inp, float * out, int n) {
+    double sum = 0.0;
+    for (int i = 0; i < n; i++) {
+        sum += inp[i] * inp[i];
+    }
+    sum = sqrt(sum);
+
+    const float norm = sum > 0.0 ? 1.0f / sum : 0.0f;
+
+    for (int i = 0; i < n; i++) {
+        out[i] = inp[i] * norm;
+    }
+}
+
+float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n){
+    double sum  = 0.0;
+    double sum1 = 0.0;
+    double sum2 = 0.0;
+
+    for (int i = 0; i < n; i++) {
+        sum  += embd1[i] * embd2[i];
+        sum1 += embd1[i] * embd1[i];
+        sum2 += embd2[i] * embd2[i];
+    }
+
+    return sum / (sqrt(sum1) * sqrt(sum2));
+}
+
+//
+// Control vector utils
+//
+
+static llama_control_vector_data llama_control_vector_load_one(const llama_control_vector_load_info & load_info) {
+    int32_t n_tensors;
+
+    size_t n_bytes = 0;
+
+    uint32_t max_direction_layer = 0;
+
+    llama_control_vector_data result = { -1, {} };
+
+    // calculate size of ctx needed for tensors, ensure tensors are f32, and find max layer
+    {
+        struct ggml_init_params meta_params = {
+            /* .mem_size   = */ ggml_tensor_overhead() * 128 + ggml_graph_overhead(),
+            /* .mem_buffer = */ nullptr,
+            /* .no_alloc   = */ true,
+        };
+        ggml_context * meta_ctx = ggml_init(meta_params);
+        struct gguf_init_params meta_gguf_params = {
+            /* .no_alloc = */ true,
+            /* .ctx      = */ &meta_ctx,
+        };
+        struct gguf_context * meta_ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params);
+        if (!meta_ctx_gguf) {
+            fprintf(stderr, "%s: failed to load control vector from %s\n", __func__, load_info.fname.c_str());
+            ggml_free(meta_ctx);
+            return result;
+        }
+
+        n_tensors = gguf_get_n_tensors(meta_ctx_gguf);
+        for (int i = 0; i < n_tensors; i++) {
+            std::string name = gguf_get_tensor_name(meta_ctx_gguf, i);
+
+            // split on '.'
+            size_t dotpos = name.find('.');
+            if (dotpos != std::string::npos && name.substr(0, dotpos) == "direction") {
+                try {
+                    uint32_t layer = std::stoi(name.substr(dotpos + 1));
+                    if (layer == 0) {
+                        fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str());
+                        ggml_free(meta_ctx);
+                        gguf_free(meta_ctx_gguf);
+                        return result;
+                    }
+                    if (layer > max_direction_layer) {
+                        max_direction_layer = layer;
+                    }
+                } catch (...) {
+                    fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str());
+                    ggml_free(meta_ctx);
+                    gguf_free(meta_ctx_gguf);
+                    return result;
+                }
+            }
+
+            struct ggml_tensor * tensor_meta = ggml_get_tensor(meta_ctx, name.c_str());
+            if (tensor_meta->type != GGML_TYPE_F32 || ggml_n_dims(tensor_meta) != 1) {
+                fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str());
+                ggml_free(meta_ctx);
+                gguf_free(meta_ctx_gguf);
+                return result;
+            }
+            if (result.n_embd == -1) {
+                result.n_embd = ggml_nelements(tensor_meta);
+            } else if (ggml_nelements(tensor_meta) != result.n_embd) {
+                fprintf(stderr, "%s: direction tensor sizes mismatched in %s\n", __func__, load_info.fname.c_str());
+                ggml_free(meta_ctx);
+                gguf_free(meta_ctx_gguf);
+                return result;
+            }
+            n_bytes += ggml_nbytes(tensor_meta);
+        }
+        ggml_free(meta_ctx);
+        gguf_free(meta_ctx_gguf);
+    }
+
+    if (n_tensors == 0) {
+        fprintf(stderr, "%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str());
+        return result;
+    }
+
+    // load and scale tensors into final control vector context
+    struct ggml_init_params ggml_params = {
+        /* .mem_size   = */ ggml_tensor_overhead() * n_tensors + n_bytes,
+        /* .mem_buffer = */ nullptr,
+        /* .no_alloc   = */ false,
+    };
+    struct ggml_context * ctx = ggml_init(ggml_params);
+
+    struct gguf_init_params params = {
+        /*.no_alloc = */ false,
+        /*.ctx      = */ &ctx,
+    };
+    struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), params);
+    if (!ctx_gguf) {
+        fprintf(stderr, "%s: failed to load control vector from %s\n", __func__, load_info.fname.c_str());
+        ggml_free(ctx);
+        return result;
+    }
+
+    // do not store data for layer 0 (it's not used)
+    result.data.resize(result.n_embd * max_direction_layer);
+
+    for (uint32_t il = 1; il <= max_direction_layer; il++) {
+        const std::string name = "direction." + std::to_string(il);
+        const ggml_tensor * tensor = ggml_get_tensor(ctx, name.c_str());
+
+        float * dst = result.data.data() + result.n_embd * (il - 1);
+
+        if (tensor) {
+            const float * src = (const float *) tensor->data;
+            for (int j = 0; j < result.n_embd; j++) {
+                dst[j] = src[j] * load_info.strength;
+            }
+        } else {
+            for (int j = 0; j < result.n_embd; j++) {
+                dst[j] = 0.0f;
+            }
+        }
+    }
+
+    return result;
+}
+
+llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos) {
+    llama_control_vector_data result = { -1, {} };
+
+    for (const auto & info : load_infos) {
+        auto cur = llama_control_vector_load_one(info);
+
+        if (cur.n_embd == -1) {
+            return result;
+        }
+        if (result.n_embd != -1 && (result.n_embd != cur.n_embd || result.data.size() != cur.data.size())) {
+            fprintf(stderr, "%s: control vector in %s does not match previous vector dimensions\n", __func__, info.fname.c_str());
+            return result;
+        }
+
+        if (result.n_embd == -1) {
+            result = std::move(cur);
+        } else {
+            for (size_t i = 0; i < cur.data.size(); i++) {
+                result.data[i] += cur.data[i];
+            }
+        }
+    }
+
+    if (result.n_embd == -1) {
+        fprintf(stderr, "%s: no vectors passed\n", __func__);
+    }
+
+    return result;
+}
diff --git a/common/common.h b/common/common.h
index 935771d44..d80344f2a 100644
--- a/common/common.h
+++ b/common/common.h
@@ -31,37 +31,43 @@
     fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET);    \
 } while(0)
 
+#define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf"
+
 // build info
 extern int LLAMA_BUILD_NUMBER;
 extern char const *LLAMA_COMMIT;
 extern char const *LLAMA_COMPILER;
 extern char const *LLAMA_BUILD_TARGET;
 
+struct llama_control_vector_load_info;
+
+int get_math_cpu_count();
+int32_t get_num_physical_cores();
+
 //
 // CLI argument parsing
 //
-int32_t get_num_physical_cores();
 
 struct gpt_params {
-    uint32_t seed                 = -1;    // RNG seed
+    uint32_t seed                 = LLAMA_DEFAULT_SEED; // RNG seed
 
-    int32_t n_threads             = get_num_physical_cores();
+    int32_t n_threads             = get_math_cpu_count();
     int32_t n_threads_draft       = -1;
     int32_t n_threads_batch       = -1;    // number of threads to use for batch processing (-1 = use n_threads)
     int32_t n_threads_batch_draft = -1;
     int32_t n_predict             = -1;    // new tokens to predict
     int32_t n_ctx                 = 512;   // context size
-    int32_t n_batch               = 512;   // batch size for prompt processing (must be >=32 to use BLAS)
+    int32_t n_batch               = 2048;  // logical batch size for prompt processing (must be >=32 to use BLAS)
+    int32_t n_ubatch              = 512;   // physical batch size for prompt processing (must be >=32 to use BLAS)
     int32_t n_keep                = 0;     // number of tokens to keep from initial prompt
-    int32_t n_draft               = 8;     // number of tokens to draft during speculative decoding
+    int32_t n_draft               = 5;     // number of tokens to draft during speculative decoding
     int32_t n_chunks              = -1;    // max number of chunks to process (-1 = unlimited)
     int32_t n_parallel            = 1;     // number of parallel sequences to decode
     int32_t n_sequences           = 1;     // number of sequences to decode
-    float   p_accept              = 0.5f;  // speculative decoding accept probability
     float   p_split               = 0.1f;  // speculative decoding split probability
     int32_t n_gpu_layers          = -1;    // number of layers to store in VRAM (-1 - use default)
     int32_t n_gpu_layers_draft    = -1;    // number of layers to store in VRAM for the draft model (-1 - use default)
-    llama_split_mode split_mode   = LLAMA_SPLIT_LAYER; // how to split the model across GPUs
+    llama_split_mode split_mode   = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
     int32_t main_gpu              = 0;     // the GPU that is used for scratch and small tensors
     float   tensor_split[128]     = {0};   // how split tensors should be distributed across GPUs
     int32_t n_beams               = 0;     // if non-zero then use beam search of given width.
@@ -75,23 +81,35 @@ struct gpt_params {
     float   yarn_beta_fast        = 32.0f; // YaRN low correction dim
     float   yarn_beta_slow        = 1.0f;  // YaRN high correction dim
     int32_t yarn_orig_ctx         = 0;     // YaRN original context length
-    int32_t rope_scaling_type     = LLAMA_ROPE_SCALING_UNSPECIFIED;
-    ggml_numa_strategy numa       = GGML_NUMA_STRATEGY_DISABLED;
+    float   defrag_thold          = -1.0f; // KV cache defragmentation threshold
+
+    ggml_backend_sched_eval_callback cb_eval = nullptr;
+    void * cb_eval_user_data                 = nullptr;
+
+    ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;
+
+    enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
+    enum llama_pooling_type      pooling_type      = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
 
     // // sampling parameters
     struct llama_sampling_params sparams;
 
-    std::string model             = "models/7B/ggml-model-f16.gguf"; // model path
-    std::string model_draft       = "";                              // draft model for speculative decoding
-    std::string model_alias       = "unknown"; // model alias
-    std::string prompt            = "";
-    std::string prompt_file       = "";  // store the external prompt file name
-    std::string path_prompt_cache = "";  // path to file for saving/loading prompt eval state
-    std::string input_prefix      = "";  // string to prefix user inputs with
-    std::string input_suffix      = "";  // string to suffix user inputs with
+    std::string model                = "";  // model path
+    std::string model_draft          = "";  // draft model for speculative decoding
+    std::string model_alias          = "unknown"; // model alias
+    std::string model_url            = "";  // model url to download
+    std::string hf_repo              = "";  // HF repo
+    std::string hf_file              = "";  // HF file
+    std::string prompt               = "";
+    std::string prompt_file          = "";  // store the external prompt file name
+    std::string path_prompt_cache    = "";  // path to file for saving/loading prompt eval state
+    std::string input_prefix         = "";  // string to prefix user inputs with
+    std::string input_suffix         = "";  // string to suffix user inputs with
     std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
-    std::string logdir            = "";  // directory in which to save YAML log files
-    std::string logits_file       = "";  // file for saving *all* logits
+    std::string logdir               = "";  // directory in which to save YAML log files
+    std::string lookup_cache_static  = ""; // path of static ngram cache file for lookup decoding
+    std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding
+    std::string logits_file          = "";  // file for saving *all* logits
 
     std::vector<llama_model_kv_override> kv_overrides;
 
@@ -99,6 +117,11 @@ struct gpt_params {
     std::vector<std::tuple<std::string, float>> lora_adapter; // lora adapter path with user defined scale
     std::string lora_base  = "";                              // base model path for the lora adapter
 
+    std::vector<llama_control_vector_load_info> control_vectors; // control vector with user defined scale
+
+    int32_t control_vector_layer_start = -1; // layer range for control vector
+    int32_t control_vector_layer_end   = -1; // layer range for control vector
+
     int  ppl_stride        = 0;     // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
     int  ppl_output_type   = 0;     // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
                                     //                                       (which is more convenient to use for plotting)
@@ -112,12 +135,13 @@ struct gpt_params {
     bool   multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt
     size_t multiple_choice_tasks = 0;     // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed
 
-    bool   kl_divergence   = false; // compute KL-divergence
+    bool   kl_divergence   = false; // compute KL divergence
 
-    bool mul_mat_q         = true;  // if true, use mul_mat_q kernels instead of cuBLAS
     bool random_prompt     = false; // do not randomize prompt if none provided
     bool use_color         = false; // use color to distinguish generations and inputs
     bool interactive       = false; // interactive mode
+    bool interactive_specials = false; // whether to allow special tokens from user, during interactive mode
+    bool conversation      = false; // conversation mode (does not print special tokens and suffix/prefix)
     bool chatml            = false; // chatml mode (used for models trained on chatml syntax)
     bool prompt_cache_all  = false; // save user input and generations to prompt cache
     bool prompt_cache_ro   = false; // open the prompt cache read-only and do not update it
@@ -127,7 +151,8 @@ struct gpt_params {
     bool interactive_first = false; // wait for user input immediately
     bool multiline_input   = false; // reverse the usage of `\`
     bool simple_io         = false; // improves compatibility with subprocesses and limited consoles
-    bool cont_batching     = false; // insert new sequences for decoding on-the-fly
+    bool cont_batching     = true;  // insert new sequences for decoding on-the-fly
+    bool flash_attn        = false; // flash attention
 
     bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
     bool ignore_eos        = false; // ignore generated EOS tokens
@@ -140,27 +165,37 @@ struct gpt_params {
     bool infill            = false; // use infill mode
     bool dump_kv_cache     = false; // dump the KV cache contents for debugging purposes
     bool no_kv_offload     = false; // disable KV offloading
+    bool warmup            = true;  // warmup run
+    bool check_tensors     = false; // validate tensor data
 
     std::string cache_type_k = "f16"; // KV cache data type for the K
     std::string cache_type_v = "f16"; // KV cache data type for the V
 
     // multimodal models (see examples/llava)
-    std::string mmproj = ""; // path to multimodal projector
-    std::string image  = ""; // path to an image file
+    std::string mmproj = "";        // path to multimodal projector
+    std::vector<std::string> image; // path to image file(s)
 };
 
+void gpt_params_handle_model_default(gpt_params & params);
+
+bool parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
+
 bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params);
 
 bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
 
 void gpt_print_usage(int argc, char ** argv, const gpt_params & params);
 
+bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param);
+
 std::string get_system_info(const gpt_params & params);
 
 std::string gpt_random_prompt(std::mt19937 & rng);
 
 void process_escapes(std::string& input);
 
+bool validate_file_name(const std::string & filename);
+
 //
 // String utils
 //
@@ -168,6 +203,7 @@ void process_escapes(std::string& input);
 std::vector<llama_sampler_type> sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
 std::vector<llama_sampler_type> sampler_types_from_chars(const std::string & names_string);
 std::vector<std::string> string_split(std::string input, char separator);
+std::string string_strip(const std::string & str);
 std::string sampler_type_to_name_string(llama_sampler_type sampler_type);
 
 //
@@ -180,6 +216,9 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
 struct llama_model_params   llama_model_params_from_gpt_params  (const gpt_params & params);
 struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params);
 
+struct llama_model * llama_load_model_from_url(const char * model_url, const char * path_model, const struct llama_model_params & params);
+struct llama_model * llama_load_model_from_hf(const char * repo, const char * file, const char * path_model, const struct llama_model_params & params);
+
 // Batch utils
 
 void llama_batch_clear(struct llama_batch & batch);
@@ -200,20 +239,21 @@ void llama_batch_add(
 std::vector<llama_token> llama_tokenize(
   const struct llama_context * ctx,
            const std::string & text,
-                        bool   add_bos,
-                        bool   special = false);
+                        bool   add_special,
+                        bool   parse_special = false);
 
 std::vector<llama_token> llama_tokenize(
     const struct llama_model * model,
            const std::string & text,
-                        bool   add_bos,
-                        bool   special = false);
+                        bool   add_special,
+                        bool   parse_special = false);
 
-// tokenizes a token into a piece
+// tokenizes a token into a piece, optionally renders special/control tokens
 // should work similar to Python's `tokenizer.id_to_piece`
 std::string llama_token_to_piece(
         const struct llama_context * ctx,
-                       llama_token   token);
+                       llama_token   token,
+                       bool          special = true);
 
 // TODO: these should be moved in llama.h C-style API under single `llama_detokenize` function
 //       that takes into account the tokenizer type and decides how to handle the leading space
@@ -258,3 +298,39 @@ void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size = 80);
 
 // Dump the KV cache view showing individual sequences in each cell (long output).
 void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40);
+
+//
+// Embedding utils
+//
+
+void llama_embd_normalize(const float * inp, float * out, int n);
+
+float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n);
+
+//
+// Control vector utils
+//
+
+struct llama_control_vector_data {
+    int n_embd;
+
+    // stores data for layers [1, n_layer] where n_layer = data.size() / n_embd
+    std::vector<float> data;
+};
+
+struct llama_control_vector_load_info {
+    float strength;
+
+    std::string fname;
+};
+
+// Load control vectors, scale each by strength, and add them together.
+// On error, returns {-1, empty}
+llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos);
+
+//
+// Split utils
+//
+static const char * const LLM_KV_SPLIT_NO            = "split.no";
+static const char * const LLM_KV_SPLIT_COUNT         = "split.count";
+static const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
diff --git a/common/grammar-parser.cpp b/common/grammar-parser.cpp
index bf89a96f3..fecb7cd71 100644
--- a/common/grammar-parser.cpp
+++ b/common/grammar-parser.cpp
@@ -142,6 +142,9 @@ namespace grammar_parser {
                 pos++;
                 last_sym_start = out_elements.size();
                 while (*pos != '"') {
+                    if (!*pos) {
+                        throw std::runtime_error("unexpected end of input");
+                    }
                     auto char_pair = parse_char(pos);
                          pos       = char_pair.second;
                     out_elements.push_back({LLAMA_GRETYPE_CHAR, char_pair.first});
@@ -156,6 +159,9 @@ namespace grammar_parser {
                 }
                 last_sym_start = out_elements.size();
                 while (*pos != ']') {
+                    if (!*pos) {
+                        throw std::runtime_error("unexpected end of input");
+                    }
                     auto char_pair = parse_char(pos);
                          pos       = char_pair.second;
                     enum llama_gretype type = last_sym_start < out_elements.size()
@@ -164,6 +170,9 @@ namespace grammar_parser {
 
                     out_elements.push_back({type, char_pair.first});
                     if (pos[0] == '-' && pos[1] != ']') {
+                        if (!pos[1]) {
+                            throw std::runtime_error("unexpected end of input");
+                        }
                         auto endchar_pair = parse_char(pos + 1);
                              pos          = endchar_pair.second;
                         out_elements.push_back({LLAMA_GRETYPE_CHAR_RNG_UPPER, endchar_pair.first});
@@ -278,6 +287,22 @@ namespace grammar_parser {
             while (*pos) {
                 pos = parse_rule(state, pos);
             }
+            // Validate the state to ensure that all rules are defined
+            for (const auto & rule : state.rules) {
+                for (const auto & elem : rule) {
+                    if (elem.type == LLAMA_GRETYPE_RULE_REF) {
+                        // Ensure that the rule at that location exists
+                        if (elem.value >= state.rules.size() || state.rules[elem.value].empty()) {
+                            // Get the name of the rule that is missing
+                            for (const auto & kv : state.symbol_ids) {
+                                if (kv.second == elem.value) {
+                                    throw std::runtime_error("Undefined rule identifier '" + kv.first + "'");
+                                }
+                            }
+                        }
+                    }
+                }
+            }
             return state;
         } catch (const std::exception & err) {
             fprintf(stderr, "%s: error parsing grammar: %s\n", __func__, err.what());
diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp
new file mode 100644
index 000000000..0f8f1b1d4
--- /dev/null
+++ b/common/json-schema-to-grammar.cpp
@@ -0,0 +1,764 @@
+#include "json-schema-to-grammar.h"
+#include <algorithm>
+#include <fstream>
+#include <map>
+#include <regex>
+#include <sstream>
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+
+using json = nlohmann::ordered_json;
+
+template <typename Iterator>
+static std::string join(Iterator begin, Iterator end, const std::string & separator);
+
+static std::string repeat(const std::string & str, size_t n);
+
+static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "", bool item_rule_is_literal = false) {
+    if (separator_rule.empty()) {
+        if (min_items == 0 && max_items == 1) {
+            return item_rule + "?";
+        } else if (min_items == 1 && max_items == std::numeric_limits<int>::max()) {
+            return item_rule + "+";
+        }
+    }
+
+    std::string result;
+    if (min_items > 0) {
+        if (item_rule_is_literal && separator_rule.empty()) {
+            result = "\"" + repeat(std::string(item_rule.begin() + 1, item_rule.end() - 1), min_items) + "\"";
+        } else {
+            std::vector<std::string> items(min_items, item_rule);
+            result = join(items.begin(), items.end(), separator_rule.empty() ? " " : " " + separator_rule + " ");
+        }
+    }
+
+    std::function<std::string(int, bool)> opt_repetitions = [&](int up_to_n, bool prefix_with_sep) -> std::string {
+        auto content = prefix_with_sep && !separator_rule.empty() ? separator_rule + " " + item_rule : item_rule;
+
+        if (up_to_n == 0) {
+            return "";
+        } else if (up_to_n == 1) {
+            return "(" + content + ")?";
+        } else if (!separator_rule.empty() && !prefix_with_sep) {
+            return "(" + content + " " + opt_repetitions(up_to_n - 1, true) + ")?";
+        } else {
+            std::string res = repeat("(" + content + " ", up_to_n);
+            // strip trailing space
+            res = res.substr(0, res.length() - 1);
+            res += repeat(")?", up_to_n);
+            return res;
+        }
+    };
+
+    if (min_items > 0 && max_items != min_items) {
+        result += " ";
+    }
+
+    if (max_items != std::numeric_limits<int>::max()) {
+        result += opt_repetitions(max_items - min_items, min_items > 0);
+    } else {
+        std::string item_operator = "(" + (separator_rule.empty() ? "" : separator_rule + " ") + item_rule + ")";
+        if (min_items == 0 && !separator_rule.empty()) {
+            result = "(" + item_rule + " " + item_operator + "*)?";
+        } else {
+            result += item_operator + "*";
+        }
+    }
+
+    return result;
+}
+
+const std::string SPACE_RULE = "\" \"?";
+
+struct BuiltinRule {
+    std::string content;
+    std::vector<std::string> deps;
+};
+
+const std::string _up_to_15_digits = build_repetition("[0-9]", 0, 15);
+
+std::unordered_map<std::string, BuiltinRule> PRIMITIVE_RULES = {
+    {"boolean", {"(\"true\" | \"false\") space", {}}},
+    {"decimal-part", {"[0-9] " + _up_to_15_digits, {}}},
+    {"integral-part", {"[0-9] | [1-9] " + _up_to_15_digits, {}}},
+    {"number", {"(\"-\"? integral-part) (\".\" decimal-part)? ([eE] [-+]? integral-part)? space", {"integral-part", "decimal-part"}}},
+    {"integer", {"(\"-\"? integral-part) space", {"integral-part"}}},
+    {"value", {"object | array | string | number | boolean | null", {"object", "array", "string", "number", "boolean", "null"}}},
+    {"object", {"\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? \"}\" space", {"string", "value"}}},
+    {"array", {"\"[\" space ( value (\",\" space value)* )? \"]\" space", {"value"}}},
+    {"uuid", {"\"\\\"\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
+                "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
+                "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
+                "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
+                "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] \"\\\"\" space", {}}},
+    {"char",   {"[^\"\\\\] | \"\\\\\" ([\"\\\\/bfnrt] | \"u\" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])", {}}},
+    {"string", {"\"\\\"\" char* \"\\\"\" space", {"char"}}},
+    {"null", {"\"null\" space", {}}},
+};
+
+std::unordered_map<std::string, BuiltinRule> STRING_FORMAT_RULES = {
+    {"date", {"[0-9] [0-9] [0-9] [0-9] \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )", {}}},
+    {"time", {"([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9] [0-9] [0-9] )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )", {}}},
+    {"date-time", {"date \"T\" time", {"date", "time"}}},
+    {"date-string", {"\"\\\"\" date \"\\\"\" space", {"date"}}},
+    {"time-string", {"\"\\\"\" time \"\\\"\" space", {"time"}}},
+    {"date-time-string", {"\"\\\"\" date-time \"\\\"\" space", {"date-time"}}}
+};
+
+static bool is_reserved_name(const std::string & name) {
+    static std::unordered_set<std::string> RESERVED_NAMES;
+    if (RESERVED_NAMES.empty()) {
+        RESERVED_NAMES.insert("root");
+        for (const auto &p : PRIMITIVE_RULES) RESERVED_NAMES.insert(p.first);
+        for (const auto &p : STRING_FORMAT_RULES) RESERVED_NAMES.insert(p.first);
+    }
+    return RESERVED_NAMES.find(name) != RESERVED_NAMES.end();
+}
+
+std::regex INVALID_RULE_CHARS_RE("[^a-zA-Z0-9-]+");
+std::regex GRAMMAR_LITERAL_ESCAPE_RE("[\r\n\"]");
+std::regex GRAMMAR_RANGE_LITERAL_ESCAPE_RE("[\r\n\"\\]\\-\\\\]");
+std::unordered_map<char, std::string> GRAMMAR_LITERAL_ESCAPES = {
+    {'\r', "\\r"}, {'\n', "\\n"}, {'"', "\\\""}, {'-', "\\-"}, {']', "\\]"}
+};
+
+std::unordered_set<char> NON_LITERAL_SET = {'|', '.', '(', ')', '[', ']', '{', '}', '*', '+', '?'};
+std::unordered_set<char> ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = {'[', ']', '(', ')', '|', '{', '}', '*', '+', '?'};
+
+template <typename Iterator>
+std::string join(Iterator begin, Iterator end, const std::string & separator) {
+    std::ostringstream result;
+    if (begin != end) {
+        result << *begin;
+        for (Iterator it = begin + 1; it != end; ++it) {
+            result << separator << *it;
+        }
+    }
+    return result.str();
+}
+
+static std::vector<std::string> split(const std::string & str, const std::string & delimiter) {
+    std::vector<std::string> tokens;
+    size_t start = 0;
+    size_t end = str.find(delimiter);
+
+    while (end != std::string::npos) {
+        tokens.push_back(str.substr(start, end - start));
+        start = end + delimiter.length();
+        end = str.find(delimiter, start);
+    }
+
+    tokens.push_back(str.substr(start));
+
+    return tokens;
+}
+
+static std::string repeat(const std::string & str, size_t n) {
+    if (n == 0) {
+        return "";
+    }
+
+    std::string result;
+    result.reserve(str.length() * n);
+
+    for (size_t i = 0; i < n; ++i) {
+        result += str;
+    }
+
+    return result;
+}
+
+static std::string replacePattern(const std::string & input, const std::regex & regex, const std::function<std::string(const std::smatch  &)> & replacement) {
+    std::smatch match;
+    std::string result;
+
+    std::string::const_iterator searchStart(input.cbegin());
+    std::string::const_iterator searchEnd(input.cend());
+
+    while (std::regex_search(searchStart, searchEnd, match, regex)) {
+        result.append(searchStart, searchStart + match.position());
+        result.append(replacement(match));
+        searchStart = match.suffix().first;
+    }
+
+    result.append(searchStart, searchEnd);
+
+    return result;
+}
+
+static std::string format_literal(const std::string & literal) {
+    std::string escaped = replacePattern(literal, GRAMMAR_LITERAL_ESCAPE_RE, [&](const std::smatch & match) {
+        char c = match.str()[0];
+        return GRAMMAR_LITERAL_ESCAPES.at(c);
+    });
+    return "\"" + escaped + "\"";
+}
+
+
+class SchemaConverter {
+private:
+    std::function<json(const std::string &)> _fetch_json;
+    bool _dotall;
+    std::map<std::string, std::string> _rules;
+    std::unordered_map<std::string, json> _refs;
+    std::unordered_set<std::string> _refs_being_resolved;
+    std::vector<std::string> _errors;
+    std::vector<std::string> _warnings;
+
+    std::string _add_rule(const std::string & name, const std::string & rule) {
+        std::string esc_name = regex_replace(name, INVALID_RULE_CHARS_RE, "-");
+        if (_rules.find(esc_name) == _rules.end() || _rules[esc_name] == rule) {
+            _rules[esc_name] = rule;
+            return esc_name;
+        } else {
+            int i = 0;
+            while (_rules.find(esc_name + std::to_string(i)) != _rules.end() && _rules[esc_name + std::to_string(i)] != rule) {
+                i++;
+            }
+            std::string key = esc_name + std::to_string(i);
+            _rules[key] = rule;
+            return key;
+        }
+    }
+
+    std::string _generate_union_rule(const std::string & name, const std::vector<json> & alt_schemas) {
+        std::vector<std::string> rules;
+        for (size_t i = 0; i < alt_schemas.size(); i++) {
+            rules.push_back(visit(alt_schemas[i], name + (name.empty() ? "alternative-" : "-") + std::to_string(i)));
+        }
+        return join(rules.begin(), rules.end(), " | ");
+    }
+
+    std::string _visit_pattern(const std::string & pattern, const std::string & name) {
+        if (!(pattern.front() == '^' && pattern.back() == '$')) {
+            _errors.push_back("Pattern must start with '^' and end with '$'");
+            return "";
+        }
+        std::string sub_pattern = pattern.substr(1, pattern.length() - 2);
+        std::unordered_map<std::string, std::string> sub_rule_ids;
+
+        size_t i = 0;
+        size_t length = sub_pattern.length();
+
+        using literal_or_rule = std::pair<std::string, bool>;
+        auto to_rule = [&](const literal_or_rule & ls) {
+            auto is_literal = ls.second;
+            auto s = ls.first;
+            return is_literal ? "\"" + s + "\"" : s;
+        };
+        std::function<literal_or_rule()> transform = [&]() -> literal_or_rule {
+            size_t start = i;
+            std::vector<literal_or_rule> seq;
+
+            auto get_dot = [&]() {
+                std::string rule;
+                if (_dotall) {
+                    rule = "[\\U00000000-\\U0010FFFF]";
+                } else {
+                    rule = "[^\\x0A\\x0D]";
+                }
+                return _add_rule("dot", rule);
+            };
+
+            // Joins the sequence, merging consecutive literals together.
+            auto join_seq = [&]() {
+                std::vector<literal_or_rule> ret;
+
+                std::string literal;
+                auto flush_literal = [&]() {
+                    if (literal.empty()) {
+                        return false;
+                    }
+                    ret.push_back(std::make_pair(literal, true));
+                    literal.clear();
+                    return true;
+                };
+
+                for (const auto & item : seq) {
+                    auto is_literal = item.second;
+                    if (is_literal) {
+                        literal += item.first;
+                    } else {
+                        flush_literal();
+                        ret.push_back(item);
+                    }
+                }
+                flush_literal();
+
+                std::vector<std::string> results;
+                for (const auto & item : ret) {
+                    results.push_back(to_rule(item));
+                }
+                return std::make_pair(join(results.begin(), results.end(), " "), false);
+            };
+
+            while (i < length) {
+                char c = sub_pattern[i];
+                if (c == '.') {
+                    seq.push_back(std::make_pair(get_dot(), false));
+                    i++;
+                } else if (c == '(') {
+                    i++;
+                    if (i < length) {
+                        if (sub_pattern[i] == '?') {
+                            _warnings.push_back("Unsupported pattern syntax");
+                        }
+                    }
+                    seq.push_back(std::make_pair("(" + to_rule(transform()) + ")", false));
+                } else if (c == ')') {
+                    i++;
+                    if (start > 0 && sub_pattern[start - 1] != '(') {
+                        _errors.push_back("Unbalanced parentheses");
+                    }
+                    return join_seq();
+                } else if (c == '[') {
+                    std::string square_brackets = std::string(1, c);
+                    i++;
+                    while (i < length && sub_pattern[i] != ']') {
+                        if (sub_pattern[i] == '\\') {
+                            square_brackets += sub_pattern.substr(i, 2);
+                            i += 2;
+                        } else {
+                            square_brackets += sub_pattern[i];
+                            i++;
+                        }
+                    }
+                    if (i >= length) {
+                        _errors.push_back("Unbalanced square brackets");
+                    }
+                    square_brackets += ']';
+                    i++;
+                    seq.push_back(std::make_pair(square_brackets, false));
+                } else if (c == '|') {
+                    seq.push_back(std::make_pair("|", false));
+                    i++;
+                } else if (c == '*' || c == '+' || c == '?') {
+                    seq.back() = std::make_pair(to_rule(seq.back()) + c, false);
+                    i++;
+                } else if (c == '{') {
+                    std::string curly_brackets = std::string(1, c);
+                    i++;
+                    while (i < length && sub_pattern[i] != '}') {
+                        curly_brackets += sub_pattern[i];
+                        i++;
+                    }
+                    if (i >= length) {
+                        _errors.push_back("Unbalanced curly brackets");
+                    }
+                    curly_brackets += '}';
+                    i++;
+                    auto nums = split(curly_brackets.substr(1, curly_brackets.length() - 2), ",");
+                    int min_times = 0;
+                    int max_times = std::numeric_limits<int>::max();
+                    try {
+                        if (nums.size() == 1) {
+                            min_times = max_times = std::stoi(nums[0]);
+                        } else if (nums.size() != 2) {
+                            _errors.push_back("Wrong number of values in curly brackets");
+                        } else {
+                            if (!nums[0].empty()) {
+                                min_times = std::stoi(nums[0]);
+                            }
+                            if (!nums[1].empty()) {
+                                max_times = std::stoi(nums[1]);
+                            }
+                        }
+                    } catch (const std::invalid_argument & e) {
+                        _errors.push_back("Invalid number in curly brackets");
+                        return std::make_pair("", false);
+                    }
+                    auto &last = seq.back();
+                    auto &sub = last.first;
+                    auto sub_is_literal = last.second;
+
+                    if (!sub_is_literal) {
+                        std::string & sub_id = sub_rule_ids[sub];
+                        if (sub_id.empty()) {
+                            sub_id = _add_rule(name + "-" + std::to_string(sub_rule_ids.size()), sub);
+                        }
+                        sub = sub_id;
+                    }
+                    seq.back().first = build_repetition(
+                        sub_is_literal ? "\"" + sub + "\"" : sub,
+                        min_times,
+                        max_times,
+                        "",
+                        sub_is_literal
+                    );
+                    seq.back().second = false;
+                } else {
+                    std::string literal;
+                    auto is_non_literal = [&](char c) {
+                        return NON_LITERAL_SET.find(c) != NON_LITERAL_SET.end();
+                    };
+                    while (i < length) {
+                        if (sub_pattern[i] == '\\' && i < length - 1) {
+                            char next = sub_pattern[i + 1];
+                            if (ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS.find(next) != ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS.end()) {
+                                i++;
+                                literal += sub_pattern[i];
+                                i++;
+                            } else {
+                                literal += sub_pattern.substr(i, 2);
+                                i += 2;
+                            }
+                        } else if (sub_pattern[i] == '"') {
+                            literal += "\\\"";
+                            i++;
+                        } else if (!is_non_literal(sub_pattern[i]) &&
+                                (i == length - 1 || literal.empty() || sub_pattern[i + 1] == '.' || !is_non_literal(sub_pattern[i + 1]))) {
+                            literal += sub_pattern[i];
+                            i++;
+                        } else {
+                            break;
+                        }
+                    }
+                    if (!literal.empty()) {
+                        seq.push_back(std::make_pair(literal, true));
+                    }
+                }
+            }
+            return join_seq();
+        };
+        return _add_rule(name, "\"\\\"\" " + to_rule(transform()) + " \"\\\"\" space");
+    }
+
+    std::string _resolve_ref(const std::string & ref) {
+        std::string ref_name = ref.substr(ref.find_last_of('/') + 1);
+        if (_rules.find(ref_name) == _rules.end() && _refs_being_resolved.find(ref) == _refs_being_resolved.end()) {
+            _refs_being_resolved.insert(ref);
+            json resolved = _refs[ref];
+            ref_name = visit(resolved, ref_name);
+            _refs_being_resolved.erase(ref);
+        }
+        return ref_name;
+    }
+
+    std::string _build_object_rule(
+        const std::vector<std::pair<std::string, json>> & properties,
+        const std::unordered_set<std::string> & required,
+        const std::string & name,
+        const json & additional_properties)
+    {
+        std::vector<std::string> required_props;
+        std::vector<std::string> optional_props;
+        std::unordered_map<std::string, std::string> prop_kv_rule_names;
+        for (const auto & kv : properties) {
+            const auto &prop_name = kv.first;
+            const auto &prop_schema = kv.second;
+
+            std::string prop_rule_name = visit(prop_schema, name + (name.empty() ? "" : "-") + prop_name);
+            prop_kv_rule_names[prop_name] = _add_rule(
+                name + (name.empty() ? "" : "-") + prop_name + "-kv",
+                format_literal(json(prop_name).dump()) + " space \":\" space " + prop_rule_name
+            );
+            if (required.find(prop_name) != required.end()) {
+                required_props.push_back(prop_name);
+            } else {
+                optional_props.push_back(prop_name);
+            }
+        }
+        if (additional_properties.is_object() || (additional_properties.is_boolean() && additional_properties.get<bool>())) {
+            std::string sub_name = name + (name.empty() ? "" : "-") + "additional";
+            std::string value_rule = visit(additional_properties.is_object() ? additional_properties : json::object(), sub_name + "-value");
+            std::string kv_rule = _add_rule(sub_name + "-kv", _add_primitive("string", PRIMITIVE_RULES.at("string")) + " \":\" space " + value_rule);
+            prop_kv_rule_names["*"] = kv_rule;
+            optional_props.push_back("*");
+        }
+
+        std::string rule = "\"{\" space ";
+        for (size_t i = 0; i < required_props.size(); i++) {
+            if (i > 0) {
+                rule += " \",\" space ";
+            }
+            rule += prop_kv_rule_names[required_props[i]];
+        }
+
+        if (!optional_props.empty()) {
+            rule += " (";
+            if (!required_props.empty()) {
+                rule += " \",\" space ( ";
+            }
+
+            std::function<std::string(const std::vector<std::string> &, bool)> get_recursive_refs = [&](const std::vector<std::string> & ks, bool first_is_optional) {
+                std::string res;
+                if (ks.empty()) {
+                    return res;
+                }
+                std::string k = ks[0];
+                std::string kv_rule_name = prop_kv_rule_names[k];
+                if (k == "*") {
+                    res = _add_rule(
+                        name + (name.empty() ? "" : "-") + "additional-kvs",
+                        kv_rule_name + " ( \",\" space " + kv_rule_name + " )*"
+                    );
+                } else if (first_is_optional) {
+                    res = "( \",\" space " + kv_rule_name + " )?";
+                } else {
+                    res = kv_rule_name;
+                }
+                if (ks.size() > 1) {
+                    res += " " + _add_rule(
+                        name + (name.empty() ? "" : "-") + k + "-rest",
+                        get_recursive_refs(std::vector<std::string>(ks.begin() + 1, ks.end()), true)
+                    );
+                }
+                return res;
+            };
+
+            for (size_t i = 0; i < optional_props.size(); i++) {
+                if (i > 0) {
+                    rule += " | ";
+                }
+                rule += get_recursive_refs(std::vector<std::string>(optional_props.begin() + i, optional_props.end()), false);
+            }
+            if (!required_props.empty()) {
+                rule += " )";
+            }
+            rule += " )?";
+        }
+
+        rule += " \"}\" space";
+
+        return rule;
+    }
+
+    std::string _add_primitive(const std::string & name, const BuiltinRule & rule) {
+        auto n = _add_rule(name, rule.content);
+        for (const auto & dep : rule.deps) {
+            BuiltinRule dep_rule;
+            auto it = PRIMITIVE_RULES.find(dep);
+            if (it == PRIMITIVE_RULES.end()) {
+                it = STRING_FORMAT_RULES.find(dep);
+                if (it == STRING_FORMAT_RULES.end()) {
+                    _errors.push_back("Rule " + dep + " not known");
+                    continue;
+                }
+            }
+            if (_rules.find(dep) == _rules.end()) {
+                _add_primitive(dep, it->second);
+            }
+        }
+        return n;
+    }
+
+public:
+    SchemaConverter(
+        const std::function<json(const std::string &)> & fetch_json,
+        bool dotall)
+          : _fetch_json(fetch_json), _dotall(dotall)
+    {
+        _rules["space"] = SPACE_RULE;
+    }
+
+    void resolve_refs(json & schema, const std::string & url) {
+        /*
+        * Resolves all $ref fields in the given schema, fetching any remote schemas,
+        * replacing each $ref with absolute reference URL and populates _refs with the
+        * respective referenced (sub)schema dictionaries.
+        */
+        std::function<void(json &)> visit_refs = [&](json & n) {
+            if (n.is_array()) {
+                for (auto & x : n) {
+                    visit_refs(x);
+                }
+            } else if (n.is_object()) {
+                if (n.contains("$ref")) {
+                    std::string ref = n["$ref"];
+                    if (_refs.find(ref) == _refs.end()) {
+                        json target;
+                        if (ref.find("https://") == 0) {
+                            std::string base_url = ref.substr(0, ref.find('#'));
+                            auto it = _refs.find(base_url);
+                            if (it != _refs.end()) {
+                                target = it->second;
+                            } else {
+                                // Fetch the referenced schema and resolve its refs
+                                auto referenced = _fetch_json(ref);
+                                resolve_refs(referenced, base_url);
+                                _refs[base_url] = referenced;
+                            }
+                            if (ref.find('#') == std::string::npos || ref.substr(ref.find('#') + 1).empty()) {
+                                return;
+                            }
+                        } else if (ref.find("#/") == 0) {
+                            target = schema;
+                            n["$ref"] = url + ref;
+                            ref = url + ref;
+                        } else {
+                            _errors.push_back("Unsupported ref: " + ref);
+                            return;
+                        }
+                        std::string pointer = ref.substr(ref.find('#') + 1);
+                        std::vector<std::string> tokens = split(pointer, "/");
+                        for (size_t i = 1; i < tokens.size(); ++i) {
+                            std::string sel = tokens[i];
+                            if (target.is_null() || !target.contains(sel)) {
+                                _errors.push_back("Error resolving ref " + ref + ": " + sel + " not in " + target.dump());
+                                return;
+                            }
+                            target = target[sel];
+                        }
+                        _refs[ref] = target;
+                    }
+                } else {
+                    for (auto & kv : n.items()) {
+                        visit_refs(kv.value());
+                    }
+                }
+            }
+        };
+
+        visit_refs(schema);
+    }
+
+    std::string _generate_constant_rule(const json & value) {
+        return format_literal(value.dump());
+    }
+
+    std::string visit(const json & schema, const std::string & name) {
+        json schema_type = schema.contains("type") ? schema["type"] : json();
+        std::string schema_format = schema.contains("format") ? schema["format"].get<std::string>() : "";
+        std::string rule_name = is_reserved_name(name) ? name + "-" : name.empty() ? "root" : name;
+
+        if (schema.contains("$ref")) {
+            return _add_rule(rule_name, _resolve_ref(schema["$ref"]));
+        } else if (schema.contains("oneOf") || schema.contains("anyOf")) {
+            std::vector<json> alt_schemas = schema.contains("oneOf") ? schema["oneOf"].get<std::vector<json>>() : schema["anyOf"].get<std::vector<json>>();
+            return _add_rule(rule_name, _generate_union_rule(name, alt_schemas));
+        } else if (schema_type.is_array()) {
+            std::vector<json> schema_types;
+            for (const auto & t : schema_type) {
+                schema_types.push_back({{"type", t}});
+            }
+            return _add_rule(rule_name, _generate_union_rule(name, schema_types));
+        } else if (schema.contains("const")) {
+            return _add_rule(rule_name, _generate_constant_rule(schema["const"]));
+        } else if (schema.contains("enum")) {
+            std::vector<std::string> enum_values;
+            for (const auto & v : schema["enum"]) {
+                enum_values.push_back(_generate_constant_rule(v));
+            }
+            return _add_rule(rule_name, join(enum_values.begin(), enum_values.end(), " | "));
+        } else if ((schema_type.is_null() || schema_type == "object")
+                && (schema.contains("properties") ||
+                    (schema.contains("additionalProperties") && schema["additionalProperties"] != true))) {
+            std::unordered_set<std::string> required;
+            if (schema.contains("required") && schema["required"].is_array()) {
+                for (const auto & item : schema["required"]) {
+                    if (item.is_string()) {
+                        required.insert(item.get<std::string>());
+                    }
+                }
+            }
+            std::vector<std::pair<std::string, json>> properties;
+            if (schema.contains("properties")) {
+                for (const auto & prop : schema["properties"].items()) {
+                    properties.emplace_back(prop.key(), prop.value());
+                }
+            }
+            return _add_rule(rule_name,
+                _build_object_rule(
+                    properties, required, name,
+                    schema.contains("additionalProperties") ? schema["additionalProperties"] : json()));
+        } else if ((schema_type.is_null() || schema_type == "object") && schema.contains("allOf")) {
+            std::unordered_set<std::string> required;
+            std::vector<std::pair<std::string, json>> properties;
+            std::string hybrid_name = name;
+            std::function<void(const json &, bool)> add_component = [&](const json & comp_schema, bool is_required) {
+                if (comp_schema.contains("$ref")) {
+                    add_component(_refs[comp_schema["$ref"]], is_required);
+                } else if (comp_schema.contains("properties")) {
+                    for (const auto & prop : comp_schema["properties"].items()) {
+                        properties.emplace_back(prop.key(), prop.value());
+                        if (is_required) {
+                            required.insert(prop.key());
+                        }
+                    }
+                } else {
+                  // todo warning
+                }
+            };
+            for (auto & t : schema["allOf"]) {
+                if (t.contains("anyOf")) {
+                    for (auto & tt : t["anyOf"]) {
+                        add_component(tt, false);
+                    }
+                } else {
+                    add_component(t, true);
+                }
+            }
+            return _add_rule(rule_name, _build_object_rule(properties, required, hybrid_name, json()));
+        } else if ((schema_type.is_null() || schema_type == "array") && (schema.contains("items") || schema.contains("prefixItems"))) {
+            json items = schema.contains("items") ? schema["items"] : schema["prefixItems"];
+            if (items.is_array()) {
+                std::string rule = "\"[\" space ";
+                for (size_t i = 0; i < items.size(); i++) {
+                    if (i > 0) {
+                        rule += " \",\" space ";
+                    }
+                    rule += visit(items[i], name + (name.empty() ? "" : "-") + "tuple-" + std::to_string(i));
+                }
+                rule += " \"]\" space";
+                return _add_rule(rule_name, rule);
+            } else {
+                std::string item_rule_name = visit(items, name + (name.empty() ? "" : "-") + "item");
+                int min_items = schema.contains("minItems") ? schema["minItems"].get<int>() : 0;
+                json max_items_json = schema.contains("maxItems") ? schema["maxItems"] : json();
+                int max_items = max_items_json.is_number_integer() ? max_items_json.get<int>() : std::numeric_limits<int>::max();
+
+                return _add_rule(rule_name, "\"[\" space " + build_repetition(item_rule_name, min_items, max_items, "\",\" space") + " \"]\" space");
+            }
+        } else if ((schema_type.is_null() || schema_type == "string") && schema.contains("pattern")) {
+            return _visit_pattern(schema["pattern"], rule_name);
+        } else if ((schema_type.is_null() || schema_type == "string") && std::regex_match(schema_format, std::regex("^uuid[1-5]?$"))) {
+            return _add_primitive(rule_name == "root" ? "root" : schema_format, PRIMITIVE_RULES.at("uuid"));
+        } else if ((schema_type.is_null() || schema_type == "string") && STRING_FORMAT_RULES.find(schema_format + "-string") != STRING_FORMAT_RULES.end()) {
+            auto prim_name = schema_format + "-string";
+            return _add_rule(rule_name, _add_primitive(prim_name, STRING_FORMAT_RULES.at(prim_name)));
+        } else if (schema_type == "string" && (schema.contains("minLength") || schema.contains("maxLength"))) {
+            std::string char_rule = _add_primitive("char", PRIMITIVE_RULES.at("char"));
+            int min_len = schema.contains("minLength") ? schema["minLength"].get<int>() : 0;
+            int max_len = schema.contains("maxLength") ? schema["maxLength"].get<int>() : std::numeric_limits<int>::max();
+            return _add_rule(rule_name, "\"\\\"\" " + build_repetition(char_rule, min_len, max_len) + " \"\\\"\" space");
+        } else if (schema.empty() || schema_type == "object") {
+            return _add_rule(rule_name, _add_primitive("object", PRIMITIVE_RULES.at("object")));
+        } else {
+            if (!schema_type.is_string() || PRIMITIVE_RULES.find(schema_type.get<std::string>()) == PRIMITIVE_RULES.end()) {
+                _errors.push_back("Unrecognized schema: " + schema.dump());
+                return "";
+            }
+            // TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero
+            return _add_primitive(rule_name == "root" ? "root" : schema_type.get<std::string>(), PRIMITIVE_RULES.at(schema_type.get<std::string>()));
+        }
+    }
+
+    void check_errors() {
+        if (!_errors.empty()) {
+            throw std::runtime_error("JSON schema conversion failed:\n" + join(_errors.begin(), _errors.end(), "\n"));
+        }
+        if (!_warnings.empty()) {
+            fprintf(stderr, "WARNING: JSON schema conversion was incomplete: %s\n", join(_warnings.begin(), _warnings.end(), "; ").c_str());
+        }
+    }
+
+    std::string format_grammar() {
+        std::stringstream ss;
+        for (const auto & kv : _rules) {
+            ss << kv.first << " ::= " << kv.second << std::endl;
+        }
+        return ss.str();
+    }
+};
+
+std::string json_schema_to_grammar(const json & schema) {
+    SchemaConverter converter([](const std::string &) { return json::object(); }, /* dotall= */ false);
+    auto copy = schema;
+    converter.resolve_refs(copy, "input");
+    converter.visit(copy, "");
+    converter.check_errors();
+    return converter.format_grammar();
+}
diff --git a/common/json-schema-to-grammar.h b/common/json-schema-to-grammar.h
new file mode 100644
index 000000000..41623b346
--- /dev/null
+++ b/common/json-schema-to-grammar.h
@@ -0,0 +1,8 @@
+#pragma once
+
+#include "ggml.h"
+// Change JSON_ASSERT from assert() to GGML_ASSERT:
+#define JSON_ASSERT GGML_ASSERT
+#include "json.hpp"
+
+std::string json_schema_to_grammar(const nlohmann::ordered_json& schema);
diff --git a/examples/server/json.hpp b/common/json.hpp
similarity index 93%
rename from examples/server/json.hpp
rename to common/json.hpp
index ea945f346..a858728c4 100644
--- a/examples/server/json.hpp
+++ b/common/json.hpp
@@ -1,9 +1,9 @@
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 /****************************************************************************\
@@ -27,7 +27,6 @@
 #endif  // JSON_NO_IO
 #include <iterator> // random_access_iterator_tag
 #include <memory> // unique_ptr
-#include <numeric> // accumulate
 #include <string> // string, stoi, to_string
 #include <utility> // declval, forward, move, pair, swap
 #include <vector> // vector
@@ -35,10 +34,10 @@
 // #include <nlohmann/adl_serializer.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -48,10 +47,10 @@
 // #include <nlohmann/detail/abi_macros.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -60,7 +59,7 @@
 
 #ifndef JSON_SKIP_LIBRARY_VERSION_CHECK
     #if defined(NLOHMANN_JSON_VERSION_MAJOR) && defined(NLOHMANN_JSON_VERSION_MINOR) && defined(NLOHMANN_JSON_VERSION_PATCH)
-        #if NLOHMANN_JSON_VERSION_MAJOR != 3 || NLOHMANN_JSON_VERSION_MINOR != 11 || NLOHMANN_JSON_VERSION_PATCH != 2
+        #if NLOHMANN_JSON_VERSION_MAJOR != 3 || NLOHMANN_JSON_VERSION_MINOR != 11 || NLOHMANN_JSON_VERSION_PATCH != 3
             #warning "Already included a different version of the library!"
         #endif
     #endif
@@ -68,7 +67,7 @@
 
 #define NLOHMANN_JSON_VERSION_MAJOR 3   // NOLINT(modernize-macro-to-enum)
 #define NLOHMANN_JSON_VERSION_MINOR 11  // NOLINT(modernize-macro-to-enum)
-#define NLOHMANN_JSON_VERSION_PATCH 2   // NOLINT(modernize-macro-to-enum)
+#define NLOHMANN_JSON_VERSION_PATCH 3   // NOLINT(modernize-macro-to-enum)
 
 #ifndef JSON_DIAGNOSTICS
     #define JSON_DIAGNOSTICS 0
@@ -150,10 +149,10 @@
 // #include <nlohmann/detail/conversions/from_json.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -173,16 +172,19 @@
 // #include <nlohmann/detail/exceptions.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
 
 #include <cstddef> // nullptr_t
 #include <exception> // exception
+#if JSON_DIAGNOSTICS
+    #include <numeric> // accumulate
+#endif
 #include <stdexcept> // runtime_error
 #include <string> // to_string
 #include <vector> // vector
@@ -190,10 +192,10 @@
 // #include <nlohmann/detail/value_t.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -206,10 +208,10 @@
 // #include <nlohmann/detail/macro_scope.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -218,10 +220,10 @@
 // #include <nlohmann/detail/meta/detected.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -231,10 +233,10 @@
 // #include <nlohmann/detail/meta/void_t.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -318,10 +320,10 @@ NLOHMANN_JSON_NAMESPACE_END
 
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-FileCopyrightText: 2016-2021 Evan Nemerson <evan@nemerson.com>
 // SPDX-License-Identifier: MIT
 
@@ -2483,6 +2485,14 @@ JSON_HEDLEY_DIAGNOSTIC_POP
     #endif
 #endif
 
+#ifndef JSON_HAS_STATIC_RTTI
+    #if !defined(_HAS_STATIC_RTTI) || _HAS_STATIC_RTTI != 0
+        #define JSON_HAS_STATIC_RTTI 1
+    #else
+        #define JSON_HAS_STATIC_RTTI 0
+    #endif
+#endif
+
 #ifdef JSON_HAS_CPP_17
     #define JSON_INLINE_VARIABLE inline
 #else
@@ -2590,12 +2600,13 @@ JSON_HEDLEY_DIAGNOSTIC_POP
              class NumberUnsignedType, class NumberFloatType,              \
              template<typename> class AllocatorType,                       \
              template<typename, typename = void> class JSONSerializer,     \
-             class BinaryType>
+             class BinaryType,                                             \
+             class CustomBaseClass>
 
 #define NLOHMANN_BASIC_JSON_TPL                                            \
     basic_json<ObjectType, ArrayType, StringType, BooleanType,             \
     NumberIntegerType, NumberUnsignedType, NumberFloatType,                \
-    AllocatorType, JSONSerializer, BinaryType>
+    AllocatorType, JSONSerializer, BinaryType, CustomBaseClass>
 
 // Macros to simplify conversion from/to types
 
@@ -2745,7 +2756,10 @@ JSON_HEDLEY_DIAGNOSTIC_POP
 
 #define NLOHMANN_DEFINE_TYPE_INTRUSIVE_WITH_DEFAULT(Type, ...)  \
     friend void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \
-    friend void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { Type nlohmann_json_default_obj; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) }
+    friend void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { const Type nlohmann_json_default_obj{}; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) }
+
+#define NLOHMANN_DEFINE_TYPE_INTRUSIVE_ONLY_SERIALIZE(Type, ...)  \
+    friend void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) }
 
 /*!
 @brief macro
@@ -2756,10 +2770,12 @@ JSON_HEDLEY_DIAGNOSTIC_POP
     inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \
     inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM, __VA_ARGS__)) }
 
+#define NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE_ONLY_SERIALIZE(Type, ...)  \
+    inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) }
+
 #define NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE_WITH_DEFAULT(Type, ...)  \
     inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \
-    inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { Type nlohmann_json_default_obj; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) }
-
+    inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { const Type nlohmann_json_default_obj{}; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) }
 
 // inspired from https://stackoverflow.com/a/26745591
 // allows to call any std function as if (e.g. with begin):
@@ -2923,10 +2939,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/string_escape.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -2998,10 +3014,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/input/position_t.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -3040,10 +3056,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/meta/cpp_future.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-FileCopyrightText: 2018 The Abseil Authors
 // SPDX-License-Identifier: MIT
 
@@ -3214,10 +3230,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/meta/type_traits.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -3226,14 +3242,15 @@ NLOHMANN_JSON_NAMESPACE_END
 #include <type_traits> // false_type, is_constructible, is_integral, is_same, true_type
 #include <utility> // declval
 #include <tuple> // tuple
+#include <string> // char_traits
 
 // #include <nlohmann/detail/iterators/iterator_traits.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -3298,10 +3315,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/meta/call_std/begin.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -3318,10 +3335,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/meta/call_std/end.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -3342,10 +3359,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/json_fwd.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 #ifndef INCLUDE_NLOHMANN_JSON_FWD_HPP_
@@ -3389,7 +3406,8 @@ NLOHMANN_JSON_NAMESPACE_END
     template<typename U> class AllocatorType = std::allocator,
     template<typename T, typename SFINAE = void> class JSONSerializer =
     adl_serializer,
-    class BinaryType = std::vector<std::uint8_t>>
+    class BinaryType = std::vector<std::uint8_t>, // cppcheck-suppress syntaxError
+    class CustomBaseClass = void>
     class basic_json;
 
     /// @brief JSON Pointer defines a string syntax for identifying a specific value within a JSON document
@@ -3577,6 +3595,63 @@ struct actual_object_comparator
 template<typename BasicJsonType>
 using actual_object_comparator_t = typename actual_object_comparator<BasicJsonType>::type;
 
+/////////////////
+// char_traits //
+/////////////////
+
+// Primary template of char_traits calls std char_traits
+template<typename T>
+struct char_traits : std::char_traits<T>
+{};
+
+// Explicitly define char traits for unsigned char since it is not standard
+template<>
+struct char_traits<unsigned char> : std::char_traits<char>
+{
+    using char_type = unsigned char;
+    using int_type = uint64_t;
+
+    // Redefine to_int_type function
+    static int_type to_int_type(char_type c) noexcept
+    {
+        return static_cast<int_type>(c);
+    }
+
+    static char_type to_char_type(int_type i) noexcept
+    {
+        return static_cast<char_type>(i);
+    }
+
+    static constexpr int_type eof() noexcept
+    {
+        return static_cast<int_type>(EOF);
+    }
+};
+
+// Explicitly define char traits for signed char since it is not standard
+template<>
+struct char_traits<signed char> : std::char_traits<char>
+{
+    using char_type = signed char;
+    using int_type = uint64_t;
+
+    // Redefine to_int_type function
+    static int_type to_int_type(char_type c) noexcept
+    {
+        return static_cast<int_type>(c);
+    }
+
+    static char_type to_char_type(int_type i) noexcept
+    {
+        return static_cast<char_type>(i);
+    }
+
+    static constexpr int_type eof() noexcept
+    {
+        return static_cast<int_type>(EOF);
+    }
+};
+
 ///////////////////
 // is_ functions //
 ///////////////////
@@ -3613,7 +3688,6 @@ template <typename... Ts>
 struct is_default_constructible<const std::tuple<Ts...>>
             : conjunction<is_default_constructible<Ts>...> {};
 
-
 template <typename T, typename... Args>
 struct is_constructible : std::is_constructible<T, Args...> {};
 
@@ -3629,7 +3703,6 @@ struct is_constructible<std::tuple<Ts...>> : is_default_constructible<std::tuple
 template <typename... Ts>
 struct is_constructible<const std::tuple<Ts...>> : is_default_constructible<const std::tuple<Ts...>> {};
 
-
 template<typename T, typename = void>
 struct is_iterator_traits : std::false_type {};
 
@@ -4039,7 +4112,6 @@ struct value_in_range_of_impl2<OfType, T, false, true>
     }
 };
 
-
 template<typename OfType, typename T>
 struct value_in_range_of_impl2<OfType, T, true, true>
 {
@@ -4138,10 +4210,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/string_concat.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -4165,28 +4237,28 @@ inline std::size_t concat_length()
 }
 
 template<typename... Args>
-inline std::size_t concat_length(const char* cstr, Args&& ... rest);
+inline std::size_t concat_length(const char* cstr, const Args& ... rest);
 
 template<typename StringType, typename... Args>
-inline std::size_t concat_length(const StringType& str, Args&& ... rest);
+inline std::size_t concat_length(const StringType& str, const Args& ... rest);
 
 template<typename... Args>
-inline std::size_t concat_length(const char /*c*/, Args&& ... rest)
+inline std::size_t concat_length(const char /*c*/, const Args& ... rest)
 {
-    return 1 + concat_length(std::forward<Args>(rest)...);
+    return 1 + concat_length(rest...);
 }
 
 template<typename... Args>
-inline std::size_t concat_length(const char* cstr, Args&& ... rest)
+inline std::size_t concat_length(const char* cstr, const Args& ... rest)
 {
     // cppcheck-suppress ignoredReturnValue
-    return ::strlen(cstr) + concat_length(std::forward<Args>(rest)...);
+    return ::strlen(cstr) + concat_length(rest...);
 }
 
 template<typename StringType, typename... Args>
-inline std::size_t concat_length(const StringType& str, Args&& ... rest)
+inline std::size_t concat_length(const StringType& str, const Args& ... rest)
 {
-    return str.size() + concat_length(std::forward<Args>(rest)...);
+    return str.size() + concat_length(rest...);
 }
 
 template<typename OutStringType>
@@ -4277,7 +4349,7 @@ template<typename OutStringType = std::string, typename... Args>
 inline OutStringType concat(Args && ... args)
 {
     OutStringType str;
-    str.reserve(concat_length(std::forward<Args>(args)...));
+    str.reserve(concat_length(args...));
     concat_into(str, std::forward<Args>(args)...);
     return str;
 }
@@ -4286,7 +4358,6 @@ inline OutStringType concat(Args && ... args)
 NLOHMANN_JSON_NAMESPACE_END
 
 
-
 NLOHMANN_JSON_NAMESPACE_BEGIN
 namespace detail
 {
@@ -4334,9 +4405,9 @@ class exception : public std::exception
             {
                 case value_t::array:
                 {
-                    for (std::size_t i = 0; i < current->m_parent->m_value.array->size(); ++i)
+                    for (std::size_t i = 0; i < current->m_parent->m_data.m_value.array->size(); ++i)
                     {
-                        if (&current->m_parent->m_value.array->operator[](i) == current)
+                        if (&current->m_parent->m_data.m_value.array->operator[](i) == current)
                         {
                             tokens.emplace_back(std::to_string(i));
                             break;
@@ -4347,7 +4418,7 @@ class exception : public std::exception
 
                 case value_t::object:
                 {
-                    for (const auto& element : *current->m_parent->m_value.object)
+                    for (const auto& element : *current->m_parent->m_data.m_value.object)
                     {
                         if (&element.second == current)
                         {
@@ -4410,17 +4481,17 @@ class parse_error : public exception
     template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
     static parse_error create(int id_, const position_t& pos, const std::string& what_arg, BasicJsonContext context)
     {
-        std::string w = concat(exception::name("parse_error", id_), "parse error",
-                               position_string(pos), ": ", exception::diagnostics(context), what_arg);
+        const std::string w = concat(exception::name("parse_error", id_), "parse error",
+                                     position_string(pos), ": ", exception::diagnostics(context), what_arg);
         return {id_, pos.chars_read_total, w.c_str()};
     }
 
     template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
     static parse_error create(int id_, std::size_t byte_, const std::string& what_arg, BasicJsonContext context)
     {
-        std::string w = concat(exception::name("parse_error", id_), "parse error",
-                               (byte_ != 0 ? (concat(" at byte ", std::to_string(byte_))) : ""),
-                               ": ", exception::diagnostics(context), what_arg);
+        const std::string w = concat(exception::name("parse_error", id_), "parse error",
+                                     (byte_ != 0 ? (concat(" at byte ", std::to_string(byte_))) : ""),
+                                     ": ", exception::diagnostics(context), what_arg);
         return {id_, byte_, w.c_str()};
     }
 
@@ -4454,7 +4525,7 @@ class invalid_iterator : public exception
     template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
     static invalid_iterator create(int id_, const std::string& what_arg, BasicJsonContext context)
     {
-        std::string w = concat(exception::name("invalid_iterator", id_), exception::diagnostics(context), what_arg);
+        const std::string w = concat(exception::name("invalid_iterator", id_), exception::diagnostics(context), what_arg);
         return {id_, w.c_str()};
     }
 
@@ -4472,7 +4543,7 @@ class type_error : public exception
     template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
     static type_error create(int id_, const std::string& what_arg, BasicJsonContext context)
     {
-        std::string w = concat(exception::name("type_error", id_), exception::diagnostics(context), what_arg);
+        const std::string w = concat(exception::name("type_error", id_), exception::diagnostics(context), what_arg);
         return {id_, w.c_str()};
     }
 
@@ -4489,7 +4560,7 @@ class out_of_range : public exception
     template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
     static out_of_range create(int id_, const std::string& what_arg, BasicJsonContext context)
     {
-        std::string w = concat(exception::name("out_of_range", id_), exception::diagnostics(context), what_arg);
+        const std::string w = concat(exception::name("out_of_range", id_), exception::diagnostics(context), what_arg);
         return {id_, w.c_str()};
     }
 
@@ -4506,7 +4577,7 @@ class other_error : public exception
     template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
     static other_error create(int id_, const std::string& what_arg, BasicJsonContext context)
     {
-        std::string w = concat(exception::name("other_error", id_), exception::diagnostics(context), what_arg);
+        const std::string w = concat(exception::name("other_error", id_), exception::diagnostics(context), what_arg);
         return {id_, w.c_str()};
     }
 
@@ -4525,10 +4596,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/meta/identity_tag.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -4549,10 +4620,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/meta/std_fs.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -5055,10 +5126,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/conversions/to_json.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -5075,10 +5146,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/iterators/iteration_proxy.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -5147,10 +5218,10 @@ template<typename IteratorType> class iteration_proxy_value
     // older GCCs are a bit fussy and require explicit noexcept specifiers on defaulted functions
     iteration_proxy_value(iteration_proxy_value&&)
     noexcept(std::is_nothrow_move_constructible<IteratorType>::value
-             && std::is_nothrow_move_constructible<string_type>::value) = default;
+             && std::is_nothrow_move_constructible<string_type>::value) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor,cppcoreguidelines-noexcept-move-operations)
     iteration_proxy_value& operator=(iteration_proxy_value&&)
     noexcept(std::is_nothrow_move_assignable<IteratorType>::value
-             && std::is_nothrow_move_assignable<string_type>::value) = default;
+             && std::is_nothrow_move_assignable<string_type>::value) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor,cppcoreguidelines-noexcept-move-operations)
     ~iteration_proxy_value() = default;
 
     /// dereference operator (needed for range-based for)
@@ -5297,11 +5368,11 @@ namespace std
     #pragma clang diagnostic ignored "-Wmismatched-tags"
 #endif
 template<typename IteratorType>
-class tuple_size<::nlohmann::detail::iteration_proxy_value<IteratorType>>
+class tuple_size<::nlohmann::detail::iteration_proxy_value<IteratorType>> // NOLINT(cert-dcl58-cpp)
             : public std::integral_constant<std::size_t, 2> {};
 
 template<std::size_t N, typename IteratorType>
-class tuple_element<N, ::nlohmann::detail::iteration_proxy_value<IteratorType >>
+class tuple_element<N, ::nlohmann::detail::iteration_proxy_value<IteratorType >> // NOLINT(cert-dcl58-cpp)
 {
   public:
     using type = decltype(
@@ -5340,7 +5411,7 @@ namespace detail
 
 /*
  * Note all external_constructor<>::construct functions need to call
- * j.m_value.destroy(j.m_type) to avoid a memory leak in case j contains an
+ * j.m_data.m_value.destroy(j.m_data.m_type) to avoid a memory leak in case j contains an
  * allocated value (e.g., a string). See bug issue
  * https://github.com/nlohmann/json/issues/2865 for more information.
  */
@@ -5353,9 +5424,9 @@ struct external_constructor<value_t::boolean>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, typename BasicJsonType::boolean_t b) noexcept
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::boolean;
-        j.m_value = b;
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::boolean;
+        j.m_data.m_value = b;
         j.assert_invariant();
     }
 };
@@ -5366,18 +5437,18 @@ struct external_constructor<value_t::string>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, const typename BasicJsonType::string_t& s)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::string;
-        j.m_value = s;
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::string;
+        j.m_data.m_value = s;
         j.assert_invariant();
     }
 
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, typename BasicJsonType::string_t&& s)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::string;
-        j.m_value = std::move(s);
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::string;
+        j.m_data.m_value = std::move(s);
         j.assert_invariant();
     }
 
@@ -5386,9 +5457,9 @@ struct external_constructor<value_t::string>
                              int > = 0 >
     static void construct(BasicJsonType& j, const CompatibleStringType& str)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::string;
-        j.m_value.string = j.template create<typename BasicJsonType::string_t>(str);
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::string;
+        j.m_data.m_value.string = j.template create<typename BasicJsonType::string_t>(str);
         j.assert_invariant();
     }
 };
@@ -5399,18 +5470,18 @@ struct external_constructor<value_t::binary>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, const typename BasicJsonType::binary_t& b)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::binary;
-        j.m_value = typename BasicJsonType::binary_t(b);
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::binary;
+        j.m_data.m_value = typename BasicJsonType::binary_t(b);
         j.assert_invariant();
     }
 
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, typename BasicJsonType::binary_t&& b)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::binary;
-        j.m_value = typename BasicJsonType::binary_t(std::move(b));
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::binary;
+        j.m_data.m_value = typename BasicJsonType::binary_t(std::move(b));
         j.assert_invariant();
     }
 };
@@ -5421,9 +5492,9 @@ struct external_constructor<value_t::number_float>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, typename BasicJsonType::number_float_t val) noexcept
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::number_float;
-        j.m_value = val;
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::number_float;
+        j.m_data.m_value = val;
         j.assert_invariant();
     }
 };
@@ -5434,9 +5505,9 @@ struct external_constructor<value_t::number_unsigned>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, typename BasicJsonType::number_unsigned_t val) noexcept
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::number_unsigned;
-        j.m_value = val;
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::number_unsigned;
+        j.m_data.m_value = val;
         j.assert_invariant();
     }
 };
@@ -5447,9 +5518,9 @@ struct external_constructor<value_t::number_integer>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, typename BasicJsonType::number_integer_t val) noexcept
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::number_integer;
-        j.m_value = val;
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::number_integer;
+        j.m_data.m_value = val;
         j.assert_invariant();
     }
 };
@@ -5460,9 +5531,9 @@ struct external_constructor<value_t::array>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, const typename BasicJsonType::array_t& arr)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::array;
-        j.m_value = arr;
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::array;
+        j.m_data.m_value = arr;
         j.set_parents();
         j.assert_invariant();
     }
@@ -5470,9 +5541,9 @@ struct external_constructor<value_t::array>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, typename BasicJsonType::array_t&& arr)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::array;
-        j.m_value = std::move(arr);
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::array;
+        j.m_data.m_value = std::move(arr);
         j.set_parents();
         j.assert_invariant();
     }
@@ -5485,9 +5556,9 @@ struct external_constructor<value_t::array>
         using std::begin;
         using std::end;
 
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::array;
-        j.m_value.array = j.template create<typename BasicJsonType::array_t>(begin(arr), end(arr));
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::array;
+        j.m_data.m_value.array = j.template create<typename BasicJsonType::array_t>(begin(arr), end(arr));
         j.set_parents();
         j.assert_invariant();
     }
@@ -5495,14 +5566,14 @@ struct external_constructor<value_t::array>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, const std::vector<bool>& arr)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::array;
-        j.m_value = value_t::array;
-        j.m_value.array->reserve(arr.size());
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::array;
+        j.m_data.m_value = value_t::array;
+        j.m_data.m_value.array->reserve(arr.size());
         for (const bool x : arr)
         {
-            j.m_value.array->push_back(x);
-            j.set_parent(j.m_value.array->back());
+            j.m_data.m_value.array->push_back(x);
+            j.set_parent(j.m_data.m_value.array->back());
         }
         j.assert_invariant();
     }
@@ -5511,13 +5582,13 @@ struct external_constructor<value_t::array>
              enable_if_t<std::is_convertible<T, BasicJsonType>::value, int> = 0>
     static void construct(BasicJsonType& j, const std::valarray<T>& arr)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::array;
-        j.m_value = value_t::array;
-        j.m_value.array->resize(arr.size());
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::array;
+        j.m_data.m_value = value_t::array;
+        j.m_data.m_value.array->resize(arr.size());
         if (arr.size() > 0)
         {
-            std::copy(std::begin(arr), std::end(arr), j.m_value.array->begin());
+            std::copy(std::begin(arr), std::end(arr), j.m_data.m_value.array->begin());
         }
         j.set_parents();
         j.assert_invariant();
@@ -5530,9 +5601,9 @@ struct external_constructor<value_t::object>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, const typename BasicJsonType::object_t& obj)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::object;
-        j.m_value = obj;
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::object;
+        j.m_data.m_value = obj;
         j.set_parents();
         j.assert_invariant();
     }
@@ -5540,9 +5611,9 @@ struct external_constructor<value_t::object>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, typename BasicJsonType::object_t&& obj)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::object;
-        j.m_value = std::move(obj);
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::object;
+        j.m_data.m_value = std::move(obj);
         j.set_parents();
         j.assert_invariant();
     }
@@ -5554,9 +5625,9 @@ struct external_constructor<value_t::object>
         using std::begin;
         using std::end;
 
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::object;
-        j.m_value.object = j.template create<typename BasicJsonType::object_t>(begin(obj), end(obj));
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::object;
+        j.m_data.m_value.object = j.template create<typename BasicJsonType::object_t>(begin(obj), end(obj));
         j.set_parents();
         j.assert_invariant();
     }
@@ -5626,7 +5697,8 @@ template<typename BasicJsonType, typename EnumType,
 inline void to_json(BasicJsonType& j, EnumType e) noexcept
 {
     using underlying_type = typename std::underlying_type<EnumType>::type;
-    external_constructor<value_t::number_integer>::construct(j, static_cast<underlying_type>(e));
+    static constexpr value_t integral_value_t = std::is_unsigned<underlying_type>::value ? value_t::number_unsigned : value_t::number_integer;
+    external_constructor<integral_value_t>::construct(j, static_cast<underlying_type>(e));
 }
 #endif  // JSON_DISABLE_ENUM_SERIALIZATION
 
@@ -5796,10 +5868,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/byte_container_with_subtype.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -5908,10 +5980,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/hash.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -6041,10 +6113,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/input/binary_reader.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -6067,10 +6139,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/input/input_adapters.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -6094,6 +6166,8 @@ NLOHMANN_JSON_NAMESPACE_END
 
 // #include <nlohmann/detail/macro_scope.hpp>
 
+// #include <nlohmann/detail/meta/type_traits.hpp>
+
 
 NLOHMANN_JSON_NAMESPACE_BEGIN
 namespace detail
@@ -6140,7 +6214,6 @@ class file_input_adapter
     std::FILE* m_file;
 };
 
-
 /*!
 Input adapter for a (caching) istream. Ignores a UFT Byte Order Mark at
 beginning of input. Does not support changing the underlying std::streambuf
@@ -6214,16 +6287,16 @@ class iterator_input_adapter
         : current(std::move(first)), end(std::move(last))
     {}
 
-    typename std::char_traits<char_type>::int_type get_character()
+    typename char_traits<char_type>::int_type get_character()
     {
         if (JSON_HEDLEY_LIKELY(current != end))
         {
-            auto result = std::char_traits<char_type>::to_int_type(*current);
+            auto result = char_traits<char_type>::to_int_type(*current);
             std::advance(current, 1);
             return result;
         }
 
-        return std::char_traits<char_type>::eof();
+        return char_traits<char_type>::eof();
     }
 
   private:
@@ -6239,7 +6312,6 @@ class iterator_input_adapter
     }
 };
 
-
 template<typename BaseInputAdapter, size_t T>
 struct wide_string_input_helper;
 
@@ -6363,7 +6435,7 @@ struct wide_string_input_helper<BaseInputAdapter, 2>
     }
 };
 
-// Wraps another input apdater to convert wide character types into individual bytes.
+// Wraps another input adapter to convert wide character types into individual bytes.
 template<typename BaseInputAdapter, typename WideCharType>
 class wide_string_input_adapter
 {
@@ -6408,7 +6480,6 @@ class wide_string_input_adapter
     std::size_t utf8_bytes_filled = 0;
 };
 
-
 template<typename IteratorType, typename Enable = void>
 struct iterator_input_adapter_factory
 {
@@ -6565,10 +6636,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/input/json_sax.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -6710,7 +6781,6 @@ struct json_sax
     virtual ~json_sax() = default;
 };
 
-
 namespace detail
 {
 /*!
@@ -6812,7 +6882,7 @@ class json_sax_dom_parser
         JSON_ASSERT(ref_stack.back()->is_object());
 
         // add null at given key and store the reference for later
-        object_element = &(ref_stack.back()->m_value.object->operator[](val));
+        object_element = &(ref_stack.back()->m_data.m_value.object->operator[](val));
         return true;
     }
 
@@ -6887,8 +6957,8 @@ class json_sax_dom_parser
 
         if (ref_stack.back()->is_array())
         {
-            ref_stack.back()->m_value.array->emplace_back(std::forward<Value>(v));
-            return &(ref_stack.back()->m_value.array->back());
+            ref_stack.back()->m_data.m_value.array->emplace_back(std::forward<Value>(v));
+            return &(ref_stack.back()->m_data.m_value.array->back());
         }
 
         JSON_ASSERT(ref_stack.back()->is_object());
@@ -7007,7 +7077,7 @@ class json_sax_dom_callback_parser
         // add discarded value at given key and store the reference for later
         if (keep && ref_stack.back())
         {
-            object_element = &(ref_stack.back()->m_value.object->operator[](val) = discarded);
+            object_element = &(ref_stack.back()->m_data.m_value.object->operator[](val) = discarded);
         }
 
         return true;
@@ -7092,7 +7162,7 @@ class json_sax_dom_callback_parser
         // remove discarded value
         if (!keep && !ref_stack.empty() && ref_stack.back()->is_array())
         {
-            ref_stack.back()->m_value.array->pop_back();
+            ref_stack.back()->m_data.m_value.array->pop_back();
         }
 
         return true;
@@ -7159,7 +7229,7 @@ class json_sax_dom_callback_parser
         if (ref_stack.empty())
         {
             root = std::move(value);
-            return {true, &root};
+            return {true, & root};
         }
 
         // skip this value if we already decided to skip the parent
@@ -7175,8 +7245,8 @@ class json_sax_dom_callback_parser
         // array
         if (ref_stack.back()->is_array())
         {
-            ref_stack.back()->m_value.array->emplace_back(std::move(value));
-            return {true, &(ref_stack.back()->m_value.array->back())};
+            ref_stack.back()->m_data.m_value.array->emplace_back(std::move(value));
+            return {true, & (ref_stack.back()->m_data.m_value.array->back())};
         }
 
         // object
@@ -7201,9 +7271,9 @@ class json_sax_dom_callback_parser
     /// stack to model hierarchy of values
     std::vector<BasicJsonType*> ref_stack {};
     /// stack to manage which values to keep
-    std::vector<bool> keep_stack {};
+    std::vector<bool> keep_stack {}; // NOLINT(readability-redundant-member-init)
     /// stack to manage which object keys to keep
-    std::vector<bool> key_keep_stack {};
+    std::vector<bool> key_keep_stack {}; // NOLINT(readability-redundant-member-init)
     /// helper to hold the reference for the next object element
     BasicJsonType* object_element = nullptr;
     /// whether a syntax error occurred
@@ -7298,10 +7368,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/input/lexer.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -7322,6 +7392,8 @@ NLOHMANN_JSON_NAMESPACE_END
 
 // #include <nlohmann/detail/macro_scope.hpp>
 
+// #include <nlohmann/detail/meta/type_traits.hpp>
+
 
 NLOHMANN_JSON_NAMESPACE_BEGIN
 namespace detail
@@ -7416,7 +7488,7 @@ class lexer : public lexer_base<BasicJsonType>
     using number_float_t = typename BasicJsonType::number_float_t;
     using string_t = typename BasicJsonType::string_t;
     using char_type = typename InputAdapterType::char_type;
-    using char_int_type = typename std::char_traits<char_type>::int_type;
+    using char_int_type = typename char_traits<char_type>::int_type;
 
   public:
     using token_type = typename lexer_base<BasicJsonType>::token_type;
@@ -7523,7 +7595,7 @@ class lexer : public lexer_base<BasicJsonType>
         for (auto range = ranges.begin(); range != ranges.end(); ++range)
         {
             get();
-            if (JSON_HEDLEY_LIKELY(*range <= current && current <= *(++range)))
+            if (JSON_HEDLEY_LIKELY(*range <= current && current <= *(++range))) // NOLINT(bugprone-inc-dec-in-conditions)
             {
                 add(current);
             }
@@ -7566,7 +7638,7 @@ class lexer : public lexer_base<BasicJsonType>
             switch (get())
             {
                 // end of file while parsing string
-                case std::char_traits<char_type>::eof():
+                case char_traits<char_type>::eof():
                 {
                     error_message = "invalid string: missing closing quote";
                     return token_type::parse_error;
@@ -8155,7 +8227,7 @@ class lexer : public lexer_base<BasicJsonType>
                     {
                         case '\n':
                         case '\r':
-                        case std::char_traits<char_type>::eof():
+                        case char_traits<char_type>::eof():
                         case '\0':
                             return true;
 
@@ -8172,7 +8244,7 @@ class lexer : public lexer_base<BasicJsonType>
                 {
                     switch (get())
                     {
-                        case std::char_traits<char_type>::eof():
+                        case char_traits<char_type>::eof():
                         case '\0':
                         {
                             error_message = "invalid comment; missing closing '*/'";
@@ -8601,10 +8673,10 @@ scan_number_done:
     token_type scan_literal(const char_type* literal_text, const std::size_t length,
                             token_type return_type)
     {
-        JSON_ASSERT(std::char_traits<char_type>::to_char_type(current) == literal_text[0]);
+        JSON_ASSERT(char_traits<char_type>::to_char_type(current) == literal_text[0]);
         for (std::size_t i = 1; i < length; ++i)
         {
-            if (JSON_HEDLEY_UNLIKELY(std::char_traits<char_type>::to_char_type(get()) != literal_text[i]))
+            if (JSON_HEDLEY_UNLIKELY(char_traits<char_type>::to_char_type(get()) != literal_text[i]))
             {
                 error_message = "invalid literal";
                 return token_type::parse_error;
@@ -8622,7 +8694,7 @@ scan_number_done:
     {
         token_buffer.clear();
         token_string.clear();
-        token_string.push_back(std::char_traits<char_type>::to_char_type(current));
+        token_string.push_back(char_traits<char_type>::to_char_type(current));
     }
 
     /*
@@ -8630,7 +8702,7 @@ scan_number_done:
 
     This function provides the interface to the used input adapter. It does
     not throw in case the input reached EOF, but returns a
-    `std::char_traits<char>::eof()` in that case.  Stores the scanned characters
+    `char_traits<char>::eof()` in that case.  Stores the scanned characters
     for use in error messages.
 
     @return character read from the input
@@ -8650,9 +8722,9 @@ scan_number_done:
             current = ia.get_character();
         }
 
-        if (JSON_HEDLEY_LIKELY(current != std::char_traits<char_type>::eof()))
+        if (JSON_HEDLEY_LIKELY(current != char_traits<char_type>::eof()))
         {
-            token_string.push_back(std::char_traits<char_type>::to_char_type(current));
+            token_string.push_back(char_traits<char_type>::to_char_type(current));
         }
 
         if (current == '\n')
@@ -8691,7 +8763,7 @@ scan_number_done:
             --position.chars_read_current_line;
         }
 
-        if (JSON_HEDLEY_LIKELY(current != std::char_traits<char_type>::eof()))
+        if (JSON_HEDLEY_LIKELY(current != char_traits<char_type>::eof()))
         {
             JSON_ASSERT(!token_string.empty());
             token_string.pop_back();
@@ -8885,7 +8957,7 @@ scan_number_done:
             // end of input (the null byte is needed when parsing from
             // string literals)
             case '\0':
-            case std::char_traits<char_type>::eof():
+            case char_traits<char_type>::eof():
                 return token_type::end_of_input;
 
             // error
@@ -8903,7 +8975,7 @@ scan_number_done:
     const bool ignore_comments = false;
 
     /// the current character
-    char_int_type current = std::char_traits<char_type>::eof();
+    char_int_type current = char_traits<char_type>::eof();
 
     /// whether the next get() call should just return current
     bool next_unget = false;
@@ -8937,10 +9009,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/meta/is_sax.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -9129,7 +9201,6 @@ static inline bool little_endianness(int num = 1) noexcept
     return *reinterpret_cast<char*>(&num) == 1;
 }
 
-
 ///////////////////
 // binary reader //
 ///////////////////
@@ -9147,7 +9218,7 @@ class binary_reader
     using binary_t = typename BasicJsonType::binary_t;
     using json_sax_t = SAX;
     using char_type = typename InputAdapterType::char_type;
-    using char_int_type = typename std::char_traits<char_type>::int_type;
+    using char_int_type = typename char_traits<char_type>::int_type;
 
   public:
     /*!
@@ -9220,7 +9291,7 @@ class binary_reader
                 get();
             }
 
-            if (JSON_HEDLEY_UNLIKELY(current != std::char_traits<char_type>::eof()))
+            if (JSON_HEDLEY_UNLIKELY(current != char_traits<char_type>::eof()))
             {
                 return sax->parse_error(chars_read, get_token_string(), parse_error::create(110, chars_read,
                                         exception_message(input_format, concat("expected end of input; last byte: 0x", get_token_string()), "value"), nullptr));
@@ -9303,7 +9374,7 @@ class binary_reader
                                     exception_message(input_format_t::bson, concat("string length must be at least 1, is ", std::to_string(len)), "string"), nullptr));
         }
 
-        return get_string(input_format_t::bson, len - static_cast<NumberType>(1), result) && get() != std::char_traits<char_type>::eof();
+        return get_string(input_format_t::bson, len - static_cast<NumberType>(1), result) && get() != char_traits<char_type>::eof();
     }
 
     /*!
@@ -9404,7 +9475,7 @@ class binary_reader
             {
                 std::array<char, 3> cr{{}};
                 static_cast<void>((std::snprintf)(cr.data(), cr.size(), "%.2hhX", static_cast<unsigned char>(element_type))); // NOLINT(cppcoreguidelines-pro-type-vararg,hicpp-vararg)
-                std::string cr_str{cr.data()};
+                const std::string cr_str{cr.data()};
                 return sax->parse_error(element_type_parse_position, cr_str,
                                         parse_error::create(114, element_type_parse_position, concat("Unsupported BSON record type 0x", cr_str), nullptr));
             }
@@ -9497,7 +9568,7 @@ class binary_reader
         switch (get_char ? get() : current)
         {
             // EOF
-            case std::char_traits<char_type>::eof():
+            case char_traits<char_type>::eof():
                 return unexpect_eof(input_format_t::cbor, "value");
 
             // Integer 0x00..0x17 (0..23)
@@ -10272,7 +10343,7 @@ class binary_reader
         switch (get())
         {
             // EOF
-            case std::char_traits<char_type>::eof():
+            case char_traits<char_type>::eof():
                 return unexpect_eof(input_format_t::msgpack, "value");
 
             // positive fixint
@@ -11339,7 +11410,7 @@ class binary_reader
                                         exception_message(input_format, concat("expected '#' after type information; last byte: 0x", last_token), "size"), nullptr));
             }
 
-            bool is_error = get_ubjson_size_value(result.first, is_ndarray);
+            const bool is_error = get_ubjson_size_value(result.first, is_ndarray);
             if (input_format == input_format_t::bjdata && is_ndarray)
             {
                 if (inside_ndarray)
@@ -11354,7 +11425,7 @@ class binary_reader
 
         if (current == '#')
         {
-            bool is_error = get_ubjson_size_value(result.first, is_ndarray);
+            const bool is_error = get_ubjson_size_value(result.first, is_ndarray);
             if (input_format == input_format_t::bjdata && is_ndarray)
             {
                 return sax->parse_error(chars_read, get_token_string(), parse_error::create(112, chars_read,
@@ -11374,7 +11445,7 @@ class binary_reader
     {
         switch (prefix)
         {
-            case std::char_traits<char_type>::eof():  // EOF
+            case char_traits<char_type>::eof():  // EOF
                 return unexpect_eof(input_format, "value");
 
             case 'T':  // true
@@ -11819,7 +11890,7 @@ class binary_reader
 
     This function provides the interface to the used input adapter. It does
     not throw in case the input reached EOF, but returns a -'ve valued
-    `std::char_traits<char_type>::eof()` in that case.
+    `char_traits<char_type>::eof()` in that case.
 
     @return character read from the input
     */
@@ -11961,7 +12032,7 @@ class binary_reader
     JSON_HEDLEY_NON_NULL(3)
     bool unexpect_eof(const input_format_t format, const char* context) const
     {
-        if (JSON_HEDLEY_UNLIKELY(current == std::char_traits<char_type>::eof()))
+        if (JSON_HEDLEY_UNLIKELY(current == char_traits<char_type>::eof()))
         {
             return sax->parse_error(chars_read, "<end of file>",
                                     parse_error::create(110, chars_read, exception_message(format, "unexpected end of input", context), nullptr));
@@ -12028,7 +12099,7 @@ class binary_reader
     InputAdapterType ia;
 
     /// the current character
-    char_int_type current = std::char_traits<char_type>::eof();
+    char_int_type current = char_traits<char_type>::eof();
 
     /// the number of characters read
     std::size_t chars_read = 0;
@@ -12090,10 +12161,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/input/parser.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -12439,13 +12510,25 @@ class parser
                                                 m_lexer.get_token_string(),
                                                 parse_error::create(101, m_lexer.get_position(), exception_message(token_type::uninitialized, "value"), nullptr));
                     }
+                    case token_type::end_of_input:
+                    {
+                        if (JSON_HEDLEY_UNLIKELY(m_lexer.get_position().chars_read_total == 1))
+                        {
+                            return sax->parse_error(m_lexer.get_position(),
+                                                    m_lexer.get_token_string(),
+                                                    parse_error::create(101, m_lexer.get_position(),
+                                                            "attempting to parse an empty input; check that your input string or stream contains the expected JSON", nullptr));
+                        }
 
+                        return sax->parse_error(m_lexer.get_position(),
+                                                m_lexer.get_token_string(),
+                                                parse_error::create(101, m_lexer.get_position(), exception_message(token_type::literal_or_value, "value"), nullptr));
+                    }
                     case token_type::uninitialized:
                     case token_type::end_array:
                     case token_type::end_object:
                     case token_type::name_separator:
                     case token_type::value_separator:
-                    case token_type::end_of_input:
                     case token_type::literal_or_value:
                     default: // the last token was unexpected
                     {
@@ -12607,10 +12690,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/iterators/internal_iterator.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -12620,10 +12703,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/iterators/primitive_iterator.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -12779,10 +12862,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/iterators/iter_impl.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -12887,7 +12970,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
     {
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
             {
@@ -12984,17 +13067,17 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
     {
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
             {
-                m_it.object_iterator = m_object->m_value.object->begin();
+                m_it.object_iterator = m_object->m_data.m_value.object->begin();
                 break;
             }
 
             case value_t::array:
             {
-                m_it.array_iterator = m_object->m_value.array->begin();
+                m_it.array_iterator = m_object->m_data.m_value.array->begin();
                 break;
             }
 
@@ -13028,17 +13111,17 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
     {
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
             {
-                m_it.object_iterator = m_object->m_value.object->end();
+                m_it.object_iterator = m_object->m_data.m_value.object->end();
                 break;
             }
 
             case value_t::array:
             {
-                m_it.array_iterator = m_object->m_value.array->end();
+                m_it.array_iterator = m_object->m_data.m_value.array->end();
                 break;
             }
 
@@ -13067,17 +13150,17 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
     {
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
             {
-                JSON_ASSERT(m_it.object_iterator != m_object->m_value.object->end());
+                JSON_ASSERT(m_it.object_iterator != m_object->m_data.m_value.object->end());
                 return m_it.object_iterator->second;
             }
 
             case value_t::array:
             {
-                JSON_ASSERT(m_it.array_iterator != m_object->m_value.array->end());
+                JSON_ASSERT(m_it.array_iterator != m_object->m_data.m_value.array->end());
                 return *m_it.array_iterator;
             }
 
@@ -13111,17 +13194,17 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
     {
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
             {
-                JSON_ASSERT(m_it.object_iterator != m_object->m_value.object->end());
+                JSON_ASSERT(m_it.object_iterator != m_object->m_data.m_value.object->end());
                 return &(m_it.object_iterator->second);
             }
 
             case value_t::array:
             {
-                JSON_ASSERT(m_it.array_iterator != m_object->m_value.array->end());
+                JSON_ASSERT(m_it.array_iterator != m_object->m_data.m_value.array->end());
                 return &*m_it.array_iterator;
             }
 
@@ -13164,7 +13247,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
     {
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
             {
@@ -13215,7 +13298,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
     {
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
             {
@@ -13262,7 +13345,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
 
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
                 return (m_it.object_iterator == other.m_it.object_iterator);
@@ -13307,7 +13390,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
 
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
                 JSON_THROW(invalid_iterator::create(213, "cannot compare order of object iterators", m_object));
@@ -13363,7 +13446,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
     {
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
                 JSON_THROW(invalid_iterator::create(209, "cannot use offsets with object iterators", m_object));
@@ -13442,7 +13525,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
     {
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
                 JSON_THROW(invalid_iterator::create(209, "cannot use offsets with object iterators", m_object));
@@ -13471,7 +13554,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
     {
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
                 JSON_THROW(invalid_iterator::create(208, "cannot use operator[] for object iterators", m_object));
@@ -13541,10 +13624,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/iterators/json_reverse_iterator.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -13673,13 +13756,55 @@ NLOHMANN_JSON_NAMESPACE_END
 
 // #include <nlohmann/detail/iterators/primitive_iterator.hpp>
 
+// #include <nlohmann/detail/json_custom_base_class.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <type_traits> // conditional, is_same
+
+// #include <nlohmann/detail/abi_macros.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+/*!
+@brief Default base class of the @ref basic_json class.
+
+So that the correct implementations of the copy / move ctors / assign operators
+of @ref basic_json do not require complex case distinctions
+(no base class / custom base class used as customization point),
+@ref basic_json always has a base class.
+By default, this class is used because it is empty and thus has no effect
+on the behavior of @ref basic_json.
+*/
+struct json_default_base {};
+
+template<class T>
+using json_base_class = typename std::conditional <
+                        std::is_same<T, void>::value,
+                        json_default_base,
+                        T
+                        >::type;
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
 // #include <nlohmann/detail/json_pointer.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -13911,7 +14036,7 @@ class json_pointer
         const char* p = s.c_str();
         char* p_end = nullptr;
         errno = 0; // strtoull doesn't reset errno
-        unsigned long long res = std::strtoull(p, &p_end, 10); // NOLINT(runtime/int)
+        const unsigned long long res = std::strtoull(p, &p_end, 10); // NOLINT(runtime/int)
         if (p == p_end // invalid input or empty string
                 || errno == ERANGE // out of range
                 || JSON_HEDLEY_UNLIKELY(static_cast<std::size_t>(p_end - p) != s.size())) // incomplete read
@@ -14067,7 +14192,7 @@ class json_pointer
                     if (reference_token == "-")
                     {
                         // explicitly treat "-" as index beyond the end
-                        ptr = &ptr->operator[](ptr->m_value.array->size());
+                        ptr = &ptr->operator[](ptr->m_data.m_value.array->size());
                     }
                     else
                     {
@@ -14119,7 +14244,7 @@ class json_pointer
                     {
                         // "-" always fails the range check
                         JSON_THROW(detail::out_of_range::create(402, detail::concat(
-                                "array index '-' (", std::to_string(ptr->m_value.array->size()),
+                                "array index '-' (", std::to_string(ptr->m_data.m_value.array->size()),
                                 ") is out of range"), ptr));
                     }
 
@@ -14176,7 +14301,7 @@ class json_pointer
                     if (JSON_HEDLEY_UNLIKELY(reference_token == "-"))
                     {
                         // "-" cannot be used for const access
-                        JSON_THROW(detail::out_of_range::create(402, detail::concat("array index '-' (", std::to_string(ptr->m_value.array->size()), ") is out of range"), ptr));
+                        JSON_THROW(detail::out_of_range::create(402, detail::concat("array index '-' (", std::to_string(ptr->m_data.m_value.array->size()), ") is out of range"), ptr));
                     }
 
                     // use unchecked array access
@@ -14226,7 +14351,7 @@ class json_pointer
                     {
                         // "-" always fails the range check
                         JSON_THROW(detail::out_of_range::create(402, detail::concat(
-                                "array index '-' (", std::to_string(ptr->m_value.array->size()),
+                                "array index '-' (", std::to_string(ptr->m_data.m_value.array->size()),
                                 ") is out of range"), ptr));
                     }
 
@@ -14421,7 +14546,7 @@ class json_pointer
         {
             case detail::value_t::array:
             {
-                if (value.m_value.array->empty())
+                if (value.m_data.m_value.array->empty())
                 {
                     // flatten empty array as null
                     result[reference_string] = nullptr;
@@ -14429,10 +14554,10 @@ class json_pointer
                 else
                 {
                     // iterate array and use index as reference string
-                    for (std::size_t i = 0; i < value.m_value.array->size(); ++i)
+                    for (std::size_t i = 0; i < value.m_data.m_value.array->size(); ++i)
                     {
                         flatten(detail::concat(reference_string, '/', std::to_string(i)),
-                                value.m_value.array->operator[](i), result);
+                                value.m_data.m_value.array->operator[](i), result);
                     }
                 }
                 break;
@@ -14440,7 +14565,7 @@ class json_pointer
 
             case detail::value_t::object:
             {
-                if (value.m_value.object->empty())
+                if (value.m_data.m_value.object->empty())
                 {
                     // flatten empty object as null
                     result[reference_string] = nullptr;
@@ -14448,7 +14573,7 @@ class json_pointer
                 else
                 {
                     // iterate object and use keys as reference string
-                    for (const auto& element : *value.m_value.object)
+                    for (const auto& element : *value.m_data.m_value.object)
                     {
                         flatten(detail::concat(reference_string, '/', detail::escape(element.first)), element.second, result);
                     }
@@ -14495,7 +14620,7 @@ class json_pointer
         BasicJsonType result;
 
         // iterate the JSON object values
-        for (const auto& element : *value.m_value.object)
+        for (const auto& element : *value.m_data.m_value.object)
         {
             if (JSON_HEDLEY_UNLIKELY(!element.second.is_primitive()))
             {
@@ -14671,10 +14796,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/json_ref.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -14763,10 +14888,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/output/binary_writer.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -14789,10 +14914,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/output/output_adapters.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -14978,7 +15103,7 @@ class binary_writer
         {
             case value_t::object:
             {
-                write_bson_object(*j.m_value.object);
+                write_bson_object(*j.m_data.m_value.object);
                 break;
             }
 
@@ -15013,7 +15138,7 @@ class binary_writer
 
             case value_t::boolean:
             {
-                oa->write_character(j.m_value.boolean
+                oa->write_character(j.m_data.m_value.boolean
                                     ? to_char_type(0xF5)
                                     : to_char_type(0xF4));
                 break;
@@ -15021,42 +15146,42 @@ class binary_writer
 
             case value_t::number_integer:
             {
-                if (j.m_value.number_integer >= 0)
+                if (j.m_data.m_value.number_integer >= 0)
                 {
                     // CBOR does not differentiate between positive signed
                     // integers and unsigned integers. Therefore, we used the
                     // code from the value_t::number_unsigned case here.
-                    if (j.m_value.number_integer <= 0x17)
+                    if (j.m_data.m_value.number_integer <= 0x17)
                     {
-                        write_number(static_cast<std::uint8_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_integer <= (std::numeric_limits<std::uint8_t>::max)())
+                    else if (j.m_data.m_value.number_integer <= (std::numeric_limits<std::uint8_t>::max)())
                     {
                         oa->write_character(to_char_type(0x18));
-                        write_number(static_cast<std::uint8_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_integer <= (std::numeric_limits<std::uint16_t>::max)())
+                    else if (j.m_data.m_value.number_integer <= (std::numeric_limits<std::uint16_t>::max)())
                     {
                         oa->write_character(to_char_type(0x19));
-                        write_number(static_cast<std::uint16_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::uint16_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_integer <= (std::numeric_limits<std::uint32_t>::max)())
+                    else if (j.m_data.m_value.number_integer <= (std::numeric_limits<std::uint32_t>::max)())
                     {
                         oa->write_character(to_char_type(0x1A));
-                        write_number(static_cast<std::uint32_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::uint32_t>(j.m_data.m_value.number_integer));
                     }
                     else
                     {
                         oa->write_character(to_char_type(0x1B));
-                        write_number(static_cast<std::uint64_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::uint64_t>(j.m_data.m_value.number_integer));
                     }
                 }
                 else
                 {
                     // The conversions below encode the sign in the first
                     // byte, and the value is converted to a positive number.
-                    const auto positive_number = -1 - j.m_value.number_integer;
-                    if (j.m_value.number_integer >= -24)
+                    const auto positive_number = -1 - j.m_data.m_value.number_integer;
+                    if (j.m_data.m_value.number_integer >= -24)
                     {
                         write_number(static_cast<std::uint8_t>(0x20 + positive_number));
                     }
@@ -15086,52 +15211,52 @@ class binary_writer
 
             case value_t::number_unsigned:
             {
-                if (j.m_value.number_unsigned <= 0x17)
+                if (j.m_data.m_value.number_unsigned <= 0x17)
                 {
-                    write_number(static_cast<std::uint8_t>(j.m_value.number_unsigned));
+                    write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_unsigned));
                 }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint8_t>::max)())
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint8_t>::max)())
                 {
                     oa->write_character(to_char_type(0x18));
-                    write_number(static_cast<std::uint8_t>(j.m_value.number_unsigned));
+                    write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_unsigned));
                 }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint16_t>::max)())
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint16_t>::max)())
                 {
                     oa->write_character(to_char_type(0x19));
-                    write_number(static_cast<std::uint16_t>(j.m_value.number_unsigned));
+                    write_number(static_cast<std::uint16_t>(j.m_data.m_value.number_unsigned));
                 }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint32_t>::max)())
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint32_t>::max)())
                 {
                     oa->write_character(to_char_type(0x1A));
-                    write_number(static_cast<std::uint32_t>(j.m_value.number_unsigned));
+                    write_number(static_cast<std::uint32_t>(j.m_data.m_value.number_unsigned));
                 }
                 else
                 {
                     oa->write_character(to_char_type(0x1B));
-                    write_number(static_cast<std::uint64_t>(j.m_value.number_unsigned));
+                    write_number(static_cast<std::uint64_t>(j.m_data.m_value.number_unsigned));
                 }
                 break;
             }
 
             case value_t::number_float:
             {
-                if (std::isnan(j.m_value.number_float))
+                if (std::isnan(j.m_data.m_value.number_float))
                 {
                     // NaN is 0xf97e00 in CBOR
                     oa->write_character(to_char_type(0xF9));
                     oa->write_character(to_char_type(0x7E));
                     oa->write_character(to_char_type(0x00));
                 }
-                else if (std::isinf(j.m_value.number_float))
+                else if (std::isinf(j.m_data.m_value.number_float))
                 {
                     // Infinity is 0xf97c00, -Infinity is 0xf9fc00
                     oa->write_character(to_char_type(0xf9));
-                    oa->write_character(j.m_value.number_float > 0 ? to_char_type(0x7C) : to_char_type(0xFC));
+                    oa->write_character(j.m_data.m_value.number_float > 0 ? to_char_type(0x7C) : to_char_type(0xFC));
                     oa->write_character(to_char_type(0x00));
                 }
                 else
                 {
-                    write_compact_float(j.m_value.number_float, detail::input_format_t::cbor);
+                    write_compact_float(j.m_data.m_value.number_float, detail::input_format_t::cbor);
                 }
                 break;
             }
@@ -15139,7 +15264,7 @@ class binary_writer
             case value_t::string:
             {
                 // step 1: write control byte and the string length
-                const auto N = j.m_value.string->size();
+                const auto N = j.m_data.m_value.string->size();
                 if (N <= 0x17)
                 {
                     write_number(static_cast<std::uint8_t>(0x60 + N));
@@ -15169,15 +15294,15 @@ class binary_writer
 
                 // step 2: write the string
                 oa->write_characters(
-                    reinterpret_cast<const CharType*>(j.m_value.string->c_str()),
-                    j.m_value.string->size());
+                    reinterpret_cast<const CharType*>(j.m_data.m_value.string->c_str()),
+                    j.m_data.m_value.string->size());
                 break;
             }
 
             case value_t::array:
             {
                 // step 1: write control byte and the array size
-                const auto N = j.m_value.array->size();
+                const auto N = j.m_data.m_value.array->size();
                 if (N <= 0x17)
                 {
                     write_number(static_cast<std::uint8_t>(0x80 + N));
@@ -15206,7 +15331,7 @@ class binary_writer
                 // LCOV_EXCL_STOP
 
                 // step 2: write each element
-                for (const auto& el : *j.m_value.array)
+                for (const auto& el : *j.m_data.m_value.array)
                 {
                     write_cbor(el);
                 }
@@ -15215,32 +15340,32 @@ class binary_writer
 
             case value_t::binary:
             {
-                if (j.m_value.binary->has_subtype())
+                if (j.m_data.m_value.binary->has_subtype())
                 {
-                    if (j.m_value.binary->subtype() <= (std::numeric_limits<std::uint8_t>::max)())
+                    if (j.m_data.m_value.binary->subtype() <= (std::numeric_limits<std::uint8_t>::max)())
                     {
                         write_number(static_cast<std::uint8_t>(0xd8));
-                        write_number(static_cast<std::uint8_t>(j.m_value.binary->subtype()));
+                        write_number(static_cast<std::uint8_t>(j.m_data.m_value.binary->subtype()));
                     }
-                    else if (j.m_value.binary->subtype() <= (std::numeric_limits<std::uint16_t>::max)())
+                    else if (j.m_data.m_value.binary->subtype() <= (std::numeric_limits<std::uint16_t>::max)())
                     {
                         write_number(static_cast<std::uint8_t>(0xd9));
-                        write_number(static_cast<std::uint16_t>(j.m_value.binary->subtype()));
+                        write_number(static_cast<std::uint16_t>(j.m_data.m_value.binary->subtype()));
                     }
-                    else if (j.m_value.binary->subtype() <= (std::numeric_limits<std::uint32_t>::max)())
+                    else if (j.m_data.m_value.binary->subtype() <= (std::numeric_limits<std::uint32_t>::max)())
                     {
                         write_number(static_cast<std::uint8_t>(0xda));
-                        write_number(static_cast<std::uint32_t>(j.m_value.binary->subtype()));
+                        write_number(static_cast<std::uint32_t>(j.m_data.m_value.binary->subtype()));
                     }
-                    else if (j.m_value.binary->subtype() <= (std::numeric_limits<std::uint64_t>::max)())
+                    else if (j.m_data.m_value.binary->subtype() <= (std::numeric_limits<std::uint64_t>::max)())
                     {
                         write_number(static_cast<std::uint8_t>(0xdb));
-                        write_number(static_cast<std::uint64_t>(j.m_value.binary->subtype()));
+                        write_number(static_cast<std::uint64_t>(j.m_data.m_value.binary->subtype()));
                     }
                 }
 
                 // step 1: write control byte and the binary array size
-                const auto N = j.m_value.binary->size();
+                const auto N = j.m_data.m_value.binary->size();
                 if (N <= 0x17)
                 {
                     write_number(static_cast<std::uint8_t>(0x40 + N));
@@ -15270,7 +15395,7 @@ class binary_writer
 
                 // step 2: write each element
                 oa->write_characters(
-                    reinterpret_cast<const CharType*>(j.m_value.binary->data()),
+                    reinterpret_cast<const CharType*>(j.m_data.m_value.binary->data()),
                     N);
 
                 break;
@@ -15279,7 +15404,7 @@ class binary_writer
             case value_t::object:
             {
                 // step 1: write control byte and the object size
-                const auto N = j.m_value.object->size();
+                const auto N = j.m_data.m_value.object->size();
                 if (N <= 0x17)
                 {
                     write_number(static_cast<std::uint8_t>(0xA0 + N));
@@ -15308,7 +15433,7 @@ class binary_writer
                 // LCOV_EXCL_STOP
 
                 // step 2: write each element
-                for (const auto& el : *j.m_value.object)
+                for (const auto& el : *j.m_data.m_value.object)
                 {
                     write_cbor(el.first);
                     write_cbor(el.second);
@@ -15337,7 +15462,7 @@ class binary_writer
 
             case value_t::boolean: // true and false
             {
-                oa->write_character(j.m_value.boolean
+                oa->write_character(j.m_data.m_value.boolean
                                     ? to_char_type(0xC3)
                                     : to_char_type(0xC2));
                 break;
@@ -15345,75 +15470,75 @@ class binary_writer
 
             case value_t::number_integer:
             {
-                if (j.m_value.number_integer >= 0)
+                if (j.m_data.m_value.number_integer >= 0)
                 {
                     // MessagePack does not differentiate between positive
                     // signed integers and unsigned integers. Therefore, we used
                     // the code from the value_t::number_unsigned case here.
-                    if (j.m_value.number_unsigned < 128)
+                    if (j.m_data.m_value.number_unsigned < 128)
                     {
                         // positive fixnum
-                        write_number(static_cast<std::uint8_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint8_t>::max)())
+                    else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint8_t>::max)())
                     {
                         // uint 8
                         oa->write_character(to_char_type(0xCC));
-                        write_number(static_cast<std::uint8_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint16_t>::max)())
+                    else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint16_t>::max)())
                     {
                         // uint 16
                         oa->write_character(to_char_type(0xCD));
-                        write_number(static_cast<std::uint16_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::uint16_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint32_t>::max)())
+                    else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint32_t>::max)())
                     {
                         // uint 32
                         oa->write_character(to_char_type(0xCE));
-                        write_number(static_cast<std::uint32_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::uint32_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint64_t>::max)())
+                    else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint64_t>::max)())
                     {
                         // uint 64
                         oa->write_character(to_char_type(0xCF));
-                        write_number(static_cast<std::uint64_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::uint64_t>(j.m_data.m_value.number_integer));
                     }
                 }
                 else
                 {
-                    if (j.m_value.number_integer >= -32)
+                    if (j.m_data.m_value.number_integer >= -32)
                     {
                         // negative fixnum
-                        write_number(static_cast<std::int8_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::int8_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_integer >= (std::numeric_limits<std::int8_t>::min)() &&
-                             j.m_value.number_integer <= (std::numeric_limits<std::int8_t>::max)())
+                    else if (j.m_data.m_value.number_integer >= (std::numeric_limits<std::int8_t>::min)() &&
+                             j.m_data.m_value.number_integer <= (std::numeric_limits<std::int8_t>::max)())
                     {
                         // int 8
                         oa->write_character(to_char_type(0xD0));
-                        write_number(static_cast<std::int8_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::int8_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_integer >= (std::numeric_limits<std::int16_t>::min)() &&
-                             j.m_value.number_integer <= (std::numeric_limits<std::int16_t>::max)())
+                    else if (j.m_data.m_value.number_integer >= (std::numeric_limits<std::int16_t>::min)() &&
+                             j.m_data.m_value.number_integer <= (std::numeric_limits<std::int16_t>::max)())
                     {
                         // int 16
                         oa->write_character(to_char_type(0xD1));
-                        write_number(static_cast<std::int16_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::int16_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_integer >= (std::numeric_limits<std::int32_t>::min)() &&
-                             j.m_value.number_integer <= (std::numeric_limits<std::int32_t>::max)())
+                    else if (j.m_data.m_value.number_integer >= (std::numeric_limits<std::int32_t>::min)() &&
+                             j.m_data.m_value.number_integer <= (std::numeric_limits<std::int32_t>::max)())
                     {
                         // int 32
                         oa->write_character(to_char_type(0xD2));
-                        write_number(static_cast<std::int32_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::int32_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_integer >= (std::numeric_limits<std::int64_t>::min)() &&
-                             j.m_value.number_integer <= (std::numeric_limits<std::int64_t>::max)())
+                    else if (j.m_data.m_value.number_integer >= (std::numeric_limits<std::int64_t>::min)() &&
+                             j.m_data.m_value.number_integer <= (std::numeric_limits<std::int64_t>::max)())
                     {
                         // int 64
                         oa->write_character(to_char_type(0xD3));
-                        write_number(static_cast<std::int64_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::int64_t>(j.m_data.m_value.number_integer));
                     }
                 }
                 break;
@@ -15421,48 +15546,48 @@ class binary_writer
 
             case value_t::number_unsigned:
             {
-                if (j.m_value.number_unsigned < 128)
+                if (j.m_data.m_value.number_unsigned < 128)
                 {
                     // positive fixnum
-                    write_number(static_cast<std::uint8_t>(j.m_value.number_integer));
+                    write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_integer));
                 }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint8_t>::max)())
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint8_t>::max)())
                 {
                     // uint 8
                     oa->write_character(to_char_type(0xCC));
-                    write_number(static_cast<std::uint8_t>(j.m_value.number_integer));
+                    write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_integer));
                 }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint16_t>::max)())
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint16_t>::max)())
                 {
                     // uint 16
                     oa->write_character(to_char_type(0xCD));
-                    write_number(static_cast<std::uint16_t>(j.m_value.number_integer));
+                    write_number(static_cast<std::uint16_t>(j.m_data.m_value.number_integer));
                 }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint32_t>::max)())
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint32_t>::max)())
                 {
                     // uint 32
                     oa->write_character(to_char_type(0xCE));
-                    write_number(static_cast<std::uint32_t>(j.m_value.number_integer));
+                    write_number(static_cast<std::uint32_t>(j.m_data.m_value.number_integer));
                 }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint64_t>::max)())
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint64_t>::max)())
                 {
                     // uint 64
                     oa->write_character(to_char_type(0xCF));
-                    write_number(static_cast<std::uint64_t>(j.m_value.number_integer));
+                    write_number(static_cast<std::uint64_t>(j.m_data.m_value.number_integer));
                 }
                 break;
             }
 
             case value_t::number_float:
             {
-                write_compact_float(j.m_value.number_float, detail::input_format_t::msgpack);
+                write_compact_float(j.m_data.m_value.number_float, detail::input_format_t::msgpack);
                 break;
             }
 
             case value_t::string:
             {
                 // step 1: write control byte and the string length
-                const auto N = j.m_value.string->size();
+                const auto N = j.m_data.m_value.string->size();
                 if (N <= 31)
                 {
                     // fixstr
@@ -15489,15 +15614,15 @@ class binary_writer
 
                 // step 2: write the string
                 oa->write_characters(
-                    reinterpret_cast<const CharType*>(j.m_value.string->c_str()),
-                    j.m_value.string->size());
+                    reinterpret_cast<const CharType*>(j.m_data.m_value.string->c_str()),
+                    j.m_data.m_value.string->size());
                 break;
             }
 
             case value_t::array:
             {
                 // step 1: write control byte and the array size
-                const auto N = j.m_value.array->size();
+                const auto N = j.m_data.m_value.array->size();
                 if (N <= 15)
                 {
                     // fixarray
@@ -15517,7 +15642,7 @@ class binary_writer
                 }
 
                 // step 2: write each element
-                for (const auto& el : *j.m_value.array)
+                for (const auto& el : *j.m_data.m_value.array)
                 {
                     write_msgpack(el);
                 }
@@ -15528,10 +15653,10 @@ class binary_writer
             {
                 // step 0: determine if the binary type has a set subtype to
                 // determine whether or not to use the ext or fixext types
-                const bool use_ext = j.m_value.binary->has_subtype();
+                const bool use_ext = j.m_data.m_value.binary->has_subtype();
 
                 // step 1: write control byte and the byte string length
-                const auto N = j.m_value.binary->size();
+                const auto N = j.m_data.m_value.binary->size();
                 if (N <= (std::numeric_limits<std::uint8_t>::max)())
                 {
                     std::uint8_t output_type{};
@@ -15576,18 +15701,18 @@ class binary_writer
                 }
                 else if (N <= (std::numeric_limits<std::uint16_t>::max)())
                 {
-                    std::uint8_t output_type = use_ext
-                                               ? 0xC8 // ext 16
-                                               : 0xC5; // bin 16
+                    const std::uint8_t output_type = use_ext
+                                                     ? 0xC8 // ext 16
+                                                     : 0xC5; // bin 16
 
                     oa->write_character(to_char_type(output_type));
                     write_number(static_cast<std::uint16_t>(N));
                 }
                 else if (N <= (std::numeric_limits<std::uint32_t>::max)())
                 {
-                    std::uint8_t output_type = use_ext
-                                               ? 0xC9 // ext 32
-                                               : 0xC6; // bin 32
+                    const std::uint8_t output_type = use_ext
+                                                     ? 0xC9 // ext 32
+                                                     : 0xC6; // bin 32
 
                     oa->write_character(to_char_type(output_type));
                     write_number(static_cast<std::uint32_t>(N));
@@ -15596,12 +15721,12 @@ class binary_writer
                 // step 1.5: if this is an ext type, write the subtype
                 if (use_ext)
                 {
-                    write_number(static_cast<std::int8_t>(j.m_value.binary->subtype()));
+                    write_number(static_cast<std::int8_t>(j.m_data.m_value.binary->subtype()));
                 }
 
                 // step 2: write the byte string
                 oa->write_characters(
-                    reinterpret_cast<const CharType*>(j.m_value.binary->data()),
+                    reinterpret_cast<const CharType*>(j.m_data.m_value.binary->data()),
                     N);
 
                 break;
@@ -15610,7 +15735,7 @@ class binary_writer
             case value_t::object:
             {
                 // step 1: write control byte and the object size
-                const auto N = j.m_value.object->size();
+                const auto N = j.m_data.m_value.object->size();
                 if (N <= 15)
                 {
                     // fixmap
@@ -15630,7 +15755,7 @@ class binary_writer
                 }
 
                 // step 2: write each element
-                for (const auto& el : *j.m_value.object)
+                for (const auto& el : *j.m_data.m_value.object)
                 {
                     write_msgpack(el.first);
                     write_msgpack(el.second);
@@ -15670,7 +15795,7 @@ class binary_writer
             {
                 if (add_prefix)
                 {
-                    oa->write_character(j.m_value.boolean
+                    oa->write_character(j.m_data.m_value.boolean
                                         ? to_char_type('T')
                                         : to_char_type('F'));
                 }
@@ -15679,19 +15804,19 @@ class binary_writer
 
             case value_t::number_integer:
             {
-                write_number_with_ubjson_prefix(j.m_value.number_integer, add_prefix, use_bjdata);
+                write_number_with_ubjson_prefix(j.m_data.m_value.number_integer, add_prefix, use_bjdata);
                 break;
             }
 
             case value_t::number_unsigned:
             {
-                write_number_with_ubjson_prefix(j.m_value.number_unsigned, add_prefix, use_bjdata);
+                write_number_with_ubjson_prefix(j.m_data.m_value.number_unsigned, add_prefix, use_bjdata);
                 break;
             }
 
             case value_t::number_float:
             {
-                write_number_with_ubjson_prefix(j.m_value.number_float, add_prefix, use_bjdata);
+                write_number_with_ubjson_prefix(j.m_data.m_value.number_float, add_prefix, use_bjdata);
                 break;
             }
 
@@ -15701,10 +15826,10 @@ class binary_writer
                 {
                     oa->write_character(to_char_type('S'));
                 }
-                write_number_with_ubjson_prefix(j.m_value.string->size(), true, use_bjdata);
+                write_number_with_ubjson_prefix(j.m_data.m_value.string->size(), true, use_bjdata);
                 oa->write_characters(
-                    reinterpret_cast<const CharType*>(j.m_value.string->c_str()),
-                    j.m_value.string->size());
+                    reinterpret_cast<const CharType*>(j.m_data.m_value.string->c_str()),
+                    j.m_data.m_value.string->size());
                 break;
             }
 
@@ -15716,7 +15841,7 @@ class binary_writer
                 }
 
                 bool prefix_required = true;
-                if (use_type && !j.m_value.array->empty())
+                if (use_type && !j.m_data.m_value.array->empty())
                 {
                     JSON_ASSERT(use_count);
                     const CharType first_prefix = ubjson_prefix(j.front(), use_bjdata);
@@ -15739,10 +15864,10 @@ class binary_writer
                 if (use_count)
                 {
                     oa->write_character(to_char_type('#'));
-                    write_number_with_ubjson_prefix(j.m_value.array->size(), true, use_bjdata);
+                    write_number_with_ubjson_prefix(j.m_data.m_value.array->size(), true, use_bjdata);
                 }
 
-                for (const auto& el : *j.m_value.array)
+                for (const auto& el : *j.m_data.m_value.array)
                 {
                     write_ubjson(el, use_count, use_type, prefix_required, use_bjdata);
                 }
@@ -15762,7 +15887,7 @@ class binary_writer
                     oa->write_character(to_char_type('['));
                 }
 
-                if (use_type && !j.m_value.binary->empty())
+                if (use_type && !j.m_data.m_value.binary->empty())
                 {
                     JSON_ASSERT(use_count);
                     oa->write_character(to_char_type('$'));
@@ -15772,21 +15897,21 @@ class binary_writer
                 if (use_count)
                 {
                     oa->write_character(to_char_type('#'));
-                    write_number_with_ubjson_prefix(j.m_value.binary->size(), true, use_bjdata);
+                    write_number_with_ubjson_prefix(j.m_data.m_value.binary->size(), true, use_bjdata);
                 }
 
                 if (use_type)
                 {
                     oa->write_characters(
-                        reinterpret_cast<const CharType*>(j.m_value.binary->data()),
-                        j.m_value.binary->size());
+                        reinterpret_cast<const CharType*>(j.m_data.m_value.binary->data()),
+                        j.m_data.m_value.binary->size());
                 }
                 else
                 {
-                    for (size_t i = 0; i < j.m_value.binary->size(); ++i)
+                    for (size_t i = 0; i < j.m_data.m_value.binary->size(); ++i)
                     {
                         oa->write_character(to_char_type('U'));
-                        oa->write_character(j.m_value.binary->data()[i]);
+                        oa->write_character(j.m_data.m_value.binary->data()[i]);
                     }
                 }
 
@@ -15800,9 +15925,9 @@ class binary_writer
 
             case value_t::object:
             {
-                if (use_bjdata && j.m_value.object->size() == 3 && j.m_value.object->find("_ArrayType_") != j.m_value.object->end() && j.m_value.object->find("_ArraySize_") != j.m_value.object->end() && j.m_value.object->find("_ArrayData_") != j.m_value.object->end())
+                if (use_bjdata && j.m_data.m_value.object->size() == 3 && j.m_data.m_value.object->find("_ArrayType_") != j.m_data.m_value.object->end() && j.m_data.m_value.object->find("_ArraySize_") != j.m_data.m_value.object->end() && j.m_data.m_value.object->find("_ArrayData_") != j.m_data.m_value.object->end())
                 {
-                    if (!write_bjdata_ndarray(*j.m_value.object, use_count, use_type))  // decode bjdata ndarray in the JData format (https://github.com/NeuroJSON/jdata)
+                    if (!write_bjdata_ndarray(*j.m_data.m_value.object, use_count, use_type))  // decode bjdata ndarray in the JData format (https://github.com/NeuroJSON/jdata)
                     {
                         break;
                     }
@@ -15814,7 +15939,7 @@ class binary_writer
                 }
 
                 bool prefix_required = true;
-                if (use_type && !j.m_value.object->empty())
+                if (use_type && !j.m_data.m_value.object->empty())
                 {
                     JSON_ASSERT(use_count);
                     const CharType first_prefix = ubjson_prefix(j.front(), use_bjdata);
@@ -15837,10 +15962,10 @@ class binary_writer
                 if (use_count)
                 {
                     oa->write_character(to_char_type('#'));
-                    write_number_with_ubjson_prefix(j.m_value.object->size(), true, use_bjdata);
+                    write_number_with_ubjson_prefix(j.m_data.m_value.object->size(), true, use_bjdata);
                 }
 
-                for (const auto& el : *j.m_value.object)
+                for (const auto& el : *j.m_data.m_value.object)
                 {
                     write_number_with_ubjson_prefix(el.first.size(), true, use_bjdata);
                     oa->write_characters(
@@ -15990,19 +16115,19 @@ class binary_writer
     void write_bson_unsigned(const string_t& name,
                              const BasicJsonType& j)
     {
-        if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int32_t>::max)()))
+        if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int32_t>::max)()))
         {
             write_bson_entry_header(name, 0x10 /* int32 */);
-            write_number<std::int32_t>(static_cast<std::int32_t>(j.m_value.number_unsigned), true);
+            write_number<std::int32_t>(static_cast<std::int32_t>(j.m_data.m_value.number_unsigned), true);
         }
-        else if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int64_t>::max)()))
+        else if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int64_t>::max)()))
         {
             write_bson_entry_header(name, 0x12 /* int64 */);
-            write_number<std::int64_t>(static_cast<std::int64_t>(j.m_value.number_unsigned), true);
+            write_number<std::int64_t>(static_cast<std::int64_t>(j.m_data.m_value.number_unsigned), true);
         }
         else
         {
-            JSON_THROW(out_of_range::create(407, concat("integer number ", std::to_string(j.m_value.number_unsigned), " cannot be represented by BSON as it does not fit int64"), &j));
+            JSON_THROW(out_of_range::create(407, concat("integer number ", std::to_string(j.m_data.m_value.number_unsigned), " cannot be represented by BSON as it does not fit int64"), &j));
         }
     }
 
@@ -16083,13 +16208,13 @@ class binary_writer
         switch (j.type())
         {
             case value_t::object:
-                return header_size + calc_bson_object_size(*j.m_value.object);
+                return header_size + calc_bson_object_size(*j.m_data.m_value.object);
 
             case value_t::array:
-                return header_size + calc_bson_array_size(*j.m_value.array);
+                return header_size + calc_bson_array_size(*j.m_data.m_value.array);
 
             case value_t::binary:
-                return header_size + calc_bson_binary_size(*j.m_value.binary);
+                return header_size + calc_bson_binary_size(*j.m_data.m_value.binary);
 
             case value_t::boolean:
                 return header_size + 1ul;
@@ -16098,13 +16223,13 @@ class binary_writer
                 return header_size + 8ul;
 
             case value_t::number_integer:
-                return header_size + calc_bson_integer_size(j.m_value.number_integer);
+                return header_size + calc_bson_integer_size(j.m_data.m_value.number_integer);
 
             case value_t::number_unsigned:
-                return header_size + calc_bson_unsigned_size(j.m_value.number_unsigned);
+                return header_size + calc_bson_unsigned_size(j.m_data.m_value.number_unsigned);
 
             case value_t::string:
-                return header_size + calc_bson_string_size(*j.m_value.string);
+                return header_size + calc_bson_string_size(*j.m_data.m_value.string);
 
             case value_t::null:
                 return header_size + 0ul;
@@ -16130,28 +16255,28 @@ class binary_writer
         switch (j.type())
         {
             case value_t::object:
-                return write_bson_object_entry(name, *j.m_value.object);
+                return write_bson_object_entry(name, *j.m_data.m_value.object);
 
             case value_t::array:
-                return write_bson_array(name, *j.m_value.array);
+                return write_bson_array(name, *j.m_data.m_value.array);
 
             case value_t::binary:
-                return write_bson_binary(name, *j.m_value.binary);
+                return write_bson_binary(name, *j.m_data.m_value.binary);
 
             case value_t::boolean:
-                return write_bson_boolean(name, j.m_value.boolean);
+                return write_bson_boolean(name, j.m_data.m_value.boolean);
 
             case value_t::number_float:
-                return write_bson_double(name, j.m_value.number_float);
+                return write_bson_double(name, j.m_data.m_value.number_float);
 
             case value_t::number_integer:
-                return write_bson_integer(name, j.m_value.number_integer);
+                return write_bson_integer(name, j.m_data.m_value.number_integer);
 
             case value_t::number_unsigned:
                 return write_bson_unsigned(name, j);
 
             case value_t::string:
-                return write_bson_string(name, *j.m_value.string);
+                return write_bson_string(name, *j.m_data.m_value.string);
 
             case value_t::null:
                 return write_bson_null(name);
@@ -16173,8 +16298,8 @@ class binary_writer
     */
     static std::size_t calc_bson_object_size(const typename BasicJsonType::object_t& value)
     {
-        std::size_t document_size = std::accumulate(value.begin(), value.end(), static_cast<std::size_t>(0),
-                                    [](size_t result, const typename BasicJsonType::object_t::value_type & el)
+        const std::size_t document_size = std::accumulate(value.begin(), value.end(), static_cast<std::size_t>(0),
+                                          [](size_t result, const typename BasicJsonType::object_t::value_type & el)
         {
             return result += calc_bson_element_size(el.first, el.second);
         });
@@ -16424,35 +16549,35 @@ class binary_writer
                 return 'Z';
 
             case value_t::boolean:
-                return j.m_value.boolean ? 'T' : 'F';
+                return j.m_data.m_value.boolean ? 'T' : 'F';
 
             case value_t::number_integer:
             {
-                if ((std::numeric_limits<std::int8_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::int8_t>::max)())
+                if ((std::numeric_limits<std::int8_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::int8_t>::max)())
                 {
                     return 'i';
                 }
-                if ((std::numeric_limits<std::uint8_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::uint8_t>::max)())
+                if ((std::numeric_limits<std::uint8_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::uint8_t>::max)())
                 {
                     return 'U';
                 }
-                if ((std::numeric_limits<std::int16_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::int16_t>::max)())
+                if ((std::numeric_limits<std::int16_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::int16_t>::max)())
                 {
                     return 'I';
                 }
-                if (use_bjdata && ((std::numeric_limits<std::uint16_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::uint16_t>::max)()))
+                if (use_bjdata && ((std::numeric_limits<std::uint16_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::uint16_t>::max)()))
                 {
                     return 'u';
                 }
-                if ((std::numeric_limits<std::int32_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::int32_t>::max)())
+                if ((std::numeric_limits<std::int32_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::int32_t>::max)())
                 {
                     return 'l';
                 }
-                if (use_bjdata && ((std::numeric_limits<std::uint32_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::uint32_t>::max)()))
+                if (use_bjdata && ((std::numeric_limits<std::uint32_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::uint32_t>::max)()))
                 {
                     return 'm';
                 }
-                if ((std::numeric_limits<std::int64_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::int64_t>::max)())
+                if ((std::numeric_limits<std::int64_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::int64_t>::max)())
                 {
                     return 'L';
                 }
@@ -16462,35 +16587,35 @@ class binary_writer
 
             case value_t::number_unsigned:
             {
-                if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int8_t>::max)()))
+                if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int8_t>::max)()))
                 {
                     return 'i';
                 }
-                if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::uint8_t>::max)()))
+                if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::uint8_t>::max)()))
                 {
                     return 'U';
                 }
-                if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int16_t>::max)()))
+                if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int16_t>::max)()))
                 {
                     return 'I';
                 }
-                if (use_bjdata && j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::uint16_t>::max)()))
+                if (use_bjdata && j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::uint16_t>::max)()))
                 {
                     return 'u';
                 }
-                if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int32_t>::max)()))
+                if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int32_t>::max)()))
                 {
                     return 'l';
                 }
-                if (use_bjdata && j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::uint32_t>::max)()))
+                if (use_bjdata && j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::uint32_t>::max)()))
                 {
                     return 'm';
                 }
-                if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int64_t>::max)()))
+                if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int64_t>::max)()))
                 {
                     return 'L';
                 }
-                if (use_bjdata && j.m_value.number_unsigned <= (std::numeric_limits<std::uint64_t>::max)())
+                if (use_bjdata && j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint64_t>::max)())
                 {
                     return 'M';
                 }
@@ -16499,7 +16624,7 @@ class binary_writer
             }
 
             case value_t::number_float:
-                return get_ubjson_float_prefix(j.m_value.number_float);
+                return get_ubjson_float_prefix(j.m_data.m_value.number_float);
 
             case value_t::string:
                 return 'S';
@@ -16548,7 +16673,7 @@ class binary_writer
         std::size_t len = (value.at(key).empty() ? 0 : 1);
         for (const auto& el : value.at(key))
         {
-            len *= static_cast<std::size_t>(el.m_value.number_unsigned);
+            len *= static_cast<std::size_t>(el.m_data.m_value.number_unsigned);
         }
 
         key = "_ArrayData_";
@@ -16570,70 +16695,70 @@ class binary_writer
         {
             for (const auto& el : value.at(key))
             {
-                write_number(static_cast<std::uint8_t>(el.m_value.number_unsigned), true);
+                write_number(static_cast<std::uint8_t>(el.m_data.m_value.number_unsigned), true);
             }
         }
         else if (dtype == 'i')
         {
             for (const auto& el : value.at(key))
             {
-                write_number(static_cast<std::int8_t>(el.m_value.number_integer), true);
+                write_number(static_cast<std::int8_t>(el.m_data.m_value.number_integer), true);
             }
         }
         else if (dtype == 'u')
         {
             for (const auto& el : value.at(key))
             {
-                write_number(static_cast<std::uint16_t>(el.m_value.number_unsigned), true);
+                write_number(static_cast<std::uint16_t>(el.m_data.m_value.number_unsigned), true);
             }
         }
         else if (dtype == 'I')
         {
             for (const auto& el : value.at(key))
             {
-                write_number(static_cast<std::int16_t>(el.m_value.number_integer), true);
+                write_number(static_cast<std::int16_t>(el.m_data.m_value.number_integer), true);
             }
         }
         else if (dtype == 'm')
         {
             for (const auto& el : value.at(key))
             {
-                write_number(static_cast<std::uint32_t>(el.m_value.number_unsigned), true);
+                write_number(static_cast<std::uint32_t>(el.m_data.m_value.number_unsigned), true);
             }
         }
         else if (dtype == 'l')
         {
             for (const auto& el : value.at(key))
             {
-                write_number(static_cast<std::int32_t>(el.m_value.number_integer), true);
+                write_number(static_cast<std::int32_t>(el.m_data.m_value.number_integer), true);
             }
         }
         else if (dtype == 'M')
         {
             for (const auto& el : value.at(key))
             {
-                write_number(static_cast<std::uint64_t>(el.m_value.number_unsigned), true);
+                write_number(static_cast<std::uint64_t>(el.m_data.m_value.number_unsigned), true);
             }
         }
         else if (dtype == 'L')
         {
             for (const auto& el : value.at(key))
             {
-                write_number(static_cast<std::int64_t>(el.m_value.number_integer), true);
+                write_number(static_cast<std::int64_t>(el.m_data.m_value.number_integer), true);
             }
         }
         else if (dtype == 'd')
         {
             for (const auto& el : value.at(key))
             {
-                write_number(static_cast<float>(el.m_value.number_float), true);
+                write_number(static_cast<float>(el.m_data.m_value.number_float), true);
             }
         }
         else if (dtype == 'D')
         {
             for (const auto& el : value.at(key))
             {
-                write_number(static_cast<double>(el.m_value.number_float), true);
+                write_number(static_cast<double>(el.m_data.m_value.number_float), true);
             }
         }
         return false;
@@ -16757,11 +16882,11 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/output/serializer.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
 // SPDX-FileCopyrightText: 2008-2009 Björn Hoehrmann <bjoern@hoehrmann.de>
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -16782,11 +16907,11 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/conversions/to_chars.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
 // SPDX-FileCopyrightText: 2009 Florian Loitsch <https://florian.loitsch.com/>
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -17692,7 +17817,7 @@ void grisu2(char* buf, int& len, int& decimal_exponent, FloatType value)
     // NB: If the neighbors are computed for single-precision numbers, there is a single float
     //     (7.0385307e-26f) which can't be recovered using strtod. The resulting double precision
     //     value is off by 1 ulp.
-#if 0
+#if 0 // NOLINT(readability-avoid-unconditional-preprocessor-if)
     const boundaries w = compute_boundaries(static_cast<double>(value));
 #else
     const boundaries w = compute_boundaries(value);
@@ -17994,11 +18119,11 @@ class serializer
               const unsigned int indent_step,
               const unsigned int current_indent = 0)
     {
-        switch (val.m_type)
+        switch (val.m_data.m_type)
         {
             case value_t::object:
             {
-                if (val.m_value.object->empty())
+                if (val.m_data.m_value.object->empty())
                 {
                     o->write_characters("{}", 2);
                     return;
@@ -18016,8 +18141,8 @@ class serializer
                     }
 
                     // first n-1 elements
-                    auto i = val.m_value.object->cbegin();
-                    for (std::size_t cnt = 0; cnt < val.m_value.object->size() - 1; ++cnt, ++i)
+                    auto i = val.m_data.m_value.object->cbegin();
+                    for (std::size_t cnt = 0; cnt < val.m_data.m_value.object->size() - 1; ++cnt, ++i)
                     {
                         o->write_characters(indent_string.c_str(), new_indent);
                         o->write_character('\"');
@@ -18028,8 +18153,8 @@ class serializer
                     }
 
                     // last element
-                    JSON_ASSERT(i != val.m_value.object->cend());
-                    JSON_ASSERT(std::next(i) == val.m_value.object->cend());
+                    JSON_ASSERT(i != val.m_data.m_value.object->cend());
+                    JSON_ASSERT(std::next(i) == val.m_data.m_value.object->cend());
                     o->write_characters(indent_string.c_str(), new_indent);
                     o->write_character('\"');
                     dump_escaped(i->first, ensure_ascii);
@@ -18045,8 +18170,8 @@ class serializer
                     o->write_character('{');
 
                     // first n-1 elements
-                    auto i = val.m_value.object->cbegin();
-                    for (std::size_t cnt = 0; cnt < val.m_value.object->size() - 1; ++cnt, ++i)
+                    auto i = val.m_data.m_value.object->cbegin();
+                    for (std::size_t cnt = 0; cnt < val.m_data.m_value.object->size() - 1; ++cnt, ++i)
                     {
                         o->write_character('\"');
                         dump_escaped(i->first, ensure_ascii);
@@ -18056,8 +18181,8 @@ class serializer
                     }
 
                     // last element
-                    JSON_ASSERT(i != val.m_value.object->cend());
-                    JSON_ASSERT(std::next(i) == val.m_value.object->cend());
+                    JSON_ASSERT(i != val.m_data.m_value.object->cend());
+                    JSON_ASSERT(std::next(i) == val.m_data.m_value.object->cend());
                     o->write_character('\"');
                     dump_escaped(i->first, ensure_ascii);
                     o->write_characters("\":", 2);
@@ -18071,7 +18196,7 @@ class serializer
 
             case value_t::array:
             {
-                if (val.m_value.array->empty())
+                if (val.m_data.m_value.array->empty())
                 {
                     o->write_characters("[]", 2);
                     return;
@@ -18089,8 +18214,8 @@ class serializer
                     }
 
                     // first n-1 elements
-                    for (auto i = val.m_value.array->cbegin();
-                            i != val.m_value.array->cend() - 1; ++i)
+                    for (auto i = val.m_data.m_value.array->cbegin();
+                            i != val.m_data.m_value.array->cend() - 1; ++i)
                     {
                         o->write_characters(indent_string.c_str(), new_indent);
                         dump(*i, true, ensure_ascii, indent_step, new_indent);
@@ -18098,9 +18223,9 @@ class serializer
                     }
 
                     // last element
-                    JSON_ASSERT(!val.m_value.array->empty());
+                    JSON_ASSERT(!val.m_data.m_value.array->empty());
                     o->write_characters(indent_string.c_str(), new_indent);
-                    dump(val.m_value.array->back(), true, ensure_ascii, indent_step, new_indent);
+                    dump(val.m_data.m_value.array->back(), true, ensure_ascii, indent_step, new_indent);
 
                     o->write_character('\n');
                     o->write_characters(indent_string.c_str(), current_indent);
@@ -18111,16 +18236,16 @@ class serializer
                     o->write_character('[');
 
                     // first n-1 elements
-                    for (auto i = val.m_value.array->cbegin();
-                            i != val.m_value.array->cend() - 1; ++i)
+                    for (auto i = val.m_data.m_value.array->cbegin();
+                            i != val.m_data.m_value.array->cend() - 1; ++i)
                     {
                         dump(*i, false, ensure_ascii, indent_step, current_indent);
                         o->write_character(',');
                     }
 
                     // last element
-                    JSON_ASSERT(!val.m_value.array->empty());
-                    dump(val.m_value.array->back(), false, ensure_ascii, indent_step, current_indent);
+                    JSON_ASSERT(!val.m_data.m_value.array->empty());
+                    dump(val.m_data.m_value.array->back(), false, ensure_ascii, indent_step, current_indent);
 
                     o->write_character(']');
                 }
@@ -18131,7 +18256,7 @@ class serializer
             case value_t::string:
             {
                 o->write_character('\"');
-                dump_escaped(*val.m_value.string, ensure_ascii);
+                dump_escaped(*val.m_data.m_value.string, ensure_ascii);
                 o->write_character('\"');
                 return;
             }
@@ -18153,24 +18278,24 @@ class serializer
 
                     o->write_characters("\"bytes\": [", 10);
 
-                    if (!val.m_value.binary->empty())
+                    if (!val.m_data.m_value.binary->empty())
                     {
-                        for (auto i = val.m_value.binary->cbegin();
-                                i != val.m_value.binary->cend() - 1; ++i)
+                        for (auto i = val.m_data.m_value.binary->cbegin();
+                                i != val.m_data.m_value.binary->cend() - 1; ++i)
                         {
                             dump_integer(*i);
                             o->write_characters(", ", 2);
                         }
-                        dump_integer(val.m_value.binary->back());
+                        dump_integer(val.m_data.m_value.binary->back());
                     }
 
                     o->write_characters("],\n", 3);
                     o->write_characters(indent_string.c_str(), new_indent);
 
                     o->write_characters("\"subtype\": ", 11);
-                    if (val.m_value.binary->has_subtype())
+                    if (val.m_data.m_value.binary->has_subtype())
                     {
-                        dump_integer(val.m_value.binary->subtype());
+                        dump_integer(val.m_data.m_value.binary->subtype());
                     }
                     else
                     {
@@ -18184,21 +18309,21 @@ class serializer
                 {
                     o->write_characters("{\"bytes\":[", 10);
 
-                    if (!val.m_value.binary->empty())
+                    if (!val.m_data.m_value.binary->empty())
                     {
-                        for (auto i = val.m_value.binary->cbegin();
-                                i != val.m_value.binary->cend() - 1; ++i)
+                        for (auto i = val.m_data.m_value.binary->cbegin();
+                                i != val.m_data.m_value.binary->cend() - 1; ++i)
                         {
                             dump_integer(*i);
                             o->write_character(',');
                         }
-                        dump_integer(val.m_value.binary->back());
+                        dump_integer(val.m_data.m_value.binary->back());
                     }
 
                     o->write_characters("],\"subtype\":", 12);
-                    if (val.m_value.binary->has_subtype())
+                    if (val.m_data.m_value.binary->has_subtype())
                     {
-                        dump_integer(val.m_value.binary->subtype());
+                        dump_integer(val.m_data.m_value.binary->subtype());
                         o->write_character('}');
                     }
                     else
@@ -18211,7 +18336,7 @@ class serializer
 
             case value_t::boolean:
             {
-                if (val.m_value.boolean)
+                if (val.m_data.m_value.boolean)
                 {
                     o->write_characters("true", 4);
                 }
@@ -18224,19 +18349,19 @@ class serializer
 
             case value_t::number_integer:
             {
-                dump_integer(val.m_value.number_integer);
+                dump_integer(val.m_data.m_value.number_integer);
                 return;
             }
 
             case value_t::number_unsigned:
             {
-                dump_integer(val.m_value.number_unsigned);
+                dump_integer(val.m_data.m_value.number_unsigned);
                 return;
             }
 
             case value_t::number_float:
             {
-                dump_float(val.m_value.number_float);
+                dump_float(val.m_data.m_value.number_float);
                 return;
             }
 
@@ -18810,8 +18935,8 @@ class serializer
                 ? (byte & 0x3fu) | (codep << 6u)
                 : (0xFFu >> type) & (byte);
 
-        std::size_t index = 256u + static_cast<size_t>(state) * 16u + static_cast<size_t>(type);
-        JSON_ASSERT(index < 400);
+        const std::size_t index = 256u + static_cast<size_t>(state) * 16u + static_cast<size_t>(type);
+        JSON_ASSERT(index < utf8d.size());
         state = utf8d[index];
         return state;
     }
@@ -18878,10 +19003,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/ordered_map.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -18998,7 +19123,7 @@ template <class Key, class T, class IgnoredLess = std::less<Key>,
 
     template<class KeyType, detail::enable_if_t<
                  detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
-    T & at(KeyType && key)
+    T & at(KeyType && key) // NOLINT(cppcoreguidelines-missing-std-forward)
     {
         for (auto it = this->begin(); it != this->end(); ++it)
         {
@@ -19026,7 +19151,7 @@ template <class Key, class T, class IgnoredLess = std::less<Key>,
 
     template<class KeyType, detail::enable_if_t<
                  detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
-    const T & at(KeyType && key) const
+    const T & at(KeyType && key) const // NOLINT(cppcoreguidelines-missing-std-forward)
     {
         for (auto it = this->begin(); it != this->end(); ++it)
         {
@@ -19060,7 +19185,7 @@ template <class Key, class T, class IgnoredLess = std::less<Key>,
 
     template<class KeyType, detail::enable_if_t<
                  detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
-    size_type erase(KeyType && key)
+    size_type erase(KeyType && key) // NOLINT(cppcoreguidelines-missing-std-forward)
     {
         for (auto it = this->begin(); it != this->end(); ++it)
         {
@@ -19151,7 +19276,7 @@ template <class Key, class T, class IgnoredLess = std::less<Key>,
 
     template<class KeyType, detail::enable_if_t<
                  detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
-    size_type count(KeyType && key) const
+    size_type count(KeyType && key) const // NOLINT(cppcoreguidelines-missing-std-forward)
     {
         for (auto it = this->begin(); it != this->end(); ++it)
         {
@@ -19177,7 +19302,7 @@ template <class Key, class T, class IgnoredLess = std::less<Key>,
 
     template<class KeyType, detail::enable_if_t<
                  detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
-    iterator find(KeyType && key)
+    iterator find(KeyType && key) // NOLINT(cppcoreguidelines-missing-std-forward)
     {
         for (auto it = this->begin(); it != this->end(); ++it)
         {
@@ -19240,7 +19365,9 @@ NLOHMANN_JSON_NAMESPACE_END
 
 
 #if defined(JSON_HAS_CPP_17)
-    #include <any>
+    #if JSON_HAS_STATIC_RTTI
+        #include <any>
+    #endif
     #include <string_view>
 #endif
 
@@ -19271,6 +19398,7 @@ The invariants are checked by member function assert_invariant().
 */
 NLOHMANN_BASIC_JSON_TPL_DECLARATION
 class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-special-member-functions)
+    : public ::nlohmann::detail::json_base_class<CustomBaseClass>
 {
   private:
     template<detail::value_t> friend struct detail::external_constructor;
@@ -19297,6 +19425,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// workaround type for MSVC
     using basic_json_t = NLOHMANN_BASIC_JSON_TPL;
+    using json_base_class_t = ::nlohmann::detail::json_base_class<CustomBaseClass>;
 
   JSON_PRIVATE_UNLESS_TESTED:
     // convenience aliases for types residing in namespace detail;
@@ -19368,7 +19497,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @}
 
-
     /////////////////////
     // container types //
     /////////////////////
@@ -19410,7 +19538,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @}
 
-
     /// @brief returns the allocator associated with the container
     /// @sa https://json.nlohmann.me/api/basic_json/get_allocator/
     static allocator_type get_allocator()
@@ -19425,7 +19552,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     {
         basic_json result;
 
-        result["copyright"] = "(C) 2013-2022 Niels Lohmann";
+        result["copyright"] = "(C) 2013-2023 Niels Lohmann";
         result["name"] = "JSON for Modern C++";
         result["url"] = "https://github.com/nlohmann/json";
         result["version"]["string"] =
@@ -19473,7 +19600,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         result["compiler"] = {{"family", "unknown"}, {"version", "unknown"}};
 #endif
 
-
 #if defined(_MSVC_LANG)
         result["compiler"]["c++"] = std::to_string(_MSVC_LANG);
 #elif defined(__cplusplus)
@@ -19484,7 +19610,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         return result;
     }
 
-
     ///////////////////////////
     // JSON value data types //
     ///////////////////////////
@@ -19692,7 +19817,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                     object = nullptr;  // silence warning, see #821
                     if (JSON_HEDLEY_UNLIKELY(t == value_t::null))
                     {
-                        JSON_THROW(other_error::create(500, "961c151d2e87f2686a955a9be24d316f1362bf21 3.11.2", nullptr)); // LCOV_EXCL_LINE
+                        JSON_THROW(other_error::create(500, "961c151d2e87f2686a955a9be24d316f1362bf21 3.11.3", nullptr)); // LCOV_EXCL_LINE
                     }
                     break;
                 }
@@ -19731,6 +19856,16 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
         void destroy(value_t t)
         {
+            if (
+                (t == value_t::object && object == nullptr) ||
+                (t == value_t::array && array == nullptr) ||
+                (t == value_t::string && string == nullptr) ||
+                (t == value_t::binary && binary == nullptr)
+            )
+            {
+                //not initialized (e.g. due to exception in the ctor)
+                return;
+            }
             if (t == value_t::array || t == value_t::object)
             {
                 // flatten the current json_value to a heap-allocated stack
@@ -19761,18 +19896,18 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                     // its children to the stack to be processed later
                     if (current_item.is_array())
                     {
-                        std::move(current_item.m_value.array->begin(), current_item.m_value.array->end(), std::back_inserter(stack));
+                        std::move(current_item.m_data.m_value.array->begin(), current_item.m_data.m_value.array->end(), std::back_inserter(stack));
 
-                        current_item.m_value.array->clear();
+                        current_item.m_data.m_value.array->clear();
                     }
                     else if (current_item.is_object())
                     {
-                        for (auto&& it : *current_item.m_value.object)
+                        for (auto&& it : *current_item.m_data.m_value.object)
                         {
                             stack.push_back(std::move(it.second));
                         }
 
-                        current_item.m_value.object->clear();
+                        current_item.m_data.m_value.object->clear();
                     }
 
                     // it's now safe that current_item get destructed
@@ -19849,10 +19984,10 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     */
     void assert_invariant(bool check_parents = true) const noexcept
     {
-        JSON_ASSERT(m_type != value_t::object || m_value.object != nullptr);
-        JSON_ASSERT(m_type != value_t::array || m_value.array != nullptr);
-        JSON_ASSERT(m_type != value_t::string || m_value.string != nullptr);
-        JSON_ASSERT(m_type != value_t::binary || m_value.binary != nullptr);
+        JSON_ASSERT(m_data.m_type != value_t::object || m_data.m_value.object != nullptr);
+        JSON_ASSERT(m_data.m_type != value_t::array || m_data.m_value.array != nullptr);
+        JSON_ASSERT(m_data.m_type != value_t::string || m_data.m_value.string != nullptr);
+        JSON_ASSERT(m_data.m_type != value_t::binary || m_data.m_value.binary != nullptr);
 
 #if JSON_DIAGNOSTICS
         JSON_TRY
@@ -19871,11 +20006,11 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     void set_parents()
     {
 #if JSON_DIAGNOSTICS
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::array:
             {
-                for (auto& element : *m_value.array)
+                for (auto& element : *m_data.m_value.array)
                 {
                     element.m_parent = this;
                 }
@@ -19884,7 +20019,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
             case value_t::object:
             {
-                for (auto& element : *m_value.object)
+                for (auto& element : *m_data.m_value.object)
                 {
                     element.second.m_parent = this;
                 }
@@ -19925,7 +20060,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         {
             // see https://github.com/nlohmann/json/issues/2838
             JSON_ASSERT(type() == value_t::array);
-            if (JSON_HEDLEY_UNLIKELY(m_value.array->capacity() != old_capacity))
+            if (JSON_HEDLEY_UNLIKELY(m_data.m_value.array->capacity() != old_capacity))
             {
                 // capacity has changed: update all parents
                 set_parents();
@@ -19981,7 +20116,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// @brief create an empty value with a given type
     /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
     basic_json(const value_t v)
-        : m_type(v), m_value(v)
+        : m_data(v)
     {
         assert_invariant();
     }
@@ -20055,12 +20190,12 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                 *this = nullptr;
                 break;
             case value_t::discarded:
-                m_type = value_t::discarded;
+                m_data.m_type = value_t::discarded;
                 break;
             default:            // LCOV_EXCL_LINE
                 JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
         }
-        JSON_ASSERT(m_type == val.type());
+        JSON_ASSERT(m_data.m_type == val.type());
         set_parents();
         assert_invariant();
     }
@@ -20076,7 +20211,10 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         bool is_an_object = std::all_of(init.begin(), init.end(),
                                         [](const detail::json_ref<basic_json>& element_ref)
         {
-            return element_ref->is_array() && element_ref->size() == 2 && (*element_ref)[0].is_string();
+            // The cast is to ensure op[size_type] is called, bearing in mind size_type may not be int;
+            // (many string types can be constructed from 0 via its null-pointer guise, so we get a
+            // broken call to op[key_type], the wrong semantics and a 4804 warning on Windows)
+            return element_ref->is_array() && element_ref->size() == 2 && (*element_ref)[static_cast<size_type>(0)].is_string();
         });
 
         // adjust type if type deduction is not wanted
@@ -20098,22 +20236,22 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         if (is_an_object)
         {
             // the initializer list is a list of pairs -> create object
-            m_type = value_t::object;
-            m_value = value_t::object;
+            m_data.m_type = value_t::object;
+            m_data.m_value = value_t::object;
 
             for (auto& element_ref : init)
             {
                 auto element = element_ref.moved_or_copied();
-                m_value.object->emplace(
-                    std::move(*((*element.m_value.array)[0].m_value.string)),
-                    std::move((*element.m_value.array)[1]));
+                m_data.m_value.object->emplace(
+                    std::move(*((*element.m_data.m_value.array)[0].m_data.m_value.string)),
+                    std::move((*element.m_data.m_value.array)[1]));
             }
         }
         else
         {
             // the initializer list describes an array -> create array
-            m_type = value_t::array;
-            m_value.array = create<array_t>(init.begin(), init.end());
+            m_data.m_type = value_t::array;
+            m_data.m_value.array = create<array_t>(init.begin(), init.end());
         }
 
         set_parents();
@@ -20126,8 +20264,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     static basic_json binary(const typename binary_t::container_type& init)
     {
         auto res = basic_json();
-        res.m_type = value_t::binary;
-        res.m_value = init;
+        res.m_data.m_type = value_t::binary;
+        res.m_data.m_value = init;
         return res;
     }
 
@@ -20137,8 +20275,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     static basic_json binary(const typename binary_t::container_type& init, typename binary_t::subtype_type subtype)
     {
         auto res = basic_json();
-        res.m_type = value_t::binary;
-        res.m_value = binary_t(init, subtype);
+        res.m_data.m_type = value_t::binary;
+        res.m_data.m_value = binary_t(init, subtype);
         return res;
     }
 
@@ -20148,8 +20286,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     static basic_json binary(typename binary_t::container_type&& init)
     {
         auto res = basic_json();
-        res.m_type = value_t::binary;
-        res.m_value = std::move(init);
+        res.m_data.m_type = value_t::binary;
+        res.m_data.m_value = std::move(init);
         return res;
     }
 
@@ -20159,8 +20297,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     static basic_json binary(typename binary_t::container_type&& init, typename binary_t::subtype_type subtype)
     {
         auto res = basic_json();
-        res.m_type = value_t::binary;
-        res.m_value = binary_t(std::move(init), subtype);
+        res.m_data.m_type = value_t::binary;
+        res.m_data.m_value = binary_t(std::move(init), subtype);
         return res;
     }
 
@@ -20182,10 +20320,9 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @brief construct an array with count copies of given value
     /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
-    basic_json(size_type cnt, const basic_json& val)
-        : m_type(value_t::array)
+    basic_json(size_type cnt, const basic_json& val):
+        m_data{cnt, val}
     {
-        m_value.array = create<array_t>(cnt, val);
         set_parents();
         assert_invariant();
     }
@@ -20207,10 +20344,10 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         }
 
         // copy type from first iterator
-        m_type = first.m_object->m_type;
+        m_data.m_type = first.m_object->m_data.m_type;
 
         // check if iterator range is complete for primitive values
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::boolean:
             case value_t::number_float:
@@ -20235,55 +20372,55 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                 break;
         }
 
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::number_integer:
             {
-                m_value.number_integer = first.m_object->m_value.number_integer;
+                m_data.m_value.number_integer = first.m_object->m_data.m_value.number_integer;
                 break;
             }
 
             case value_t::number_unsigned:
             {
-                m_value.number_unsigned = first.m_object->m_value.number_unsigned;
+                m_data.m_value.number_unsigned = first.m_object->m_data.m_value.number_unsigned;
                 break;
             }
 
             case value_t::number_float:
             {
-                m_value.number_float = first.m_object->m_value.number_float;
+                m_data.m_value.number_float = first.m_object->m_data.m_value.number_float;
                 break;
             }
 
             case value_t::boolean:
             {
-                m_value.boolean = first.m_object->m_value.boolean;
+                m_data.m_value.boolean = first.m_object->m_data.m_value.boolean;
                 break;
             }
 
             case value_t::string:
             {
-                m_value = *first.m_object->m_value.string;
+                m_data.m_value = *first.m_object->m_data.m_value.string;
                 break;
             }
 
             case value_t::object:
             {
-                m_value.object = create<object_t>(first.m_it.object_iterator,
-                                                  last.m_it.object_iterator);
+                m_data.m_value.object = create<object_t>(first.m_it.object_iterator,
+                                        last.m_it.object_iterator);
                 break;
             }
 
             case value_t::array:
             {
-                m_value.array = create<array_t>(first.m_it.array_iterator,
-                                                last.m_it.array_iterator);
+                m_data.m_value.array = create<array_t>(first.m_it.array_iterator,
+                                                       last.m_it.array_iterator);
                 break;
             }
 
             case value_t::binary:
             {
-                m_value = *first.m_object->m_value.binary;
+                m_data.m_value = *first.m_object->m_data.m_value.binary;
                 break;
             }
 
@@ -20297,7 +20434,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         assert_invariant();
     }
 
-
     ///////////////////////////////////////
     // other constructors and destructor //
     ///////////////////////////////////////
@@ -20310,58 +20446,59 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// @brief copy constructor
     /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
     basic_json(const basic_json& other)
-        : m_type(other.m_type)
+        : json_base_class_t(other)
     {
+        m_data.m_type = other.m_data.m_type;
         // check of passed value is valid
         other.assert_invariant();
 
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::object:
             {
-                m_value = *other.m_value.object;
+                m_data.m_value = *other.m_data.m_value.object;
                 break;
             }
 
             case value_t::array:
             {
-                m_value = *other.m_value.array;
+                m_data.m_value = *other.m_data.m_value.array;
                 break;
             }
 
             case value_t::string:
             {
-                m_value = *other.m_value.string;
+                m_data.m_value = *other.m_data.m_value.string;
                 break;
             }
 
             case value_t::boolean:
             {
-                m_value = other.m_value.boolean;
+                m_data.m_value = other.m_data.m_value.boolean;
                 break;
             }
 
             case value_t::number_integer:
             {
-                m_value = other.m_value.number_integer;
+                m_data.m_value = other.m_data.m_value.number_integer;
                 break;
             }
 
             case value_t::number_unsigned:
             {
-                m_value = other.m_value.number_unsigned;
+                m_data.m_value = other.m_data.m_value.number_unsigned;
                 break;
             }
 
             case value_t::number_float:
             {
-                m_value = other.m_value.number_float;
+                m_data.m_value = other.m_data.m_value.number_float;
                 break;
             }
 
             case value_t::binary:
             {
-                m_value = *other.m_value.binary;
+                m_data.m_value = *other.m_data.m_value.binary;
                 break;
             }
 
@@ -20378,15 +20515,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// @brief move constructor
     /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
     basic_json(basic_json&& other) noexcept
-        : m_type(std::move(other.m_type)),
-          m_value(std::move(other.m_value))
+        : json_base_class_t(std::forward<json_base_class_t>(other)),
+          m_data(std::move(other.m_data))
     {
         // check that passed value is valid
         other.assert_invariant(false);
 
         // invalidate payload
-        other.m_type = value_t::null;
-        other.m_value = {};
+        other.m_data.m_type = value_t::null;
+        other.m_data.m_value = {};
 
         set_parents();
         assert_invariant();
@@ -20398,15 +20535,17 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         std::is_nothrow_move_constructible<value_t>::value&&
         std::is_nothrow_move_assignable<value_t>::value&&
         std::is_nothrow_move_constructible<json_value>::value&&
-        std::is_nothrow_move_assignable<json_value>::value
+        std::is_nothrow_move_assignable<json_value>::value&&
+        std::is_nothrow_move_assignable<json_base_class_t>::value
     )
     {
         // check that passed value is valid
         other.assert_invariant();
 
         using std::swap;
-        swap(m_type, other.m_type);
-        swap(m_value, other.m_value);
+        swap(m_data.m_type, other.m_data.m_type);
+        swap(m_data.m_value, other.m_data.m_value);
+        json_base_class_t::operator=(std::move(other));
 
         set_parents();
         assert_invariant();
@@ -20418,7 +20557,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     ~basic_json() noexcept
     {
         assert_invariant(false);
-        m_value.destroy(m_type);
     }
 
     /// @}
@@ -20458,7 +20596,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// @sa https://json.nlohmann.me/api/basic_json/type/
     constexpr value_t type() const noexcept
     {
-        return m_type;
+        return m_data.m_type;
     }
 
     /// @brief return whether type is primitive
@@ -20479,14 +20617,14 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// @sa https://json.nlohmann.me/api/basic_json/is_null/
     constexpr bool is_null() const noexcept
     {
-        return m_type == value_t::null;
+        return m_data.m_type == value_t::null;
     }
 
     /// @brief return whether value is a boolean
     /// @sa https://json.nlohmann.me/api/basic_json/is_boolean/
     constexpr bool is_boolean() const noexcept
     {
-        return m_type == value_t::boolean;
+        return m_data.m_type == value_t::boolean;
     }
 
     /// @brief return whether value is a number
@@ -20500,63 +20638,63 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// @sa https://json.nlohmann.me/api/basic_json/is_number_integer/
     constexpr bool is_number_integer() const noexcept
     {
-        return m_type == value_t::number_integer || m_type == value_t::number_unsigned;
+        return m_data.m_type == value_t::number_integer || m_data.m_type == value_t::number_unsigned;
     }
 
     /// @brief return whether value is an unsigned integer number
     /// @sa https://json.nlohmann.me/api/basic_json/is_number_unsigned/
     constexpr bool is_number_unsigned() const noexcept
     {
-        return m_type == value_t::number_unsigned;
+        return m_data.m_type == value_t::number_unsigned;
     }
 
     /// @brief return whether value is a floating-point number
     /// @sa https://json.nlohmann.me/api/basic_json/is_number_float/
     constexpr bool is_number_float() const noexcept
     {
-        return m_type == value_t::number_float;
+        return m_data.m_type == value_t::number_float;
     }
 
     /// @brief return whether value is an object
     /// @sa https://json.nlohmann.me/api/basic_json/is_object/
     constexpr bool is_object() const noexcept
     {
-        return m_type == value_t::object;
+        return m_data.m_type == value_t::object;
     }
 
     /// @brief return whether value is an array
     /// @sa https://json.nlohmann.me/api/basic_json/is_array/
     constexpr bool is_array() const noexcept
     {
-        return m_type == value_t::array;
+        return m_data.m_type == value_t::array;
     }
 
     /// @brief return whether value is a string
     /// @sa https://json.nlohmann.me/api/basic_json/is_string/
     constexpr bool is_string() const noexcept
     {
-        return m_type == value_t::string;
+        return m_data.m_type == value_t::string;
     }
 
     /// @brief return whether value is a binary array
     /// @sa https://json.nlohmann.me/api/basic_json/is_binary/
     constexpr bool is_binary() const noexcept
     {
-        return m_type == value_t::binary;
+        return m_data.m_type == value_t::binary;
     }
 
     /// @brief return whether value is discarded
     /// @sa https://json.nlohmann.me/api/basic_json/is_discarded/
     constexpr bool is_discarded() const noexcept
     {
-        return m_type == value_t::discarded;
+        return m_data.m_type == value_t::discarded;
     }
 
     /// @brief return the type of the JSON value (implicit)
     /// @sa https://json.nlohmann.me/api/basic_json/operator_value_t/
     constexpr operator value_t() const noexcept
     {
-        return m_type;
+        return m_data.m_type;
     }
 
     /// @}
@@ -20571,7 +20709,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     {
         if (JSON_HEDLEY_LIKELY(is_boolean()))
         {
-            return m_value.boolean;
+            return m_data.m_value.boolean;
         }
 
         JSON_THROW(type_error::create(302, detail::concat("type must be boolean, but is ", type_name()), this));
@@ -20580,97 +20718,97 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// get a pointer to the value (object)
     object_t* get_impl_ptr(object_t* /*unused*/) noexcept
     {
-        return is_object() ? m_value.object : nullptr;
+        return is_object() ? m_data.m_value.object : nullptr;
     }
 
     /// get a pointer to the value (object)
     constexpr const object_t* get_impl_ptr(const object_t* /*unused*/) const noexcept
     {
-        return is_object() ? m_value.object : nullptr;
+        return is_object() ? m_data.m_value.object : nullptr;
     }
 
     /// get a pointer to the value (array)
     array_t* get_impl_ptr(array_t* /*unused*/) noexcept
     {
-        return is_array() ? m_value.array : nullptr;
+        return is_array() ? m_data.m_value.array : nullptr;
     }
 
     /// get a pointer to the value (array)
     constexpr const array_t* get_impl_ptr(const array_t* /*unused*/) const noexcept
     {
-        return is_array() ? m_value.array : nullptr;
+        return is_array() ? m_data.m_value.array : nullptr;
     }
 
     /// get a pointer to the value (string)
     string_t* get_impl_ptr(string_t* /*unused*/) noexcept
     {
-        return is_string() ? m_value.string : nullptr;
+        return is_string() ? m_data.m_value.string : nullptr;
     }
 
     /// get a pointer to the value (string)
     constexpr const string_t* get_impl_ptr(const string_t* /*unused*/) const noexcept
     {
-        return is_string() ? m_value.string : nullptr;
+        return is_string() ? m_data.m_value.string : nullptr;
     }
 
     /// get a pointer to the value (boolean)
     boolean_t* get_impl_ptr(boolean_t* /*unused*/) noexcept
     {
-        return is_boolean() ? &m_value.boolean : nullptr;
+        return is_boolean() ? &m_data.m_value.boolean : nullptr;
     }
 
     /// get a pointer to the value (boolean)
     constexpr const boolean_t* get_impl_ptr(const boolean_t* /*unused*/) const noexcept
     {
-        return is_boolean() ? &m_value.boolean : nullptr;
+        return is_boolean() ? &m_data.m_value.boolean : nullptr;
     }
 
     /// get a pointer to the value (integer number)
     number_integer_t* get_impl_ptr(number_integer_t* /*unused*/) noexcept
     {
-        return is_number_integer() ? &m_value.number_integer : nullptr;
+        return is_number_integer() ? &m_data.m_value.number_integer : nullptr;
     }
 
     /// get a pointer to the value (integer number)
     constexpr const number_integer_t* get_impl_ptr(const number_integer_t* /*unused*/) const noexcept
     {
-        return is_number_integer() ? &m_value.number_integer : nullptr;
+        return is_number_integer() ? &m_data.m_value.number_integer : nullptr;
     }
 
     /// get a pointer to the value (unsigned number)
     number_unsigned_t* get_impl_ptr(number_unsigned_t* /*unused*/) noexcept
     {
-        return is_number_unsigned() ? &m_value.number_unsigned : nullptr;
+        return is_number_unsigned() ? &m_data.m_value.number_unsigned : nullptr;
     }
 
     /// get a pointer to the value (unsigned number)
     constexpr const number_unsigned_t* get_impl_ptr(const number_unsigned_t* /*unused*/) const noexcept
     {
-        return is_number_unsigned() ? &m_value.number_unsigned : nullptr;
+        return is_number_unsigned() ? &m_data.m_value.number_unsigned : nullptr;
     }
 
     /// get a pointer to the value (floating-point number)
     number_float_t* get_impl_ptr(number_float_t* /*unused*/) noexcept
     {
-        return is_number_float() ? &m_value.number_float : nullptr;
+        return is_number_float() ? &m_data.m_value.number_float : nullptr;
     }
 
     /// get a pointer to the value (floating-point number)
     constexpr const number_float_t* get_impl_ptr(const number_float_t* /*unused*/) const noexcept
     {
-        return is_number_float() ? &m_value.number_float : nullptr;
+        return is_number_float() ? &m_data.m_value.number_float : nullptr;
     }
 
     /// get a pointer to the value (binary)
     binary_t* get_impl_ptr(binary_t* /*unused*/) noexcept
     {
-        return is_binary() ? m_value.binary : nullptr;
+        return is_binary() ? m_data.m_value.binary : nullptr;
     }
 
     /// get a pointer to the value (binary)
     constexpr const binary_t* get_impl_ptr(const binary_t* /*unused*/) const noexcept
     {
-        return is_binary() ? m_value.binary : nullptr;
+        return is_binary() ? m_data.m_value.binary : nullptr;
     }
 
     /*!
@@ -21053,7 +21191,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 #if defined(JSON_HAS_CPP_17) && (defined(__GNUC__) || (defined(_MSC_VER) && _MSC_VER >= 1910 && _MSC_VER <= 1914))
                                                 detail::negation<std::is_same<ValueType, std::string_view>>,
 #endif
-#if defined(JSON_HAS_CPP_17)
+#if defined(JSON_HAS_CPP_17) && JSON_HAS_STATIC_RTTI
                                                 detail::negation<std::is_same<ValueType, std::any>>,
 #endif
                                                 detail::is_detected_lazy<detail::get_template_function, const basic_json_t&, ValueType>
@@ -21090,7 +21228,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @}
 
-
     ////////////////////
     // element access //
     ////////////////////
@@ -21108,7 +21245,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         {
             JSON_TRY
             {
-                return set_parent(m_value.array->at(idx));
+                return set_parent(m_data.m_value.array->at(idx));
             }
             JSON_CATCH (std::out_of_range&)
             {
@@ -21131,7 +21268,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         {
             JSON_TRY
             {
-                return m_value.array->at(idx);
+                return m_data.m_value.array->at(idx);
             }
             JSON_CATCH (std::out_of_range&)
             {
@@ -21155,8 +21292,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this));
         }
 
-        auto it = m_value.object->find(key);
-        if (it == m_value.object->end())
+        auto it = m_data.m_value.object->find(key);
+        if (it == m_data.m_value.object->end())
         {
             JSON_THROW(out_of_range::create(403, detail::concat("key '", key, "' not found"), this));
         }
@@ -21175,8 +21312,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this));
         }
 
-        auto it = m_value.object->find(std::forward<KeyType>(key));
-        if (it == m_value.object->end())
+        auto it = m_data.m_value.object->find(std::forward<KeyType>(key));
+        if (it == m_data.m_value.object->end())
         {
             JSON_THROW(out_of_range::create(403, detail::concat("key '", string_t(std::forward<KeyType>(key)), "' not found"), this));
         }
@@ -21193,8 +21330,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this));
         }
 
-        auto it = m_value.object->find(key);
-        if (it == m_value.object->end())
+        auto it = m_data.m_value.object->find(key);
+        if (it == m_data.m_value.object->end())
         {
             JSON_THROW(out_of_range::create(403, detail::concat("key '", key, "' not found"), this));
         }
@@ -21213,8 +21350,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this));
         }
 
-        auto it = m_value.object->find(std::forward<KeyType>(key));
-        if (it == m_value.object->end())
+        auto it = m_data.m_value.object->find(std::forward<KeyType>(key));
+        if (it == m_data.m_value.object->end())
         {
             JSON_THROW(out_of_range::create(403, detail::concat("key '", string_t(std::forward<KeyType>(key)), "' not found"), this));
         }
@@ -21228,8 +21365,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // implicitly convert null value to an empty array
         if (is_null())
         {
-            m_type = value_t::array;
-            m_value.array = create<array_t>();
+            m_data.m_type = value_t::array;
+            m_data.m_value.array = create<array_t>();
             assert_invariant();
         }
 
@@ -21237,17 +21374,17 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         if (JSON_HEDLEY_LIKELY(is_array()))
         {
             // fill up array with null values if given idx is outside range
-            if (idx >= m_value.array->size())
+            if (idx >= m_data.m_value.array->size())
             {
 #if JSON_DIAGNOSTICS
                 // remember array size & capacity before resizing
-                const auto old_size = m_value.array->size();
-                const auto old_capacity = m_value.array->capacity();
+                const auto old_size = m_data.m_value.array->size();
+                const auto old_capacity = m_data.m_value.array->capacity();
 #endif
-                m_value.array->resize(idx + 1);
+                m_data.m_value.array->resize(idx + 1);
 
 #if JSON_DIAGNOSTICS
-                if (JSON_HEDLEY_UNLIKELY(m_value.array->capacity() != old_capacity))
+                if (JSON_HEDLEY_UNLIKELY(m_data.m_value.array->capacity() != old_capacity))
                 {
                     // capacity has changed: update all parents
                     set_parents();
@@ -21261,7 +21398,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                 assert_invariant();
             }
 
-            return m_value.array->operator[](idx);
+            return m_data.m_value.array->operator[](idx);
         }
 
         JSON_THROW(type_error::create(305, detail::concat("cannot use operator[] with a numeric argument with ", type_name()), this));
@@ -21274,7 +21411,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // const operator[] only works for arrays
         if (JSON_HEDLEY_LIKELY(is_array()))
         {
-            return m_value.array->operator[](idx);
+            return m_data.m_value.array->operator[](idx);
         }
 
         JSON_THROW(type_error::create(305, detail::concat("cannot use operator[] with a numeric argument with ", type_name()), this));
@@ -21287,15 +21424,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // implicitly convert null value to an empty object
         if (is_null())
         {
-            m_type = value_t::object;
-            m_value.object = create<object_t>();
+            m_data.m_type = value_t::object;
+            m_data.m_value.object = create<object_t>();
             assert_invariant();
         }
 
         // operator[] only works for objects
         if (JSON_HEDLEY_LIKELY(is_object()))
         {
-            auto result = m_value.object->emplace(std::move(key), nullptr);
+            auto result = m_data.m_value.object->emplace(std::move(key), nullptr);
             return set_parent(result.first->second);
         }
 
@@ -21309,8 +21446,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // const operator[] only works for objects
         if (JSON_HEDLEY_LIKELY(is_object()))
         {
-            auto it = m_value.object->find(key);
-            JSON_ASSERT(it != m_value.object->end());
+            auto it = m_data.m_value.object->find(key);
+            JSON_ASSERT(it != m_data.m_value.object->end());
             return it->second;
         }
 
@@ -21340,15 +21477,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // implicitly convert null value to an empty object
         if (is_null())
         {
-            m_type = value_t::object;
-            m_value.object = create<object_t>();
+            m_data.m_type = value_t::object;
+            m_data.m_value.object = create<object_t>();
             assert_invariant();
         }
 
         // operator[] only works for objects
         if (JSON_HEDLEY_LIKELY(is_object()))
         {
-            auto result = m_value.object->emplace(std::forward<KeyType>(key), nullptr);
+            auto result = m_data.m_value.object->emplace(std::forward<KeyType>(key), nullptr);
             return set_parent(result.first->second);
         }
 
@@ -21364,8 +21501,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // const operator[] only works for objects
         if (JSON_HEDLEY_LIKELY(is_object()))
         {
-            auto it = m_value.object->find(std::forward<KeyType>(key));
-            JSON_ASSERT(it != m_value.object->end());
+            auto it = m_data.m_value.object->find(std::forward<KeyType>(key));
+            JSON_ASSERT(it != m_data.m_value.object->end());
             return it->second;
         }
 
@@ -21602,7 +21739,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
         IteratorType result = end();
 
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::boolean:
             case value_t::number_float:
@@ -21619,32 +21756,32 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                 if (is_string())
                 {
                     AllocatorType<string_t> alloc;
-                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_value.string);
-                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_value.string, 1);
-                    m_value.string = nullptr;
+                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_data.m_value.string);
+                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_data.m_value.string, 1);
+                    m_data.m_value.string = nullptr;
                 }
                 else if (is_binary())
                 {
                     AllocatorType<binary_t> alloc;
-                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_value.binary);
-                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_value.binary, 1);
-                    m_value.binary = nullptr;
+                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_data.m_value.binary);
+                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_data.m_value.binary, 1);
+                    m_data.m_value.binary = nullptr;
                 }
 
-                m_type = value_t::null;
+                m_data.m_type = value_t::null;
                 assert_invariant();
                 break;
             }
 
             case value_t::object:
             {
-                result.m_it.object_iterator = m_value.object->erase(pos.m_it.object_iterator);
+                result.m_it.object_iterator = m_data.m_value.object->erase(pos.m_it.object_iterator);
                 break;
             }
 
             case value_t::array:
             {
-                result.m_it.array_iterator = m_value.array->erase(pos.m_it.array_iterator);
+                result.m_it.array_iterator = m_data.m_value.array->erase(pos.m_it.array_iterator);
                 break;
             }
 
@@ -21672,7 +21809,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
         IteratorType result = end();
 
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::boolean:
             case value_t::number_float:
@@ -21690,33 +21827,33 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                 if (is_string())
                 {
                     AllocatorType<string_t> alloc;
-                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_value.string);
-                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_value.string, 1);
-                    m_value.string = nullptr;
+                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_data.m_value.string);
+                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_data.m_value.string, 1);
+                    m_data.m_value.string = nullptr;
                 }
                 else if (is_binary())
                 {
                     AllocatorType<binary_t> alloc;
-                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_value.binary);
-                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_value.binary, 1);
-                    m_value.binary = nullptr;
+                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_data.m_value.binary);
+                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_data.m_value.binary, 1);
+                    m_data.m_value.binary = nullptr;
                 }
 
-                m_type = value_t::null;
+                m_data.m_type = value_t::null;
                 assert_invariant();
                 break;
             }
 
             case value_t::object:
             {
-                result.m_it.object_iterator = m_value.object->erase(first.m_it.object_iterator,
+                result.m_it.object_iterator = m_data.m_value.object->erase(first.m_it.object_iterator,
                                               last.m_it.object_iterator);
                 break;
             }
 
             case value_t::array:
             {
-                result.m_it.array_iterator = m_value.array->erase(first.m_it.array_iterator,
+                result.m_it.array_iterator = m_data.m_value.array->erase(first.m_it.array_iterator,
                                              last.m_it.array_iterator);
                 break;
             }
@@ -21741,7 +21878,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             JSON_THROW(type_error::create(307, detail::concat("cannot use erase() with ", type_name()), this));
         }
 
-        return m_value.object->erase(std::forward<KeyType>(key));
+        return m_data.m_value.object->erase(std::forward<KeyType>(key));
     }
 
     template < typename KeyType, detail::enable_if_t <
@@ -21754,10 +21891,10 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             JSON_THROW(type_error::create(307, detail::concat("cannot use erase() with ", type_name()), this));
         }
 
-        const auto it = m_value.object->find(std::forward<KeyType>(key));
-        if (it != m_value.object->end())
+        const auto it = m_data.m_value.object->find(std::forward<KeyType>(key));
+        if (it != m_data.m_value.object->end())
         {
-            m_value.object->erase(it);
+            m_data.m_value.object->erase(it);
             return 1;
         }
         return 0;
@@ -21795,7 +21932,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                 JSON_THROW(out_of_range::create(401, detail::concat("array index ", std::to_string(idx), " is out of range"), this));
             }
 
-            m_value.array->erase(m_value.array->begin() + static_cast<difference_type>(idx));
+            m_data.m_value.array->erase(m_data.m_value.array->begin() + static_cast<difference_type>(idx));
         }
         else
         {
@@ -21805,7 +21942,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @}
 
-
     ////////////
     // lookup //
     ////////////
@@ -21821,7 +21957,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
         if (is_object())
         {
-            result.m_it.object_iterator = m_value.object->find(key);
+            result.m_it.object_iterator = m_data.m_value.object->find(key);
         }
 
         return result;
@@ -21835,7 +21971,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
         if (is_object())
         {
-            result.m_it.object_iterator = m_value.object->find(key);
+            result.m_it.object_iterator = m_data.m_value.object->find(key);
         }
 
         return result;
@@ -21851,7 +21987,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
         if (is_object())
         {
-            result.m_it.object_iterator = m_value.object->find(std::forward<KeyType>(key));
+            result.m_it.object_iterator = m_data.m_value.object->find(std::forward<KeyType>(key));
         }
 
         return result;
@@ -21867,7 +22003,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
         if (is_object())
         {
-            result.m_it.object_iterator = m_value.object->find(std::forward<KeyType>(key));
+            result.m_it.object_iterator = m_data.m_value.object->find(std::forward<KeyType>(key));
         }
 
         return result;
@@ -21878,7 +22014,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     size_type count(const typename object_t::key_type& key) const
     {
         // return 0 for all nonobject types
-        return is_object() ? m_value.object->count(key) : 0;
+        return is_object() ? m_data.m_value.object->count(key) : 0;
     }
 
     /// @brief returns the number of occurrences of a key in a JSON object
@@ -21888,14 +22024,14 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     size_type count(KeyType && key) const
     {
         // return 0 for all nonobject types
-        return is_object() ? m_value.object->count(std::forward<KeyType>(key)) : 0;
+        return is_object() ? m_data.m_value.object->count(std::forward<KeyType>(key)) : 0;
     }
 
     /// @brief check the existence of an element in a JSON object
     /// @sa https://json.nlohmann.me/api/basic_json/contains/
     bool contains(const typename object_t::key_type& key) const
     {
-        return is_object() && m_value.object->find(key) != m_value.object->end();
+        return is_object() && m_data.m_value.object->find(key) != m_data.m_value.object->end();
     }
 
     /// @brief check the existence of an element in a JSON object
@@ -21904,7 +22040,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                  detail::is_usable_as_basic_json_key_type<basic_json_t, KeyType>::value, int> = 0>
     bool contains(KeyType && key) const
     {
-        return is_object() && m_value.object->find(std::forward<KeyType>(key)) != m_value.object->end();
+        return is_object() && m_data.m_value.object->find(std::forward<KeyType>(key)) != m_data.m_value.object->end();
     }
 
     /// @brief check the existence of an element in a JSON object given a JSON pointer
@@ -21923,7 +22059,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @}
 
-
     ///////////////
     // iterators //
     ///////////////
@@ -22062,7 +22197,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @}
 
-
     //////////////
     // capacity //
     //////////////
@@ -22074,7 +22208,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// @sa https://json.nlohmann.me/api/basic_json/empty/
     bool empty() const noexcept
     {
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::null:
             {
@@ -22085,13 +22219,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             case value_t::array:
             {
                 // delegate call to array_t::empty()
-                return m_value.array->empty();
+                return m_data.m_value.array->empty();
             }
 
             case value_t::object:
             {
                 // delegate call to object_t::empty()
-                return m_value.object->empty();
+                return m_data.m_value.object->empty();
             }
 
             case value_t::string:
@@ -22113,7 +22247,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// @sa https://json.nlohmann.me/api/basic_json/size/
     size_type size() const noexcept
     {
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::null:
             {
@@ -22124,13 +22258,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             case value_t::array:
             {
                 // delegate call to array_t::size()
-                return m_value.array->size();
+                return m_data.m_value.array->size();
             }
 
             case value_t::object:
             {
                 // delegate call to object_t::size()
-                return m_value.object->size();
+                return m_data.m_value.object->size();
             }
 
             case value_t::string:
@@ -22152,18 +22286,18 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// @sa https://json.nlohmann.me/api/basic_json/max_size/
     size_type max_size() const noexcept
     {
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::array:
             {
                 // delegate call to array_t::max_size()
-                return m_value.array->max_size();
+                return m_data.m_value.array->max_size();
             }
 
             case value_t::object:
             {
                 // delegate call to object_t::max_size()
-                return m_value.object->max_size();
+                return m_data.m_value.object->max_size();
             }
 
             case value_t::null:
@@ -22184,7 +22318,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @}
 
-
     ///////////////
     // modifiers //
     ///////////////
@@ -22196,53 +22329,53 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// @sa https://json.nlohmann.me/api/basic_json/clear/
     void clear() noexcept
     {
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::number_integer:
             {
-                m_value.number_integer = 0;
+                m_data.m_value.number_integer = 0;
                 break;
             }
 
             case value_t::number_unsigned:
             {
-                m_value.number_unsigned = 0;
+                m_data.m_value.number_unsigned = 0;
                 break;
             }
 
             case value_t::number_float:
             {
-                m_value.number_float = 0.0;
+                m_data.m_value.number_float = 0.0;
                 break;
             }
 
             case value_t::boolean:
             {
-                m_value.boolean = false;
+                m_data.m_value.boolean = false;
                 break;
             }
 
             case value_t::string:
             {
-                m_value.string->clear();
+                m_data.m_value.string->clear();
                 break;
             }
 
             case value_t::binary:
             {
-                m_value.binary->clear();
+                m_data.m_value.binary->clear();
                 break;
             }
 
             case value_t::array:
             {
-                m_value.array->clear();
+                m_data.m_value.array->clear();
                 break;
             }
 
             case value_t::object:
             {
-                m_value.object->clear();
+                m_data.m_value.object->clear();
                 break;
             }
 
@@ -22266,15 +22399,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // transform null object into an array
         if (is_null())
         {
-            m_type = value_t::array;
-            m_value = value_t::array;
+            m_data.m_type = value_t::array;
+            m_data.m_value = value_t::array;
             assert_invariant();
         }
 
         // add element to array (move semantics)
-        const auto old_capacity = m_value.array->capacity();
-        m_value.array->push_back(std::move(val));
-        set_parent(m_value.array->back(), old_capacity);
+        const auto old_capacity = m_data.m_value.array->capacity();
+        m_data.m_value.array->push_back(std::move(val));
+        set_parent(m_data.m_value.array->back(), old_capacity);
         // if val is moved from, basic_json move constructor marks it null, so we do not call the destructor
     }
 
@@ -22299,15 +22432,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // transform null object into an array
         if (is_null())
         {
-            m_type = value_t::array;
-            m_value = value_t::array;
+            m_data.m_type = value_t::array;
+            m_data.m_value = value_t::array;
             assert_invariant();
         }
 
         // add element to array
-        const auto old_capacity = m_value.array->capacity();
-        m_value.array->push_back(val);
-        set_parent(m_value.array->back(), old_capacity);
+        const auto old_capacity = m_data.m_value.array->capacity();
+        m_data.m_value.array->push_back(val);
+        set_parent(m_data.m_value.array->back(), old_capacity);
     }
 
     /// @brief add an object to an array
@@ -22331,13 +22464,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // transform null object into an object
         if (is_null())
         {
-            m_type = value_t::object;
-            m_value = value_t::object;
+            m_data.m_type = value_t::object;
+            m_data.m_value = value_t::object;
             assert_invariant();
         }
 
         // add element to object
-        auto res = m_value.object->insert(val);
+        auto res = m_data.m_value.object->insert(val);
         set_parent(res.first->second);
     }
 
@@ -22387,15 +22520,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // transform null object into an array
         if (is_null())
         {
-            m_type = value_t::array;
-            m_value = value_t::array;
+            m_data.m_type = value_t::array;
+            m_data.m_value = value_t::array;
             assert_invariant();
         }
 
         // add element to array (perfect forwarding)
-        const auto old_capacity = m_value.array->capacity();
-        m_value.array->emplace_back(std::forward<Args>(args)...);
-        return set_parent(m_value.array->back(), old_capacity);
+        const auto old_capacity = m_data.m_value.array->capacity();
+        m_data.m_value.array->emplace_back(std::forward<Args>(args)...);
+        return set_parent(m_data.m_value.array->back(), old_capacity);
     }
 
     /// @brief add an object to an object if key does not exist
@@ -22412,13 +22545,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // transform null object into an object
         if (is_null())
         {
-            m_type = value_t::object;
-            m_value = value_t::object;
+            m_data.m_type = value_t::object;
+            m_data.m_value = value_t::object;
             assert_invariant();
         }
 
         // add element to array (perfect forwarding)
-        auto res = m_value.object->emplace(std::forward<Args>(args)...);
+        auto res = m_data.m_value.object->emplace(std::forward<Args>(args)...);
         set_parent(res.first->second);
 
         // create result iterator and set iterator to the result of emplace
@@ -22436,14 +22569,14 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     iterator insert_iterator(const_iterator pos, Args&& ... args)
     {
         iterator result(this);
-        JSON_ASSERT(m_value.array != nullptr);
+        JSON_ASSERT(m_data.m_value.array != nullptr);
 
-        auto insert_pos = std::distance(m_value.array->begin(), pos.m_it.array_iterator);
-        m_value.array->insert(pos.m_it.array_iterator, std::forward<Args>(args)...);
-        result.m_it.array_iterator = m_value.array->begin() + insert_pos;
+        auto insert_pos = std::distance(m_data.m_value.array->begin(), pos.m_it.array_iterator);
+        m_data.m_value.array->insert(pos.m_it.array_iterator, std::forward<Args>(args)...);
+        result.m_it.array_iterator = m_data.m_value.array->begin() + insert_pos;
 
         // This could have been written as:
-        // result.m_it.array_iterator = m_value.array->insert(pos.m_it.array_iterator, cnt, val);
+        // result.m_it.array_iterator = m_data.m_value.array->insert(pos.m_it.array_iterator, cnt, val);
         // but the return value of insert is missing in GCC 4.8, so it is written this way instead.
 
         set_parents();
@@ -22570,7 +22703,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             JSON_THROW(invalid_iterator::create(202, "iterators first and last must point to objects", this));
         }
 
-        m_value.object->insert(first.m_it.object_iterator, last.m_it.object_iterator);
+        m_data.m_value.object->insert(first.m_it.object_iterator, last.m_it.object_iterator);
     }
 
     /// @brief updates a JSON object from another object, overwriting existing keys
@@ -22587,8 +22720,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // implicitly convert null value to an empty object
         if (is_null())
         {
-            m_type = value_t::object;
-            m_value.object = create<object_t>();
+            m_data.m_type = value_t::object;
+            m_data.m_value.object = create<object_t>();
             assert_invariant();
         }
 
@@ -22613,16 +22746,16 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         {
             if (merge_objects && it.value().is_object())
             {
-                auto it2 = m_value.object->find(it.key());
-                if (it2 != m_value.object->end())
+                auto it2 = m_data.m_value.object->find(it.key());
+                if (it2 != m_data.m_value.object->end())
                 {
                     it2->second.update(it.value(), true);
                     continue;
                 }
             }
-            m_value.object->operator[](it.key()) = it.value();
+            m_data.m_value.object->operator[](it.key()) = it.value();
 #if JSON_DIAGNOSTICS
-            m_value.object->operator[](it.key()).m_parent = this;
+            m_data.m_value.object->operator[](it.key()).m_parent = this;
 #endif
         }
     }
@@ -22632,12 +22765,12 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     void swap(reference other) noexcept (
         std::is_nothrow_move_constructible<value_t>::value&&
         std::is_nothrow_move_assignable<value_t>::value&&
-        std::is_nothrow_move_constructible<json_value>::value&&
+        std::is_nothrow_move_constructible<json_value>::value&& // NOLINT(cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
         std::is_nothrow_move_assignable<json_value>::value
     )
     {
-        std::swap(m_type, other.m_type);
-        std::swap(m_value, other.m_value);
+        std::swap(m_data.m_type, other.m_data.m_type);
+        std::swap(m_data.m_value, other.m_data.m_value);
 
         set_parents();
         other.set_parents();
@@ -22649,7 +22782,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     friend void swap(reference left, reference right) noexcept (
         std::is_nothrow_move_constructible<value_t>::value&&
         std::is_nothrow_move_assignable<value_t>::value&&
-        std::is_nothrow_move_constructible<json_value>::value&&
+        std::is_nothrow_move_constructible<json_value>::value&& // NOLINT(cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
         std::is_nothrow_move_assignable<json_value>::value
     )
     {
@@ -22658,13 +22791,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @brief exchanges the values
     /// @sa https://json.nlohmann.me/api/basic_json/swap/
-    void swap(array_t& other) // NOLINT(bugprone-exception-escape)
+    void swap(array_t& other) // NOLINT(bugprone-exception-escape,cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
     {
         // swap only works for arrays
         if (JSON_HEDLEY_LIKELY(is_array()))
         {
             using std::swap;
-            swap(*(m_value.array), other);
+            swap(*(m_data.m_value.array), other);
         }
         else
         {
@@ -22674,13 +22807,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @brief exchanges the values
     /// @sa https://json.nlohmann.me/api/basic_json/swap/
-    void swap(object_t& other) // NOLINT(bugprone-exception-escape)
+    void swap(object_t& other) // NOLINT(bugprone-exception-escape,cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
     {
         // swap only works for objects
         if (JSON_HEDLEY_LIKELY(is_object()))
         {
             using std::swap;
-            swap(*(m_value.object), other);
+            swap(*(m_data.m_value.object), other);
         }
         else
         {
@@ -22690,13 +22823,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @brief exchanges the values
     /// @sa https://json.nlohmann.me/api/basic_json/swap/
-    void swap(string_t& other) // NOLINT(bugprone-exception-escape)
+    void swap(string_t& other) // NOLINT(bugprone-exception-escape,cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
     {
         // swap only works for strings
         if (JSON_HEDLEY_LIKELY(is_string()))
         {
             using std::swap;
-            swap(*(m_value.string), other);
+            swap(*(m_data.m_value.string), other);
         }
         else
         {
@@ -22706,13 +22839,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @brief exchanges the values
     /// @sa https://json.nlohmann.me/api/basic_json/swap/
-    void swap(binary_t& other) // NOLINT(bugprone-exception-escape)
+    void swap(binary_t& other) // NOLINT(bugprone-exception-escape,cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
     {
         // swap only works for strings
         if (JSON_HEDLEY_LIKELY(is_binary()))
         {
             using std::swap;
-            swap(*(m_value.binary), other);
+            swap(*(m_data.m_value.binary), other);
         }
         else
         {
@@ -22728,7 +22861,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         if (JSON_HEDLEY_LIKELY(is_binary()))
         {
             using std::swap;
-            swap(*(m_value.binary), other);
+            swap(*(m_data.m_value.binary), other);
         }
         else
         {
@@ -22756,31 +22889,31 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         switch (lhs_type)                                                                                \
         {                                                                                                \
             case value_t::array:                                                                         \
-                return (*lhs.m_value.array) op (*rhs.m_value.array);                                     \
+                return (*lhs.m_data.m_value.array) op (*rhs.m_data.m_value.array);                                     \
                 \
             case value_t::object:                                                                        \
-                return (*lhs.m_value.object) op (*rhs.m_value.object);                                   \
+                return (*lhs.m_data.m_value.object) op (*rhs.m_data.m_value.object);                                   \
                 \
             case value_t::null:                                                                          \
                 return (null_result);                                                                    \
                 \
             case value_t::string:                                                                        \
-                return (*lhs.m_value.string) op (*rhs.m_value.string);                                   \
+                return (*lhs.m_data.m_value.string) op (*rhs.m_data.m_value.string);                                   \
                 \
             case value_t::boolean:                                                                       \
-                return (lhs.m_value.boolean) op (rhs.m_value.boolean);                                   \
+                return (lhs.m_data.m_value.boolean) op (rhs.m_data.m_value.boolean);                                   \
                 \
             case value_t::number_integer:                                                                \
-                return (lhs.m_value.number_integer) op (rhs.m_value.number_integer);                     \
+                return (lhs.m_data.m_value.number_integer) op (rhs.m_data.m_value.number_integer);                     \
                 \
             case value_t::number_unsigned:                                                               \
-                return (lhs.m_value.number_unsigned) op (rhs.m_value.number_unsigned);                   \
+                return (lhs.m_data.m_value.number_unsigned) op (rhs.m_data.m_value.number_unsigned);                   \
                 \
             case value_t::number_float:                                                                  \
-                return (lhs.m_value.number_float) op (rhs.m_value.number_float);                         \
+                return (lhs.m_data.m_value.number_float) op (rhs.m_data.m_value.number_float);                         \
                 \
             case value_t::binary:                                                                        \
-                return (*lhs.m_value.binary) op (*rhs.m_value.binary);                                   \
+                return (*lhs.m_data.m_value.binary) op (*rhs.m_data.m_value.binary);                                   \
                 \
             case value_t::discarded:                                                                     \
             default:                                                                                     \
@@ -22789,27 +22922,27 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     }                                                                                                    \
     else if (lhs_type == value_t::number_integer && rhs_type == value_t::number_float)                   \
     {                                                                                                    \
-        return static_cast<number_float_t>(lhs.m_value.number_integer) op rhs.m_value.number_float;      \
+        return static_cast<number_float_t>(lhs.m_data.m_value.number_integer) op rhs.m_data.m_value.number_float;      \
     }                                                                                                    \
     else if (lhs_type == value_t::number_float && rhs_type == value_t::number_integer)                   \
     {                                                                                                    \
-        return lhs.m_value.number_float op static_cast<number_float_t>(rhs.m_value.number_integer);      \
+        return lhs.m_data.m_value.number_float op static_cast<number_float_t>(rhs.m_data.m_value.number_integer);      \
     }                                                                                                    \
     else if (lhs_type == value_t::number_unsigned && rhs_type == value_t::number_float)                  \
     {                                                                                                    \
-        return static_cast<number_float_t>(lhs.m_value.number_unsigned) op rhs.m_value.number_float;     \
+        return static_cast<number_float_t>(lhs.m_data.m_value.number_unsigned) op rhs.m_data.m_value.number_float;     \
     }                                                                                                    \
     else if (lhs_type == value_t::number_float && rhs_type == value_t::number_unsigned)                  \
     {                                                                                                    \
-        return lhs.m_value.number_float op static_cast<number_float_t>(rhs.m_value.number_unsigned);     \
+        return lhs.m_data.m_value.number_float op static_cast<number_float_t>(rhs.m_data.m_value.number_unsigned);     \
     }                                                                                                    \
     else if (lhs_type == value_t::number_unsigned && rhs_type == value_t::number_integer)                \
     {                                                                                                    \
-        return static_cast<number_integer_t>(lhs.m_value.number_unsigned) op rhs.m_value.number_integer; \
+        return static_cast<number_integer_t>(lhs.m_data.m_value.number_unsigned) op rhs.m_data.m_value.number_integer; \
     }                                                                                                    \
     else if (lhs_type == value_t::number_integer && rhs_type == value_t::number_unsigned)                \
     {                                                                                                    \
-        return lhs.m_value.number_integer op static_cast<number_integer_t>(rhs.m_value.number_unsigned); \
+        return lhs.m_data.m_value.number_integer op static_cast<number_integer_t>(rhs.m_data.m_value.number_unsigned); \
     }                                                                                                    \
     else if(compares_unordered(lhs, rhs))\
     {\
@@ -22826,8 +22959,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     // an operation is computed as an odd number of inverses of others
     static bool compares_unordered(const_reference lhs, const_reference rhs, bool inverse = false) noexcept
     {
-        if ((lhs.is_number_float() && std::isnan(lhs.m_value.number_float) && rhs.is_number())
-                || (rhs.is_number_float() && std::isnan(rhs.m_value.number_float) && lhs.is_number()))
+        if ((lhs.is_number_float() && std::isnan(lhs.m_data.m_value.number_float) && rhs.is_number())
+                || (rhs.is_number_float() && std::isnan(rhs.m_data.m_value.number_float) && lhs.is_number()))
         {
             return true;
         }
@@ -23171,7 +23304,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 #endif  // JSON_NO_IO
     /// @}
 
-
     /////////////////////
     // deserialization //
     /////////////////////
@@ -23328,7 +23460,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     JSON_HEDLEY_RETURNS_NON_NULL
     const char* type_name() const noexcept
     {
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::null:
                 return "null";
@@ -23352,17 +23484,43 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         }
     }
 
-
   JSON_PRIVATE_UNLESS_TESTED:
     //////////////////////
     // member variables //
     //////////////////////
 
-    /// the type of the current element
-    value_t m_type = value_t::null;
+    struct data
+    {
+        /// the type of the current element
+        value_t m_type = value_t::null;
 
-    /// the value of the current element
-    json_value m_value = {};
+        /// the value of the current element
+        json_value m_value = {};
+
+        data(const value_t v)
+            : m_type(v), m_value(v)
+        {
+        }
+
+        data(size_type cnt, const basic_json& val)
+            : m_type(value_t::array)
+        {
+            m_value.array = create<array_t>(cnt, val);
+        }
+
+        data() noexcept = default;
+        data(data&&) noexcept = default;
+        data(const data&) noexcept = delete;
+        data& operator=(data&&) noexcept = delete;
+        data& operator=(const data&) noexcept = delete;
+
+        ~data() noexcept
+        {
+            m_value.destroy(m_type);
+        }
+    };
+
+    data m_data = {};
 
 #if JSON_DIAGNOSTICS
     /// a pointer to a parent value (for debugging purposes)
@@ -23543,7 +23701,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         return from_cbor(ptr, ptr + len, strict, allow_exceptions, tag_handler);
     }
 
-
     JSON_HEDLEY_WARN_UNUSED_RESULT
     JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_cbor(ptr, ptr + len))
     static basic_json from_cbor(detail::span_input_adapter&& i,
@@ -23667,7 +23824,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         return res ? result : basic_json(value_t::discarded);
     }
 
-
     /// @brief create a JSON value from an input in BJData format
     /// @sa https://json.nlohmann.me/api/basic_json/from_bjdata/
     template<typename InputType>
@@ -23890,7 +24046,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             }
 
             // make sure the top element of the pointer exists
-            json_pointer top_pointer = ptr.top();
+            json_pointer const top_pointer = ptr.top();
             if (top_pointer != ptr)
             {
                 result.at(top_pointer);
@@ -23902,7 +24058,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             // parent must exist when performing patch add per RFC6902 specs
             basic_json& parent = result.at(ptr);
 
-            switch (parent.m_type)
+            switch (parent.m_data.m_type)
             {
                 case value_t::null:
                 case value_t::object:
@@ -23948,7 +24104,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         };
 
         // wrapper for "remove" operation; remove value at ptr
-        const auto operation_remove = [this, &result](json_pointer & ptr)
+        const auto operation_remove = [this, & result](json_pointer & ptr)
         {
             // get reference to parent of JSON pointer ptr
             const auto last_path = ptr.back();
@@ -23991,13 +24147,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                                           bool string_type) -> basic_json &
             {
                 // find value
-                auto it = val.m_value.object->find(member);
+                auto it = val.m_data.m_value.object->find(member);
 
                 // context-sensitive error message
-                const auto error_msg = (op == "op") ? "operation" : detail::concat("operation '", op, '\'');
+                const auto error_msg = (op == "op") ? "operation" : detail::concat("operation '", op, '\''); // NOLINT(bugprone-unused-local-non-trivial-variable)
 
                 // check if desired value is present
-                if (JSON_HEDLEY_UNLIKELY(it == val.m_value.object->end()))
+                if (JSON_HEDLEY_UNLIKELY(it == val.m_data.m_value.object->end()))
                 {
                     // NOLINTNEXTLINE(performance-inefficient-string-concatenation)
                     JSON_THROW(parse_error::create(105, 0, detail::concat(error_msg, " must have member '", member, "'"), &val));
@@ -24052,7 +24208,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                     json_pointer from_ptr(from_path);
 
                     // the "from" location must exist - use at()
-                    basic_json v = result.at(from_ptr);
+                    basic_json const v = result.at(from_ptr);
 
                     // The move operation is functionally identical to a
                     // "remove" operation on the "from" location, followed
@@ -24069,7 +24225,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                     const json_pointer from_ptr(from_path);
 
                     // the "from" location must exist - use at()
-                    basic_json v = result.at(from_ptr);
+                    basic_json const v = result.at(from_ptr);
 
                     // The copy is functionally identical to an "add"
                     // operation at the target location using the value
@@ -24311,7 +24467,11 @@ inline namespace json_literals
 /// @brief user-defined string literal for JSON values
 /// @sa https://json.nlohmann.me/api/basic_json/operator_literal_json/
 JSON_HEDLEY_NON_NULL(1)
-inline nlohmann::json operator "" _json(const char* s, std::size_t n)
+#if !defined(JSON_HEDLEY_GCC_VERSION) || JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0)
+    inline nlohmann::json operator ""_json(const char* s, std::size_t n)
+#else
+    inline nlohmann::json operator "" _json(const char* s, std::size_t n)
+#endif
 {
     return nlohmann::json::parse(s, s + n);
 }
@@ -24319,7 +24479,11 @@ inline nlohmann::json operator "" _json(const char* s, std::size_t n)
 /// @brief user-defined string literal for JSON pointer
 /// @sa https://json.nlohmann.me/api/basic_json/operator_literal_json_pointer/
 JSON_HEDLEY_NON_NULL(1)
-inline nlohmann::json::json_pointer operator "" _json_pointer(const char* s, std::size_t n)
+#if !defined(JSON_HEDLEY_GCC_VERSION) || JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0)
+    inline nlohmann::json::json_pointer operator ""_json_pointer(const char* s, std::size_t n)
+#else
+    inline nlohmann::json::json_pointer operator "" _json_pointer(const char* s, std::size_t n)
+#endif
 {
     return nlohmann::json::json_pointer(std::string(s, n));
 }
@@ -24338,7 +24502,7 @@ namespace std // NOLINT(cert-dcl58-cpp)
 /// @brief hash value for JSON objects
 /// @sa https://json.nlohmann.me/api/basic_json/std_hash/
 NLOHMANN_BASIC_JSON_TPL_DECLARATION
-struct hash<nlohmann::NLOHMANN_BASIC_JSON_TPL>
+struct hash<nlohmann::NLOHMANN_BASIC_JSON_TPL> // NOLINT(cert-dcl58-cpp)
 {
     std::size_t operator()(const nlohmann::NLOHMANN_BASIC_JSON_TPL& j) const
     {
@@ -24371,8 +24535,8 @@ struct less< ::nlohmann::detail::value_t> // do not remove the space after '<',
 /// @brief exchanges the values of two JSON objects
 /// @sa https://json.nlohmann.me/api/basic_json/std_swap/
 NLOHMANN_BASIC_JSON_TPL_DECLARATION
-inline void swap(nlohmann::NLOHMANN_BASIC_JSON_TPL& j1, nlohmann::NLOHMANN_BASIC_JSON_TPL& j2) noexcept(  // NOLINT(readability-inconsistent-declaration-parameter-name)
-    is_nothrow_move_constructible<nlohmann::NLOHMANN_BASIC_JSON_TPL>::value&&                          // NOLINT(misc-redundant-expression)
+inline void swap(nlohmann::NLOHMANN_BASIC_JSON_TPL& j1, nlohmann::NLOHMANN_BASIC_JSON_TPL& j2) noexcept(  // NOLINT(readability-inconsistent-declaration-parameter-name, cert-dcl58-cpp)
+    is_nothrow_move_constructible<nlohmann::NLOHMANN_BASIC_JSON_TPL>::value&&                          // NOLINT(misc-redundant-expression,cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
     is_nothrow_move_assignable<nlohmann::NLOHMANN_BASIC_JSON_TPL>::value)
 {
     j1.swap(j2);
@@ -24383,17 +24547,22 @@ inline void swap(nlohmann::NLOHMANN_BASIC_JSON_TPL& j1, nlohmann::NLOHMANN_BASIC
 }  // namespace std
 
 #if JSON_USE_GLOBAL_UDLS
-    using nlohmann::literals::json_literals::operator "" _json; // NOLINT(misc-unused-using-decls,google-global-names-in-headers)
-    using nlohmann::literals::json_literals::operator "" _json_pointer; //NOLINT(misc-unused-using-decls,google-global-names-in-headers)
+    #if !defined(JSON_HEDLEY_GCC_VERSION) || JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0)
+        using nlohmann::literals::json_literals::operator ""_json; // NOLINT(misc-unused-using-decls,google-global-names-in-headers)
+        using nlohmann::literals::json_literals::operator ""_json_pointer; //NOLINT(misc-unused-using-decls,google-global-names-in-headers)
+    #else
+        using nlohmann::literals::json_literals::operator "" _json; // NOLINT(misc-unused-using-decls,google-global-names-in-headers)
+        using nlohmann::literals::json_literals::operator "" _json_pointer; //NOLINT(misc-unused-using-decls,google-global-names-in-headers)
+    #endif
 #endif
 
 // #include <nlohmann/detail/macro_unscope.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -24428,16 +24597,17 @@ inline void swap(nlohmann::NLOHMANN_BASIC_JSON_TPL& j1, nlohmann::NLOHMANN_BASIC
     #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
     #undef JSON_HAS_THREE_WAY_COMPARISON
     #undef JSON_HAS_RANGES
+    #undef JSON_HAS_STATIC_RTTI
     #undef JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON
 #endif
 
 // #include <nlohmann/thirdparty/hedley/hedley_undef.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
diff --git a/common/log.h b/common/log.h
index e4e1b9f4f..6934c57b2 100644
--- a/common/log.h
+++ b/common/log.h
@@ -234,7 +234,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
 // INTERNAL, DO NOT USE
 //  USE LOG() INSTEAD
 //
-#ifndef _MSC_VER
+#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER) || defined(__clang__)
     #define LOG_IMPL(str, ...)                                                                                      \
     do {                                                                                                            \
         if (LOG_TARGET != nullptr)                                                                                  \
@@ -257,7 +257,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
 // INTERNAL, DO NOT USE
 //  USE LOG_TEE() INSTEAD
 //
-#ifndef _MSC_VER
+#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER) || defined(__clang__)
     #define LOG_TEE_IMPL(str, ...)                                                                                                      \
     do {                                                                                                                                \
         if (LOG_TARGET != nullptr)                                                                                                      \
@@ -297,7 +297,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
 #ifndef _MSC_VER
     #define LOG(...) LOG_IMPL(__VA_ARGS__, "")
 #else
-    #define LOG(str, ...) LOG_IMPL("%s" str, "", __VA_ARGS__, "")
+    #define LOG(str, ...) LOG_IMPL("%s" str, "", ##__VA_ARGS__, "")
 #endif
 
 // Main TEE macro.
@@ -311,7 +311,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
 #ifndef _MSC_VER
     #define LOG_TEE(...) LOG_TEE_IMPL(__VA_ARGS__, "")
 #else
-    #define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", __VA_ARGS__, "")
+    #define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", ##__VA_ARGS__, "")
 #endif
 
 // LOG macro variants with auto endline.
@@ -319,8 +319,8 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
     #define LOGLN(...) LOG_IMPL(__VA_ARGS__, "\n")
     #define LOG_TEELN(...) LOG_TEE_IMPL(__VA_ARGS__, "\n")
 #else
-    #define LOGLN(str, ...) LOG_IMPL("%s" str, "", __VA_ARGS__, "\n")
-    #define LOG_TEELN(str, ...) LOG_TEE_IMPL("%s" str, "", __VA_ARGS__, "\n")
+    #define LOGLN(str, ...) LOG_IMPL("%s" str, "", ##__VA_ARGS__, "\n")
+    #define LOG_TEELN(str, ...) LOG_TEE_IMPL("%s" str, "", ##__VA_ARGS__, "\n")
 #endif
 
 // INTERNAL, DO NOT USE
@@ -566,6 +566,7 @@ inline void log_print_usage()
     printf("  --log-new             Create a separate new log file on start. "
                                    "Each log file will have unique name: \"<name>.<ID>.log\"\n");
     printf("  --log-append          Don't truncate the old log file.\n");
+    printf("\n");
 }
 
 #define log_dump_cmdline(argc, argv) log_dump_cmdline_impl(argc, argv)
diff --git a/common/ngram-cache.cpp b/common/ngram-cache.cpp
new file mode 100644
index 000000000..3ca112ef1
--- /dev/null
+++ b/common/ngram-cache.cpp
@@ -0,0 +1,282 @@
+#include "ngram-cache.h"
+#include "common.h"
+#include "log.h"
+
+#include <cstdint>
+#include <fstream>
+
+void llama_ngram_cache_update(llama_ngram_cache & ngram_cache, int ngram_min, int ngram_max,
+                              std::vector<llama_token> & inp, int nnew, bool print_progress) {
+    const int64_t t_start_ms = ggml_time_ms();
+    const int64_t inp_size = inp.size();
+
+    const int64_t n_todo = inp_size * (ngram_max - ngram_min + 1);
+    int64_t n_done = 0;
+
+    for (int64_t ngram_size = ngram_min; ngram_size <= ngram_max; ++ngram_size) {
+        const int64_t i_start = std::max(inp_size - nnew, ngram_size);
+        for (int64_t i = i_start; i < inp_size; ++i) {
+            const int64_t ngram_start = i - ngram_size;
+            llama_ngram ngram(&inp[ngram_start], ngram_size);
+            const llama_token token = inp[i];
+
+            llama_ngram_cache::iterator part_it = ngram_cache.find(ngram);
+            if (part_it == ngram_cache.end()) {
+                llama_ngram_cache_part part;
+                part.emplace(token, 1);
+                ngram_cache.emplace(ngram, part);
+            } else {
+                llama_ngram_cache_part::iterator token_count_it = part_it->second.find(token);
+                if (token_count_it == part_it->second.end()) {
+                    part_it->second.emplace(token, 1);
+                } else {
+                    token_count_it->second++;
+                }
+            }
+            ++n_done;
+
+            if (print_progress && n_done % 10000000 == 0) {
+                const int64_t t_now_ms = ggml_time_ms();
+                const int64_t eta_ms   = (inp_size*(ngram_max-ngram_min+1) - n_done) * (t_now_ms - t_start_ms) / n_done;
+                const int64_t eta_min  = eta_ms / (60*1000);
+                const int64_t eta_s    = (eta_ms - 60*1000*eta_min) / 1000;
+
+                fprintf(stderr, "%s: %" PRId64 "/%" PRId64 " done, ETA: %02" PRId64 ":%02" PRId64 "\n", __func__, n_done, n_todo, eta_min, eta_s);
+            }
+        }
+    }
+}
+
+// Helper function to get a token from the combined, speculative sequence of inp and draft.
+static llama_token get_token(const std::vector<llama_token> & inp, const std::vector<llama_token> & draft, const size_t i) {
+    return i < inp.size() ? inp[i] : draft[1 + i - inp.size()];
+}
+
+// If sample size or percentage are below these thresholds the draft is aborted early:
+constexpr int    draft_min_sample_size_lax[LLAMA_NGRAM_MAX] = { 2,  2,  1,  1};
+constexpr int        draft_min_percent_lax[LLAMA_NGRAM_MAX] = {66, 50, 50, 50};
+constexpr int draft_min_sample_size_strict[LLAMA_NGRAM_MAX] = { 4,  3,  2,  2};
+constexpr int     draft_min_percent_strict[LLAMA_NGRAM_MAX] = {75, 66, 66, 66};
+
+// Helper function that tries to draft a token from only the static ngram cache:
+static llama_token try_draft(llama_ngram_cache & nc_static, const llama_ngram ngram_static) {
+    llama_ngram_cache::iterator part_static_it = nc_static.find(ngram_static);
+    if (part_static_it == nc_static.end()) {
+        return -1;
+    }
+    const llama_ngram_cache_part part_static = part_static_it->second;
+
+    int max_count_static  = 0;
+    int sum_count_static  = 0;
+    llama_token max_token = -1;
+
+    for (std::pair<llama_token, int> token_count_static : part_static) {
+        const llama_token token = token_count_static.first;
+        const int32_t count_static  = token_count_static.second;
+
+        if (count_static > max_count_static) {
+            max_token        = token;
+            max_count_static = count_static;
+        }
+        sum_count_static += count_static;
+    }
+
+    if (sum_count_static < draft_min_sample_size_lax[LLAMA_NGRAM_STATIC-1]) {
+        return -1;
+    }
+    if (100*max_count_static < draft_min_percent_lax[LLAMA_NGRAM_STATIC-1]*sum_count_static) {
+        return -1;
+    }
+    return max_token;
+}
+
+// Try to draft a token from primary cache (context/dynamic), validate with static cache:
+static llama_token try_draft(
+    llama_ngram_cache & nc_primary, const std::vector<llama_ngram> & ngrams_primary, llama_ngram_cache_part & part_static,
+    const int * min_sample_size, const int * min_percent) {
+
+    llama_token drafted_token = -1;
+
+    for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == -1; --i) {
+        const llama_ngram ngram_primary = ngrams_primary[i];
+
+        llama_ngram_cache::iterator part_primary_it = nc_primary.find(ngram_primary);
+        if (part_primary_it == nc_primary.end()) {
+            continue;
+        }
+        const llama_ngram_cache_part part_primary = part_primary_it->second;
+
+        int max_count_primary = 0;
+        int max_count_static  = 0;
+        int sum_count_primary = 0;
+        llama_token max_token = -1;
+
+        for (std::pair<llama_token, int> token_count_primary : part_primary) {
+            const llama_token token = token_count_primary.first;
+
+            llama_ngram_cache_part::iterator token_count_static_it = part_static.find(token);
+
+            const int32_t count_primary = token_count_primary.second;
+            const int32_t count_static  = token_count_static_it != part_static.end() ? 100*token_count_static_it->second : 1;
+
+            if (count_primary*count_static > max_count_primary*max_count_static) {
+                max_token         = token;
+                max_count_primary = count_primary;
+                max_count_static  = count_static;
+            }
+            sum_count_primary += count_primary;
+        }
+
+        if (sum_count_primary < min_sample_size[i]) {
+            continue;
+        }
+        if (100*max_count_primary < min_percent[i]*sum_count_primary) {
+            continue;;
+        }
+        drafted_token = max_token;
+    }
+
+    return drafted_token;
+}
+
+void llama_ngram_cache_draft(
+    std::vector<llama_token> & inp, std::vector<llama_token> & draft, int n_draft, int ngram_min, int ngram_max,
+    llama_ngram_cache & nc_context, llama_ngram_cache & nc_dynamic, llama_ngram_cache & nc_static
+) {
+    GGML_ASSERT(draft.size() == 1);
+    const int inp_size = inp.size();
+
+    if (inp_size < LLAMA_NGRAM_STATIC) {
+        return;
+    }
+
+    while ((int) draft.size()-1 < n_draft) {
+        llama_token drafted_token = -1;
+
+        const int ngram_start_static = inp_size-LLAMA_NGRAM_STATIC + draft.size()-1;
+        llama_ngram ngram_static;
+        for (int j = ngram_start_static; j < ngram_start_static + LLAMA_NGRAM_STATIC; ++j) {
+            ngram_static.tokens[j-ngram_start_static] = get_token(inp, draft, j);
+        }
+        llama_ngram_cache::iterator part_static_it = nc_static.find(ngram_static);
+        llama_ngram_cache_part part_static;
+        if (part_static_it != nc_static.end()) {
+            part_static = part_static_it->second;
+        }
+
+        // cd = context + dynamic
+        std::vector<llama_ngram> ngrams_cd;
+        for (int ngram_size_cd = ngram_min; ngram_size_cd <= ngram_max; ++ngram_size_cd) {
+            const int ngram_start_cd = inp_size-ngram_size_cd + draft.size()-1;
+            llama_ngram ngram_cd;
+            for (int j = ngram_start_cd; j < ngram_start_cd + ngram_size_cd; ++j) {
+                ngram_cd.tokens[j-ngram_start_cd] = get_token(inp, draft, j);
+            }
+            ngrams_cd.push_back(ngram_cd);
+        }
+        if (drafted_token == -1) {
+            drafted_token = try_draft(nc_context, ngrams_cd, part_static, draft_min_sample_size_lax, draft_min_percent_lax);
+        }
+        if (drafted_token == -1) {
+            drafted_token = try_draft(nc_dynamic, ngrams_cd, part_static, draft_min_sample_size_strict, draft_min_percent_strict);
+        }
+        if (drafted_token == -1) {
+            drafted_token = try_draft(nc_static, ngram_static);
+        }
+
+        if (drafted_token == -1) {
+            break;
+        }
+
+        LOG(" - draft candidate: token=%d\n", drafted_token);
+        draft.push_back(drafted_token);
+    }
+}
+
+void llama_ngram_cache_save(llama_ngram_cache & ngram_cache, std::string & filename) {
+    std::ofstream file_out(filename, std::ios::binary);
+    for (std::pair<llama_ngram, llama_ngram_cache_part> item : ngram_cache) {
+        const llama_ngram      ngram        = item.first;
+        llama_ngram_cache_part token_counts = item.second;
+        GGML_ASSERT(!token_counts.empty());
+        const int32_t ntokens = token_counts.size();
+        GGML_ASSERT(ntokens > 0);
+
+        file_out.write(reinterpret_cast<const char *>(&ngram),   sizeof(llama_ngram));
+        file_out.write(reinterpret_cast<const char *>(&ntokens), sizeof(int32_t));
+        for (std::pair<llama_token, int32_t> item2 : token_counts) {
+            const llama_token token = item2.first;
+            const int32_t     count = item2.second;
+            GGML_ASSERT(count > 0);
+
+            file_out.write(reinterpret_cast<const char *>(&token), sizeof(llama_token));
+            file_out.write(reinterpret_cast<const char *>(&count), sizeof(int32_t));
+        }
+    }
+
+}
+
+llama_ngram_cache llama_ngram_cache_load(std::string & filename) {
+    std::ifstream hashmap_file(filename, std::ios::binary);
+    if (!hashmap_file) {
+        throw std::ifstream::failure("Unable to open file " + filename);
+    }
+    llama_ngram_cache ngram_cache;
+
+    llama_ngram ngram;
+    int32_t     ntokens;
+    llama_token token;
+    int32_t     count;
+
+    char * ngramc   = reinterpret_cast<char*>(&ngram);
+    char * ntokensc = reinterpret_cast<char*>(&ntokens);
+    char * tokenc   = reinterpret_cast<char*>(&token);
+    char * countc   = reinterpret_cast<char*>(&count);
+    while(hashmap_file.read(ngramc, sizeof(llama_ngram))) {
+        GGML_ASSERT(!hashmap_file.eof());
+        GGML_ASSERT(hashmap_file.read(ntokensc, sizeof(int32_t)));
+        GGML_ASSERT(ntokens > 0);
+        llama_ngram_cache_part token_counts;
+
+        for (int i = 0; i < ntokens; ++i) {
+            GGML_ASSERT(!hashmap_file.eof());
+            GGML_ASSERT(hashmap_file.read(tokenc, sizeof(llama_token)));
+            GGML_ASSERT(!hashmap_file.eof());
+            GGML_ASSERT(hashmap_file.read(countc, sizeof(int32_t)));
+            GGML_ASSERT(count > 0);
+            token_counts.emplace(token, count);
+        }
+
+        ngram_cache.emplace(ngram, token_counts);
+    }
+    GGML_ASSERT(hashmap_file.eof());
+
+    return ngram_cache;
+}
+
+void llama_ngram_cache_merge(llama_ngram_cache & ngram_cache_target, llama_ngram_cache & ngram_cache_add) {
+    for (std::pair<llama_ngram, llama_ngram_cache_part> ngram_part : ngram_cache_add) {
+        const llama_ngram      ngram = ngram_part.first;
+        llama_ngram_cache_part  part = ngram_part.second;
+
+        llama_ngram_cache::iterator part_merged_it = ngram_cache_target.find(ngram);
+        if (part_merged_it == ngram_cache_target.end()) {
+            ngram_cache_target.emplace(ngram, part);
+            continue;
+        }
+
+        for (std::pair<llama_token, int32_t> token_count : part) {
+            const llama_token token = token_count.first;
+            const int32_t     count = token_count.second;
+            GGML_ASSERT(count > 0);
+
+            llama_ngram_cache_part::iterator token_count_merged_it = part_merged_it->second.find(token);
+            if (token_count_merged_it == part_merged_it->second.end()) {
+                part_merged_it->second.emplace(token, count);
+                continue;
+            }
+
+            token_count_merged_it->second += count;
+        }
+    }
+}
diff --git a/common/ngram-cache.h b/common/ngram-cache.h
new file mode 100644
index 000000000..e4fa4cbd1
--- /dev/null
+++ b/common/ngram-cache.h
@@ -0,0 +1,94 @@
+#pragma once
+
+#include "llama.h"
+
+#include <unordered_map>
+#include <string>
+#include <vector>
+
+#define LLAMA_NGRAM_MIN    1
+#define LLAMA_NGRAM_MAX    4
+#define LLAMA_NGRAM_STATIC 2
+
+// Data structures to map n-grams to empirical token probabilities:
+
+struct llama_ngram {
+    llama_token tokens[LLAMA_NGRAM_MAX];
+
+    llama_ngram() {
+        for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
+            tokens[i] = -1;
+        }
+    }
+
+    llama_ngram(const llama_token * input, const int ngram_size) {
+        for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
+            tokens[i] = i < ngram_size ? input[i] : -1;
+        }
+    }
+
+    bool operator==(const llama_ngram & other) const {
+        for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
+            if (tokens[i] != other.tokens[i]) {
+                return false;
+            }
+        }
+        return true;
+    }
+};
+
+struct llama_ngram_hash_function {
+    size_t operator()(const llama_ngram & ngram) const {
+        size_t hash = 0;
+        for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
+            hash ^= std::hash<llama_token>{}(ngram.tokens[i]);
+        }
+        return hash;
+    }
+};
+
+// token -> number of times token has been seen
+typedef std::unordered_map<llama_token, int32_t> llama_ngram_cache_part;
+
+// n-gram -> empirical distribution of following tokens
+typedef std::unordered_map<llama_ngram, llama_ngram_cache_part, llama_ngram_hash_function> llama_ngram_cache;
+
+
+// Update an ngram cache with tokens.
+// ngram_cache:         the cache to modify.
+// ngram_min/ngram_max: the min/max size of the ngrams to extract from inp_data.
+// inp_data:            the token sequence with which to update ngram_cache.
+// nnew:                how many new tokens have been appended to inp_data since the last call to this function.
+// print_progress:      whether to print progress to stderr.
+//
+// In order to get correct results inp_data can ONLY BE APPENDED TO.
+// Changes in the middle need a complete rebuild.
+void llama_ngram_cache_update(
+    llama_ngram_cache & ngram_cache, int ngram_min, int ngram_max, std::vector<llama_token> & inp_data, int nnew, bool print_progress);
+
+// Try to draft tokens from ngram caches.
+// inp:                the tokens generated so far.
+// draft:              the token sequence to draft. Expected to initially contain the previously sampled token.
+// n_draft:            maximum number of tokens to add to draft.
+// ngram_min/gram_max: the min/max size of the ngrams in nc_context and nc_dynamic.
+// nc_context:         ngram cache based on current context.
+// nc_dynamic:         ngram cache based on previous user generations.
+// nc_static:          ngram cache generated from a large text corpus, used for validation.
+void llama_ngram_cache_draft(
+    std::vector<llama_token> & inp, std::vector<llama_token> & draft, int n_draft, int ngram_min, int ngram_max,
+    llama_ngram_cache & nc_context, llama_ngram_cache & nc_dynamic, llama_ngram_cache & nc_static);
+
+// Save an ngram cache to a file.
+// ngram_cache: the ngram cache to save.
+// filename:    the path under which to save the ngram cache.
+void llama_ngram_cache_save(llama_ngram_cache & ngram_cache, std::string & filename);
+
+// Load an ngram cache saved with llama_ngram_cache_save.
+// filename: the path from which to load the ngram cache.
+// returns:  an ngram cache containing the information saved to filename.
+llama_ngram_cache llama_ngram_cache_load(std::string & filename);
+
+// Merge two ngram caches.
+// ngram_cache_target: the ngram cache to which to add the information from ngram_cache_add.
+// ngram_cache_add:    the ngram cache to add to ngram_cache_target.
+void llama_ngram_cache_merge(llama_ngram_cache & ngram_cache_target, llama_ngram_cache & ngram_cache_add);
diff --git a/common/sampling.cpp b/common/sampling.cpp
index de4331a11..f0f1b92d3 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -1,4 +1,6 @@
+#define LLAMA_API_INTERNAL
 #include "sampling.h"
+#include <random>
 
 struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_params & params) {
     struct llama_sampling_context * result = new llama_sampling_context();
@@ -17,6 +19,13 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_
             return nullptr;
         }
 
+        // Ensure that there is a "root" node.
+        if (result->parsed_grammar.symbol_ids.find("root") == result->parsed_grammar.symbol_ids.end()) {
+            fprintf(stderr, "%s: grammar does not contain a 'root' symbol\n", __func__);
+            delete result;
+            return nullptr;
+        }
+
         std::vector<const llama_grammar_element *> grammar_rules(result->parsed_grammar.c_rules());
 
         result->grammar = llama_grammar_init(
@@ -26,6 +35,10 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_
 
     result->prev.resize(params.n_prev);
 
+    result->n_valid = 0;
+
+    llama_sampling_set_rng_seed(result, params.seed);
+
     return result;
 }
 
@@ -53,6 +66,14 @@ void llama_sampling_reset(llama_sampling_context * ctx) {
 
     std::fill(ctx->prev.begin(), ctx->prev.end(), 0);
     ctx->cur.clear();
+    ctx->n_valid = 0;
+}
+
+void llama_sampling_set_rng_seed(struct llama_sampling_context * ctx, uint32_t seed) {
+    if (seed == LLAMA_DEFAULT_SEED) {
+        seed = std::random_device{}();
+    }
+    ctx->rng.seed(seed);
 }
 
 void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst) {
@@ -161,32 +182,112 @@ static llama_token llama_sampling_sample_impl(
                   bool is_resampling) {  // Add a parameter to indicate if we are resampling
     const llama_sampling_params & params = ctx_sampling->params;
 
+    const float   temp            = params.temp;
+    const int     mirostat        = params.mirostat;
+    const float   mirostat_tau    = params.mirostat_tau;
+    const float   mirostat_eta    = params.mirostat_eta;
+
+    std::vector<float> original_logits;
+    auto cur_p = llama_sampling_prepare(ctx_sampling, ctx_main, ctx_cfg, idx, !is_resampling, &original_logits);
+    if (!is_resampling) {
+        GGML_ASSERT(!original_logits.empty());
+    }
+    llama_token id = 0;
+    // Get a pointer to the logits
+    float * logits = llama_get_logits_ith(ctx_main, idx);
+
+    if (temp < 0.0) {
+        // greedy sampling, with probs
+        llama_sample_softmax(ctx_main, &cur_p);
+        id = cur_p.data[0].id;
+    } else if (temp == 0.0) {
+        // greedy sampling, no probs
+        id = llama_sample_token_greedy(ctx_main, &cur_p);
+    } else {
+        if (mirostat == 1) {
+            const int mirostat_m = 100;
+            llama_sample_temp(ctx_main, &cur_p, temp);
+            id = llama_sample_token_mirostat(ctx_main, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &ctx_sampling->mirostat_mu);
+        } else if (mirostat == 2) {
+            llama_sample_temp(ctx_main, &cur_p, temp);
+            id = llama_sample_token_mirostat_v2(ctx_main, &cur_p, mirostat_tau, mirostat_eta, &ctx_sampling->mirostat_mu);
+        } else {
+            // temperature sampling
+            size_t min_keep = std::max(1, params.min_keep);
+
+            sampler_queue(ctx_main, params, cur_p, min_keep);
+
+            id = llama_sample_token_with_rng(ctx_main, &cur_p, ctx_sampling->rng);
+
+            //{
+            //    const int n_top = 10;
+            //    LOG("top %d candidates:\n", n_top);
+
+            //    for (int i = 0; i < n_top; i++) {
+            //        const llama_token id = cur_p.data[i].id;
+            //        (void)id; // To avoid a warning that id is unused when logging is disabled.
+            //        LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx_main, id).c_str(), cur_p.data[i].p);
+            //    }
+            //}
+
+            //LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx_main, id).c_str());
+        }
+    }
+
+    if (ctx_sampling->grammar != NULL && !is_resampling) {
+        // Create an array with a single token data element for the sampled id
+        llama_token_data single_token_data = {id, logits[id], 0.0f};
+        llama_token_data_array single_token_data_array = { &single_token_data, 1, false };
+
+        // Apply grammar constraints to the single token
+        llama_sample_grammar(ctx_main, &single_token_data_array, ctx_sampling->grammar);
+
+        // Check if the token is valid according to the grammar by seeing if its logit has been set to -INFINITY
+        bool is_valid = single_token_data_array.data[0].logit != -INFINITY;
+
+        // If the token is not valid according to the grammar, perform resampling
+        if (!is_valid) {
+            LOG("Resampling because token %d: '%s' does not meet grammar rules\n", id, llama_token_to_piece(ctx_main, id).c_str());
+
+            // Restore logits from the copy
+            std::copy(original_logits.begin(), original_logits.end(), logits);
+
+            return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, true);  // Pass true for is_resampling
+        }
+    }
+
+    ctx_sampling->n_valid = temp == 0.0f ? 0 : cur_p.size;
+
+    return id;
+}
+
+static llama_token_data_array llama_sampling_prepare_impl(
+                  struct llama_sampling_context * ctx_sampling,
+                  struct llama_context * ctx_main,
+                  struct llama_context * ctx_cfg,
+                  const int idx,
+                  bool apply_grammar,
+                  std::vector<float> * original_logits) {
+    const llama_sampling_params & params = ctx_sampling->params;
+
     const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));
 
-    const float   temp            = params.temp;
     const int32_t penalty_last_n  = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n;
     const float   penalty_repeat  = params.penalty_repeat;
     const float   penalty_freq    = params.penalty_freq;
     const float   penalty_present = params.penalty_present;
-    const int     mirostat        = params.mirostat;
-    const float   mirostat_tau    = params.mirostat_tau;
-    const float   mirostat_eta    = params.mirostat_eta;
+
     const bool    penalize_nl     = params.penalize_nl;
 
     auto & prev = ctx_sampling->prev;
     auto & cur  = ctx_sampling->cur;
 
-    llama_token id = 0;
-
     // Get a pointer to the logits
     float * logits = llama_get_logits_ith(ctx_main, idx);
 
-    // Declare original_logits at the beginning of the function scope
-    std::vector<float> original_logits;
-
-    if (!is_resampling) {
-        // Only make a copy of the original logits if we are not in the resampling phase, not sure if I actually have to do this.
-        original_logits = std::vector<float>(logits, logits + llama_n_vocab(llama_get_model(ctx_main)));
+    if (apply_grammar && original_logits != NULL) {
+        // Only make a copy of the original logits if we are not applying grammar checks, not sure if I actually have to do this.
+        *original_logits = {logits, logits + llama_n_vocab(llama_get_model(ctx_main))};
     }
 
     // apply params.logit_bias map
@@ -227,72 +328,12 @@ static llama_token llama_sampling_sample_impl(
         }
     }
 
-    // If we are in the resampling phase, apply grammar checks before sampling logic
-    if (is_resampling && ctx_sampling->grammar != NULL) {
+    // apply grammar checks before sampling logic
+    if (apply_grammar && ctx_sampling->grammar != NULL) {
         llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar);
     }
 
-    if (temp < 0.0) {
-        // greedy sampling, with probs
-        llama_sample_softmax(ctx_main, &cur_p);
-        id = cur_p.data[0].id;
-    } else if (temp == 0.0) {
-        // greedy sampling, no probs
-        id = llama_sample_token_greedy(ctx_main, &cur_p);
-    } else {
-        if (mirostat == 1) {
-            const int mirostat_m = 100;
-            llama_sample_temp(ctx_main, &cur_p, temp);
-            id = llama_sample_token_mirostat(ctx_main, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &ctx_sampling->mirostat_mu);
-        } else if (mirostat == 2) {
-            llama_sample_temp(ctx_main, &cur_p, temp);
-            id = llama_sample_token_mirostat_v2(ctx_main, &cur_p, mirostat_tau, mirostat_eta, &ctx_sampling->mirostat_mu);
-        } else {
-            // temperature sampling
-            size_t min_keep = std::max(1, params.min_keep);
-
-            sampler_queue(ctx_main, params, cur_p, min_keep);
-
-            id = llama_sample_token(ctx_main, &cur_p);
-
-            //{
-            //    const int n_top = 10;
-            //    LOG("top %d candidates:\n", n_top);
-
-            //    for (int i = 0; i < n_top; i++) {
-            //        const llama_token id = cur_p.data[i].id;
-            //        (void)id; // To avoid a warning that id is unused when logging is disabled.
-            //        LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx_main, id).c_str(), cur_p.data[i].p);
-            //    }
-            //}
-
-            LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx_main, id).c_str());
-        }
-    }
-
-    if (ctx_sampling->grammar != NULL && !is_resampling) {
-        // Create an array with a single token data element for the sampled id
-        llama_token_data single_token_data = {id, logits[id], 0.0f};
-        llama_token_data_array single_token_data_array = { &single_token_data, 1, false };
-
-        // Apply grammar constraints to the single token
-        llama_sample_grammar(ctx_main, &single_token_data_array, ctx_sampling->grammar);
-
-        // Check if the token is valid according to the grammar by seeing if its logit has been set to -INFINITY
-        bool is_valid = single_token_data_array.data[0].logit != -INFINITY;
-
-        // If the token is not valid according to the grammar, perform resampling
-        if (!is_valid) {
-            LOG("Resampling because token %d: '%s' does not meet grammar rules\n", id, llama_token_to_piece(ctx_main, id).c_str());
-
-            // Restore logits from the copy
-            std::copy(original_logits.begin(), original_logits.end(), logits);
-
-            return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, true);  // Pass true for is_resampling
-        }
-    }
-
-    return id;
+    return cur_p;
 }
 
 llama_token llama_sampling_sample(
@@ -304,6 +345,16 @@ llama_token llama_sampling_sample(
     return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, false);
 }
 
+llama_token_data_array llama_sampling_prepare(
+                  struct llama_sampling_context * ctx_sampling,
+                  struct llama_context * ctx_main,
+                  struct llama_context * ctx_cfg,
+                  const int idx,
+                  bool apply_grammar,
+                  std::vector<float> * original_logits) {
+    return llama_sampling_prepare_impl(ctx_sampling,ctx_main, ctx_cfg, idx, apply_grammar, original_logits);
+}
+
 void llama_sampling_accept(
         struct llama_sampling_context * ctx_sampling,
         struct llama_context * ctx_main,
diff --git a/common/sampling.h b/common/sampling.h
index 95d875394..655732ad1 100644
--- a/common/sampling.h
+++ b/common/sampling.h
@@ -4,9 +4,10 @@
 
 #include "grammar-parser.h"
 
+#include <random>
 #include <string>
-#include <vector>
 #include <unordered_map>
+#include <vector>
 
 // sampler types
 enum class llama_sampler_type : char {
@@ -20,25 +21,26 @@ enum class llama_sampler_type : char {
 
 // sampling parameters
 typedef struct llama_sampling_params {
-    int32_t     n_prev                = 64;       // number of previous tokens to remember
-    int32_t     n_probs               = 0;        // if greater than 0, output the probabilities of top n_probs tokens.
-    int32_t     min_keep              = 0;        // 0 = disabled, otherwise samplers should return at least min_keep tokens
-    int32_t     top_k                 = 40;       // <= 0 to use vocab size
-    float       top_p                 = 0.95f;    // 1.0 = disabled
-    float       min_p                 = 0.05f;    // 0.0 = disabled
-    float       tfs_z                 = 1.00f;    // 1.0 = disabled
-    float       typical_p             = 1.00f;    // 1.0 = disabled
-    float       temp                  = 0.80f;    // <= 0.0 to sample greedily, 0.0 to not output probabilities
-    float       dynatemp_range        = 0.00f;    // 0.0 = disabled
-    float       dynatemp_exponent     = 1.00f;    // controls how entropy maps to temperature in dynamic temperature sampler
-    int32_t     penalty_last_n        = 64;       // last n tokens to penalize (0 = disable penalty, -1 = context size)
-    float       penalty_repeat        = 1.10f;    // 1.0 = disabled
-    float       penalty_freq          = 0.00f;    // 0.0 = disabled
-    float       penalty_present       = 0.00f;    // 0.0 = disabled
-    int32_t     mirostat              = 0;        // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
-    float       mirostat_tau          = 5.00f;    // target entropy
-    float       mirostat_eta          = 0.10f;    // learning rate
-    bool        penalize_nl           = true;     // consider newlines as a repeatable token
+    int32_t     n_prev                = 64;                 // number of previous tokens to remember
+    int32_t     n_probs               = 0;                  // if greater than 0, output the probabilities of top n_probs tokens.
+    int32_t     min_keep              = 0;                  // 0 = disabled, otherwise samplers should return at least min_keep tokens
+    int32_t     top_k                 = 40;                 // <= 0 to use vocab size
+    float       top_p                 = 0.95f;              // 1.0 = disabled
+    float       min_p                 = 0.05f;              // 0.0 = disabled
+    float       tfs_z                 = 1.00f;              // 1.0 = disabled
+    float       typical_p             = 1.00f;              // 1.0 = disabled
+    float       temp                  = 0.80f;              // <= 0.0 to sample greedily, 0.0 to not output probabilities
+    float       dynatemp_range        = 0.00f;              // 0.0 = disabled
+    float       dynatemp_exponent     = 1.00f;              // controls how entropy maps to temperature in dynamic temperature sampler
+    int32_t     penalty_last_n        = 64;                 // last n tokens to penalize (0 = disable penalty, -1 = context size)
+    float       penalty_repeat        = 1.00f;              // 1.0 = disabled
+    float       penalty_freq          = 0.00f;              // 0.0 = disabled
+    float       penalty_present       = 0.00f;              // 0.0 = disabled
+    int32_t     mirostat              = 0;                  // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
+    float       mirostat_tau          = 5.00f;              // target entropy
+    float       mirostat_eta          = 0.10f;              // learning rate
+    bool        penalize_nl           = false;              // consider newlines as a repeatable token
+    uint32_t    seed                  = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampling_context
 
     std::vector<llama_sampler_type> samplers_sequence = {
         llama_sampler_type::TOP_K,
@@ -79,6 +81,9 @@ struct llama_sampling_context {
     // TODO: replace with ring-buffer
     std::vector<llama_token>      prev;
     std::vector<llama_token_data> cur;
+    size_t n_valid; // Number of correct top tokens with correct probabilities.
+
+    std::mt19937 rng;
 };
 
 #include "common.h"
@@ -93,6 +98,9 @@ void llama_sampling_free(struct llama_sampling_context * ctx);
 // - reset grammar
 void llama_sampling_reset(llama_sampling_context * ctx);
 
+// Set the sampler seed
+void llama_sampling_set_rng_seed(struct llama_sampling_context * ctx, uint32_t seed);
+
 // Copy the sampler context
 void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst);
 
@@ -129,7 +137,16 @@ llama_token llama_sampling_sample(
         struct llama_sampling_context * ctx_sampling,
         struct llama_context * ctx_main,
         struct llama_context * ctx_cfg,
-        int idx = 0);
+        int idx = -1);
+
+// Prepares and adjusts the set of token candidates for sampling based on penalties, biases, and sampling parameters.
+llama_token_data_array llama_sampling_prepare(
+        struct llama_sampling_context * ctx_sampling,
+        struct llama_context * ctx_main,
+        struct llama_context * ctx_cfg,
+        int idx = 0,
+        bool apply_grammar = true,
+        std::vector<float> * original_logits = nullptr);
 
 void llama_sampling_accept(
         struct llama_sampling_context * ctx_sampling,
diff --git a/common/train.cpp b/common/train.cpp
index e4c3d5df6..0dbfd24df 100644
--- a/common/train.cpp
+++ b/common/train.cpp
@@ -31,7 +31,7 @@ struct train_state  * init_train_state() {
 
     state->opt = new struct ggml_opt_context;
     state->opt->ctx = NULL;
-    state->opt->params = ggml_opt_default_params(GGML_OPT_ADAM);
+    state->opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
     state->opt->params.graph_size = LLAMA_TRAIN_MAX_NODES;
     state->opt->loss_after = 0.0f;
 
@@ -556,7 +556,7 @@ void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_g
     std::string opt_type;
     GGUF_GET_KEY(fctx, opt_type, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_OPTIMIZER_TYPE);
     if (opt_type == LLM_KV_OPTIMIZER_TYPE_ADAM) {
-        opt->params.type = GGML_OPT_ADAM;
+        opt->params.type = GGML_OPT_TYPE_ADAM;
 
         GGUF_GET_KEY(fctx, opt->adam.fx_best,          gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS);
         GGUF_GET_KEY(fctx, opt->adam.fx_prev,          gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS);
@@ -568,7 +568,7 @@ void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_g
         copy_tensor_by_name(opt->adam.v,  f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS);
         copy_tensor_by_name(opt->adam.pf, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES);
     } else if (opt_type == LLM_KV_OPTIMIZER_TYPE_LBFGS) {
-        opt->params.type = GGML_OPT_LBFGS;
+        opt->params.type = GGML_OPT_TYPE_LBFGS;
 
         GGUF_GET_KEY(fctx, opt->params.lbfgs.m,         gguf_get_val_u32, GGUF_TYPE_UINT32,  true, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT);
         GGUF_GET_KEY(fctx, opt->lbfgs.fx_best,          gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS);
@@ -603,7 +603,7 @@ void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context *
     gguf_set_val_bool(fctx, LLM_KV_OPTIMIZER_JUST_INITIALIZED, opt->just_initialized);
 
     switch (opt->params.type) {
-        case GGML_OPT_ADAM:
+        case GGML_OPT_TYPE_ADAM:
             {
                 gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM);
                 gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS,            opt->adam.fx_best);
@@ -622,7 +622,7 @@ void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context *
                     gguf_add_tensor(fctx, opt->adam.pf);
                 }
             } break;
-        case GGML_OPT_LBFGS:
+        case GGML_OPT_TYPE_LBFGS:
             {
                 gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS);
                 gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, opt->params.lbfgs.m);
diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py
new file mode 100755
index 000000000..14aa0c45a
--- /dev/null
+++ b/convert-hf-to-gguf-update.py
@@ -0,0 +1,334 @@
+#!/usr/bin/env python3
+
+# This script downloads the tokenizer models of the specified models from Huggingface and
+# generates the get_vocab_base_pre() function for convert-hf-to-gguf.py
+#
+# This is necessary in order to analyze the type of pre-tokenizer used by the model and
+# provide the necessary information to llama.cpp via the GGUF header in order to implement
+# the same pre-tokenizer.
+#
+# ref: https://github.com/ggerganov/llama.cpp/pull/6920
+#
+# Instructions:
+#
+# - Add a new model to the "models" list
+# - Run the script with your huggingface token:
+#
+#   python3 convert-hf-to-gguf-update.py <huggingface_token>
+#
+# - Copy-paste the generated get_vocab_base_pre() function into convert-hf-to-gguf.py
+# - Update llama.cpp with the new pre-tokenizer if necessary
+#
+# TODO: generate tokenizer tests for llama.cpp
+# TODO: automate the update of convert-hf-to-gguf.py
+#
+
+import logging
+import os
+import requests
+import sys
+import json
+
+from hashlib import sha256
+from enum import IntEnum, auto
+from transformers import AutoTokenizer
+
+logging.basicConfig(level=logging.DEBUG)
+logger = logging.getLogger("convert-hf-to-gguf-update")
+
+
+class TOKENIZER_TYPE(IntEnum):
+    SPM = auto()
+    BPE = auto()
+    WPM = auto()
+
+
+# TODO: this string has to exercise as much pre-tokenizer functionality as possible
+#       will be updated with time - contributions welcome
+chktxt = '\n \n\n \n\n\n \t \t\t \t\n  \n   \n    \n     \n🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````\"\"\"\"......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL'
+
+if len(sys.argv) == 2:
+    token = sys.argv[1]
+    if not token.startswith("hf_"):
+        logger.info("Huggingface token seems invalid")
+        logger.info("Usage: python convert-hf-to-gguf-update.py <huggingface_token>")
+        sys.exit(1)
+else:
+    logger.info("Usage: python convert-hf-to-gguf-update.py <huggingface_token>")
+    sys.exit(1)
+
+# TODO: add models here, base models preferred
+models = [
+    {"name": "llama-spm",      "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", },
+    {"name": "llama-bpe",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", },
+    {"name": "phi-3",          "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", },
+    {"name": "deepseek-llm",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-llm-7b-base", },
+    {"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
+    {"name": "falcon",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
+    {"name": "bert-bge",       "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
+    {"name": "mpt",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
+    {"name": "starcoder",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
+    {"name": "gpt-2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", },
+    {"name": "refact",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", },
+    {"name": "command-r",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", },
+    {"name": "qwen2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", },
+    {"name": "olmo",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", },
+    {"name": "dbrx",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", },
+    {"name": "jina-v2-en",     "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM!
+    {"name": "jina-v2-es",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", },
+    {"name": "jina-v2-de",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", },
+]
+
+# make directory "models/tokenizers" if it doesn't exist
+if not os.path.exists("models/tokenizers"):
+    os.makedirs("models/tokenizers")
+
+
+def download_file_with_auth(url, token, save_path):
+    headers = {"Authorization": f"Bearer {token}"}
+    response = requests.get(url, headers=headers)
+    if response.status_code == 200:
+        with open(save_path, 'wb') as f:
+            f.write(response.content)
+        logger.info(f"File {save_path} downloaded successfully")
+    else:
+        logger.info(f"Failed to download file. Status code: {response.status_code}")
+
+
+# download the tokenizer models
+for model in models:
+    name = model["name"]
+    repo = model["repo"]
+    tokt = model["tokt"]
+
+    if not os.path.exists(f"models/tokenizers/{name}"):
+        os.makedirs(f"models/tokenizers/{name}")
+    else:
+        logger.info(f"Directory models/tokenizers/{name} already exists - skipping")
+        continue
+
+    logger.info(f"Downloading {name} to models/tokenizers/{name}")
+
+    url = f"{repo}/raw/main/config.json"
+    save_path = f"models/tokenizers/{name}/config.json"
+    download_file_with_auth(url, token, save_path)
+
+    url = f"{repo}/raw/main/tokenizer.json"
+    save_path = f"models/tokenizers/{name}/tokenizer.json"
+    download_file_with_auth(url, token, save_path)
+
+    # if downloaded file is less than 1KB, we likely need to download an LFS instead
+    if os.path.getsize(save_path) < 1024:
+        # remove the file
+        os.remove(save_path)
+        url = f"{repo}/resolve/main/tokenizer.json"
+        save_path = f"models/tokenizers/{name}/tokenizer.json"
+        download_file_with_auth(url, token, save_path)
+
+    if tokt == TOKENIZER_TYPE.SPM:
+        url = f"{repo}/resolve/main/tokenizer.model"
+        save_path = f"models/tokenizers/{name}/tokenizer.model"
+        download_file_with_auth(url, token, save_path)
+
+    url = f"{repo}/raw/main/tokenizer_config.json"
+    save_path = f"models/tokenizers/{name}/tokenizer_config.json"
+    download_file_with_auth(url, token, save_path)
+
+# generate the source code for the convert-hf-to-gguf.py:get_vocab_base_pre() function:
+# TODO: auto-update convert-hf-to-gguf.py with the generated function
+
+src_ifs = ""
+for model in models:
+    name = model["name"]
+    tokt = model["tokt"]
+
+    if tokt == TOKENIZER_TYPE.SPM:
+        continue
+
+    # Skip if the tokenizer folder does not exist or there are other download issues previously
+    if not os.path.exists(f"models/tokenizers/{name}"):
+        logger.warning(f"Directory for tokenizer {name} not found. Skipping...")
+        continue
+
+    # create the tokenizer
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
+    except OSError as e:
+        logger.error(f"Error loading tokenizer for model {name}. The model may not exist or is not accessible with the provided token. Error: {e}")
+        continue  # Skip to the next model if the tokenizer can't be loaded
+
+    chktok = tokenizer.encode(chktxt)
+    chkhsh = sha256(str(chktok).encode()).hexdigest()
+
+    logger.info(f"model: {name}")
+    logger.info(f"tokt: {tokt}")
+    logger.info(f"repo: {model['repo']}")
+    logger.info(f"chktok: {chktok}")
+    logger.info(f"chkhsh: {chkhsh}")
+
+    # print the "pre_tokenizer" content from the tokenizer.json
+    with open(f"models/tokenizers/{name}/tokenizer.json", "r", encoding="utf-8") as f:
+        cfg = json.load(f)
+        normalizer = cfg["normalizer"]
+        logger.info("normalizer: " + json.dumps(normalizer, indent=4))
+        pre_tokenizer = cfg["pre_tokenizer"]
+        logger.info("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4))
+        if "ignore_merges" in cfg["model"]:
+            logger.info("ignore_merges: " + json.dumps(cfg["model"]["ignore_merges"], indent=4))
+
+    logger.info("")
+
+    src_ifs += f"        if chkhsh == \"{chkhsh}\":\n"
+    src_ifs += f"            # ref: {model['repo']}\n"
+    src_ifs += f"            res = \"{name}\"\n"
+
+src_func = f"""
+    def get_vocab_base_pre(self, tokenizer) -> str:
+        # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that
+        # is specific for the BPE pre-tokenizer used by the model
+        # we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can
+        # use in llama.cpp to implement the same pre-tokenizer
+
+        chktxt = {repr(chktxt)}
+
+        chktok = tokenizer.encode(chktxt)
+        chkhsh = sha256(str(chktok).encode()).hexdigest()
+
+        logger.debug(f"chktok: {{chktok}}")
+        logger.debug(f"chkhsh: {{chkhsh}}")
+
+        res = None
+
+        # NOTE: if you get an error here, you need to update the convert-hf-to-gguf-update.py script
+        #       or pull the latest version of the model from Huggingface
+        #       don't edit the hashes manually!
+{src_ifs}
+        if res is None:
+            logger.warning("\\n")
+            logger.warning("**************************************************************************************")
+            logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!")
+            logger.warning("**          There are 2 possible reasons for this:")
+            logger.warning("**          - the model has not been added to convert-hf-to-gguf-update.py yet")
+            logger.warning("**          - the pre-tokenization config has changed upstream")
+            logger.warning("**          Check your model files and convert-hf-to-gguf-update.py and update them accordingly.")
+            logger.warning("** ref:     https://github.com/ggerganov/llama.cpp/pull/6920")
+            logger.warning("**")
+            logger.warning(f"** chkhsh:  {{chkhsh}}")
+            logger.warning("**************************************************************************************")
+            logger.warning("\\n")
+            raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()")
+
+        logger.debug(f"tokenizer.ggml.pre: {{repr(res)}}")
+        logger.debug(f"chkhsh: {{chkhsh}}")
+
+        return res
+"""
+
+print(src_func) # noqa: NP100
+
+logger.info("\n")
+logger.info("!!! Copy-paste the function above into convert-hf-to-gguf.py !!!")
+logger.info("\n")
+
+# generate tests for each tokenizer model
+
+tests = [
+    "ied 4 ½ months",
+    "Führer",
+    "",
+    " ",
+    "  ",
+    "   ",
+    "\t",
+    "\n",
+    "\n\n",
+    "\n\n\n",
+    "\t\n",
+    "Hello world",
+    " Hello world",
+    "Hello World",
+    " Hello World",
+    " Hello World!",
+    "Hello, world!",
+    " Hello, world!",
+    " this is 🦙.cpp",
+    "w048 7tuijk dsdfhu",
+    "нещо на Български",
+    "កាន់តែពិសេសអាចខលចេញ",
+    "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
+    "Hello",
+    " Hello",
+    "  Hello",
+    "   Hello",
+    "    Hello",
+    "    Hello\n    Hello",
+    " (",
+    "\n =",
+    "' era",
+    "Hello, y'all! How are you 😁 ?我想在apple工作1314151天～",
+    "3",
+    "33",
+    "333",
+    "3333",
+    "33333",
+    "333333",
+    "3333333",
+    "33333333",
+    "333333333",
+    # "Cửa Việt", # llama-bpe fails on this
+    chktxt,
+]
+
+# write the tests to ./models/ggml-vocab-{name}.gguf.inp
+# the format is:
+#
+# test0
+# __ggml_vocab_test__
+# test1
+# __ggml_vocab_test__
+# ...
+#
+
+# with each model, encode all tests and write the results in ./models/ggml-vocab-{name}.gguf.out
+# for each test, write the resulting tokens on a separate line
+
+for model in models:
+    name = model["name"]
+    tokt = model["tokt"]
+
+    # Skip if the tokenizer folder does not exist or there are other download issues previously
+    if not os.path.exists(f"models/tokenizers/{name}"):
+        logger.warning(f"Directory for tokenizer {name} not found. Skipping...")
+        continue
+
+    # create the tokenizer
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
+    except OSError as e:
+        logger.error(f"Failed to load tokenizer for model {name}. Error: {e}")
+        continue  # Skip this model and continue with the next one in the loop
+
+    with open(f"models/ggml-vocab-{name}.gguf.inp", "w", encoding="utf-8") as f:
+        for text in tests:
+            f.write(f"{text}")
+            f.write("\n__ggml_vocab_test__\n")
+
+    with open(f"models/ggml-vocab-{name}.gguf.out", "w") as f:
+        for text in tests:
+            res = tokenizer.encode(text, add_special_tokens=False)
+            for r in res:
+                f.write(f" {r}")
+            f.write("\n")
+
+    logger.info(f"Tests for {name} written in ./models/ggml-vocab-{name}.gguf.*")
+
+# generate commands for creating vocab files
+
+logger.info("\nRun the following commands to generate the vocab files for testing:\n")
+
+for model in models:
+    name = model["name"]
+
+    print(f"python3 convert-hf-to-gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only") # noqa: NP100
+
+logger.info("\n")
diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py
index 9771fccf9..cd875fa4a 100755
--- a/convert-hf-to-gguf.py
+++ b/convert-hf-to-gguf.py
@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import logging
 import argparse
 import contextlib
 import json
@@ -10,7 +11,8 @@ import re
 import sys
 from enum import IntEnum
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, ContextManager, Iterator, Sequence, cast
+from hashlib import sha256
+from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Sequence, TypeVar, cast
 
 import numpy as np
 import torch
@@ -22,7 +24,9 @@ if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf
 
-from convert import HfVocab
+from convert import LlamaHfVocab
+
+logger = logging.getLogger("hf-to-gguf")
 
 
 ###### MODEL DEFINITIONS ######
@@ -36,22 +40,70 @@ class SentencePieceTokenTypes(IntEnum):
     BYTE = 6
 
 
+AnyModel = TypeVar("AnyModel", bound="type[Model]")
+
+
 class Model:
-    def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool):
+    _model_classes: dict[str, type[Model]] = {}
+
+    dir_model: Path
+    ftype: int
+    is_big_endian: bool
+    endianess: gguf.GGUFEndian
+    use_temp_file: bool
+    lazy: bool
+    part_names: list[str]
+    is_safetensors: bool
+    hparams: dict[str, Any]
+    block_count: int
+    tensor_map: gguf.TensorNameMap
+    tensor_names: set[str] | None
+    fname_out: Path
+    gguf_writer: gguf.GGUFWriter
+
+    # subclasses should define this!
+    model_arch: gguf.MODEL_ARCH
+
+    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool, use_temp_file: bool, eager: bool):
+        if type(self) is Model:
+            raise TypeError(f"{type(self).__name__!r} should not be directly instantiated")
         self.dir_model = dir_model
         self.ftype = ftype
-        self.fname_out = fname_out
         self.is_big_endian = is_big_endian
         self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
-        self.is_safetensors = self._is_model_safetensors()
-        self.num_parts = Model.count_model_parts(self.dir_model, ".safetensors" if self.is_safetensors else ".bin")
-        self.part_names = self._get_part_names()
+        self.use_temp_file = use_temp_file
+        self.lazy = not eager
+        self.part_names = Model.get_model_part_names(self.dir_model, ".safetensors")
+        self.is_safetensors = len(self.part_names) > 0
+        if not self.is_safetensors:
+            self.part_names = Model.get_model_part_names(self.dir_model, ".bin")
         self.hparams = Model.load_hparams(self.dir_model)
-        self.model_arch = self._get_model_architecture()
-        self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=False)
         self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"])
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+        self.tensor_names = None
+        if self.ftype == gguf.LlamaFileType.GUESSED:
+            # NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie.
+            _, first_tensor = next(self.get_tensors())
+            if first_tensor.dtype == torch.float16:
+                logger.info(f"choosing --outtype f16 from first tensor type ({first_tensor.dtype})")
+                self.ftype = gguf.LlamaFileType.MOSTLY_F16
+            else:
+                logger.info(f"choosing --outtype bf16 from first tensor type ({first_tensor.dtype})")
+                self.ftype = gguf.LlamaFileType.MOSTLY_BF16
+        ftype_up: str = self.ftype.name.partition("_")[2].upper()
+        ftype_lw: str = ftype_up.lower()
+        # allow templating the file name with the output ftype, useful with the "auto" ftype
+        self.fname_out = fname_out.parent / fname_out.name.format(ftype_lw, outtype=ftype_lw, ftype=ftype_lw, OUTTYPE=ftype_up, FTYPE=ftype_up)
+        self.gguf_writer = gguf.GGUFWriter(self.fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file)
 
-    def find_hparam(self, keys: Sequence[str], optional: bool = False) -> Any:
+    @classmethod
+    def __init_subclass__(cls):
+        # can't use an abstract property, because overriding it without type errors
+        # would require using decorated functions instead of simply defining the property
+        if "model_arch" not in cls.__dict__:
+            raise TypeError(f"Missing property 'model_arch' for {cls.__name__!r}")
+
+    def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any:
         key = next((k for k in keys if k in self.hparams), None)
         if key is not None:
             return self.hparams[key]
@@ -63,8 +115,24 @@ class Model:
         self._set_vocab_gpt2()
 
     def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
+        tensor_names_from_parts: set[str] = set()
+
+        if len(self.part_names) > 1:
+            self.tensor_names = set()
+            index_name = "model.safetensors" if self.is_safetensors else "pytorch_model.bin"
+            index_name += ".index.json"
+            logger.info(f"gguf: loading model weight map from '{index_name}'")
+            with open(self.dir_model / index_name, "r", encoding="utf-8") as f:
+                index: dict[str, Any] = json.load(f)
+                weight_map = index.get("weight_map")
+                if weight_map is None or not isinstance(weight_map, dict):
+                    raise ValueError(f"Can't load 'weight_map' from {index_name!r}")
+                self.tensor_names.update(weight_map.keys())
+        else:
+            self.tensor_names = tensor_names_from_parts
+
         for part_name in self.part_names:
-            print(f"gguf: loading model part '{part_name}'")
+            logger.info(f"gguf: loading model part '{part_name}'")
             ctx: ContextManager[Any]
             if self.is_safetensors:
                 from safetensors import safe_open
@@ -73,46 +141,110 @@ class Model:
                 ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True))
 
             with ctx as model_part:
+                tensor_names_from_parts.update(model_part.keys())
+
                 for name in model_part.keys():
                     data = model_part.get_tensor(name) if self.is_safetensors else model_part[name]
+                    if self.lazy:
+                        data = LazyTorchTensor.from_eager(data)
                     yield name, data
 
+        # only verify tensor name presence; it doesn't matter if they are not in the right files
+        if len(sym_diff := tensor_names_from_parts.symmetric_difference(self.tensor_names)) > 0:
+            raise ValueError(f"Mismatch between weight map and model parts for tensor names: {sym_diff}")
+
+    def format_tensor_name(self, key: gguf.MODEL_TENSOR, bid: int | None = None, suffix: str = ".weight") -> str:
+        if key not in gguf.MODEL_TENSORS[self.model_arch]:
+            raise ValueError(f"Missing {key!r} for MODEL_TENSORS of {self.model_arch!r}")
+        name: str = gguf.TENSOR_NAMES[key]
+        if "{bid}" in name:
+            assert bid is not None
+            name = name.format(bid=bid)
+        return name + suffix
+
+    def match_model_tensor_name(self, name: str, key: gguf.MODEL_TENSOR, bid: int | None, suffix: str = ".weight") -> bool:
+        if key not in gguf.MODEL_TENSORS[self.model_arch]:
+            return False
+        key_name: str = gguf.TENSOR_NAMES[key]
+        if "{bid}" in key_name:
+            if bid is None:
+                return False
+            key_name = key_name.format(bid=bid)
+        else:
+            if bid is not None:
+                return False
+        return name == (key_name + suffix)
+
+    def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str:
+        new_name = self.tensor_map.get_name(key=name, try_suffixes=try_suffixes)
+        if new_name is None:
+            raise ValueError(f"Can not map tensor {name!r}")
+        return new_name
+
     def set_gguf_parameters(self):
         self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_block_count(self.block_count)
 
         if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None:
             self.gguf_writer.add_context_length(n_ctx)
+            logger.info(f"gguf: context length = {n_ctx}")
 
         n_embd = self.find_hparam(["hidden_size", "n_embd"])
         self.gguf_writer.add_embedding_length(n_embd)
+        logger.info(f"gguf: embedding length = {n_embd}")
 
         if (n_ff := self.find_hparam(["intermediate_size", "n_inner"], optional=True)) is not None:
             self.gguf_writer.add_feed_forward_length(n_ff)
+            logger.info(f"gguf: feed forward length = {n_ff}")
 
         n_head = self.find_hparam(["num_attention_heads", "n_head"])
         self.gguf_writer.add_head_count(n_head)
+        logger.info(f"gguf: head count = {n_head}")
 
         if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None:
             self.gguf_writer.add_head_count_kv(n_head_kv)
+            logger.info(f"gguf: key-value head count = {n_head_kv}")
 
+        if (rope_theta := self.hparams.get("rope_theta")) is not None:
+            self.gguf_writer.add_rope_freq_base(rope_theta)
+            logger.info(f"gguf: rope theta = {rope_theta}")
         if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
             self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
-        if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon"], optional=True)) is not None:
+            logger.info(f"gguf: rms norm epsilon = {f_rms_eps}")
+        if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None:
             self.gguf_writer.add_layer_norm_eps(f_norm_eps)
+            logger.info(f"gguf: layer norm epsilon = {f_norm_eps}")
         if (n_experts := self.hparams.get("num_local_experts")) is not None:
             self.gguf_writer.add_expert_count(n_experts)
+            logger.info(f"gguf: expert count = {n_experts}")
         if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
             self.gguf_writer.add_expert_used_count(n_experts_used)
+            logger.info(f"gguf: experts used count = {n_experts_used}")
 
         self.gguf_writer.add_file_type(self.ftype)
+        logger.info(f"gguf: file type = {self.ftype}")
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+    def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
+        del name, new_name, bid, n_dims  # unused
+
+        return False
+
+    def extra_f16_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
+        del name, new_name, bid, n_dims  # unused
+
+        return False
 
     def write_tensors(self):
-        block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+        max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
+
         for name, data_torch in self.get_tensors():
             # we don't need these
-            if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")):
+            if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
                 continue
 
             old_dtype = data_torch.dtype
@@ -121,38 +253,81 @@ class Model:
             if data_torch.dtype not in (torch.float16, torch.float32):
                 data_torch = data_torch.to(torch.float32)
 
-            data = data_torch.squeeze().numpy()
+            # use the first number-like part of the tensor name as the block id
+            bid = None
+            for part in name.split("."):
+                if part.isdecimal():
+                    bid = int(part)
+                    break
 
-            # map tensor names
-            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
-            if new_name is None:
-                print(f"Can not map tensor {name!r}")
-                sys.exit()
+            for new_name, data in ((n, d.squeeze().numpy()) for n, d in self.modify_tensors(data_torch, name, bid)):
+                data: np.ndarray = data  # type hint
+                n_dims = len(data.shape)
+                data_dtype = data.dtype
+                data_qtype: gguf.GGMLQuantizationType | None = None
 
-            n_dims = len(data.shape)
-            data_dtype = data.dtype
+                # when both are True, f32 should win
+                extra_f32 = self.extra_f32_tensors(name, new_name, bid, n_dims)
+                extra_f16 = self.extra_f16_tensors(name, new_name, bid, n_dims)
 
-            # if f32 desired, convert any float16 to float32
-            if self.ftype == 0 and data_dtype == np.float16:
-                data = data.astype(np.float32)
+                # Most of the codebase that takes in 1D tensors or norms only handles F32 tensors
+                # Conditions should closely match those in llama_model_quantize_internal in llama.cpp
+                extra_f32 = any(cond for cond in (
+                    extra_f32,
+                    n_dims == 1,
+                    new_name.endswith("_norm.weight"),
+                ))
 
-            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
-            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
-                data = data.astype(np.float32)
+                # Some tensor types are always in float32
+                extra_f32 = extra_f32 or any(self.match_model_tensor_name(new_name, key, bid) for key in (
+                    gguf.MODEL_TENSOR.FFN_GATE_INP,
+                    gguf.MODEL_TENSOR.POS_EMBD,
+                    gguf.MODEL_TENSOR.TOKEN_TYPES,
+                ))
 
-            # if f16 desired, convert any float32 2-dim weight tensors to float16
-            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
-                data = data.astype(np.float16)
+                # if f16 desired, convert any float32 2-dim weight tensors to float16
+                extra_f16 = any(cond for cond in (
+                    extra_f16,
+                    (name.endswith(".weight") and n_dims >= 2),
+                ))
 
-            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
+                if self.ftype != gguf.LlamaFileType.ALL_F32 and extra_f16 and not extra_f32:
+                    if self.ftype == gguf.LlamaFileType.MOSTLY_BF16:
+                        data = gguf.quantize_bf16(data)
+                        assert data.dtype == np.int16
+                        data_qtype = gguf.GGMLQuantizationType.BF16
 
-            self.gguf_writer.add_tensor(new_name, data)
+                    elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0 and gguf.can_quantize_to_q8_0(data):
+                        data = gguf.quantize_q8_0(data)
+                        assert data.dtype == np.uint8
+                        data_qtype = gguf.GGMLQuantizationType.Q8_0
+
+                    else:  # default to float16 for quantized tensors
+                        if data_dtype != np.float16:
+                            data = data.astype(np.float16)
+                        data_qtype = gguf.GGMLQuantizationType.F16
+
+                if data_qtype is None:  # by default, convert to float32
+                    if data_dtype != np.float32:
+                        data = data.astype(np.float32)
+                    data_qtype = gguf.GGMLQuantizationType.F32
+
+                block_size, type_size = gguf.GGML_QUANT_SIZES[data_qtype]
+                # reverse shape to make it similar to the internal ggml dimension order
+                shape_str = f"""{{{', '.join(str(n) for n in reversed(
+                    (*data.shape[:-1], data.shape[-1] * data.dtype.itemsize // type_size * block_size))
+                )}}}"""
+
+                # n_dims is implicit in the shape
+                logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")
+
+                self.gguf_writer.add_tensor(new_name, data, raw_dtype=data_qtype)
 
     def write(self):
         self.write_tensors()
         self.gguf_writer.write_header_to_file()
         self.gguf_writer.write_kv_data_to_file()
-        self.gguf_writer.write_tensors_to_file()
+        self.gguf_writer.write_tensors_to_file(progress=True)
         self.gguf_writer.close()
 
     def write_vocab(self):
@@ -161,143 +336,56 @@ class Model:
         self.gguf_writer.close()
 
     @staticmethod
-    def count_model_parts(dir_model: Path, prefix: str) -> int:
-        num_parts = 0
+    def get_model_part_names(dir_model: Path, suffix: str) -> list[str]:
+        part_names: list[str] = []
         for filename in os.listdir(dir_model):
-            if filename.endswith(prefix):
-                num_parts += 1
+            if filename.endswith(suffix):
+                part_names.append(filename)
 
-        return num_parts
+        part_names.sort()
+
+        return part_names
 
     @staticmethod
-    def load_hparams(dir_model):
+    def load_hparams(dir_model: Path):
         with open(dir_model / "config.json", "r", encoding="utf-8") as f:
             return json.load(f)
 
-    @staticmethod
-    def from_model_architecture(model_architecture):
-        if model_architecture == "GPTNeoXForCausalLM":
-            return GPTNeoXModel
-        if model_architecture == "BloomForCausalLM":
-            return BloomModel
-        if model_architecture == "MPTForCausalLM":
-            return MPTModel
-        if model_architecture in ("BaichuanForCausalLM", "BaiChuanForCausalLM"):
-            return BaichuanModel
-        if model_architecture in ("FalconForCausalLM", "RWForCausalLM"):
-            return FalconModel
-        if model_architecture == "GPTBigCodeForCausalLM":
-            return StarCoderModel
-        if model_architecture == "GPTRefactForCausalLM":
-            return RefactModel
-        if model_architecture == "PersimmonForCausalLM":
-            return PersimmonModel
-        if model_architecture in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
-            return StableLMModel
-        if model_architecture == "QWenLMHeadModel":
-            return QwenModel
-        if model_architecture == "Qwen2ForCausalLM":
-            return Model
-        if model_architecture == "MixtralForCausalLM":
-            return MixtralModel
-        if model_architecture == "GPT2LMHeadModel":
-            return GPT2Model
-        if model_architecture == "PhiForCausalLM":
-            return Phi2Model
-        if model_architecture == "PlamoForCausalLM":
-            return PlamoModel
-        if model_architecture == "CodeShellForCausalLM":
-            return CodeShellModel
-        if model_architecture == "OrionForCausalLM":
-            return OrionModel
-        if model_architecture == "InternLM2ForCausalLM":
-            return InternLM2Model
-        if model_architecture == "MiniCPMForCausalLM":
-            return MiniCPMModel
-        if model_architecture == "BertModel":
-            return BertModel
-        if model_architecture == "NomicBertModel":
-            return NomicBertModel
-        return Model
+    @classmethod
+    def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]:
+        assert names
 
-    def _is_model_safetensors(self) -> bool:
-        return Model.count_model_parts(self.dir_model, ".safetensors") > 0
+        def func(modelcls: AnyModel) -> AnyModel:
+            for name in names:
+                cls._model_classes[name] = modelcls
+            return modelcls
+        return func
 
-    def _get_part_names(self):
-        if self.is_safetensors:
-            if self.num_parts == 1:  # there's only one .safetensors file
-                return ("model.safetensors",)
-            return (f"model-{n:05}-of-{self.num_parts:05}.safetensors" for n in range(1, self.num_parts + 1))
+    @classmethod
+    def from_model_architecture(cls, arch: str) -> type[Model]:
+        try:
+            return cls._model_classes[arch]
+        except KeyError:
+            raise NotImplementedError(f'Architecture {arch!r} not supported!') from None
 
-        if self.num_parts == 1:  # there's only one .bin file
-            return ("pytorch_model.bin",)
-        return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1))
-
-    def _get_model_architecture(self) -> gguf.MODEL_ARCH:
-        arch = self.hparams["architectures"][0]
-        if arch == "GPTNeoXForCausalLM":
-            return gguf.MODEL_ARCH.GPTNEOX
-        if arch == "BloomForCausalLM":
-            return gguf.MODEL_ARCH.BLOOM
-        if arch == "MPTForCausalLM":
-            return gguf.MODEL_ARCH.MPT
-        if arch in ("BaichuanForCausalLM", "BaiChuanForCausalLM"):
-            return gguf.MODEL_ARCH.BAICHUAN
-        if arch in ("FalconForCausalLM", "RWForCausalLM"):
-            return gguf.MODEL_ARCH.FALCON
-        if arch == "GPTBigCodeForCausalLM":
-            return gguf.MODEL_ARCH.STARCODER
-        if arch == "GPTRefactForCausalLM":
-            return gguf.MODEL_ARCH.REFACT
-        if arch == "PersimmonForCausalLM":
-            return gguf.MODEL_ARCH.PERSIMMON
-        if arch in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
-            return gguf.MODEL_ARCH.STABLELM
-        if arch == "QWenLMHeadModel":
-            return gguf.MODEL_ARCH.QWEN
-        if arch == "Qwen2ForCausalLM":
-            return gguf.MODEL_ARCH.QWEN2
-        if arch == "MixtralForCausalLM":
-            return gguf.MODEL_ARCH.LLAMA
-        if arch == "GPT2LMHeadModel":
-            return gguf.MODEL_ARCH.GPT2
-        if arch == "PhiForCausalLM":
-            return gguf.MODEL_ARCH.PHI2
-        if arch == "PlamoForCausalLM":
-            return gguf.MODEL_ARCH.PLAMO
-        if arch == "CodeShellForCausalLM":
-            return gguf.MODEL_ARCH.CODESHELL
-        if arch == "OrionForCausalLM":
-            return gguf.MODEL_ARCH.ORION
-        if arch == "InternLM2ForCausalLM":
-            return gguf.MODEL_ARCH.INTERNLM2
-        if arch == "MiniCPMForCausalLM":
-            return gguf.MODEL_ARCH.MINICPM
-        if arch == "BertModel":
-            return gguf.MODEL_ARCH.BERT
-        if arch == "NomicBertModel":
-            return gguf.MODEL_ARCH.NOMIC_BERT
-
-        raise NotImplementedError(f'Architecture "{arch}" not supported!')
-
-    def _set_vocab_gpt2(self):
-        dir_model = self.dir_model
-        hparams = self.hparams
-        tokens: list[bytearray] = []
+    # used for GPT-2 BPE and WordPiece vocabs
+    def get_vocab_base(self) -> tuple[list[str], list[int], str]:
+        tokens: list[str] = []
         toktypes: list[int] = []
 
         from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(dir_model)
-        vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
+        vocab_size = self.hparams.get("vocab_size", len(tokenizer.vocab))
         assert max(tokenizer.vocab.values()) < vocab_size
 
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
         reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
         added_vocab = tokenizer.get_added_vocab()
 
         for i in range(vocab_size):
             if i not in reverse_vocab:
-                pad_token = f"[PAD{i}]".encode('utf-8')
-                tokens.append(bytearray(pad_token))
+                tokens.append(f"[PAD{i}]")
                 toktypes.append(gguf.TokenType.USER_DEFINED)
             elif reverse_vocab[i] in added_vocab:
                 tokens.append(reverse_vocab[i])
@@ -309,17 +397,113 @@ class Model:
                 tokens.append(reverse_vocab[i])
                 toktypes.append(gguf.TokenType.NORMAL)
 
+        return tokens, toktypes, tokpre
+
+    # NOTE: this function is generated by convert-hf-to-gguf-update.py
+    #       do not modify it manually!
+    # ref:  https://github.com/ggerganov/llama.cpp/pull/6920
+    def get_vocab_base_pre(self, tokenizer) -> str:
+        # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that
+        # is specific for the BPE pre-tokenizer used by the model
+        # we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can
+        # use in llama.cpp to implement the same pre-tokenizer
+
+        chktxt = '\n \n\n \n\n\n \t \t\t \t\n  \n   \n    \n     \n🚀 (normal) 😶\u200d🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````""""......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL'
+
+        chktok = tokenizer.encode(chktxt)
+        chkhsh = sha256(str(chktok).encode()).hexdigest()
+
+        logger.debug(f"chktok: {chktok}")
+        logger.debug(f"chkhsh: {chkhsh}")
+
+        res = None
+
+        # NOTE: if you get an error here, you need to update the convert-hf-to-gguf-update.py script
+        #       or pull the latest version of the model from Huggingface
+        #       don't edit the hashes manually!
+        if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
+            # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
+            res = "llama-bpe"
+        if chkhsh == "049ecf7629871e3041641907f3de7c733e4dbfdc736f57d882ba0b0845599754":
+            # ref: https://huggingface.co/deepseek-ai/deepseek-llm-7b-base
+            res = "deepseek-llm"
+        if chkhsh == "347715f544604f9118bb75ed199f68779f423cabb20db6de6f31b908d04d7821":
+            # ref: https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base
+            res = "deepseek-coder"
+        if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed":
+            # ref: https://huggingface.co/tiiuae/falcon-7b
+            res = "falcon"
+        if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
+            # ref: https://huggingface.co/BAAI/bge-small-en-v1.5
+            res = "bert-bge"
+        if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166":
+            # ref: https://huggingface.co/mosaicml/mpt-7b
+            res = "mpt"
+        if chkhsh == "35d91631860c815f952d711435f48d356ebac988362536bed955d43bfa436e34":
+            # ref: https://huggingface.co/bigcode/starcoder2-3b
+            res = "starcoder"
+        if chkhsh == "3ce83efda5659b07b1ad37ca97ca5797ea4285d9b9ab0dc679e4a720c9da7454":
+            # ref: https://huggingface.co/openai-community/gpt2
+            res = "gpt-2"
+        if chkhsh == "6221ad2852e85ce96f791f476e0b390cf9b474c9e3d1362f53a24a06dc8220ff":
+            # ref: https://huggingface.co/smallcloudai/Refact-1_6-base
+            res = "refact"
+        if chkhsh == "9c2227e4dd922002fb81bde4fc02b0483ca4f12911410dee2255e4987644e3f8":
+            # ref: https://huggingface.co/CohereForAI/c4ai-command-r-v01
+            res = "command-r"
+        if chkhsh == "e636dc30a262dcc0d8c323492e32ae2b70728f4df7dfe9737d9f920a282b8aea":
+            # ref: https://huggingface.co/Qwen/Qwen1.5-7B
+            res = "qwen2"
+        if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166":
+            # ref: https://huggingface.co/allenai/OLMo-1.7-7B-hf
+            res = "olmo"
+        if chkhsh == "a8594e3edff7c29c003940395316294b2c623e09894deebbc65f33f1515df79e":
+            # ref: https://huggingface.co/databricks/dbrx-base
+            res = "dbrx"
+        if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
+            # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-en
+            res = "jina-v2-en"
+        if chkhsh == "171aeeedd6fb548d418a7461d053f11b6f1f1fc9b387bd66640d28a4b9f5c643":
+            # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-es
+            res = "jina-v2-es"
+        if chkhsh == "27949a2493fc4a9f53f5b9b029c82689cfbe5d3a1929bb25e043089e28466de6":
+            # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-de
+            res = "jina-v2-de"
+
+        if res is None:
+            logger.warning("\n")
+            logger.warning("**************************************************************************************")
+            logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!")
+            logger.warning("**          There are 2 possible reasons for this:")
+            logger.warning("**          - the model has not been added to convert-hf-to-gguf-update.py yet")
+            logger.warning("**          - the pre-tokenization config has changed upstream")
+            logger.warning("**          Check your model files and convert-hf-to-gguf-update.py and update them accordingly.")
+            logger.warning("** ref:     https://github.com/ggerganov/llama.cpp/pull/6920")
+            logger.warning("**")
+            logger.warning(f"** chkhsh:  {chkhsh}")
+            logger.warning("**************************************************************************************")
+            logger.warning("\n")
+            raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()")
+
+        logger.debug(f"tokenizer.ggml.pre: {repr(res)}")
+        logger.debug(f"chkhsh: {chkhsh}")
+
+        return res
+
+    def _set_vocab_gpt2(self) -> None:
+        tokens, toktypes, tokpre = self.get_vocab_base()
         self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
         self.gguf_writer.add_token_list(tokens)
         self.gguf_writer.add_token_types(toktypes)
 
-        special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
         special_vocab.add_to_gguf(self.gguf_writer)
 
     def _set_vocab_qwen(self):
         dir_model = self.dir_model
         hparams = self.hparams
-        tokens: list[bytearray] = []
+        tokens: list[str] = []
         toktypes: list[int] = []
 
         from transformers import AutoTokenizer
@@ -327,6 +511,8 @@ class Model:
         vocab_size = hparams["vocab_size"]
         assert max(tokenizer.get_vocab().values()) < vocab_size
 
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
         merges = []
         vocab = {}
         mergeable_ranks = tokenizer.mergeable_ranks
@@ -344,8 +530,7 @@ class Model:
 
         for i in range(vocab_size):
             if i not in reverse_vocab:
-                pad_token = f"[PAD{i}]".encode("utf-8")
-                tokens.append(bytearray(pad_token))
+                tokens.append(f"[PAD{i}]")
                 toktypes.append(gguf.TokenType.USER_DEFINED)
             elif reverse_vocab[i] in added_vocab:
                 tokens.append(reverse_vocab[i])
@@ -355,6 +540,7 @@ class Model:
                 toktypes.append(gguf.TokenType.NORMAL)
 
         self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
         self.gguf_writer.add_token_list(tokens)
         self.gguf_writer.add_token_types(toktypes)
 
@@ -378,25 +564,26 @@ class Model:
         toktypes: list[int] = []
 
         if not tokenizer_path.is_file():
-            print(f'Error: Missing {tokenizer_path}', file=sys.stderr)
-            sys.exit(1)
+            raise FileNotFoundError(f"File not found: {tokenizer_path}")
+
+        tokenizer = SentencePieceProcessor()
+        tokenizer.LoadFromFile(str(tokenizer_path))
 
-        tokenizer = SentencePieceProcessor(str(tokenizer_path))
         vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
 
-        for token_id in range(vocab_size):
-            piece = tokenizer.id_to_piece(token_id)
+        for token_id in range(tokenizer.vocab_size()):
+            piece = tokenizer.IdToPiece(token_id)
             text = piece.encode("utf-8")
-            score = tokenizer.get_score(token_id)
+            score = tokenizer.GetScore(token_id)
 
             toktype = SentencePieceTokenTypes.NORMAL
-            if tokenizer.is_unknown(token_id):
+            if tokenizer.IsUnknown(token_id):
                 toktype = SentencePieceTokenTypes.UNKNOWN
-            elif tokenizer.is_control(token_id):
+            elif tokenizer.IsControl(token_id):
                 toktype = SentencePieceTokenTypes.CONTROL
-            elif tokenizer.is_unused(token_id):
+            elif tokenizer.IsUnused(token_id):
                 toktype = SentencePieceTokenTypes.UNUSED
-            elif tokenizer.is_byte(token_id):
+            elif tokenizer.IsByte(token_id):
                 toktype = SentencePieceTokenTypes.BYTE
 
             tokens.append(text)
@@ -409,11 +596,24 @@ class Model:
                 added_tokens_json = json.load(f)
 
                 for key in added_tokens_json:
-                    tokens.append(key.encode("utf-8"))
-                    scores.append(-1000.0)
-                    toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
+                    key = key.encode("utf-8")
+                    if key not in tokens:
+                        tokens.append(key)
+                        scores.append(-1000.0)
+                        toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
+
+        if vocab_size > len(tokens):
+            pad_count = vocab_size - len(tokens)
+            logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
+            for i in range(1, pad_count + 1):
+                tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
+                scores.append(-1000.0)
+                toktypes.append(SentencePieceTokenTypes.UNUSED)
+
+        assert len(tokens) == vocab_size
 
         self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
         self.gguf_writer.add_token_list(tokens)
         self.gguf_writer.add_token_scores(scores)
         self.gguf_writer.add_token_types(toktypes)
@@ -421,12 +621,8 @@ class Model:
         special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
         special_vocab.add_to_gguf(self.gguf_writer)
 
-    def _set_vocab_hf(self):
-        path = self.dir_model
-        added_tokens_path = self.dir_model
-        vocab = HfVocab(
-            path, added_tokens_path if added_tokens_path.exists() else None
-        )
+    def _set_vocab_llama_hf(self):
+        vocab = LlamaHfVocab(self.dir_model)
         tokens = []
         scores = []
         toktypes = []
@@ -439,6 +635,7 @@ class Model:
         assert len(tokens) == vocab.vocab_size
 
         self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
         self.gguf_writer.add_token_list(tokens)
         self.gguf_writer.add_token_scores(scores)
         self.gguf_writer.add_token_types(toktypes)
@@ -447,7 +644,10 @@ class Model:
         special_vocab.add_to_gguf(self.gguf_writer)
 
 
+@Model.register("GPTNeoXForCausalLM")
 class GPTNeoXModel(Model):
+    model_arch = gguf.MODEL_ARCH.GPTNEOX
+
     def set_gguf_parameters(self):
         block_count = self.hparams["num_hidden_layers"]
 
@@ -464,7 +664,10 @@ class GPTNeoXModel(Model):
         self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
 
 
+@Model.register("BloomForCausalLM")
 class BloomModel(Model):
+    model_arch = gguf.MODEL_ARCH.BLOOM
+
     def set_gguf_parameters(self):
         self.gguf_writer.add_name("Bloom")
         n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
@@ -478,85 +681,69 @@ class BloomModel(Model):
         self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
         self.gguf_writer.add_file_type(self.ftype)
 
-    def write_tensors(self):
-        block_count = self.hparams["n_layer"]
-        tensors = dict(self.get_tensors())
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
-        has_lm_head = True
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
         n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
         n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
 
-        for name, data_torch in tensors.items():
-            if "lm_head.weight" not in tensors.keys() and "output.weight" not in tensors.keys():
-                has_lm_head = False
+        name = re.sub(r'transformer\.', '', name)
 
-            name = re.sub(r'transformer\.', '', name)
+        tensors: list[tuple[str, Tensor]] = []
 
-            old_dtype = data_torch.dtype
+        if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name):
+            # Map bloom-style qkv_linear to gpt-style qkv_linear
+            # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252  # noqa
+            # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312  # noqa
+            qkv_weights = data_torch.reshape((n_head, 3, n_embed // n_head, n_embed))
+            data_torch = torch.cat(
+                (
+                    qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
+                    qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
+                    qkv_weights[:, 2, :, :].reshape((-1, n_embed)),
+                ),
+                dim=0,
+            )
+            logger.info("re-format attention.linear_qkv.weight")
+        elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name):
+            qkv_bias = data_torch.reshape((n_head, 3, n_embed // n_head))
+            data_torch = torch.cat(
+                (
+                    qkv_bias[:, 0, :].reshape((n_embed,)),
+                    qkv_bias[:, 1, :].reshape((n_embed,)),
+                    qkv_bias[:, 2, :].reshape((n_embed,)),
+                ),
+                dim=0,
+            )
+            logger.info("re-format attention.linear_qkv.bias")
 
-            # convert any unsupported data types to float32
-            if data_torch.dtype not in (torch.float16, torch.float32):
-                data_torch = data_torch.to(torch.float32)
+        tensors.append((self.map_tensor_name(name), data_torch))
 
-            data = data_torch.squeeze().numpy()
+        if name == "word_embeddings.weight":
+            assert self.tensor_names is not None
 
-            if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name):
-                # Map bloom-style qkv_linear to gpt-style qkv_linear
-                # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252  # noqa
-                # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312  # noqa
-                qkv_weights = data.reshape((n_head, 3, n_embed // n_head, n_embed))
-                data = np.concatenate(
-                    (
-                        qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
-                        qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
-                        qkv_weights[:, 2, :, :].reshape((-1, n_embed)),
-                    ),
-                    axis=0,
-                )
-                print("re-format attention.linear_qkv.weight")
-            elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name):
-                qkv_bias = data.reshape((n_head, 3, n_embed // n_head))
-                data = np.concatenate(
-                    (
-                        qkv_bias[:, 0, :].reshape((n_embed,)),
-                        qkv_bias[:, 1, :].reshape((n_embed,)),
-                        qkv_bias[:, 2, :].reshape((n_embed,)),
-                    ),
-                    axis=0,
-                )
-                print("re-format attention.linear_qkv.bias")
+            # TODO: tie them at runtime, don't duplicate in the model file
+            if all(s not in self.tensor_names for s in ("lm_head.weight", "output.weight")):
+                tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch))
 
-            # map tensor names
-            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
-            if new_name is None:
-                print(f"Can not map tensor {name!r}")
-                sys.exit()
-
-            n_dims = len(data.shape)
-            data_dtype = data.dtype
-
-            # if f32 desired, convert any float16 to float32
-            if self.ftype == 0 and data_dtype == np.float16:
-                data = data.astype(np.float32)
-
-            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
-            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
-                data = data.astype(np.float32)
-
-            # if f16 desired, convert any float32 2-dim weight tensors to float16
-            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
-                data = data.astype(np.float16)
-
-            print(f"=> {new_name}, shape = {data.shape}, {old_dtype} --> {data.dtype}")
-
-            self.gguf_writer.add_tensor(new_name, data)
-
-            if not has_lm_head and name == "word_embeddings.weight":
-                self.gguf_writer.add_tensor("output.weight", data)
-                print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}")
+        return tensors
 
 
+@Model.register("MPTForCausalLM")
 class MPTModel(Model):
+    model_arch = gguf.MODEL_ARCH.MPT
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_gpt2()
+        except Exception:
+            # Fallback for SEA-LION model
+            self._set_vocab_sentencepiece()
+            self.gguf_writer.add_add_bos_token(False)
+            self.gguf_writer.add_pad_token_id(3)
+            self.gguf_writer.add_eos_token_id(1)
+            self.gguf_writer.add_unk_token_id(0)
+
     def set_gguf_parameters(self):
         block_count = self.hparams["n_layers"]
         self.gguf_writer.add_name(self.dir_model.name)
@@ -570,61 +757,27 @@ class MPTModel(Model):
         self.gguf_writer.add_layer_norm_eps(1e-5)
         if self.hparams["attn_config"]["clip_qkv"] is not None:
             self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"])
-        self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"])
+        if self.hparams["attn_config"]["alibi"]:
+            self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"])
+        else:
+            self.gguf_writer.add_max_alibi_bias(0.0)
 
-    def write_tensors(self):
-        block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers"))
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
-        for name, data_torch in self.get_tensors():
-            # we don't need these
-            if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")):
-                continue
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
 
-            old_dtype = data_torch.dtype
+        if "scales" in name:
+            new_name = self.map_tensor_name(name, try_suffixes=(".weight", ".bias", ".scales"))
+            new_name = new_name.replace("scales", "act.scales")
+        else:
+            new_name = self.map_tensor_name(name, try_suffixes=(".weight", ".bias"))
 
-            # convert any unsupported data types to float32
-            if data_torch.dtype not in (torch.float16, torch.float32):
-                data_torch = data_torch.to(torch.float32)
-
-            data = data_torch.squeeze().numpy()
-
-            # map tensor names
-            if "scales" in name:
-                new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias", ".scales"))
-                if new_name is not None:
-                    new_name = new_name.replace("scales", "act.scales")
-            else:
-                new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
-            if new_name is None:
-                print(f"Can not map tensor {name!r}")
-                sys.exit()
-
-            n_dims = len(data.shape)
-            data_dtype = data.dtype
-
-            # if f32 desired, convert any float16 to float32
-            if self.ftype == 0 and data_dtype == np.float16:
-                data = data.astype(np.float32)
-
-            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
-            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
-                data = data.astype(np.float32)
-
-            # if f16 desired, convert any float32 2-dim weight tensors to float16
-            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
-                data = data.astype(np.float16)
-
-            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
-
-            self.gguf_writer.add_tensor(new_name, data)
-
-            # note: MPT output is tied to (same as) wte in original model;
-            # for easier implementation in llama.cpp it's duplicated in GGUF, though :/
-            if new_name == "token_embd.weight":
-                self.gguf_writer.add_tensor("output.weight", data)
+        return [(new_name, data_torch)]
 
 
+@Model.register("OrionForCausalLM")
 class OrionModel(Model):
+    model_arch = gguf.MODEL_ARCH.ORION
+
     def set_vocab(self):
         self._set_vocab_sentencepiece()
 
@@ -642,8 +795,7 @@ class OrionModel(Model):
         elif "model_max_length" in self.hparams:
             ctx_length = self.hparams["model_max_length"]
         else:
-            print("gguf: can not find ctx length parameter.")
-            sys.exit()
+            raise ValueError("gguf: can not find ctx length parameter.")
 
         self.gguf_writer.add_file_type(self.ftype)
         self.gguf_writer.add_name(self.dir_model.name)
@@ -655,53 +807,15 @@ class OrionModel(Model):
         self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
         self.gguf_writer.add_head_count(head_count)
         self.gguf_writer.add_head_count_kv(head_count_kv)
+        # note: config provides rms norm but it is actually layer norm
+        # ref:  https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/276a17221ce42beb45f66fac657a41540e71f4f5/modeling_orion.py#L570-L571
         self.gguf_writer.add_layer_norm_eps(self.hparams["rms_norm_eps"])
 
-    def write_tensors(self):
-        # Collect tensors from generator object
-        model_kv = dict(self.get_tensors())
-        block_count = self.hparams["num_hidden_layers"]
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
-
-        for name, data_torch in model_kv.items():
-            # we don't need these
-            if name.endswith(".rotary_emb.inv_freq"):
-                continue
-
-            old_dtype = data_torch.dtype
-
-            # convert any unsupported data types to float32
-            if data_torch.dtype not in (torch.float16, torch.float32):
-                data_torch = data_torch.to(torch.float32)
-
-            data = data_torch.squeeze().numpy()
-
-            # map tensor names
-            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
-            if new_name is None:
-                print(f"Can not map tensor {name!r}")
-                sys.exit()
-
-            n_dims = len(data.shape)
-            data_dtype = data.dtype
-
-            # if f32 desired, convert any float16 to float32
-            if self.ftype == 0 and data_dtype == np.float16:
-                data = data.astype(np.float32)
-
-            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
-            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
-                data = data.astype(np.float32)
-
-            # if f16 desired, convert any float32 2-dim weight tensors to float16
-            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
-                data = data.astype(np.float16)
-
-            print(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
-            self.gguf_writer.add_tensor(new_name, data)
-
 
+@Model.register("BaichuanForCausalLM", "BaiChuanForCausalLM")
 class BaichuanModel(Model):
+    model_arch = gguf.MODEL_ARCH.BAICHUAN
+
     def set_vocab(self):
         self._set_vocab_sentencepiece()
 
@@ -719,8 +833,7 @@ class BaichuanModel(Model):
         elif "model_max_length" in self.hparams:
             ctx_length = self.hparams["model_max_length"]
         else:
-            print("gguf: can not find ctx length parameter.")
-            sys.exit()
+            raise ValueError("gguf: can not find ctx length parameter.")
 
         self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_source_hf_repo(hf_repo)
@@ -733,67 +846,33 @@ class BaichuanModel(Model):
         self.gguf_writer.add_head_count(head_count)
         self.gguf_writer.add_head_count_kv(head_count_kv)
         self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
+        self.gguf_writer.add_file_type(self.ftype)
 
         if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
             if self.hparams["rope_scaling"].get("type") == "linear":
                 self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
                 self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
 
-    def write_tensors(self):
-        # Collect tensors from generator object
-        model_kv = dict(self.get_tensors())
-        block_count = self.hparams["num_hidden_layers"]
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         head_count = self.hparams["num_attention_heads"]
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
         head_count_kv = self.hparams.get("num_key_value_heads", head_count)
 
-        for i in range(block_count):
-            if (w := model_kv.get(f"model.layers.{i}.self_attn.W_pack.weight")) is not None:
-                print(f"Unpacking and permuting layer {i}")
-                model_kv[f"model.layers.{i}.self_attn.q_proj.weight"] = \
-                    self._reverse_hf_permute_part(w, 0, head_count, head_count)
-                model_kv[f"model.layers.{i}.self_attn.k_proj.weight"] = \
-                    self._reverse_hf_permute_part(w, 1, head_count, head_count_kv)
-                model_kv[f"model.layers.{i}.self_attn.v_proj.weight"] = \
-                    self._reverse_hf_part(w, 2)
-                del model_kv[f"model.layers.{i}.self_attn.W_pack.weight"]
+        tensors: list[tuple[str, Tensor]] = []
 
-        for name, data_torch in model_kv.items():
-            # we don't need these
-            if name.endswith(".rotary_emb.inv_freq"):
-                continue
+        if bid is not None and name == f"model.layers.{bid}.self_attn.W_pack.weight":
+            logger.info(f"Unpacking and permuting layer {bid}")
+            tensors = [
+                (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid),
+                    self._reverse_hf_permute_part(data_torch, 0, head_count, head_count)),
+                (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid),
+                    self._reverse_hf_permute_part(data_torch, 1, head_count, head_count_kv)),
+                (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid),
+                    self._reverse_hf_part(data_torch, 2)),
+            ]
+        else:
+            tensors = [(self.map_tensor_name(name), data_torch)]
 
-            old_dtype = data_torch.dtype
-
-            # convert any unsupported data types to float32
-            if data_torch.dtype not in (torch.float16, torch.float32):
-                data_torch = data_torch.to(torch.float32)
-
-            data = data_torch.squeeze().numpy()
-
-            # map tensor names
-            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
-            if new_name is None:
-                print(f"Can not map tensor {name!r}")
-                sys.exit()
-
-            n_dims = len(data.shape)
-            data_dtype = data.dtype
-
-            # if f32 desired, convert any float16 to float32
-            if self.ftype == 0 and data_dtype == np.float16:
-                data = data.astype(np.float32)
-
-            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
-            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
-                data = data.astype(np.float32)
-
-            # if f16 desired, convert any float32 2-dim weight tensors to float16
-            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
-                data = data.astype(np.float16)
-
-            print(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
-            self.gguf_writer.add_tensor(new_name, data)
+        return tensors
 
     def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
         if n_kv_head is not None and n_head != n_kv_head:
@@ -816,7 +895,116 @@ class BaichuanModel(Model):
         return weights[r * n_part:r * n_part + r, ...]
 
 
+@Model.register("XverseForCausalLM")
+class XverseModel(Model):
+    model_arch = gguf.MODEL_ARCH.XVERSE
+
+    def set_vocab(self):
+        assert (self.dir_model / "tokenizer.json").is_file()
+        dir_model = self.dir_model
+        hparams = self.hparams
+
+        tokens: list[bytes] = []
+        toktypes: list[int] = []
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(dir_model)
+        vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
+        assert max(tokenizer.vocab.values()) < vocab_size
+
+        reverse_vocab: dict[int, str] = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
+        added_vocab = tokenizer.get_added_vocab()
+
+        for token_id in range(vocab_size):
+            token_text = reverse_vocab[token_id].encode('utf-8')
+            # replace "\x00" to string with length > 0
+            if token_text == b"\x00":
+                toktype = gguf.TokenType.BYTE  # special
+                token_text = f"<{token_text}>".encode('utf-8')
+            elif re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
+                toktype = gguf.TokenType.BYTE  # special
+            elif reverse_vocab[token_id] in added_vocab:
+                if tokenizer.added_tokens_decoder[token_id].special:
+                    toktype = gguf.TokenType.CONTROL
+                else:
+                    toktype = gguf.TokenType.USER_DEFINED
+            else:
+                toktype = gguf.TokenType.NORMAL
+
+            tokens.append(token_text)
+            toktypes.append(toktype)
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        block_count = self.hparams["num_hidden_layers"]
+        head_count = self.hparams["num_attention_heads"]
+        head_count_kv = self.hparams.get("num_key_value_heads", head_count)
+        hf_repo = self.hparams.get("_name_or_path", "")
+
+        ctx_length = 0
+        if "max_sequence_length" in self.hparams:
+            ctx_length = self.hparams["max_sequence_length"]
+        elif "max_position_embeddings" in self.hparams:
+            ctx_length = self.hparams["max_position_embeddings"]
+        elif "model_max_length" in self.hparams:
+            ctx_length = self.hparams["model_max_length"]
+        else:
+            raise ValueError("gguf: can not find ctx length parameter.")
+
+        self.gguf_writer.add_name(self.dir_model.name)
+        self.gguf_writer.add_source_hf_repo(hf_repo)
+        self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
+        self.gguf_writer.add_context_length(ctx_length)
+        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
+        self.gguf_writer.add_block_count(block_count)
+        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
+        self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count(head_count)
+        self.gguf_writer.add_head_count_kv(head_count_kv)
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+        if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
+            if self.hparams["rope_scaling"].get("type") == "linear":
+                self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+                self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        head_count = self.hparams["num_attention_heads"]
+        head_count_kv = self.hparams.get("num_key_value_heads", head_count)
+
+        # HF models permute some of the tensors, so we need to undo that
+        if name.endswith("q_proj.weight"):
+            data_torch = self._reverse_hf_permute(data_torch, head_count, head_count)
+        if name.endswith("k_proj.weight"):
+            data_torch = self._reverse_hf_permute(data_torch, head_count, head_count_kv)
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+    def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
+        if n_kv_head is not None and n_head != n_kv_head:
+            n_head //= n_kv_head
+
+        return (
+            weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
+            .swapaxes(1, 2)
+            .reshape(weights.shape)
+        )
+
+
+@Model.register("FalconForCausalLM", "RWForCausalLM")
 class FalconModel(Model):
+    model_arch = gguf.MODEL_ARCH.FALCON
+
     def set_gguf_parameters(self):
         block_count = self.hparams.get("num_hidden_layers")
         if block_count is None:
@@ -841,75 +1029,37 @@ class FalconModel(Model):
         self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
         self.gguf_writer.add_file_type(self.ftype)
 
-    def write_tensors(self):
-        block_count = self.hparams.get("num_hidden_layers")
-        if block_count is None:
-            block_count = self.hparams["n_layer"]  # old name
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
 
-        n_head = self.hparams.get("num_attention_heads")
-        if n_head is None:
-            n_head = self.hparams["n_head"]  # old name
+        # QKV tensor transform
+        # The original query_key_value tensor contains n_head_kv "kv groups",
+        # each consisting of n_head/n_head_kv query weights followed by one key
+        # and one value weight (shared by all query heads in the kv group).
+        # This layout makes it a big pain to work with in GGML.
+        # So we rearrange them here,, so that we have n_head query weights
+        # followed by n_head_kv key weights followed by n_head_kv value weights,
+        # in contiguous fashion.
+        # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py
 
-        n_head_kv = self.hparams.get("num_kv_heads")
-        if n_head_kv is None:
-            n_head_kv = self.hparams.get("n_head_kv", 1)  # old name
+        if "query_key_value" in name:
+            n_head = self.find_hparam(["num_attention_heads", "n_head"])
+            n_head_kv = self.find_hparam(["num_kv_heads", "n_head_kv"], optional=True) or 1
+            head_dim = self.hparams["hidden_size"] // n_head
 
-        head_dim = self.hparams["hidden_size"] // n_head
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+            qkv = data_torch.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head)
+            q = qkv[:, :-2].reshape(n_head * head_dim, head_dim * n_head)
+            k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head)
+            v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head)
+            data_torch = torch.cat((q, k, v)).reshape_as(data_torch)
 
-        for name, data_torch in self.get_tensors():
-            old_dtype = data_torch.dtype
-
-            # convert any unsupported data types to float32
-            if data_torch.dtype not in (torch.float16, torch.float32):
-                data_torch = data_torch.to(torch.float32)
-
-            # QKV tensor transform
-            # The original query_key_value tensor contains n_head_kv "kv groups",
-            # each consisting of n_head/n_head_kv query weights followed by one key
-            # and one value weight (shared by all query heads in the kv group).
-            # This layout makes it a big pain to work with in GGML.
-            # So we rearrange them here,, so that we have n_head query weights
-            # followed by n_head_kv key weights followed by n_head_kv value weights,
-            # in contiguous fashion.
-            # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py
-
-            if "query_key_value" in name:
-                qkv = data_torch.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head)
-                q = qkv[:, :-2].reshape(n_head * head_dim, head_dim * n_head)
-                k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head)
-                v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head)
-                data_torch = torch.cat((q, k, v)).reshape_as(data_torch)
-
-            data = data_torch.squeeze().numpy()
-
-            # map tensor names
-            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
-            if new_name is None:
-                print(f"Can not map tensor {name!r}")
-                sys.exit()
-
-            n_dims = len(data.shape)
-            data_dtype = data.dtype
-
-            # if f32 desired, convert any float16 to float32
-            if self.ftype == 0 and data_dtype == np.float16:
-                data = data.astype(np.float32)
-
-            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
-            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
-                data = data.astype(np.float32)
-
-            # if f16 desired, convert any float32 2-dim weight tensors to float16
-            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
-                data = data.astype(np.float16)
-
-            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
-
-            self.gguf_writer.add_tensor(new_name, data)
+        return [(self.map_tensor_name(name), data_torch)]
 
 
+@Model.register("GPTBigCodeForCausalLM")
 class StarCoderModel(Model):
+    model_arch = gguf.MODEL_ARCH.STARCODER
+
     def set_gguf_parameters(self):
         block_count = self.hparams["n_layer"]
 
@@ -924,7 +1074,22 @@ class StarCoderModel(Model):
         self.gguf_writer.add_file_type(self.ftype)
 
 
+@Model.register("GPTRefactForCausalLM")
 class RefactModel(Model):
+    model_arch = gguf.MODEL_ARCH.REFACT
+
+    def set_vocab(self):
+        super().set_vocab()
+
+        # TODO: how to determine special FIM tokens automatically?
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
+                                          special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot'])
+        special_vocab._set_special_token("prefix", 1)
+        special_vocab._set_special_token("suffix", 3)
+        special_vocab._set_special_token("middle", 2)
+        special_vocab._set_special_token("fsep",   4) # is this correct?
+        special_vocab.add_to_gguf(self.gguf_writer)
+
     def set_gguf_parameters(self):
         hidden_dim = self.hparams["n_embd"]
         inner_dim = 4 * hidden_dim
@@ -946,7 +1111,7 @@ class RefactModel(Model):
         self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
         self.gguf_writer.add_file_type(self.ftype)
 
-    def write_tensors(self):
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         hidden_dim = self.hparams["n_embd"]
         inner_dim = 4 * hidden_dim
         hidden_dim = int(2 * inner_dim / 3)
@@ -955,60 +1120,29 @@ class RefactModel(Model):
         n_head = self.hparams["n_head"]
         n_head_kv = 1
         head_dim = self.hparams["n_embd"] // n_head
-        block_count = self.hparams["n_layer"]
 
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+        tensors: list[tuple[str, Tensor]] = []
 
-        tensors = dict(self.get_tensors())
-        for i in range(block_count):
-            if (w := tensors.get(f"transformer.h.{i}.attn.kv.weight")) is not None:
-                tensors[f"model.layers.{i}.self_attn.k_proj.weight"] = w[:n_head_kv * head_dim]
-                tensors[f"model.layers.{i}.self_attn.v_proj.weight"] = w[n_head_kv * head_dim:]
-                del tensors[f"transformer.h.{i}.attn.kv.weight"]
-            if (w := tensors.get(f"transformer.h.{i}.attn.q.weight")) is not None:
-                tensors[f"model.layers.{i}.self_attn.q_proj.weight"] = w
-                del tensors[f"transformer.h.{i}.attn.q.weight"]
-            if (w := tensors.get(f"transformer.h.{i}.mlp.gate_up_proj.weight")) is not None:
-                tensors[f"model.layers.{i}.mlp.gate_proj.weight"] = w[:ff_dim]
-                tensors[f"model.layers.{i}.mlp.up_proj.weight"] = w[ff_dim:]
-                del tensors[f"transformer.h.{i}.mlp.gate_up_proj.weight"]
+        if bid is not None:
+            if name == f"transformer.h.{bid}.attn.kv.weight":
+                tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), data_torch[:n_head_kv * head_dim]))
+                tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), data_torch[n_head_kv * head_dim:]))
+            elif name == f"transformer.h.{bid}.attn.q.weight":
+                tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), data_torch))
+            elif name == f"transformer.h.{bid}.mlp.gate_up_proj.weight":
+                tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), data_torch[:ff_dim]))
+                tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), data_torch[ff_dim:]))
 
-        for name, data_torch in tensors.items():
-            old_dtype = data_torch.dtype
+        if len(tensors) == 0:
+            tensors.append((self.map_tensor_name(name), data_torch))
 
-            # convert any unsupported data types to float32
-            if data_torch.dtype not in (torch.float16, torch.float32):
-                data_torch = data_torch.to(torch.float32)
-
-            data = data_torch.squeeze().numpy()
-
-            # map tensor names
-            new_name = tensor_map.get_name(name, try_suffixes=(".weight",))
-            if new_name is None:
-                print(f"Can not map tensor {name!r}")
-                sys.exit()
-
-            n_dims = len(data.shape)
-            data_dtype = data.dtype
-
-            # if f32 desired, convert any float16 to float32
-            if self.ftype == 0 and data_dtype == np.float16:
-                data = data.astype(np.float32)
-
-            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
-            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
-                data = data.astype(np.float32)
-
-            # if f16 desired, convert any float32 2-dim weight tensors to float16
-            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
-                data = data.astype(np.float16)
-
-            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
-
-            self.gguf_writer.add_tensor(new_name, data)
+        return tensors
 
 
+@Model.register("PersimmonForCausalLM")
 class PersimmonModel(Model):
+    model_arch = gguf.MODEL_ARCH.PERSIMMON
+
     def set_gguf_parameters(self):
         block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers"))
         head_count = self.hparams["num_attention_heads"]
@@ -1031,33 +1165,23 @@ class PersimmonModel(Model):
         self.gguf_writer.add_head_count_kv(head_count_kv)
         self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"])
         self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
 
     def set_vocab(self):
         self._set_vocab_sentencepiece()
         # self.gguf_writer.add_bos_token_id(71013)
         # self.gguf_writer.add_eos_token_id(71013)
 
-    def write_tensors(self):
-        block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers"))
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+    def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
+        del name, new_name, bid, n_dims  # unused
 
-        for name, data_torch in self.get_tensors():
-            if name.endswith(".self_attention.rotary_emb.inv_freq"):
-                continue
-            old_dtype = data_torch.dtype
-            # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?)
-            data = data_torch.to(torch.float32).squeeze().numpy()
-            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
-            if new_name is None:
-                print(f"Can not map tensor {name!r}")
-                sys.exit()
-            n_dims = len(data.shape)
-            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
-            self.gguf_writer.add_tensor(new_name, data)
+        # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?)
+        return True
 
 
+@Model.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM")
 class StableLMModel(Model):
+    model_arch = gguf.MODEL_ARCH.STABLELM
+
     def set_vocab(self):
         if (self.dir_model / "tokenizer.json").is_file():
             self._set_vocab_gpt2()
@@ -1074,18 +1198,308 @@ class StableLMModel(Model):
         self.gguf_writer.add_embedding_length(hparams["hidden_size"])
         self.gguf_writer.add_block_count(block_count)
         self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
-        self.gguf_writer.add_rope_dimension_count(int(hparams["rope_pct"] * (hparams["hidden_size"] // hparams["num_attention_heads"])))
+        rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"])
+        self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
         self.gguf_writer.add_head_count(hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"])
         self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
-        self.gguf_writer.add_layer_norm_eps(1e-5)
+        self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"]))
+        self.gguf_writer.add_file_type(self.ftype)
+
+    _q_norms: list[dict[str, Tensor]] | None = None
+    _k_norms: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams["num_attention_heads"]
+        n_kv_head = self.hparams["num_key_value_heads"]
+
+        if name.find("q_layernorm.norms") != -1:
+            assert bid is not None
+
+            if self._q_norms is None:
+                self._q_norms = [{} for _ in range(self.block_count)]
+
+            self._q_norms[bid][name] = data_torch
+
+            if len(self._q_norms[bid]) >= n_head:
+                return self._stack_qk_norm(bid, n_head, self._q_norms[bid], "q_layernorm")
+            else:
+                return []
+
+        if name.find("k_layernorm.norms") != -1:
+            assert bid is not None
+
+            if self._k_norms is None:
+                self._k_norms = [{} for _ in range(self.block_count)]
+
+            self._k_norms[bid][name] = data_torch
+
+            if len(self._k_norms[bid]) >= n_kv_head:
+                return self._stack_qk_norm(bid, n_kv_head, self._k_norms[bid], "k_layernorm")
+            else:
+                return []
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+    def _stack_qk_norm(self, bid: int, n_head: int, norms: dict[str, Tensor], layer_name: str = "q_layernorm"):
+        datas: list[Tensor] = []
+        # extract the norms in order
+        for xid in range(n_head):
+            ename = f"model.layers.{bid}.self_attn.{layer_name}.norms.{xid}.weight"
+            datas.append(norms[ename])
+            del norms[ename]
+        data_torch = torch.stack(datas, dim=0)
+
+        merged_name = f"model.layers.{bid}.self_attn.{layer_name}.weight"
+        new_name = self.map_tensor_name(merged_name)
+
+        return [(new_name, data_torch)]
+
+    def write_tensors(self):
+        super().write_tensors()
+
+        if self._q_norms is not None or self._k_norms is not None:
+            # flatten two `list[dict[str, Tensor]]` into a single `list[str]`
+            norms = (
+                [k for d in self._q_norms for k in d.keys()] if self._q_norms is not None else []
+            ) + (
+                [k for d in self._k_norms for k in d.keys()] if self._k_norms is not None else []
+            )
+            if len(norms) > 0:
+                raise ValueError(f"Unprocessed norms: {norms}")
 
 
-class MixtralModel(Model):
+@Model.register("LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM")
+class LlamaModel(Model):
+    model_arch = gguf.MODEL_ARCH.LLAMA
+
+    def set_vocab(self):
+        try:
+            self. _set_vocab_sentencepiece()
+        except FileNotFoundError:
+            try:
+                self._set_vocab_llama_hf()
+            except (FileNotFoundError, TypeError):
+                # Llama 3
+                self._set_vocab_gpt2()
+
+        # Apply to CodeLlama only (and ignore for Llama 3 with a vocab size of 128256)
+        if self.hparams.get("vocab_size", 32000) == 32016:
+            special_vocab = gguf.SpecialVocab(
+                self.dir_model, load_merges=False,
+                special_token_types = ['prefix', 'suffix', 'middle', 'eot']
+            )
+            special_vocab._set_special_token("prefix", 32007)
+            special_vocab._set_special_token("suffix", 32008)
+            special_vocab._set_special_token("middle", 32009)
+            special_vocab._set_special_token("eot",    32010)
+            special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+        self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
+
+        if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
+            if self.hparams["rope_scaling"].get("type") == "linear":
+                self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+                self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
+
+    @staticmethod
+    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
+        if n_head_kv is not None and n_head != n_head_kv:
+            n_head = n_head_kv
+        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
+                .swapaxes(1, 2)
+                .reshape(weights.shape))
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams["num_attention_heads"]
+        n_kv_head = self.hparams.get("num_key_value_heads")
+
+        if name.endswith("q_proj.weight"):
+            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
+        if name.endswith("k_proj.weight"):
+            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
+
+        # process the experts separately
+        if name.find("block_sparse_moe.experts") != -1:
+            n_experts = self.hparams["num_local_experts"]
+
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                tensors: list[tuple[str, Tensor]] = []
+
+                # merge the experts into a single 3d tensor
+                for wid in ["w1", "w2", "w3"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+
+                    tensors.append((new_name, data_torch))
+                return tensors
+            else:
+                return []
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+    def write_tensors(self):
+        super().write_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
+@Model.register("GrokForCausalLM")
+class GrokModel(Model):
+    model_arch = gguf.MODEL_ARCH.GROK
+
     def set_vocab(self):
         self._set_vocab_sentencepiece()
 
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
 
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_name("Grok")
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # process the experts separately
+        if name.find(".moe.") != -1:
+            n_experts = self.hparams["num_local_experts"]
+
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                tensors: list[tuple[str, Tensor]] = []
+
+                # merge the experts into a single 3d tensor
+                for wid in ["linear", "linear_1", "linear_v"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"transformer.decoder_layer.{bid}.moe.{wid}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+
+                    tensors.append((new_name, data_torch))
+                return tensors
+            else:
+                return []
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+
+@Model.register("DbrxForCausalLM")
+class DbrxModel(Model):
+    model_arch = gguf.MODEL_ARCH.DBRX
+
+    def set_gguf_parameters(self):
+        ffn_config = self.hparams["ffn_config"]
+        attn_config = self.hparams["attn_config"]
+        self.gguf_writer.add_name(self.hparams["model_type"])
+        self.gguf_writer.add_block_count(self.hparams["n_layers"])
+
+        self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
+        self.gguf_writer.add_embedding_length(self.hparams["d_model"])
+        self.gguf_writer.add_feed_forward_length(ffn_config["ffn_hidden_size"])
+
+        self.gguf_writer.add_head_count(self.hparams["n_heads"])
+        self.gguf_writer.add_head_count_kv(attn_config["kv_n_heads"])
+
+        self.gguf_writer.add_rope_freq_base(attn_config["rope_theta"])
+
+        self.gguf_writer.add_clamp_kqv(attn_config["clip_qkv"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+        self.gguf_writer.add_expert_count(ffn_config["moe_num_experts"])
+        self.gguf_writer.add_expert_used_count(ffn_config["moe_top_k"])
+
+        self.gguf_writer.add_layer_norm_eps(1e-5)
+
+        self.gguf_writer.add_file_type(self.ftype)
+        logger.info(f"gguf: file type = {self.ftype}")
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        n_expert = self.hparams["ffn_config"]["moe_num_experts"]
+        n_ff = self.hparams["ffn_config"]["ffn_hidden_size"]
+        n_embd = self.hparams["d_model"]
+
+        # Specific behavior for experts tensors: suffix .weight, view as 3D and transpose
+        # original implementation expects (n_expert, n_ff, n_embd) for all experts weights
+        # But llama.cpp moe graph works differently
+        # AND the dimensions in ggml are typically in the reverse order of the pytorch dimensions
+        # so (n_expert, n_ff, n_embd) in pytorch is {n_embd, n_ff, n_expert} in ggml_tensor
+        exp_tensor_names = {"ffn.experts.mlp.w1": None,       # LLM_TENSOR_FFN_GATE_EXPS ggml_tensor->ne{n_embd, n_ff,   n_expert}
+                            "ffn.experts.mlp.w2": (0, 2, 1),  # LLM_TENSOR_FFN_DOWN_EXPS ggml_tensor->ne{n_ff,   n_embd, n_expert}
+                            "ffn.experts.mlp.v1": None}       # LLM_TENSOR_FFN_UP_EXPS   ggml_tensor->ne{n_embd, n_ff,   n_expert}
+        experts = False
+
+        for exp_tensor_name in exp_tensor_names.keys():
+            if name.find(exp_tensor_name) != -1 and name.find(".weight") == -1:
+                experts = True
+                data_torch = data_torch.view(n_expert, n_ff, n_embd)
+                if (permute_tensor := exp_tensor_names[exp_tensor_name]) is not None:
+                    data_torch = data_torch.permute(*permute_tensor)
+                break
+
+        # map tensor names
+        # In MoE models the ffn tensors are typically most of the model weights,
+        # and need to be quantizable. Quantize expects tensor names to be suffixed by .weight.
+        # Every other model has the weight names ending in .weight,
+        # let's assume that is the convention which is not the case for dbrx:
+        # https://huggingface.co/databricks/dbrx-instruct/blob/main/model.safetensors.index.json#L15
+        new_name = self.map_tensor_name(name if not experts else name + ".weight", try_suffixes=(".weight",))
+
+        return [(new_name, data_torch)]
+
+    def extra_f16_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
+        del name, new_name, bid  # unused
+
+        return n_dims > 1
+
+
+@Model.register("MiniCPMForCausalLM")
 class MiniCPMModel(Model):
+    model_arch = gguf.MODEL_ARCH.MINICPM
+
     def set_gguf_parameters(self):
         block_count = self.hparams["num_hidden_layers"]
         self.gguf_writer.add_name("MiniCPM")
@@ -1100,7 +1514,7 @@ class MiniCPMModel(Model):
         self.gguf_writer.add_file_type(self.ftype)
 
     def set_vocab(self):
-        self._set_vocab_hf()
+        self._set_vocab_llama_hf()
 
     def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
         if n_kv_head is not None and n_head != n_kv_head:
@@ -1112,57 +1526,25 @@ class MiniCPMModel(Model):
             .reshape(weights.shape)
         )
 
-    def write_tensors(self):
-        block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
-        n_head = self.hparams.get("num_attention_heads")
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        n_head = self.hparams["num_attention_heads"]
         n_kv_head = self.hparams.get("num_key_value_heads")
-        for name, data_torch in self.get_tensors():
-            # we don't need these
-            if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")):
-                continue
 
-            old_dtype = data_torch.dtype
+        # HF models permute some of the tensors, so we need to undo that
+        if name.endswith(("q_proj.weight")):
+            data_torch = self._reverse_hf_permute(data_torch, n_head, n_head)
+        if name.endswith(("k_proj.weight")):
+            data_torch = self._reverse_hf_permute(data_torch, n_head, n_kv_head)
 
-            # convert any unsupported data types to float32
-            if data_torch.dtype not in (torch.float16, torch.float32):
-                data_torch = data_torch.to(torch.float32)
-
-            # HF models permute some of the tensors, so we need to undo that
-            if name.endswith(("q_proj.weight")):
-                data_torch = self._reverse_hf_permute(data_torch, n_head, n_head)
-            if name.endswith(("k_proj.weight")):
-                data_torch = self._reverse_hf_permute(data_torch, n_head, n_kv_head)
-
-            data = data_torch.squeeze().numpy()
-
-            # map tensor names
-            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
-            if new_name is None:
-                print(f"Can not map tensor {name!r}")
-                sys.exit()
-
-            n_dims = len(data.shape)
-            data_dtype = data.dtype
-
-            # if f32 desired, convert any float16 to float32
-            if self.ftype == 0 and data_dtype == np.float16:
-                data = data.astype(np.float32)
-
-            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
-            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
-                data = data.astype(np.float32)
-
-            # if f16 desired, convert any float32 2-dim weight tensors to float16
-            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
-                data = data.astype(np.float16)
-
-            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
-
-            self.gguf_writer.add_tensor(new_name, data)
+        return [(self.map_tensor_name(name), data_torch)]
 
 
+@Model.register("QWenLMHeadModel")
 class QwenModel(Model):
+    model_arch = gguf.MODEL_ARCH.QWEN
+
     @staticmethod
     def token_bytes_to_string(b):
         from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
@@ -1199,50 +1581,81 @@ class QwenModel(Model):
         self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
         self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
         self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+
+@Model.register("Qwen2ForCausalLM")
+class Qwen2Model(Model):
+    model_arch = gguf.MODEL_ARCH.QWEN2
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_gpt2()
+
+
+@Model.register("Qwen2MoeForCausalLM")
+class Qwen2MoeModel(Model):
+    model_arch = gguf.MODEL_ARCH.QWEN2MOE
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if (n_experts := self.hparams.get("num_experts")) is not None:
+            self.gguf_writer.add_expert_count(n_experts)
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # process the experts separately
+        if name.find("experts") != -1:
+            n_experts = self.hparams["num_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                tensors: list[tuple[str, Tensor]] = []
+
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+
+                    tensors.append((new_name, data_torch))
+                return tensors
+            else:
+                return []
+
+        return [(self.map_tensor_name(name), data_torch)]
 
     def write_tensors(self):
-        block_count = self.hparams["num_hidden_layers"]
-        model_kv = dict(self.get_tensors())
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
-        for name, data_torch in model_kv.items():
-            # we don't need these
-            if name.endswith(".rotary_emb.inv_freq"):
-                continue
+        super().write_tensors()
 
-            old_dtype = data_torch.dtype
-
-            # convert any unsupported data types to float32
-            if data_torch.dtype not in (torch.float16, torch.float32):
-                data_torch = data_torch.to(torch.float32)
-
-            data = data_torch.squeeze().numpy()
-
-            # map tensor names
-            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
-            if new_name is None:
-                print(f"Can not map tensor {name!r}")
-                sys.exit()
-
-            n_dims = len(data.shape)
-            data_dtype = data.dtype
-
-            # if f32 desired, convert any float16 to float32
-            if self.ftype == 0 and data_dtype == np.float16:
-                data = data.astype(np.float32)
-
-            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
-            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
-                data = data.astype(np.float32)
-
-            # if f16 desired, convert any float32 2-dim weight tensors to float16
-            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
-                data = data.astype(np.float16)
-
-            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
-            self.gguf_writer.add_tensor(new_name, data)
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
 
 
+@Model.register("GPT2LMHeadModel")
 class GPT2Model(Model):
+    model_arch = gguf.MODEL_ARCH.GPT2
+
     def set_gguf_parameters(self):
         self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_block_count(self.hparams["n_layer"])
@@ -1253,58 +1666,33 @@ class GPT2Model(Model):
         self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
         self.gguf_writer.add_file_type(self.ftype)
 
-    def write_tensors(self):
-        block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
 
-        for name, data_torch in self.get_tensors():
-            # we don't need these
-            if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq", ".attn.bias", ".attn.masked_bias")):
-                continue
+        tensors: list[tuple[str, Tensor]] = []
 
-            if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")):
-                data_torch = data_torch.transpose(1, 0)
+        # we don't need these
+        if name.endswith((".attn.bias", ".attn.masked_bias")):
+            return tensors
 
-            old_dtype = data_torch.dtype
+        if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")):
+            data_torch = data_torch.transpose(1, 0)
 
-            # convert any unsupported data types to float32
-            if data_torch.dtype not in (torch.float16, torch.float32):
-                data_torch = data_torch.to(torch.float32)
+        new_name = self.map_tensor_name(name)
 
-            data = data_torch.squeeze().numpy()
+        tensors.append((new_name, data_torch))
 
-            # map tensor names
-            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
-            if new_name is None:
-                print(f"Can not map tensor {name!r}")
-                sys.exit()
+        # note: GPT2 output is tied to (same as) wte in original model
+        if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD):
+            tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch))
 
-            n_dims = len(data.shape)
-            data_dtype = data.dtype
-
-            # if f32 desired, convert any float16 to float32
-            if self.ftype == 0 and data_dtype == np.float16:
-                data = data.astype(np.float32)
-
-            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
-            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
-                data = data.astype(np.float32)
-
-            # if f16 desired, convert any float32 2-dim weight tensors to float16
-            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
-                data = data.astype(np.float16)
-
-            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
-
-            self.gguf_writer.add_tensor(new_name, data)
-
-            # note: GPT2 output is tied to (same as) wte in original model
-            if new_name == "token_embd.weight":
-                print(f"output.weight, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
-                self.gguf_writer.add_tensor("output.weight", data)
+        return tensors
 
 
+@Model.register("PhiForCausalLM")
 class Phi2Model(Model):
+    model_arch = gguf.MODEL_ARCH.PHI2
+
     def set_gguf_parameters(self):
         block_count = self.find_hparam(["num_hidden_layers", "n_layer"])
 
@@ -1326,7 +1714,96 @@ class Phi2Model(Model):
         self.gguf_writer.add_add_bos_token(False)
 
 
+@Model.register("Phi3ForCausalLM")
+class Phi3MiniModel(Model):
+    model_arch = gguf.MODEL_ARCH.PHI3
+
+    def set_vocab(self):
+        from sentencepiece import SentencePieceProcessor
+
+        tokenizer_path = self.dir_model / 'tokenizer.model'
+
+        if not tokenizer_path.is_file():
+            raise ValueError(f'Error: Missing {tokenizer_path}')
+
+        tokenizer = SentencePieceProcessor()
+        tokenizer.LoadFromFile(str(tokenizer_path))
+
+        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
+
+        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
+        scores: list[float] = [-10000.0] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
+
+        for token_id in range(tokenizer.vocab_size()):
+
+            piece = tokenizer.IdToPiece(token_id)
+            text = piece.encode("utf-8")
+            score = tokenizer.GetScore(token_id)
+
+            toktype = SentencePieceTokenTypes.NORMAL
+            if tokenizer.IsUnknown(token_id):
+                toktype = SentencePieceTokenTypes.UNKNOWN
+            elif tokenizer.IsControl(token_id):
+                toktype = SentencePieceTokenTypes.CONTROL
+            elif tokenizer.IsUnused(token_id):
+                toktype = SentencePieceTokenTypes.UNUSED
+            elif tokenizer.IsByte(token_id):
+                toktype = SentencePieceTokenTypes.BYTE
+
+            tokens[token_id] = text
+            scores[token_id] = score
+            toktypes[token_id] = toktype
+
+        added_tokens_file = self.dir_model / 'added_tokens.json'
+        if added_tokens_file.is_file():
+            with open(added_tokens_file, "r", encoding="utf-8") as f:
+                added_tokens_json = json.load(f)
+
+                for key in added_tokens_json:
+                    token_id = added_tokens_json[key]
+                    if (token_id >= vocab_size):
+                        logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
+                        continue
+
+                    tokens[token_id] = key.encode("utf-8")
+                    scores[token_id] = -1000.0
+                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        block_count = self.find_hparam(["num_hidden_layers", "n_layer"])
+
+        rot_pct = 1.0
+        n_embd = self.find_hparam(["hidden_size", "n_embd"])
+        n_head = self.find_hparam(["num_attention_heads", "n_head"])
+        rms_eps = self.find_hparam(["rms_norm_eps"])
+
+        self.gguf_writer.add_name("Phi3")
+        self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"]))
+
+        self.gguf_writer.add_embedding_length(n_embd)
+        self.gguf_writer.add_feed_forward_length(8192)
+        self.gguf_writer.add_block_count(block_count)
+        self.gguf_writer.add_head_count(n_head)
+        self.gguf_writer.add_head_count_kv(n_head)
+        self.gguf_writer.add_layer_norm_rms_eps(rms_eps)
+        self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
+        self.gguf_writer.add_file_type(self.ftype)
+
+
+@Model.register("PlamoForCausalLM")
 class PlamoModel(Model):
+    model_arch = gguf.MODEL_ARCH.PLAMO
+
     def set_vocab(self):
         self._set_vocab_sentencepiece()
 
@@ -1342,6 +1819,7 @@ class PlamoModel(Model):
         self.gguf_writer.add_head_count(hparams["num_attention_heads"])
         self.gguf_writer.add_head_count_kv(5)  # hparams["num_key_value_heads"]) is wrong
         self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
+        self.gguf_writer.add_file_type(self.ftype)
 
     def shuffle_attn_q_weight(self, data_torch):
         assert data_torch.size() == (5120, 5120)
@@ -1357,55 +1835,24 @@ class PlamoModel(Model):
         data_torch = torch.reshape(data_torch, (5120, 5120))
         return data_torch
 
-    def write_tensors(self):
-        block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers"))
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
 
-        for name, data_torch in self.get_tensors():
-            if "self_attn.rotary_emb.inv_freq" in name:
-                continue
+        new_name = self.map_tensor_name(name)
 
-            # map tensor names
-            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
-            if new_name is None:
-                print(f"Can not map tensor {name!r}")
-                sys.exit()
+        # shuffle for broadcasting of gqa in ggml_mul_mat
+        if new_name.endswith("attn_q.weight"):
+            data_torch = self.shuffle_attn_q_weight(data_torch)
+        elif new_name.endswith("attn_output.weight"):
+            data_torch = self.shuffle_attn_output_weight(data_torch)
 
-            # shuffle for broadcasting of gqa in ggml_mul_mat
-            if new_name.endswith("attn_q.weight"):
-                data_torch = self.shuffle_attn_q_weight(data_torch)
-            elif new_name.endswith("attn_output.weight"):
-                data_torch = self.shuffle_attn_output_weight(data_torch)
-
-            old_dtype = data_torch.dtype
-
-            # convert any unsupported data types to float32
-            if data_torch.dtype not in (torch.float16, torch.float32):
-                data_torch = data_torch.to(torch.float32)
-
-            data = data_torch.squeeze().numpy()
-
-            n_dims = len(data.shape)
-            data_dtype = data.dtype
-
-            # if f32 desired, convert any float16 to float32
-            if self.ftype == 0 and data_dtype == np.float16:
-                data = data.astype(np.float32)
-
-            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
-            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
-                data = data.astype(np.float32)
-
-            # if f16 desired, convert any float32 2-dim weight tensors to float16
-            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
-                data = data.astype(np.float16)
-
-            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
-
-            self.gguf_writer.add_tensor(new_name, data)
+        return [(new_name, data_torch)]
 
 
+@Model.register("CodeShellForCausalLM")
 class CodeShellModel(Model):
+    model_arch = gguf.MODEL_ARCH.CODESHELL
+
     def set_gguf_parameters(self):
         block_count = self.hparams["n_layer"]
 
@@ -1422,55 +1869,27 @@ class CodeShellModel(Model):
         self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
         self.gguf_writer.add_rope_scaling_factor(1.0)
 
-    def write_tensors(self):
-        block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
-        tensors = dict(self.get_tensors())
-        has_lm_head = "lm_head.weight" in tensors.keys() or "output.weight" in tensors.keys()
-        for name, data_torch in tensors.items():
-            # we don't need these
-            if name.endswith((".attn.rotary_emb.inv_freq")):
-                continue
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
 
-            old_dtype = data_torch.dtype
+        new_name = self.map_tensor_name(name)
 
-            # convert any unsupported data types to float32
-            if data_torch.dtype not in (torch.float16, torch.float32):
-                data_torch = data_torch.to(torch.float32)
+        tensors: list[tuple[str, Tensor]] = [(new_name, data_torch)]
 
-            data = data_torch.squeeze().numpy()
+        if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD):
+            assert self.tensor_names is not None
 
-            # map tensor names
-            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
-            if new_name is None:
-                print(f"Can not map tensor {name!r}")
-                sys.exit()
+            if all(s not in self.tensor_names for s in ("lm_head.weight", "output.weight")):
+                # copy tok_embd.weight to output.weight
+                tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch))
 
-            n_dims = len(data.shape)
-            data_dtype = data.dtype
-
-            # if f32 desired, convert any float16 to float32
-            if self.ftype == 0 and data_dtype == np.float16:
-                data = data.astype(np.float32)
-
-            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
-            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
-                data = data.astype(np.float32)
-
-            # if f16 desired, convert any float32 2-dim weight tensors to float16
-            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
-                data = data.astype(np.float16)
-
-            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
-
-            self.gguf_writer.add_tensor(new_name, data)
-
-            if not has_lm_head and name == "transformer.wte.weight":
-                self.gguf_writer.add_tensor("output.weight", data)
-                print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}")
+        return tensors
 
 
+@Model.register("InternLM2ForCausalLM")
 class InternLM2Model(Model):
+    model_arch = gguf.MODEL_ARCH.INTERNLM2
+
     def set_vocab(self):
         # (TODO): Is there a better way?
         # Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character
@@ -1486,34 +1905,36 @@ class InternLM2Model(Model):
         toktypes: list[int] = []
 
         if not tokenizer_path.is_file():
-            print(f'Error: Missing {tokenizer_path}', file=sys.stderr)
+            logger.error(f'Error: Missing {tokenizer_path}')
             sys.exit(1)
 
         sentencepiece_model = model.ModelProto()
         sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
         add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
 
-        tokenizer = SentencePieceProcessor(str(tokenizer_path))
+        tokenizer = SentencePieceProcessor()
+        tokenizer.LoadFromFile(str(tokenizer_path))
+
         vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
 
         for token_id in range(vocab_size):
-            piece = tokenizer.id_to_piece(token_id)
+            piece = tokenizer.IdToPiece(token_id)
             text = piece.encode("utf-8")
-            score = tokenizer.get_score(token_id)
+            score = tokenizer.GetScore(token_id)
             if text == b"\x00":
                 # (TODO): fixme
                 # Hack here and replace the \x00 characters.
-                print(f"InternLM2 convert token '{text}' to '🐉'!")
-                text = "🐉"
+                logger.warning(f"InternLM2 convert token '{text}' to '🐉'!")
+                text = "🐉".encode("utf-8")
 
             toktype = SentencePieceTokenTypes.NORMAL
-            if tokenizer.is_unknown(token_id):
+            if tokenizer.IsUnknown(token_id):
                 toktype = SentencePieceTokenTypes.UNKNOWN
-            elif tokenizer.is_control(token_id):
+            elif tokenizer.IsControl(token_id):
                 toktype = SentencePieceTokenTypes.CONTROL
-            elif tokenizer.is_unused(token_id):
+            elif tokenizer.IsUnused(token_id):
                 toktype = SentencePieceTokenTypes.UNUSED
-            elif tokenizer.is_byte(token_id):
+            elif tokenizer.IsByte(token_id):
                 toktype = SentencePieceTokenTypes.BYTE
 
             tokens.append(text)
@@ -1531,6 +1952,7 @@ class InternLM2Model(Model):
                     toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
 
         self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
         self.gguf_writer.add_token_list(tokens)
         self.gguf_writer.add_token_scores(scores)
         self.gguf_writer.add_token_types(toktypes)
@@ -1540,20 +1962,24 @@ class InternLM2Model(Model):
         old_eos = special_vocab.special_token_ids["eos"]
         if "chat" in os.path.basename(self.dir_model.absolute()):
             # For the chat model, we replace the eos with '<|im_end|>'.
+            # TODO: this is a hack, should be fixed
+            #       https://github.com/ggerganov/llama.cpp/pull/6745#issuecomment-2067687048
             special_vocab.special_token_ids["eos"] = self._try_get_sft_eos(tokenizer)
-            print(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \
+            logger.warning(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \
 in chat mode so that the conversation can end normally.")
 
         special_vocab.add_to_gguf(self.gguf_writer)
 
     def _try_get_sft_eos(self, tokenizer):
-        unused_145_list = tokenizer.encode('[UNUSED_TOKEN_145]')
-        im_end_list = tokenizer.encode('<|im_end|>')
+        unused_145_list = tokenizer.Encode('[UNUSED_TOKEN_145]')
+        im_end_list = tokenizer.Encode('<|im_end|>')
+        eos_token = None
         assert (len(unused_145_list) == 1) ^ (len(im_end_list) == 1)
         if len(unused_145_list) == 1:
             eos_token = unused_145_list[0]
         if len(im_end_list) == 1:
             eos_token = im_end_list[0]
+        assert eos_token
         return eos_token
 
     def _hf_permute_qk(self, weights, n_head: int, n_head_kv: int):
@@ -1573,76 +1999,44 @@ in chat mode so that the conversation can end normally.")
         self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
         self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
         self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
+        self.gguf_writer.add_file_type(self.ftype)
 
-    def post_write_tensors(self, tensor_map, name, data_torch):
-        old_dtype = data_torch.dtype
-
-        # convert any unsupported data types to float32
-        if data_torch.dtype not in (torch.float16, torch.float32):
-            data_torch = data_torch.to(torch.float32)
-
-        data = data_torch.squeeze().numpy()
-
-        # map tensor names
-        new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
-        if new_name is None:
-            print(f"Can not map tensor {name!r}")
-            sys.exit()
-
-        n_dims = len(data.shape)
-        data_dtype = data.dtype
-
-        # if f32 desired, convert any float16 to float32
-        if self.ftype == 0 and data_dtype == np.float16:
-            data = data.astype(np.float32)
-
-        # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
-        if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
-            data = data.astype(np.float32)
-
-        # if f16 desired, convert any float32 2-dim weight tensors to float16
-        if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
-            data = data.astype(np.float16)
-
-        print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
-        self.gguf_writer.add_tensor(new_name, data)
-
-    def write_tensors(self):
-        from einops import rearrange
-
-        num_heads = self.hparams.get("num_attention_heads")
-        num_kv_heads = self.hparams.get("num_key_value_heads")
-        hidden_size = self.hparams.get("hidden_size")
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        num_heads = self.hparams["num_attention_heads"]
+        num_kv_heads = self.hparams["num_key_value_heads"]
+        hidden_size = self.hparams["hidden_size"]
         q_per_kv = num_heads // num_kv_heads
         head_dim = hidden_size // num_heads
         num_groups = num_heads // q_per_kv
 
-        block_count = self.hparams["num_hidden_layers"]
-        model_kv = dict(self.get_tensors())
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
         qkv_pattern = r"model\.layers\.(\d+)\.attention\.wqkv"
-        for name, data_torch in model_kv.items():
-            # we don't need these
-            if name.endswith(".rotary_emb.inv_freq"):
-                continue
 
-            if re.match(qkv_pattern, name):
-                bid = re.findall(qkv_pattern, name)[0]
-                qkv = data_torch
-                qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim)
-                q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :]
-                # The model weights of q and k equire additional reshape.
-                q = self._hf_permute_qk(rearrange(q, " o g n i ->  o (g n i)").T, num_heads, num_heads)
-                k = self._hf_permute_qk(rearrange(k, " o g n i ->  o (g n i)").T, num_heads, num_kv_heads)
-                v = rearrange(v, " o g n i ->  o (g n i)").T
-                self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wq.weight", q)
-                self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wk.weight", k)
-                self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wv.weight", v)
-            else:
-                self.post_write_tensors(tensor_map, name, data_torch)
+        if re.match(qkv_pattern, name):
+            bid = re.findall(qkv_pattern, name)[0]
+            qkv = data_torch
+            # qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim)
+            qkv = qkv.T.reshape((-1, num_groups, q_per_kv + 2, head_dim))
+            q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :]
+            # The model weights of q and k equire additional reshape.
+            # q = self._hf_permute_qk(rearrange(q, " o g n i ->  o (g n i)").T, num_heads, num_heads)
+            q = self._hf_permute_qk(q.reshape((q.shape[0], -1)).T, num_heads, num_heads)
+            # k = self._hf_permute_qk(rearrange(k, " o g n i ->  o (g n i)").T, num_heads, num_kv_heads)
+            k = self._hf_permute_qk(k.reshape((k.shape[0], -1)).T, num_heads, num_kv_heads)
+            # v = rearrange(v, " o g n i ->  o (g n i)").T
+            v = v.reshape((v.shape[0], -1)).T
+            return [
+                (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), q),
+                (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), k),
+                (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), v),
+            ]
+        else:
+            return [(self.map_tensor_name(name), data_torch)]
 
 
+@Model.register("BertModel", "CamembertModel")
 class BertModel(Model):
+    model_arch = gguf.MODEL_ARCH.BERT
+
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.vocab_size = None
@@ -1652,16 +2046,17 @@ class BertModel(Model):
         self.gguf_writer.add_causal_attention(False)
 
         # get pooling path
-        with open(self.dir_model / "modules.json", encoding="utf-8") as f:
-            modules = json.load(f)
         pooling_path = None
-        for mod in modules:
-            if mod["type"] == "sentence_transformers.models.Pooling":
-                pooling_path = mod["path"]
-                break
+        module_path = self.dir_model / "modules.json"
+        if module_path.is_file():
+            with open(module_path, encoding="utf-8") as f:
+                modules = json.load(f)
+            for mod in modules:
+                if mod["type"] == "sentence_transformers.models.Pooling":
+                    pooling_path = mod["path"]
+                    break
 
         # get pooling type
-        pooling_type = gguf.PoolingType.NONE
         if pooling_path is not None:
             with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f:
                 pooling = json.load(f)
@@ -1671,84 +2066,49 @@ class BertModel(Model):
                 pooling_type = gguf.PoolingType.CLS
             else:
                 raise NotImplementedError("Only MEAN and CLS pooling types supported")
-
-        self.gguf_writer.add_pooling_type(pooling_type.value)
+            self.gguf_writer.add_pooling_type(pooling_type)
 
     def set_vocab(self):
-        path = self.dir_model
-        added_tokens_path = self.dir_model if self.dir_model.exists() else None
-
-        # use huggingface vocab to get all tokens
-        vocab = HfVocab(path, added_tokens_path)
-        tokens, scores, toktypes = zip(*vocab.all_tokens())
-        assert len(tokens) == vocab.vocab_size
-        self.vocab_size = vocab.vocab_size
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        self.vocab_size = len(tokens)
 
         # we need this to validate the size of the token_type embeddings
         # though currently we are passing all zeros to the token_type embeddings
-        n_token_types = len(set(toktypes))
-        self.gguf_writer.add_token_type_count(n_token_types)
+        self.gguf_writer.add_token_type_count(2)  # "Sequence A" or "Sequence B"
 
         # convert to phantom space vocab
-        def phantom(tok, typ):
-            if tok.startswith(b"[") and tok.endswith(b"]"):
+        def phantom(tok):
+            if tok.startswith("[") and tok.endswith("]"):
                 return tok
-            if tok.startswith(b"##"):
+            if tok.startswith("##"):
                 return tok[2:]
-            return b"\xe2\x96\x81" + tok
-        tokens = tuple(phantom(t, y) for t, y in zip(tokens, toktypes))
-
-        # set up bos and eos tokens (cls and sep)
-        self.gguf_writer.add_bos_token_id(vocab.tokenizer.cls_token_id)
-        self.gguf_writer.add_eos_token_id(vocab.tokenizer.sep_token_id)
+            return "\u2581" + tok
+        tokens = list(map(phantom, tokens))
 
         # add vocab to gguf
         self.gguf_writer.add_tokenizer_model("bert")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
         self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_scores(scores)
         self.gguf_writer.add_token_types(toktypes)
 
         # handle special tokens
         special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
         special_vocab.add_to_gguf(self.gguf_writer)
 
-    def write_tensors(self):
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
-        tensors = dict(self.get_tensors())
-        for name, data_torch in tensors.items():
-            # we are only using BERT for embeddings so we don't need the pooling layer
-            if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"):
-                continue  # we don't need these
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
 
-            # map tensor names
-            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
-            if new_name is None:
-                print(f"Can not map tensor {name!r}")
-                sys.exit()
+        # we are only using BERT for embeddings so we don't need the pooling layer
+        if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"):
+            return [] # we don't need these
 
-            data = data_torch.squeeze().numpy()
-            n_dims = len(data.shape)
-            new_dtype: type[np.floating[Any]]
-
-            if (
-                self.ftype == 1 and name.endswith(".weight") and n_dims == 2
-                and name != "embeddings.token_type_embeddings.weight"  # not used with get_rows, must be F32
-            ):
-                # if f16 desired, convert any float32 2-dim weight tensors to float16
-                new_dtype = np.float16
-            else:
-                # if f32 desired, convert any float16 to float32
-                new_dtype = np.float32
-
-            print(f"{new_name}, n_dims = {n_dims}, {data_torch.dtype} --> {new_dtype}")
-
-            if data.dtype != new_dtype:
-                data = data.astype(new_dtype)
-
-            self.gguf_writer.add_tensor(new_name, data)
+        return [(self.map_tensor_name(name), data_torch)]
 
 
+@Model.register("NomicBertModel")
 class NomicBertModel(BertModel):
+    model_arch = gguf.MODEL_ARCH.NOMIC_BERT
+
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
@@ -1774,20 +2134,303 @@ class NomicBertModel(BertModel):
         super().set_gguf_parameters()
         self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
 
+
+@Model.register("GemmaForCausalLM")
+class GemmaModel(Model):
+    model_arch = gguf.MODEL_ARCH.GEMMA
+
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+
+        # TODO: these special tokens should be exported only for the CodeGemma family
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
+                                          special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot'])
+        special_vocab._set_special_token("prefix", 67)
+        special_vocab._set_special_token("suffix", 69)
+        special_vocab._set_special_token("middle", 68)
+        special_vocab._set_special_token("fsep",   70)
+        special_vocab._set_special_token("eot",    107)
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        hparams = self.hparams
+        block_count = hparams["num_hidden_layers"]
+
+        self.gguf_writer.add_name(self.dir_model.name)
+        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
+        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
+        self.gguf_writer.add_block_count(block_count)
+        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
+        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
+        self.gguf_writer.add_key_length(hparams["head_dim"])
+        self.gguf_writer.add_value_length(hparams["head_dim"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        # lm_head is not used in llama.cpp, while autoawq will include this tensor in model
+        # To prevent errors, skip loading lm_head.weight.
+        if name == "lm_head.weight":
+            logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.")
+            return []
+
+        # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89
+        if name.endswith("norm.weight"):
+            data_torch = data_torch + 1
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+
+@Model.register("Starcoder2ForCausalLM")
+class StarCoder2Model(Model):
+    model_arch = gguf.MODEL_ARCH.STARCODER2
+
+
+@Model.register("MambaForCausalLM", "MambaLMHeadModel")
+class MambaModel(Model):
+    model_arch = gguf.MODEL_ARCH.MAMBA
+
+    def set_vocab(self):
+        vocab_size = self.hparams["vocab_size"]
+        # Round vocab size to next multiple of 8
+        pad_vocab = self.hparams.get("pad_vocab_size_multiple", 8)
+        # pad using ceiling division
+        # ref: https://stackoverflow.com/a/17511341/22827863
+        vocab_size = -(vocab_size // -pad_vocab) * pad_vocab
+        self.hparams["vocab_size"] = vocab_size
+
+        if (self.dir_model / "tokenizer.json").is_file():
+            self._set_vocab_gpt2()
+        elif (self.dir_model / "tokenizer.model").is_file():
+            self._set_vocab_sentencepiece()
+        else:
+            # Use the GPT-NeoX tokenizer when no tokenizer files are present
+            tokenizer_path = Path(sys.path[0]) / "models" / "ggml-vocab-gpt-neox.gguf"
+            logger.warning(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'")
+            neox_reader = gguf.GGUFReader(tokenizer_path, "r")
+
+            field = neox_reader.get_field(gguf.Keys.Tokenizer.MODEL)
+            self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]).decode("utf-8") if field else "gpt2")
+
+            field = neox_reader.get_field(gguf.Keys.Tokenizer.PRE)
+            self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1]).decode("utf-8") if field else "mpt")
+
+            field = neox_reader.get_field(gguf.Keys.Tokenizer.LIST)
+            assert field
+            self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size])
+
+            field = neox_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE)
+            assert field
+            self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
+
+            field = neox_reader.get_field(gguf.Keys.Tokenizer.MERGES)
+            assert field
+            self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data])
+
+            field = neox_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)
+            self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0] if field else 1)
+
+            field = neox_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)
+            self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0] if field else 0)
+
+            field = neox_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)
+            self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0] if field else 0)
+
+            field = neox_reader.get_field(gguf.Keys.Tokenizer.PAD_ID)
+            self.gguf_writer.add_pad_token_id(field.parts[-1].tolist()[0] if field else 0)
+
+    def set_gguf_parameters(self):
+        d_model = self.find_hparam(["hidden_size",       "d_model"])
+        d_conv  = self.find_hparam(["conv_kernel",       "d_conv"],  optional=True) or 4
+        d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model
+        d_state = self.find_hparam(["state_size",        "d_state"], optional=True) or 16
+        # ceiling division
+        # ref: https://stackoverflow.com/a/17511341/22827863
+        # ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58
+        dt_rank      = self.find_hparam(["time_step_rank",     "dt_rank"],      optional=True) or -(d_model // -16)
+        rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5
+
+        # Fail early for models which don't have a block expansion factor of 2
+        assert d_inner == 2 * d_model
+
+        self.gguf_writer.add_name(self.dir_model.name)
+        self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default
+        self.gguf_writer.add_embedding_length(d_model)
+        self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading
+        self.gguf_writer.add_head_count(0) # unused, but seemingly required when loading
+        self.gguf_writer.add_block_count(self.hparams["n_layer"])
+        self.gguf_writer.add_ssm_conv_kernel(d_conv)
+        self.gguf_writer.add_ssm_inner_size(d_inner)
+        self.gguf_writer.add_ssm_state_size(d_state)
+        self.gguf_writer.add_ssm_time_step_rank(dt_rank)
+        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
+        self.gguf_writer.add_file_type(self.ftype)
+
+    _tok_embd = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT)
+        tok_embd_name = self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD)
+
+        new_name = self.map_tensor_name(name)
+
+        if name.endswith(".A_log"):
+            logger.debug("A_log --> A ==> " + new_name)
+            data_torch = -torch.exp(data_torch)
+
+        # assuming token_embd.weight is seen before output.weight
+        if self._tok_embd is not None and new_name == output_name:
+            if torch.equal(self._tok_embd, data_torch):
+                logger.debug(f"{output_name} is equivalent to {tok_embd_name}, omitting")
+                return []
+        elif new_name == tok_embd_name:
+            self._tok_embd = data_torch
+
+        return [(new_name, data_torch)]
+
+    def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
+        del n_dims  # unused
+
+        return bid is not None and new_name in (
+            self.format_tensor_name(n, bid, ".weight" if name.endswith(".weight") else "") for n in [
+                gguf.MODEL_TENSOR.SSM_CONV1D,
+                gguf.MODEL_TENSOR.SSM_X,
+                gguf.MODEL_TENSOR.SSM_DT,
+                gguf.MODEL_TENSOR.SSM_A,
+                gguf.MODEL_TENSOR.SSM_D,
+            ]
+        )
+
+
+@Model.register("CohereForCausalLM")
+class CommandR2Model(Model):
+    model_arch = gguf.MODEL_ARCH.COMMAND_R
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # max_position_embeddings = 8192 in config.json but model was actually
+        # trained on 128k context length
+        self.hparams["max_position_embeddings"] = self.hparams["model_max_length"]
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_logit_scale(self.hparams["logit_scale"])
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+
+
+@Model.register("OlmoForCausalLM")
+@Model.register("OLMoForCausalLM")
+class OlmoModel(Model):
+    model_arch = gguf.MODEL_ARCH.OLMO
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_layer_norm_eps(1e-5)
+        clip_qkv = self.hparams.get("clip_qkv")
+        if clip_qkv is not None:
+            self.gguf_writer.add_clamp_kqv(clip_qkv)
+
+    # Same as super class, but permuting q_proj, k_proj
+    # Copied from: LlamaModel
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        n_head = self.hparams["num_attention_heads"]
+        n_kv_head = self.hparams.get("num_key_value_heads")
+
+        if name.endswith("q_proj.weight"):
+            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
+        if name.endswith("k_proj.weight"):
+            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+
+@Model.register("JinaBertModel", "JinaBertForMaskedLM")
+class JinaBertV2Model(BertModel):
+    model_arch = gguf.MODEL_ARCH.JINA_BERT_V2
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.intermediate_size = self.hparams["intermediate_size"]
+
     def get_tensors(self):
-        assert self.vocab_size is not None
         for name, data in super().get_tensors():
-            # Nomic Embed's token embeddings tensor is padded, but llama.cpp wants tensor sizes to match exactly.
-            if name == 'embeddings.word_embeddings.weight' and data.shape[1] != self.vocab_size:
-                rounded_vocab_size = (self.vocab_size + 63) // 64 * 64
-                assert data.shape == (rounded_vocab_size, self.hparams["n_embd"])
-                data = data[:self.vocab_size, :]
+            if 'gated_layers' in name:
+                d1 = data[:self.intermediate_size, :]
+                name1 = name.replace('gated_layers', 'gated_layers_w')
+                d2 = data[self.intermediate_size:, :]
+                name2 = name.replace('gated_layers', 'gated_layers_v')
+                yield name1, d1
+                yield name2, d2
+                continue
+
             yield name, data
 
+    def set_vocab(self, *args, **kwargs):
+        tokenizer_class = 'BertTokenizer'
+        with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f:
+            tokenizer_class = json.load(f)['tokenizer_class']
+
+        if tokenizer_class == 'BertTokenizer':
+            super().set_vocab()
+        elif tokenizer_class == 'RobertaTokenizer':
+            self._set_vocab_gpt2()
+            self.gguf_writer.add_token_type_count(2)
+        else:
+            raise NotImplementedError(f'Tokenizer {tokenizer_class} is not supported for JinaBertModel')
+        self.gguf_writer.add_add_bos_token(True)
+        self.gguf_writer.add_add_eos_token(True)
+
 
 ###### CONVERSION LOGIC ######
 
 
+# tree of lazy tensors
+class LazyTorchTensor(gguf.LazyBase):
+    _tensor_type = torch.Tensor
+    # to keep the type-checker happy
+    dtype: torch.dtype
+    shape: torch.Size
+
+    # only used when converting a torch.Tensor to a np.ndarray
+    _dtype_map: dict[torch.dtype, type] = {
+        torch.float16: np.float16,
+        torch.float32: np.float32,
+    }
+
+    def numpy(self) -> gguf.LazyNumpyTensor:
+        dtype = self._dtype_map[self.dtype]
+        return gguf.LazyNumpyTensor(
+            meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(dtype, self.shape),
+            lazy=self._lazy,
+            args=(self,),
+            func=(lambda s: s[0].numpy())
+        )
+
+    @classmethod
+    def meta_with_dtype_and_shape(cls, dtype: torch.dtype, shape: torch.Size) -> Tensor:
+        return torch.empty(size=shape, dtype=dtype, device="meta")
+
+    @classmethod
+    def __torch_function__(cls, func, types, args=(), kwargs=None):
+        del types  # unused
+
+        if kwargs is None:
+            kwargs = {}
+
+        if func is torch.Tensor.numpy:
+            return args[0].numpy()
+
+        return LazyTorchTensor._wrap_fn(func)(*args, **kwargs)
+
+
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(
         description="Convert a huggingface model to a GGML compatible file")
@@ -1797,20 +2440,40 @@ def parse_args() -> argparse.Namespace:
     )
     parser.add_argument(
         "--awq-path", type=Path, default=None,
-        help="Path to scale awq cache file")
+        help="Path to scale awq cache file",
+    )
     parser.add_argument(
         "--outfile", type=Path,
-        help="path to write to; default: based on input",
+        help="path to write to; default: based on input. {ftype} will be replaced by the outtype.",
     )
     parser.add_argument(
-        "--outtype", type=str, choices=["f32", "f16"], default="f16",
-        help="output format - use f32 for float32, f16 for float16",
+        "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "auto"], default="f16",
+        help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type",
+    )
+    parser.add_argument(
+        "--bigendian", action="store_true",
+        help="model is executed on big endian machine",
     )
-    parser.add_argument("--bigendian", action="store_true", help="model is executed on big endian machine")
     parser.add_argument(
         "model", type=Path,
         help="directory containing model file",
     )
+    parser.add_argument(
+        "--use-temp-file", action="store_true",
+        help="use the tempfile library while processing (helpful when running out of memory, process killed)",
+    )
+    parser.add_argument(
+        "--no-lazy", action="store_true",
+        help="use more RAM by computing all outputs before writing (use in case lazy evaluation is broken)",
+    )
+    parser.add_argument(
+        "--model-name", type=str, default=None,
+        help="name of the model",
+    )
+    parser.add_argument(
+        "--verbose", action="store_true",
+        help="increase output verbosity",
+    )
 
     return parser.parse_args()
 
@@ -1818,6 +2481,8 @@ def parse_args() -> argparse.Namespace:
 def main() -> None:
     args = parse_args()
 
+    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
+
     dir_model = args.model
 
     if args.awq_path:
@@ -1826,50 +2491,55 @@ def main() -> None:
         tmp_model_path = args.model / "weighted_model"
         dir_model = tmp_model_path
         if tmp_model_path.is_dir():
-            print(f"{tmp_model_path} exists as a weighted model.")
+            logger.info(f"{tmp_model_path} exists as a weighted model.")
         else:
             tmp_model_path.mkdir(parents=True, exist_ok=True)
-            print("Saving new weighted model ...")
+            logger.info("Saving new weighted model ...")
             add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path))
-            print(f"Saved weighted model at {tmp_model_path}.")
+            logger.info(f"Saved weighted model at {tmp_model_path}.")
 
     if not dir_model.is_dir():
-        print(f'Error: {args.model} is not a directory', file=sys.stderr)
+        logger.error(f'Error: {args.model} is not a directory')
         sys.exit(1)
 
-    ftype_map = {
-        "f32": gguf.GGMLQuantizationType.F32,
-        "f16": gguf.GGMLQuantizationType.F16,
+    ftype_map: dict[str, gguf.LlamaFileType] = {
+        "f32": gguf.LlamaFileType.ALL_F32,
+        "f16": gguf.LlamaFileType.MOSTLY_F16,
+        "bf16": gguf.LlamaFileType.MOSTLY_BF16,
+        "q8_0": gguf.LlamaFileType.MOSTLY_Q8_0,
+        "auto": gguf.LlamaFileType.GUESSED,
     }
 
     if args.outfile is not None:
         fname_out = args.outfile
     else:
         # output in the same directory as the model by default
-        fname_out = dir_model / f'ggml-model-{args.outtype}.gguf'
+        fname_out = dir_model / 'ggml-model-{ftype}.gguf'
 
-    print(f"Loading model: {dir_model.name}")
+    logger.info(f"Loading model: {dir_model.name}")
 
     hparams = Model.load_hparams(dir_model)
 
     with torch.inference_mode():
         model_class = Model.from_model_architecture(hparams["architectures"][0])
-        model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian)
+        model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file, args.no_lazy)
 
-        print("Set model parameters")
+        logger.info("Set model parameters")
         model_instance.set_gguf_parameters()
 
-        print("Set model tokenizer")
+        logger.info("Set model tokenizer")
         model_instance.set_vocab()
 
+        model_instance.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
+
         if args.vocab_only:
-            print(f"Exporting model vocab to '{fname_out}'")
+            logger.info(f"Exporting model vocab to '{model_instance.fname_out}'")
             model_instance.write_vocab()
         else:
-            print(f"Exporting model to '{fname_out}'")
+            logger.info(f"Exporting model to '{model_instance.fname_out}'")
             model_instance.write()
 
-        print(f"Model successfully exported to '{fname_out}'")
+        logger.info(f"Model successfully exported to '{model_instance.fname_out}'")
 
 
 if __name__ == '__main__':
diff --git a/convert-llama-ggml-to-gguf.py b/convert-llama-ggml-to-gguf.py
index b33108062..9349de3b3 100755
--- a/convert-llama-ggml-to-gguf.py
+++ b/convert-llama-ggml-to-gguf.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 from __future__ import annotations
 
+import logging
 import argparse
 import os
 import struct
@@ -14,6 +15,8 @@ if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf
 
+logger = logging.getLogger("ggml-to-gguf")
+
 
 class GGMLFormat(IntEnum):
     GGML = 0
@@ -125,7 +128,6 @@ class Tensor:
         self.start_offset = offset
         self.len_bytes = n_bytes
         offset += n_bytes
-        # print(n_dims, name_len, dtype, self.dims, self.name, pad)
         return offset - orig_offset
 
 
@@ -175,7 +177,7 @@ class GGMLModel:
         offset += self.validate_header(data, offset)
         hp = Hyperparameters()
         offset += hp.load(data, offset)
-        print(f'* File format: {self.file_format.name}v{self.format_version} with ftype {hp.ftype.name}')
+        logger.info(f'* File format: {self.file_format.name}v{self.format_version} with ftype {hp.ftype.name}')
         self.validate_conversion(hp.ftype)
         vocab = Vocab(load_scores = self.file_format > GGMLFormat.GGML)
         offset += vocab.load(data, offset, hp.n_vocab)
@@ -215,12 +217,12 @@ class GGMLToGGUF:
                     if float(hp.n_head) / float(x) == gqa:
                         n_kv_head = x
                 assert n_kv_head is not None, "Couldn't determine n_kv_head from GQA param"
-                print(f'- Guessed n_kv_head = {n_kv_head} based on GQA {cfg.gqa}')
+                logger.info(f'- Guessed n_kv_head = {n_kv_head} based on GQA {cfg.gqa}')
         self.n_kv_head = n_kv_head
         self.name_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.LLAMA, ggml_model.hyperparameters.n_layer)
 
     def save(self):
-        print('* Preparing to save GGUF file')
+        logger.info('* Preparing to save GGUF file')
         gguf_writer = gguf.GGUFWriter(
             self.cfg.output,
             gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA],
@@ -230,11 +232,11 @@ class GGMLToGGUF:
         if self.special_vocab is not None:
             self.special_vocab.add_to_gguf(gguf_writer)
         self.add_tensors(gguf_writer)
-        print("    gguf: write header")
+        logger.info("    gguf: write header")
         gguf_writer.write_header_to_file()
-        print("    gguf: write metadata")
+        logger.info("    gguf: write metadata")
         gguf_writer.write_kv_data_to_file()
-        print("    gguf: write tensors")
+        logger.info("    gguf: write tensors")
         gguf_writer.write_tensors_to_file()
         gguf_writer.close()
 
@@ -250,7 +252,7 @@ class GGMLToGGUF:
             name = cfg.name if cfg.name is not None else cfg.input.name
         except UnicodeDecodeError:
             name = None
-        print('* Adding model parameters and KV items')
+        logger.info('* Adding model parameters and KV items')
         if name is not None:
             gguf_writer.add_name(name)
         gguf_writer.add_description(desc)
@@ -281,12 +283,13 @@ class GGMLToGGUF:
     def add_vocab(self, gguf_writer):
         hp = self.model.hyperparameters
         gguf_writer.add_tokenizer_model('llama')
+        gguf_writer.add_tokenizer_pre('default')
         tokens = []
         scores = []
         toktypes = []
         if self.vocab_override is not None:
             vo = self.vocab_override
-            print('* Adding vocab item(s)')
+            logger.info('* Adding vocab item(s)')
             for (idx, (vbytes, score, ttype)) in enumerate(vo.all_tokens()):
                 tokens.append(vbytes)
                 scores.append(score)
@@ -298,7 +301,7 @@ class GGMLToGGUF:
             if len(toktypes) > 0:
                 gguf_writer.add_token_types(toktypes)
             return
-        print(f'* Adding {hp.n_vocab} vocab item(s)')
+        logger.info(f'* Adding {hp.n_vocab} vocab item(s)')
         assert len(self.model.vocab.items) >= 3, 'Cannot handle unexpectedly short model vocab'
         for (tokid, (vbytes, vscore)) in enumerate(self.model.vocab.items):
             tt = 1 # Normal
@@ -333,7 +336,7 @@ class GGMLToGGUF:
     def add_tensors(self, gguf_writer):
         tensor_map = self.name_map
         data = self.data
-        print(f'* Adding {len(self.model.tensors)} tensor(s)')
+        logger.info(f'* Adding {len(self.model.tensors)} tensor(s)')
         for tensor in self.model.tensors:
             name = str(tensor.name, 'UTF-8')
             mapped_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
@@ -343,7 +346,6 @@ class GGMLToGGUF:
                 temp = tempdims[1]
                 tempdims[1] = tempdims[0]
                 tempdims[0] = temp
-            # print(f'+ {tensor.name} | {mapped_name} {tensor.dims} :: {tempdims}')
             gguf_writer.add_tensor(
                 mapped_name,
                 data[tensor.start_offset:tensor.start_offset + tensor.len_bytes],
@@ -373,7 +375,7 @@ def handle_metadata(cfg, hp):
         raise ValueError('Unable to load metadata')
     vocab_path = Path(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir)
     vocab_factory = convert.VocabFactory(vocab_path)
-    vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype, cfg.model_metadata_dir)
+    vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype.split(","), cfg.model_metadata_dir)
     convert.check_vocab_size(params, vocab)
     return params, vocab, special_vocab
 
@@ -398,35 +400,37 @@ def handle_args():
                         help ='Load HuggingFace/.pth vocab and metadata from the specified directory')
     parser.add_argument("--vocab-dir", type=Path,
                         help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir")
-    parser.add_argument("--vocabtype", choices=["spm", "bpe"], default="spm",
-                        help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)")
+    parser.add_argument("--vocabtype", default="spm,hfft",
+                        help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm,hfft)")
+    parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
     return parser.parse_args()
 
 
 def main():
     cfg = handle_args()
-    print(f'* Using config: {cfg}')
-    print('\n=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===\n')
+    logging.basicConfig(level=logging.DEBUG if cfg.verbose else logging.INFO)
+    logger.info(f'* Using config: {cfg}')
+    logger.warning('=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===')
     if cfg.model_metadata_dir is None and (cfg.gqa == 1 or cfg.eps == '5.0e-06'):
-        print('- Note: If converting LLaMA2, specifying "--eps 1e-5" is required. 70B models also need "--gqa 8".')
+        logger.info('- Note: If converting LLaMA2, specifying "--eps 1e-5" is required. 70B models also need "--gqa 8".')
     data = np.memmap(cfg.input, mode = 'r')
     model = GGMLModel()
-    print('* Scanning GGML input file')
+    logger.info('* Scanning GGML input file')
     offset = model.load(data, 0)  # noqa
-    print(f'* GGML model hyperparameters: {model.hyperparameters}')
+    logger.info(f'* GGML model hyperparameters: {model.hyperparameters}')
     vocab_override = None
     params_override = None
     special_vocab = None
     if cfg.model_metadata_dir is not None:
         (params_override, vocab_override, special_vocab) = handle_metadata(cfg, model.hyperparameters)
-        print('!! Note: When overriding params the --gqa, --eps and --context-length options are ignored.')
-        print(f'* Overriding params: {params_override}')
-        print(f'* Overriding vocab: {vocab_override}')
-        print(f'* Special vocab: {special_vocab}')
+        logger.info('!! Note: When overriding params the --gqa, --eps and --context-length options are ignored.')
+        logger.info(f'* Overriding params: {params_override}')
+        logger.info(f'* Overriding vocab: {vocab_override}')
+        logger.info(f'* Special vocab: {special_vocab}')
     else:
-        print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n')
+        logger.warning('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n')
         if model.file_format == GGMLFormat.GGML:
-            print('! This is a very old GGML file that does not contain vocab scores. Strongly recommend using model metadata!')
+            logger.info('! This is a very old GGML file that does not contain vocab scores. Strongly recommend using model metadata!')
     converter = GGMLToGGUF(
         model, data, cfg,
         params_override = params_override,
@@ -434,7 +438,7 @@ def main():
         special_vocab = special_vocab
     )
     converter.save()
-    print(f'* Successful completion. Output saved to: {cfg.output}')
+    logger.info(f'* Successful completion. Output saved to: {cfg.output}')
 
 
 if __name__ == '__main__':
diff --git a/convert-lora-to-ggml.py b/convert-lora-to-ggml.py
deleted file mode 100755
index 9a9936dec..000000000
--- a/convert-lora-to-ggml.py
+++ /dev/null
@@ -1,148 +0,0 @@
-#!/usr/bin/env python3
-from __future__ import annotations
-
-import json
-import os
-import struct
-import sys
-from pathlib import Path
-from typing import Any, BinaryIO, Sequence
-
-import numpy as np
-import torch
-
-if 'NO_LOCAL_GGUF' not in os.environ:
-    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
-import gguf
-
-NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1}
-
-
-def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None:
-    fout.write(b"ggla"[::-1])  # magic (ggml lora)
-    fout.write(struct.pack("i", 1))  # file version
-    fout.write(struct.pack("i", params["r"]))
-    # https://opendelta.readthedocs.io/en/latest/modules/deltas.html says that `lora_alpha` is an int
-    # but some models ship a float value instead
-    # let's convert to int, but fail if lossless conversion is not possible
-    assert (
-        int(params["lora_alpha"]) == params["lora_alpha"]
-    ), "cannot convert float to int losslessly"
-    fout.write(struct.pack("i", int(params["lora_alpha"])))
-
-
-def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_type: np.dtype[Any]) -> None:
-    sname = name.encode("utf-8")
-    fout.write(
-        struct.pack(
-            "iii",
-            len(shape),
-            len(sname),
-            NUMPY_TYPE_TO_FTYPE[data_type.name],
-        )
-    )
-    fout.write(struct.pack("i" * len(shape), *shape[::-1]))
-    fout.write(sname)
-    fout.seek((fout.tell() + 31) & -32)
-
-
-if __name__ == '__main__':
-    if len(sys.argv) < 2:
-        print(f"Usage: python {sys.argv[0]} <path> [arch]")
-        print(
-            "Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'"
-        )
-        print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)")
-        sys.exit(1)
-
-    input_json = os.path.join(sys.argv[1], "adapter_config.json")
-    input_model = os.path.join(sys.argv[1], "adapter_model.bin")
-    output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin")
-
-    if os.path.exists(input_model):
-        model = torch.load(input_model, map_location="cpu")
-    else:
-        input_model = os.path.join(sys.argv[1], "adapter_model.safetensors")
-        # lazy import load_file only if lora is in safetensors format.
-        from safetensors.torch import load_file
-        model = load_file(input_model, device="cpu")
-
-    arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama"
-
-    if arch_name not in gguf.MODEL_ARCH_NAMES.values():
-        print(f"Error: unsupported architecture {arch_name}")
-        sys.exit(1)
-
-    arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)]
-    name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone
-
-    with open(input_json, "r") as f:
-        params = json.load(f)
-
-    if params["peft_type"] != "LORA":
-        print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA")
-        sys.exit(1)
-
-    if params["fan_in_fan_out"] is True:
-        print("Error: param fan_in_fan_out is not supported")
-        sys.exit(1)
-
-    if params["bias"] is not None and params["bias"] != "none":
-        print("Error: param bias is not supported")
-        sys.exit(1)
-
-    # TODO: these seem to be layers that have been trained but without lora.
-    # doesn't seem widely used but eventually should be supported
-    if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0:
-        print("Error: param modules_to_save is not supported")
-        sys.exit(1)
-
-    with open(output_path, "wb") as fout:
-        fout.truncate()
-
-        write_file_header(fout, params)
-        for k, v in model.items():
-            orig_k = k
-            if k.endswith(".default.weight"):
-                k = k.replace(".default.weight", ".weight")
-            if k in ["llama_proj.weight", "llama_proj.bias"]:
-                continue
-            if k.endswith("lora_A.weight"):
-                if v.dtype != torch.float16 and v.dtype != torch.float32:
-                    v = v.float()
-                v = v.T
-            else:
-                v = v.float()
-
-            t = v.detach().numpy()
-
-            prefix = "base_model.model."
-            if k.startswith(prefix):
-                k = k[len(prefix) :]
-
-            lora_suffixes = (".lora_A.weight", ".lora_B.weight")
-            if k.endswith(lora_suffixes):
-                suffix = k[-len(lora_suffixes[0]):]
-                k = k[: -len(lora_suffixes[0])]
-            else:
-                print(f"Error: unrecognized tensor name {orig_k}")
-                sys.exit(1)
-
-            tname = name_map.get_name(k)
-            if tname is None:
-                print(f"Error: could not map tensor name {orig_k}")
-                print(" Note: the arch parameter must be specified if the model is not llama")
-                sys.exit(1)
-
-            if suffix == ".lora_A.weight":
-                tname += ".weight.loraA"
-            elif suffix == ".lora_B.weight":
-                tname += ".weight.loraB"
-            else:
-                assert False
-
-            print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB")
-            write_tensor_header(fout, tname, t.shape, t.dtype)
-            t.tofile(fout)
-
-    print(f"Converted {input_json} and {input_model} to {output_path}")
diff --git a/convert-persimmon-to-gguf.py b/convert-persimmon-to-gguf.py
index def210531..07dcade74 100755
--- a/convert-persimmon-to-gguf.py
+++ b/convert-persimmon-to-gguf.py
@@ -1,4 +1,7 @@
 #!/usr/bin/env python3
+from __future__ import annotations
+
+import logging
 import argparse
 import os
 import sys
@@ -12,6 +15,8 @@ if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf
 
+logger = logging.getLogger("persimmon-to-gguf")
+
 
 def _flatten_dict(dct, tensors, prefix=None):
     assert isinstance(dct, dict)
@@ -28,9 +33,9 @@ def _flatten_dict(dct, tensors, prefix=None):
 
 def _get_sentencepiece_tokenizer_info(dir_model: Path):
     tokenizer_path = dir_model / 'adept_vocab.model'
-    print('gguf: getting sentencepiece tokenizer from', tokenizer_path)
+    logger.info('getting sentencepiece tokenizer from', tokenizer_path)
     tokenizer = SentencePieceProcessor(str(tokenizer_path))
-    print('gguf: adding tokens')
+    logger.info('adding tokens')
     tokens: list[bytes] = []
     scores: list[float] = []
     toktypes: list[int] = []
@@ -65,8 +70,10 @@ def main():
     parser.add_argument("--outfile",             type=Path, help="path to write to; default: based on input")
     parser.add_argument("--ckpt-path",           type=Path, help="path to persimmon checkpoint .pt file")
     parser.add_argument("--model-dir",           type=Path, help="directory containing model e.g. 8b_chat_model_release")
-    parser.add_argument("--adept-inference-dir", type=str, help="path to adept-inference code directory")
+    parser.add_argument("--adept-inference-dir", type=str,  help="path to adept-inference code directory")
+    parser.add_argument("--verbose",  action="store_true",  help="increase output verbosity")
     args = parser.parse_args()
+    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
     sys.path.append(str(args.adept_inference_dir))
     persimmon_model = torch.load(args.ckpt_path)
     hparams = persimmon_model['args']
@@ -97,6 +104,7 @@ def main():
 
     tokens, scores, toktypes = _get_sentencepiece_tokenizer_info(args.model_dir)
     gguf_writer.add_tokenizer_model('llama')
+    gguf_writer.add_tokenizer_pre('default')
     gguf_writer.add_token_list(tokens)
     gguf_writer.add_token_scores(scores)
     gguf_writer.add_token_types(toktypes)
@@ -104,32 +112,31 @@ def main():
     gguf_writer.add_eos_token_id(71013)
 
     tensor_map = gguf.get_tensor_name_map(arch, block_count)
-    print(tensor_map)
+    logger.info(tensor_map)
     for name in tensors.keys():
-        data = tensors[name]
+        data_torch = tensors[name]
         if name.endswith(".self_attention.rotary_emb.inv_freq"):
             continue
-        old_dtype = data.dtype
+        old_dtype = data_torch.dtype
         # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?)
-        data = data.to(torch.float32).squeeze().numpy()
+        data = data_torch.to(torch.float32).squeeze().numpy()
         new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
         if new_name is None:
-            print("Can not map tensor '" + name + "'")
-            sys.exit()
+            raise ValueError(f"Can not map tensor '{name}'")
+
         n_dims = len(data.shape)
-        print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
+        logger.debug(f"{new_name}, n_dims = {str(n_dims)}, {str(old_dtype)} --> {str(data.dtype)}")
         gguf_writer.add_tensor(new_name, data)
-    print("gguf: write header")
+    logger.info("gguf: write header")
     gguf_writer.write_header_to_file()
-    print("gguf: write metadata")
+    logger.info("gguf: write metadata")
     gguf_writer.write_kv_data_to_file()
-    print("gguf: write tensors")
+    logger.info("gguf: write tensors")
     gguf_writer.write_tensors_to_file()
 
     gguf_writer.close()
 
-    print(f"gguf: model successfully exported to '{args.outfile}'")
-    print("")
+    logger.info(f"gguf: model successfully exported to '{args.outfile}'")
 
 
 if __name__ == '__main__':
diff --git a/convert.py b/convert.py
index 63285f238..da1247957 100755
--- a/convert.py
+++ b/convert.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 from __future__ import annotations
 
+import logging
 import argparse
 import concurrent.futures
 import enum
@@ -16,13 +17,14 @@ import re
 import signal
 import struct
 import sys
+import textwrap
 import time
 import zipfile
-from abc import ABCMeta, abstractmethod
+from abc import ABC, abstractmethod
 from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
 from dataclasses import dataclass
 from pathlib import Path
-from typing import IO, TYPE_CHECKING, Any, Callable, Iterable, Literal, TypeVar
+from typing import TYPE_CHECKING, Any, Callable, ClassVar, IO, Iterable, Literal, Protocol, TypeVar, runtime_checkable, Optional
 
 import numpy as np
 from sentencepiece import SentencePieceProcessor
@@ -32,7 +34,9 @@ if 'NO_LOCAL_GGUF' not in os.environ:
 import gguf
 
 if TYPE_CHECKING:
-    from typing import TypeAlias
+    from typing_extensions import Self, TypeAlias
+
+logger = logging.getLogger("convert")
 
 if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'):
     faulthandler.register(signal.SIGUSR1)
@@ -43,6 +47,9 @@ ARCH = gguf.MODEL_ARCH.LLAMA
 
 DEFAULT_CONCURRENCY = 8
 
+ADDED_TOKENS_FILE = 'added_tokens.json'
+FAST_TOKENIZER_FILE = 'tokenizer.json'
+
 #
 # data types
 #
@@ -135,7 +142,8 @@ class GGMLFileType(enum.IntEnum):
         dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self)
         if dt is None:
             raise ValueError(self)
-        # 1D tensors are always F32.
+        # Convert all 1D tensors to F32.  Most of the codebase that takes in 1D tensors only handles F32 tensors, and most of the outputs tensors are F32.
+        #  Also The 1d tensors aren't much of a performance/size issue.  So instead of having to have separate F32 and F16 implementations of both, just convert everything to F32 for now.
         return dt if len(tensor.shape) > 1 else DT_F32
 
 
@@ -188,8 +196,10 @@ class Params:
             n_layer = next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model)
 
         if n_layer < 1:
-            raise Exception("failed to guess 'n_layer'. This model is unknown or unsupported.\n"
-                            "Suggestion: provide 'config.json' of the model in the same directory containing model files.")
+            msg = """\
+                failed to guess 'n_layer'. This model is unknown or unsupported.
+                Suggestion: provide 'config.json' of the model in the same directory containing model files."""
+            raise KeyError(textwrap.dedent(msg))
 
         n_head = n_embd // 128 # guessed
         n_mult = 256           # guessed
@@ -211,7 +221,8 @@ class Params:
 
     @staticmethod
     def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params:
-        config = json.load(open(config_path))
+        with open(config_path) as f:
+            config = json.load(f)
 
         rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None
         rope_scaling = config.get("rope_scaling")
@@ -233,8 +244,10 @@ class Params:
         elif "max_position_embeddings" in config:
             n_ctx = config["max_position_embeddings"]
         else:
-            raise Exception("failed to guess 'n_ctx'. This model is unknown or unsupported.\n"
-                            "Suggestion: provide 'config.json' of the model in the same directory containing model files.")
+            msg = """\
+                failed to guess 'n_ctx'. This model is unknown or unsupported.
+                Suggestion: provide 'config.json' of the model in the same directory containing model files."""
+            raise KeyError(textwrap.dedent(msg))
 
         n_experts      = None
         n_experts_used = None
@@ -265,11 +278,13 @@ class Params:
     # {"dim": 8192, "multiple_of": 4096, "ffn_dim_multiplier": 1.3, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-05, "vocab_size": -1}
     @staticmethod
     def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params:
-        config = json.load(open(config_path))
+        with open(config_path) as f:
+            config = json.load(f)
 
         n_experts      = None
         n_experts_used = None
         f_rope_freq_base = None
+        n_ff = None
 
         # hack to determine LLaMA v1 vs v2 vs CodeLlama
         if config.get("moe"):
@@ -294,6 +309,8 @@ class Params:
             n_experts_used = config["moe"]["num_experts_per_tok"]
             f_rope_freq_base = 1e6
 
+        assert n_ff is not None
+
         return Params(
             n_vocab          = model["tok_embeddings.weight"].shape[0],
             n_embd           = config["dim"],
@@ -327,48 +344,127 @@ class Params:
         return params
 
 
+@dataclass
+class Metadata:
+    name: Optional[str] = None
+    author: Optional[str] = None
+    version: Optional[str] = None
+    url: Optional[str] = None
+    description: Optional[str] = None
+    licence: Optional[str] = None
+    source_url: Optional[str] = None
+    source_hf_repo: Optional[str] = None
+
+    @staticmethod
+    def load(metadata_path: Path) -> Metadata:
+        if metadata_path is None or not metadata_path.exists():
+            return Metadata()
+
+        with open(metadata_path, 'r') as file:
+            data = json.load(file)
+
+        # Create a new Metadata instance
+        metadata = Metadata()
+
+        # Assigning values to Metadata attributes if they exist in the JSON file
+        # This is based on LLM_KV_NAMES mapping in llama.cpp
+        metadata.name = data.get("general.name")
+        metadata.author = data.get("general.author")
+        metadata.version = data.get("general.version")
+        metadata.url = data.get("general.url")
+        metadata.description = data.get("general.description")
+        metadata.license = data.get("general.license")
+        metadata.source_url = data.get("general.source.url")
+        metadata.source_hf_repo = data.get("general.source.huggingface.repository")
+
+        return metadata
+
+
 #
 # vocab
 #
 
-class BpeVocab:
-    def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
-        self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read())
-        if isinstance(self.bpe_tokenizer.get('model'), dict):
-            self.vocab = self.bpe_tokenizer["model"]["vocab"]
-        else:
-            self.vocab = self.bpe_tokenizer
-        added_tokens: dict[str, int]
-        if fname_added_tokens is not None:
-            # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
-            added_tokens = json.load(open(fname_added_tokens, encoding="utf-8"))
-        else:
-            # Fall back to trying to find the added tokens in tokenizer.json
-            tokenizer_json_file = fname_tokenizer.parent / 'tokenizer.json'
-            if not tokenizer_json_file.is_file():
-                added_tokens = {}
-            else:
-                tokenizer_json = json.load(open(tokenizer_json_file, encoding="utf-8"))
-                added_tokens = dict(
-                    (item['content'], item['id'])
-                    for item in tokenizer_json.get('added_tokens', [])
-                    # Added tokens here can be duplicates of the main vocabulary.
-                    if item['content'] not in self.bpe_tokenizer)
 
-        vocab_size: int = len(self.vocab)
-        expected_ids    = list(range(vocab_size, vocab_size + len(added_tokens)))
-        actual_ids      = sorted(added_tokens.values())
+@runtime_checkable
+class BaseVocab(Protocol):
+    tokenizer_model: ClassVar[str]
+    name: ClassVar[str]
+
+
+class NoVocab(BaseVocab):
+    tokenizer_model = "no_vocab"
+    name = "no_vocab"
+
+    def __repr__(self) -> str:
+        return "<NoVocab for a model without integrated vocabulary>"
+
+
+@runtime_checkable
+class Vocab(BaseVocab, Protocol):
+    vocab_size: int
+    added_tokens_dict: dict[str, int]
+    added_tokens_list: list[str]
+    fname_tokenizer: Path
+
+    def __init__(self, base_path: Path): ...
+    def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ...
+
+
+class BpeVocab(Vocab):
+    tokenizer_model = "gpt2"
+    name = "bpe"
+
+    def __init__(self, base_path: Path):
+        added_tokens: dict[str, int] = {}
+
+        if (fname_tokenizer := base_path / 'vocab.json').exists():
+            # "slow" tokenizer
+            with open(fname_tokenizer, encoding="utf-8") as f:
+                self.vocab = json.load(f)
+
+            try:
+                # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
+                with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f:
+                    added_tokens = json.load(f)
+            except FileNotFoundError:
+                pass
+        else:
+            # "fast" tokenizer
+            fname_tokenizer = base_path / FAST_TOKENIZER_FILE
+
+            # if this fails, FileNotFoundError propagates to caller
+            with open(fname_tokenizer, encoding="utf-8") as f:
+                tokenizer_json = json.load(f)
+
+            tokenizer_model: dict[str, Any] = tokenizer_json['model']
+            if (
+                tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False)
+                or tokenizer_json['decoder']['type'] != 'ByteLevel'
+            ):
+                raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer')
+
+            self.vocab = tokenizer_model["vocab"]
+
+            if (added := tokenizer_json.get('added_tokens')) is not None:
+                # Added tokens here can be duplicates of the main vocabulary.
+                added_tokens = {item['content']: item['id']
+                                for item in added
+                                if item['content'] not in self.vocab}
+
+        vocab_size   = len(self.vocab)
+        expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
+        actual_ids   = sorted(added_tokens.values())
         if expected_ids != actual_ids:
             expected_end_id = vocab_size + len(actual_ids) - 1
-            raise Exception(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range {vocab_size} - {expected_end_id}; got {actual_ids}")
+            raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range "
+                             f"{vocab_size} - {expected_end_id}; got {actual_ids}")
 
         items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
         self.added_tokens_dict    = added_tokens
         self.added_tokens_list    = [text for (text, idx) in items]
-        self.vocab_size_base: int = vocab_size
-        self.vocab_size: int      = self.vocab_size_base + len(self.added_tokens_list)
+        self.vocab_size_base      = vocab_size
+        self.vocab_size           = self.vocab_size_base + len(self.added_tokens_list)
         self.fname_tokenizer      = fname_tokenizer
-        self.fname_added_tokens   = fname_added_tokens
 
     def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
         reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()}
@@ -389,16 +485,26 @@ class BpeVocab:
         return f"<BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
 
 
-class SentencePieceVocab:
-    def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
-        self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
-        added_tokens: dict[str, int]
-        if fname_added_tokens is not None:
-            added_tokens = json.load(open(fname_added_tokens, encoding="utf-8"))
-        else:
-            added_tokens = {}
+class SentencePieceVocab(Vocab):
+    tokenizer_model = "llama"
+    name = "spm"
 
-        vocab_size: int = self.sentencepiece_tokenizer.vocab_size()
+    def __init__(self, base_path: Path):
+        added_tokens: dict[str, int] = {}
+        if (fname_tokenizer := base_path / 'tokenizer.model').exists():
+            # normal location
+            try:
+                with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f:
+                    added_tokens = json.load(f)
+            except FileNotFoundError:
+                pass
+        elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists():
+            # not found in alternate location either
+            raise FileNotFoundError('Cannot find tokenizer.model')
+
+        self.sentencepiece_tokenizer = SentencePieceProcessor()
+        self.sentencepiece_tokenizer.LoadFromFile(str(fname_tokenizer))
+        vocab_size = self.sentencepiece_tokenizer.vocab_size()
 
         new_tokens       = {id: piece for piece, id in added_tokens.items() if id >= vocab_size}
         expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens)))
@@ -408,33 +514,32 @@ class SentencePieceVocab:
             raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}")
 
         # Token pieces that were added to the base vocabulary.
-        self.added_tokens_dict = added_tokens
+        self.added_tokens_dict  = added_tokens
         self.added_tokens_list  = [new_tokens[id] for id in actual_new_ids]
         self.vocab_size_base    = vocab_size
         self.vocab_size         = self.vocab_size_base + len(self.added_tokens_list)
         self.fname_tokenizer    = fname_tokenizer
-        self.fname_added_tokens = fname_added_tokens
 
     def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
         tokenizer = self.sentencepiece_tokenizer
         for i in range(tokenizer.vocab_size()):
-            piece = tokenizer.id_to_piece(i)
-            text: bytes = piece.encode("utf-8")
-            score: float = tokenizer.get_score(i)
+            piece = tokenizer.IdToPiece(i)
+            text         = piece.encode("utf-8")
+            score: float = tokenizer.GetScore(i)
 
             toktype = gguf.TokenType.NORMAL
-            if tokenizer.is_unknown(i):
+            if tokenizer.IsUnknown(i):
                 toktype = gguf.TokenType.UNKNOWN
-            if tokenizer.is_control(i):
+            if tokenizer.IsControl(i):
                 toktype = gguf.TokenType.CONTROL
 
             # NOTE: I think added_tokens are user defined.
             # ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto
             # if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED
 
-            if tokenizer.is_unused(i):
+            if tokenizer.IsUnused(i):
                 toktype = gguf.TokenType.UNUSED
-            if tokenizer.is_byte(i):
+            if tokenizer.IsByte(i):
                 toktype = gguf.TokenType.BYTE
 
             yield text, score, toktype
@@ -452,24 +557,47 @@ class SentencePieceVocab:
         return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
 
 
-class HfVocab:
-    def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None = None) -> None:
+class LlamaHfVocab(Vocab):
+    tokenizer_model = "llama"
+    name = "hfft"
+
+    def __init__(self, base_path: Path):
+        fname_tokenizer = base_path / FAST_TOKENIZER_FILE
+        # if this fails, FileNotFoundError propagates to caller
+        with open(fname_tokenizer, encoding='utf-8') as f:
+            tokenizer_json = json.load(f)
+
+        # pre-check so we know if we need transformers
+        tokenizer_model: dict[str, Any] = tokenizer_json['model']
+        is_llama3 = (
+            tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False)
+            and not tokenizer_model.get('byte_fallback', True)
+        )
+        if is_llama3:
+            raise TypeError('Llama 3 must be converted with BpeVocab')
+
+        if not is_llama3 and (
+            tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False)
+            or tokenizer_json['decoder']['type'] != 'Sequence'
+        ):
+            raise FileNotFoundError('Cannot find Llama BPE tokenizer')
+
         try:
             from transformers import AutoTokenizer
         except ImportError as e:
             raise ImportError(
-                "To use HfVocab, please install the `transformers` package. "
+                "To use LlamaHfVocab, please install the `transformers` package. "
                 "You can install it with `pip install transformers`."
             ) from e
 
-        print("fname_tokenizer:", fname_tokenizer)
         # Allow the tokenizer to default to slow or fast versions.
         # Explicitly set tokenizer to use local paths.
         self.tokenizer = AutoTokenizer.from_pretrained(
-            fname_tokenizer,
-            cache_dir=fname_tokenizer,
+            base_path,
+            cache_dir=base_path,
             local_files_only=True,
         )
+        assert self.tokenizer.is_fast  # assume tokenizer.json is used
 
         # Initialize lists and dictionaries for added tokens
         self.added_tokens_list = []
@@ -497,8 +625,7 @@ class HfVocab:
         self.vocab_size_base = self.tokenizer.vocab_size
         self.vocab_size      = self.vocab_size_base + len(self.added_tokens_list)
 
-        self.fname_tokenizer    = fname_tokenizer
-        self.fname_added_tokens = fname_added_tokens
+        self.fname_tokenizer = fname_tokenizer
 
     def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
         reverse_vocab = {
@@ -550,10 +677,7 @@ class HfVocab:
         yield from self.added_tokens()
 
     def __repr__(self) -> str:
-        return f"<HfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
-
-
-Vocab: TypeAlias = "BpeVocab | SentencePieceVocab | HfVocab"
+        return f"<LlamaHfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
 
 
 #
@@ -563,7 +687,6 @@ Vocab: TypeAlias = "BpeVocab | SentencePieceVocab | HfVocab"
 
 
 def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray:
-    # print( "permute debug " + str(weights.shape[0]) + " x " + str(weights.shape[1]) + " nhead " + str(n_head) + " nheadkv " + str(n_kv_head) )
     if n_head_kv is not None and n_head != n_head_kv:
         n_head = n_head_kv
     return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
@@ -571,17 +694,18 @@ def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray:
             .reshape(weights.shape))
 
 
-class Tensor(metaclass=ABCMeta):
+class Tensor(ABC):
+    ndarray: NDArray
     data_type: DataType
 
     @abstractmethod
-    def astype(self, data_type: DataType) -> Tensor: ...
+    def astype(self, data_type: DataType) -> Self: ...
     @abstractmethod
-    def permute(self, n_head: int, n_head_kv: int) -> Tensor: ...
+    def permute(self, n_head: int, n_head_kv: int) -> Self: ...
     @abstractmethod
-    def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor: ...
+    def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> Self: ...
     @abstractmethod
-    def part(self, n_part: int) -> UnquantizedTensor: ...
+    def part(self, n_part: int) -> Self: ...
     @abstractmethod
     def to_ggml(self) -> GGMLCompatibleTensor: ...
 
@@ -593,18 +717,18 @@ def bf16_to_fp32(bf16_arr: np.ndarray[Any, np.dtype[np.uint16]]) -> NDArray:
 
 
 class UnquantizedTensor(Tensor):
-    def __init__(self, ndarray: NDArray) -> None:
+    def __init__(self, ndarray: NDArray):
         assert isinstance(ndarray, np.ndarray)
         self.ndarray = ndarray
         self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype]
 
-    def astype(self, data_type: DataType) -> Tensor:
+    def astype(self, data_type: DataType) -> UnquantizedTensor:
         dtype = data_type.dtype
         if self.data_type == DT_BF16:
             self.ndarray = bf16_to_fp32(self.ndarray)
         return UnquantizedTensor(self.ndarray.astype(dtype))
 
-    def to_ggml(self) -> UnquantizedTensor:
+    def to_ggml(self) -> Self:
         return self
 
     def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor:
@@ -672,7 +796,7 @@ class ModelPlus:
     model: LazyModel
     paths: list[Path]  # Where this was read from.
     format: Literal['ggml', 'torch', 'safetensors', 'none']
-    vocab: Vocab | None  # For GGML models (which have vocab built in), the vocab.
+    vocab: BaseVocab | None  # For GGML models (which have vocab built in), the vocab.
 
 
 def merge_sharded(models: list[LazyModel]) -> LazyModel:
@@ -681,7 +805,7 @@ def merge_sharded(models: list[LazyModel]) -> LazyModel:
     names = {name: None for model in models for name in model}
 
     def convert(name: str) -> LazyTensor:
-        lazy_tensors: list[LazyTensor] = [model[name] for model in models]
+        lazy_tensors = [model[name] for model in models]
         if len(lazy_tensors) == 1:
             # only one file; don't go through this procedure since there might
             # be quantized tensors
@@ -702,7 +826,7 @@ def merge_sharded(models: list[LazyModel]) -> LazyModel:
 
         def load() -> UnquantizedTensor:
             ndarrays = [load_unquantized(tensor) for tensor in lazy_tensors]
-            concatenated: NDArray = np.concatenate(ndarrays, axis=axis)
+            concatenated = np.concatenate(ndarrays, axis=axis)
             return UnquantizedTensor(concatenated)
         description = 'concatenated[[' + '] | ['.join(lt.description for lt in lazy_tensors) + ']]'
         return LazyTensor(load, concatenated_shape, lazy_tensors[0].data_type, description)
@@ -754,6 +878,15 @@ def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor:
     return LazyTensor(load, s, lazy_tensor.data_type, 'part ' + lazy_tensor.description)
 
 
+def pack_experts_lazy(lazy_tensors: list[LazyTensor]) -> LazyTensor:
+    def load() -> Tensor:
+        tensors = [lazy_tensor.load() for lazy_tensor in lazy_tensors]
+        return UnquantizedTensor(np.array([tensor.ndarray for tensor in tensors]))
+    s = lazy_tensors[0].shape.copy()
+    s.insert(0, len(lazy_tensors))
+    return LazyTensor(load, s, lazy_tensors[0].data_type, 'pack_experts ' + ' | '.join(lt.description for lt in lazy_tensors))
+
+
 # Functionality that simulates `torch.load` but where individual tensors are
 # only loaded into memory on demand, not all at once.
 # PyTorch can't do this natively as of time of writing:
@@ -790,10 +923,10 @@ class LazyUnpickler(pickle.Unpickler):
 
         def load(offset: int, elm_count: int) -> NDArray:
             dtype = data_type.dtype
-            fp = self.zip_file.open(info)
-            fp.seek(offset * dtype.itemsize)
-            size = elm_count * dtype.itemsize
-            data = fp.read(size)
+            with self.zip_file.open(info) as fp:
+                fp.seek(offset * dtype.itemsize)
+                size = elm_count * dtype.itemsize
+                data = fp.read(size)
             assert len(data) == size
             return np.frombuffer(data, dtype)
         description = f'storage data_type={data_type} path-in-zip={filename} path={self.zip_file.filename}'
@@ -814,7 +947,7 @@ class LazyUnpickler(pickle.Unpickler):
     def rebuild_from_type_v2(func, new_type, args, state):
         return func(*args)
 
-    CLASSES: dict[tuple[str, str], Any] = {
+    CLASSES: dict[tuple[str, str], type[LazyTensor] | LazyStorageKind] = {
         # getattr used here as a workaround for mypy not being smart enough to determine
         # the staticmethods have a __func__ attribute.
         ('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'),
@@ -873,7 +1006,7 @@ def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus:
 def must_read(fp: IO[bytes], length: int) -> bytes:
     ret = fp.read(length)
     if len(ret) < length:
-        raise Exception("unexpectedly reached end of file")
+        raise EOFError("unexpectedly reached end of file")
     return ret
 
 
@@ -931,21 +1064,24 @@ def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], conc
             yield result
 
 
-def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> None:
+def check_vocab_size(params: Params, vocab: BaseVocab, pad_vocab: bool = False) -> None:
     # Handle special case where the model's vocab size is not set
     if params.n_vocab == -1:
         raise ValueError(
-            f"The model's vocab size is set to -1 in params.json. Please update it manually. Maybe {vocab.vocab_size}?"
+            "The model's vocab size is set to -1 in params.json. Please update it manually."
+            + (f" Maybe {vocab.vocab_size}?" if isinstance(vocab, Vocab) else ""),
         )
+    if not isinstance(vocab, Vocab):
+        return  # model has no vocab
 
     # Check for a vocab size mismatch
     if params.n_vocab == vocab.vocab_size:
-        print("Ignoring added_tokens.json since model matches vocab size without it.")
+        logger.warning("Ignoring added_tokens.json since model matches vocab size without it.")
         return
 
     if pad_vocab and params.n_vocab > vocab.vocab_size:
         pad_count = params.n_vocab - vocab.vocab_size
-        print(
+        logger.debug(
             f"Padding vocab with {pad_count} token(s) - <dummy00001> through <dummy{pad_count:05}>"
         )
         for i in range(1, pad_count + 1):
@@ -960,27 +1096,49 @@ def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> N
     if vocab.vocab_size < params.n_vocab:
         msg += " Add the --pad-vocab option and try again."
 
-    raise Exception(msg)
+    raise ValueError(msg)
 
 
 class OutputFile:
-    def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE) -> None:
+    def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE):
         self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess)
 
-    def add_meta_arch(self, params: Params) -> None:
+    def add_meta_model(self, params: Params, metadata: Metadata) -> None:
+        # Metadata About The Model And Its Provenence
         name = "LLaMA"
-
-        # TODO: better logic to determine model name
-        if params.n_ctx == 4096:
-            name = "LLaMA v2"
+        if metadata is not None and metadata.name is not None:
+            name = metadata.name
         elif params.path_model is not None:
             name = params.path_model.name
+        elif params.n_ctx == 4096:
+            # Heuristic detection of LLaMA v2 model
+            name = "LLaMA v2"
 
-        self.gguf.add_name                (name)
-        self.gguf.add_context_length      (params.n_ctx)
-        self.gguf.add_embedding_length    (params.n_embd)
-        self.gguf.add_block_count         (params.n_layer)
-        self.gguf.add_feed_forward_length (params.n_ff)
+        self.gguf.add_name(name)
+
+        if metadata is not None:
+            if metadata.author is not None:
+                self.gguf.add_author(metadata.author)
+            if metadata.version is not None:
+                self.gguf.add_version(metadata.version)
+            if metadata.url is not None:
+                self.gguf.add_url(metadata.url)
+            if metadata.description is not None:
+                self.gguf.add_description(metadata.description)
+            if metadata.licence is not None:
+                self.gguf.add_licence(metadata.licence)
+            if metadata.source_url is not None:
+                self.gguf.add_source_url(metadata.source_url)
+            if metadata.source_hf_repo is not None:
+                self.gguf.add_source_hf_repo(metadata.source_hf_repo)
+
+    def add_meta_arch(self, params: Params) -> None:
+        # Metadata About The Neural Architecture Itself
+        self.gguf.add_vocab_size(params.n_vocab)
+        self.gguf.add_context_length(params.n_ctx)
+        self.gguf.add_embedding_length(params.n_embd)
+        self.gguf.add_block_count(params.n_layer)
+        self.gguf.add_feed_forward_length(params.n_ff)
         self.gguf.add_rope_dimension_count(params.n_embd // params.n_head)
         self.gguf.add_head_count          (params.n_head)
         self.gguf.add_head_count_kv       (params.n_head_kv)
@@ -1013,20 +1171,6 @@ class OutputFile:
         if params.ftype is not None:
             self.gguf.add_file_type(params.ftype)
 
-    def handle_tokenizer_model(self, vocab: Vocab) -> str:
-        # Map the vocab types to the supported tokenizer models
-        tokenizer_model = {
-            SentencePieceVocab: "llama",
-            HfVocab: "llama",
-            BpeVocab: "gpt2",
-        }.get(type(vocab))
-
-        # Block if vocab type is not predefined
-        if tokenizer_model is None:
-            raise ValueError("Unknown vocab type: Not supported")
-
-        return tokenizer_model
-
     def extract_vocabulary_from_model(self, vocab: Vocab) -> tuple[list[bytes], list[float], list[gguf.TokenType]]:
         tokens = []
         scores = []
@@ -1043,11 +1187,8 @@ class OutputFile:
         return tokens, scores, toktypes
 
     def add_meta_vocab(self, vocab: Vocab) -> None:
-        # Handle the tokenizer model
-        tokenizer_model = self.handle_tokenizer_model(vocab)
-
         # Ensure that tokenizer_model is added to the GGUF model
-        self.gguf.add_tokenizer_model(tokenizer_model)
+        self.gguf.add_tokenizer_model(vocab.tokenizer_model)
 
         # Extract model vocabulary for model conversion
         tokens, scores, toktypes = self.extract_vocabulary_from_model(vocab)
@@ -1074,19 +1215,40 @@ class OutputFile:
     def write_tensor_info(self) -> None:
         self.gguf.write_ti_data_to_file()
 
+    def write_tensor_data(self, ftype: GGMLFileType, model: LazyModel, concurrency: int) -> None:
+        ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency=concurrency)
+        if ftype == GGMLFileType.MostlyQ8_0:
+            ndarrays = bounded_parallel_map(
+                OutputFile.maybe_do_quantize, ndarrays_inner, concurrency=concurrency, max_workers=concurrency,
+                use_processpool_executor=True,
+            )
+        else:
+            ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)
+
+        start = time.time()
+        for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
+            elapsed = time.time() - start
+            size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
+            padi = len(str(len(model)))
+            logger.info(
+                f"[{i + 1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}"
+            )
+            self.gguf.write_tensor_data(ndarray)
+
     def close(self) -> None:
         self.gguf.close()
 
     @staticmethod
     def write_vocab_only(
         fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab,
-        endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False,
+        endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False, metadata: Metadata = None,
     ) -> None:
-        check_vocab_size(params, vocab, pad_vocab = pad_vocab)
+        check_vocab_size(params, vocab, pad_vocab=pad_vocab)
 
         of = OutputFile(fname_out, endianess=endianess)
 
         # meta data
+        of.add_meta_model(params, metadata)
         of.add_meta_arch(params)
         of.add_meta_vocab(vocab)
         of.add_meta_special_vocab(svocab)
@@ -1110,18 +1272,23 @@ class OutputFile:
 
     @staticmethod
     def write_all(
-        fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab,
+        fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: BaseVocab, svocab: gguf.SpecialVocab,
         concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE,
         pad_vocab: bool = False,
+        metadata: Metadata = None,
     ) -> None:
         check_vocab_size(params, vocab, pad_vocab=pad_vocab)
 
         of = OutputFile(fname_out, endianess=endianess)
 
         # meta data
+        of.add_meta_model(params, metadata)
         of.add_meta_arch(params)
-        of.add_meta_vocab(vocab)
-        of.add_meta_special_vocab(svocab)
+        if isinstance(vocab, Vocab):
+            of.add_meta_vocab(vocab)
+            of.add_meta_special_vocab(svocab)
+        else:  # NoVocab
+            of.gguf.add_tokenizer_model(vocab.tokenizer_model)
 
         # tensor info
         for name, lazy_tensor in model.items():
@@ -1131,24 +1298,7 @@ class OutputFile:
         of.write_tensor_info()
 
         # tensor data
-        ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency = concurrency)
-        if ftype == GGMLFileType.MostlyQ8_0:
-            ndarrays = bounded_parallel_map(
-                OutputFile.maybe_do_quantize, ndarrays_inner, concurrency=concurrency, max_workers=concurrency,
-                use_processpool_executor=True,
-            )
-        else:
-            ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)
-
-        start = time.time()
-        for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
-            elapsed = time.time() - start
-            size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
-            padi = len(str(len(model)))
-            print(
-                f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}"
-            )
-            of.gguf.write_tensor_data(ndarray)
+        of.write_tensor_data(ftype, model, concurrency)
 
         of.close()
 
@@ -1156,16 +1306,47 @@ class OutputFile:
 def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType:
     wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0) + ".weight"].data_type
 
-    if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32):
+    if output_type_str == "f32" or (output_type_str is None and wq_type in (DT_F32, DT_BF16)):
         return GGMLFileType.AllF32
-    if output_type_str == "f16" or (output_type_str is None and wq_type in (DT_F16, DT_BF16)):
+    if output_type_str == "f16" or (output_type_str is None and wq_type == DT_F16):
         return GGMLFileType.MostlyF16
     if output_type_str == "q8_0":
         return GGMLFileType.MostlyQ8_0
 
     name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()}
 
-    raise Exception(f"Unexpected combination of types: {name_to_type}")
+    raise ValueError(f"Unexpected combination of types: {name_to_type}")
+
+
+def model_parameter_count(model: LazyModel) -> int:
+    total_model_parameters = 0
+    for i, (name, lazy_tensor) in enumerate(model.items()):
+        sum_weights_in_tensor = 1
+        for dim in lazy_tensor.shape:
+            sum_weights_in_tensor *= dim
+        total_model_parameters += sum_weights_in_tensor
+    return total_model_parameters
+
+
+def model_parameter_count_rounded_notation(model_params_count: int) -> str:
+    if model_params_count > 1e12 :
+        # Trillions Of Parameters
+        scaled_model_params = model_params_count * 1e-12
+        scale_suffix = "T"
+    elif model_params_count > 1e9 :
+        # Billions Of Parameters
+        scaled_model_params = model_params_count * 1e-9
+        scale_suffix = "B"
+    elif model_params_count > 1e6 :
+        # Millions Of Parameters
+        scaled_model_params = model_params_count * 1e-6
+        scale_suffix = "M"
+    else:
+        # Thousands Of Parameters
+        scaled_model_params = model_params_count * 1e-3
+        scale_suffix = "K"
+
+    return f"{round(scaled_model_params)}{scale_suffix}"
 
 
 def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel:
@@ -1175,19 +1356,35 @@ def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyM
 
 def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) -> LazyModel:
     tmap = gguf.TensorNameMap(ARCH, params.n_layer)
-    should_skip: set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, []))
+    should_skip = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, []))
 
     tmp = model
 
+    # merge experts into one tensor
+    if params.n_experts and params.n_experts > 0:
+        for i_l in range(params.n_layer):
+            for w in range(1, 4):
+                experts = []
+                for e in range(params.n_experts):
+                    if f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight" in model:
+                        experts.append(model[f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight"])
+                        del tmp[f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight"]
+                    elif f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight" in model:
+                        experts.append(model[f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight"])
+                        del tmp[f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight"]
+                    else:
+                        raise ValueError(f"Expert tensor not found: layers.{i_l}.feed_forward.experts.{e}.w{w}.weight")
+                tmp[f"layers.{i_l}.feed_forward.experts.w{w}.weight"] = pack_experts_lazy(experts)
+
     # HF models permut or pack some of the tensors, so we need to undo that
     for i in itertools.count():
         if f"model.layers.{i}.self_attn.q_proj.weight" in model:
-            print(f"Permuting layer {i}")
+            logger.debug(f"Permuting layer {i}")
             tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head, params.n_head)
             tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head, params.n_head_kv)
             # tmp[f"model.layers.{i}.self_attn.v_proj.weight"] =              model[f"model.layers.{i}.self_attn.v_proj.weight"]
         elif f"model.layers.{i}.self_attn.W_pack.weight" in model:
-            print(f"Unpacking and permuting layer {i}")
+            logger.debug(f"Unpacking and permuting layer {i}")
             tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head, params.n_head)
             tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head, params.n_head_kv)
             tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = part_lazy        (model[f"model.layers.{i}.self_attn.W_pack.weight"], 2)
@@ -1200,16 +1397,15 @@ def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) ->
         tensor_type, name_new = tmap.get_type_and_name(name, try_suffixes = (".weight", ".bias")) or (None, None)
         if name_new is None:
             if skip_unknown:
-                print(f"Unexpected tensor name: {name} - skipping")
+                logger.warning(f"Unexpected tensor name: {name} - skipping")
                 continue
-            else:
-                raise Exception(f"Unexpected tensor name: {name}. Use --skip-unknown to ignore it (e.g. LLaVA)")
+            raise ValueError(f"Unexpected tensor name: {name}. Use --skip-unknown to ignore it (e.g. LLaVA)")
 
         if tensor_type in should_skip:
-            print(f"skipping tensor {name_new}")
+            logger.debug(f"skipping tensor {name_new}")
             continue
 
-        print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}")
+        logger.debug(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}")
         out[name_new] = lazy_tensor
 
     return out
@@ -1220,7 +1416,7 @@ def nth_multifile_path(path: Path, n: int) -> Path | None:
     the nth path in the model.
     '''
     # Support the following patterns:
-    patterns: list[tuple[str, str]] = [
+    patterns = [
         # - x.00.pth, x.01.pth, etc.
         (r'\.[0-9]{2}\.pth$', f'.{n:02}.pth'),
         # - x-00001-of-00002.bin, x-00002-of-00002.bin, etc.
@@ -1259,22 +1455,22 @@ def load_some_model(path: Path) -> ModelPlus:
     # Be extra-friendly and accept either a file or a directory:
     if path.is_dir():
         # Check if it's a set of safetensors files first
-        globs = ["model-00001-of-*.safetensors", "model.safetensors"]
+        globs = ["model-00001-of-*.safetensors", "model.safetensors", "consolidated.safetensors"]
         files = [file for glob in globs for file in path.glob(glob)]
         if not files:
             # Try the PyTorch patterns too, with lower priority
             globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt", "pytorch_model.bin"]
             files = [file for glob in globs for file in path.glob(glob)]
         if not files:
-            raise Exception(f"Can't find model in directory {path}")
+            raise FileNotFoundError(f"Can't find model in directory {path}")
         if len(files) > 1:
-            raise Exception(f"Found multiple models in {path}, not sure which to pick: {files}")
+            raise ValueError(f"Found multiple models in {path}, not sure which to pick: {files}")
         path = files[0]
 
     paths = find_multifile_paths(path)
     models_plus: list[ModelPlus] = []
     for path in paths:
-        print(f"Loading model file {path}")
+        logger.info(f"Loading model file {path}")
         models_plus.append(lazy_load_file(path))
 
     model_plus = merge_multifile_models(models_plus)
@@ -1282,39 +1478,14 @@ def load_some_model(path: Path) -> ModelPlus:
 
 
 class VocabFactory:
+    _VOCAB_CLASSES: list[type[Vocab]] = [SentencePieceVocab, BpeVocab, LlamaHfVocab]
+
     def __init__(self, path: Path):
         self.path = path
-        self.files: dict[str, Path | None] = {
-            "tokenizer.model": None,
-            "vocab.json": None,
-            "tokenizer.json": None,
-        }
-        self._detect_files()
 
-    def _detect_files(self):
-        for file in self.files.keys():
-            file_path = self.path / file
-            parent_file_path = self.path.parent / file
-            if file_path.exists():
-                self.files[file] = file_path
-            elif parent_file_path.exists():
-                self.files[file] = parent_file_path
-        print(f"Found vocab files: {self.files}")
-
-    def _select_file(self, vocabtype: str | None) -> Path:
-        if vocabtype in ["spm", "bpe"]:
-            for file_key in self.files.keys():
-                if (file := self.files[file_key]) is not None:
-                    return file
-            raise FileNotFoundError(f"{vocabtype} vocab not found.")
-        if vocabtype == "hfft":
-            # For Hugging Face Fast Tokenizer, return the directory path instead of a specific file
-            return self.path
-        raise ValueError(f"Unsupported vocabulary type {vocabtype}")
-
-    def _create_special_vocab(self, vocab: Vocab, vocabtype: str, model_parent_path: Path) -> gguf.SpecialVocab:
-        load_merges = vocabtype == "bpe"
-        n_vocab = vocab.vocab_size if hasattr(vocab, "vocab_size") else None
+    def _create_special_vocab(self, vocab: BaseVocab, model_parent_path: Path) -> gguf.SpecialVocab:
+        load_merges = vocab.name == "bpe"
+        n_vocab = vocab.vocab_size if isinstance(vocab, Vocab) else None
         return gguf.SpecialVocab(
             model_parent_path,
             load_merges=load_merges,
@@ -1322,56 +1493,84 @@ class VocabFactory:
             n_vocab=n_vocab,
         )
 
-    def load_vocab(self, vocabtype: str, model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]:
-        path = self._select_file(vocabtype)
-        print(f"Loading vocab file '{path}', type '{vocabtype}'")
+    def _create_vocab_by_path(self, vocab_types: list[str]) -> Vocab:
+        vocab_classes: dict[str, type[Vocab]] = {cls.name: cls for cls in self._VOCAB_CLASSES}
+        selected_vocabs: dict[str, type[Vocab]] = {}
+        for vtype in vocab_types:
+            try:
+                selected_vocabs[vtype] = vocab_classes[vtype]
+            except KeyError:
+                raise ValueError(f"Unsupported vocabulary type {vtype}") from None
 
-        added_tokens_path = path.parent / "added_tokens.json"
-        vocab: Vocab
-        if vocabtype == "bpe":
-            vocab = BpeVocab(
-                path, added_tokens_path if added_tokens_path.exists() else None
-            )
-        elif vocabtype == "spm":
-            vocab = SentencePieceVocab(
-                path, added_tokens_path if added_tokens_path.exists() else None
-            )
-        elif vocabtype == "hfft":
-            vocab = HfVocab(
-                path, added_tokens_path if added_tokens_path.exists() else None
-            )
+        for vtype, cls in selected_vocabs.items():
+            try:
+                vocab = cls(self.path)
+                break
+            except FileNotFoundError:
+                pass  # ignore unavailable tokenizers
         else:
-            raise ValueError(f"Unsupported vocabulary type {vocabtype}")
+            raise FileNotFoundError(f"Could not find a tokenizer matching any of {vocab_types}")
+
+        logger.info(f"Loaded vocab file {vocab.fname_tokenizer!r}, type {vocab.name!r}")
+        return vocab
+
+    def load_vocab(self, vocab_types: list[str] | None, model_parent_path: Path) -> tuple[BaseVocab, gguf.SpecialVocab]:
+        vocab: BaseVocab
+        if vocab_types is None:
+            vocab = NoVocab()
+        else:
+            vocab = self._create_vocab_by_path(vocab_types)
         # FIXME: Respect --vocab-dir?
         special_vocab = self._create_special_vocab(
             vocab,
-            vocabtype,
             model_parent_path,
         )
         return vocab, special_vocab
 
 
-def default_outfile(model_paths: list[Path], file_type: GGMLFileType) -> Path:
-    namestr = {
-        GGMLFileType.AllF32:    "f32",
-        GGMLFileType.MostlyF16: "f16",
-        GGMLFileType.MostlyQ8_0:"q8_0",
+def default_convention_outfile(file_type: GGMLFileType, params: Params, model_params_count: int, metadata: Metadata) -> str:
+    quantization = {
+        GGMLFileType.AllF32:    "F32",
+        GGMLFileType.MostlyF16: "F16",
+        GGMLFileType.MostlyQ8_0: "Q8_0",
     }[file_type]
-    ret = model_paths[0].parent / f"ggml-model-{namestr}.gguf"
+
+    parameters = model_parameter_count_rounded_notation(model_params_count)
+
+    expert_count = ""
+    if params.n_experts is not None:
+        expert_count = f"{params.n_experts}x"
+
+    version = ""
+    if metadata is not None and metadata.version is not None:
+        version = f"-{metadata.version}"
+
+    name = "ggml-model"
+    if metadata is not None and metadata.name is not None:
+        name = metadata.name
+    elif params.path_model is not None:
+        name = params.path_model.name
+
+    return f"{name}{version}-{expert_count}{parameters}-{quantization}"
+
+
+def default_outfile(model_paths: list[Path], file_type: GGMLFileType, params: Params, model_params_count: int, metadata: Metadata) -> Path:
+    default_filename = default_convention_outfile(file_type, params, model_params_count, metadata)
+    ret = model_paths[0].parent / f"{default_filename}.gguf"
     if ret in model_paths:
-        sys.stderr.write(
+        logger.error(
             f"Error: Default output path ({ret}) would overwrite the input. "
-            "Please explicitly specify a path using --outfile.\n")
+            "Please explicitly specify a path using --outfile.")
         sys.exit(1)
     return ret
 
 
 def do_dump_model(model_plus: ModelPlus) -> None:
-    print(f"model_plus.paths = {model_plus.paths!r}")
-    print(f"model_plus.format = {model_plus.format!r}")
-    print(f"model_plus.vocab = {model_plus.vocab!r}")
+    print(f"model_plus.paths = {model_plus.paths!r}") # noqa: NP100
+    print(f"model_plus.format = {model_plus.format!r}") # noqa: NP100
+    print(f"model_plus.vocab = {model_plus.vocab!r}") # noqa: NP100
     for name, lazy_tensor in model_plus.model.items():
-        print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}")
+        print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}") # noqa: NP100
 
 
 def main(args_in: list[str] | None = None) -> None:
@@ -1379,15 +1578,14 @@ def main(args_in: list[str] | None = None) -> None:
     if np.uint32(1) == np.uint32(1).newbyteorder("<"):
         # We currently only support Q8_0 output on little endian systems.
         output_choices.append("q8_0")
-    vocab_types = ["spm", "bpe", "hfft"]
-    parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file")
-    parser.add_argument("--awq-path",     type=Path,              help="Path to scale awq cache file", default=None)
+    parser = argparse.ArgumentParser(description="Convert a LLaMA model to a GGML compatible file")
     parser.add_argument("--dump",         action="store_true",    help="don't convert, just show what's in the model")
     parser.add_argument("--dump-single",  action="store_true",    help="don't convert, just show what's in a single model file")
     parser.add_argument("--vocab-only",   action="store_true",    help="extract only the vocab")
+    parser.add_argument("--no-vocab",     action="store_true",    help="store model without the vocab")
     parser.add_argument("--outtype",      choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
     parser.add_argument("--vocab-dir",    type=Path,              help="directory containing tokenizer.model, if separate from model file")
-    parser.add_argument("--vocab-type",   choices=vocab_types,    help="The vocabulary format used to define the tokenizer model (default: spm)", default="spm")
+    parser.add_argument("--vocab-type",                           help="vocab types to try in order, choose from 'spm', 'bpe', 'hfft' (default: spm,hfft)", default="spm,hfft")
     parser.add_argument("--outfile",      type=Path,              help="path to write to; default: based on input")
     parser.add_argument("model",          type=Path,              help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
     parser.add_argument("--ctx",          type=int,               help="model training context (default: based on input)")
@@ -1395,20 +1593,33 @@ def main(args_in: list[str] | None = None) -> None:
     parser.add_argument("--big-endian",   action="store_true",    help="model is executed on big endian machine")
     parser.add_argument("--pad-vocab",    action="store_true",    help="add pad tokens when model vocab expects more than tokenizer metadata provides")
     parser.add_argument("--skip-unknown", action="store_true",    help="skip unknown tensor names instead of failing")
+    parser.add_argument("--verbose",      action="store_true",    help="increase output verbosity")
+    parser.add_argument("--metadata",     type=Path,              help="Specify the path for a metadata file")
+    parser.add_argument("--get-outfile",  action="store_true",    help="get calculated default outfile name")
 
     args = parser.parse_args(args_in)
-    if args.awq_path:
-        sys.path.insert(1, str(Path(__file__).parent / 'awq-py'))
-        from awq.apply_awq import add_scale_weights  # type: ignore[import-not-found]
-        tmp_model_path = args.model / "weighted_model"
-        if tmp_model_path.is_dir():
-            print(f"{tmp_model_path} exists as a weighted model.")
-        else:
-            tmp_model_path.mkdir(parents=True, exist_ok=True)
-            print("Saving new weighted model ...")
-            add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path))
-            print(f"Saved weighted model at {tmp_model_path}.")
-        args.model = tmp_model_path
+
+    if args.verbose:
+        logging.basicConfig(level=logging.DEBUG)
+    elif args.dump_single or args.dump or args.get_outfile:
+        # Avoid printing anything besides the dump output
+        logging.basicConfig(level=logging.WARNING)
+    else:
+        logging.basicConfig(level=logging.INFO)
+
+    metadata = Metadata.load(args.metadata)
+
+    if args.get_outfile:
+        model_plus = load_some_model(args.model)
+        params = Params.load(model_plus)
+        model   = convert_model_names(model_plus.model, params, args.skip_unknown)
+        model_params_count = model_parameter_count(model_plus.model)
+        ftype   = pick_output_type(model, args.outtype)
+        print(f"{default_convention_outfile(ftype, params, model_params_count, metadata)}") # noqa: NP100
+        return
+
+    if args.no_vocab and args.vocab_only:
+        raise ValueError("--vocab-only does not make sense with --no-vocab")
 
     if args.dump_single:
         model_plus = lazy_load_file(args.model)
@@ -1420,63 +1631,83 @@ def main(args_in: list[str] | None = None) -> None:
     else:
         model_plus = ModelPlus(model = {}, paths = [args.model / 'dummy'], format = 'none', vocab = None)
 
+    model_params_count = model_parameter_count(model_plus.model)
+    logger.info(f"model parameters count : {model_params_count} ({model_parameter_count_rounded_notation(model_params_count)})")
+
     if args.dump:
         do_dump_model(model_plus)
         return
+
     endianess = gguf.GGUFEndian.LITTLE
     if args.big_endian:
         endianess = gguf.GGUFEndian.BIG
 
-    params = Params.load(model_plus)
-    if params.n_ctx == -1:
-        if args.ctx is None:
-            raise Exception("The model doesn't have a context size, and you didn't specify one with --ctx\n"
-                            "Please specify one with --ctx:\n"
-                            " - LLaMA v1: --ctx 2048\n"
-                            " - LLaMA v2: --ctx 4096\n")
-        params.n_ctx = args.ctx
+    params = None
+    if args.pad_vocab or not args.vocab_only:
+        params = Params.load(model_plus)
+        if params.n_ctx == -1:
+            if args.ctx is None:
+                msg = """\
+                    The model doesn't have a context size, and you didn't specify one with --ctx
+                    Please specify one with --ctx:
+                     - LLaMA v1: --ctx 2048
+                     - LLaMA v2: --ctx 4096"""
+                parser.error(textwrap.dedent(msg))
+            params.n_ctx = args.ctx
 
-    if args.outtype:
-        params.ftype = {
-            "f32": GGMLFileType.AllF32,
-            "f16": GGMLFileType.MostlyF16,
-            "q8_0": GGMLFileType.MostlyQ8_0,
-        }[args.outtype]
+        if args.outtype:
+            params.ftype = {
+                "f32": GGMLFileType.AllF32,
+                "f16": GGMLFileType.MostlyF16,
+                "q8_0": GGMLFileType.MostlyQ8_0,
+            }[args.outtype]
 
-    print(f"params = {params}")
+        logger.info(f"params = {params}")
 
     model_parent_path = model_plus.paths[0].parent
     vocab_path = Path(args.vocab_dir or args.model or model_parent_path)
     vocab_factory = VocabFactory(vocab_path)
-    vocab, special_vocab = vocab_factory.load_vocab(args.vocab_type, model_parent_path)
+    vocab_types = None if args.no_vocab else args.vocab_type.split(",")
+    vocab, special_vocab = vocab_factory.load_vocab(vocab_types, model_parent_path)
 
     if args.vocab_only:
+        assert isinstance(vocab, Vocab)
         if not args.outfile:
             raise ValueError("need --outfile if using --vocab-only")
         outfile = args.outfile
+        if params is None:
+            params = Params(
+                n_vocab    = vocab.vocab_size,
+                n_embd     = 1,
+                n_layer    = 1,
+                n_ctx      = 1,
+                n_ff       = 1,
+                n_head     = 1,
+                n_head_kv  = 1,
+                f_norm_eps = 1e-5,
+            )
         OutputFile.write_vocab_only(outfile, params, vocab, special_vocab,
-                                    endianess=endianess, pad_vocab=args.pad_vocab)
-        print(f"Wrote {outfile}")
+                                    endianess=endianess, pad_vocab=args.pad_vocab, metadata=metadata)
+        logger.info(f"Wrote {outfile}")
         return
 
-    if model_plus.vocab is not None and args.vocab_dir is None:
+    if model_plus.vocab is not None and args.vocab_dir is None and not args.no_vocab:
         vocab = model_plus.vocab
 
-    print(f"Vocab info: {vocab}")
-    print(f"Special vocab info: {special_vocab}")
-
+    logger.info(f"Vocab info: {vocab}")
+    logger.info(f"Special vocab info: {special_vocab}")
     model   = model_plus.model
     model   = convert_model_names(model, params, args.skip_unknown)
     ftype   = pick_output_type(model, args.outtype)
     model   = convert_to_output_type(model, ftype)
-    outfile = args.outfile or default_outfile(model_plus.paths, ftype)
+    outfile = args.outfile or default_outfile(model_plus.paths, ftype, params, model_params_count, metadata)
 
     params.ftype = ftype
-    print(f"Writing {outfile}, format {ftype}")
+    logger.info(f"Writing {outfile}, format {ftype}")
 
     OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab,
-                         concurrency=args.concurrency, endianess=endianess, pad_vocab=args.pad_vocab)
-    print(f"Wrote {outfile}")
+                         concurrency=args.concurrency, endianess=endianess, pad_vocab=args.pad_vocab, metadata=metadata)
+    logger.info(f"Wrote {outfile}")
 
 
 if __name__ == '__main__':
diff --git a/docs/BLIS.md b/docs/BLIS.md
index 0bcd6eeef..c933766b7 100644
--- a/docs/BLIS.md
+++ b/docs/BLIS.md
@@ -23,7 +23,7 @@ Install BLIS:
 sudo make install
 ```
 
-We recommend using openmp since it's easier to modify the cores been used.
+We recommend using openmp since it's easier to modify the cores being used.
 
 ### llama.cpp compilation
 
diff --git a/docs/HOWTO-add-model.md b/docs/HOWTO-add-model.md
new file mode 100644
index 000000000..48769cdf6
--- /dev/null
+++ b/docs/HOWTO-add-model.md
@@ -0,0 +1,119 @@
+## Add a new model architecture to `llama.cpp`
+
+Adding a model requires few steps:
+
+1. Convert the model to GGUF
+2. Define the model architecture in `llama.cpp`
+3. Build the GGML graph implementation
+
+After following these steps, you can open PR.
+
+Also, it is important to check that the examples and main ggml backends (CUDA, METAL, CPU) are working with the new architecture, especially:
+- [main](../examples/main)
+- [imatrix](../examples/imatrix)
+- [quantize](../examples/quantize)
+- [server](../examples/server)
+
+### 1. Convert the model to GGUF
+
+This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library.
+Depending on the model architecture, you can use either [convert.py](../convert.py) or [convert-hf-to-gguf.py](../convert-hf-to-gguf.py).
+
+The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors.
+
+The required steps to implement for an HF model are:
+
+1. Define the model `Model.register` annotation in a new `Model` subclass, example:
+
+```python
+@Model.register("MyModelForCausalLM")
+class MyModel(Model):
+    model_arch = gguf.MODEL_ARCH.GROK
+```
+
+2. Define the layout of the GGUF tensors in [constants.py](../gguf-py/gguf/constants.py)
+
+Add an enum entry in `MODEL_ARCH`, the model human friendly name in `MODEL_ARCH_NAMES` and the GGUF tensor names in `MODEL_TENSORS`.
+
+Example for `falcon` model:
+```python
+    MODEL_ARCH.FALCON: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_NORM_2,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ]
+```
+
+3. Map the original tensor names to the standardize equivalent in GGUF
+
+As a general rule, before adding a new tensor name to GGUF, be sure the equivalent naming does not already exist.
+
+Once you have found the GGUF tensor name equivalent, add it to the [tensor_mapping.py](../gguf-py/gguf/tensor_mapping.py) file.
+
+If the tensor name is part of a repetitive layer/block, the key word `bid` substitutes it.
+
+Example for the normalization tensor in attention layers:
+
+```python
+block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
+        # Attention norm
+        MODEL_TENSOR.ATTN_NORM: (
+            "gpt_neox.layers.{bid}.input_layernorm",                # gptneox
+            "transformer.h.{bid}.ln_1",                             # gpt2 gpt-j refact qwen
+            "transformer.blocks.{bid}.norm_1",                      # mpt
+            ...
+        )
+}
+```
+
+`transformer.blocks.{bid}.norm_1` will be mapped to `blk.{bid}.attn_norm` in GGUF.
+
+Depending on the model configuration, tokenizer, code and tensors layout, you will have to override:
+- `Model#set_gguf_parameters`
+- `Model#set_vocab`
+- `Model#write_tensors`
+
+NOTE: Tensor names must end with `.weight` suffix, that is the convention and several tools like `quantize` expect this to proceed the weights.
+
+### 2. Define the model architecture in `llama.cpp`
+
+The model params and tensors layout must be defined in `llama.cpp`:
+1. Define a new `llm_arch`
+2. Define the tensors layout in `LLM_TENSOR_NAMES`
+3. Add any non standard metadata in `llm_load_hparams`
+4. Create the tensors for inference in `llm_load_tensors`
+5. If the model has a RoPE operation, add the rope type in `llama_rope_type`
+
+NOTE: The dimensions in `ggml` are typically in the reverse order of the `pytorch` dimensions.
+
+### 3. Build the GGML graph implementation
+
+This is the funniest part, you have to provide the inference graph implementation of the new model architecture in `llama_build_graph`.
+
+Have a look at existing implementation like `build_llama`, `build_dbrx` or `build_bert`.
+
+When implementing a new graph, please note that the underlying `ggml` backends might not support them all, support for missing backend operations can be added in another PR.
+
+Note: to debug the inference graph: you can use [eval-callback](../examples/eval-callback).
+
+## GGUF specification
+
+https://github.com/ggerganov/ggml/blob/master/docs/gguf.md
+
+## Resources
+
+- YaRN RoPE scaling https://github.com/ggerganov/llama.cpp/pull/2268
+- support Baichuan serial models https://github.com/ggerganov/llama.cpp/pull/3009
+- support attention bias https://github.com/ggerganov/llama.cpp/pull/4283
+- Mixtral support https://github.com/ggerganov/llama.cpp/pull/4406
+- BERT embeddings https://github.com/ggerganov/llama.cpp/pull/5423
+- Grok-1 support https://github.com/ggerganov/llama.cpp/pull/6204
+- Command R Plus support https://github.com/ggerganov/llama.cpp/pull/6491
+- support arch DBRX https://github.com/ggerganov/llama.cpp/pull/6515
+- How to convert HuggingFace model to GGUF format https://github.com/ggerganov/llama.cpp/discussions/2948
diff --git a/docs/debugging-tests.md b/docs/debugging-tests.md
new file mode 100644
index 000000000..51a125e19
--- /dev/null
+++ b/docs/debugging-tests.md
@@ -0,0 +1,88 @@
+# Debugging Tests Tips
+
+## How to run & debug a specific test without anything else to keep the feedback loop short?
+
+There is a script called debug-test.sh in the scripts folder whose parameter takes a REGEX and an optional test number.
+
+For example, running the following command will output an interactive list from which you can select a test. It takes this form:
+
+`debug-test.sh [OPTION]... <test_regex> <test_number>`
+
+It will then build & run in the debugger for you.
+
+```bash
+./scripts/debug-test.sh test-tokenizer
+
+# Once in the debugger, i.e. at the chevrons prompt, setting a breakpoint could be as follows:
+>>> b main
+```
+
+For further reference use `debug-test.sh -h` to print help.
+
+&nbsp;
+
+### How does the script work?
+If you want to be able to use the concepts contained in the script separately, the important ones are briefly outlined below.
+
+#### Step 1: Reset and Setup folder context
+
+From base of this repository, let's create `build-ci-debug` as our build context.
+
+```bash
+rm -rf build-ci-debug && mkdir build-ci-debug && cd build-ci-debug
+```
+
+#### Step 2: Setup Build Environment and Compile Test Binaries
+
+Setup and trigger a build under debug mode. You may adapt the arguments as needed, but in this case these are sane defaults.
+
+```bash
+cmake -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_FATAL_WARNINGS=ON ..
+make -j
+```
+
+#### Step 3.1: Identify Test Command for Debugging
+
+The output of this command will give you the command & arguments needed to run GDB.
+
+* `-R test-tokenizer` : looks for all the test files named `test-tokenizer*` (R=Regex)
+* `-N` : "show-only" disables test execution & shows test commands that you can feed to GDB.
+* `-V` : Verbose Mode
+
+```bash
+ctest -R "test-tokenizer" -V -N
+```
+
+This may return output similar to below (focusing on key lines to pay attention to):
+
+```bash
+...
+1: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf"
+1: Working Directory: .
+Labels: main
+  Test  #1: test-tokenizer-0-llama-spm
+...
+4: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-falcon.gguf"
+4: Working Directory: .
+Labels: main
+  Test  #4: test-tokenizer-0-falcon
+...
+```
+
+So for test #1 we can tell these two pieces of relevant information:
+* Test Binary: `~/llama.cpp/build-ci-debug/bin/test-tokenizer-0`
+* Test GGUF Model: `~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf`
+
+#### Step 3.2: Run GDB on test command
+
+Based on the ctest 'test command' report above we can then run a gdb session via this command below:
+
+```bash
+gdb --args ${Test Binary} ${Test GGUF Model}
+```
+
+Example:
+
+```bash
+gdb --args ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf"
+```
diff --git a/docs/token_generation_performance_tips.md b/docs/token_generation_performance_tips.md
index d7e863dff..3c4343147 100644
--- a/docs/token_generation_performance_tips.md
+++ b/docs/token_generation_performance_tips.md
@@ -1,7 +1,7 @@
 # Token generation performance troubleshooting
 
-## Verifying that the model is running on the GPU with cuBLAS
-Make sure you compiled llama with the correct env variables according to [this guide](../README.md#cublas), so that llama accepts the `-ngl N` (or `--n-gpu-layers N`) flag. When running llama, you may configure `N` to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example:
+## Verifying that the model is running on the GPU with CUDA
+Make sure you compiled llama with the correct env variables according to [this guide](../README.md#CUDA), so that llama accepts the `-ngl N` (or `--n-gpu-layers N`) flag. When running llama, you may configure `N` to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example:
 ```shell
 ./main -m "path/to/model.gguf" -ngl 200000 -p "Please sir, may I have some "
 ```
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 653abc73a..f421769cc 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -19,7 +19,10 @@ else()
     add_subdirectory(benchmark)
     add_subdirectory(convert-llama2c-to-ggml)
     add_subdirectory(embedding)
+    add_subdirectory(eval-callback)
     add_subdirectory(finetune)
+    add_subdirectory(gritlm)
+    add_subdirectory(gguf-split)
     add_subdirectory(infill)
     add_subdirectory(llama-bench)
     add_subdirectory(llava)
@@ -32,6 +35,7 @@ else()
     add_subdirectory(perplexity)
     add_subdirectory(quantize)
     add_subdirectory(quantize-stats)
+    add_subdirectory(retrieval)
     add_subdirectory(save-load-state)
     add_subdirectory(simple)
     add_subdirectory(passkey)
diff --git a/examples/baby-llama/baby-llama.cpp b/examples/baby-llama/baby-llama.cpp
index 65bb238a0..bf0125e75 100644
--- a/examples/baby-llama/baby-llama.cpp
+++ b/examples/baby-llama/baby-llama.cpp
@@ -1547,7 +1547,7 @@ int main(int argc, char ** argv) {
 
         float error_before_opt = ggml_get_f32_1d(e, 0);
 
-        struct ggml_opt_params opt_params_lbfgs = ggml_opt_default_params(GGML_OPT_LBFGS);
+        struct ggml_opt_params opt_params_lbfgs = ggml_opt_default_params(GGML_OPT_TYPE_LBFGS);
         opt_params_lbfgs.print_forward_graph = false;
         opt_params_lbfgs.print_backward_graph = false;
         opt_params_lbfgs.lbfgs.n_iter = 16;
diff --git a/examples/batched-bench/README.md b/examples/batched-bench/README.md
index 34b343f66..bf951baf7 100644
--- a/examples/batched-bench/README.md
+++ b/examples/batched-bench/README.md
@@ -10,16 +10,16 @@ There are 2 modes of operation:
 - `prompt is shared` - there is a common prompt of size `PP` used by all batches (i.e. `N_KV = PP + B*TG`)
 
 ```bash
-./batched-bench MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ] <PP> <TG> <PL>
+./batched-bench MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [IS_PP_SHARED] [NGL] [MMQ] <PP> <TG> <PL>
 
 # LLaMA 7B, F16, N_KV_MAX = 16384 (8GB), prompt not shared
-./batched-bench ./models/llama-7b/ggml-model-f16.gguf 16384 0 99
+./batched-bench ./models/llama-7b/ggml-model-f16.gguf 16384 2048 512 0 99
 
 # LLaMA 7B, Q8_0, N_KV_MAX = 16384 (8GB), prompt is shared
-./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 16384 1 99
+./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 16384 2048 512 1 99
 
 # custom set of batches
-./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32
+./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 2048 512 512 0 999 0 128,256,512 128,256 1,2,4,8,16,32
 ```
 
 ## Sample results
diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp
index b4b8a38e1..2924d8116 100644
--- a/examples/batched-bench/batched-bench.cpp
+++ b/examples/batched-bench/batched-bench.cpp
@@ -32,16 +32,18 @@ int main(int argc, char ** argv) {
     gpt_params params;
 
     if (argc == 1 || argv[1][0] == '-') {
-        printf("usage: %s MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ] <PP> <TG> <PL>\n" , argv[0]);
+        printf("usage: %s MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [FATTN] [IS_PP_SHARED] [NGL] <PP> <TG> <PL>\n" , argv[0]);
         printf("  <PP>, <TG> and PL are comma-separated lists of numbers without spaces\n\n");
-        printf("  example: %s ggml-model-f16.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]);
+        printf("  example: %s ggml-model-f16.gguf 2048 2048 512 0 999 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]);
         return 1 ;
     }
 
     int n_kv_max     = 2048;
+    int n_batch      = 2048;
+    int n_ubatch     = 512;
+    bool flash_attn  = false;
     int is_pp_shared = 0;
     int n_gpu_layers = 0;
-    int mmq          = 0;
 
     std::vector<int> n_pp = { 128, 256, 512, 1024, 2048, 3584, 7680, };
     std::vector<int> n_tg = { 128, 256, };
@@ -57,27 +59,35 @@ int main(int argc, char ** argv) {
     }
 
     if (argc >= 4) {
-        is_pp_shared = std::atoi(argv[3]);
+        n_batch = std::atoi(argv[3]);
     }
 
     if (argc >= 5) {
-        n_gpu_layers = std::atoi(argv[4]);
+        n_ubatch = std::atoi(argv[4]);
     }
 
     if (argc >= 6) {
-        mmq = std::atoi(argv[5]);
+        flash_attn = std::atoi(argv[5]);
     }
 
     if (argc >= 7) {
-        n_pp = parse_list(argv[6]);
+        is_pp_shared = std::atoi(argv[6]);
     }
 
     if (argc >= 8) {
-        n_tg = parse_list(argv[7]);
+        n_gpu_layers = std::atoi(argv[7]);
     }
 
     if (argc >= 9) {
-        n_pl = parse_list(argv[8]);
+        n_pp = parse_list(argv[8]);
+    }
+
+    if (argc >= 10) {
+        n_tg = parse_list(argv[9]);
+    }
+
+    if (argc >= 11) {
+        n_pl = parse_list(argv[10]);
     }
 
     // init LLM
@@ -103,14 +113,18 @@ int main(int argc, char ** argv) {
 
     llama_context_params ctx_params = llama_context_default_params();
 
-    ctx_params.seed      = 1234;
-    ctx_params.n_ctx     = n_kv_max;
-    ctx_params.n_batch   = 512;
-    ctx_params.mul_mat_q = mmq;
+    ctx_params.seed       = 1234;
+    ctx_params.n_ctx      = n_kv_max;
+    ctx_params.n_batch    = n_batch;
+    ctx_params.n_ubatch   = n_ubatch;
+    ctx_params.flash_attn = flash_attn;
 
     ctx_params.n_threads       = params.n_threads;
     ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
 
+    // ensure enough sequences are available
+    ctx_params.n_seq_max = *std::max_element(n_pl.begin(), n_pl.end());
+
     llama_context * ctx = llama_new_context_with_model(model, ctx_params);
 
     if (ctx == NULL) {
@@ -141,6 +155,8 @@ int main(int argc, char ** argv) {
                 LOG_TEE("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret);
                 return false;
             }
+
+            llama_synchronize(ctx);
         }
 
         return true;
@@ -159,7 +175,7 @@ int main(int argc, char ** argv) {
     }
 
     LOG_TEE("\n");
-    LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq, ctx_params.n_threads, ctx_params.n_threads_batch);
+    LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, n_batch, n_ubatch, flash_attn, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
     LOG_TEE("\n");
 
     LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP",     "TG",     "B",    "N_KV",     "T_PP s",   "S_PP t/s", "T_TG s",   "S_TG t/s", "T s",      "S t/s");
@@ -180,10 +196,10 @@ int main(int argc, char ** argv) {
 
                 llama_batch_clear(batch);
 
-                const int n_tokens = is_pp_shared ? pp : pl*pp;
-
-                for (int i = 0; i < n_tokens; ++i) {
-                    llama_batch_add(batch, 0, i, { 0 }, false);
+                for (int i = 0; i < pp; ++i) {
+                    for (int j = 0; j < (is_pp_shared ? 1 : pl); ++j) {
+                        llama_batch_add(batch, 0, i, { j }, false);
+                    }
                 }
                 batch.logits[batch.n_tokens - 1] = true;
 
@@ -198,7 +214,7 @@ int main(int argc, char ** argv) {
 
                 if (is_pp_shared) {
                     for (int32_t i = 1; i < pl; ++i) {
-                        llama_kv_cache_seq_cp(ctx, 0, i, 0, pp);
+                        llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
                     }
                 }
 
diff --git a/examples/batched.swift/Sources/main.swift b/examples/batched.swift/Sources/main.swift
index d75c503d5..dbbd06da5 100644
--- a/examples/batched.swift/Sources/main.swift
+++ b/examples/batched.swift/Sources/main.swift
@@ -153,7 +153,7 @@ while n_cur <= n_len {
         // const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
 
         // is it an end of stream? -> mark the stream as finished
-        if new_token_id == llama_token_eos(model) || n_cur == n_len {
+        if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
             i_batch[i] = -1
             // print("")
             if n_parallel > 1 {
@@ -229,7 +229,7 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
 
 private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String? {
     var result = [CChar](repeating: 0, count: 8)
-    let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count))
+    let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count), false)
     if nTokens < 0 {
         let actualTokensCount = -Int(nTokens)
         result = .init(repeating: 0, count: actualTokensCount)
@@ -237,7 +237,8 @@ private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String
             model,
             token,
             &result,
-            Int32(result.count)
+            Int32(result.count),
+            false
         )
         assert(check == actualTokensCount)
     } else {
diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp
index 9be7eb56b..be30d20bf 100644
--- a/examples/batched/batched.cpp
+++ b/examples/batched/batched.cpp
@@ -48,6 +48,8 @@ int main(int argc, char ** argv) {
         params.prompt = "Hello my name is";
     }
 
+    process_escapes(params.prompt);
+
     // init LLM
 
     llama_backend_init();
@@ -78,8 +80,9 @@ int main(int argc, char ** argv) {
     llama_context_params ctx_params = llama_context_default_params();
 
     ctx_params.seed  = 1234;
-    ctx_params.n_ctx = n_kv_req;
+    ctx_params.n_ctx   = n_kv_req;
     ctx_params.n_batch = std::max(n_len, n_parallel);
+    ctx_params.n_seq_max       = n_parallel;
     ctx_params.n_threads       = params.n_threads;
     ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
 
@@ -132,7 +135,7 @@ int main(int argc, char ** argv) {
     // assign the system KV cache to all parallel sequences
     // this way, the parallel sequences will "reuse" the prompt tokens without having to copy them
     for (int32_t i = 1; i < n_parallel; ++i) {
-        llama_kv_cache_seq_cp(ctx, 0, i, 0, batch.n_tokens);
+        llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
     }
 
     if (n_parallel > 1) {
@@ -188,8 +191,8 @@ int main(int argc, char ** argv) {
 
             //const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
 
-            // is it an end of stream? -> mark the stream as finished
-            if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
+            // is it an end of generation? -> mark the stream as finished
+            if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
                 i_batch[i] = -1;
                 LOG_TEE("\n");
                 if (n_parallel > 1) {
diff --git a/examples/beam-search/beam-search.cpp b/examples/beam-search/beam-search.cpp
index 866c6d7a6..3d34378a5 100644
--- a/examples/beam-search/beam-search.cpp
+++ b/examples/beam-search/beam-search.cpp
@@ -47,7 +47,7 @@ struct beam_search_callback_data {
 // In this case, end-of-beam (eob) is equivalent to end-of-sentence (eos) but this need not always be the same.
 // For example, eob can be flagged due to maximum token length, stop words, etc.
 static bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, size_t n_tokens) {
-    return n_tokens && tokens[n_tokens-1] == llama_token_eos(llama_get_model(callback_data.ctx));
+    return n_tokens && llama_token_is_eog(llama_get_model(callback_data.ctx), tokens[n_tokens-1]);
 }
 
 // Function matching type llama_beam_search_callback_fn_t.
diff --git a/examples/benchmark/benchmark-matmult.cpp b/examples/benchmark/benchmark-matmult.cpp
index e89f3de2f..47cb16c69 100644
--- a/examples/benchmark/benchmark-matmult.cpp
+++ b/examples/benchmark/benchmark-matmult.cpp
@@ -189,12 +189,10 @@ int main(int argc, char ** argv)  {
 
     int32_t nelements = sizex*sizey;
 
-    std::vector<int64_t> hist_cur(1 << 4, 0);
-
     // Set up a the benchmark matrices
     // printf("Creating new tensor q11 & Running quantize\n");
     struct ggml_tensor * q11 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey);
-    ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements/m11->ne[0], m11->ne[0], hist_cur.data(), nullptr);
+    ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements/m11->ne[0], m11->ne[0], nullptr);
 
     // Set up a the compute graph
     // printf("Creating new tensor q31\n");
@@ -207,7 +205,7 @@ int main(int argc, char ** argv)  {
     // Set up a second graph computation to make sure we override the CPU cache lines
     // printf("Creating new tensor q12 & Running quantize\n");
     struct ggml_tensor * q12 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey);
-    ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements/m12->ne[0], m12->ne[0], hist_cur.data(), nullptr);
+    ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements/m12->ne[0], m12->ne[0], nullptr);
 
     // printf("Creating new tensor q32\n");
     struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2);
diff --git a/examples/convert-llama2c-to-ggml/README.md b/examples/convert-llama2c-to-ggml/README.md
index 0f37d295b..742dcf7a3 100644
--- a/examples/convert-llama2c-to-ggml/README.md
+++ b/examples/convert-llama2c-to-ggml/README.md
@@ -2,7 +2,7 @@
 
 This example reads weights from project [llama2.c](https://github.com/karpathy/llama2.c) and saves them in ggml compatible format. The vocab that is available in `models/ggml-vocab.bin` is used by default.
 
-To convert the model first download the models from the [llma2.c](https://github.com/karpathy/llama2.c) repository:
+To convert the model first download the models from the [llama2.c](https://github.com/karpathy/llama2.c) repository:
 
 `$ make -j`
 
@@ -21,6 +21,8 @@ An example command using a model from [karpathy/tinyllamas](https://huggingface.
 
 `$ ./convert-llama2c-to-ggml --copy-vocab-from-model llama-2-7b-chat.gguf.q2_K.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.gguf.bin`
 
+Note: The vocabulary for `stories260K.bin` should be its own tokenizer `tok512.bin` found in [karpathy/tinyllamas/stories260K](https://huggingface.co/karpathy/tinyllamas/tree/main/stories260K).
+
 Now you can use the model with a command like:
 
 `$ ./main -m stories42M.gguf.bin -p "One day, Lily met a Shoggoth" -n 500 -c 256`
diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
index 8209dcb64..746c3fbef 100644
--- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
+++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
@@ -1,6 +1,7 @@
 #include "ggml.h"
 #include "llama.h"
 #include "common.h"
+#include "log.h"
 
 #include <unordered_map>
 #include <vector>
@@ -78,111 +79,101 @@ typedef struct {
 
 struct TransformerWeights {
     // token embedding table
-    float* token_embedding_table;    // (vocab_size, dim)
+    std::vector<float> token_embedding_table;    // (vocab_size, dim)
     // weights for rmsnorms
-    float* rms_att_weight; // (layer, dim) rmsnorm weights
-    float* rms_ffn_weight; // (layer, dim)
+    std::vector<float> rms_att_weight; // (layer, dim) rmsnorm weights
+    std::vector<float> rms_ffn_weight; // (layer, dim)
     // weights for matmuls
-    float* wq; // (layer, dim, dim)
-    float* wk; // (layer, dim, dim)
-    float* wv; // (layer, dim, dim)
-    float* wo; // (layer, dim, dim)
+    std::vector<float> wq; // (layer, dim, dim)
+    std::vector<float> wk; // (layer, dim, dim)
+    std::vector<float> wv; // (layer, dim, dim)
+    std::vector<float> wo; // (layer, dim, dim)
     // weights for ffn
-    float* w1; // (layer, hidden_dim, dim)
-    float* w2; // (layer, dim, hidden_dim)
-    float* w3; // (layer, hidden_dim, dim)
+    std::vector<float> w1; // (layer, hidden_dim, dim)
+    std::vector<float> w2; // (layer, dim, hidden_dim)
+    std::vector<float> w3; // (layer, hidden_dim, dim)
     // final rmsnorm
-    float* rms_final_weight; // (dim,)
+    std::vector<float> rms_final_weight; // (dim,)
     // freq_cis for RoPE relatively positional embeddings
-    // float* freq_cis_real; // (seq_len, dim/2)
-    // float* freq_cis_imag; // (seq_len, dim/2)
+    // std::vector<float> freq_cis_real; // (seq_len, dim/2)
+    // std::vector<float> freq_cis_imag; // (seq_len, dim/2)
     // (optional) classifier weights for the logits, on the last layer
-    float* wcls;
-
-    ~TransformerWeights() {
-        delete[] token_embedding_table;
-        delete[] rms_att_weight;
-        delete[] rms_ffn_weight;
-        delete[] wq;
-        delete[] wk;
-        delete[] wv;
-        delete[] wo;
-        delete[] w1;
-        delete[] w2;
-        delete[] w3;
-        delete[] rms_final_weight;
-        delete[] wcls;
-    }
+    std::vector<float> wcls;
 };
 
-static void malloc_weights(TransformerWeights* w, Config* p, bool shared_weights) {
-    // we calloc instead of malloc to keep valgrind happy
-    w->token_embedding_table = new float[p->vocab_size * p->dim]();
-    printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->token_embedding_table\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
+static void alloc_weights(TransformerWeights * w, const Config * p, bool shared_weights) {
+    const int n_multiqueries = p->n_kv_heads <= 0 || p->n_kv_heads >= p->n_heads ? 1 : p->n_heads / p->n_kv_heads;
+    try {
+        w->token_embedding_table.resize(p->vocab_size * p->dim);
+        LOG("%s: Allocating [%d] x [%d] = [%d] float space for w->token_embedding_table\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
 
-    w->rms_att_weight = new float[p->n_layers * p->dim]();
-    printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->rms_att_weight\n",__func__,p->n_layers, p->dim, p->n_layers * p->dim);
+        w->rms_att_weight.resize(p->n_layers * p->dim);
+        LOG("%s: Allocating [%d] x [%d] = [%d] float space for w->rms_att_weight\n",__func__,p->n_layers, p->dim, p->n_layers * p->dim);
 
-    w->rms_ffn_weight = new float[p->n_layers * p->dim]();
-    printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->rms_ffn_weight\n",__func__,p->n_layers , p->dim, p->n_layers * p->dim);
+        w->rms_ffn_weight.resize(p->n_layers * p->dim);
+        LOG("%s: Allocating [%d] x [%d] = [%d] float space for w->rms_ffn_weight\n",__func__,p->n_layers , p->dim, p->n_layers * p->dim);
 
-    w->wq = new float[p->n_layers * p->dim * p->dim]();
-    printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wq\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
+        w->wq.resize(p->n_layers * p->dim * p->dim);
+        LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wq\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
 
-    w->wk = new float[p->n_layers * p->dim * p->dim]();
-    printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wk\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
+        w->wk.resize(p->n_layers * p->dim * p->dim / n_multiqueries);
+        LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wk\n",__func__,p->n_layers, p->dim, p->dim / n_multiqueries, p->n_layers * p->dim * p->dim / n_multiqueries);
 
-    w->wv = new float[p->n_layers * p->dim * p->dim]();
-    printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wv\n",__func__, p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
+        w->wv.resize(p->n_layers * p->dim * p->dim / n_multiqueries);
+        LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wv\n",__func__, p->n_layers, p->dim, p->dim / n_multiqueries, p->n_layers * p->dim * p->dim / n_multiqueries);
 
-    w->wo = new float[p->n_layers * p->dim * p->dim]();
-    printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wo\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
+        w->wo.resize(p->n_layers * p->dim * p->dim);
+        LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wo\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
 
-    w->w1 = new float[p->n_layers * p->hidden_dim * p->dim]();
-    printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w1\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim);
+        w->w1.resize(p->n_layers * p->hidden_dim * p->dim);
+        LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->w1\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim);
 
-    w->w2 = new float[p->n_layers * p->hidden_dim * p->dim]();
-    printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w2\n",__func__,p->n_layers, p->dim, p->hidden_dim, p->n_layers * p->hidden_dim * p->dim);
+        w->w2.resize(p->n_layers * p->hidden_dim * p->dim);
+        LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->w2\n",__func__,p->n_layers, p->dim, p->hidden_dim, p->n_layers * p->hidden_dim * p->dim);
 
-    w->w3 = new float[p->n_layers * p->hidden_dim * p->dim]();
-    printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w3\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim);
+        w->w3.resize(p->n_layers * p->hidden_dim * p->dim);
+        LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->w3\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim);
 
-    w->rms_final_weight = new float[p->dim]();
-    printf("[%s:AK] Allocating [%d] float space for w->rms_final_weight\n",__func__,p->dim);
+        w->rms_final_weight.resize(p->dim);
+        LOG("%s: Allocating [%d] float space for w->rms_final_weight\n",__func__,p->dim);
 
-    if (shared_weights) {
-        w->wcls = NULL;
-    } else {
-        w->wcls = new float[p->vocab_size * p->dim]();
-        printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->wcls\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
+        if (shared_weights) {
+            w->wcls = {};
+        } else {
+            w->wcls.resize(p->vocab_size * p->dim);
+            LOG("%s: Allocating [%d] x [%d] = [%d] float space for w->wcls\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
+        }
+    }
+    catch (std::length_error &) {
+        die("Invalid configuration. Failed to allocate memory for weights");
     }
 }
 
-static int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f, bool shared_weights) {
-    if (fread(w->token_embedding_table, sizeof(float), p->vocab_size * p->dim, f) != static_cast<size_t>(p->vocab_size * p->dim)) return 1;
-    if (fread(w->rms_att_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim)) return 1;
-    if (fread(w->wq, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
-    if (fread(w->wk, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
-    if (fread(w->wv, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
-    if (fread(w->wo, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
-    if (fread(w->rms_ffn_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim)) return 1;
-    if (fread(w->w1, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->hidden_dim)) return 1;
-    if (fread(w->w2, sizeof(float), p->n_layers * p->hidden_dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->hidden_dim * p->dim)) return 1;
-    if (fread(w->w3, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->hidden_dim)) return 1;
-    if (fread(w->rms_final_weight, sizeof(float), p->dim, f) != static_cast<size_t>(p->dim)) return 1;
+static int checkpoint_init_weights(TransformerWeights * w, const Config * p, FILE * f, bool shared_weights) {
+    if (fread(w->token_embedding_table.data(), sizeof(float), w->token_embedding_table.size(), f) != w->token_embedding_table.size()) return 1;
+    if (fread(w->rms_att_weight.data(), sizeof(float), w->rms_att_weight.size(), f) != w->rms_att_weight.size()) return 1;
+    if (fread(w->wq.data(), sizeof(float), w->wq.size(), f) != w->wq.size()) return 1;
+    if (fread(w->wk.data(), sizeof(float), w->wk.size(), f) != w->wk.size()) return 1;
+    if (fread(w->wv.data(), sizeof(float), w->wv.size(), f) != w->wv.size()) return 1;
+    if (fread(w->wo.data(), sizeof(float), w->wo.size(), f) != w->wo.size()) return 1;
+    if (fread(w->rms_ffn_weight.data(), sizeof(float), w->rms_ffn_weight.size(), f) != w->rms_ffn_weight.size()) return 1;
+    if (fread(w->w1.data(), sizeof(float), w->w1.size(), f) != w->w1.size()) return 1;
+    if (fread(w->w2.data(), sizeof(float), w->w2.size(), f) != w->w2.size()) return 1;
+    if (fread(w->w3.data(), sizeof(float), w->w3.size(), f) != w->w3.size()) return 1;
+    if (fread(w->rms_final_weight.data(), sizeof(float), w->rms_final_weight.size(), f) != w->rms_final_weight.size()) return 1;
 
     // Skip freq_cis_real & freq_cis_imag
     int head_size = p->dim / p->n_heads;
     fseek(f, p->seq_len * head_size * sizeof(float), SEEK_CUR);
 
-    if (!shared_weights && fread(w->wcls, sizeof(float), p->vocab_size * p->dim, f) != static_cast<size_t>(p->vocab_size * p->dim)) return 1;
+    if (!shared_weights && fread(w->wcls.data(), sizeof(float), w->wcls.size(), f) != w->wcls.size()) return 1;
 
     // Check we didn't forget to read anything
     auto curr = ftell(f);
     fseek(f, 0, SEEK_END);
     auto end = ftell(f);
     if (curr != end) {
-        printf("Error: failed to read the checkpoint file to the end (curr = %ld, end =  %ld)\n", curr, end);
+        LOG("%s: Error: failed to read the checkpoint file to the end (curr = %ld, end =  %ld)\n", __func__, curr, end);
         return 1;
     }
 
@@ -190,20 +181,20 @@ static int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f, bo
 }
 
 static void print_sample_weights(TransformerWeights *w){
-    printf("----- Quick print of first of the weight vales of all the variables\n");
-    printf("%f\n", w->token_embedding_table[0]);
-    printf("%f\n", w->rms_att_weight[0]);
-    printf("%f\n", w->rms_ffn_weight[0]);
+    LOG("----- Quick print of first of the weight vales of all the variables\n");
+    LOG("%f\n", w->token_embedding_table[0]);
+    LOG("%f\n", w->rms_att_weight[0]);
+    LOG("%f\n", w->rms_ffn_weight[0]);
 
-    printf("%f\n", w->wq[0]);
-    printf("%f\n", w->wk[0]);
-    printf("%f\n", w->wv[0]);
-    printf("%f\n", w->wo[0]);
-    printf("%f\n", w->w1[0]);
-    printf("%f\n", w->w2[0]);
-    printf("%f\n", w->w3[0]);
-    printf("%f\n", w->rms_att_weight[0]);
-    if (w->wcls) printf("%f\n", w->wcls[0]);
+    LOG("%f\n", w->wq[0]);
+    LOG("%f\n", w->wk[0]);
+    LOG("%f\n", w->wv[0]);
+    LOG("%f\n", w->wo[0]);
+    LOG("%f\n", w->w1[0]);
+    LOG("%f\n", w->w2[0]);
+    LOG("%f\n", w->w3[0]);
+    LOG("%f\n", w->rms_att_weight[0]);
+    if (!w->wcls.empty()) LOG("%f\n", w->wcls[0]);
 }
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////
 
@@ -225,14 +216,16 @@ struct llama_vocab {
 };
 
 struct my_llama_hparams {
-    uint32_t n_vocab = 32000;
-    uint32_t n_ctx   = 512;   // this is provided as user input?
-    uint32_t n_embd  = 4096;
-    uint32_t n_ff    = 11008;
-    uint32_t n_mult  = 4;
-    uint32_t n_head  = 32;
-    uint32_t n_layer = 32;
-    uint32_t n_rot   = 64;
+    uint32_t n_vocab   = 32000;
+    uint32_t n_ctx     = 512;   // this is provided as user input?
+    uint32_t n_embd    = 4096;
+    uint32_t n_ff      = 11008;
+    uint32_t n_mult    = 4;
+    uint32_t n_head    = 32;
+    uint32_t n_head_kv = 32;
+    uint32_t n_layer   = 32;
+    uint32_t n_rot     = 64;
+
     bool operator!=(const my_llama_hparams& other) const {
         return memcmp(this, &other, sizeof(my_llama_hparams));
     }
@@ -325,14 +318,30 @@ struct train_params {
 };
 
 static void print_params(struct my_llama_hparams * params) {
-    printf("%s: n_vocab: %u\n", __func__, params->n_vocab);
-    printf("%s: n_ctx:   %u\n", __func__, params->n_ctx);
-    printf("%s: n_embd:  %u\n", __func__, params->n_embd);
-    printf("%s: n_mult:  %u\n", __func__, params->n_mult);
-    printf("%s: n_head:  %u\n", __func__, params->n_head);
-    printf("%s: n_ff:    %u\n", __func__, params->n_ff);
-    printf("%s: n_layer: %u\n", __func__, params->n_layer);
-    printf("%s: n_rot:   %u\n", __func__, params->n_rot);
+    LOG("%s: n_vocab:   %u\n", __func__, params->n_vocab);
+    LOG("%s: n_ctx:     %u\n", __func__, params->n_ctx);
+    LOG("%s: n_embd:    %u\n", __func__, params->n_embd);
+    LOG("%s: n_mult:    %u\n", __func__, params->n_mult);
+    LOG("%s: n_head:    %u\n", __func__, params->n_head);
+    LOG("%s: n_head_kv: %u\n", __func__, params->n_head_kv);
+    LOG("%s: n_ff:      %u\n", __func__, params->n_ff);
+    LOG("%s: n_layer:   %u\n", __func__, params->n_layer);
+    LOG("%s: n_rot:     %u\n", __func__, params->n_rot);
+}
+
+static void print_tensor_info(const struct ggml_context * ctx) {
+    for (auto t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+        LOG("%s: Allocating ", __func__);
+        int64_t total = 1;
+        int i = 0;
+        for (; i < ggml_n_dims(t); ++i) {
+            if (i > 0) LOG("x ");
+            LOG("[%" PRId64 "] ", t->ne[i]);
+            total *= t->ne[i];
+        }
+        if (i > 1) LOG("= [%" PRId64 "] ", total);
+        LOG("float space for %s\n", ggml_get_name(t));
+    }
 }
 
 static void init_model(struct my_llama_model * model) {
@@ -342,6 +351,8 @@ static void init_model(struct my_llama_model * model) {
     const uint32_t n_layer = hparams.n_layer;
     const uint32_t n_vocab = hparams.n_vocab;
 
+    const uint32_t n_multiqueries = hparams.n_head_kv <= 0 || hparams.n_head_kv >= hparams.n_head ? 1 : hparams.n_head / hparams.n_head_kv;
+
     const uint32_t n_ff = hparams.n_ff;
     struct ggml_context * ctx = model->ctx;
 
@@ -350,25 +361,8 @@ static void init_model(struct my_llama_model * model) {
     model->train_tokens = 0;
 
     model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
-    printf("[%s:GG] Allocating [%u] x [%u] = [%u] float space for model->tok_embeddings\n",__func__,n_embd , n_vocab, n_embd * n_vocab);
-
     model->norm           = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
-    printf("[%s:GG] Allocating [%u] float space for model->norm\n",__func__,n_embd);
-
     model->output         = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
-    printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for model->output\n",__func__,n_embd, n_vocab, n_embd * n_vocab);
-
-    // printing the per-layer allocations here so we dont print in the for loop.
-    printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.wq for [%u] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
-    printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.wk for [%u] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
-    printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.wv for [%u] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
-    printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.wo for [%u] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
-
-    printf("[%s:GG] Allocating [%u] float space for layer.ffn_norm for [%u] layers\n",__func__,n_embd, n_layer);
-
-    printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.w1 for [%u] layers\n",__func__, n_ff, n_embd, n_embd * n_ff, n_layer);
-    printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.w2 for [%u] layers\n",__func__, n_embd, n_ff, n_ff * n_embd, n_layer);
-    printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.w3 for [%u] layers\n",__func__, n_ff, n_embd, n_embd * n_ff, n_layer);
 
     ggml_set_name(model->tok_embeddings, "tok_embeddings.weight");
     ggml_set_name(model->norm,           "norm.weight");
@@ -383,8 +377,8 @@ static void init_model(struct my_llama_model * model) {
         layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
 
         layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
-        layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
-        layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
+        layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd / n_multiqueries);
+        layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd / n_multiqueries);
         layer.wo = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
 
         layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
@@ -406,6 +400,8 @@ static void init_model(struct my_llama_model * model) {
         ggml_format_name(layer.w2, "%s.feed_forward.w2.weight", layers_i.c_str());
         ggml_format_name(layer.w3, "%s.feed_forward.w3.weight", layers_i.c_str());
     }
+
+    print_tensor_info(ctx);
 }
 
 static float get_f32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) {
@@ -421,9 +417,9 @@ static int32_t get_i32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) {
 static void print_row(struct ggml_tensor * probs, int i) {
     for (int k = 0; k < probs->ne[0]; ++k) {
         float p = get_f32_2d(probs, k, i);
-        printf(" %f", p);
+        LOG(" %f", p);
     }
-    printf("\n");
+    LOG("\n");
 }
 
 static void print_matrix(struct ggml_tensor * probs) {
@@ -431,33 +427,12 @@ static void print_matrix(struct ggml_tensor * probs) {
     for (int i = 0; i < probs->ne[1]; ++i) {
         for (int k = 0; k < probs->ne[0]; ++k) {
             float p = get_f32_2d(probs, k, i);
-            printf(" %.2f", p);
+            LOG(" %.2f", p);
         }
-        printf("\n");
+        LOG("\n");
     }
 }
 
-#ifdef __GNUC__
-#ifdef __MINGW32__
-__attribute__((format(gnu_printf, 1, 2)))
-#else
-__attribute__((format(printf, 1, 2)))
-#endif
-#endif
-static std::string format(const char * fmt, ...) {
-    va_list ap, ap2;
-    va_start(ap, fmt);
-    va_copy(ap2, ap);
-    int size = vsnprintf(NULL, 0, fmt, ap);
-    GGML_ASSERT(size >= 0 && size < INT_MAX);
-    std::vector<char> buf(size + 1);
-    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
-    GGML_ASSERT(size2 == size);
-    va_end(ap2);
-    va_end(ap);
-    return std::string(buf.data(), size);
-}
-
 struct llama_file {
     // use FILE * so we don't have to re-open the file to mmap
     FILE * fp;
@@ -549,8 +524,9 @@ static std::string llama_escape_whitespaces(const std::string & text) {
     return out.str();
 }
 
-static void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) {
+static void load_vocab(const char * filename, const Config * config, struct llama_vocab * vocab) {
     if (is_ggml_file(filename)) {
+        LOG("%s: Loading vocabulary from gguf file %s\n", __func__, filename);
         struct ggml_context * ctx_data = NULL;
 
         struct gguf_init_params params = {
@@ -578,6 +554,9 @@ static void load_vocab(const char *filename, Config *config, struct llama_vocab
         const int * toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
 
         const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);
+        if (n_vocab != static_cast<uint32_t>(config->vocab_size)) {
+            die_fmt("vocab size mismatch: (gguf) %u != (llama2c) %d", n_vocab, config->vocab_size);
+        }
 
         vocab->id_to_token.resize(n_vocab);
 
@@ -595,7 +574,7 @@ static void load_vocab(const char *filename, Config *config, struct llama_vocab
         gguf_free(ctx);
     } else {
         // assume llama2.c vocabulary
-        printf("Assuming llama2.c vocabulary since %s is not a gguf file\n", filename);
+        LOG("%s: Assuming llama2.c vocabulary since %s is not a gguf file\n", __func__, filename);
         llama_file file(filename, "rb");
         if (!file.fp) {
             die_fmt("%s: %s", strerror(errno), filename);
@@ -638,38 +617,15 @@ static void load_vocab(const char *filename, Config *config, struct llama_vocab
 }
 
 static void convert_weights_ak_to_gg(struct ggml_tensor * gg_weights, const float * karpathy_weights) {
-    int ct;
-    switch (ggml_n_dims(gg_weights)) {
-        case 1:
-            ct = 0;
-            for (int i0 = 0; i0 < gg_weights->ne[0]; i0++){
-                float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0]);
-                *ptr = karpathy_weights[ct];
-                ct++;
-            }
-            break;
-        case 2:
-            ct = 0;
-            for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) {
-                for (int i0 = 0; i0 < gg_weights->ne[0]; i0++) {
-                    float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0] + i1*gg_weights->nb[1]);
-                    *ptr = karpathy_weights[ct];
-                    ct++;
-                }
-            }
-            break;
-        case 3:
-            ct = 0;
-            for (int i2 = 0; i2 < gg_weights->ne[2]; i2++) {
-                for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) {
-                    for (int i0 = 0; i0 < gg_weights->ne[0]; i0++) {
-                        float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0] + i1*gg_weights->nb[1] + i2*gg_weights->nb[2]);
-                        *ptr = karpathy_weights[ct];
-                        ct++;
-                    }
-                }
-            }
-            break;
+    int size = 1;
+    for (int dim = 0; dim < ggml_n_dims(gg_weights); ++dim) {
+        size *= gg_weights->ne[dim];
+    }
+    for (int ct = 0; ct < size; ++ct) {
+        int64_t i0 = 0; int64_t i1 = 0;
+        int64_t i2 = 0; int64_t i3 = 0;
+        ggml_unravel_index(gg_weights, ct, &i0, &i1, &i2, &i3);
+        ggml_set_f32_nd(gg_weights, i0, i1, i2, i3, karpathy_weights[ct]);
     }
 }
 
@@ -679,16 +635,18 @@ static void save_as_llama_model(
     // convert AK weights into GG weights one by one.
     // w->token_embedding_table -> model->tok_embeddings
     // float*                   -> struct ggml_tensor
-    convert_weights_ak_to_gg(model->tok_embeddings, w->token_embedding_table);
-    convert_weights_ak_to_gg(model->output, w->wcls ? w->wcls : w->token_embedding_table);
+    convert_weights_ak_to_gg(model->tok_embeddings, w->token_embedding_table.data());
+    convert_weights_ak_to_gg(model->output, !w->wcls.empty() ? w->wcls.data() : w->token_embedding_table.data());
 
-    convert_weights_ak_to_gg(model->norm, w->rms_final_weight);
+    convert_weights_ak_to_gg(model->norm, w->rms_final_weight.data());
     //print_row(model->norm, 0);
 
     // for rms-att-weight
     int row_length = model->hparams.n_embd;
     int n_ff = model->hparams.n_ff;
 
+    const uint32_t n_multiqueries = model->hparams.n_head_kv <= 0 || model->hparams.n_head_kv >= model->hparams.n_head ? 1 : model->hparams.n_head / model->hparams.n_head_kv;
+
     for (uint32_t i = 0; i < model->hparams.n_layer; ++i){
         auto & layer = model->layers[i];
         // 1d
@@ -697,9 +655,10 @@ static void save_as_llama_model(
 
         // from 3d matrix layer x dim x dim to 2d matrix dim x dim
         convert_weights_ak_to_gg(layer.wq            , &w->wq[i*row_length*row_length]);
-        convert_weights_ak_to_gg(layer.wk            , &w->wk[i*row_length*row_length]);
-        convert_weights_ak_to_gg(layer.wv            , &w->wv[i*row_length*row_length]);
         convert_weights_ak_to_gg(layer.wo            , &w->wo[i*row_length*row_length]);
+        // from 3d matrix layer x dim x dim to 2d matrix dim x dim / n_multiqueries
+        convert_weights_ak_to_gg(layer.wk            , &w->wk[i*row_length*row_length/n_multiqueries]);
+        convert_weights_ak_to_gg(layer.wv            , &w->wv[i*row_length*row_length/n_multiqueries]);
 
         convert_weights_ak_to_gg(layer.w1            , &w->w1[i*row_length*n_ff]);
         convert_weights_ak_to_gg(layer.w2            , &w->w2[i*n_ff*row_length]);
@@ -736,8 +695,8 @@ static void save_as_llama_model(
     gguf_set_val_u32(ctx, KV_EMBEDDING_LENGTH, model->hparams.n_embd);
     gguf_set_val_u32(ctx, KV_FEED_FORWARD_LENGTH, model->hparams.n_ff);
     gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT, model->hparams.n_head);
-    // n_head_kv is optional, default to n_head
-    // gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT_KV, ...);
+    gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT, model->hparams.n_head);
+    gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT_KV, model->hparams.n_head_kv);
     gguf_set_val_u32(ctx, KV_BLOCK_COUNT, model->hparams.n_layer);
     gguf_set_val_u32(ctx, KV_ROPE_DIMENSION_COUNT, model->hparams.n_rot);
     gguf_set_val_f32(ctx, KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f);
@@ -789,12 +748,12 @@ static void save_as_llama_model(
 
 static struct train_params get_default_train_params() {
     struct train_params params;
-    params.fn_vocab_model    = "models/7B/ggml-model-f16.gguf";
+    params.fn_vocab_model          = "models/7B/ggml-model-f16.gguf";
     params.fn_llama2c_output_model = "ak_llama_model.bin";
-    params.fn_train_data     = "shakespeare.txt";
-    params.fn_checkpoint_in  = "checkpoint.bin";
-    params.fn_checkpoint_out = "checkpoint.bin";
-    params.fn_model_out      = "ggml-checkpoint-f32.bin";
+    params.fn_train_data           = "shakespeare.txt";
+    params.fn_checkpoint_in        = "checkpoint.bin";
+    params.fn_checkpoint_out       = "checkpoint.bin";
+    params.fn_model_out            = "ggml-checkpoint-f32.bin";
 
     params.seed       =   -1;
 
@@ -829,8 +788,8 @@ static struct train_params get_default_train_params() {
     params.adam_alpha        = 1e-3f;
     params.adam_decay        = 1e-3f;
 
-    params.mem_model_gb   = 2;
-    params.mem_compute_gb = 24;
+    params.mem_model_gb    = 2;
+    params.mem_compute_gb  = 24;
     params.mem_compute0_gb = 8;
     params.mem_compute1_gb = 2;
 
@@ -916,19 +875,30 @@ int main(int argc, char ** argv) {
     if (!params_parse(argc, argv, &params)) {
         return 1;
     }
+    log_set_target(stdout);
     Config config;
     TransformerWeights weights = {};
     {
-        FILE *file = fopen(params.fn_llama2c_model, "rb");
-        if (!file) { printf("Unable to open the checkpoint file %s!\n", params.fn_llama2c_model); return 1; }
+        LOG("%s: Loading llama2c model from %s\n", __func__, params.fn_llama2c_model);
+        FILE * file = fopen(params.fn_llama2c_model, "rb");
+        if (!file) {
+            LOG("%s: Unable to open the checkpoint file %s!\n", __func__, params.fn_llama2c_model);
+            return 1;
+        }
         // read in the config header
-        if(fread(&config, sizeof(Config), 1, file) != 1) { return 1; }
+        if (fread(&config, sizeof(Config), 1, file) != 1) {
+            LOG("%s: Unable to read llama2c config from %s!\n",__func__,params.fn_llama2c_model);
+            return 1;
+        }
         auto shared_weights = config.vocab_size > 0;
         config.vocab_size = abs(config.vocab_size);
 
         // read in the Transformer weights
-        malloc_weights(&weights, &config, shared_weights);
-        if(checkpoint_init_weights(&weights, &config, file, shared_weights)) { return 1; }
+        alloc_weights(&weights, &config, shared_weights);
+        if (checkpoint_init_weights(&weights, &config, file, shared_weights)) {
+            LOG("%s: Unable to initialize transformer weights from %s!",__func__,params.fn_llama2c_model);
+            return 1;
+        }
         fclose(file);
     }
 
@@ -936,15 +906,18 @@ int main(int argc, char ** argv) {
     load_vocab(params.fn_vocab_model, &config, &vocab);
 
     struct my_llama_model model;
-    model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx);
-    model.hparams.n_ctx   = params.n_ctx;
-    model.hparams.n_embd  = config.dim; //params.n_embd;
-    model.hparams.n_ff    = config.hidden_dim;
-    model.hparams.n_mult  = 32;//params.n_mult;
-    model.hparams.n_head  = config.n_heads; //params.n_head;
-    model.hparams.n_layer = config.n_layers; //params.n_layer;
-    model.hparams.n_rot   = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head);
+    model.hparams.n_vocab   = config.vocab_size; //llama_n_vocab(lctx);
+    model.hparams.n_ctx     = params.n_ctx;
+    model.hparams.n_embd    = config.dim; //params.n_embd;
+    model.hparams.n_ff      = config.hidden_dim;
+    model.hparams.n_mult    = 32;//params.n_mult;
+    model.hparams.n_head    = config.n_heads; //params.n_head;
+    model.hparams.n_head_kv = config.n_kv_heads;
+    model.hparams.n_layer   = config.n_layers; //params.n_layer;
+    model.hparams.n_rot     = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head);
+
     print_params(&model.hparams);
+
     struct ggml_init_params lcparams;
     lcparams.mem_size   = 1024ll*1024ll*1024ll*((size_t) params.mem_model_gb);
     lcparams.mem_buffer = NULL;
@@ -956,7 +929,7 @@ int main(int argc, char ** argv) {
     model.name = basename(params.fn_llama2c_model);
     save_as_llama_model(&vocab, &model, &weights, params.fn_llama2c_output_model);
 
-    printf("Saving llama.c model file %s in ggml format at %s\n", params.fn_llama2c_model, params.fn_llama2c_output_model);
+    LOG("%s: Saving llama.c model file %s in ggml format at %s\n", __func__, params.fn_llama2c_model, params.fn_llama2c_output_model);
 
     ggml_free(model.ctx);
     return 0;
diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp
index acff715e9..c85a2da53 100644
--- a/examples/embedding/embedding.cpp
+++ b/examples/embedding/embedding.cpp
@@ -19,18 +19,7 @@ static std::vector<std::string> split_lines(const std::string & s) {
 
 static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, int seq_id) {
     for (size_t i = 0; i < tokens.size(); i++) {
-        llama_batch_add(batch, tokens[i], i, { seq_id }, false);
-    }
-}
-
-static void normalize(float * vec, float * out, int n) {
-    float norm = 0;
-    for (int i = 0; i < n; i++) {
-        norm += vec[i] * vec[i];
-    }
-    norm = sqrt(norm);
-    for (int i = 0; i < n; i++) {
-        out[i] = vec[i] / norm;
+        llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
     }
 }
 
@@ -44,11 +33,29 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
         fprintf(stderr, "%s : failed to decode\n", __func__);
     }
 
-    // normalize on copy
-    for (int k = 0; k < n_seq; k++) {
-        float * emb = llama_get_embeddings_ith(ctx, k);
-        float * out = output + k * n_embd;
-        normalize(emb, out, n_embd);
+    for (int i = 0; i < batch.n_tokens; i++) {
+        if (!batch.logits[i]) {
+            continue;
+        }
+
+        // try to get sequence embeddings - supported only when pooling_type is not NONE
+        const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
+        if (embd == NULL) {
+            embd = llama_get_embeddings_ith(ctx, i);
+            if (embd == NULL) {
+                fprintf(stderr, "%s: failed to get embeddings for token %d\n", __func__, i);
+                continue;
+            }
+        }
+
+        float * out = output + batch.seq_id[i][0] * n_embd;
+        //TODO: I would also add a parameter here to enable normalization or not.
+        /*fprintf(stdout, "unnormalized_embedding:");
+        for (int hh = 0; hh < n_embd; hh++) {
+            fprintf(stdout, "%9.6f ", embd[hh]);
+        }
+        fprintf(stdout, "\n");*/
+        llama_embd_normalize(embd, out, n_embd);
     }
 }
 
@@ -60,6 +67,8 @@ int main(int argc, char ** argv) {
     }
 
     params.embedding = true;
+    // For non-causal models, batch size must be equal to ubatch size
+    params.n_ubatch = params.n_batch;
 
     print_build_info();
 
@@ -106,18 +115,29 @@ int main(int argc, char ** argv) {
 
     // max batch size
     const uint64_t n_batch = params.n_batch;
-    GGML_ASSERT(params.n_batch == params.n_ctx);
+    GGML_ASSERT(params.n_batch >= params.n_ctx);
 
     // tokenize the prompts and trim
     std::vector<std::vector<int32_t>> inputs;
     for (const auto & prompt : prompts) {
-        auto inp = ::llama_tokenize(ctx, prompt, true);
+        auto inp = ::llama_tokenize(ctx, prompt, true, false);
         if (inp.size() > n_batch) {
-            inp.resize(n_batch);
+            fprintf(stderr, "%s: error: number of tokens in input line (%lld) exceeds batch size (%lld), increase batch size and re-run\n",
+                    __func__, (long long int) inp.size(), (long long int) n_batch);
+            return 1;
         }
         inputs.push_back(inp);
     }
 
+    // check if the last token is SEP
+    // it should be automatically added by the tokenizer when 'tokenizer.ggml.add_eos_token' is set to 'true'
+    for (auto & inp : inputs) {
+        if (inp.empty() || inp.back() != llama_token_sep(model)) {
+            fprintf(stderr, "%s: warning: last token in the prompt is not SEP\n", __func__);
+            fprintf(stderr, "%s:          'tokenizer.ggml.add_eos_token' should be set to 'true' in the GGUF header\n", __func__);
+        }
+    }
+
     // tokenization stats
     if (params.verbose_prompt) {
         for (int i = 0; i < (int) inputs.size(); i++) {
@@ -132,7 +152,7 @@ int main(int argc, char ** argv) {
 
     // initialize batch
     const int n_prompts = prompts.size();
-    struct llama_batch batch = llama_batch_init(n_batch, 0, n_prompts);
+    struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
 
     // allocate output
     const int n_embd = llama_n_embd(model);
@@ -145,6 +165,7 @@ int main(int argc, char ** argv) {
     for (int k = 0; k < n_prompts; k++) {
         // clamp to n_batch tokens
         auto & inp = inputs[k];
+
         const uint64_t n_toks = inp.size();
 
         // encode if at capacity
@@ -165,15 +186,28 @@ int main(int argc, char ** argv) {
     float * out = emb + p * n_embd;
     batch_decode(ctx, batch, out, s, n_embd);
 
-    // print first 3 embeddings
-    for (int j = 0; j < std::min(3, n_prompts); j++) {
-        fprintf(stderr, "embedding %d: ", j);
-        for (int i = 0; i < n_embd; i++) {
-            fprintf(stderr, "%f ", emb[j * n_embd + i]);
+    // print the first part of the embeddings or for a single prompt, the full embedding
+    fprintf(stdout, "\n");
+    for (int j = 0; j < n_prompts; j++) {
+        fprintf(stdout, "embedding %d: ", j);
+        for (int i = 0; i < (n_prompts > 1 ? std::min(16, n_embd) : n_embd); i++) {
+            fprintf(stdout, "%9.6f ", emb[j * n_embd + i]);
+        }
+        fprintf(stdout, "\n");
+    }
+
+    // print cosine similarity matrix
+    if (n_prompts > 1) {
+        fprintf(stdout, "\n");
+        printf("cosine similarity matrix:\n\n");
+        for (int i = 0; i < n_prompts; i++) {
+            for (int j = 0; j < n_prompts; j++) {
+                float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
+                fprintf(stdout, "%6.2f ", sim);
+            }
+            fprintf(stdout, "\n");
         }
-        fprintf(stderr, "\n\n");
     }
-    fprintf(stderr, "\n");
 
     // clean up
     llama_print_timings(ctx);
diff --git a/examples/eval-callback/CMakeLists.txt b/examples/eval-callback/CMakeLists.txt
new file mode 100644
index 000000000..c56ba780b
--- /dev/null
+++ b/examples/eval-callback/CMakeLists.txt
@@ -0,0 +1,9 @@
+set(TARGET eval-callback)
+add_executable(${TARGET} eval-callback.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
+
+set(TEST_TARGET test-eval-callback)
+add_test(NAME ${TEST_TARGET} COMMAND eval-callback --hf-repo ggml-org/models --hf-file tinyllamas/stories260K.gguf --model stories260K.gguf --prompt hello --seed 42 -ngl 0)
+set_property(TEST ${TEST_TARGET} PROPERTY LABELS eval-callback curl)
diff --git a/examples/eval-callback/README.md b/examples/eval-callback/README.md
new file mode 100644
index 000000000..66a37e878
--- /dev/null
+++ b/examples/eval-callback/README.md
@@ -0,0 +1,95 @@
+# llama.cpp/examples/eval-callback
+
+A simple example which demonstrates how to use callback during the inference.
+It simply prints to the console all operations and tensor data.
+
+Usage:
+
+```shell
+eval-callback \
+  --hf-repo ggml-org/models \
+  --hf-file phi-2/ggml-model-q4_0.gguf \
+  --model phi-2-q4_0.gguf \
+  --prompt hello \
+  --seed 42 \
+  -ngl 33
+```
+
+Will print:
+
+```shell
+llm_load_tensors: offloaded 33/33 layers to GPU
+...
+llama_new_context_with_model: n_ctx      = 512
+...
+llama_new_context_with_model:      CUDA0 compute buffer size =   105.00 MiB
+llama_new_context_with_model:  CUDA_Host compute buffer size =     6.01 MiB
+llama_new_context_with_model: graph nodes  = 1225
+llama_new_context_with_model: graph splits = 2
+ggml_debug:                 inp_embd = (f32)   GET_ROWS(token_embd.weight{2560, 51200, 1, 1}, inp_tokens{1, 1, 1, 1}}) = {2560, 1, 1, 1}
+                                     [
+                                      [
+                                       [ -0.0181,   0.0272,   0.0272, ...],
+                                      ],
+                                     ]
+ggml_debug:                   norm-0 = (f32)       NORM(CUDA0#inp_embd#0{2560, 1, 1, 1}, }) = {2560, 1, 1, 1}
+                                     [
+                                      [
+                                       [ -0.6989,   1.0636,   1.0636, ...],
+                                      ],
+                                     ]
+ggml_debug:                 norm_w-0 = (f32)        MUL(norm-0{2560, 1, 1, 1}, blk.0.attn_norm.weight{2560, 1, 1, 1}}) = {2560, 1, 1, 1}
+                                     [
+                                      [
+                                       [ -0.1800,   0.2817,   0.2632, ...],
+                                      ],
+                                     ]
+ggml_debug:              attn_norm-0 = (f32)        ADD(norm_w-0{2560, 1, 1, 1}, blk.0.attn_norm.bias{2560, 1, 1, 1}}) = {2560, 1, 1, 1}
+                                     [
+                                      [
+                                       [ -0.1863,   0.2970,   0.2604, ...],
+                                      ],
+                                     ]
+ggml_debug:                   wqkv-0 = (f32)    MUL_MAT(blk.0.attn_qkv.weight{2560, 7680, 1, 1}, attn_norm-0{2560, 1, 1, 1}}) = {7680, 1, 1, 1}
+                                     [
+                                      [
+                                       [ -1.1238,   1.2876,  -1.8086, ...],
+                                      ],
+                                     ]
+ggml_debug:                   bqkv-0 = (f32)        ADD(wqkv-0{7680, 1, 1, 1}, blk.0.attn_qkv.bias{7680, 1, 1, 1}}) = {7680, 1, 1, 1}
+                                     [
+                                      [
+                                       [ -1.1135,   1.4604,  -1.9226, ...],
+                                      ],
+                                     ]
+ggml_debug:            bqkv-0 (view) = (f32)       VIEW(bqkv-0{7680, 1, 1, 1}, }) = {2560, 1, 1, 1}
+                                     [
+                                      [
+                                       [ -1.1135,   1.4604,  -1.9226, ...],
+                                      ],
+                                     ]
+ggml_debug:                   Qcur-0 = (f32)       CONT(bqkv-0 (view){2560, 1, 1, 1}, }) = {2560, 1, 1, 1}
+                                     [
+                                      [
+                                       [ -1.1135,   1.4604,  -1.9226, ...],
+                                      ],
+                                     ]
+ggml_debug:        Qcur-0 (reshaped) = (f32)    RESHAPE(Qcur-0{2560, 1, 1, 1}, }) = {80, 32, 1, 1}
+                                     [
+                                      [
+                                       [ -1.1135,   1.4604,  -1.9226, ...],
+                                       [ -0.3608,   0.5076,  -1.8866, ...],
+                                       [  1.7643,   0.0273,  -2.1065, ...],
+                                       ...
+                                      ],
+                                     ]
+ggml_debug:                   Qcur-0 = (f32)       ROPE(Qcur-0 (reshaped){80, 32, 1, 1}, CUDA0#inp_pos#0{1, 1, 1, 1}}) = {80, 32, 1, 1}
+                                     [
+                                      [
+                                       [ -1.1135,   1.4604,  -1.9226, ...],
+                                       [ -0.3608,   0.5076,  -1.8866, ...],
+                                       [  1.7643,   0.0273,  -2.1065, ...],
+                                       ...
+                                      ],
+                                     ]
+```
diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp
new file mode 100644
index 000000000..e670d3769
--- /dev/null
+++ b/examples/eval-callback/eval-callback.cpp
@@ -0,0 +1,195 @@
+#include "common.h"
+#include "llama.h"
+#include "ggml.h"
+
+#include <cstdio>
+#include <random>
+#include <string>
+#include <tuple>
+#include <vector>
+
+/**
+ * This the arbitrary data which will be passed to each callback.
+ * Later on we can for example add operation or tensor name filter from the CLI arg, or a file descriptor to dump the tensor.
+ */
+struct callback_data {
+    std::vector<uint8_t> data;
+};
+
+static std::string ggml_ne_string(const ggml_tensor * t) {
+    std::string str;
+    for (int i = 0; i < GGML_MAX_DIMS; ++i) {
+        str += std::to_string(t->ne[i]);
+        if (i + 1 < GGML_MAX_DIMS) {
+            str += ", ";
+        }
+    }
+    return str;
+}
+
+static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) {
+    GGML_ASSERT(n > 0);
+    float sum = 0;
+    for (int64_t i3 = 0; i3 < ne[3]; i3++) {
+        printf("                                     [\n");
+        for (int64_t i2 = 0; i2 < ne[2]; i2++) {
+            if (i2 == n && ne[2] > 2*n) {
+                printf("                                      ..., \n");
+                i2 = ne[2] - n;
+            }
+            printf("                                      [\n");
+            for (int64_t i1 = 0; i1 < ne[1]; i1++) {
+                if (i1 == n && ne[1] > 2*n) {
+                    printf("                                       ..., \n");
+                    i1 = ne[1] - n;
+                }
+                printf("                                       [");
+                for (int64_t i0 = 0; i0 < ne[0]; i0++) {
+                    if (i0 == n && ne[0] > 2*n) {
+                        printf("..., ");
+                        i0 = ne[0] - n;
+                    }
+                    size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
+                    float v;
+                    if (type == GGML_TYPE_F16) {
+                        v = ggml_fp16_to_fp32(*(ggml_fp16_t *) &data[i]);
+                    } else if (type == GGML_TYPE_F32) {
+                        v = *(float *) &data[i];
+                    } else if (type == GGML_TYPE_I32) {
+                        v = (float) *(int32_t *) &data[i];
+                    } else if (type == GGML_TYPE_I16) {
+                        v = (float) *(int16_t *) &data[i];
+                    } else if (type == GGML_TYPE_I8) {
+                        v = (float) *(int8_t *) &data[i];
+                    } else {
+                        GGML_ASSERT(false);
+                    }
+                    printf("%12.4f", v);
+                    sum += v;
+                    if (i0 < ne[0] - 1) printf(", ");
+                }
+                printf("],\n");
+            }
+            printf("                                      ],\n");
+        }
+        printf("                                     ]\n");
+        printf("                                     sum = %f\n", sum);
+    }
+}
+
+/**
+ * GGML operations callback during the graph execution.
+ *
+ * @param t current tensor
+ * @param ask when ask is true, the scheduler wants to know if we are interested in data from this tensor
+ *            if we return true, a follow-up call will be made with ask=false in which we can do the actual collection.
+ *            see ggml_backend_sched_eval_callback
+ * @param user_data user data to pass at each call back
+ * @return true to receive data or continue the graph, false otherwise
+ */
+static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) {
+    auto * cb_data = (callback_data *) user_data;
+
+    const struct ggml_tensor * src0 = t->src[0];
+    const struct ggml_tensor * src1 = t->src[1];
+
+    if (ask) {
+        return true; // Always retrieve data
+    }
+
+    char src1_str[128] = {0};
+    if (src1) {
+        sprintf(src1_str, "%s{%s}", src1->name, ggml_ne_string(src1).c_str());
+    }
+
+    printf("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__,
+           t->name, ggml_type_name(t->type), ggml_op_desc(t),
+           src0->name, ggml_ne_string(src0).c_str(),
+           src1 ? src1_str : "",
+           ggml_ne_string(t).c_str());
+
+
+    // copy the data from the GPU memory if needed
+    const bool is_host = ggml_backend_buffer_is_host(t->buffer);
+
+    if (!is_host) {
+        auto n_bytes = ggml_nbytes(t);
+        cb_data->data.resize(n_bytes);
+        ggml_backend_tensor_get(t, cb_data->data.data(), 0, n_bytes);
+    }
+
+    if (!ggml_is_quantized(t->type)) {
+        uint8_t * data = is_host ? (uint8_t *) t->data : cb_data->data.data();
+        ggml_print_tensor(data, t->type, t->ne, t->nb, 3);
+    }
+
+    return true;
+}
+
+static bool run(llama_context * ctx, const gpt_params & params) {
+    const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
+
+    std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
+
+    if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) {
+        fprintf(stderr, "%s : failed to eval\n", __func__);
+        return false;
+    }
+
+    return true;
+}
+
+int main(int argc, char ** argv) {
+
+    callback_data cb_data;
+
+    gpt_params params;
+    if (!gpt_params_parse(argc, argv, params)) {
+        return 1;
+    }
+
+    print_build_info();
+
+    std::mt19937 rng(params.seed);
+    if (params.random_prompt) {
+        params.prompt = gpt_random_prompt(rng);
+    }
+
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    // pass the callback to the backend scheduler
+    // it will be executed for each node during the graph computation
+    params.cb_eval = ggml_debug;
+    params.cb_eval_user_data = &cb_data;
+    params.warmup = false;
+
+    // init
+    llama_model * model;
+    llama_context * ctx;
+    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    if (model == nullptr || ctx == nullptr) {
+        fprintf(stderr, "%s : failed to init\n", __func__);
+        return 1;
+    }
+
+    // print system information
+    {
+        fprintf(stderr, "\n");
+        fprintf(stderr, "%s\n", get_system_info(params).c_str());
+    }
+
+    bool OK = run(ctx, params);
+    if (!OK) {
+        return 1;
+    }
+
+    llama_print_timings(ctx);
+
+    llama_free(ctx);
+    llama_free_model(model);
+
+    llama_backend_free();
+
+    return 0;
+}
diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp
index 98bf5a07a..22743b1bf 100644
--- a/examples/finetune/finetune.cpp
+++ b/examples/finetune/finetune.cpp
@@ -575,7 +575,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
     GGML_ASSERT(tokens_input->type == GGML_TYPE_I32);
 
     auto add_to_f32 = [] (struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
-        if (ggml_is_quantized(a->type) || a->type == GGML_TYPE_F16) {
+        if (ggml_is_quantized(a->type) || a->type == GGML_TYPE_F16 || a->type == GGML_TYPE_BF16) {
             return ggml_add_cast(ctx, a, b, GGML_TYPE_F32);
         } else if (a->type == GGML_TYPE_F32) {
             return ggml_add(ctx, a, b);
@@ -1531,7 +1531,7 @@ int main(int argc, char ** argv) {
     lora.hparams.n_rank_output         = n_rank_output;
 
     // set opt params from command line
-    opt->params = ggml_opt_default_params(GGML_OPT_ADAM);
+    opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
     opt->params.print_forward_graph     = false;
     opt->params.print_backward_graph    = false;
     opt->params.graph_size              = LLAMA_TRAIN_MAX_NODES;
diff --git a/examples/gbnf-validator/CMakeLists.txt b/examples/gbnf-validator/CMakeLists.txt
new file mode 100644
index 000000000..166e3ad2a
--- /dev/null
+++ b/examples/gbnf-validator/CMakeLists.txt
@@ -0,0 +1,5 @@
+set(TARGET gbnf-validator)
+add_executable(${TARGET} gbnf-validator.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common grammar-parser llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/gbnf-validator/gbnf-validator.cpp b/examples/gbnf-validator/gbnf-validator.cpp
new file mode 100644
index 000000000..091069ffa
--- /dev/null
+++ b/examples/gbnf-validator/gbnf-validator.cpp
@@ -0,0 +1,132 @@
+#define LLAMA_API_INTERNAL
+
+#include "grammar-parser.h"
+#include "ggml.h"
+#include "llama.h"
+#include "unicode.h"
+
+#include <cstdio>
+#include <cstdlib>
+#include <string>
+#include <vector>
+
+static bool llama_sample_grammar_string(struct llama_grammar * grammar, const std::string & input_str, size_t & error_pos, std::string & error_msg) {
+    auto decoded = decode_utf8(input_str, {});
+    const auto & code_points = decoded.first;
+
+    size_t pos = 0;
+    for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
+        auto prev_stacks = grammar->stacks;
+        llama_grammar_accept(grammar->rules, prev_stacks, *it, grammar->stacks);
+        if (grammar->stacks.empty()) {
+            error_pos = pos;
+            error_msg = "Unexpected character '" + unicode_cpt_to_utf8(*it) + "'";
+            grammar->stacks = prev_stacks;
+            return false;
+        }
+        ++pos;
+    }
+
+    for (const auto & stack : grammar->stacks) {
+        if (stack.empty()) {
+            return true;
+        }
+    }
+
+    error_pos = pos;
+    error_msg = "Unexpected end of input";
+    return false;
+}
+
+static void print_error_message(const std::string & input_str, size_t error_pos, const std::string & error_msg) {
+    fprintf(stdout, "Input string is invalid according to the grammar.\n");
+    fprintf(stdout, "Error: %s at position %zu\n", error_msg.c_str(), error_pos);
+    fprintf(stdout, "\n");
+    fprintf(stdout, "Input string:\n");
+    fprintf(stdout, "%s", input_str.substr(0, error_pos).c_str());
+    if (error_pos < input_str.size()) {
+        fprintf(stdout, "\033[1;31m%c", input_str[error_pos]);
+        if (error_pos+1 < input_str.size()) {
+            fprintf(stdout, "\033[0;31m%s", input_str.substr(error_pos+1).c_str());
+        }
+        fprintf(stdout, "\033[0m\n");
+    }
+}
+
+int main(int argc, char** argv) {
+    if (argc != 3) {
+        fprintf(stdout, "Usage: %s <grammar_filename> <input_filename>\n", argv[0]);
+        return 1;
+    }
+
+    const std::string grammar_filename = argv[1];
+    const std::string input_filename = argv[2];
+
+    // Read the GBNF grammar file
+    FILE* grammar_file = fopen(grammar_filename.c_str(), "r");
+    if (!grammar_file) {
+        fprintf(stdout, "Failed to open grammar file: %s\n", grammar_filename.c_str());
+        return 1;
+    }
+
+    fseek(grammar_file, 0, SEEK_END);
+    size_t grammar_size = ftell(grammar_file);
+    fseek(grammar_file, 0, SEEK_SET);
+
+    std::string grammar_str(grammar_size, ' ');
+    fread(&grammar_str[0], 1, grammar_size, grammar_file);
+    fclose(grammar_file);
+
+    // Parse the GBNF grammar
+    auto parsed_grammar = grammar_parser::parse(grammar_str.c_str());
+
+    // will be empty (default) if there are parse errors
+    if (parsed_grammar.rules.empty()) {
+        fprintf(stdout, "%s: failed to parse grammar\n", __func__);
+        return 1;
+    }
+
+    // Ensure that there is a "root" node.
+    if (parsed_grammar.symbol_ids.find("root") == parsed_grammar.symbol_ids.end()) {
+        fprintf(stdout, "%s: grammar does not contain a 'root' symbol\n", __func__);
+        return 1;
+    }
+
+    std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
+
+    // Create the LLAMA grammar
+    auto grammar = llama_grammar_init(
+            grammar_rules.data(),
+            grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
+
+    // Read the input file
+    FILE* input_file = fopen(input_filename.c_str(), "r");
+    if (!input_file) {
+        fprintf(stdout, "Failed to open input file: %s\n", input_filename.c_str());
+        return 1;
+    }
+
+    fseek(input_file, 0, SEEK_END);
+    size_t input_size = ftell(input_file);
+    fseek(input_file, 0, SEEK_SET);
+
+    std::string input_str(input_size, ' ');
+    fread(&input_str[0], 1, input_size, input_file);
+    fclose(input_file);
+
+    // Validate the input string against the grammar
+    size_t error_pos;
+    std::string error_msg;
+    bool is_valid = llama_sample_grammar_string(grammar, input_str, error_pos, error_msg);
+
+    if (is_valid) {
+        fprintf(stdout, "Input string is valid according to the grammar.\n");
+    } else {
+        print_error_message(input_str, error_pos, error_msg);
+    }
+
+    // Clean up
+    llama_grammar_free(grammar);
+
+    return 0;
+}
diff --git a/examples/gguf-split/CMakeLists.txt b/examples/gguf-split/CMakeLists.txt
new file mode 100644
index 000000000..828e62435
--- /dev/null
+++ b/examples/gguf-split/CMakeLists.txt
@@ -0,0 +1,5 @@
+set(TARGET gguf-split)
+add_executable(${TARGET} gguf-split.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/gguf-split/README.md b/examples/gguf-split/README.md
new file mode 100644
index 000000000..ad1d86651
--- /dev/null
+++ b/examples/gguf-split/README.md
@@ -0,0 +1,10 @@
+## GGUF split Example
+
+CLI to split / merge GGUF files.
+
+**Command line options:**
+
+- `--split`: split GGUF to multiple GGUF, default operation.
+- `--split-max-size`: max size per split in `M` or `G`, f.ex. `500M` or `2G`.
+- `--split-max-tensors`: maximum tensors in each split: default(128)
+- `--merge`: merge multiple GGUF to a single GGUF.
diff --git a/examples/gguf-split/gguf-split.cpp b/examples/gguf-split/gguf-split.cpp
new file mode 100644
index 000000000..e04feeae3
--- /dev/null
+++ b/examples/gguf-split/gguf-split.cpp
@@ -0,0 +1,564 @@
+#include "llama.h"
+#include "common.h"
+
+#include <algorithm>
+#include <cmath>
+#include <cstdlib>
+#include <fstream>
+#include <string>
+#include <vector>
+
+#include <stdio.h>
+#include <string.h>
+#include <climits>
+#include <stdexcept>
+
+#if defined(_WIN32)
+    #include <windows.h>
+    #ifndef PATH_MAX
+        #define PATH_MAX MAX_PATH
+    #endif
+    #include <io.h>
+#endif
+
+enum split_operation : uint8_t {
+    SPLIT_OP_SPLIT,
+    SPLIT_OP_MERGE,
+};
+
+struct split_params {
+    split_operation operation = SPLIT_OP_SPLIT;
+    size_t n_bytes_split = 0;
+    int n_split_tensors = 128;
+    std::string input;
+    std::string output;
+    bool no_tensor_first_split = false;
+    bool dry_run = false;
+};
+
+static void split_print_usage(const char * executable) {
+    const split_params default_params;
+    printf("\n");
+    printf("usage: %s [options] GGUF_IN GGUF_OUT\n", executable);
+    printf("\n");
+    printf("Apply a GGUF operation on IN to OUT.");
+    printf("\n");
+    printf("options:\n");
+    printf("  -h, --help              show this help message and exit\n");
+    printf("  --version               show version and build info\n");
+    printf("  --split                 split GGUF to multiple GGUF (enabled by default)\n");
+    printf("  --merge                 merge multiple GGUF to a single GGUF\n");
+    printf("  --split-max-tensors     max tensors in each split (default: %d)\n", default_params.n_split_tensors);
+    printf("  --split-max-size N(M|G) max size per split\n");
+    printf("  --no-tensor-first-split do not add tensors to the first split (disabled by default)\n");
+    printf("  --dry-run               only print out a split plan and exit, without writing any new files\n");
+    printf("\n");
+}
+
+// return convert string, for example "128M" or "4G" to number of bytes
+static size_t split_str_to_n_bytes(std::string str) {
+    size_t n_bytes = 0;
+    int n;
+    if (str.back() == 'M') {
+        sscanf(str.c_str(), "%d", &n);
+        n_bytes = (size_t)n * 1024 * 1024; // megabytes
+    } else if (str.back() == 'G') {
+        sscanf(str.c_str(), "%d", &n);
+        n_bytes = (size_t)n * 1024 * 1024 * 1024; // gigabytes
+    } else {
+        throw std::invalid_argument("error: supported units are M (megabytes) or G (gigabytes), but got: " + std::string(1, str.back()));
+    }
+    if (n <= 0) {
+        throw std::invalid_argument("error: size must be a positive value");
+    }
+    return n_bytes;
+}
+
+static void split_params_parse_ex(int argc, const char ** argv, split_params & params) {
+    std::string arg;
+    const std::string arg_prefix = "--";
+    bool invalid_param = false;
+
+    int arg_idx = 1;
+    for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) {
+        arg = argv[arg_idx];
+        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
+            std::replace(arg.begin(), arg.end(), '_', '-');
+        }
+
+        bool arg_found = false;
+        bool is_op_set = false;
+        bool is_mode_set = false;
+        if (arg == "-h" || arg == "--help") {
+            split_print_usage(argv[0]);
+            exit(0);
+        }
+        if (arg == "--version") {
+            fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
+            fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
+            exit(0);
+        }
+        if (arg == "--dry-run") {
+            arg_found = true;
+            params.dry_run = true;
+        }
+        if (arg == "--no-tensor-first-split") {
+            arg_found = true;
+            params.no_tensor_first_split = true;
+        }
+
+        if (is_op_set) {
+            throw std::invalid_argument("error: either --split or --merge can be specified, but not both");
+        }
+        if (arg == "--merge") {
+            arg_found = true;
+            is_op_set = true;
+            params.operation = SPLIT_OP_MERGE;
+        }
+        if (arg == "--split") {
+            arg_found = true;
+            is_op_set = true;
+            params.operation = SPLIT_OP_SPLIT;
+        }
+
+        if (is_mode_set) {
+            throw std::invalid_argument("error: either --split-max-tensors or --split-max-size can be specified, but not both");
+        }
+        if (arg == "--split-max-tensors") {
+            if (++arg_idx >= argc) {
+                invalid_param = true;
+                break;
+            }
+            arg_found = true;
+            is_mode_set = true;
+            params.n_split_tensors = atoi(argv[arg_idx]);
+        }
+        if (arg == "--split-max-size") {
+            if (++arg_idx >= argc) {
+                invalid_param = true;
+                break;
+            }
+            arg_found = true;
+            is_mode_set = true;
+            params.n_bytes_split = split_str_to_n_bytes(argv[arg_idx]);
+        }
+
+        if (!arg_found) {
+            throw std::invalid_argument("error: unknown argument: " + arg);
+        }
+    }
+
+    if (invalid_param) {
+        throw std::invalid_argument("error: invalid parameter for argument: " + arg);
+    }
+
+    if (argc - arg_idx < 2) {
+        throw std::invalid_argument("error: bad arguments");
+    }
+
+    params.input = argv[arg_idx++];
+    params.output = argv[arg_idx++];
+}
+
+static bool split_params_parse(int argc, const char ** argv, split_params & params) {
+    bool result = true;
+    try {
+        split_params_parse_ex(argc, argv, params);
+    }
+    catch (const std::invalid_argument & ex) {
+        fprintf(stderr, "%s\n", ex.what());
+        split_print_usage(argv[0]);
+        exit(EXIT_FAILURE);
+    }
+    return result;
+}
+
+static void zeros(std::ofstream & file, size_t n) {
+    char zero = 0;
+    for (size_t i = 0; i < n; ++i) {
+        file.write(&zero, 1);
+    }
+}
+
+struct split_strategy {
+    const split_params params;
+    std::ifstream & f_input;
+    struct gguf_context * ctx_gguf;
+    struct ggml_context * ctx_meta = NULL;
+    const int n_tensors;
+
+    // one ctx_out per one output file
+    std::vector<struct gguf_context *> ctx_outs;
+
+    // temporary buffer for reading in tensor data
+    std::vector<uint8_t> read_buf;
+
+    split_strategy(const split_params & params,
+            std::ifstream & f_input,
+            struct gguf_context * ctx_gguf,
+            struct ggml_context * ctx_meta) :
+        params(params),
+        f_input(f_input),
+        ctx_gguf(ctx_gguf),
+        ctx_meta(ctx_meta),
+        n_tensors(gguf_get_n_tensors(ctx_gguf)) {
+
+        // because we need to know list of tensors for each file in advance, we will build all the ctx_out for all output splits
+        int i_split = -1;
+        struct gguf_context * ctx_out = NULL;
+        auto new_ctx_out = [&](bool allow_no_tensors) {
+            i_split++;
+            if (ctx_out != NULL) {
+                if (gguf_get_n_tensors(ctx_out) == 0 && !allow_no_tensors) {
+                    fprintf(stderr, "error: one of splits have 0 tensors. Maybe size or tensors limit is too small\n");
+                    exit(EXIT_FAILURE);
+                }
+                ctx_outs.push_back(ctx_out);
+            }
+            ctx_out = gguf_init_empty();
+            // Save all metadata in first split only
+            if (i_split == 0) {
+                gguf_set_kv(ctx_out, ctx_gguf);
+            }
+            gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_NO, i_split);
+            gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_COUNT, 0); // placeholder
+            gguf_set_val_i32(ctx_out, LLM_KV_SPLIT_TENSORS_COUNT, n_tensors);
+        };
+
+        // initialize ctx_out for the first split
+        new_ctx_out(false);
+
+        // skip first split if no_tensor_first_split is set
+        if (params.no_tensor_first_split) {
+            new_ctx_out(true);
+        }
+
+        // process tensors one by one
+        size_t curr_tensors_size = 0; // current size by counting only tensors size (without metadata)
+        for (int i = 0; i < n_tensors; ++i) {
+            struct ggml_tensor * t = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
+            // calculate the "imaginary" size = the current size + next tensor size
+            size_t n_bytes = GGML_PAD(ggml_nbytes(t), GGUF_DEFAULT_ALIGNMENT);
+            size_t next_tensors_size = curr_tensors_size + n_bytes;
+            if (should_split(i, next_tensors_size)) {
+                new_ctx_out(false);
+                curr_tensors_size = n_bytes;
+            } else {
+                curr_tensors_size = next_tensors_size;
+            }
+            gguf_add_tensor(ctx_out, t);
+        }
+
+        // push the last ctx_out
+        ctx_outs.push_back(ctx_out);
+
+        // set the correct n_split for all ctx_out
+        for (auto & ctx : ctx_outs) {
+            gguf_set_val_u16(ctx, LLM_KV_SPLIT_COUNT, ctx_outs.size());
+        }
+    }
+
+    ~split_strategy() {
+        for (auto & ctx_out : ctx_outs) {
+            gguf_free(ctx_out);
+        }
+    }
+
+    bool should_split(int i_tensor, size_t next_size) {
+        if (params.n_bytes_split > 0) {
+            // split by max size per file
+            return next_size > params.n_bytes_split;
+        } else {
+            // split by number of tensors per file
+            return i_tensor > 0 && i_tensor < n_tensors && i_tensor % params.n_split_tensors == 0;
+        }
+    }
+
+    void print_info() {
+        printf("n_split: %ld\n", ctx_outs.size());
+        int i_split = 0;
+        for (auto & ctx_out : ctx_outs) {
+            // re-calculate the real gguf size for each split (= metadata size + total size of all tensors)
+            size_t total_size = gguf_get_meta_size(ctx_out);
+            for (int i = 0; i < gguf_get_n_tensors(ctx_out); ++i) {
+                struct ggml_tensor * t = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_out, i));
+                total_size += ggml_nbytes(t);
+            }
+            total_size = total_size / 1024 / 1024; // convert to megabytes
+            printf("split %05d: n_tensors = %d, total_size = %ldM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size);
+            i_split++;
+        }
+    }
+
+    void write() {
+        int i_split = 0;
+        int n_split = ctx_outs.size();
+        for (auto & ctx_out : ctx_outs) {
+            // construct file path
+            char split_path[PATH_MAX] = {0};
+            llama_split_path(split_path, sizeof(split_path), params.output.c_str(), i_split, n_split);
+
+            // open the output file
+            printf("Writing file %s ... ", split_path);
+            fflush(stdout);
+            std::ofstream fout = std::ofstream(split_path, std::ios::binary);
+            fout.exceptions(std::ofstream::failbit); // fail fast on write errors
+
+            // write metadata
+            std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
+            gguf_get_meta_data(ctx_out, data.data());
+            fout.write((const char *)data.data(), data.size());
+
+            // write tensors
+            for (int i = 0; i < gguf_get_n_tensors(ctx_out); ++i) {
+                // read tensor meta and prepare buffer
+                const char * t_name = gguf_get_tensor_name(ctx_out, i);
+                struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
+                auto n_bytes = ggml_nbytes(t);
+                read_buf.resize(n_bytes);
+
+                // calculate offset
+                auto i_tensor_in = gguf_find_tensor(ctx_gguf, t_name); // idx of tensor in the input file
+                auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor_in);
+
+                // copy tensor from input to output file
+                copy_file_to_file(f_input, fout, offset, n_bytes);
+                zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes);
+            }
+
+            printf("done\n");
+            // close the file
+            fout.close();
+            i_split++;
+        }
+    }
+
+    void copy_file_to_file(std::ifstream & f_in, std::ofstream & f_out, const size_t in_offset, const size_t len) {
+        // TODO: detect OS and use copy_file_range() here for better performance
+        if (read_buf.size() < len) {
+            read_buf.resize(len);
+        }
+        f_in.seekg(in_offset);
+        f_in.read((char *)read_buf.data(), len);
+        f_out.write((const char *)read_buf.data(), len);
+    }
+};
+
+static void gguf_split(const split_params & split_params) {
+    struct ggml_context * ctx_meta = NULL;
+
+    struct gguf_init_params params = {
+        /*.no_alloc = */ true,
+        /*.ctx      = */ &ctx_meta,
+    };
+
+    std::ifstream f_input(split_params.input.c_str(), std::ios::binary);
+    if (!f_input.is_open()) {
+        fprintf(stderr, "%s:  failed to open input GGUF from %s\n", __func__, split_params.input.c_str());
+        exit(EXIT_FAILURE);
+    }
+
+    auto * ctx_gguf = gguf_init_from_file(split_params.input.c_str(), params);
+    if (!ctx_gguf) {
+        fprintf(stderr, "%s:  failed to load input GGUF from %s\n", __func__, split_params.input.c_str());
+        exit(EXIT_FAILURE);
+    }
+
+    // prepare the strategy
+    split_strategy strategy(split_params, f_input, ctx_gguf, ctx_meta);
+    int n_split = strategy.ctx_outs.size();
+    strategy.print_info();
+
+    if (!split_params.dry_run) {
+        // write all output splits
+        strategy.write();
+    }
+
+    // done, clean up
+    gguf_free(ctx_gguf);
+    f_input.close();
+
+    fprintf(stderr, "%s: %d gguf split written with a total of %d tensors.\n",
+            __func__, n_split, strategy.n_tensors);
+}
+
+static void gguf_merge(const split_params & split_params) {
+    fprintf(stderr, "%s: %s -> %s\n",
+            __func__, split_params.input.c_str(),
+            split_params.output.c_str());
+    int n_split = 1;
+    int total_tensors = 0;
+
+    auto * ctx_out = gguf_init_empty();
+    std::ofstream fout(split_params.output.c_str(), std::ios::binary);
+    fout.exceptions(std::ofstream::failbit); // fail fast on write errors
+
+    std::vector<uint8_t> read_data;
+    std::vector<ggml_context *> ctx_metas;
+    std::vector<gguf_context *> ctx_ggufs;
+
+    char split_path[PATH_MAX] = {0};
+    strncpy(split_path, split_params.input.c_str(), sizeof(split_path) - 1);
+    char split_prefix[PATH_MAX] = {0};
+
+    // First pass to find KV and tensors metadata
+    for (int i_split = 0; i_split < n_split; i_split++) {
+        struct ggml_context * ctx_meta = NULL;
+
+        struct gguf_init_params params = {
+            /*.no_alloc = */ true,
+            /*.ctx      = */ &ctx_meta,
+        };
+
+        if (i_split > 0) {
+            llama_split_path(split_path, sizeof(split_path), split_prefix, i_split, n_split);
+        }
+        fprintf(stderr, "%s: reading metadata %s ...", __func__, split_path);
+
+        auto * ctx_gguf = gguf_init_from_file(split_path, params);
+        if (!ctx_gguf) {
+            fprintf(stderr, "\n%s:  failed to load input GGUF from %s\n", __func__, split_params.input.c_str());
+            exit(EXIT_FAILURE);
+        }
+        ctx_ggufs.push_back(ctx_gguf);
+        ctx_metas.push_back(ctx_meta);
+
+        if (i_split == 0) {
+            auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT);
+            if (key_n_split < 0) {
+                fprintf(stderr,
+                        "\n%s: input file does not contain %s metadata\n",
+                        __func__,
+                        LLM_KV_SPLIT_COUNT);
+                gguf_free(ctx_gguf);
+                ggml_free(ctx_meta);
+                gguf_free(ctx_out);
+                fout.close();
+                exit(EXIT_FAILURE);
+            }
+
+            n_split = gguf_get_val_u16(ctx_gguf, key_n_split);
+            if (n_split < 1) {
+                fprintf(stderr,
+                        "\n%s: input file does not contain a valid split count %d\n",
+                        __func__,
+                        n_split);
+                gguf_free(ctx_gguf);
+                ggml_free(ctx_meta);
+                gguf_free(ctx_out);
+                fout.close();
+                exit(EXIT_FAILURE);
+            }
+
+            // Verify the file naming and extract split_prefix
+            if (!llama_split_prefix(split_prefix, sizeof (split_prefix), split_path, i_split, n_split)) {
+                fprintf(stderr, "\n%s: unexpected input file name: %s"
+                                " i_split=%d"
+                                " n_split=%d\n", __func__,
+                        split_path, i_split, n_split);
+                gguf_free(ctx_gguf);
+                ggml_free(ctx_meta);
+                gguf_free(ctx_out);
+                fout.close();
+                exit(EXIT_FAILURE);
+            }
+
+            // Do not trigger merge if we try to merge again the output
+            gguf_set_val_u16(ctx_gguf, LLM_KV_SPLIT_COUNT, 0);
+
+            // Set metadata from the first split
+            gguf_set_kv(ctx_out, ctx_gguf);
+        }
+
+        auto n_tensors = gguf_get_n_tensors(ctx_gguf);
+        for (int i_tensor = 0; i_tensor < n_tensors; i_tensor++) {
+            const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor);
+            struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
+            gguf_add_tensor(ctx_out, t);
+        }
+        total_tensors += n_tensors;
+
+        fprintf(stderr, "\033[3Ddone\n");
+    }
+
+    // placeholder for the meta data
+    {
+        auto meta_size = gguf_get_meta_size(ctx_out);
+        ::zeros(fout, meta_size);
+    }
+
+    // Write tensors data
+    for (int i_split = 0; i_split < n_split; i_split++) {
+        llama_split_path(split_path, sizeof(split_path), split_prefix, i_split, n_split);
+        std::ifstream f_input(split_path, std::ios::binary);
+        if (!f_input.is_open()) {
+            fprintf(stderr, "%s:  failed to open input GGUF from %s\n", __func__, split_path);
+            for (uint32_t i = 0; i < ctx_ggufs.size(); i++) {
+                gguf_free(ctx_ggufs[i]);
+                ggml_free(ctx_metas[i]);
+            }
+            gguf_free(ctx_out);
+            fout.close();
+            exit(EXIT_FAILURE);
+        }
+        fprintf(stderr, "%s: writing tensors %s ...", __func__, split_path);
+
+        auto * ctx_gguf = ctx_ggufs[i_split];
+        auto * ctx_meta = ctx_metas[i_split];
+
+        auto n_tensors = gguf_get_n_tensors(ctx_gguf);
+        for (int i_tensor = 0; i_tensor < n_tensors; i_tensor++) {
+            const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor);
+            struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
+
+            auto n_bytes = ggml_nbytes(t);
+
+            if (read_data.size() < n_bytes) {
+                read_data.resize(n_bytes);
+            }
+
+            auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor);
+            f_input.seekg(offset);
+            f_input.read((char *)read_data.data(), n_bytes);
+
+            // write tensor data + padding
+            fout.write((const char *)read_data.data(), n_bytes);
+            zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes);
+        }
+
+        gguf_free(ctx_gguf);
+        ggml_free(ctx_meta);
+        f_input.close();
+        fprintf(stderr, "\033[3Ddone\n");
+    }
+
+    {
+        // go back to beginning of file and write the updated metadata
+        fout.seekp(0);
+        std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
+        gguf_get_meta_data(ctx_out, data.data());
+        fout.write((const char *)data.data(), data.size());
+
+        fout.close();
+        gguf_free(ctx_out);
+    }
+
+    fprintf(stderr, "%s: %s merged from %d split with %d tensors.\n",
+            __func__, split_params.output.c_str(), n_split, total_tensors);
+}
+
+int main(int argc, const char ** argv) {
+    split_params params;
+    split_params_parse(argc, argv, params);
+
+    switch (params.operation) {
+        case SPLIT_OP_SPLIT: gguf_split(params);
+            break;
+        case SPLIT_OP_MERGE: gguf_merge(params);
+            break;
+        default: split_print_usage(argv[0]);
+            exit(EXIT_FAILURE);
+    }
+
+    return 0;
+}
diff --git a/examples/gguf-split/tests.sh b/examples/gguf-split/tests.sh
new file mode 100755
index 000000000..7ca6fa7f2
--- /dev/null
+++ b/examples/gguf-split/tests.sh
@@ -0,0 +1,89 @@
+#!/bin/bash
+
+set -eu
+
+if [ $# -lt 1 ]
+then
+    echo "usage:   $0 path_to_build_binary [path_to_temp_folder]"
+    echo "example: $0 ../../build/bin ../../tmp"
+    exit 1
+fi
+
+if [ $# -gt 1 ]
+then
+    TMP_DIR=$2
+else
+    TMP_DIR=/tmp
+fi
+
+set -x
+
+SPLIT=$1/gguf-split
+MAIN=$1/main
+WORK_PATH=$TMP_DIR/gguf-split
+ROOT_DIR=$(realpath $(dirname $0)/../../)
+
+mkdir -p "$WORK_PATH"
+
+# Clean up in case of previously failed test
+rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-merge*.gguf
+
+# 1. Get a model
+(
+cd $WORK_PATH
+"$ROOT_DIR"/scripts/hf.sh --repo ggml-org/gemma-1.1-2b-it-Q8_0-GGUF --file gemma-1.1-2b-it.Q8_0.gguf
+)
+echo PASS
+
+# 2. Split with max tensors strategy
+$SPLIT --split-max-tensors 28  $WORK_PATH/gemma-1.1-2b-it.Q8_0.gguf $WORK_PATH/ggml-model-split
+echo PASS
+echo
+
+# 2b. Test the sharded model is loading properly
+$MAIN --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --random-prompt --n-predict 32
+echo PASS
+echo
+
+# 3. Merge
+$SPLIT --merge $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-merge.gguf
+echo PASS
+echo
+
+# 3b. Test the merged model is loading properly
+$MAIN --model $WORK_PATH/ggml-model-merge.gguf --random-prompt --n-predict 32
+echo PASS
+echo
+
+# 4. Split with no tensors in the first split
+$SPLIT --split-max-tensors 32 --no-tensor-first-split $WORK_PATH/ggml-model-merge.gguf $WORK_PATH/ggml-model-split-32-tensors
+echo PASS
+echo
+
+# 4b. Test the sharded model is loading properly
+$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00007.gguf --random-prompt --n-predict 32
+echo PASS
+echo
+
+# 5. Merge
+#$SPLIT --merge $WORK_PATH/ggml-model-split-32-tensors-00001-of-00006.gguf $WORK_PATH/ggml-model-merge-2.gguf
+#echo PASS
+#echo
+
+# 5b. Test the merged model is loading properly
+#$MAIN --model $WORK_PATH/ggml-model-merge-2.gguf --random-prompt --n-predict 32
+#echo PASS
+#echo
+
+# 6. Split with size strategy
+$SPLIT --split-max-size 2G $WORK_PATH/ggml-model-merge.gguf $WORK_PATH/ggml-model-split-2G
+echo PASS
+echo
+
+# 6b. Test the sharded model is loading properly
+$MAIN --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --random-prompt --n-predict 32
+echo PASS
+echo
+
+# Clean up
+rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-merge*.gguf
diff --git a/examples/gguf/gguf.cpp b/examples/gguf/gguf.cpp
index e67be4fb2..575143771 100644
--- a/examples/gguf/gguf.cpp
+++ b/examples/gguf/gguf.cpp
@@ -142,7 +142,7 @@ static bool gguf_ex_read_0(const std::string & fname) {
 }
 
 // read and create ggml_context containing the tensors and their data
-static bool gguf_ex_read_1(const std::string & fname) {
+static bool gguf_ex_read_1(const std::string & fname, bool check_data) {
     struct ggml_context * ctx_data = NULL;
 
     struct gguf_init_params params = {
@@ -206,11 +206,12 @@ static bool gguf_ex_read_1(const std::string & fname) {
             printf("\n\n");
 
             // check data
-            {
+            if (check_data) {
                 const float * data = (const float *) cur->data;
                 for (int j = 0; j < ggml_nelements(cur); ++j) {
                     if (data[j] != 100 + i) {
                         fprintf(stderr, "%s: tensor[%d]: data[%d] = %f\n", __func__, i, j, data[j]);
+                        gguf_free(ctx);
                         return false;
                     }
                 }
@@ -228,9 +229,16 @@ static bool gguf_ex_read_1(const std::string & fname) {
 
 int main(int argc, char ** argv) {
     if (argc < 3) {
-        printf("usage: %s data.gguf r|w\n", argv[0]);
+        printf("usage: %s data.gguf r|w [n]\n", argv[0]);
+        printf("r: read data.gguf file\n");
+        printf("w: write data.gguf file\n");
+        printf("n: no check of tensor data\n");
         return -1;
     }
+    bool check_data = true;
+    if (argc == 4) {
+        check_data = false;
+    }
 
     const std::string fname(argv[1]);
     const std::string mode (argv[2]);
@@ -241,7 +249,7 @@ int main(int argc, char ** argv) {
         GGML_ASSERT(gguf_ex_write(fname) && "failed to write gguf file");
     } else if (mode == "r") {
         GGML_ASSERT(gguf_ex_read_0(fname) && "failed to read gguf file");
-        GGML_ASSERT(gguf_ex_read_1(fname) && "failed to read gguf file");
+        GGML_ASSERT(gguf_ex_read_1(fname, check_data) && "failed to read gguf file");
     }
 
     return 0;
diff --git a/examples/gritlm/CMakeLists.txt b/examples/gritlm/CMakeLists.txt
new file mode 100644
index 000000000..ac4a5ae79
--- /dev/null
+++ b/examples/gritlm/CMakeLists.txt
@@ -0,0 +1,5 @@
+set(TARGET gritlm)
+add_executable(${TARGET} gritlm.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/gritlm/README.md b/examples/gritlm/README.md
new file mode 100644
index 000000000..a3a3c1389
--- /dev/null
+++ b/examples/gritlm/README.md
@@ -0,0 +1,62 @@
+## Generative Representational Instruction Tuning (GRIT) Example
+[gritlm] a model which can generate embeddings as well as "normal" text
+generation depending on the instructions in the prompt.
+
+* Paper: https://arxiv.org/pdf/2402.09906.pdf
+
+### Retrieval-Augmented Generation (RAG) use case
+One use case for `gritlm` is to use it with RAG. If we recall how RAG works is
+that we take documents that we want to use as context, to ground the large
+language model (LLM), and we create token embeddings for them. We then store
+these token embeddings in a vector database.
+
+When we perform a query, prompt the LLM, we will first create token embeddings
+for the query and then search the vector database to retrieve the most
+similar vectors, and return those documents so they can be passed to the LLM as
+context. Then the query and the context will be passed to the LLM which will
+have to _again_ create token embeddings for the query. But because gritlm is used
+the first query can be cached and the second query tokenization generation does
+not have to be performed at all.
+
+### Running the example
+Download a Grit model:
+```console
+$ scripts/hf.sh --repo cohesionet/GritLM-7B_gguf --file gritlm-7b_q4_1.gguf --outdir models
+```
+
+Run the example using the downloaded model:
+```console
+$ ./gritlm -m models/gritlm-7b_q4_1.gguf
+
+Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "A purely peer-to-peer version of electronic cash w" is: 0.605
+Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "All text-based language problems can be reduced to" is: 0.103
+Cosine similarity between "Generative Representational Instruction Tuning" and "A purely peer-to-peer version of electronic cash w" is: 0.112
+Cosine similarity between "Generative Representational Instruction Tuning" and "All text-based language problems can be reduced to" is: 0.547
+
+Oh, brave adventurer, who dared to climb
+The lofty peak of Mt. Fuji in the night,
+When shadows lurk and ghosts do roam,
+And darkness reigns, a fearsome sight.
+
+Thou didst set out, with heart aglow,
+To conquer this mountain, so high,
+And reach the summit, where the stars do glow,
+And the moon shines bright, up in the sky.
+
+Through the mist and fog, thou didst press on,
+With steadfast courage, and a steadfast will,
+Through the darkness, thou didst not be gone,
+But didst climb on, with a steadfast skill.
+
+At last, thou didst reach the summit's crest,
+And gazed upon the world below,
+And saw the beauty of the night's best,
+And felt the peace, that only nature knows.
+
+Oh, brave adventurer, who dared to climb
+The lofty peak of Mt. Fuji in the night,
+Thou art a hero, in the eyes of all,
+For thou didst conquer this mountain, so bright.
+```
+
+[gritlm]: https://github.com/ContextualAI/gritlm
diff --git a/examples/gritlm/gritlm.cpp b/examples/gritlm/gritlm.cpp
new file mode 100644
index 000000000..52fd719b3
--- /dev/null
+++ b/examples/gritlm/gritlm.cpp
@@ -0,0 +1,215 @@
+#include "common.h"
+#include "llama.h"
+
+#include <string>
+#include <vector>
+
+// #define GRIT_DEBUG
+
+static std::vector<std::vector<float>> encode(llama_context * ctx, const std::vector<std::string> & sentences, const std::string & instruction) {
+    std::vector<std::vector<float>> result;
+
+    const llama_model * mdl = llama_get_model(ctx);
+
+    llama_batch batch = llama_batch_init(llama_n_batch(ctx), 0, 1);
+
+    for (uint64_t i = 0; i < sentences.size(); i++) {
+        llama_batch_clear(batch);
+
+        const std::string input_string = instruction + sentences[i];
+
+        std::vector<llama_token> inputs = llama_tokenize(mdl, input_string, true, false);
+
+        const int32_t n_toks = inputs.size();
+
+        // GritLM seems to have EOS = ""
+        // https://github.com/ContextualAI/gritlm/blob/92025b16534712b31b3c4aaaf069350e222bd5f8/gritlm/gritlm.py#L18
+        // inputs.push_back(llama_token_eos(mdl));
+
+        // we want to ignore instruction tokens for mean pooling
+        const int32_t n_inst = llama_tokenize(mdl, instruction, true, false).size();
+
+#ifdef GRIT_DEBUG
+        // debug tokens - should be matching as referenced in the GritLM sample
+        std::for_each(inputs.begin(), inputs.end(), [&ctx](llama_token t) {
+            std::printf("[%u:%s]", t, llama_token_to_piece(ctx, t).c_str());
+        });
+        std::printf("\n");
+#endif
+
+        // add input to batch (this increments n_tokens)
+        for (int32_t j = 0; j < n_toks; j++) {
+            llama_batch_add(batch, inputs[j], j, { 0 }, j >= n_inst);
+        }
+
+        // clear previous kv_cache values (irrelevant for embeddings)
+        llama_kv_cache_clear(ctx);
+        llama_set_causal_attn(ctx, false);
+
+        // run model
+        llama_decode(ctx, batch);
+
+        // get embedding dimensions
+        uint64_t n_embd = llama_n_embd(mdl);
+
+        // allocate embedding output
+        std::vector<float> emb_unorm(n_embd, 0.0f);
+
+        // sum up all token embeddings
+        for (int32_t k = n_inst; k < n_toks; k++) {
+            float * emb = llama_get_embeddings_ith(ctx, k);
+            for (uint64_t j = 0; j < n_embd; j++) {
+                emb_unorm[j] += emb[j];
+            }
+        }
+
+        // divide by number of tokens (mean pooling)
+        {
+            const uint64_t n_sent = n_toks - n_inst;
+
+            for (uint64_t j = 0; j < n_embd; j++) {
+                emb_unorm[j] /= n_sent;
+            }
+        }
+
+        std::vector<float> emb_norm(emb_unorm.size());
+        llama_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd);
+        result.push_back(emb_norm);
+
+#ifdef GRIT_DEBUG
+        // print out emb_norm
+        std::printf("embedding %ld: ", i);
+        for (uint64_t j = 0; j < n_embd; j++) {
+            std::printf("%.5f ", emb_norm[j]);
+        }
+        std::printf("\n\n");
+#endif
+    }
+
+    llama_batch_free(batch);
+
+    return result;
+}
+
+static std::string generate(llama_context * ctx, const std::string & prompt, bool stream) {
+    std::string result;
+
+    const llama_model * mdl = llama_get_model(ctx);
+    llama_token eos_token = llama_token_eos(mdl);
+
+    llama_kv_cache_clear(ctx);
+    llama_set_causal_attn(ctx, true);
+    llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1);
+
+    std::vector<llama_token> inputs = llama_tokenize(mdl, prompt, false, true);
+    int32_t i_current_token = 0;
+
+    while (true) {
+        llama_batch_clear(bat);
+        auto n_inputs = (int32_t)inputs.size();
+        for (int32_t i = 0; i < n_inputs; i++) {
+            llama_batch_add(bat, inputs[i], i_current_token++, { 0 }, i == n_inputs - 1);
+        }
+        inputs.clear();
+
+        llama_decode(ctx, bat);
+        auto logits = llama_get_logits_ith(ctx, bat.n_tokens - 1);
+
+        auto candidates = std::vector<llama_token_data>(llama_n_vocab(mdl));
+        auto n_candidates = (int32_t)candidates.size();
+        for (int32_t token = 0; token < n_candidates; token++) {
+            candidates[token] = llama_token_data{ token, logits[token], 0.0f };
+        }
+        auto candidates_p = llama_token_data_array{ candidates.data(), candidates.size(), false };
+
+        llama_token token = llama_sample_token_greedy(ctx, &candidates_p);
+        if (token == eos_token) {
+            break;
+        }
+
+        std::string piece = llama_token_to_piece(ctx, token);
+        if (stream) {
+            std::printf("%s", piece.c_str());
+            std::fflush(stdout);
+        }
+
+        inputs.push_back(token);
+
+        result += piece;
+    }
+
+    if (stream) {
+        std::printf("\n");
+    }
+
+    llama_batch_free(bat);
+
+    return result;
+}
+
+static std::string gritlm_instruction(const std::string & instruction) {
+    return !instruction.empty() ? "<|user|>\n" + instruction + "\n<|embed|>\n" : "<|embed|>\n";
+}
+
+int main(int argc, char * argv[]) {
+    gpt_params params;
+    if (!gpt_params_parse(argc, argv, params)) {
+        return 1;
+    }
+
+    llama_model_params mparams = llama_model_params_from_gpt_params(params);
+    llama_context_params cparams = llama_context_params_from_gpt_params(params);
+
+    llama_backend_init();
+
+    llama_model * mdl = llama_load_model_from_file(params.model.c_str(), mparams);
+
+    // create new context - set to embedding mode
+    cparams.embeddings = true;
+    llama_context * ctx = llama_new_context_with_model(mdl, cparams);
+
+    // ### Embedding/Representation ###
+    // samples taken from: https://github.com/ContextualAI/gritlm#basic
+    {
+        const std::string instruction = "Given a scientific paper title, retrieve the paper's abstract";
+
+        const std::vector<std::string> queries = {
+            "Bitcoin: A Peer-to-Peer Electronic Cash System",
+            "Generative Representational Instruction Tuning",
+        };
+
+        const std::vector<std::string> documents = {
+            "A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending. We propose a solution to the double-spending problem using a peer-to-peer network. The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power. As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers. The network itself requires minimal structure. Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone.",
+            "All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational instruction tuning (GRIT) whereby a large language model is trained to handle both generative and embedding tasks by distinguishing between them through instructions. Compared to other open models, our resulting GritLM 7B sets a new state of the art on the Massive Text Embedding Benchmark (MTEB) and outperforms all models up to its size on a range of generative tasks. By scaling up further, GritLM 8X7B outperforms all open generative language models that we tried while still being among the best embedding models. Notably, we find that GRIT matches training on only generative or embedding data, thus we can unify both at no performance loss. Among other benefits, the unification via GRIT speeds up Retrieval-Augmented Generation (RAG) by > 60% for long documents, by no longer requiring separate retrieval and generation models. Models, code, etc. are freely available at https://github.com/ContextualAI/gritlm.",
+        };
+
+        // No need to add instruction for retrieval documents
+        const std::vector<std::vector<float>> d_rep = encode(ctx, documents, gritlm_instruction(""));
+        const std::vector<std::vector<float>> q_rep = encode(ctx, queries,   gritlm_instruction(instruction));
+
+        const int n_embd = llama_n_embd(mdl);
+
+        const float cosine_sim_q0_d0 = llama_embd_similarity_cos(q_rep[0].data(), d_rep[0].data(), n_embd);
+        const float cosine_sim_q0_d1 = llama_embd_similarity_cos(q_rep[0].data(), d_rep[1].data(), n_embd);
+        const float cosine_sim_q1_d0 = llama_embd_similarity_cos(q_rep[1].data(), d_rep[0].data(), n_embd);
+        const float cosine_sim_q1_d1 = llama_embd_similarity_cos(q_rep[1].data(), d_rep[1].data(), n_embd);
+
+        std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[0].c_str(), cosine_sim_q0_d0);
+        std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[1].c_str(), cosine_sim_q0_d1);
+        std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[1].c_str(), documents[0].c_str(), cosine_sim_q1_d0);
+        std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[1].c_str(), documents[1].c_str(), cosine_sim_q1_d1);
+    }
+
+    // ### Generation ###
+    // GritLM models are not finetuned with system prompts, as you can just include system-like instructions together with your user instruction
+    {
+        const std::string prompt = "<|user|>\nPlease write me a poem about my recent hike of Mt. Fuji at midnight in the style of Shakespeare.\n<|assistant|>\n";
+        std::string response = generate(ctx, prompt, true);
+    }
+
+    llama_free(ctx);
+    llama_free_model(mdl);
+    llama_backend_free();
+
+    return 0;
+}
diff --git a/examples/imatrix/README.md b/examples/imatrix/README.md
index 578e8fc27..458c01b87 100644
--- a/examples/imatrix/README.md
+++ b/examples/imatrix/README.md
@@ -22,7 +22,7 @@ For faster computation, make sure to use GPU offloading via the `-ngl` argument
 ## Example
 
 ```bash
-LLAMA_CUBLAS=1 make -j
+LLAMA_CUDA=1 make -j
 
 # generate importance matrix (imatrix.dat)
 ./imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99
diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp
index f21bc48f3..82b19fc4f 100644
--- a/examples/imatrix/imatrix.cpp
+++ b/examples/imatrix/imatrix.cpp
@@ -19,10 +19,12 @@
 
 struct Stats {
     std::vector<float> values;
+    std::vector<int> counts;
     int ncall = 0;
 };
 
 struct StatParams {
+    std::string dataset;
     std::string ofile = "imatrix.dat";
     int         n_output_frequency = 10;
     int         verbosity = 1;
@@ -44,25 +46,46 @@ private:
     std::mutex                             m_mutex;
     int                                    m_last_call = 0;
     std::vector<float>                     m_src1_data;
-    std::vector<int>                       m_ids; // the expert ids from ggml_mul_mat_id
+    std::vector<char>                      m_ids; // the expert ids from ggml_mul_mat_id
                                                   //
-    void save_imatrix(const char * file_name) const;
+    void save_imatrix(const char * file_name, const char * dataset) const;
     void keep_imatrix(int ncall) const;
 };
 
+// remove any prefix and suffixes from the name
+// CUDA0#blk.0.attn_k.weight#0 => blk.0.attn_k.weight
+static std::string filter_tensor_name(const char * name) {
+    std::string wname;
+    const char * p = strchr(name, '#');
+    if (p != NULL) {
+        p = p + 1;
+        const char * q = strchr(p, '#');
+        if (q != NULL) {
+            wname = std::string(p, q - p);
+        } else {
+            wname = p;
+        }
+    } else {
+        wname = name;
+    }
+    return wname;
+}
+
 bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) {
     GGML_UNUSED(user_data);
 
     const struct ggml_tensor * src0 = t->src[0];
     const struct ggml_tensor * src1 = t->src[1];
+    std::string wname = filter_tensor_name(src0->name);
 
     // when ask is true, the scheduler wants to know if we are interested in data from this tensor
     // if we return true, a follow-up call will be made with ask=false in which we can do the actual collection
     if (ask) {
         if (t->op == GGML_OP_MUL_MAT_ID) return true; // collect all indirect matrix multiplications
         if (t->op != GGML_OP_MUL_MAT) return false;
+        // why are small batches ignored (<16 tokens)?
         if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return false;
-        if (!(strncmp(src0->name, "blk.", 4) == 0 || (m_params.collect_output_weight && strcmp(src0->name, "output.weight") == 0))) return false;
+        if (!(wname.substr(0, 4) == "blk." || (m_params.collect_output_weight && wname == "output.weight"))) return false;
         return true;
     }
 
@@ -78,44 +101,59 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
 
     const float * data = is_host ? (const float *) src1->data : m_src1_data.data();
 
+    // this has been adapted to the new format of storing merged experts in a single 3d tensor
+    // ref: https://github.com/ggerganov/llama.cpp/pull/6387
     if (t->op == GGML_OP_MUL_MAT_ID) {
-        const int idx  = ((int32_t *) t->op_params)[0];
-        const int n_as = ((int32_t *) t->op_params)[1];
+        //   ids  -> [n_experts_used, n_tokens]
+        //   src1 -> [cols, n_expert_used, n_tokens]
+        const ggml_tensor * ids = t->src[2];
+        const int n_as = src0->ne[2];
+        const int n_ids = ids->ne[0];
 
-        // the top-k selected expert ids are stored in the src0 tensor
-        // for simplicity, always copy src0 to host, because it is small
-        // take into account that src0 is not contiguous!
-        GGML_ASSERT(src0->ne[1] == src1->ne[1]);
-        GGML_ASSERT(n_as*ggml_nrows(src0)*sizeof(int) == GGML_PAD(ggml_nbytes(src0), n_as*sizeof(int)));
-        m_ids.resize(ggml_nbytes(src0)/sizeof(int));
-        ggml_backend_tensor_get(src0, m_ids.data(), 0, ggml_nbytes(src0));
+        // the top-k selected expert ids are stored in the ids tensor
+        // for simplicity, always copy ids to host, because it is small
+        // take into account that ids is not contiguous!
 
+        GGML_ASSERT(ids->ne[1] == src1->ne[2]);
+
+        m_ids.resize(ggml_nbytes(ids));
+        ggml_backend_tensor_get(ids, m_ids.data(), 0, ggml_nbytes(ids));
+
+        auto & e = m_stats[wname];
+
+        ++e.ncall;
+
+        if (e.values.empty()) {
+            e.values.resize(src1->ne[0]*n_as, 0);
+            e.counts.resize(src1->ne[0]*n_as, 0);
+        }
+        else if (e.values.size() != (size_t)src1->ne[0]*n_as) {
+            fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]*n_as);
+            exit(1); //GGML_ASSERT(false);
+        }
+        if (m_params.verbosity > 1) {
+            printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[2], (int)src1->type);
+        }
         // loop over all possible experts, regardless if they are used or not in the batch
-        // this is necessary to guarantee equal number of "ncall" for each tensor
         for (int ex = 0; ex < n_as; ++ex) {
-            src0 = t->src[2 + ex];
-            auto& e = m_stats[src0->name];
-            if (e.values.empty()) {
-                e.values.resize(src1->ne[0], 0);
-            }
-            else if (e.values.size() != (size_t)src1->ne[0]) {
-                fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", src0->name, (int)e.values.size(), (int)src1->ne[0]);
-                exit(1); //GGML_ASSERT(false);
-            }
-            // NOTE: since we select top-k experts, the number of calls for the expert tensors will be k times larger
-            //       using the following line, we can correct for that if needed
-            //if (idx == t->src[0]->ne[0] - 1) ++e.ncall;
-            ++e.ncall;
-            if (m_params.verbosity > 1) {
-                printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, src0->name, ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
-            }
-            for (int row = 0; row < (int)src1->ne[1]; ++row) {
-                const int excur = m_ids[row*n_as + idx];
-                GGML_ASSERT(excur >= 0 && excur < n_as); // sanity check
-                if (excur != ex) continue;
-                const float * x = data + row * src1->ne[0];
-                for (int j = 0; j < (int)src1->ne[0]; ++j) {
-                    e.values[j] += x[j]*x[j];
+            size_t e_start = ex*src1->ne[0];
+
+            for (int idx = 0; idx < n_ids; ++idx) {
+                for (int row = 0; row < (int)src1->ne[2]; ++row) {
+                    const int excur = *(const int32_t *) (m_ids.data() + row*ids->nb[1] + idx*ids->nb[0]);
+
+                    GGML_ASSERT(excur >= 0 && excur < n_as); // sanity check
+
+                    if (excur != ex) continue;
+
+                    const int64_t i11 = idx % src1->ne[1];
+                    const int64_t i12 = row;
+                    const float * x = (const float *)((const char *)data + i11*src1->nb[1] + i12*src1->nb[2]);
+
+                    for (int j = 0; j < (int)src1->ne[0]; ++j) {
+                        e.values[e_start + j] += x[j]*x[j];
+                        e.counts[e_start + j]++;
+                    }
                 }
             }
             if (e.ncall > m_last_call) {
@@ -129,22 +167,24 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
             }
         }
     } else {
-        auto& e = m_stats[src0->name];
+        auto& e = m_stats[wname];
         if (e.values.empty()) {
             e.values.resize(src1->ne[0], 0);
+            e.counts.resize(src1->ne[0], 0);
         }
         else if (e.values.size() != (size_t)src1->ne[0]) {
-            fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", src0->name, (int)e.values.size(), (int)src1->ne[0]);
+            fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]);
             exit(1); //GGML_ASSERT(false);
         }
         ++e.ncall;
         if (m_params.verbosity > 1) {
-            printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, src0->name, ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
+            printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
         }
         for (int row = 0; row < (int)src1->ne[1]; ++row) {
             const float * x = data + row * src1->ne[0];
             for (int j = 0; j < (int)src1->ne[0]; ++j) {
                 e.values[j] += x[j]*x[j];
+                e.counts[j]++;
             }
         }
         if (e.ncall > m_last_call) {
@@ -162,7 +202,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
 }
 
 void IMatrixCollector::save_imatrix() const {
-    save_imatrix(m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str());
+    save_imatrix(m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str(), m_params.dataset.c_str());
 }
 
 void IMatrixCollector::keep_imatrix(int ncall) const {
@@ -170,24 +210,39 @@ void IMatrixCollector::keep_imatrix(int ncall) const {
     if (file_name.empty()) file_name = "imatrix.dat";
     file_name += ".at_";
     file_name += std::to_string(ncall);
-    save_imatrix(file_name.c_str());
+    save_imatrix(file_name.c_str(), m_params.dataset.c_str());
 }
 
-void IMatrixCollector::save_imatrix(const char * fname) const {
+void IMatrixCollector::save_imatrix(const char * fname, const char * dataset) const {
     std::ofstream out(fname, std::ios::binary);
     int n_entries = m_stats.size();
-    out.write((const char*)&n_entries, sizeof(n_entries));
-    for (auto& p : m_stats) {
+    out.write((const char *) &n_entries, sizeof(n_entries));
+    for (const auto & p : m_stats) {
         int len = p.first.size();
-        out.write((const char*)&len, sizeof(len));
+        out.write((const char *) &len, sizeof(len));
         out.write(p.first.c_str(), len);
-        out.write((const char*)&p.second.ncall, sizeof(p.second.ncall));
+        out.write((const char *) &p.second.ncall, sizeof(p.second.ncall));
         int nval = p.second.values.size();
-        out.write((const char*)&nval, sizeof(nval));
-        if (nval > 0) out.write((const char*)p.second.values.data(), nval*sizeof(float));
+        out.write((const char *) &nval, sizeof(nval));
+        if (nval > 0) {
+            std::vector<float> tmp(nval);
+            for (int i = 0; i < nval; i++) {
+                tmp[i] = (p.second.values[i] / static_cast<float>(p.second.counts[i])) * static_cast<float>(p.second.ncall);
+            }
+            out.write((const char*)tmp.data(), nval*sizeof(float));
+        }
     }
+
+    // Write the number of call the matrix was computed with
+    out.write((const char *) &m_last_call, sizeof(m_last_call));
+
+    // Write the dataset name at the end of the file to later on specify it in quantize
+    int n_dataset = strlen(dataset);
+    out.write((const char *) &n_dataset, sizeof(n_dataset));
+    out.write(dataset, n_dataset);
+
     if (m_params.verbosity > 0) {
-        fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n",__func__,m_last_call,fname);
+        fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname);
     }
 }
 
@@ -223,14 +278,28 @@ bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_ma
             imatrix_data = {};
             return false;
         }
-        e.values.resize(nval);
-        in.read((char*)e.values.data(), nval*sizeof(float));
+
+        // When re-called from load_imatrix() with add set, this will already be created.
+        if (e.values.empty()) {
+            e.values.resize(nval, 0);
+            e.counts.resize(nval, 0);
+        }
+
+        std::vector<float> tmp(nval);
+        in.read((char*)tmp.data(), nval*sizeof(float));
         if (in.fail()) {
             printf("%s: failed reading data for entry %d\n",__func__,i);
             imatrix_data = {};
             return false;
         }
-        e.ncall = ncall;
+
+        // Recreate the state as expected by save_imatrix(), and corerct for weighted sum.
+        for (int i = 0; i < nval; i++) {
+            e.values[i] += tmp[i];
+            e.counts[i] += ncall;
+        }
+        e.ncall += ncall;
+
     }
     return true;
 }
@@ -325,12 +394,13 @@ static void process_logits(
 static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl, int from_chunk) {
 
     const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
+    GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1);
     const int n_ctx = llama_n_ctx(ctx);
 
     auto tim1 = std::chrono::high_resolution_clock::now();
     fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
 
-    std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
+    std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, true);
 
     auto tim2 = std::chrono::high_resolution_clock::now();
     fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
@@ -403,6 +473,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
                 tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
             }
 
+            // TODO: use batch.logits to save computations instead of relying on logits_all == true
             if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
                 fprintf(stderr, "%s : failed to eval\n", __func__);
                 return false;
@@ -508,6 +579,29 @@ int main(int argc, char ** argv) {
         }
     }
 
+    gpt_params params;
+    params.n_batch = 512;
+    if (!gpt_params_parse(args.size(), args.data(), params)) {
+        return 1;
+    }
+
+    params.logits_all = true;
+    params.n_batch = std::min(params.n_batch, params.n_ctx);
+
+    print_build_info();
+
+    if (params.seed == LLAMA_DEFAULT_SEED) {
+        params.seed = time(NULL);
+    }
+
+    fprintf(stderr, "%s: seed  = %u\n", __func__, params.seed);
+
+    std::mt19937 rng(params.seed);
+    if (params.random_prompt) {
+        params.prompt = gpt_random_prompt(rng);
+    }
+
+    sparams.dataset = params.prompt_file;
     g_collector.set_parameters(std::move(sparams));
 
     if (!combine_files.empty()) {
@@ -546,49 +640,21 @@ int main(int argc, char ** argv) {
         }
     }
 
-    gpt_params params;
-    params.n_batch = 512;
-    if (!gpt_params_parse(args.size(), args.data(), params)) {
-        return 1;
-    }
-
-    params.logits_all = true;
-    params.n_batch = std::min(params.n_batch, params.n_ctx);
-
-    print_build_info();
-
-    if (params.seed == LLAMA_DEFAULT_SEED) {
-        params.seed = time(NULL);
-    }
-
-    fprintf(stderr, "%s: seed  = %u\n", __func__, params.seed);
-
-    std::mt19937 rng(params.seed);
-    if (params.random_prompt) {
-        params.prompt = gpt_random_prompt(rng);
-    }
-
     llama_backend_init();
     llama_numa_init(params.numa);
 
-    llama_model_params mparams = llama_model_params_from_gpt_params(params);
-
-    llama_model * model = llama_load_model_from_file(params.model.c_str(), mparams);
-    if (model == NULL) {
-        fprintf(stderr, "%s: error: unable to load model\n", __func__);
-        return 1;
-    }
-
-    llama_context_params cparams = llama_context_params_from_gpt_params(params);
-
     // pass the callback to the backend scheduler
     // it will be executed for each node during the graph computation
-    cparams.cb_eval = ik_collect_imatrix;
-    cparams.cb_eval_user_data = NULL;
+    params.cb_eval = ik_collect_imatrix;
+    params.cb_eval_user_data = NULL;
+    params.warmup = false;
 
-    llama_context * ctx = llama_new_context_with_model(model, cparams);
-    if (ctx == NULL) {
-        fprintf(stderr, "%s: error: unable to create context\n", __func__);
+    // init
+    llama_model * model;
+    llama_context * ctx;
+    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    if (model == nullptr || ctx == nullptr) {
+        fprintf(stderr, "%s : failed to init\n", __func__);
         return 1;
     }
 
diff --git a/examples/infill/README.md b/examples/infill/README.md
index 8c97f719b..6b076c839 100644
--- a/examples/infill/README.md
+++ b/examples/infill/README.md
@@ -36,6 +36,11 @@ The `infill` program offers a seamless way to interact with LLaMA models, allowi
 
 ### Example
 
+Download a model that supports infill, for example CodeLlama:
+```console
+scripts/hf.sh --repo TheBloke/CodeLlama-13B-GGUF --file codellama-13b.Q5_K_S.gguf --outdir models
+```
+
 ```bash
 ./infill -t 10 -ngl 0 -m models/codellama-13b.Q5_K_S.gguf -c 4096 --temp 0.7 --repeat_penalty 1.1 -n 20 --in-prefix "def helloworld():\n    print(\"hell" --in-suffix "\n   print(\"goodbye world\")\n    "
 ```
diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp
index 92c67b7cf..afac145f6 100644
--- a/examples/infill/infill.cpp
+++ b/examples/infill/infill.cpp
@@ -239,6 +239,7 @@ int main(int argc, char ** argv) {
         LOG_TEE("%s\n", get_system_info(params).c_str());
     }
     const bool add_bos = llama_should_add_bos_token(model);
+    GGML_ASSERT(llama_add_eos_token(model) != 1);
     LOG("add_bos: %d\n", add_bos);
 
     bool suff_rm_leading_spc = params.escape;
@@ -279,10 +280,10 @@ int main(int argc, char ** argv) {
     if (ctx_guidance) {
         LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt));
 
-        guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos);
+        guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, true);
         LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str());
 
-        std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
+        std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, true);
         LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str());
 
         original_prompt_len = original_inp.size();
@@ -378,10 +379,10 @@ int main(int argc, char ** argv) {
     if (params.interactive) {
         const char *control_message;
         if (params.multiline_input) {
-            control_message = " - To return control to LLaMa, end your input with '\\'.\n"
+            control_message = " - To return control to LLaMA, end your input with '\\'.\n"
                               " - To return control without starting a new line, end your input with '/'.\n";
         } else {
-            control_message = " - Press Return to return control to LLaMa.\n"
+            control_message = " - Press Return to return control to LLaMA.\n"
                               " - To return control without starting a new line, end your input with '/'.\n"
                               " - If you want to submit another line, end your input with '\\'.\n";
         }
@@ -447,8 +448,8 @@ int main(int argc, char ** argv) {
                 LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
                     n_past, n_left, n_ctx, params.n_keep, n_discard);
 
-                llama_kv_cache_seq_rm   (ctx, 0, params.n_keep + 1            , params.n_keep + n_discard + 1);
-                llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);
+                llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1            , params.n_keep + n_discard + 1);
+                llama_kv_cache_seq_add(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);
 
                 n_past -= n_discard;
 
@@ -585,7 +586,7 @@ int main(int argc, char ** argv) {
 
             // deal with eot token in infill mode
             if ((llama_sampling_last(ctx_sampling) == llama_token_eot(model) || is_interacting) && params.interactive){
-                if(is_interacting && !params.interactive_first) {
+                if (is_interacting && !params.interactive_first) {
                     // print an eot token
                     printf("%s", llama_token_to_piece(ctx, llama_token_eot(model)).c_str());
                 }
@@ -650,8 +651,8 @@ int main(int argc, char ** argv) {
                 // LOG_TEE("took new input\n");
                 is_interacting = false;
             }
-            // deal with end of text token in interactive mode
-            else if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) {
+            // deal with end of generation tokens in interactive mode
+            else if (llama_token_is_eog(model, llama_sampling_last(ctx_sampling))) {
                 LOG("found EOS token\n");
 
                 if (params.interactive) {
@@ -730,8 +731,8 @@ int main(int argc, char ** argv) {
             }
         }
 
-        // end of text token
-        if (!embd.empty() && embd.back() == llama_token_eos(model) && !params.interactive) {
+        // end of generation
+        if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !params.interactive) {
             break;
         }
 
diff --git a/examples/json-schema-pydantic-example.py b/examples/json-schema-pydantic-example.py
new file mode 100644
index 000000000..69ebfd409
--- /dev/null
+++ b/examples/json-schema-pydantic-example.py
@@ -0,0 +1,74 @@
+# Usage:
+#! ./server -m some-model.gguf &
+#! pip install pydantic
+#! python json-schema-pydantic-example.py
+
+from pydantic import BaseModel, TypeAdapter
+from annotated_types import MinLen
+from typing import Annotated, List, Optional
+import json, requests
+
+if True:
+
+    def create_completion(*, response_model=None, endpoint="http://localhost:8080/v1/chat/completions", messages, **kwargs):
+        '''
+        Creates a chat completion using an OpenAI-compatible endpoint w/ JSON schema support
+        (llama.cpp server, llama-cpp-python, Anyscale / Together...)
+
+        The response_model param takes a type (+ supports Pydantic) and behaves just as w/ Instructor (see below)
+        '''
+        if response_model:
+            type_adapter = TypeAdapter(response_model)
+            schema = type_adapter.json_schema()
+            messages = [{
+                "role": "system",
+                "content": f"You respond in JSON format with the following schema: {json.dumps(schema, indent=2)}"
+            }] + messages
+            response_format={"type": "json_object", "schema": schema}
+
+        data = requests.post(endpoint, headers={"Content-Type": "application/json"},
+                             json=dict(messages=messages, response_format=response_format, **kwargs)).json()
+        if 'error' in data:
+            raise Exception(data['error']['message'])
+
+        content = data["choices"][0]["message"]["content"]
+        return type_adapter.validate_json(content) if type_adapter else content
+
+else:
+
+    # This alternative branch uses Instructor + OpenAI client lib.
+    # Instructor support streamed iterable responses, retry & more.
+    # (see https://python.useinstructor.com/)
+    #! pip install instructor openai
+    import instructor, openai
+    client = instructor.patch(
+        openai.OpenAI(api_key="123", base_url="http://localhost:8080"),
+        mode=instructor.Mode.JSON_SCHEMA)
+    create_completion = client.chat.completions.create
+
+
+if __name__ == '__main__':
+
+    class QAPair(BaseModel):
+        question: str
+        concise_answer: str
+        justification: str
+
+    class PyramidalSummary(BaseModel):
+        title: str
+        summary: str
+        question_answers: Annotated[List[QAPair], MinLen(2)]
+        sub_sections: Optional[Annotated[List['PyramidalSummary'], MinLen(2)]]
+
+    print("# Summary\n", create_completion(
+        model="...",
+        response_model=PyramidalSummary,
+        messages=[{
+            "role": "user",
+            "content": f"""
+                You are a highly efficient corporate document summarizer.
+                Create a pyramidal summary of an imaginary internal document about our company processes
+                (starting high-level, going down to each sub sections).
+                Keep questions short, and answers even shorter (trivia / quizz style).
+            """
+        }]))
diff --git a/examples/json-schema-to-grammar.py b/examples/json-schema-to-grammar.py
deleted file mode 100755
index 6a977f031..000000000
--- a/examples/json-schema-to-grammar.py
+++ /dev/null
@@ -1,147 +0,0 @@
-#!/usr/bin/env python3
-import argparse
-import json
-import re
-import sys
-
-# whitespace is constrained to a single space char to prevent model "running away" in
-# whitespace. Also maybe improves generation quality?
-SPACE_RULE = '" "?'
-
-PRIMITIVE_RULES = {
-    'boolean': '("true" | "false") space',
-    'number': '("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space',
-    'integer': '("-"? ([0-9] | [1-9] [0-9]*)) space',
-    'string': r''' "\"" (
-        [^"\\] |
-        "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
-      )* "\"" space ''',
-    'null': '"null" space',
-}
-
-INVALID_RULE_CHARS_RE = re.compile(r'[^a-zA-Z0-9-]+')
-GRAMMAR_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"]')
-GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"'}
-
-
-class SchemaConverter:
-    def __init__(self, prop_order):
-        self._prop_order = prop_order
-        self._rules = {'space': SPACE_RULE}
-
-    def _format_literal(self, literal):
-        escaped = GRAMMAR_LITERAL_ESCAPE_RE.sub(
-            lambda m: GRAMMAR_LITERAL_ESCAPES.get(m.group(0)), json.dumps(literal)
-        )
-        return f'"{escaped}"'
-
-    def _add_rule(self, name, rule):
-        esc_name = INVALID_RULE_CHARS_RE.sub('-', name)
-        if esc_name not in self._rules or self._rules[esc_name] == rule:
-            key = esc_name
-        else:
-            i = 0
-            while f'{esc_name}{i}' in self._rules:
-                i += 1
-            key = f'{esc_name}{i}'
-        self._rules[key] = rule
-        return key
-
-    def visit(self, schema, name):
-        schema_type = schema.get('type')
-        rule_name = name or 'root'
-
-        if 'oneOf' in schema or 'anyOf' in schema:
-            rule = ' | '.join((
-                self.visit(alt_schema, f'{name}{"-" if name else ""}{i}')
-                for i, alt_schema in enumerate(schema.get('oneOf') or schema['anyOf'])
-            ))
-            return self._add_rule(rule_name, rule)
-
-        elif 'const' in schema:
-            return self._add_rule(rule_name, self._format_literal(schema['const']))
-
-        elif 'enum' in schema:
-            rule = ' | '.join((self._format_literal(v) for v in schema['enum']))
-            return self._add_rule(rule_name, rule)
-
-        elif schema_type == 'object' and 'properties' in schema:
-            # TODO: `required` keyword
-            prop_order = self._prop_order
-            prop_pairs = sorted(
-                schema['properties'].items(),
-                # sort by position in prop_order (if specified) then by key
-                key=lambda kv: (prop_order.get(kv[0], len(prop_order)), kv[0]),
-            )
-
-            rule = '"{" space'
-            for i, (prop_name, prop_schema) in enumerate(prop_pairs):
-                prop_rule_name = self.visit(prop_schema, f'{name}{"-" if name else ""}{prop_name}')
-                if i > 0:
-                    rule += ' "," space'
-                rule += fr' {self._format_literal(prop_name)} space ":" space {prop_rule_name}'
-            rule += ' "}" space'
-
-            return self._add_rule(rule_name, rule)
-
-        elif schema_type == 'array' and 'items' in schema:
-            # TODO `prefixItems` keyword
-            item_rule_name = self.visit(schema['items'], f'{name}{"-" if name else ""}item')
-            list_item_operator = f'("," space {item_rule_name})'
-            successive_items = ""
-            min_items = schema.get("minItems", 0)
-            if min_items > 0:
-               first_item = f"({item_rule_name})"
-               successive_items = list_item_operator * (min_items - 1)
-               min_items -= 1
-            else:
-               first_item = f"({item_rule_name})?"
-            max_items = schema.get("maxItems")
-            if max_items is not None and max_items > min_items:
-                successive_items += (list_item_operator + "?") * (max_items - min_items - 1)
-            else:
-                successive_items += list_item_operator + "*"
-            rule = f'"[" space {first_item} {successive_items} "]" space'
-            return self._add_rule(rule_name, rule)
-
-        else:
-            assert schema_type in PRIMITIVE_RULES, f'Unrecognized schema: {schema}'
-            return self._add_rule(
-                'root' if rule_name == 'root' else schema_type,
-                PRIMITIVE_RULES[schema_type]
-            )
-
-    def format_grammar(self):
-        return '\n'.join((f'{name} ::= {rule}' for name, rule in self._rules.items()))
-
-
-def main(args_in = None):
-    parser = argparse.ArgumentParser(
-        description='''
-            Generates a grammar (suitable for use in ./main) that produces JSON conforming to a
-            given JSON schema. Only a subset of JSON schema features are supported; more may be
-            added in the future.
-        ''',
-    )
-    parser.add_argument(
-        '--prop-order',
-        default=[],
-        type=lambda s: s.split(','),
-        help='''
-            comma-separated property names defining the order of precedence for object properties;
-            properties not specified here are given lower precedence than those that are, and are
-            sorted alphabetically
-        '''
-    )
-    parser.add_argument('schema', help='file containing JSON schema ("-" for stdin)')
-    args = parser.parse_args(args_in)
-
-    schema = json.load(sys.stdin if args.schema == '-' else open(args.schema))
-    prop_order = {name: idx for idx, name in enumerate(args.prop_order)}
-    converter = SchemaConverter(prop_order)
-    converter.visit(schema, '')
-    print(converter.format_grammar())
-
-
-if __name__ == '__main__':
-    main()
diff --git a/examples/json_schema_to_grammar.py b/examples/json_schema_to_grammar.py
new file mode 100755
index 000000000..826cd3f72
--- /dev/null
+++ b/examples/json_schema_to_grammar.py
@@ -0,0 +1,616 @@
+#!/usr/bin/env python3
+import argparse
+import itertools
+import json
+import re
+import sys
+from typing import Any, Dict, List, Set, Tuple, Union
+
+def _build_repetition(item_rule, min_items, max_items, separator_rule=None, item_rule_is_literal=False):
+    if not separator_rule:
+        if min_items == 0 and max_items == 1:
+            return f'{item_rule}?'
+        elif min_items == 1 and max_items is None:
+            return f'{item_rule}+'
+
+    result = ''
+
+    if min_items > 0:
+        if item_rule_is_literal and separator_rule is None:
+            result = '"' + (item_rule[1:-1] * min_items) + '"'
+        else:
+            result = (f' {separator_rule} ' if separator_rule else ' ').join([item_rule] * min_items)
+
+    def opt_repetitions(up_to_n, prefix_with_sep=False):
+        '''
+            - n=4, no sep:             '(a (a (a (a)?)?)?)?'
+            - n=4, sep=',', prefix:    '("," a ("," a ("," a ("," a)?)?)?)?'
+            - n=4, sep=',', no prefix: '(a ("," a ("," a ("," a)?)?)?)?'
+        '''
+
+        content = f'{separator_rule} {item_rule}' if prefix_with_sep and separator_rule else item_rule
+        if up_to_n == 0:
+            return ''
+        elif up_to_n == 1:
+            return f'({content})?'
+        elif separator_rule and not prefix_with_sep:
+            return f'({content} {opt_repetitions(up_to_n - 1, prefix_with_sep=True)})?'
+        else:
+            return (f'({content} ' * up_to_n).rstrip() + (')?' * up_to_n)
+
+    if min_items > 0 and max_items != min_items:
+        result += ' '
+
+    if max_items is not None:
+        result += opt_repetitions(max_items - min_items, prefix_with_sep=min_items > 0)
+    else:
+        item_operator = f'({separator_rule + " " if separator_rule else ""}{item_rule})'
+
+        if min_items == 0 and separator_rule:
+            result = f'({item_rule} {item_operator}*)?'
+        else:
+            result += f'{item_operator}*'
+
+    return result
+
+
+class BuiltinRule:
+    def __init__(self, content: str, deps: list = None):
+        self.content = content
+        self.deps = deps or []
+
+_up_to_15_digits = _build_repetition('[0-9]', 0, 15)
+
+# whitespace is constrained to a single space char to prevent model "running away" in
+# whitespace. Also maybe improves generation quality?
+SPACE_RULE = '" "?'
+
+PRIMITIVE_RULES = {
+    'boolean'      : BuiltinRule('("true" | "false") space', []),
+    'decimal-part' : BuiltinRule('[0-9] ' + _up_to_15_digits, []),
+    'integral-part': BuiltinRule('[0-9] | [1-9] ' + _up_to_15_digits, []),
+    'number'       : BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']),
+    'integer'      : BuiltinRule('("-"? integral-part) space', ['integral-part']),
+    'value'        : BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']),
+    'object'       : BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']),
+    'array'        : BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']),
+    'uuid'         : BuiltinRule(r'"\"" ' + ' "-" '.join('[0-9a-fA-F]' * n for n in [8, 4, 4, 4, 12]) + r' "\"" space', []),
+    'char'         : BuiltinRule(r'[^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])', []),
+    'string'       : BuiltinRule(r'"\"" char* "\"" space', ['char']),
+    'null'         : BuiltinRule('"null" space', []),
+}
+
+# TODO: support "uri", "email" string formats
+STRING_FORMAT_RULES = {
+    'date'            : BuiltinRule('[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []),
+    'time'            : BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []),
+    'date-time'       : BuiltinRule('date "T" time', ['date', 'time']),
+    'date-string'     : BuiltinRule('"\\"" date "\\"" space', ['date']),
+    'time-string'     : BuiltinRule('"\\"" time "\\"" space', ['time']),
+    'date-time-string': BuiltinRule('"\\"" date-time "\\"" space', ['date-time']),
+}
+
+DOTALL = '[\\U00000000-\\U0010FFFF]'
+DOT = '[^\\x0A\\x0D]'
+
+RESERVED_NAMES = set(["root", "dot", *PRIMITIVE_RULES.keys(), *STRING_FORMAT_RULES.keys()])
+
+INVALID_RULE_CHARS_RE = re.compile(r'[^a-zA-Z0-9-]+')
+GRAMMAR_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"]')
+GRAMMAR_RANGE_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"\]\-\\]')
+GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']': '\\]'}
+
+NON_LITERAL_SET = set('|.()[]{}*+?')
+ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = set('[]()|{}*+?')
+
+
+class SchemaConverter:
+    def __init__(self, *, prop_order, allow_fetch, dotall, raw_pattern):
+        self._prop_order = prop_order
+        self._allow_fetch = allow_fetch
+        self._dotall = dotall
+        self._raw_pattern = raw_pattern
+        self._rules = {
+            'space': SPACE_RULE,
+        }
+        self._refs = {}
+        self._refs_being_resolved = set()
+
+    def _format_literal(self, literal):
+        escaped = GRAMMAR_LITERAL_ESCAPE_RE.sub(
+            lambda m: GRAMMAR_LITERAL_ESCAPES.get(m.group(0)), literal
+        )
+        return f'"{escaped}"'
+
+    def not_literal(self, literal: str, dotall: bool = True, maybe_escaped_underscores = False) -> str:
+        '''
+            not_literal('a') -> '[^a]'
+            not_literal('abc') -> '([^a] | "a" ([^b] | "b" ([^c])?)?)?'
+        '''
+        assert len(literal) > 0, 'Empty literal not supported'
+        def recurse(i: int):
+            c = literal[i]
+            if maybe_escaped_underscores and c == '_':
+                yield f'[^{c}\\\\]'
+                yield ' | '
+                yield f'"\\\\"? "{c}"'
+            else:
+                yield f'[^{c}]'
+            if i < len(literal) - 1:
+                yield ' | '
+                yield self._format_literal(c)
+                yield ' ('
+                yield from recurse(i + 1)
+                yield ')?'
+
+        return ''.join(('(', *recurse(0), ')'))
+
+    def _add_rule(self, name, rule):
+        esc_name = INVALID_RULE_CHARS_RE.sub('-', name)
+        if esc_name not in self._rules or self._rules[esc_name] == rule:
+            key = esc_name
+        else:
+            i = 0
+            while f'{esc_name}{i}' in self._rules and self._rules[f'{esc_name}{i}'] != rule:
+                i += 1
+            key = f'{esc_name}{i}'
+        self._rules[key] = rule
+        return key
+
+    def resolve_refs(self, schema: dict, url: str):
+        '''
+            Resolves all $ref fields in the given schema, fetching any remote schemas,
+            replacing $ref with absolute reference URL and populating self._refs with the
+            respective referenced (sub)schema dictionaries.
+        '''
+        def visit(n: dict):
+            if isinstance(n, list):
+                return [visit(x) for x in n]
+            elif isinstance(n, dict):
+                ref = n.get('$ref')
+                if ref is not None and ref not in self._refs:
+                    if ref.startswith('https://'):
+                        assert self._allow_fetch, 'Fetching remote schemas is not allowed (use --allow-fetch for force)'
+                        import requests
+
+                        frag_split = ref.split('#')
+                        base_url = frag_split[0]
+
+                        target = self._refs.get(base_url)
+                        if target is None:
+                            target = self.resolve_refs(requests.get(ref).json(), base_url)
+                            self._refs[base_url] = target
+
+                        if len(frag_split) == 1 or frag_split[-1] == '':
+                            return target
+                    elif ref.startswith('#/'):
+                        target = schema
+                        ref = f'{url}{ref}'
+                        n['$ref'] = ref
+                    else:
+                        raise ValueError(f'Unsupported ref {ref}')
+
+                    for sel in ref.split('#')[-1].split('/')[1:]:
+                        assert target is not None and sel in target, f'Error resolving ref {ref}: {sel} not in {target}'
+                        target = target[sel]
+
+                    self._refs[ref] = target
+                else:
+                    for v in n.values():
+                        visit(v)
+
+            return n
+        return visit(schema)
+
+    def _generate_union_rule(self, name, alt_schemas):
+        return ' | '.join((
+            self.visit(alt_schema, f'{name}{"-" if name else "alternative-"}{i}')
+            for i, alt_schema in enumerate(alt_schemas)
+        ))
+
+    def _visit_pattern(self, pattern, name):
+        '''
+            Transforms a regular expression pattern into a GBNF rule.
+
+            Input: https://json-schema.org/understanding-json-schema/reference/regular_expressions
+            Output: https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md
+
+            Unsupported features: negative/positive lookaheads, greedy/non-greedy modifiers.
+
+            Mostly a 1:1 translation, except for {x} / {x,} / {x,y} quantifiers for which
+            we define sub-rules to keep the output lean.
+        '''
+
+        assert pattern.startswith('^') and pattern.endswith('$'), 'Pattern must start with "^" and end with "$"'
+        pattern = pattern[1:-1]
+        sub_rule_ids = {}
+
+        i = 0
+        length = len(pattern)
+
+        def to_rule(s: Tuple[str, bool]) -> str:
+            (txt, is_literal) = s
+            return "\"" + txt + "\"" if is_literal else txt
+
+        def transform() -> Tuple[str, bool]:
+            '''
+                Parse a unit at index i (advancing it), and return its string representation + whether it's a literal.
+            '''
+            nonlocal i
+            nonlocal pattern
+            nonlocal sub_rule_ids
+
+            start = i
+            # For each component of this sequence, store its string representation and whether it's a literal.
+            # We only need a flat structure here to apply repetition operators to the last item, and
+            # to merge literals at the and (we're parsing grouped ( sequences ) recursively and don't treat '|' specially
+            # (GBNF's syntax is luckily very close to regular expressions!)
+            seq: list[Tuple[str, bool]] = []
+
+            def get_dot():
+                if self._dotall:
+                    rule = DOTALL
+                else:
+                    # Accept any character... except \n and \r line break chars (\x0A and \xOD)
+                    rule = DOT
+                return self._add_rule(f'dot', rule)
+
+            def join_seq():
+                nonlocal seq
+                ret = []
+                for is_literal, g in itertools.groupby(seq, lambda x: x[1]):
+                    if is_literal:
+                        ret.append((''.join(x[0] for x in g), True))
+                    else:
+                        ret.extend(g)
+                if len(ret) == 1:
+                    return ret[0]
+                return (' '.join(to_rule(x) for x in seq), False)
+
+            while i < length:
+                c = pattern[i]
+                if c == '.':
+                    seq.append((get_dot(), False))
+                    i += 1
+                elif c == '(':
+                    i += 1
+                    if i < length:
+                        assert pattern[i] != '?', f'Unsupported pattern syntax "{pattern[i]}" at index {i} of /{pattern}/'
+                    seq.append((f'({to_rule(transform())})', False))
+                elif c == ')':
+                    i += 1
+                    assert start > 0 and pattern[start-1] == '(', f'Unbalanced parentheses; start = {start}, i = {i}, pattern = {pattern}'
+                    return join_seq()
+                elif c == '[':
+                    square_brackets = c
+                    i += 1
+                    while i < length and pattern[i] != ']':
+                        if pattern[i] == '\\':
+                            square_brackets += pattern[i:i+2]
+                            i += 2
+                        else:
+                            square_brackets += pattern[i]
+                            i += 1
+                    assert i < length, f'Unbalanced square brackets; start = {start}, i = {i}, pattern = {pattern}'
+                    square_brackets += ']'
+                    i += 1
+                    seq.append((square_brackets, False))
+                elif c == '|':
+                    seq.append(('|', False))
+                    i += 1
+                elif c in ('*', '+', '?'):
+                    seq[-1] = (to_rule(seq[-1]) + c, False)
+                    i += 1
+                elif c == '{':
+                    curly_brackets = c
+                    i += 1
+                    while i < length and pattern[i] != '}':
+                        curly_brackets += pattern[i]
+                        i += 1
+                    assert i < length, f'Unbalanced curly brackets; start = {start}, i = {i}, pattern = {pattern}'
+                    curly_brackets += '}'
+                    i += 1
+                    nums = [s.strip() for s in curly_brackets[1:-1].split(',')]
+                    min_times = 0
+                    max_times = None
+                    try:
+                        if len(nums) == 1:
+                            min_times = int(nums[0])
+                            max_times = min_times
+                        else:
+                            assert len(nums) == 2
+                            min_times = int(nums[0]) if nums[0] else 0
+                            max_times = int(nums[1]) if nums[1] else None
+                    except ValueError:
+                        raise ValueError(f'Invalid quantifier {curly_brackets} in /{pattern}/')
+
+                    (sub, sub_is_literal) = seq[-1]
+
+                    if not sub_is_literal:
+                        id = sub_rule_ids.get(sub)
+                        if id is None:
+                            id = self._add_rule(f'{name}-{len(sub_rule_ids) + 1}', sub)
+                            sub_rule_ids[sub] = id
+                        sub = id
+
+                    seq[-1] = (_build_repetition(f'"{sub}"' if sub_is_literal else sub, min_times, max_times, item_rule_is_literal=sub_is_literal), False)
+                else:
+                    literal = ''
+                    while i < length:
+                        if pattern[i] == '\\' and i < length - 1:
+                            next = pattern[i + 1]
+                            if next in ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS:
+                                i += 1
+                                literal += pattern[i]
+                                i += 1
+                            else:
+                                literal += pattern[i:i+2]
+                                i += 2
+                        elif pattern[i] == '"' and not self._raw_pattern:
+                            literal += '\\"'
+                            i += 1
+                        elif pattern[i] not in NON_LITERAL_SET and \
+                                (i == length - 1 or literal == '' or pattern[i+1] == '.' or pattern[i+1] not in NON_LITERAL_SET):
+                            literal += pattern[i]
+                            i += 1
+                        else:
+                            break
+                    if literal:
+                        seq.append((literal, True))
+
+            return join_seq()
+
+        return self._add_rule(
+            name,
+            to_rule(transform()) if self._raw_pattern \
+                else "\"\\\"\" " + to_rule(transform()) + " \"\\\"\" space")
+
+
+    def _resolve_ref(self, ref):
+        ref_name = ref.split('/')[-1]
+        if ref_name not in self._rules and ref not in self._refs_being_resolved:
+            self._refs_being_resolved.add(ref)
+            resolved = self._refs[ref]
+            ref_name = self.visit(resolved, ref_name)
+            self._refs_being_resolved.remove(ref)
+        return ref_name
+
+    def _generate_constant_rule(self, value):
+        return self._format_literal(json.dumps(value))
+
+    def visit(self, schema, name):
+        schema_type = schema.get('type')
+        schema_format = schema.get('format')
+        rule_name = name + '-' if name in RESERVED_NAMES else name or 'root'
+
+        if (ref := schema.get('$ref')) is not None:
+            return self._add_rule(rule_name, self._resolve_ref(ref))
+
+        elif 'oneOf' in schema or 'anyOf' in schema:
+            return self._add_rule(rule_name, self._generate_union_rule(name, schema.get('oneOf') or schema['anyOf']))
+
+        elif isinstance(schema_type, list):
+            return self._add_rule(rule_name, self._generate_union_rule(name, [{'type': t} for t in schema_type]))
+
+        elif 'const' in schema:
+            return self._add_rule(rule_name, self._generate_constant_rule(schema['const']))
+
+        elif 'enum' in schema:
+            rule = ' | '.join((self._generate_constant_rule(v) for v in schema['enum']))
+            return self._add_rule(rule_name, rule)
+
+        elif schema_type in (None, 'object') and \
+             ('properties' in schema or \
+              ('additionalProperties' in schema and schema['additionalProperties'] is not True)):
+            required = set(schema.get('required', []))
+            properties = list(schema.get('properties', {}).items())
+            return self._add_rule(rule_name, self._build_object_rule(properties, required, name, schema.get('additionalProperties')))
+
+        elif schema_type in (None, 'object') and 'allOf' in schema:
+            required = set()
+            properties = []
+            hybrid_name = name
+            def add_component(comp_schema, is_required):
+                if (ref := comp_schema.get('$ref')) is not None:
+                    comp_schema = self._refs[ref]
+
+                if 'properties' in comp_schema:
+                    for prop_name, prop_schema in comp_schema['properties'].items():
+                        properties.append((prop_name, prop_schema))
+                        if is_required:
+                            required.add(prop_name)
+
+            for t in schema['allOf']:
+                if 'anyOf' in t:
+                    for tt in t['anyOf']:
+                        add_component(tt, is_required=False)
+                else:
+                    add_component(t, is_required=True)
+
+            return self._add_rule(rule_name, self._build_object_rule(properties, required, hybrid_name, additional_properties=[]))
+
+        elif schema_type in (None, 'array') and ('items' in schema or 'prefixItems' in schema):
+            items = schema.get('items') or schema['prefixItems']
+            if isinstance(items, list):
+                return self._add_rule(
+                    rule_name,
+                    '"[" space ' +
+                    ' "," space '.join(
+                        self.visit(item, f'{name}{"-" if name else ""}tuple-{i}')
+                        for i, item in enumerate(items)) +
+                    ' "]" space')
+            else:
+                item_rule_name = self.visit(items, f'{name}{"-" if name else ""}item')
+                min_items = schema.get("minItems", 0)
+                max_items = schema.get("maxItems")
+                return self._add_rule(rule_name, '"[" space ' + _build_repetition(item_rule_name, min_items, max_items, separator_rule='"," space') + ' "]" space')
+
+        elif schema_type in (None, 'string') and 'pattern' in schema:
+            return self._visit_pattern(schema['pattern'], rule_name)
+
+        elif schema_type in (None, 'string') and re.match(r'^uuid[1-5]?$', schema_format or ''):
+            return self._add_primitive(
+                'root' if rule_name == 'root' else schema_format,
+                PRIMITIVE_RULES['uuid']
+            )
+
+        elif schema_type in (None, 'string') and f'{schema_format}-string' in STRING_FORMAT_RULES:
+            prim_name = f'{schema_format}-string'
+            return self._add_rule(rule_name, self._add_primitive(prim_name, STRING_FORMAT_RULES[prim_name]))
+
+        elif schema_type == 'string' and ('minLength' in schema or 'maxLength' in schema):
+            char_rule = self._add_primitive('char', PRIMITIVE_RULES['char'])
+            min_len = schema.get('minLength', 0)
+            max_len = schema.get('maxLength')
+
+            return self._add_rule(rule_name, r'"\"" ' + _build_repetition(char_rule, min_len, max_len) + r' "\"" space')
+
+        elif (schema_type == 'object') or (len(schema) == 0):
+            return self._add_rule(rule_name, self._add_primitive('object', PRIMITIVE_RULES['object']))
+
+        else:
+            assert schema_type in PRIMITIVE_RULES, f'Unrecognized schema: {schema}'
+            # TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero
+            return self._add_primitive('root' if rule_name == 'root' else schema_type, PRIMITIVE_RULES[schema_type])
+
+    def _add_primitive(self, name: str, rule: BuiltinRule):
+        n = self._add_rule(name, rule.content)
+
+        for dep in rule.deps:
+            dep_rule = PRIMITIVE_RULES.get(dep) or STRING_FORMAT_RULES.get(dep)
+            assert dep_rule, f'Rule {dep} not known'
+            if dep not in self._rules:
+                self._add_primitive(dep, dep_rule)
+        return n
+
+    def _build_object_rule(self, properties: List[Tuple[str, Any]], required: Set[str], name: str, additional_properties: Union[bool, Any]):
+        prop_order = self._prop_order
+        # sort by position in prop_order (if specified) then by original order
+        sorted_props = [kv[0] for _, kv in sorted(enumerate(properties), key=lambda ikv: (prop_order.get(ikv[1][0], len(prop_order)), ikv[0]))]
+
+        prop_kv_rule_names = {}
+        for prop_name, prop_schema in properties:
+            prop_rule_name = self.visit(prop_schema, f'{name}{"-" if name else ""}{prop_name}')
+            prop_kv_rule_names[prop_name] = self._add_rule(
+                f'{name}{"-" if name else ""}{prop_name}-kv',
+                fr'{self._format_literal(json.dumps(prop_name))} space ":" space {prop_rule_name}'
+            )
+        required_props = [k for k in sorted_props if k in required]
+        optional_props = [k for k in sorted_props if k not in required]
+
+        if additional_properties == True or isinstance(additional_properties, dict):
+            sub_name = f'{name}{"-" if name else ""}additional'
+            value_rule = self.visit({} if additional_properties == True else additional_properties, f'{sub_name}-value')
+            prop_kv_rule_names["*"] = self._add_rule(
+                f'{sub_name}-kv',
+                self._add_primitive('string', PRIMITIVE_RULES['string']) + f' ":" space {value_rule}'
+            )
+            optional_props.append("*")
+
+        rule = '"{" space '
+        rule += ' "," space '.join(prop_kv_rule_names[k] for k in required_props)
+
+        if optional_props:
+            rule += ' ('
+            if required_props:
+                rule += ' "," space ( '
+
+            def get_recursive_refs(ks, first_is_optional):
+                [k, *rest] = ks
+                kv_rule_name = prop_kv_rule_names[k]
+                if k == '*':
+                    res = self._add_rule(
+                        f'{name}{"-" if name else ""}additional-kvs',
+                        f'{kv_rule_name} ( "," space ' + kv_rule_name + ' )*'
+                    )
+                elif first_is_optional:
+                    res = f'( "," space {kv_rule_name} )?'
+                else:
+                    res = kv_rule_name
+                if len(rest) > 0:
+                    res += ' ' + self._add_rule(
+                        f'{name}{"-" if name else ""}{k}-rest',
+                        get_recursive_refs(rest, first_is_optional=True)
+                    )
+                return res
+
+            rule += ' | '.join(
+                get_recursive_refs(optional_props[i:], first_is_optional=False)
+                for i in range(len(optional_props))
+            )
+            if required_props:
+                rule += ' )'
+            rule += ' )?'
+
+        rule += ' "}" space'
+
+        return rule
+
+    def format_grammar(self):
+        return '\n'.join(
+            f'{name} ::= {rule}'
+            for name, rule in sorted(self._rules.items(), key=lambda kv: kv[0])
+        )
+
+
+def main(args_in = None):
+    parser = argparse.ArgumentParser(
+        description='''
+            Generates a grammar (suitable for use in ./main) that produces JSON conforming to a
+            given JSON schema. Only a subset of JSON schema features are supported; more may be
+            added in the future.
+        ''',
+    )
+    parser.add_argument(
+        '--prop-order',
+        default=[],
+        type=lambda s: s.split(','),
+        help='''
+            comma-separated property names defining the order of precedence for object properties;
+            properties not specified here are given lower precedence than those that are, and
+            are kept in their original order from the schema. Required properties are always
+            given precedence over optional properties.
+        '''
+    )
+    parser.add_argument(
+        '--allow-fetch',
+        action='store_true',
+        default=False,
+        help='Whether to allow fetching referenced schemas over HTTPS')
+    parser.add_argument(
+        '--dotall',
+        action='store_true',
+        default=False,
+        help='Whether to treat dot (".") as matching all chars including line breaks in regular expression patterns')
+    parser.add_argument(
+        '--raw-pattern',
+        action='store_true',
+        default=False,
+        help='Treats string patterns as raw patterns w/o quotes (or quote escapes)')
+
+    parser.add_argument('schema', help='file containing JSON schema ("-" for stdin)')
+    args = parser.parse_args(args_in)
+
+    if args.schema.startswith('https://'):
+        url = args.schema
+        import requests
+        schema = requests.get(url).json()
+    elif args.schema == '-':
+        url = 'stdin'
+        schema = json.load(sys.stdin)
+    else:
+        url = f'file://{args.schema}'
+        with open(args.schema) as f:
+            schema = json.load(f)
+    converter = SchemaConverter(
+        prop_order={name: idx for idx, name in enumerate(args.prop_order)},
+        allow_fetch=args.allow_fetch,
+        dotall=args.dotall,
+        raw_pattern=args.raw_pattern)
+    schema = converter.resolve_refs(schema, url)
+    converter.visit(schema, '')
+    print(converter.format_grammar())
+
+
+if __name__ == '__main__':
+    main()
diff --git a/examples/llama-bench/README.md b/examples/llama-bench/README.md
index 374e40a7d..857840564 100644
--- a/examples/llama-bench/README.md
+++ b/examples/llama-bench/README.md
@@ -26,17 +26,21 @@ options:
   -m, --model <filename>              (default: models/7B/ggml-model-q4_0.gguf)
   -p, --n-prompt <n>                  (default: 512)
   -n, --n-gen <n>                     (default: 128)
-  -b, --batch-size <n>                (default: 512)
-  -ctk <t>, --cache-type-k <t>        (default: f16)
-  -ctv <t>, --cache-type-v <t>        (default: f16)
-  -t, --threads <n>                   (default: 112)
+  -pg <pp,tg>                         (default: 512,128)
+  -b, --batch-size <n>                (default: 2048)
+  -ub, --ubatch-size <n>              (default: 512)
+  -ctk, --cache-type-k <t>            (default: f16)
+  -ctv, --cache-type-v <t>            (default: f16)
+  -t, --threads <n>                   (default: 16)
   -ngl, --n-gpu-layers <n>            (default: 99)
   -sm, --split-mode <none|layer|row>  (default: layer)
   -mg, --main-gpu <i>                 (default: 0)
   -nkvo, --no-kv-offload <0|1>        (default: 0)
+  -fa, --flash-attn <0|1>             (default: 0)
   -mmp, --mmap <0|1>                  (default: 1)
-  -mmq, --mul-mat-q <0|1>             (default: 1)
-  -ts, --tensor_split <ts0/ts1/..>    (default: 0)
+  --numa <distribute|isolate|numactl> (default: disabled)
+  -embd, --embeddings <0|1>           (default: 0)
+  -ts, --tensor-split <ts0/ts1/..>    (default: 0)
   -r, --repetitions <n>               (default: 5)
   -o, --output <csv|json|md|sql>      (default: md)
   -v, --verbose                       (default: 0)
@@ -44,10 +48,11 @@ options:
 Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.
 ```
 
-llama-bench can perform two types of tests:
+llama-bench can perform three types of tests:
 
 - Prompt processing (pp): processing a prompt in batches (`-p`)
 - Text generation (tg): generating a sequence of tokens (`-n`)
+- Prompt processing + text generation (pg): processing a prompt followed by generating a sequence of tokens (`-pg`)
 
 With the exception of `-r`, `-o` and `-v`, all options can be specified multiple times to run multiple tests. Each pp and tg test is run with all combinations of the specified options. To specify multiple values for an option, the values can be separated by commas (e.g. `-n 16,32`), or the option can be specified multiple times (e.g. `-n 16 -n 32`).
 
diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp
index 11410f8ae..8b965e199 100644
--- a/examples/llama-bench/llama-bench.cpp
+++ b/examples/llama-bench/llama-bench.cpp
@@ -8,6 +8,7 @@
 #include <cstdio>
 #include <cstring>
 #include <ctime>
+#include <cstdlib>
 #include <iterator>
 #include <map>
 #include <numeric>
@@ -103,6 +104,7 @@ static std::string get_cpu_info() {
                 }
             }
         }
+        fclose(f);
     }
 #endif
     // TODO: other platforms
@@ -111,11 +113,11 @@ static std::string get_cpu_info() {
 
 static std::string get_gpu_info() {
     std::string id;
-#ifdef GGML_USE_CUBLAS
-    int count = ggml_cuda_get_device_count();
+#ifdef GGML_USE_CUDA
+    int count = ggml_backend_cuda_get_device_count();
     for (int i = 0; i < count; i++) {
         char buf[128];
-        ggml_cuda_get_device_description(i, buf, sizeof(buf));
+        ggml_backend_cuda_get_device_description(i, buf, sizeof(buf));
         id += buf;
         if (i < count - 1) {
             id += "/";
@@ -123,20 +125,15 @@ static std::string get_gpu_info() {
     }
 #endif
 #ifdef GGML_USE_SYCL
-    int device_list[GGML_SYCL_MAX_DEVICES];
-    ggml_sycl_get_gpu_list(device_list, GGML_SYCL_MAX_DEVICES);
-
-    for (int i = 0; i < GGML_SYCL_MAX_DEVICES; i++) {
-        if (device_list[i] >0 ){
-            char buf[128];
-            ggml_sycl_get_device_description(i, buf, sizeof(buf));
-            id += buf;
+    int count = ggml_backend_sycl_get_device_count();
+    for (int i = 0; i < count; i++) {
+        char buf[128];
+        ggml_sycl_get_device_description(i, buf, sizeof(buf));
+        id += buf;
+        if (i < count - 1) {
             id += "/";
         }
     }
-    if (id.length() >2 ) {
-        id.pop_back();
-    }
 #endif
     // TODO: other backends
     return id;
@@ -157,18 +154,26 @@ static const char * output_format_str(output_formats format) {
 
 static const char * split_mode_str(llama_split_mode mode) {
     switch (mode) {
-        case LLAMA_SPLIT_NONE:  return "none";
-        case LLAMA_SPLIT_LAYER: return "layer";
-        case LLAMA_SPLIT_ROW:   return "row";
+        case LLAMA_SPLIT_MODE_NONE:  return "none";
+        case LLAMA_SPLIT_MODE_LAYER: return "layer";
+        case LLAMA_SPLIT_MODE_ROW:   return "row";
         default: GGML_ASSERT(!"invalid split mode");
     }
 }
 
+static std::string pair_str(const std::pair<int, int> & p) {
+    static char buf[32];
+    snprintf(buf, sizeof(buf), "%d,%d", p.first, p.second);
+    return buf;
+}
+
 struct cmd_params {
     std::vector<std::string> model;
     std::vector<int> n_prompt;
     std::vector<int> n_gen;
+    std::vector<std::pair<int, int>> n_pg;
     std::vector<int> n_batch;
+    std::vector<int> n_ubatch;
     std::vector<ggml_type> type_k;
     std::vector<ggml_type> type_v;
     std::vector<int> n_threads;
@@ -176,9 +181,11 @@ struct cmd_params {
     std::vector<llama_split_mode> split_mode;
     std::vector<int> main_gpu;
     std::vector<bool> no_kv_offload;
-    std::vector<bool> mul_mat_q;
+    std::vector<bool> flash_attn;
     std::vector<std::vector<float>> tensor_split;
     std::vector<bool> use_mmap;
+    std::vector<bool> embeddings;
+    ggml_numa_strategy numa;
     int reps;
     bool verbose;
     output_formats output_format;
@@ -188,17 +195,21 @@ static const cmd_params cmd_params_defaults = {
     /* model         */ {"models/7B/ggml-model-q4_0.gguf"},
     /* n_prompt      */ {512},
     /* n_gen         */ {128},
-    /* n_batch       */ {512},
+    /* n_pg          */ {{512, 128}},
+    /* n_batch       */ {2048},
+    /* n_ubatch      */ {512},
     /* type_k        */ {GGML_TYPE_F16},
     /* type_v        */ {GGML_TYPE_F16},
-    /* n_threads     */ {get_num_physical_cores()},
+    /* n_threads     */ {get_math_cpu_count()},
     /* n_gpu_layers  */ {99},
-    /* split_mode    */ {LLAMA_SPLIT_LAYER},
+    /* split_mode    */ {LLAMA_SPLIT_MODE_LAYER},
     /* main_gpu      */ {0},
     /* no_kv_offload */ {false},
-    /* mul_mat_q     */ {true},
+    /* flash_attn    */ {false},
     /* tensor_split  */ {std::vector<float>(llama_max_devices(), 0.0f)},
     /* use_mmap      */ {true},
+    /* embeddings    */ {false},
+    /* numa          */ GGML_NUMA_STRATEGY_DISABLED,
     /* reps          */ 5,
     /* verbose       */ false,
     /* output_format */ MARKDOWN
@@ -212,17 +223,21 @@ static void print_usage(int /* argc */, char ** argv) {
     printf("  -m, --model <filename>              (default: %s)\n", join(cmd_params_defaults.model, ",").c_str());
     printf("  -p, --n-prompt <n>                  (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str());
     printf("  -n, --n-gen <n>                     (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str());
+    printf("  -pg <pp,tg>                         (default: %s)\n", join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str());
     printf("  -b, --batch-size <n>                (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str());
-    printf("  -ctk <t>, --cache-type-k <t>        (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str());
-    printf("  -ctv <t>, --cache-type-v <t>        (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
+    printf("  -ub, --ubatch-size <n>              (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str());
+    printf("  -ctk, --cache-type-k <t>            (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str());
+    printf("  -ctv, --cache-type-v <t>            (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
     printf("  -t, --threads <n>                   (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
     printf("  -ngl, --n-gpu-layers <n>            (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
     printf("  -sm, --split-mode <none|layer|row>  (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str());
     printf("  -mg, --main-gpu <i>                 (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
     printf("  -nkvo, --no-kv-offload <0|1>        (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
+    printf("  -fa, --flash-attn <0|1>             (default: %s)\n", join(cmd_params_defaults.flash_attn, ",").c_str());
     printf("  -mmp, --mmap <0|1>                  (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str());
-    printf("  -mmq, --mul-mat-q <0|1>             (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str());
-    printf("  -ts, --tensor_split <ts0/ts1/..>    (default: 0)\n");
+    printf("  --numa <distribute|isolate|numactl> (default: disabled)\n");
+    printf("  -embd, --embeddings <0|1>           (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str());
+    printf("  -ts, --tensor-split <ts0/ts1/..>    (default: 0)\n");
     printf("  -r, --repetitions <n>               (default: %d)\n", cmd_params_defaults.reps);
     printf("  -o, --output <csv|json|md|sql>      (default: %s)\n", output_format_str(cmd_params_defaults.output_format));
     printf("  -v, --verbose                       (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
@@ -249,6 +264,9 @@ static ggml_type ggml_type_from_name(const std::string & s) {
     if (s == "q5_1") {
         return GGML_TYPE_Q5_1;
     }
+    if (s == "iq4_nl") {
+        return GGML_TYPE_IQ4_NL;
+    }
 
     return GGML_TYPE_COUNT;
 }
@@ -295,6 +313,17 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
             }
             auto p = split<int>(argv[i], split_delim);
             params.n_gen.insert(params.n_gen.end(), p.begin(), p.end());
+        } else if (arg == "-pg") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            auto p = split<std::string>(argv[i], ',');
+            if (p.size() != 2) {
+                invalid_param = true;
+                break;
+            }
+            params.n_pg.push_back({std::stoi(p[0]), std::stoi(p[1])});
         } else if (arg == "-b" || arg == "--batch-size") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -302,6 +331,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
             }
             auto p = split<int>(argv[i], split_delim);
             params.n_batch.insert(params.n_batch.end(), p.begin(), p.end());
+        } else if (arg == "-ub" || arg == "--ubatch-size") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            auto p = split<int>(argv[i], split_delim);
+            params.n_ubatch.insert(params.n_ubatch.end(), p.begin(), p.end());
         } else if (arg == "-ctk" || arg == "--cache-type-k") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -358,11 +394,11 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
             for (const auto & m : p) {
                 llama_split_mode mode;
                 if (m == "none") {
-                    mode = LLAMA_SPLIT_NONE;
+                    mode = LLAMA_SPLIT_MODE_NONE;
                 } else if (m == "layer") {
-                    mode = LLAMA_SPLIT_LAYER;
+                    mode = LLAMA_SPLIT_MODE_LAYER;
                 } else if (m == "row") {
-                    mode = LLAMA_SPLIT_ROW;
+                    mode = LLAMA_SPLIT_MODE_ROW;
                 } else {
                     invalid_param = true;
                     break;
@@ -383,13 +419,24 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
             }
             auto p = split<bool>(argv[i], split_delim);
             params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end());
-        } else if (arg == "-mmq" || arg == "--mul-mat-q") {
+        } else if (arg == "--numa") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            } else {
+                std::string value(argv[i]);
+                /**/ if (value == "distribute" || value == "" ) { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
+                else if (value == "isolate")                    { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
+                else if (value == "numactl")                    { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
+                else { invalid_param = true; break; }
+            }
+        } else if (arg == "-fa" || arg == "--flash-attn") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             auto p = split<bool>(argv[i], split_delim);
-            params.mul_mat_q.insert(params.mul_mat_q.end(), p.begin(), p.end());
+            params.flash_attn.insert(params.flash_attn.end(), p.begin(), p.end());
         } else if (arg == "-mmp" || arg == "--mmap") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -397,6 +444,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
             }
             auto p = split<bool>(argv[i], split_delim);
             params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end());
+        } else if (arg == "-embd" || arg == "--embeddings") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            auto p = split<bool>(argv[i], split_delim);
+            params.embeddings.insert(params.embeddings.end(), p.begin(), p.end());
         } else if (arg == "-ts" || arg == "--tensor-split") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -459,16 +513,19 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
     if (params.model.empty())        { params.model = cmd_params_defaults.model; }
     if (params.n_prompt.empty())     { params.n_prompt = cmd_params_defaults.n_prompt; }
     if (params.n_gen.empty())        { params.n_gen = cmd_params_defaults.n_gen; }
+    if (params.n_pg.empty())         { params.n_pg = cmd_params_defaults.n_pg; }
     if (params.n_batch.empty())      { params.n_batch = cmd_params_defaults.n_batch; }
+    if (params.n_ubatch.empty())     { params.n_ubatch = cmd_params_defaults.n_ubatch; }
     if (params.type_k.empty())       { params.type_k = cmd_params_defaults.type_k; }
     if (params.type_v.empty())       { params.type_v = cmd_params_defaults.type_v; }
     if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; }
     if (params.split_mode.empty())   { params.split_mode = cmd_params_defaults.split_mode; }
     if (params.main_gpu.empty())     { params.main_gpu = cmd_params_defaults.main_gpu; }
     if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; }
-    if (params.mul_mat_q.empty())    { params.mul_mat_q = cmd_params_defaults.mul_mat_q; }
+    if (params.flash_attn.empty())   { params.flash_attn = cmd_params_defaults.flash_attn; }
     if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; }
     if (params.use_mmap.empty())     { params.use_mmap = cmd_params_defaults.use_mmap; }
+    if (params.embeddings.empty())   { params.embeddings = cmd_params_defaults.embeddings; }
     if (params.n_threads.empty())    { params.n_threads = cmd_params_defaults.n_threads; }
 
     return params;
@@ -479,6 +536,7 @@ struct cmd_params_instance {
     int n_prompt;
     int n_gen;
     int n_batch;
+    int n_ubatch;
     ggml_type type_k;
     ggml_type type_v;
     int n_threads;
@@ -486,9 +544,10 @@ struct cmd_params_instance {
     llama_split_mode split_mode;
     int main_gpu;
     bool no_kv_offload;
-    bool mul_mat_q;
+    bool flash_attn;
     std::vector<float> tensor_split;
     bool use_mmap;
+    bool embeddings;
 
     llama_model_params to_llama_mparams() const {
         llama_model_params mparams = llama_model_default_params();
@@ -516,10 +575,12 @@ struct cmd_params_instance {
 
         cparams.n_ctx = n_prompt + n_gen;
         cparams.n_batch = n_batch;
+        cparams.n_ubatch = n_ubatch;
         cparams.type_k = type_k;
         cparams.type_v = type_v;
-        cparams.mul_mat_q = mul_mat_q;
         cparams.offload_kqv = !no_kv_offload;
+        cparams.flash_attn = flash_attn;
+        cparams.embeddings = embeddings;
 
         return cparams;
     }
@@ -535,11 +596,13 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
     for (const auto & mg : params.main_gpu)
     for (const auto & ts : params.tensor_split)
     for (const auto & mmp : params.use_mmap)
+    for (const auto & embd : params.embeddings)
     for (const auto & nb : params.n_batch)
+    for (const auto & nub : params.n_ubatch)
     for (const auto & tk : params.type_k)
     for (const auto & tv : params.type_v)
-    for (const auto & mmq : params.mul_mat_q)
     for (const auto & nkvo : params.no_kv_offload)
+    for (const auto & fa : params.flash_attn)
     for (const auto & nt : params.n_threads) {
         for (const auto & n_prompt : params.n_prompt) {
             if (n_prompt == 0) {
@@ -550,6 +613,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .n_prompt     = */ n_prompt,
                 /* .n_gen        = */ 0,
                 /* .n_batch      = */ nb,
+                /* .n_ubatch     = */ nub,
                 /* .type_k       = */ tk,
                 /* .type_v       = */ tv,
                 /* .n_threads    = */ nt,
@@ -557,9 +621,10 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .split_mode   = */ sm,
                 /* .main_gpu     = */ mg,
                 /* .no_kv_offload= */ nkvo,
-                /* .mul_mat_q    = */ mmq,
+                /* .flash_attn   = */ fa,
                 /* .tensor_split = */ ts,
                 /* .use_mmap     = */ mmp,
+                /* .embeddings   = */ embd,
             };
             instances.push_back(instance);
         }
@@ -573,6 +638,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .n_prompt     = */ 0,
                 /* .n_gen        = */ n_gen,
                 /* .n_batch      = */ nb,
+                /* .n_ubatch     = */ nub,
                 /* .type_k       = */ tk,
                 /* .type_v       = */ tv,
                 /* .n_threads    = */ nt,
@@ -580,9 +646,35 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .split_mode   = */ sm,
                 /* .main_gpu     = */ mg,
                 /* .no_kv_offload= */ nkvo,
-                /* .mul_mat_q    = */ mmq,
+                /* .flash_attn   = */ fa,
                 /* .tensor_split = */ ts,
                 /* .use_mmap     = */ mmp,
+                /* .embeddings   = */ embd,
+            };
+            instances.push_back(instance);
+        }
+
+        for (const auto & n_pg : params.n_pg) {
+            if (n_pg.first == 0 && n_pg.second == 0) {
+                continue;
+            }
+            cmd_params_instance instance = {
+                /* .model        = */ m,
+                /* .n_prompt     = */ n_pg.first,
+                /* .n_gen        = */ n_pg.second,
+                /* .n_batch      = */ nb,
+                /* .n_ubatch     = */ nub,
+                /* .type_k       = */ tk,
+                /* .type_v       = */ tv,
+                /* .n_threads    = */ nt,
+                /* .n_gpu_layers = */ nl,
+                /* .split_mode   = */ sm,
+                /* .main_gpu     = */ mg,
+                /* .no_kv_offload= */ nkvo,
+                /* .flash_attn   = */ fa,
+                /* .tensor_split = */ ts,
+                /* .use_mmap     = */ mmp,
+                /* .embeddings   = */ embd,
             };
             instances.push_back(instance);
         }
@@ -609,6 +701,7 @@ struct test {
     uint64_t model_size;
     uint64_t model_n_params;
     int n_batch;
+    int n_ubatch;
     int n_threads;
     ggml_type type_k;
     ggml_type type_v;
@@ -616,9 +709,10 @@ struct test {
     llama_split_mode split_mode;
     int main_gpu;
     bool no_kv_offload;
-    bool mul_mat_q;
+    bool flash_attn;
     std::vector<float> tensor_split;
     bool use_mmap;
+    bool embeddings;
     int n_prompt;
     int n_gen;
     std::string test_time;
@@ -632,6 +726,7 @@ struct test {
         model_size = llama_model_size(lmodel);
         model_n_params = llama_model_n_params(lmodel);
         n_batch = inst.n_batch;
+        n_ubatch = inst.n_ubatch;
         n_threads = inst.n_threads;
         type_k = inst.type_k;
         type_v = inst.type_v;
@@ -639,9 +734,10 @@ struct test {
         split_mode = inst.split_mode;
         main_gpu = inst.main_gpu;
         no_kv_offload = inst.no_kv_offload;
-        mul_mat_q = inst.mul_mat_q;
+        flash_attn = inst.flash_attn;
         tensor_split = inst.tensor_split;
         use_mmap = inst.use_mmap;
+        embeddings = inst.embeddings;
         n_prompt = inst.n_prompt;
         n_gen = inst.n_gen;
         // RFC 3339 date-time format
@@ -710,10 +806,11 @@ struct test {
             "cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas",
             "cpu_info", "gpu_info",
             "model_filename", "model_type", "model_size", "model_n_params",
-            "n_batch", "n_threads", "type_k", "type_v",
+            "n_batch", "n_ubatch",
+            "n_threads", "type_k", "type_v",
             "n_gpu_layers", "split_mode",
-            "main_gpu", "no_kv_offload",
-            "mul_mat_q", "tensor_split", "use_mmap",
+            "main_gpu", "no_kv_offload", "flash_attn",
+            "tensor_split", "use_mmap", "embeddings",
             "n_prompt", "n_gen", "test_time",
             "avg_ns", "stddev_ns",
             "avg_ts", "stddev_ts"
@@ -724,7 +821,8 @@ struct test {
     enum field_type {STRING, BOOL, INT, FLOAT};
 
     static field_type get_field_type(const std::string & field) {
-        if (field == "build_number" || field == "n_batch" || field == "n_threads" ||
+        if (field == "build_number" || field == "n_batch" || field == "n_ubatch" ||
+            field == "n_threads" ||
             field == "model_size" || field == "model_n_params" ||
             field == "n_gpu_layers" || field == "main_gpu" ||
             field == "n_prompt" || field == "n_gen" ||
@@ -733,7 +831,7 @@ struct test {
         }
         if (field == "cuda" || field == "opencl"  || field == "vulkan" || field == "kompute" || field == "metal" ||
             field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" ||
-            field == "mul_mat_q" || field == "use_mmap") {
+            field == "flash_attn" || field == "use_mmap" || field == "embeddings") {
             return BOOL;
         }
         if (field == "avg_ts" || field == "stddev_ts") {
@@ -764,10 +862,11 @@ struct test {
             std::to_string(metal), std::to_string(sycl), std::to_string(gpu_blas), std::to_string(blas),
             cpu_info, gpu_info,
             model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
-            std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
+            std::to_string(n_batch), std::to_string(n_ubatch),
+            std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
             std::to_string(n_gpu_layers), split_mode_str(split_mode),
-            std::to_string(main_gpu), std::to_string(no_kv_offload),
-            std::to_string(mul_mat_q), tensor_split_str, std::to_string(use_mmap),
+            std::to_string(main_gpu), std::to_string(no_kv_offload), std::to_string(flash_attn),
+            tensor_split_str, std::to_string(use_mmap), std::to_string(embeddings),
             std::to_string(n_prompt), std::to_string(n_gen), test_time,
             std::to_string(avg_ns()), std::to_string(stdev_ns()),
             std::to_string(avg_ts()), std::to_string(stdev_ts())
@@ -787,7 +886,7 @@ struct test {
 
 const std::string test::build_commit = LLAMA_COMMIT;
 const int         test::build_number = LLAMA_BUILD_NUMBER;
-const bool        test::cuda         = !!ggml_cpu_has_cublas();
+const bool        test::cuda         = !!ggml_cpu_has_cuda();
 const bool        test::opencl       = !!ggml_cpu_has_clblast();
 const bool        test::vulkan       = !!ggml_cpu_has_vulkan();
 const bool        test::kompute      = !!ggml_cpu_has_kompute();
@@ -912,6 +1011,9 @@ struct markdown_printer : public printer {
         if (field == "n_gpu_layers") {
             return 3;
         }
+        if (field == "test") {
+            return 13;
+        }
 
         int width = std::max((int)field.length(), 10);
 
@@ -931,15 +1033,18 @@ struct markdown_printer : public printer {
         if (field == "n_threads") {
             return "threads";
         }
-        if (field == "mul_mat_q") {
-            return "mmq";
-        }
         if (field == "no_kv_offload") {
             return "nkvo";
         }
+        if (field == "flash_attn") {
+            return "fa";
+        }
         if (field == "use_mmap") {
             return "mmap";
         }
+        if (field == "embeddings") {
+            return "embd";
+        }
         if (field == "tensor_split") {
             return "ts";
         }
@@ -962,6 +1067,9 @@ struct markdown_printer : public printer {
         if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) {
             fields.emplace_back("n_batch");
         }
+        if (params.n_ubatch.size() > 1 || params.n_ubatch != cmd_params_defaults.n_ubatch) {
+            fields.emplace_back("n_ubatch");
+        }
         if (params.type_k.size() > 1 || params.type_k != cmd_params_defaults.type_k) {
             fields.emplace_back("type_k");
         }
@@ -974,18 +1082,21 @@ struct markdown_printer : public printer {
         if (params.split_mode.size() > 1 || params.split_mode != cmd_params_defaults.split_mode) {
             fields.emplace_back("split_mode");
         }
-        if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) {
-            fields.emplace_back("mul_mat_q");
-        }
         if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) {
             fields.emplace_back("no_kv_offload");
         }
+        if (params.flash_attn.size() > 1 || params.flash_attn != cmd_params_defaults.flash_attn) {
+            fields.emplace_back("flash_attn");
+        }
         if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) {
             fields.emplace_back("tensor_split");
         }
         if (params.use_mmap.size() > 1 || params.use_mmap != cmd_params_defaults.use_mmap) {
             fields.emplace_back("use_mmap");
         }
+        if (params.embeddings.size() > 1 || params.embeddings != cmd_params_defaults.embeddings) {
+            fields.emplace_back("embeddings");
+        }
         fields.emplace_back("test");
         fields.emplace_back("t/s");
 
@@ -1029,12 +1140,11 @@ struct markdown_printer : public printer {
                 value = test::get_backend();
             } else if (field == "test") {
                 if (t.n_prompt > 0 && t.n_gen == 0) {
-                    snprintf(buf, sizeof(buf), "pp %d", t.n_prompt);
+                    snprintf(buf, sizeof(buf), "pp%d", t.n_prompt);
                 } else if (t.n_gen > 0 && t.n_prompt == 0) {
-                    snprintf(buf, sizeof(buf), "tg %d", t.n_gen);
+                    snprintf(buf, sizeof(buf), "tg%d", t.n_gen);
                 } else {
-                    assert(false);
-                    exit(1);
+                    snprintf(buf, sizeof(buf), "pp%d+tg%d", t.n_prompt, t.n_gen);
                 }
                 value = buf;
             } else if (field == "t/s") {
@@ -1101,25 +1211,40 @@ struct sql_printer : public printer {
 };
 
 static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) {
-    std::vector<llama_token> tokens(n_batch, llama_token_bos(llama_get_model(ctx)));
-    int n_processed = 0;
-
     llama_set_n_threads(ctx, n_threads, n_threads);
 
+    const llama_model * model = llama_get_model(ctx);
+    const int32_t n_vocab = llama_n_vocab(model);
+
+    std::vector<llama_token> tokens(n_batch);
+
+    int n_processed = 0;
+
     while (n_processed < n_prompt) {
         int n_tokens = std::min(n_prompt - n_processed, n_batch);
+        tokens[0] = n_processed == 0 && llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab;
+        for (int i = 1; i < n_tokens; i++) {
+            tokens[i] = std::rand() % n_vocab;
+        }
         llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens, n_past + n_processed, 0));
         n_processed += n_tokens;
     }
+
+    llama_synchronize(ctx);
 }
 
 static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) {
-    llama_token token = llama_token_bos(llama_get_model(ctx));
-
     llama_set_n_threads(ctx, n_threads, n_threads);
 
+    const llama_model * model = llama_get_model(ctx);
+    const int32_t n_vocab = llama_n_vocab(model);
+
+    llama_token token = llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab;
+
     for (int i = 0; i < n_gen; i++) {
         llama_decode(ctx, llama_batch_get_one(&token, 1, n_past + i, 0));
+        llama_synchronize(ctx);
+        token = std::rand() % n_vocab;
     }
 }
 
@@ -1152,6 +1277,7 @@ int main(int argc, char ** argv) {
         llama_log_set(llama_null_log_callback, NULL);
     }
     llama_backend_init();
+    llama_numa_init(params.numa);
 
     // initialize printer
     std::unique_ptr<printer> p;
@@ -1208,7 +1334,8 @@ int main(int argc, char ** argv) {
 
         // warmup run
         if (t.n_prompt > 0) {
-            test_prompt(ctx, std::min(2, t.n_batch), 0, t.n_batch, t.n_threads);
+            //test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads);
+            test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads);
         }
         if (t.n_gen > 0) {
             test_gen(ctx, 1, 0, t.n_threads);
@@ -1218,12 +1345,14 @@ int main(int argc, char ** argv) {
             llama_kv_cache_clear(ctx);
 
             uint64_t t_start = get_time_ns();
+
             if (t.n_prompt > 0) {
                 test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads);
             }
             if (t.n_gen > 0) {
                 test_gen(ctx, t.n_gen, t.n_prompt, t.n_threads);
             }
+
             uint64_t t_ns = get_time_ns() - t_start;
             t.samples_ns.push_back(t_ns);
         }
diff --git a/examples/llama.android/app/build.gradle.kts b/examples/llama.android/app/build.gradle.kts
index aadbe22c9..d42140efe 100644
--- a/examples/llama.android/app/build.gradle.kts
+++ b/examples/llama.android/app/build.gradle.kts
@@ -21,12 +21,8 @@ android {
             useSupportLibrary = true
         }
         ndk {
-            // Workaround for https://github.com/llvm/llvm-project/issues/65820
-            // affecting armeabi-v7a. Skip armeabi-v7a when invoked with
-            // -Pskip-armeabi-v7a (e.g., ./gradlew build -Pskip-armeabi-v7a).
-            if (project.hasProperty("skip-armeabi-v7a")) {
-                abiFilters += listOf("arm64-v8a", "x86_64", "x86")
-            }
+            // Add NDK properties if wanted, e.g.
+            // abiFilters += listOf("arm64-v8a")
         }
         externalNativeBuild {
             cmake {
diff --git a/examples/llama.android/app/src/main/cpp/llama-android.cpp b/examples/llama.android/app/src/main/cpp/llama-android.cpp
index 2beb1e0d5..4af9de303 100644
--- a/examples/llama.android/app/src/main/cpp/llama-android.cpp
+++ b/examples/llama.android/app/src/main/cpp/llama-android.cpp
@@ -33,6 +33,45 @@ jclass la_int_var;
 jmethodID la_int_var_value;
 jmethodID la_int_var_inc;
 
+std::string cached_token_chars;
+
+bool is_valid_utf8(const char * string) {
+    if (!string) {
+        return true;
+    }
+
+    const unsigned char * bytes = (const unsigned char *)string;
+    int num;
+
+    while (*bytes != 0x00) {
+        if ((*bytes & 0x80) == 0x00) {
+            // U+0000 to U+007F
+            num = 1;
+        } else if ((*bytes & 0xE0) == 0xC0) {
+            // U+0080 to U+07FF
+            num = 2;
+        } else if ((*bytes & 0xF0) == 0xE0) {
+            // U+0800 to U+FFFF
+            num = 3;
+        } else if ((*bytes & 0xF8) == 0xF0) {
+            // U+10000 to U+10FFFF
+            num = 4;
+        } else {
+            return false;
+        }
+
+        bytes += 1;
+        for (int i = 1; i < num; ++i) {
+            if ((*bytes & 0xC0) != 0x80) {
+                return false;
+            }
+            bytes += 1;
+        }
+    }
+
+    return true;
+}
+
 static void log_callback(ggml_log_level level, const char * fmt, void * data) {
     if (level == GGML_LOG_LEVEL_ERROR)     __android_log_print(ANDROID_LOG_ERROR, TAG, fmt, data);
     else if (level == GGML_LOG_LEVEL_INFO) __android_log_print(ANDROID_LOG_INFO, TAG, fmt, data);
@@ -295,6 +334,8 @@ Java_com_example_llama_Llm_completion_1init(
         jint n_len
     ) {
 
+    cached_token_chars.clear();
+
     const auto text = env->GetStringUTFChars(jtext, 0);
     const auto context = reinterpret_cast<llama_context *>(context_pointer);
     const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
@@ -367,13 +408,21 @@ Java_com_example_llama_Llm_completion_1loop(
     const auto new_token_id = llama_sample_token_greedy(context, &candidates_p);
 
     const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value);
-    if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
+    if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
         return env->NewStringUTF("");
     }
 
     auto new_token_chars = llama_token_to_piece(context, new_token_id);
-    LOGi("new_token_chars: `%s`", new_token_chars.c_str());
-    auto new_token = env->NewStringUTF(new_token_chars.c_str());
+    cached_token_chars += new_token_chars;
+
+    jstring new_token = nullptr;
+    if (is_valid_utf8(cached_token_chars.c_str())) {
+        new_token = env->NewStringUTF(cached_token_chars.c_str());
+        LOGi("cached: %s, new_token_chars: `%s`, id: %d", cached_token_chars.c_str(), new_token_chars.c_str(), new_token_id);
+        cached_token_chars.clear();
+    } else {
+        new_token = env->NewStringUTF("");
+    }
 
     llama_batch_clear(*batch);
     llama_batch_add(*batch, new_token_id, n_cur, { 0 }, true);
diff --git a/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt b/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt
index 5f3270372..d86afee37 100644
--- a/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt
+++ b/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt
@@ -71,7 +71,7 @@ class Llm {
         batch: Long,
         nLen: Int,
         ncur: IntVar
-    ): String
+    ): String?
 
     private external fun kv_cache_clear(context: Long)
 
@@ -115,7 +115,7 @@ class Llm {
                 val ncur = IntVar(completion_init(state.context, state.batch, message, nlen))
                 while (ncur.value <= nlen) {
                     val str = completion_loop(state.context, state.batch, nlen, ncur)
-                    if (str.isEmpty()) {
+                    if (str == null) {
                         break
                     }
                     emit(str)
diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
index 58fcf40c6..737f882fb 100644
--- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
+++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
@@ -158,7 +158,7 @@ actor LlamaContext {
             new_token_id = llama_sample_token_greedy(context, &candidates_p)
         }
 
-        if new_token_id == llama_token_eos(model) || n_cur == n_len {
+        if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
             print("\n")
             let new_token_str = String(cString: temporary_invalid_cchars + [0])
             temporary_invalid_cchars.removeAll()
@@ -221,6 +221,7 @@ actor LlamaContext {
             if llama_decode(context, batch) != 0 {
                 print("llama_decode() failed during prompt")
             }
+            llama_synchronize(context)
 
             let t_pp_end = ggml_time_us()
 
@@ -240,6 +241,7 @@ actor LlamaContext {
                 if llama_decode(context, batch) != 0 {
                     print("llama_decode() failed during text generation")
                 }
+                llama_synchronize(context)
             }
 
             let t_tg_end = ggml_time_us()
@@ -320,7 +322,7 @@ actor LlamaContext {
         defer {
             result.deallocate()
         }
-        let nTokens = llama_token_to_piece(model, token, result, 8)
+        let nTokens = llama_token_to_piece(model, token, result, 8, false)
 
         if nTokens < 0 {
             let newResult = UnsafeMutablePointer<Int8>.allocate(capacity: Int(-nTokens))
@@ -328,7 +330,7 @@ actor LlamaContext {
             defer {
                 newResult.deallocate()
             }
-            let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens)
+            let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens, false)
             let bufferPointer = UnsafeBufferPointer(start: newResult, count: Int(nNewTokens))
             return Array(bufferPointer)
         } else {
diff --git a/examples/llava/MobileVLM-README.md b/examples/llava/MobileVLM-README.md
index 9eba791da..413e433dd 100644
--- a/examples/llava/MobileVLM-README.md
+++ b/examples/llava/MobileVLM-README.md
@@ -1,11 +1,13 @@
 # MobileVLM
 
-Currently this implementation supports [MobileVLM-v1.7](https://huggingface.co/mtgv/MobileVLM-1.7B) variants.
+Currently this implementation supports [MobileVLM-1.7B](https://huggingface.co/mtgv/MobileVLM-1.7B) / [MobileVLM_V2-1.7B](https://huggingface.co/mtgv/MobileVLM_V2-1.7B) variants.
 
 for more information, please go to [Meituan-AutoML/MobileVLM](https://github.com/Meituan-AutoML/MobileVLM)
 
 The implementation is based on llava, and is compatible with llava and mobileVLM. The usage is basically same as llava.
 
+Notice: The overall process of model inference for both **MobileVLM** and **MobileVLM_V2** models is the same, but the process of model conversion is a little different. Therefore, using **MobileVLM-1.7B** as an example, the different conversion step will be shown.
+
 ## Usage
 Build with cmake or run `make llava-cli` to build it.
 
@@ -20,7 +22,7 @@ After building, run: `./llava-cli` to see the usage. For example:
 
 ## Model conversion
 
-- Clone `mobileVLM-1.7B` and `clip-vit-large-patch14-336` locally:
+1. Clone `mobileVLM-1.7B` and `clip-vit-large-patch14-336` locally:
 
 ```sh
 git clone https://huggingface.co/mtgv/MobileVLM-1.7B
@@ -34,7 +36,7 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336
 python ./examples/llava/llava-surgery.py -m path/to/MobileVLM-1.7B
 ```
 
-3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` to convert the LLaVA image encoder to GGUF:
+3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` (for **V2** please use `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF:
 
 ```sh
 python ./examples/llava/convert-image-encoder-to-gguf \
@@ -44,6 +46,14 @@ python ./examples/llava/convert-image-encoder-to-gguf \
     --projector-type ldp
 ```
 
+```sh
+python ./examples/llava/convert-image-encoder-to-gguf \
+    -m path/to/clip-vit-large-patch14-336 \
+    --llava-projector path/to/MobileVLM-1.7B_V2/llava.projector \
+    --output-dir path/to/MobileVLM-1.7B_V2 \
+    --projector-type ldpv2
+```
+
 4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF:
 
 ```sh
@@ -68,7 +78,7 @@ cd examples/llava/android/build_64
 ### run on Android
 refer to `android/adb_run.sh`, modify resources' `name` and `path`
 
-## some result on Android with `Snapdragon 888` chip
+## Some result on Android with `Snapdragon 888` chip
 ### case 1
 **input**
 ```sh
@@ -99,7 +109,6 @@ llama_print_timings:       total time =   34731.93 ms
     --image /data/local/tmp/cat.jpeg \
     -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat is in the image? ASSISTANT:"
 ```
-
 **output**
 ```sh
 encode_image_with_clip: image encoded in 21149.51 ms by CLIP (  146.87 ms per image patch)
@@ -111,12 +120,82 @@ llama_print_timings:        eval time =    1279.03 ms /    18 runs   (   71.06 m
 llama_print_timings:       total time =   34570.79 ms
 ```
 
+
+## Some result on Android with `Snapdragon 778G` chip
+### MobileVLM-1.7B case
+#### llava-cli release-b2005
+**input**
+```sh
+/data/local/tmp/llava-cli \
+    -m /data/local/tmp/ggml-model-q4_k.gguf \
+    --mmproj /data/local/tmp/mmproj-model-f16.gguf \
+    -t 4 \
+    --image /data/local/tmp/many_llamas.jpeg \
+    -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat's that? ASSISTANT:"
+```
+**output**
+```sh
+encode_image_with_clip: image encoded in 18728.52 ms by CLIP (  130.06 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that? ASSISTANT:
+
+ A group of llamas are standing in a green pasture.
+
+llama_print_timings:        load time =   20357.33 ms
+llama_print_timings:      sample time =       2.96 ms /    14 runs   (    0.21 ms per token,  4734.53 tokens per second)
+llama_print_timings: prompt eval time =    8119.49 ms /   191 tokens (   42.51 ms per token,    23.52 tokens per second)
+llama_print_timings:        eval time =    1005.75 ms /    14 runs   (   71.84 ms per token,    13.92 tokens per second)
+llama_print_timings:       total time =   28038.34 ms /   205 tokens
+```
+#### llava-cli latest-version
+**input**
+
+Just the same as above.
+
+**output**(seems to be much slower)
+```sh
+encode_image_with_clip: image embedding created: 144 tokens
+
+encode_image_with_clip: image encoded in 288268.88 ms by CLIP ( 2001.87 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that? ASSISTANT:
+
+ It is a group of sheep standing together in a grass field.
+
+llama_print_timings:        load time =  818120.91 ms
+llama_print_timings:      sample time =       3.44 ms /    14 runs   (    0.25 ms per token,  4067.40 tokens per second)
+llama_print_timings: prompt eval time =  529274.69 ms /   191 tokens ( 2771.07 ms per token,     0.36 tokens per second)
+llama_print_timings:        eval time =   43894.02 ms /    13 runs   ( 3376.46 ms per token,     0.30 tokens per second)
+llama_print_timings:       total time =  865441.76 ms /   204 tokens
+```
+### MobileVLM_V2-1.7B case
+#### llava-cli release-2005b
+**input**
+
+Just the same as above.
+
+**output**
+```sh
+encode_image_with_clip: image encoded in 20609.61 ms by CLIP (  143.12 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that? ASSISTANT:
+
+ This image captures a lively scene of 20 llamas in motion on an expansive, grassy field. The llama is scattered across the landscape with some standing and others sitting down as if taking rest or observing their surroundings from different vantage points within this verdant setting.
+
+The background offers glimpses into a picturesque town nestled amidst hills under an overcast sky, adding depth to the scene while also emphasizing that distance between these llama and human-made structures like houses or roads in which they roam freely without any barriers around them. The image is framed by text at both right angles on white backgrounds against a contrasting blue backdrop with green foliage, further drawing attention to the llamas amidst their natural habitat while also inviting viewers into this picturesque landscape within town limits of Alta Llama
+
+llama_print_timings:        load time =   22406.77 ms
+llama_print_timings:      sample time =      49.26 ms /   186 runs   (    0.26 ms per token,  3776.27 tokens per second)
+llama_print_timings: prompt eval time =    9044.54 ms /   191 tokens (   47.35 ms per token,    21.12 tokens per second)
+llama_print_timings:        eval time =   14497.49 ms /   186 runs   (   77.94 ms per token,    12.83 tokens per second)
+llama_print_timings:       total time =   44411.01 ms /   377 tokens
+```
+
 ## Orin compile and run
 ### compile
 ```sh
-make LLAMA_CUBLAS=1 CUDA_DOCKER_ARCH=sm_87 LLAMA_CUDA_F16=1 -j 32
+make LLAMA_CUDA=1 CUDA_DOCKER_ARCH=sm_87 LLAMA_CUDA_F16=1 -j 32
 ```
-
 ### run on Orin
 ### case 1
 **input**
@@ -165,8 +244,121 @@ llama_print_timings:        eval time =     166.65 ms /    11 runs   (   15.15 m
 llama_print_timings:       total time =    1365.47 ms /   243 tokens
 ```
 
-## Minor shortcomings
-The `n_patch` of output in `ldp` is 1/4 of the input. In order to implement quickly, we uniformly modified `clip_n_patches` function to a quarter. when counting the time consumption, the calculated time will be 4 times bigger than the real cost.
+## Running on Intel(R) Core(TM) i7-10750H
+### Operating system
+Ubuntu22.04
+### compile
+```sh
+make -j32
+```
+### MobileVLM-1.7B case
+**input**
+```sh
+-m /path/to/ggml-model-q4_k.gguf \
+    --mmproj /path/to/mmproj-model-f16.gguf \
+    --image /path/to/many_llamas.jpeg
+    -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat's that? ASSISTANT:" \
+```
+**output**
+```sh
+encode_image_with_clip: image embedding created: 144 tokens
+
+encode_image_with_clip: image encoded in  2730.94 ms by CLIP (   18.96 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that?ASSISTANT:
+
+ A group of llamas are walking together in a field.
+
+llama_print_timings:        load time =    5506.60 ms
+llama_print_timings:      sample time =       0.44 ms /    13 runs   (    0.03 ms per token, 29545.45 tokens per second)
+llama_print_timings: prompt eval time =    2031.58 ms /   190 tokens (   10.69 ms per token,    93.52 tokens per second)
+llama_print_timings:        eval time =     438.92 ms /    12 runs   (   36.58 ms per token,    27.34 tokens per second)
+llama_print_timings:       total time =    5990.25 ms /   202 tokens
+```
+
+### MobileVLM_V2-1.7B case
+**input**
+
+Just the same as above.
+
+**ouput**
+```sh
+encode_image_with_clip: image embedding created: 144 tokens
+
+encode_image_with_clip: image encoded in  3223.89 ms by CLIP (   22.39 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that?ASSISTANT:
+
+ The image captures a tranquil scene in a park, where a group of approximately 20 llamas are gathered. The llamas, a mix of white and black, are standing in a line, their black and white patterns contrasting with the lush green grass of the park. The lamas are arranged in a line, suggesting a social order.
+
+The park itself is lush and green, with trees dotting the landscape in the background. A sign reading "Llamas Tico  Ana" is also visible in the image, possibly indicating the location or the breed of the llamas. The image seems to be taken from a distance, providing a wide view of the scene and the surrounding environment.
+
+The llamas' positions relative to each other, the sign, and the trees create a harmonious composition. The image does not contain any discernible text. The overall scene is one of peace and natural beauty, with the llamas in their natural habitat, surrounded by the vibrant colors and lush greenery of the park.
+
+llama_print_timings:        load time =    6642.61 ms
+llama_print_timings:      sample time =       8.15 ms /   223 runs   (    0.04 ms per token, 27358.61 tokens per second)
+llama_print_timings: prompt eval time =    2475.07 ms /   190 tokens (   13.03 ms per token,    76.77 tokens per second)
+llama_print_timings:        eval time =    8760.60 ms /   222 runs   (   39.46 ms per token,    25.34 tokens per second)
+llama_print_timings:       total time =   15513.95 ms /   412 tokens
+```
+
+## Run on Intel(R) Core(TM) Ultra7 115H
+### operation system
+Windows11
+### comiple
+```sh
+make -j32
+```
+### MobileVLM-1.7B case
+**input**
+```sh
+-m /path/to/ggml-model-q4_k.gguf \
+    --mmproj /path/to/tmp/mmproj-model-f16.gguf \
+    -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat's that? ASSISTANT:" \
+```
+**output**
+```sh
+encode_image_with_clip: image encoded in  4902.81 ms by CLIP (   34.05 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that? ASSISTANT:
+
+ The image features a group of brown and white llamas standing in a grassy field.
+
+llama_print_timings:        load time =    7441.06 ms
+llama_print_timings:      sample time =       0.72 ms /    19 runs   (    0.04 ms per token, 26279.39 tokens per second)
+llama_print_timings: prompt eval time =    2090.71 ms /   191 tokens (   10.95 ms per token,    91.36 tokens per second)
+llama_print_timings:        eval time =     512.35 ms /    18 runs   (   28.46 ms per token,    35.13 tokens per second)
+llama_print_timings:       total time =    7987.23 ms /   209 tokens
+```
+
+### MobileVLM_V2-1.7B case
+**input**
+
+Just the same as above.
+
+**output**
+```sh
+encode_image_with_clip: image encoded in  4682.44 ms by CLIP (   32.52 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that? ASSISTANT:
+
+ This image captures a lively scene of a group of 14 llamas in a grassy field. The llamas, with their distinctive black and white coats, are standing and walking in a line, seemingly engaged in a social activity. One
+ of them, possibly the first in the line, has its back turned, perhaps observing something in the distance.
+
+The llama in the front of the line stands out due to its black and white coloring, which is quite unusual for llama patterns. The llama in the front also seems to be more aware of its surroundings, as it faces the camera, giving a sense of engagement with the viewer.
+
+The image is taken from the side of the llama, providing a clear view of the llama in the front and its companions. The lameness in the llama in
+ front is not visible, indicating that it might not be the main focus of the photo.
+
+The background of the image features a grassy field, with a fence and a tree visible in the distance. The tree appears to be bare, suggesting that it might be during a time of year when most trees are dormant or have shed their leaves.
+
+
+llama_print_timings:        load time =    7015.35 ms
+llama_print_timings:      sample time =      10.61 ms /   256 runs   (    0.04 ms per token, 24119.09 tokens per second)
+llama_print_timings: prompt eval time =    2052.45 ms /   191 tokens (   10.75 ms per token,    93.06 tokens per second)
+llama_print_timings:        eval time =    7259.43 ms /   255 runs   (   28.47 ms per token,    35.13 tokens per second)
+llama_print_timings:       total time =   14371.19 ms /   446 tokens
+```
 
 ## TODO
 
@@ -181,5 +373,5 @@ The `n_patch` of output in `ldp` is 1/4 of the input. In order to implement quic
 
 ## contributor
 ```sh
-zhangjidong05, yangyang260, huyiming03, chenxiaotao03
+zhangjidong05, yangyang260, huyiming03, chenxiaotao03, ZiangWu-77
 ```
diff --git a/examples/llava/README.md b/examples/llava/README.md
index e42db6e5a..4fb0cf381 100644
--- a/examples/llava/README.md
+++ b/examples/llava/README.md
@@ -24,7 +24,7 @@ After building, run: `./llava-cli` to see the usage. For example:
 
 ## LLaVA 1.5
 
-- Clone a LLaVA and a CLIP model ([available options](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)). For example:
+1. Clone a LLaVA and a CLIP model ([available options](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)). For example:
 
 ```sh
 git clone https://huggingface.co/liuhaotian/llava-v1.5-7b
@@ -56,17 +56,50 @@ python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-pa
 python ./convert.py ../llava-v1.5-7b --skip-unknown
 ```
 
-Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` directory.
+Now both the LLaMA part and the image encoder are in the `llava-v1.5-7b` directory.
 
 ## LLaVA 1.6 gguf conversion
+1) First clone a LLaVA 1.6 model:
+```console
+git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b
+```
 
-1) Backup your pth/safetensor model files as llava-surgery modifies them
-2) Use `python llava-surgery-v2.py -C -m /path/to/hf-model` which also supports llava-1.5 variants pytorch as well as safetensor models:
+2) Install the required Python packages:
+
+```sh
+pip install -r examples/llava/requirements.txt
+```
+
+3) Use `llava-surgery-v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models:
+```console
+python examples/llava/llava-surgery-v2.py -C -m ../llava-v1.6-vicuna-7b/
+```
 - you will find a llava.projector and a llava.clip file in your model directory
-3) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory (https://huggingface.co/cmp-nct/llava-1.6-gguf/blob/main/config_vit.json) and rename it to config.json.
-4) Create the visual gguf model: `python ./examples/llava/convert-image-encoder-to-gguf.py -m ../path/to/vit --llava-projector ../path/to/llava.projector --output-dir ../path/to/output --clip-model-is-vision`
+
+4) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory:
+```console
+mkdir vit
+cp ../llava-v1.6-vicuna-7b/llava.clip vit/pytorch_model.bin
+cp ../llava-v1.6-vicuna-7b/llava.projector vit/
+curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.json -o vit/config.json
+```
+
+5) Create the visual gguf model:
+```console
+python ./examples/llava/convert-image-encoder-to-gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision
+```
 - This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP
-5) Everything else as usual: convert.py the hf model, quantize as needed
+
+6) Then convert the model to gguf format:
+```console
+python ./convert.py ../llava-v1.6-vicuna-7b/ --skip-unknown
+```
+
+7) And finally we can run the llava-cli using the 1.6 model version:
+```console
+./llava-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf --image some-image.jpg -c 4096
+```
+
 **note** llava-1.6 needs more context than llava-1.5, at least 3000 is needed (just run it at -c 4096)
 **note** llava-1.6 greatly benefits from batched prompt processing (defaults work)
 
diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index ef9e4ba7a..95fbe3d02 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -3,11 +3,12 @@
 // I'll gradually clean and extend it
 // Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch
 #include "clip.h"
+#include "log.h"
 #include "ggml.h"
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
 
-#ifdef GGML_USE_CUBLAS
+#ifdef GGML_USE_CUDA
 #include "ggml-cuda.h"
 #endif
 
@@ -23,7 +24,6 @@
 #include <cstdlib>
 #include <cstring>
 #include <fstream>
-#include <iostream>
 #include <map>
 #include <regex>
 #include <stdexcept>
@@ -104,6 +104,7 @@ static std::string format(const char * fmt, ...) {
 #define TN_POS_EMBD        "%s.position_embd.weight"
 #define TN_CLASS_EMBD      "v.class_embd"
 #define TN_PATCH_EMBD      "v.patch_embd.weight"
+#define TN_PATCH_BIAS      "v.patch_embd.bias"
 #define TN_ATTN_K          "%s.blk.%d.attn_k.%s"
 #define TN_ATTN_Q          "%s.blk.%d.attn_q.%s"
 #define TN_ATTN_V          "%s.blk.%d.attn_v.%s"
@@ -119,6 +120,7 @@ static std::string format(const char * fmt, ...) {
 #define TN_LLAVA_PROJ      "mm.%d.%s"
 #define TN_MVLM_PROJ_MLP   "mm.model.mlp.%d.%s"
 #define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s"
+#define TN_MVLM_PROJ_PEG   "mm.model.peg.%d.%s"
 #define TN_IMAGE_NEWLINE   "model.image_newline"
 
 
@@ -126,12 +128,14 @@ enum projector_type {
     PROJECTOR_TYPE_MLP,
     PROJECTOR_TYPE_MLP_NORM,
     PROJECTOR_TYPE_LDP,
+    PROJECTOR_TYPE_LDPV2,
     PROJECTOR_TYPE_UNKNOWN,
 };
 
 static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_MLP, "mlp" },
     { PROJECTOR_TYPE_LDP, "ldp" },
+    { PROJECTOR_TYPE_LDPV2, "ldpv2"},
 };
 
 
@@ -142,7 +146,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
 static int get_key_idx(const gguf_context * ctx, const char * key) {
     int i = gguf_find_key(ctx, key);
     if (i == -1) {
-        fprintf(stderr, "key %s not found in file\n", key);
+        LOG_TEE("key %s not found in file\n", key);
         throw std::runtime_error(format("Missing required key: %s", key));
     }
 
@@ -244,7 +248,7 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
 
 static void print_tensor_info(const ggml_tensor * tensor, const char * prefix = "") {
     size_t tensor_size = ggml_nbytes(tensor);
-    printf("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n",
+    LOG_TEE("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n",
             prefix, ggml_n_dims(tensor), tensor->name, tensor_size,
             tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], ggml_type_name(tensor->type));
 }
@@ -262,7 +266,7 @@ static projector_type clip_projector_type_from_string(const std::string & name)
 static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::string& filename) {
     std::ofstream file(filename, std::ios::binary);
     if (!file.is_open()) {
-        std::cerr << "Failed to open file for writing: " << filename << std::endl;
+        LOG_TEE("Failed to open file for writing: %s\n", filename.c_str());
         return;
     }
 
@@ -281,7 +285,7 @@ static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::s
 static void clip_image_save_to_bmp(const clip_image_u8& img, const std::string& filename) {
     std::ofstream file(filename, std::ios::binary);
     if (!file.is_open()) {
-        std::cerr << "Failed to open file for writing: " << filename << std::endl;
+        LOG_TEE("Failed to open file for writing: %s\n", filename.c_str());
         return;
     }
 
@@ -422,6 +426,7 @@ struct clip_vision_model {
     // embeddings
     struct ggml_tensor * class_embedding;
     struct ggml_tensor * patch_embeddings;
+    struct ggml_tensor * patch_bias;
     struct ggml_tensor * position_embeddings;
 
     struct ggml_tensor * pre_ln_w;
@@ -475,6 +480,14 @@ struct clip_vision_model {
     struct ggml_tensor * mm_model_block_2_block_2_0_w;
     struct ggml_tensor * mm_model_block_2_block_2_1_w;
     struct ggml_tensor * mm_model_block_2_block_2_1_b;
+
+    // MobileVLM_V2 projection
+    struct ggml_tensor * mm_model_mlp_0_w;
+    struct ggml_tensor * mm_model_mlp_0_b;
+    struct ggml_tensor * mm_model_mlp_2_w;
+    struct ggml_tensor * mm_model_mlp_2_b;
+    struct ggml_tensor * mm_model_peg_0_w;
+    struct ggml_tensor * mm_model_peg_0_b;
 };
 
 struct clip_ctx {
@@ -490,6 +503,11 @@ struct clip_ctx {
     bool use_gelu = false;
     int32_t ftype = 1;
 
+    bool has_class_embedding = true;
+    bool has_pre_norm = true;
+    bool has_post_norm = false;
+    bool has_patch_bias = false;
+
     struct gguf_context * ctx_gguf;
     struct ggml_context * ctx_data;
 
@@ -497,7 +515,6 @@ struct clip_ctx {
 
     // memory buffers to evaluate the model
     ggml_backend_buffer_t params_buffer  = NULL;
-    ggml_backend_buffer_t compute_buffer = NULL;
 
     ggml_backend_t backend       = NULL;
     ggml_gallocr_t compute_alloc = NULL;
@@ -505,7 +522,7 @@ struct clip_ctx {
 
 static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs) {
     if (!ctx->has_vision_encoder) {
-        printf("This gguf file seems to have no vision encoder\n");
+        LOG_TEE("This gguf file seems to have no vision encoder\n");
         return nullptr;
     }
 
@@ -516,7 +533,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     const int patch_size           = hparams.patch_size;
     const int num_patches          = ((image_size / patch_size) * (image_size / patch_size));
     const int num_patches_per_side = image_size / patch_size; GGML_UNUSED(num_patches_per_side);
-    const int num_positions        = num_patches + 1;
+    const int num_positions        = num_patches + (ctx->has_class_embedding ? 1 : 0);
     const int hidden_size          = hparams.hidden_size;
     const int n_head               = hparams.n_head;
     const int d_head               = hidden_size / n_head;
@@ -547,16 +564,23 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size);
     inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3));
 
+    if (ctx->has_patch_bias) {
+        // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp));
+        inp = ggml_add(ctx0, inp, model.patch_bias);
+    }
+
     // concat class_embeddings and patch_embeddings
-    struct ggml_tensor * embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size);
-    ggml_set_name(embeddings, "embeddings");
-    ggml_set_input(embeddings);
+    struct ggml_tensor * embeddings = inp;
+    if (ctx->has_class_embedding) {
+        embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size);
+        ggml_set_name(embeddings, "embeddings");
+        ggml_set_input(embeddings);
+        embeddings = ggml_acc(ctx0, embeddings, model.class_embedding,
+                embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0);
+        embeddings = ggml_acc(ctx0, embeddings, inp,
+                embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]);
+    }
 
-    embeddings = ggml_acc(ctx0, embeddings, model.class_embedding,
-            embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0);
-
-    embeddings = ggml_acc(ctx0, embeddings, inp,
-            embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]);
 
     struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions);
     ggml_set_name(positions, "positions");
@@ -566,7 +590,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
         ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions));
 
     // pre-layernorm
-    {
+    if (ctx->has_pre_norm) {
         embeddings = ggml_norm(ctx0, embeddings, eps);
         ggml_set_name(embeddings, "pre_ln");
 
@@ -654,6 +678,14 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
         embeddings = cur;
     }
 
+    // post-layernorm
+    if (ctx->has_post_norm) {
+        embeddings = ggml_norm(ctx0, embeddings, eps);
+        ggml_set_name(embeddings, "post_ln");
+
+        embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b);
+    }
+
     // llava projector
     {
         embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]);
@@ -808,6 +840,30 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             }
             embeddings = block_1;
         }
+        else if (ctx->proj_type == PROJECTOR_TYPE_LDPV2)
+        {
+            int n_patch = 24;
+            struct ggml_tensor * mlp_0 = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings);
+            mlp_0 = ggml_add(ctx0, mlp_0, model.mm_model_mlp_0_b);
+            mlp_0 = ggml_gelu(ctx0, mlp_0);
+            struct ggml_tensor * mlp_2 = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, mlp_0);
+            mlp_2 = ggml_add(ctx0, mlp_2, model.mm_model_mlp_2_b);
+            // mlp_2 ne = [2048, 576, 1, 1]
+            // // AVG Pool Layer 2*2, strides = 2
+            mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 0, 2, 3));
+            // mlp_2 ne = [576, 2048, 1, 1]
+            mlp_2 = ggml_reshape_4d(ctx0, mlp_2, n_patch, n_patch, mlp_2->ne[1], mlp_2->ne[2]);
+            // mlp_2 ne [24, 24, 2048, 1]
+            mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0);
+            // weight ne = [3, 3, 2048, 1]
+            struct ggml_tensor * peg_0 = ggml_conv_depthwise_2d(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1);
+            peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3));
+            peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b);
+            mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 2, 0, 3));
+            peg_0 = ggml_add(ctx0, peg_0, mlp_2);
+            peg_0 = ggml_reshape_3d(ctx0, peg_0, peg_0->ne[0], peg_0->ne[1] * peg_0->ne[2], peg_0->ne[3]);
+            embeddings = peg_0;
+        }
         else {
             GGML_ASSERT(false);
         }
@@ -845,21 +901,21 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         const int idx_name = gguf_find_key(ctx, KEY_NAME);
         if (idx_name != -1) { // make name optional temporarily as some of the uploaded models missing it due to a bug
             const std::string name = gguf_get_val_str(ctx, idx_name);
-            printf("%s: model name:   %s\n", __func__, name.c_str());
+            LOG_TEE("%s: model name:   %s\n", __func__, name.c_str());
         }
-        printf("%s: description:  %s\n", __func__, description.c_str());
-        printf("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx));
-        printf("%s: alignment:    %zu\n", __func__, gguf_get_alignment(ctx));
-        printf("%s: n_tensors:    %d\n", __func__, n_tensors);
-        printf("%s: n_kv:         %d\n", __func__, n_kv);
-        printf("%s: ftype:        %s\n", __func__, ftype_str.c_str());
-        printf("\n");
+        LOG_TEE("%s: description:  %s\n", __func__, description.c_str());
+        LOG_TEE("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx));
+        LOG_TEE("%s: alignment:    %zu\n", __func__, gguf_get_alignment(ctx));
+        LOG_TEE("%s: n_tensors:    %d\n", __func__, n_tensors);
+        LOG_TEE("%s: n_kv:         %d\n", __func__, n_kv);
+        LOG_TEE("%s: ftype:        %s\n", __func__, ftype_str.c_str());
+        LOG_TEE("\n");
     }
     const int n_tensors = gguf_get_n_tensors(ctx);
 
     // kv
     const int n_kv = gguf_get_n_kv(ctx);
-    printf("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n",
+    LOG_TEE("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n",
         __func__, n_kv, n_tensors, fname);
     {
         std::map<enum ggml_type, uint32_t> n_type;
@@ -870,7 +926,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             n_type[type]++;
         }
 
-        printf("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
+        LOG_TEE("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
         for (int i = 0; i < n_kv; i++) {
             const char * name           = gguf_get_key(ctx, i);
             const enum gguf_type type   = gguf_get_kv_type(ctx, i);
@@ -886,7 +942,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             }
             replace_all(value, "\n", "\\n");
 
-            printf("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
+            LOG_TEE("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
         }
 
         // print type counts
@@ -895,7 +951,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
                 continue;
             }
 
-            printf("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
+            LOG_TEE("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
         }
     }
 
@@ -910,7 +966,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             size_t tensor_size = ggml_nbytes(cur);
             model_size += tensor_size;
             if (verbosity >= 3) {
-                printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
+                LOG_TEE("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
                        __func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type));
             }
         }
@@ -935,20 +991,20 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         }
     }
 
-#ifdef GGML_USE_CUBLAS
+#ifdef GGML_USE_CUDA
     new_clip->backend = ggml_backend_cuda_init(0);
-    printf("%s: CLIP using CUDA backend\n", __func__);
+    LOG_TEE("%s: CLIP using CUDA backend\n", __func__);
 #endif
 
 #ifdef GGML_USE_METAL
     new_clip->backend = ggml_backend_metal_init();
-    printf("%s: CLIP using Metal backend\n", __func__);
+    LOG_TEE("%s: CLIP using Metal backend\n", __func__);
 #endif
 
 
     if (!new_clip->backend) {
         new_clip->backend = ggml_backend_cpu_init();
-        printf("%s: CLIP using CPU backend\n", __func__);
+        LOG_TEE("%s: CLIP using CPU backend\n", __func__);
     }
 
     // model size and capabilities
@@ -972,15 +1028,15 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         new_clip->use_gelu = gguf_get_val_bool(ctx, idx);
 
         if (verbosity >= 1) {
-            printf("%s: text_encoder:   %d\n", __func__, new_clip->has_text_encoder);
-            printf("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
-            printf("%s: llava_projector:  %d\n", __func__, new_clip->has_llava_projector);
-            printf("%s: model size:     %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
-            printf("%s: metadata size:  %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
+            LOG_TEE("%s: text_encoder:   %d\n", __func__, new_clip->has_text_encoder);
+            LOG_TEE("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
+            LOG_TEE("%s: llava_projector:  %d\n", __func__, new_clip->has_llava_projector);
+            LOG_TEE("%s: model size:     %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
+            LOG_TEE("%s: metadata size:  %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
         }
     }
 
-    printf("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors);
+    LOG_TEE("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors);
 
     // load tensors
     {
@@ -993,15 +1049,17 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
 
         new_clip->ctx_data = ggml_init(params);
         if (!new_clip->ctx_data) {
-            fprintf(stderr, "%s: ggml_init() failed\n", __func__);
+            LOG_TEE("%s: ggml_init() failed\n", __func__);
             clip_free(new_clip);
+            gguf_free(ctx);
             return nullptr;
         }
 
         auto fin = std::ifstream(fname, std::ios::binary);
         if (!fin) {
-            printf("cannot open model file for loading tensors\n");
+            LOG_TEE("cannot open model file for loading tensors\n");
             clip_free(new_clip);
+            gguf_free(ctx);
             return nullptr;
         }
 
@@ -1021,8 +1079,9 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i);
             fin.seekg(offset, std::ios::beg);
             if (!fin) {
-                printf("%s: failed to seek for tensor %s\n", __func__, name);
+                LOG_TEE("%s: failed to seek for tensor %s\n", __func__, name);
                 clip_free(new_clip);
+                gguf_free(ctx);
                 return nullptr;
             }
             int num_bytes = ggml_nbytes(cur);
@@ -1091,34 +1150,61 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         }
 
         if (verbosity >= 2) {
-            printf("\n%s: vision model hparams\n", __func__);
-            printf("image_size         %d\n", hparams.image_size);
-            printf("patch_size         %d\n", hparams.patch_size);
-            printf("v_hidden_size      %d\n", hparams.hidden_size);
-            printf("v_n_intermediate   %d\n", hparams.n_intermediate);
-            printf("v_projection_dim   %d\n", hparams.projection_dim);
-            printf("v_n_head           %d\n", hparams.n_head);
-            printf("v_n_layer          %d\n", hparams.n_layer);
-            printf("v_eps              %f\n", hparams.eps);
-            printf("v_image_mean       %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]);
-            printf("v_image_std        %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]);
-            printf("v_image_grid_pinpoints: ");
+            LOG_TEE("\n%s: vision model hparams\n", __func__);
+            LOG_TEE("image_size         %d\n", hparams.image_size);
+            LOG_TEE("patch_size         %d\n", hparams.patch_size);
+            LOG_TEE("v_hidden_size      %d\n", hparams.hidden_size);
+            LOG_TEE("v_n_intermediate   %d\n", hparams.n_intermediate);
+            LOG_TEE("v_projection_dim   %d\n", hparams.projection_dim);
+            LOG_TEE("v_n_head           %d\n", hparams.n_head);
+            LOG_TEE("v_n_layer          %d\n", hparams.n_layer);
+            LOG_TEE("v_eps              %f\n", hparams.eps);
+            LOG_TEE("v_image_mean       %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]);
+            LOG_TEE("v_image_std        %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]);
+            LOG_TEE("v_image_grid_pinpoints: ");
             for (int i = 0; i < 32 && (hparams.image_grid_pinpoints[i] != 0); ++i) {
-                printf("%d ", hparams.image_grid_pinpoints[i]);
+                LOG_TEE("%d ", hparams.image_grid_pinpoints[i]);
             }
-            printf("\n");
-            printf("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type);
+            LOG_TEE("\n");
+            LOG_TEE("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type);
 
         }
 
+        try {
+            vision_model.class_embedding  = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD);
+            new_clip->has_class_embedding = true;
+        } catch (const std::exception& e) {
+            new_clip->has_class_embedding = false;
+        }
+
+        try {
+            vision_model.pre_ln_w  = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
+            vision_model.pre_ln_b  = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
+            new_clip->has_pre_norm = true;
+        } catch (std::exception & e) {
+            new_clip->has_pre_norm = false;
+        }
+
+        try {
+            vision_model.post_ln_w  = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "weight"));
+            vision_model.post_ln_b  = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "bias"));
+            new_clip->has_post_norm = true;
+        } catch (std::exception & e) {
+            new_clip->has_post_norm = false;
+        }
+
+        try {
+            vision_model.patch_bias = get_tensor(new_clip->ctx_data, TN_PATCH_BIAS);
+            new_clip->has_patch_bias = true;
+        } catch (std::exception & e) {
+            new_clip->has_patch_bias = false;
+        }
+
         try {
             vision_model.patch_embeddings    = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
-            vision_model.class_embedding     = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD);
             vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v"));
-            vision_model.pre_ln_w            = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
-            vision_model.pre_ln_b            = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
         } catch(const std::exception& e) {
-            fprintf(stderr, "%s: failed to load vision model tensors\n", __func__);
+            LOG_TEE("%s: failed to load vision model tensors\n", __func__);
         }
 
         // LLaVA projection
@@ -1147,7 +1233,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             } catch (std::runtime_error & e) {  }
             try {
                 vision_model.image_newline = get_tensor(new_clip->ctx_data, TN_IMAGE_NEWLINE);
-                // fprintf(stderr, "%s: image_newline tensor (llava-1.6) found\n", __func__);
+                // LOG_TEE("%s: image_newline tensor (llava-1.6) found\n", __func__);
             } catch (std::runtime_error & e) {  }
         } else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) {
             // MobileVLM projection
@@ -1175,7 +1261,18 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             vision_model.mm_model_block_2_block_2_0_w   = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "0.weight"));
             vision_model.mm_model_block_2_block_2_1_w   = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.weight"));
             vision_model.mm_model_block_2_block_2_1_b   = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.bias"));
-        } else {
+        }
+        else if (new_clip->proj_type == PROJECTOR_TYPE_LDPV2)
+        {
+            // MobilVLM_V2 projection
+            vision_model.mm_model_mlp_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 0, "weight"));
+            vision_model.mm_model_mlp_0_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 0, "bias"));
+            vision_model.mm_model_mlp_2_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 2, "weight"));
+            vision_model.mm_model_mlp_2_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 2, "bias"));
+            vision_model.mm_model_peg_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_PEG, 0, "weight"));
+            vision_model.mm_model_peg_0_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_PEG, 0, "bias"));
+        }
+        else {
             std::string proj_type = PROJECTOR_TYPE_NAMES[new_clip->proj_type];
             throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
         }
@@ -1216,7 +1313,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch);
         ggml_gallocr_reserve(new_clip->compute_alloc, gf);
         size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0);
-        printf("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
+        LOG_TEE("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
     }
 
     return new_clip;
@@ -1232,16 +1329,16 @@ struct clip_image_f32 * clip_image_f32_init() {
 
 void clip_image_u8_free(struct clip_image_u8  * img) { delete img; }
 void clip_image_f32_free(struct clip_image_f32 * img) { delete img; }
-void clip_image_u8_batch_free(struct clip_image_u8_batch  & batch) {
-    if (batch.size > 0) {
-        delete[] batch.data;
-        batch.size = 0;
+void clip_image_u8_batch_free(struct clip_image_u8_batch  * batch) {
+    if (batch->size > 0) {
+        delete[] batch->data;
+        batch->size = 0;
     }
 }
-void clip_image_f32_batch_free(struct clip_image_f32_batch  & batch) {
-    if (batch.size > 0) {
-        delete[] batch.data;
-        batch.size = 0;
+void clip_image_f32_batch_free(struct clip_image_f32_batch  * batch) {
+    if (batch->size > 0) {
+        delete[] batch->data;
+        batch->size = 0;
     }
 }
 
@@ -1256,7 +1353,7 @@ bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) {
     int nx, ny, nc;
     auto * data = stbi_load(fname, &nx, &ny, &nc, 3);
     if (!data) {
-        fprintf(stderr, "%s: failed to load image '%s'\n", __func__, fname);
+        LOG_TEE("%s: failed to load image '%s'\n", __func__, fname);
         return false;
     }
     build_clip_img_from_data(data, nx, ny, img);
@@ -1268,7 +1365,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length
     int nx, ny, nc;
     auto * data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3);
     if (!data) {
-        fprintf(stderr, "%s: failed to decode image bytes\n", __func__);
+        LOG_TEE("%s: failed to decode image bytes\n", __func__);
         return false;
     }
     build_clip_img_from_data(data, nx, ny, img);
@@ -1277,7 +1374,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length
 }
 
 // Linear interpolation between two points
-inline float lerp(float s, float e, float t) {
+inline float clip_lerp(float s, float e, float t) {
     return s + (e - s) * t;
 }
 // Bilinear resize function
@@ -1299,17 +1396,17 @@ static void bilinear_resize(const clip_image_u8& src, clip_image_u8& dst, int ta
             float y_lerp = py - y_floor;
 
             for (int c = 0; c < 3; c++) {
-                float top = lerp(
+                float top = clip_lerp(
                     static_cast<float>(src.buf[3 * (y_floor * src.nx + x_floor) + c]),
                     static_cast<float>(src.buf[3 * (y_floor * src.nx + (x_floor + 1)) + c]),
                     x_lerp
                 );
-                float bottom = lerp(
+                float bottom = clip_lerp(
                     static_cast<float>(src.buf[3 * ((y_floor + 1) * src.nx + x_floor) + c]),
                     static_cast<float>(src.buf[3 * ((y_floor + 1) * src.nx + (x_floor + 1)) + c]),
                     x_lerp
                 );
-                dst.buf[3 * (y * target_width + x) + c] = static_cast<uint8_t>(lerp(top, bottom, y_lerp));
+                dst.buf[3 * (y * target_width + x) + c] = static_cast<uint8_t>(clip_lerp(top, bottom, y_lerp));
             }
         }
     }
@@ -1458,7 +1555,7 @@ static std::pair<int, int> select_best_resolution(const std::pair<int, int> & or
         int downscaled_height = static_cast<int>(original_height * scale);
         int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
         int wasted_resolution = (width * height) - effective_resolution;
-        // fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
+        // LOG_TEE("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
         if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
             max_effective_resolution = effective_resolution;
             min_wasted_resolution = wasted_resolution;
@@ -1494,10 +1591,10 @@ static std::vector<clip_image_u8*> divide_to_patches_u8(const clip_image_u8 & im
 
 // returns the normalized float tensor for llava-1.5, for spatial_unpad with anyres processing for llava-1.6 it returns the normalized image patch tensors as a vector
 // res_imgs memory is being allocated here, previous allocations will be freed if found
-bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch & res_imgs) {
+bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch * res_imgs) {
     bool pad_to_square = true;
     if (!ctx->has_vision_encoder) {
-        printf("This gguf file seems to have no vision encoder\n");
+        LOG_TEE("This gguf file seems to have no vision encoder\n");
         return false;
     }
     auto & params = ctx->vision_model.hparams;
@@ -1506,11 +1603,11 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
         pad_to_square = false;
     }
     // free the previous res_imgs if any set
-    if (res_imgs.size > 0) {
+    if (res_imgs->size > 0) {
         clip_image_f32_batch_free(res_imgs);
     }
-    res_imgs.data = nullptr;
-    res_imgs.size = 0;
+    res_imgs->data = nullptr;
+    res_imgs->size = 0;
 
     // the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104)
     // see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156
@@ -1565,16 +1662,16 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
             bicubic_resize(*img, *image_original_resize, params.image_size, params.image_size); // in python this is "shortest_edge", but all CLIP are square
             patches.insert(patches.begin(), image_original_resize);
             // clip_image_f32_batch_init(patches.size());
-            res_imgs.size = patches.size();
-            res_imgs.data = new clip_image_f32[res_imgs.size];
+            res_imgs->size = patches.size();
+            res_imgs->data = new clip_image_f32[res_imgs->size];
             int num=0;
             for (auto& patch : patches) {
-                normalize_image_u8_to_f32(patch, &res_imgs.data[num], ctx->image_mean, ctx->image_std);
+                normalize_image_u8_to_f32(patch, &res_imgs->data[num], ctx->image_mean, ctx->image_std);
                 num++;
             }
 
             for (size_t i = 0; i < patches.size(); i++) {
-                // printf("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny);
+                // LOG_TEE("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny);
                 clip_image_u8_free(patches[i]);
             }
 
@@ -1657,9 +1754,9 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
     // }
     // res_imgs.push_back(res);
 
-    res_imgs.size = 1;
-    res_imgs.data = new clip_image_f32[res_imgs.size];
-    res_imgs.data[0] = *res;
+    res_imgs->size = 1;
+    res_imgs->data = new clip_image_f32[res_imgs->size];
+    res_imgs->data[0] = *res;
     clip_image_f32_free(res);
 
     return true;
@@ -1673,6 +1770,9 @@ void clip_free(clip_ctx * ctx) {
     ggml_free(ctx->ctx_data);
     gguf_free(ctx->ctx_gguf);
 
+    ggml_backend_buffer_free(ctx->params_buffer);
+    ggml_backend_free(ctx->backend);
+    ggml_gallocr_free(ctx->compute_alloc);
     delete ctx;
 }
 
@@ -1705,7 +1805,7 @@ int clip_n_patches(const struct clip_ctx * ctx) {
 
     int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
 
-    if (ctx->proj_type == PROJECTOR_TYPE_LDP) {
+    if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2) {
         n_patches /= 4;
     }
 
@@ -1714,7 +1814,7 @@ int clip_n_patches(const struct clip_ctx * ctx) {
 
 bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {
     if (!ctx->has_vision_encoder) {
-        printf("This gguf file seems to have no vision encoder\n");
+        LOG_TEE("This gguf file seems to have no vision encoder\n");
         return false;
     }
 
@@ -1726,7 +1826,7 @@ bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f3
 
 bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) {
     if (!ctx->has_vision_encoder) {
-        printf("This gguf file seems to have no vision encoder\n");
+        LOG_TEE("This gguf file seems to have no vision encoder\n");
         return false;
     }
 
@@ -1746,7 +1846,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     const int image_size    = hparams.image_size;
     const int patch_size    = hparams.patch_size;
     const int num_patches   = ((image_size / patch_size) * (image_size / patch_size));
-    const int num_positions = num_patches + 1;
+    const int num_positions = num_patches + (ctx->has_class_embedding ? 1 : 0);
 
     {
         struct ggml_tensor * inp_raw = ggml_graph_get_tensor(gf, "inp_raw");
@@ -1774,12 +1874,14 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     }
 
     {
-        struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings");
+        if (ctx->has_class_embedding) {
+            struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings");
 
-        void* zero_mem = malloc(ggml_nbytes(embeddings));
-        memset(zero_mem, 0, ggml_nbytes(embeddings));
-        ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings));
-        free(zero_mem);
+            void* zero_mem = malloc(ggml_nbytes(embeddings));
+            memset(zero_mem, 0, ggml_nbytes(embeddings));
+            ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings));
+            free(zero_mem);
+        }
     }
 
     {
@@ -1862,7 +1964,6 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
 
     std::vector<uint8_t> work(512);
     std::vector<float> conv_buf(512);
-    std::vector<int64_t> hist_all(1 << 4, 0);
     size_t total_size_org = 0;
     size_t total_size_new = 0;
 
@@ -1889,7 +1990,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
             new_type = type;
             if (new_type >= GGML_TYPE_Q2_K && name.find("embd") != std::string::npos) {
                 new_type = GGML_TYPE_Q8_0; // ggml_get_rows needs non K type
-                // fprintf(stderr, "%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type));
+                // LOG_TEE("%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type));
             }
             const size_t n_elms = ggml_nelements(cur);
             float * f32_data;
@@ -1908,7 +2009,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
                 f32_data = (float *)conv_buf.data();
                 break;
             default:
-                printf("Please use an input file in f32 or f16\n");
+                LOG_TEE("Please use an input file in f32 or f16\n");
+                gguf_free(ctx_out);
                 return false;
             }
 
@@ -1917,48 +2019,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
             }
             new_data = work.data();
 
-            std::vector<int64_t> hist_cur(1 << 4, 0);
-
-            switch (new_type) {
-                case GGML_TYPE_Q4_0: {
-                    new_size = ggml_quantize_q4_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
-                } break;
-                case GGML_TYPE_Q4_1: {
-                    new_size = ggml_quantize_q4_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
-                } break;
-                case GGML_TYPE_Q5_0: {
-                    new_size = ggml_quantize_q5_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
-                } break;
-                case GGML_TYPE_Q5_1: {
-                    new_size = ggml_quantize_q5_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
-                } break;
-                case GGML_TYPE_Q8_0: {
-                    new_size = ggml_quantize_q8_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
-                } break;
-                case GGML_TYPE_Q2_K: {
-                    new_size = ggml_quantize_q2_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
-                } break;
-                case GGML_TYPE_Q3_K: {
-                    new_size = ggml_quantize_q3_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
-                } break;
-                case GGML_TYPE_Q4_K: {
-                    new_size = ggml_quantize_q4_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
-                } break;
-                case GGML_TYPE_Q5_K: {
-                    new_size = ggml_quantize_q5_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
-                } break;
-                case GGML_TYPE_Q6_K: {
-                    new_size = ggml_quantize_q6_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
-                } break;
-                default: {
-                    fprintf(stderr, "%s: unsupported quantization type %d\n", __func__, new_type);
-                    return false;
-                }
-            }
-
-            for (size_t j = 0; j < hist_cur.size(); ++j) {
-                hist_all[j] += hist_cur[j];
-            }
+            new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, n_elms/cur->ne[0], cur->ne[0], nullptr);
         } else {
             new_type = cur->type;
             new_data = cur->data;
@@ -1975,7 +2036,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
             fout.put(0);
         }
 
-        printf("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
+        LOG_TEE("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
                orig_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
     }
 
@@ -1991,19 +2052,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
     gguf_free(ctx_out);
 
     {
-        printf("%s: original  size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
-        printf("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);
-
-        int64_t sum_all = 0;
-        for (size_t i = 0; i < hist_all.size(); ++i) {
-            sum_all += hist_all[i];
-        }
-
-        printf("%s: hist: ", __func__);
-        for (size_t i = 0; i < hist_all.size(); ++i) {
-            printf("%5.3f ", hist_all[i] / (float)sum_all);
-        }
-        printf("\n");
+        LOG_TEE("%s: original  size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
+        LOG_TEE("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);
     }
 
     return true;
@@ -2013,6 +2063,9 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
     if (ctx->proj_type == PROJECTOR_TYPE_LDP) {
         return ctx->vision_model.mm_model_block_1_block_2_1_b->ne[0];
     }
+    if (ctx->proj_type == PROJECTOR_TYPE_LDPV2) {
+        return ctx->vision_model.mm_model_peg_0_b->ne[0];
+    }
     if (ctx->proj_type == PROJECTOR_TYPE_MLP) {
         return ctx->vision_model.mm_2_b->ne[0];
     }
diff --git a/examples/llava/clip.h b/examples/llava/clip.h
index e5bd54924..45bdad689 100644
--- a/examples/llava/clip.h
+++ b/examples/llava/clip.h
@@ -60,8 +60,8 @@ CLIP_API struct clip_image_f32 * clip_image_f32_init();
 
 CLIP_API void clip_image_u8_free (struct clip_image_u8  * img);
 CLIP_API void clip_image_f32_free(struct clip_image_f32 * img);
-CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch  & batch);
-CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch & batch);
+CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch  * batch);
+CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch * batch);
 
 CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img);
 
@@ -69,7 +69,7 @@ CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8
 CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img);
 
 /** preprocess img and store the result in res_imgs, pad_to_square may be overriden to false depending on model configuration */
-CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch & res_imgs );
+CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs );
 
 CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx);
 
diff --git a/examples/llava/convert-image-encoder-to-gguf.py b/examples/llava/convert-image-encoder-to-gguf.py
index c69f89ac2..b00bf7c6d 100644
--- a/examples/llava/convert-image-encoder-to-gguf.py
+++ b/examples/llava/convert-image-encoder-to-gguf.py
@@ -1,6 +1,7 @@
 import argparse
 import os
 import json
+import re
 
 import torch
 import numpy as np
@@ -38,9 +39,11 @@ def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: b
 def get_tensor_name(name: str) -> str:
     if "projection" in name:
         return name
-
     if "mm_projector" in name:
-        return name.replace("model.mm_projector", "mm")
+        name = name.replace("model.mm_projector", "mm")
+        name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1)
+        name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1)
+        return name
 
     return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln")
 
@@ -83,7 +86,7 @@ ap.add_argument("--clip-model-is-vision", action="store_true", required=False,
 ap.add_argument("--clip-model-is-openclip", action="store_true", required=False,
                 help="The clip model is from openclip (for ViT-SO400M type))")
 ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
-ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp", choices=["mlp", "ldp"], default="mlp")
+ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2"], default="mlp")
 ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
 # Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
 # Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5
diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp
index e29da6cb2..a6d67e5d7 100644
--- a/examples/llava/llava-cli.cpp
+++ b/examples/llava/llava-cli.cpp
@@ -1,4 +1,5 @@
 #include "ggml.h"
+#include "log.h"
 #include "common.h"
 #include "clip.h"
 #include "llava.h"
@@ -18,7 +19,7 @@ static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_toke
             n_eval = n_batch;
         }
         if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) {
-            fprintf(stderr, "%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
+            LOG_TEE("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
             return false;
         }
         *n_past += n_eval;
@@ -45,7 +46,7 @@ static const char * sample(struct llama_sampling_context * ctx_sampling,
     const llama_token id = llama_sampling_sample(ctx_sampling, ctx_llama, NULL);
     llama_sampling_accept(ctx_sampling, ctx_llama, id, true);
     static std::string ret;
-    if (id == llama_token_eos(llama_get_model(ctx_llama))) {
+    if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
         ret = "</s>";
     } else {
         ret = llama_token_to_piece(ctx_llama, id);
@@ -73,7 +74,7 @@ static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip
     size_t img_base64_str_start, img_base64_str_end;
     find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end);
     if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) {
-        fprintf(stderr, "%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
+        LOG_TEE("%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
         return NULL;
     }
 
@@ -87,7 +88,7 @@ static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip
 
     auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size());
     if (!embed) {
-        fprintf(stderr, "%s: could not load image from base64 string.\n", __func__);
+        LOG_TEE("%s: could not load image from base64 string.\n", __func__);
         return NULL;
     }
 
@@ -112,29 +113,29 @@ struct llava_context {
 };
 
 static void show_additional_info(int /*argc*/, char ** argv) {
-    fprintf(stderr, "\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
-    fprintf(stderr, "  note: a lower temperature value like 0.1 is recommended for better quality.\n");
+    LOG_TEE("\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
+    LOG_TEE("  note: a lower temperature value like 0.1 is recommended for better quality.\n");
 }
 
-static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params) {
+static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params, const std::string & fname) {
 
     // load and preprocess the image
     llava_image_embed * embed = NULL;
     auto prompt = params->prompt;
     if (prompt_contains_image(prompt)) {
         if (!params->image.empty()) {
-            fprintf(stderr, "using base64 encoded image instead of command line image path\n");
+            LOG_TEE("using base64 encoded image instead of command line image path\n");
         }
         embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->n_threads, prompt);
         if (!embed) {
-            fprintf(stderr, "%s: can't load image from prompt\n", __func__);
+            LOG_TEE("%s: can't load image from prompt\n", __func__);
             return NULL;
         }
         params->prompt = remove_image_from_prompt(prompt);
     } else {
-        embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, params->image.c_str());
+        embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, fname.c_str());
         if (!embed) {
-            fprintf(stderr, "%s: is %s really an image file?\n", __func__, params->image.c_str());
+            fprintf(stderr, "%s: is %s really an image file?\n", __func__, fname.c_str());
             return NULL;
         }
     }
@@ -146,7 +147,6 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
     int n_past = 0;
 
     const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict;
-    const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx_llava->ctx_llama));
 
     std::string system_prompt, user_prompt;
     size_t image_pos = prompt.find("<image>");
@@ -154,18 +154,18 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
         // new templating mode: Provide the full prompt including system message and use <image> as a placeholder for the image
         system_prompt = prompt.substr(0, image_pos);
         user_prompt = prompt.substr(image_pos + std::string("<image>").length());
-        printf("system_prompt: %s\n", system_prompt.c_str());
+        LOG_TEE("system_prompt: %s\n", system_prompt.c_str());
         if (params->verbose_prompt) {
             auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
             for (int i = 0; i < (int) tmp.size(); i++) {
-                printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+                LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
             }
         }
-        printf("user_prompt: %s\n", user_prompt.c_str());
+        LOG_TEE("user_prompt: %s\n", user_prompt.c_str());
         if (params->verbose_prompt) {
             auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
             for (int i = 0; i < (int) tmp.size(); i++) {
-                printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+                LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
             }
         }
     } else {
@@ -175,20 +175,25 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
         if (params->verbose_prompt) {
             auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
             for (int i = 0; i < (int) tmp.size(); i++) {
-                printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+                LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
             }
         }
     }
 
-    eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, add_bos);
+    eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, true);
     llava_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past);
     eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, false);
 
     // generate the response
 
-    fprintf(stderr, "\n");
+    LOG_TEE("\n");
 
     struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams);
+    if (!ctx_sampling) {
+        fprintf(stderr, "%s: failed to initialize sampling subsystem\n", __func__);
+        exit(1);
+    }
+
     std::string response = "";
     for (int i = 0; i < max_tgt_len; i++) {
         const char * tmp = sample(ctx_sampling, ctx_llava->ctx_llama, &n_past);
@@ -207,8 +212,21 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
     printf("\n");
 }
 
+static struct llama_model * llava_init(gpt_params * params) {
+    llama_backend_init();
+    llama_numa_init(params->numa);
 
-static struct llava_context * llava_init(gpt_params * params) {
+    llama_model_params model_params = llama_model_params_from_gpt_params(*params);
+
+    llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
+    if (model == NULL) {
+        LOG_TEE("%s: error: unable to load model\n" , __func__);
+        return NULL;
+    }
+    return model;
+}
+
+static struct llava_context * llava_init_context(gpt_params * params, llama_model * model) {
     const char * clip_path = params->mmproj.c_str();
 
     auto prompt = params->prompt;
@@ -218,16 +236,6 @@ static struct llava_context * llava_init(gpt_params * params) {
 
     auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
 
-    llama_backend_init();
-    llama_numa_init(params->numa);
-
-    llama_model_params model_params = llama_model_params_from_gpt_params(*params);
-
-    llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
-    if (model == NULL) {
-        fprintf(stderr , "%s: error: unable to load model\n" , __func__);
-        return NULL;
-    }
 
     llama_context_params ctx_params = llama_context_params_from_gpt_params(*params);
     ctx_params.n_ctx           = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings
@@ -235,7 +243,7 @@ static struct llava_context * llava_init(gpt_params * params) {
     llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
 
     if (ctx_llama == NULL) {
-        fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
+        LOG_TEE("%s: error: failed to create the llama_context\n" , __func__);
         return NULL;
     }
 
@@ -258,6 +266,12 @@ static void llava_free(struct llava_context * ctx_llava) {
     llama_backend_free();
 }
 
+static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
+    (void) level;
+    (void) user_data;
+    LOG_TEE("%s", text);
+}
+
 int main(int argc, char ** argv) {
     ggml_time_init();
 
@@ -267,29 +281,58 @@ int main(int argc, char ** argv) {
         show_additional_info(argc, argv);
         return 1;
     }
+
+#ifndef LOG_DISABLE_LOGS
+    log_set_target(log_filename_generator("llava", "log"));
+    LOG_TEE("Log start\n");
+    log_dump_cmdline(argc, argv);
+    llama_log_set(llama_log_callback_logTee, nullptr);
+#endif // LOG_DISABLE_LOGS
+
     if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
         gpt_print_usage(argc, argv, params);
         show_additional_info(argc, argv);
         return 1;
     }
-
-    auto ctx_llava = llava_init(&params);
-    if (ctx_llava == NULL) {
-        fprintf(stderr, "%s: error: failed to init llava\n", __func__);
+    auto model = llava_init(&params);
+    if (model == NULL) {
+        fprintf(stderr, "%s: error: failed to init llava model\n", __func__);
         return 1;
     }
 
-    auto image_embed = load_image(ctx_llava, &params);
-    if (!image_embed) {
-        return 1;
+    if (prompt_contains_image(params.prompt)) {
+        auto ctx_llava = llava_init_context(&params, model);
+
+        auto image_embed = load_image(ctx_llava, &params, "");
+
+        // process the prompt
+        process_prompt(ctx_llava, image_embed, &params, params.prompt);
+
+        llama_print_timings(ctx_llava->ctx_llama);
+        llava_image_embed_free(image_embed);
+        ctx_llava->model = NULL;
+        llava_free(ctx_llava);
+    } else {
+        for (auto & image : params.image) {
+            auto ctx_llava = llava_init_context(&params, model);
+
+            auto image_embed = load_image(ctx_llava, &params, image);
+            if (!image_embed) {
+                std::cerr << "error: failed to load image " << image << ". Terminating\n\n";
+                return 1;
+            }
+
+            // process the prompt
+            process_prompt(ctx_llava, image_embed, &params, params.prompt);
+
+            llama_print_timings(ctx_llava->ctx_llama);
+            llava_image_embed_free(image_embed);
+            ctx_llava->model = NULL;
+            llava_free(ctx_llava);
+        }
     }
 
-    // process the prompt
-    process_prompt(ctx_llava, image_embed, &params, params.prompt);
+    llama_free_model(model);
 
-    llama_print_timings(ctx_llava->ctx_llama);
-
-    llava_image_embed_free(image_embed);
-    llava_free(ctx_llava);
     return 0;
 }
diff --git a/examples/llava/llava-surgery-v2.py b/examples/llava/llava-surgery-v2.py
index 5bc5bc513..eb56d6988 100644
--- a/examples/llava/llava-surgery-v2.py
+++ b/examples/llava/llava-surgery-v2.py
@@ -65,9 +65,7 @@ def clean_vision_tower_from_checkpoint(checkpoint_path):
         for name in clip_tensors:
             del checkpoint[name]
 
-        # Save the updated checkpoint
         checkpoint_path = checkpoint_path
-        save_model(checkpoint, checkpoint_path, file_type)
         return True
     return False
 
@@ -152,16 +150,6 @@ for name in first_mm_tensors:
 if len(projector) > 0:
     save_model(projector, f"{args.model}/llava.projector", 'pytorch')
 
-for name in mm_tensors:
-    del last_checkpoint[name]
-for name in first_mm_tensors:
-    del first_checkpoint[name]
-
-if len(mm_tensors) > 0:
-    save_model(last_checkpoint, projector_checkpoint_path, file_type)
-if len(first_mm_tensors) > 0:
-    save_model(first_checkpoint, newline_checkpoint_path, file_type)
-
 print("Done!")
 print(f"Now you can convert {args.model} to a a regular LLaMA GGUF file.")
 print(f"Also, use {args.model}/llava.projector to prepare a llava-encoder.gguf file.")
diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp
index 4cb65a07b..9a990bb18 100644
--- a/examples/llava/llava.cpp
+++ b/examples/llava/llava.cpp
@@ -54,7 +54,7 @@ static std::pair<int, int> select_best_resolution(const std::pair<int, int>& ori
         int downscaled_height = static_cast<int>(original_height * scale);
         int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
         int wasted_resolution = (width * height) - effective_resolution;
-        // fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
+        // LOG_TEE("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
         if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
             max_effective_resolution = effective_resolution;
             min_wasted_resolution = wasted_resolution;
@@ -152,15 +152,15 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
 
     ggml_tensor * newline_tmp = clip_get_newline_tensor(ctx_clip);
     model.newline = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, newline_tmp->ne[0]);
-    if (newline_tmp->backend != GGML_BACKEND_CPU) {
+    if (newline_tmp->backend != GGML_BACKEND_TYPE_CPU) {
         if (newline_tmp->buffer == NULL) {
-            printf("newline_tmp tensor buffer is NULL\n");
+            LOG_TEE("newline_tmp tensor buffer is NULL\n");
         }
         ggml_backend_tensor_get(newline_tmp, model.newline->data, 0, ggml_nbytes(newline_tmp));
     } else {
         model.newline->data = newline_tmp->data;
         if (model.newline->data == NULL) {
-            printf("newline_tmp tensor data is NULL\n");
+            LOG_TEE("newline_tmp tensor data is NULL\n");
         }
     }
 
@@ -223,8 +223,8 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
     clip_image_f32_batch img_res_v;
     img_res_v.size = 0;
     img_res_v.data = nullptr;
-    if (!clip_image_preprocess(ctx_clip, img, img_res_v)) {
-        fprintf(stderr, "%s: unable to preprocess image\n", __func__);
+    if (!clip_image_preprocess(ctx_clip, img, &img_res_v)) {
+        LOG_TEE("%s: unable to preprocess image\n", __func__);
         delete[] img_res_v.data;
         return false;
     }
@@ -239,7 +239,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
         bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd); // image_embd shape is 576 x 4096
         delete[] img_res_v.data;
         if (!encoded) {
-            fprintf(stderr, "Unable to encode image\n");
+            LOG_TEE("Unable to encode image\n");
 
             return false;
         }
@@ -252,12 +252,12 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
             image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip)); // 576 patches * 4096 embeddings * 4 bytes = 9437184
             const bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]); // image data is in 3x336x336 format and will be converted to 336x336x3 inside
             if (!encoded) {
-                fprintf(stderr, "Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
+                LOG_TEE("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
                 return false;
             }
         }
         const int64_t t_img_enc_batch_us = ggml_time_us();
-        printf("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
+        LOG_TEE("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
 
         const int32_t * image_grid = clip_image_grid(ctx_clip);
 
@@ -290,12 +290,12 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
         // clip_image_save_to_bmp(*tmp, "image_feature.bmp");
     }
 
-    printf("%s: image embedding created: %d tokens\n", __func__, *n_img_pos);
+    LOG_TEE("%s: image embedding created: %d tokens\n", __func__, *n_img_pos);
 
     const int64_t t_img_enc_end_us = ggml_time_us();
     float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0;
 
-    printf("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos);
+    LOG_TEE("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos);
 
     return true;
 }
@@ -305,22 +305,22 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx *
     int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama));
     auto n_image_embd = clip_n_mmproj_embd(ctx_clip);
     if (n_image_embd != n_llama_embd) {
-        printf("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
+        LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
         return false;
     }
     return true;
 }
 
-static bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) {
+bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) {
     float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*6); // TODO: base on gridsize/llava model
     if (!image_embd) {
-        fprintf(stderr, "Unable to allocate memory for image embeddings\n");
+        LOG_TEE("Unable to allocate memory for image embeddings\n");
         return false;
     }
 
     int n_img_pos;
     if (!encode_image_with_clip(ctx_clip, n_threads, img, image_embd, &n_img_pos)) {
-        fprintf(stderr, "%s: cannot encode image, aborting\n", __func__);
+        LOG_TEE("%s: cannot encode image, aborting\n", __func__);
         free(image_embd);
         return false;
     }
@@ -340,7 +340,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
         }
         llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };
         if (llama_decode(ctx_llama, batch)) {
-            fprintf(stderr, "%s : failed to eval\n", __func__);
+            LOG_TEE("%s : failed to eval\n", __func__);
             return false;
         }
         *n_past += n_eval;
@@ -352,7 +352,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
     clip_image_u8 * img = clip_image_u8_init();
     if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) {
         clip_image_u8_free(img);
-        fprintf(stderr, "%s: can't load image from bytes, is it a valid image?", __func__);
+        LOG_TEE("%s: can't load image from bytes, is it a valid image?", __func__);
         return NULL;
     }
 
@@ -361,7 +361,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
     bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos);
     if (!image_embed_result) {
         clip_image_u8_free(img);
-        fprintf(stderr, "%s: coulnd't embed the image\n", __func__);
+        LOG_TEE("%s: coulnd't embed the image\n", __func__);
         return NULL;
     }
 
@@ -375,7 +375,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
 static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long *sizeOut) {
     auto file = fopen(path, "rb");
     if (file == NULL) {
-        fprintf(stderr, "%s: can't read file %s\n", __func__, path);
+        LOG_TEE("%s: can't read file %s\n", __func__, path);
         return false;
     }
 
@@ -385,7 +385,7 @@ static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long
 
     auto buffer = (unsigned char *)malloc(fileSize); // Allocate memory to hold the file data
     if (buffer == NULL) {
-        fprintf(stderr, "%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path);
+        LOG_TEE("%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path);
         perror("Memory allocation error");
         fclose(file);
         return false;
@@ -410,7 +410,7 @@ struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx
     long image_bytes_length;
     auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length);
     if (!loaded) {
-        fprintf(stderr, "%s: failed to load %s\n", __func__, image_path);
+        LOG_TEE("%s: failed to load %s\n", __func__, image_path);
         return NULL;
     }
 
diff --git a/examples/llava/llava.h b/examples/llava/llava.h
index 9e9466a5d..19212f6e9 100644
--- a/examples/llava/llava.h
+++ b/examples/llava/llava.h
@@ -29,7 +29,9 @@ struct llava_image_embed {
 };
 
 /** sanity check for clip <-> llava embed size match */
-LLAVA_API bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip);
+LLAVA_API bool llava_validate_embed_size(const struct llama_context * ctx_llama, const struct clip_ctx * ctx_clip);
+
+LLAVA_API bool llava_image_embed_make_with_clip_img(struct clip_ctx * ctx_clip, int n_threads, const struct clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out);
 
 /** build an image embed from image file bytes */
 LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length);
diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp
index e2551e7a4..9c3540b20 100644
--- a/examples/lookahead/lookahead.cpp
+++ b/examples/lookahead/lookahead.cpp
@@ -64,13 +64,10 @@ int main(int argc, char ** argv) {
     std::tie(model, ctx) = llama_init_from_gpt_params(params);
 
     // Tokenize the prompt
-    const bool add_bos = llama_should_add_bos_token(model);
-    LOG("add_bos tgt: %d\n", add_bos);
-
     std::vector<llama_token> inp;
     std::vector<llama_token> all;
 
-    inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
+    inp = ::llama_tokenize(ctx, params.prompt, true, true);
     all = inp;
 
     const int max_context_size     = llama_n_ctx(ctx);
@@ -302,7 +299,7 @@ int main(int argc, char ** argv) {
                 }
                 fflush(stdout);
 
-                if (id == llama_token_eos(model)) {
+                if (llama_token_is_eog(model, id)) {
                     has_eos = true;
                 }
 
diff --git a/examples/lookup/CMakeLists.txt b/examples/lookup/CMakeLists.txt
index c060b8f56..b91633f63 100644
--- a/examples/lookup/CMakeLists.txt
+++ b/examples/lookup/CMakeLists.txt
@@ -3,3 +3,21 @@ add_executable(${TARGET} lookup.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
+
+set(TARGET lookup-create)
+add_executable(${TARGET} lookup-create.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
+
+set(TARGET lookup-merge)
+add_executable(${TARGET} lookup-merge.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
+
+set(TARGET lookup-stats)
+add_executable(${TARGET} lookup-stats.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/lookup/lookup-create.cpp b/examples/lookup/lookup-create.cpp
new file mode 100644
index 000000000..1c230c966
--- /dev/null
+++ b/examples/lookup/lookup-create.cpp
@@ -0,0 +1,41 @@
+#include "ggml.h"
+#include "llama.h"
+#include "common.h"
+#include "ngram-cache.h"
+
+#include <cstdint>
+#include <fstream>
+#include <iostream>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+int main(int argc, char ** argv){
+    gpt_params params;
+
+    if (!gpt_params_parse(argc, argv, params)) {
+        return 1;
+    }
+    // init llama.cpp
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    llama_model * model = NULL;
+    llama_context * ctx = NULL;
+
+    // load the model
+    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    GGML_ASSERT(model != nullptr);
+
+    // tokenize the prompt
+    std::vector<llama_token> inp;
+    inp = ::llama_tokenize(ctx, params.prompt, true, true);
+    fprintf(stderr, "%s: tokenization done\n", __func__);
+
+
+    llama_ngram_cache ngram_cache;
+    llama_ngram_cache_update(ngram_cache, LLAMA_NGRAM_STATIC, LLAMA_NGRAM_STATIC, inp, inp.size(), true);
+    fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.lookup_cache_static.c_str());
+
+    llama_ngram_cache_save(ngram_cache, params.lookup_cache_static);
+}
diff --git a/examples/lookup/lookup-merge.cpp b/examples/lookup/lookup-merge.cpp
new file mode 100644
index 000000000..07c93eb8d
--- /dev/null
+++ b/examples/lookup/lookup-merge.cpp
@@ -0,0 +1,47 @@
+#include "ggml.h"
+#include "llama.h"
+#include "common.h"
+#include "ngram-cache.h"
+
+#include <cstdint>
+#include <cstdio>
+#include <fstream>
+#include <iostream>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+static void print_usage() {
+    fprintf(stderr, "Merges multiple lookup cache files into a single one.\n");
+    fprintf(stderr, "Usage: lookup-merge [--help] lookup_part_1.bin lookup_part_2.bin ... lookup_merged.bin\n");
+}
+
+int main(int argc, char ** argv){
+    if (argc < 3) {
+        print_usage();
+        exit(1);
+    }
+
+    std::vector<std::string> args;
+    args.resize(argc-1);
+    for (int i = 0; i < argc-1; ++i) {
+        args[i] = argv[i+1];
+        if (args[i] == "-h" || args[i] == "--help") {
+            print_usage();
+            exit(0);
+        }
+    }
+
+    fprintf(stderr, "lookup-merge: loading file %s\n", args[0].c_str());
+    llama_ngram_cache ngram_cache_merged = llama_ngram_cache_load(args[0]);
+
+    for (size_t i = 1; i < args.size()-1; ++i) {
+        fprintf(stderr, "lookup-merge: loading file %s\n", args[i].c_str());
+        llama_ngram_cache ngram_cache = llama_ngram_cache_load(args[i]);
+
+        llama_ngram_cache_merge(ngram_cache_merged, ngram_cache);
+    }
+
+    fprintf(stderr, "lookup-merge: saving file %s\n", args.back().c_str());
+    llama_ngram_cache_save(ngram_cache_merged, args.back());
+}
diff --git a/examples/lookup/lookup-stats.cpp b/examples/lookup/lookup-stats.cpp
new file mode 100644
index 000000000..87ecc0a4f
--- /dev/null
+++ b/examples/lookup/lookup-stats.cpp
@@ -0,0 +1,159 @@
+#include "ggml.h"
+#include "common.h"
+#include "llama.h"
+#include "log.h"
+#include "ngram-cache.h"
+
+#include <cmath>
+#include <cstdint>
+#include <cstdio>
+#include <fstream>
+#include <string>
+#include <vector>
+#include <unordered_map>
+
+int main(int argc, char ** argv){
+    gpt_params params;
+
+    if (!gpt_params_parse(argc, argv, params)) {
+        return 1;
+    }
+
+    const int n_draft = params.n_draft;
+
+    // init llama.cpp
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    llama_model * model = NULL;
+    llama_context * ctx = NULL;
+
+    // load the model
+    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    GGML_ASSERT(llama_n_vocab(model) < (1 << 16));
+
+    // tokenize the prompt
+    std::vector<llama_token> inp;
+    inp = ::llama_tokenize(ctx, params.prompt, true, true);
+
+    llama_ngram_cache ngram_cache_context;
+    llama_ngram_cache ngram_cache_dynamic;
+    llama_ngram_cache ngram_cache_static;
+    int64_t t_draft_flat_us = 0;
+    int64_t t_draft_us = 0;
+
+    {
+        const int64_t t_start_draft_us = ggml_time_us();
+
+        if (!params.lookup_cache_static.empty()) {
+            try {
+                ngram_cache_static = llama_ngram_cache_load(params.lookup_cache_static);
+            } catch (std::ifstream::failure const &) {
+                fprintf(stderr, "error: failed to open static lookup cache: %s", params.lookup_cache_static.c_str());
+                exit(1);
+            }
+        }
+
+        if (!params.lookup_cache_dynamic.empty()) {
+            try {
+                ngram_cache_dynamic = llama_ngram_cache_load(params.lookup_cache_dynamic);
+            } catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program
+        }
+
+        t_draft_flat_us += ggml_time_us() - t_start_draft_us;
+    }
+
+    const int n_input = inp.size();
+    const int n_ctx = params.n_ctx;
+
+    int n_drafted = 0;
+    int n_accept  = 0;
+
+    const int64_t t_start_ms = ggml_time_ms();
+
+    // Iterate over input tokens in chunks of size n_ctx.
+    // Each chunk is treated as if a sequential generation but with pre-determined tokens to ensure reproducibility.
+    for (int i_start = 0; i_start + n_ctx < n_input; i_start += n_ctx) {
+        const std::vector<llama_token> inp_slice(inp.begin() + i_start, inp.begin() + i_start + n_ctx);
+        std::vector<llama_token> pseudo_output;
+        pseudo_output.push_back(inp_slice[0]);
+
+        while ((int) pseudo_output.size() < n_ctx) {
+            // Simulate drafting and decoding from draft:
+            std::vector<llama_token> draft;
+            draft.push_back(pseudo_output.back());
+
+            {
+                const int64_t t_start_draft_us = ggml_time_us();
+                llama_ngram_cache_draft(pseudo_output, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static);
+                t_draft_us += ggml_time_us() - t_start_draft_us;
+            }
+
+            n_drafted += draft.size() - 1;
+
+            for (size_t j = 1; j < draft.size() && (int) pseudo_output.size() < n_ctx; ++j) {
+                const llama_token ground_truth = inp_slice[pseudo_output.size()];
+                const llama_token drafted = draft[j];
+
+                if (ground_truth != drafted) {
+                    break;
+                }
+
+                ++n_accept;
+                pseudo_output.push_back(ground_truth);
+
+                {
+                    const int64_t t_start_draft_us = ggml_time_us();
+                    llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, pseudo_output, 1, false);
+                    t_draft_us += ggml_time_us() - t_start_draft_us;
+                }
+            }
+
+            // After each simulated batch decoding simulate the sampling of a single token:
+            if ((int) pseudo_output.size() < n_ctx) {
+                pseudo_output.push_back(inp_slice[pseudo_output.size()]);
+                {
+                    const int64_t t_start_draft_us = ggml_time_us();
+                    llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, pseudo_output, 1, false);
+                    t_draft_us += ggml_time_us() - t_start_draft_us;
+                }
+            }
+
+            draft.erase(draft.begin());
+
+        }
+        if (i_start > 0 && i_start / 100000 != (i_start - n_ctx) / 100000) {
+            const int64_t t_now_ms = ggml_time_ms();
+            const int64_t eta_ms   = (n_input - i_start) * (t_now_ms - t_start_ms) / i_start;
+            const int64_t eta_min  = eta_ms / (60*1000);
+            const int64_t eta_s    = (eta_ms - 60*1000*eta_min) / 1000;
+
+            LOG_TEE("lookup-stats: %d/%d done, ETA: %02" PRId64 ":%02" PRId64 "\n", i_start, n_input, eta_min, eta_s);
+        }
+
+        // After each chunk, update the dynamic ngram cache with the context ngram cache:
+        llama_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context);
+        ngram_cache_context.clear();
+    }
+
+    LOG_TEE("\n");
+
+    LOG_TEE("\n");
+    LOG_TEE("n_draft      = %d\n", n_draft);
+    LOG_TEE("n_predict    = %d\n", n_input - n_input % n_ctx);
+    LOG_TEE("n_drafted    = %d\n", n_drafted);
+    LOG_TEE("t_draft_flat = %.2f ms\n", t_draft_flat_us*1e-3);
+    LOG_TEE("t_draft      = %.2f ms, %.2f us per token, %.2f tokens per second\n",
+            t_draft_us*1e-3, 1.0f*t_draft_us/n_drafted, n_drafted/(1e-6*t_draft_us));
+    LOG_TEE("n_accept     = %d\n", n_accept);
+    LOG_TEE("accept       = %.3f%%\n", 100.0f * n_accept / n_drafted);
+
+    llama_free(ctx);
+    llama_free_model(model);
+
+    llama_backend_free();
+
+    fprintf(stderr, "\n\n");
+
+    return 0;
+}
diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp
index b53fae110..eebbd00a5 100644
--- a/examples/lookup/lookup.cpp
+++ b/examples/lookup/lookup.cpp
@@ -1,12 +1,15 @@
-#include "common.h"
 #include "ggml.h"
 #include "llama.h"
+#include "common.h"
+#include "ngram-cache.h"
 
 #include <cmath>
 #include <cstdint>
 #include <cstdio>
+#include <fstream>
 #include <string>
 #include <vector>
+#include <unordered_map>
 
 int main(int argc, char ** argv){
     gpt_params params;
@@ -15,11 +18,7 @@ int main(int argc, char ** argv){
         return 1;
     }
 
-    // max/min n-grams size to search for in prompt
-    const int ngram_max = 4;
-    const int ngram_min = 1;
-
-    // length of the candidate / draft sequence, if match is found
+    // max. number of additional tokens to draft if match is found
     const int n_draft = params.n_draft;
 
     const bool dump_kv_cache = params.dump_kv_cache;
@@ -39,13 +38,40 @@ int main(int argc, char ** argv){
 
     // load the model
     std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    GGML_ASSERT(llama_n_vocab(model) < (1 << 16));
 
     // tokenize the prompt
-    const bool add_bos = llama_should_add_bos_token(model);
-    LOG("add_bos tgt: %d\n", add_bos);
-
     std::vector<llama_token> inp;
-    inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
+    inp = ::llama_tokenize(ctx, params.prompt, true, true);
+
+    llama_ngram_cache ngram_cache_context;
+    llama_ngram_cache ngram_cache_dynamic;
+    llama_ngram_cache ngram_cache_static;
+    int64_t t_draft_flat_us = 0;
+    int64_t t_draft_us = 0;
+
+    {
+        // Fill up context ngram cache with tokens from user input:
+        const int64_t t_start_draft_us = ggml_time_us();
+        llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, inp.size(), false);
+
+        if (!params.lookup_cache_static.empty()) {
+            try {
+                ngram_cache_static = llama_ngram_cache_load(params.lookup_cache_static);
+            } catch (std::ifstream::failure const &) {
+                fprintf(stderr, "error: failed to open static lookup cache: %s", params.lookup_cache_static.c_str());
+                exit(1);
+            }
+        }
+
+        if (!params.lookup_cache_dynamic.empty()) {
+            try {
+                ngram_cache_dynamic = llama_ngram_cache_load(params.lookup_cache_dynamic);
+            } catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program
+        }
+
+        t_draft_flat_us += ggml_time_us() - t_start_draft_us;
+    }
 
     const int max_context_size     = llama_n_ctx(ctx);
     const int max_tokens_list_size = max_context_size - 4;
@@ -76,8 +102,6 @@ int main(int argc, char ** argv){
     int n_drafted = 0;
     int n_accept  = 0;
 
-    int64_t t_draft_us = 0;
-
     int n_past = inp.size();
 
     bool has_eos = false;
@@ -116,7 +140,7 @@ int main(int argc, char ** argv){
                 printf("%s", token_str.c_str());
             }
 
-            if (id == llama_token_eos(model)) {
+            if (llama_token_is_eog(model, id)) {
                 has_eos = true;
             }
 
@@ -129,6 +153,12 @@ int main(int argc, char ** argv){
                 ++n_past;
                 ++i_dft;
                 inp.push_back(id);
+                {
+                    // Update context ngram cache with the newly accepted token:
+                    const int64_t t_start_draft_us = ggml_time_us();
+                    llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, 1, false);
+                    t_draft_us += ggml_time_us() - t_start_draft_us;
+                }
 
                 if (params.use_color) {
                     // color accepted draft token
@@ -149,6 +179,12 @@ int main(int argc, char ** argv){
             draft.clear();
             draft.push_back(id);
             inp.push_back(id);
+            {
+                // Update context ngram cache with the newly accepted token:
+                const int64_t t_start_draft_us = ggml_time_us();
+                llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, 1, false);
+                t_draft_us += ggml_time_us() - t_start_draft_us;
+            }
             break;
         }
 
@@ -163,44 +199,19 @@ int main(int argc, char ** argv){
         llama_batch_clear(batch_tgt);
         llama_batch_add(batch_tgt, draft[0], n_past, { 0 }, true);
 
-        // generate n_pred tokens through prompt lookup
-        auto prompt_lookup = [&]() -> void {
-            const int inp_size = inp.size();
-            for (int ngram_size = ngram_max ; ngram_size > ngram_min; --ngram_size){
-                const llama_token * ngram = &inp[inp_size - ngram_size];
-
-                for (int i = 0; i <= (int) inp_size - (ngram_size * 2); ++i) {
-                    bool match = true;
-                    for (int j = 0; j < ngram_size; ++j) {
-                        if (inp[i + j] != ngram[j]) {
-                            match = false;
-                            break;
-                        }
-                    }
-
-                    if (match) {
-                        const int startIdx = i + ngram_size;
-                        const int endIdx = startIdx + n_draft;
-                        if (endIdx < inp_size) {
-                            for (int j = startIdx; j < endIdx; ++j) {
-                                LOG(" - draft candidate %d: %d\n", j, inp[j]);
-                                draft.push_back(inp[j]);
-                                llama_batch_add(batch_tgt, inp[j], n_past + (j - startIdx) + 1, { 0 }, true);
-                                ++n_drafted;
-                            }
-                            return;
-                        }
-                    }
-                }
-            }
-            return;
-        };
-
+        // Draft already contains a single token sampled from the model:
+        GGML_ASSERT(draft.size() == 1);
+        GGML_ASSERT(draft[0] == inp.back());
         const int64_t t_start_draft_us = ggml_time_us();
 
-        prompt_lookup();
+        llama_ngram_cache_draft(inp, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static);
+
+        for (size_t i = 1; i < draft.size(); ++i) {
+            llama_batch_add(batch_tgt, draft[i], n_past + i, { 0 }, true);
+        }
 
         t_draft_us += ggml_time_us() - t_start_draft_us;
+        n_drafted += draft.size() - 1;
 
         llama_decode(ctx, batch_tgt);
         ++n_past;
@@ -210,19 +221,24 @@ int main(int argc, char ** argv){
 
     auto t_dec_end = ggml_time_us();
 
+    // Update dynamic ngram cache with context ngram cache and save it to disk:
+    llama_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context);
+    llama_ngram_cache_save(ngram_cache_dynamic, params.lookup_cache_dynamic);
+
     LOG_TEE("\n\n");
 
     LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input,   (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
     LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict  / ((t_dec_end - t_dec_start) / 1e6f));
 
     LOG_TEE("\n");
-    LOG_TEE("n_draft   = %d\n", n_draft);
-    LOG_TEE("n_predict = %d\n", n_predict);
-    LOG_TEE("n_drafted = %d\n", n_drafted);
-    LOG_TEE("t_draft   = %.2f ms, %.2f us per token, %.2f tokens per second\n",
+    LOG_TEE("n_draft      = %d\n", n_draft);
+    LOG_TEE("n_predict    = %d\n", n_predict);
+    LOG_TEE("n_drafted    = %d\n", n_drafted);
+    LOG_TEE("t_draft_flat = %.2f ms\n", t_draft_flat_us*1e-3);
+    LOG_TEE("t_draft      = %.2f ms, %.2f us per token, %.2f tokens per second\n",
             t_draft_us*1e-3, 1.0f*t_draft_us/n_drafted, n_drafted/(1e-6*t_draft_us));
-    LOG_TEE("n_accept  = %d\n", n_accept);
-    LOG_TEE("accept    = %.3f%%\n", 100.0f * n_accept / n_drafted);
+    LOG_TEE("n_accept     = %d\n", n_accept);
+    LOG_TEE("accept       = %.3f%%\n", 100.0f * n_accept / n_drafted);
 
     LOG_TEE("\ntarget:\n");
     llama_print_timings(ctx);
diff --git a/examples/main-cmake-pkg/README.md b/examples/main-cmake-pkg/README.md
index 6d665f28f..edf20d8db 100644
--- a/examples/main-cmake-pkg/README.md
+++ b/examples/main-cmake-pkg/README.md
@@ -8,7 +8,7 @@ Because this example is "outside of the source tree", it is important to first b
 
 ### Considerations
 
-When hardware acceleration libraries are used (e.g. CUBlas, Metal, CLBlast, etc.), CMake must be able to locate the associated CMake package. In the example below, when building _main-cmake-pkg_ notice the `CMAKE_PREFIX_PATH` includes the Llama CMake package location _in addition to_ the CLBlast package—which was used when compiling _llama.cpp_.
+When hardware acceleration libraries are used (e.g. CUDA, Metal, CLBlast, etc.), CMake must be able to locate the associated CMake package. In the example below, when building _main-cmake-pkg_ notice the `CMAKE_PREFIX_PATH` includes the Llama CMake package location _in addition to_ the CLBlast package—which was used when compiling _llama.cpp_.
 
 ### Build llama.cpp and install to C:\LlamaCPP directory
 
@@ -17,11 +17,9 @@ In this case, CLBlast was already installed so the CMake package is referenced i
 ```cmd
 git clone https://github.com/ggerganov/llama.cpp
 cd llama.cpp
-mkdir build
-cd build
-cmake .. -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=C:/CLBlast/lib/cmake/CLBlast -G "Visual Studio 17 2022" -A x64
-cmake --build . --config Release
-cmake --install . --prefix C:/LlamaCPP
+cmake -B build -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=C:/CLBlast/lib/cmake/CLBlast -G "Visual Studio 17 2022" -A x64
+cmake --build build --config Release
+cmake --install build --prefix C:/LlamaCPP
 ```
 
 ### Build main-cmake-pkg
@@ -29,9 +27,7 @@ cmake --install . --prefix C:/LlamaCPP
 
 ```cmd
 cd ..\examples\main-cmake-pkg
-mkdir build
-cd build
-cmake .. -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/CLBlast/lib/cmake/CLBlast;C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64
-cmake --build . --config Release
-cmake --install . --prefix C:/MyLlamaApp
+cmake -B build -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/CLBlast/lib/cmake/CLBlast;C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64
+cmake --build build --config Release
+cmake --install build --prefix C:/MyLlamaApp
 ```
diff --git a/examples/main/README.md b/examples/main/README.md
index 7f84e4262..97e2ae4c2 100644
--- a/examples/main/README.md
+++ b/examples/main/README.md
@@ -66,7 +66,8 @@ main.exe -m models\7B\ggml-model.bin --ignore-eos -n -1 --random-prompt
 
 In this section, we cover the most commonly used options for running the `main` program with the LLaMA models:
 
--   `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
+-   `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`; inferred from `--model-url` if set).
+-   `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf).
 -   `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses.
 -   `-ins, --instruct`: Run the program in instruction mode, which is particularly useful when working with Alpaca models.
 -   `-n N, --n-predict N`: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text.
@@ -142,7 +143,7 @@ The `--ctx-size` option allows you to set the size of the prompt context used by
 
 ### Extended Context Size
 
-Some fine-tuned models have extended the context length by scaling RoPE. For example, if the original pre-trained model have a context length (max sequence length) of 4096 (4k) and the fine-tuned model have 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8.
+Some fine-tuned models have extended the context length by scaling RoPE. For example, if the original pre-trained model has a context length (max sequence length) of 4096 (4k) and the fine-tuned model has 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8.
 
 -   `--rope-scale N`: Where N is the linear scaling factor used by the fine-tuned model.
 
@@ -285,7 +286,7 @@ These options help improve the performance and memory usage of the LLaMA models.
 
 -   `--numa distribute`: Pin an equal proportion of the threads to the cores on each NUMA node. This will spread the load amongst all cores on the system, utilitizing all memory channels at the expense of potentially requiring memory to travel over the slow links between nodes.
 -   `--numa isolate`: Pin all threads to the NUMA node that the program starts on. This limits the number of cores and amount of memory that can be used, but guarantees all memory access remains local to the NUMA node.
--   `--numa numactl`: Pin threads to the CPUMAP that is passed to the program by starting it with the numactl utility. This is the most flexible mode, and allow arbitraty core usage patterns, for example a map that uses all the cores on one NUMA nodes, and just enough cores on a second node to saturate the inter-node memory bus.
+-   `--numa numactl`: Pin threads to the CPUMAP that is passed to the program by starting it with the numactl utility. This is the most flexible mode, and allow arbitrary core usage patterns, for example a map that uses all the cores on one NUMA nodes, and just enough cores on a second node to saturate the inter-node memory bus.
 
  These flags attempt optimizations that help on some systems with non-uniform memory access. This currently consists of one of the above strategies, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop_caches' as root.
 
@@ -295,19 +296,23 @@ These options help improve the performance and memory usage of the LLaMA models.
 
 ### Batch Size
 
--   `-b N, --batch-size N`: Set the batch size for prompt processing (default: 512). This large batch size benefits users who have BLAS installed and enabled it during the build. If you don't have BLAS enabled ("BLAS=0"), you can use a smaller number, such as 8, to see the prompt progress as it's evaluated in some situations.
+-   `-b N, --batch-size N`: Set the batch size for prompt processing (default: `2048`). This large batch size benefits users who have BLAS installed and enabled it during the build. If you don't have BLAS enabled ("BLAS=0"), you can use a smaller number, such as 8, to see the prompt progress as it's evaluated in some situations.
+
+- `-ub N`, `--ubatch-size N`: physical maximum batch size. This is for pipeline parallelization. Default: `512`.
 
 ### Prompt Caching
 
 -   `--prompt-cache FNAME`: Specify a file to cache the model state after the initial prompt. This can significantly speed up the startup time when you're using longer prompts. The file is created during the first run and is reused and updated in subsequent runs. **Note**: Restoring a cached prompt does not imply restoring the exact state of the session at the point it was saved. So even when specifying a specific seed, you are not guaranteed to get the same sequence of tokens as the original generation.
 
-### Grammars
+### Grammars & JSON schemas
 
 -   `--grammar GRAMMAR`, `--grammar-file FILE`: Specify a grammar (defined inline or in a file) to constrain model output to a specific format. For example, you could force the model to output JSON or to speak only in emojis. See the [GBNF guide](../../grammars/README.md) for details on the syntax.
 
+-   `--json-schema SCHEMA`: Specify a [JSON schema](https://json-schema.org/) to constrain model output to (e.g. `{}` for any JSON object, or `{"items": {"type": "string", "minLength": 10, "maxLength": 100}, "minItems": 10}` for a JSON array of strings with size constraints). If a schema uses external `$ref`s, you should use `--grammar "$( python examples/json_schema_to_grammar.py myschema.json )"` instead.
+
 ### Quantization
 
-For information about 4-bit quantization, which can significantly improve performance and reduce memory usage, please refer to llama.cpp's primary [README](../../README.md#prepare-data--run).
+For information about 4-bit quantization, which can significantly improve performance and reduce memory usage, please refer to llama.cpp's primary [README](../../README.md#prepare-and-quantize).
 
 ## Additional Options
 
@@ -315,8 +320,8 @@ These options provide extra functionality and customization when running the LLa
 
 -   `-h, --help`: Display a help message showing all available options and their default values. This is particularly useful for checking the latest options and default values, as they can change frequently, and the information in this document may become outdated.
 -   `--verbose-prompt`: Print the prompt before generating text.
--   `-ngl N, --n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
--   `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.
--   `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS.
+-   `-ngl N, --n-gpu-layers N`: When compiled with GPU support, this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
+-   `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used.
+-   `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance.
 -   `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
 -   `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index f5d2f4893..9dee41001 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -235,17 +235,17 @@ int main(int argc, char ** argv) {
             // The file exists and is not empty
             session_tokens.resize(n_ctx);
             size_t n_token_count_out = 0;
-            if (!llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
+            if (!llama_state_load_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
                 LOG_TEE("%s: error: failed to load session file '%s'\n", __func__, path_session.c_str());
                 return 1;
             }
             session_tokens.resize(n_token_count_out);
-            llama_set_rng_seed(ctx, params.seed);
             LOG_TEE("%s: loaded a session with prompt size of %d tokens\n", __func__, (int)session_tokens.size());
         }
     }
 
     const bool add_bos = llama_should_add_bos_token(model);
+    GGML_ASSERT(llama_add_eos_token(model) != 1);
     LOG("add_bos: %d\n", add_bos);
 
     std::vector<llama_token> embd_inp;
@@ -255,7 +255,7 @@ int main(int argc, char ** argv) {
         if (params.chatml) {
             params.prompt = "<|im_start|>system\n" + params.prompt + "<|im_end|>";
         }
-        embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
+        embd_inp = ::llama_tokenize(ctx, params.prompt, true, true);
     } else {
         LOG("use session tokens\n");
         embd_inp = session_tokens;
@@ -277,10 +277,10 @@ int main(int argc, char ** argv) {
     if (ctx_guidance) {
         LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt));
 
-        guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos, true);
+        guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, true, true);
         LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str());
 
-        std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
+        std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, true, true);
         LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str());
 
         original_prompt_len = original_inp.size();
@@ -324,7 +324,7 @@ int main(int argc, char ** argv) {
             log_tostr(embd_inp.empty()), n_matching_session_tokens, embd_inp.size(), session_tokens.size(), embd_inp.size());
 
     // if we will use the cache for the full prompt without reaching the end of the cache, force
-    // reevaluation of the last token token to recalculate the cached logits
+    // reevaluation of the last token to recalculate the cached logits
     if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() && session_tokens.size() > embd_inp.size()) {
         LOGLN("recalculate the cached logits (do): session_tokens.resize( %zu )", embd_inp.size() - 1);
 
@@ -334,17 +334,19 @@ int main(int argc, char ** argv) {
     // number of tokens to keep when resetting context
     if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct || params.chatml) {
         params.n_keep = (int)embd_inp.size();
+    } else {
+        params.n_keep += add_bos; // always keep the BOS token
     }
 
     // prefix & suffix for instruct mode
-    const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos, true);
-    const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n",    false,   true);
+    const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", true,  true);
+    const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n",    false, true);
 
     LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str());
     LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str());
 
     // chatml prefix & suffix
-    const auto cml_pfx = ::llama_tokenize(ctx, "\n<|im_start|>user\n", add_bos, true);
+    const auto cml_pfx = ::llama_tokenize(ctx, "\n<|im_start|>user\n", true, true);
     const auto cml_sfx = ::llama_tokenize(ctx, "<|im_end|>\n<|im_start|>assistant\n", false, true);
 
     LOG("cml_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_pfx).c_str());
@@ -360,6 +362,9 @@ int main(int argc, char ** argv) {
         params.interactive_first = true;
         params.antiprompt.emplace_back("<|im_start|>user\n");
     }
+    else if (params.conversation) {
+        params.interactive_first = true;
+    }
 
     // enable interactive mode if interactive start is specified
     if (params.interactive_first) {
@@ -383,8 +388,8 @@ int main(int argc, char ** argv) {
             }
         }
 
-        if (params.n_keep > 0) {
-        LOG_TEE("%s: static prompt based on n_keep: '", __func__);
+        if (params.n_keep > add_bos) {
+            LOG_TEE("%s: static prompt based on n_keep: '", __func__);
             for (int i = 0; i < params.n_keep; i++) {
                 LOG_TEE("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str());
             }
@@ -509,7 +514,19 @@ int main(int argc, char ** argv) {
     std::vector<llama_token> embd;
     std::vector<llama_token> embd_guidance;
 
+    // tokenized antiprompts
+    std::vector<std::vector<llama_token>> antiprompt_ids;
+
+    antiprompt_ids.reserve(params.antiprompt.size());
+    for (const std::string & antiprompt : params.antiprompt) {
+        antiprompt_ids.emplace_back(::llama_tokenize(ctx, antiprompt, false, true));
+    }
+
     struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams);
+    if (!ctx_sampling) {
+        fprintf(stderr, "%s: failed to initialize sampling subsystem\n", __func__);
+        exit(1);
+    }
 
     while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
         // predict
@@ -534,20 +551,20 @@ int main(int argc, char ** argv) {
                 // if we run out of context:
                 // - take the n_keep first tokens from the original prompt (via n_past)
                 // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
-                if (n_past + (int) embd.size() + std::max<int>(0, guidance_offset) > n_ctx) {
+                if (n_past + (int) embd.size() + std::max<int>(0, guidance_offset) >= n_ctx) {
                     if (params.n_predict == -2) {
                         LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
                         break;
                     }
 
-                    const int n_left    = n_past - params.n_keep - 1;
+                    const int n_left    = n_past - params.n_keep;
                     const int n_discard = n_left/2;
 
                     LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
                             n_past, n_left, n_ctx, params.n_keep, n_discard);
 
-                    llama_kv_cache_seq_rm   (ctx, 0, params.n_keep + 1            , params.n_keep + n_discard + 1);
-                    llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);
+                    llama_kv_cache_seq_rm (ctx, 0, params.n_keep            , params.n_keep + n_discard);
+                    llama_kv_cache_seq_add(ctx, 0, params.n_keep + n_discard, n_past, -n_discard);
 
                     n_past -= n_discard;
 
@@ -574,9 +591,9 @@ int main(int argc, char ** argv) {
                     LOG("div:   [%6d, %6d] / %6d -> [%6d, %6d]\n", ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n, (ga_i + ib*bd)/ga_n, (ga_i + ib*bd + ga_w)/ga_n);
                     LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i + ib*bd + ga_w, n_past + ib*bd, dd, ga_i + ib*bd + ga_w + dd, n_past + ib*bd + dd);
 
-                    llama_kv_cache_seq_shift(ctx, 0, ga_i,                n_past,              ib*bd);
-                    llama_kv_cache_seq_div  (ctx, 0, ga_i + ib*bd,        ga_i + ib*bd + ga_w, ga_n);
-                    llama_kv_cache_seq_shift(ctx, 0, ga_i + ib*bd + ga_w, n_past + ib*bd,      dd);
+                    llama_kv_cache_seq_add(ctx, 0, ga_i,                n_past,              ib*bd);
+                    llama_kv_cache_seq_div(ctx, 0, ga_i + ib*bd,        ga_i + ib*bd + ga_w, ga_n);
+                    llama_kv_cache_seq_add(ctx, 0, ga_i + ib*bd + ga_w, n_past + ib*bd,      dd);
 
                     n_past -= bd;
 
@@ -683,7 +700,7 @@ int main(int argc, char ** argv) {
             // optionally save the session on first sample (for faster prompt loading next time)
             if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) {
                 need_to_save_session = false;
-                llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
+                llama_state_save_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
 
                 LOG("saved session to %s\n", path_session.c_str());
             }
@@ -723,7 +740,7 @@ int main(int argc, char ** argv) {
         // display text
         if (input_echo && display) {
             for (auto id : embd) {
-                const std::string token_str = llama_token_to_piece(ctx, id);
+                const std::string token_str = llama_token_to_piece(ctx, id, !params.conversation);
                 printf("%s", token_str.c_str());
 
                 if (embd.size() > 1) {
@@ -767,14 +784,26 @@ int main(int argc, char ** argv) {
                     }
                 }
 
+                // check for reverse prompt using special tokens
+                llama_token last_token = llama_sampling_last(ctx_sampling);
+                for (std::vector<llama_token> ids : antiprompt_ids) {
+                    if (ids.size() == 1 && last_token == ids[0]) {
+                        if (params.interactive) {
+                            is_interacting = true;
+                        }
+                        is_antiprompt = true;
+                        break;
+                    }
+                }
+
                 if (is_antiprompt) {
                     LOG("found antiprompt: %s\n", last_output.c_str());
                 }
             }
 
-            // deal with end of text token in interactive mode
-            if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) {
-                LOG("found EOS token\n");
+            // deal with end of generation tokens in interactive mode
+            if (llama_token_is_eog(model, llama_sampling_last(ctx_sampling))) {
+                LOG("found an EOG token\n");
 
                 if (params.interactive) {
                     if (!params.antiprompt.empty()) {
@@ -794,7 +823,7 @@ int main(int argc, char ** argv) {
             if (n_past > 0 && is_interacting) {
                 LOG("waiting for user input\n");
 
-                if (params.instruct || params.chatml) {
+                if (params.conversation || params.instruct || params.chatml) {
                     printf("\n> ");
                 }
 
@@ -804,7 +833,7 @@ int main(int argc, char ** argv) {
                 }
 
                 std::string buffer;
-                if (!params.input_prefix.empty()) {
+                if (!params.input_prefix.empty() && !params.conversation) {
                     LOG("appending input prefix: '%s'\n", params.input_prefix.c_str());
                     printf("%s", params.input_prefix.c_str());
                 }
@@ -828,7 +857,7 @@ int main(int argc, char ** argv) {
                 // Entering a empty line lets the user pass control back
                 if (buffer.length() > 1) {
                     // append input suffix if any
-                    if (!params.input_suffix.empty()) {
+                    if (!params.input_suffix.empty() && !params.conversation) {
                         LOG("appending input suffix: '%s'\n", params.input_suffix.c_str());
                         printf("%s", params.input_suffix.c_str());
                     }
@@ -854,8 +883,9 @@ int main(int argc, char ** argv) {
                     }
 
                     const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true);
-                    const auto line_inp = ::llama_tokenize(ctx, buffer,              false, false);
+                    const auto line_inp = ::llama_tokenize(ctx, buffer,              false, params.interactive_specials);
                     const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true);
+
                     LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str());
 
                     embd_inp.insert(embd_inp.end(), line_pfx.begin(), line_pfx.end());
@@ -896,8 +926,8 @@ int main(int argc, char ** argv) {
             }
         }
 
-        // end of text token
-        if (!embd.empty() && embd.back() == llama_token_eos(model) && !(params.instruct || params.interactive || params.chatml)) {
+        // end of generation
+        if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !(params.instruct || params.interactive || params.chatml)) {
             LOG_TEE(" [end of text]\n");
             break;
         }
@@ -912,7 +942,7 @@ int main(int argc, char ** argv) {
 
     if (!path_session.empty() && params.prompt_cache_all && !params.prompt_cache_ro) {
         LOG_TEE("\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str());
-        llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
+        llama_state_save_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
     }
 
     llama_print_timings(ctx);
diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp
index 7d11fcd59..7c5595d6e 100644
--- a/examples/parallel/parallel.cpp
+++ b/examples/parallel/parallel.cpp
@@ -107,6 +107,9 @@ int main(int argc, char ** argv) {
     // number of simultaneous "clients" to simulate
     const int32_t n_clients = params.n_parallel;
 
+    // dedicate one sequence to the system prompt
+    params.n_parallel += 1;
+
     // requests to simulate
     const int32_t n_seq = params.n_sequences;
 
@@ -129,7 +132,6 @@ int main(int argc, char ** argv) {
     llama_context * ctx = NULL;
 
     // load the target model
-    params.logits_all = true;
     std::tie(model, ctx) = llama_init_from_gpt_params(params);
 
     // load the prompts from an external file if there are any
@@ -196,8 +198,8 @@ int main(int argc, char ** argv) {
         }
 
         // assign the system KV cache to all parallel sequences
-        for (int32_t i = 1; i < n_clients; ++i) {
-            llama_kv_cache_seq_cp(ctx, 0, i, 0, n_tokens_system);
+        for (int32_t i = 1; i <= n_clients; ++i) {
+            llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
         }
 
         LOG_TEE("\n");
@@ -221,15 +223,17 @@ int main(int argc, char ** argv) {
 
             client.i_batch = batch.n_tokens;
 
-            llama_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id }, true);
+            llama_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id + 1 }, true);
 
             client.n_decoded += 1;
         }
 
         if (batch.n_tokens == 0) {
             // all sequences have ended - clear the entire KV cache
-            for (int i = 0; i < n_clients; ++i) {
-                llama_kv_cache_seq_rm(ctx, i, n_tokens_system, -1);
+            for (int i = 1; i <= n_clients; ++i) {
+                llama_kv_cache_seq_rm(ctx, i, -1, -1);
+                // but keep the system prompt
+                llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
             }
 
             LOG_TEE("%s: clearing the KV cache\n", __func__);
@@ -255,7 +259,7 @@ int main(int argc, char ** argv) {
                     tokens_prompt = ::llama_tokenize(ctx, client.prompt, false);
 
                     for (size_t i = 0; i < tokens_prompt.size(); ++i) {
-                        llama_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id }, false);
+                        llama_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id + 1 }, false);
                     }
 
                     // extract the logits only for the last token
@@ -355,7 +359,7 @@ int main(int argc, char ** argv) {
                 //        client.id, client.seq_id, id, client.n_decoded, client.i_batch, token_str.c_str());
 
                 if (client.n_decoded > 2 &&
-                        (id == llama_token_eos(model) ||
+                        (llama_token_is_eog(model, id) ||
                          (params.n_predict > 0 && client.n_decoded + client.n_prompt >= params.n_predict) ||
                          client.response.find("User:") != std::string::npos ||
                          client.response.find('\n') != std::string::npos)) {
@@ -366,7 +370,8 @@ int main(int argc, char ** argv) {
                     }
 
                     // delete only the generated part of the sequence, i.e. keep the system prompt in the cache
-                    llama_kv_cache_seq_rm(ctx, client.id, n_tokens_system, -1);
+                    llama_kv_cache_seq_rm(ctx, client.id + 1, -1, -1);
+                    llama_kv_cache_seq_cp(ctx, 0, client.id + 1, -1, -1);
 
                     const auto t_main_end = ggml_time_us();
 
diff --git a/examples/passkey/passkey.cpp b/examples/passkey/passkey.cpp
index e12a1cdf1..f2ef9ca10 100644
--- a/examples/passkey/passkey.cpp
+++ b/examples/passkey/passkey.cpp
@@ -126,7 +126,7 @@ int main(int argc, char ** argv) {
     const int n_batch     = ctx_params.n_batch;
     const int n_batch_grp = ctx_params.n_batch/n_grp;
 
-    LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d, n_grp = %d, n_batch = %d\n", __func__, n_len, n_ctx, n_kv_req, n_grp, n_batch);
+    LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d, n_grp = %d, n_batch = %d, n_junk = %d, i_pos = %d\n", __func__, n_len, n_ctx, n_kv_req, n_grp, n_batch, n_junk, i_pos);
 
     // print the prompt token-by-token
 
@@ -146,10 +146,11 @@ int main(int argc, char ** argv) {
             const int ib = i/n_batch - 1;
             const int bd = n_batch_grp*(n_grp - 1);
 
-            llama_kv_cache_seq_shift(ctx, 0, n_past - n_batch,         n_past,         ib*bd);
-            llama_kv_cache_seq_div  (ctx, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp);
+            llama_kv_cache_seq_add (ctx, 0, n_past - n_batch,         n_past,         ib*bd);
+            llama_kv_cache_seq_div (ctx, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp);
+            llama_kv_cache_update  (ctx);
 
-            n_past -= bd;
+            n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1;
         }
 
         llama_batch_clear(batch);
@@ -179,10 +180,12 @@ int main(int argc, char ** argv) {
 
         LOG_TEE("%s: shifting KV cache with %d\n", __func__, n_discard);
 
-        llama_kv_cache_seq_rm   (ctx, 0, n_keep            , n_keep + n_discard);
-        llama_kv_cache_seq_shift(ctx, 0, n_keep + n_discard, n_ctx,  -n_discard);
+        llama_kv_cache_seq_rm (ctx, 0, n_keep            , n_keep + n_discard);
+        llama_kv_cache_seq_add(ctx, 0, n_keep + n_discard, n_ctx,  -n_discard);
+      //llama_kv_cache_defrag (ctx);
+        llama_kv_cache_update (ctx);
 
-        n_past -= n_discard;
+        n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1;
 
         llama_batch_clear(batch);
 
@@ -208,10 +211,12 @@ int main(int argc, char ** argv) {
         if (n_discard > 0) {
             LOG_TEE("%s: shifting KV cache with %d to free space for the answer\n", __func__, n_discard);
 
-            llama_kv_cache_seq_rm   (ctx, 0, n_keep            , n_keep + n_discard);
-            llama_kv_cache_seq_shift(ctx, 0, n_keep + n_discard, n_ctx,  -n_discard);
+            llama_kv_cache_seq_rm (ctx, 0, n_keep            , n_keep + n_discard);
+            llama_kv_cache_seq_add(ctx, 0, n_keep + n_discard, n_ctx,  -n_discard);
+          //llama_kv_cache_defrag (ctx);
+            llama_kv_cache_update (ctx);
 
-            n_past -= n_discard;
+            n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1;
         }
     }
 
@@ -247,8 +252,8 @@ int main(int argc, char ** argv) {
             // sample the most likely token
             const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
 
-            // is it an end of stream?
-            if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
+            // is it an end of generation?
+            if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
                 LOG_TEE("\n");
 
                 break;
diff --git a/examples/perplexity/README.md b/examples/perplexity/README.md
index 50e1af011..c2a3c5ce9 100644
--- a/examples/perplexity/README.md
+++ b/examples/perplexity/README.md
@@ -1,21 +1,190 @@
-# perplexity
+# Perplexity
 
-TODO
+The `perplexity` example can be used to calculate the so-called perplexity value of a language model over a given text corpus.
+Perplexity measures how well the model can predict the next token with lower values being better.
+Note that perplexity is **not** directly comparable between models, especially if they use different tokenizers.
+Also note that finetunes typically result in a higher perplexity value even though the human-rated quality of outputs increases.
 
-## Llama 2 70B Scorechart
-Quantization | Model size (GiB) | Perplexity | Delta to fp16
--- | -- | -- | --
-Q4_0 | 36.20 | 3.5550 | 3.61%
-Q4_1 | 40.20 | 3.5125 | 2.37%
-Q5_0 | 44.20 | 3.4744 | 1.26%
-Q2_K | 27.27 | 3.7339 | 8.82%
-Q3_K_S | 27.86 | 3.7019 | 7.89%
-Q3_K_M | 30.83 | 3.5932 | 4.72%
-Q3_K_L | 33.67 | 3.5617 | 3.80%
-Q4_K_S | 36.39 | 3.4852 | 1.57%
-Q4_K_M | 38.54 | 3.4725 | 1.20%
-Q5_K_S | 44.20 | 3.4483 | 0.50%
-Q5_K_M | 45.41 | 3.4451 | 0.40%
-Q6_K | 52.70 | 3.4367 | 0.16%
-fp16 | 128.5 | 3.4313 | -
+Within llama.cpp the perplexity of base models is used primarily to judge the quality loss from e.g. quantized models vs. FP16.
+The convention among contributors is to use the Wikitext-2 test set for testing unless noted otherwise (can be obtained with `scripts/get-wikitext-2.sh`).
+When numbers are listed all command line arguments and compilation options are left at their defaults unless noted otherwise.
+llama.cpp numbers are **not** directly comparable to those of other projects because the exact values depend strongly on the implementation details.
 
+By default only the mean perplexity value and the corresponding uncertainty is calculated.
+The uncertainty is determined empirically by assuming a Gaussian distribution of the "correct" logits per and then applying error propagation.
+
+More statistics can be obtained by recording the logits from the FP16 version of a model.
+To do this, supply `perplexity` with `--kl-divergence-base path/to/logit/binary/file.kld`.
+The program will then record all logits and save them to the provided path in binary format.
+**The logit file will be very large, 11 GiB for LLaMA 2 or 37 GiB for LLaMA 3 when using the Wikitext-2 test set.**
+Once you have the file, supply `perplexity` with the quantized model, the logits file via `--kl-divergence-base`,
+and finally the `--kl-divergence` argument to indicate that the program should calculate the so-called Kullback-Leibler divergence.
+This is a measure of how similar the FP16 and the quantized logit distributions are with a value of 0 indicating that the distribution are the same.
+The uncertainty on the mean KL divergence is calculated by assuming the KL divergence per token follows a Gaussian distribution.
+
+In addition to the KL divergence the following statistics are calculated with `--kl-divergence`:
+
+* Ratio of mean FP16 PPL and quantized PPL. Uncertainty is estimated on logits, then propagated. The logarithm of this metric is also calculated and printed, it is 0 if the logit distributions are the same.
+* Difference of mean FP16 PPL and quantized PPL. Uncertainty is estimated on logits, then propagated.
+* Mean change in "correct" token probability. Positive values mean the model gets better at prediction, negative values mean it gets worse.
+* Pearson correlation coefficient of the "correct" token probabilites between models.
+* Percentiles of change in "correct" token probability. Positive values mean the model gets better at prediction, negative values mean it gets worse. Can be used to judge noise vs. quality loss from quantization. If the percentiles are symmetric then the quantization is essentially just adding noise. If the negative values are significantly larger than the positive values then this indicates that the model is actually becoming worse from the quantization.
+* The root mean square of the change in token probabilities. If you were to assume that the quantization simply causes Gaussian noise on the token probabilities then this would be the standard deviation of said noise. The uncertainty on the value is calculated that the change in token probabilities follows a Gaussian distribution. Related discussion: https://github.com/ggerganov/llama.cpp/discussions/2875 .
+* Same top p: Percentage of how often the token was assigned the highest probabilites by both models. The uncertainty is calculated from the Gaussian approximation of the binomial distribution.
+
+## LLaMA 3 8b Scoreboard
+
+| Revision | f364eb6f           |
+|:---------|:-------------------|
+| Backend  | CUDA               |
+| CPU      | AMD Epyc 7742      |
+| GPU      | 1x NVIDIA RTX 4090 |
+
+Results were generated using the CUDA backend and are sorted by Kullback-Leibler divergence relative to FP16.
+The "WT" importance matrices were created using varying numbers of Wikitext tokens and can be found [here](https://huggingface.co/JohannesGaessler/llama.cpp_importance_matrices/blob/main/imatrix-llama_3-8b-f16-2.7m_tokens.dat).
+
+| Quantization | imatrix | Model size [GiB] | PPL                    | ΔPPL                   | KLD                   | Mean Δp           | RMS Δp           |
+|--------------|---------|------------------|------------------------|------------------------|-----------------------|-------------------|------------------|
+| f16          | None    |            14.97 | 6.233160 ±   0.037828  | -                      | -                     | -                 | -                |
+| q8_0         | None    |             7.96 | 6.234284 ±   0.037878  | 0.002650 ±   0.001006  | 0.001355 ±   0.000006 | -0.019 ± 0.003 %  | 1.198 ± 0.007 %  |
+| q6_K         | None    |             6.14 | 6.253382 ±   0.038078  | 0.021748 ±   0.001852  | 0.005452 ±   0.000035 | -0.007 ± 0.006 %  | 2.295 ± 0.019 %  |
+| q5_K_M       | None    |             5.33 | 6.288607 ±   0.038338  | 0.056974 ±   0.002598  | 0.010762 ±   0.000079 | -0.114 ± 0.008 %  | 3.160 ± 0.031 %  |
+| q5_K_S       | None    |             5.21 | 6.336598 ±   0.038755  | 0.104964 ±   0.003331  | 0.016595 ±   0.000122 | -0.223 ± 0.010 %  | 3.918 ± 0.036 %  |
+| q5_1         | None    |             5.65 | 6.337857 ±   0.038677  | 0.106223 ±   0.003476  | 0.018045 ±   0.000139 | -0.287 ± 0.011 %  | 4.123 ± 0.039 %  |
+| q5_0         | None    |             5.21 | 6.363224 ±   0.038861  | 0.131591 ±   0.003894  | 0.022239 ±   0.000166 | -0.416 ± 0.012 %  | 4.634 ± 0.043 %  |
+| q4_K_M       | WT 10m  |             4.58 | 6.382937 ±   0.039055  | 0.151303 ±   0.004429  | 0.028152 ±   0.000240 | -0.389 ± 0.014 %  | 5.251 ± 0.049 %  |
+| q4_K_M       | None    |             4.58 | 6.407115 ±   0.039119  | 0.175482 ±   0.004620  | 0.031273 ±   0.000238 | -0.596 ± 0.014 %  | 5.519 ± 0.050 %  |
+| q4_K_S       | WT 10m  |             4.37 | 6.409697 ±   0.039189  | 0.178064 ±   0.004744  | 0.031951 ±   0.000259 | -0.531 ± 0.015 %  | 5.645 ± 0.051 %  |
+| iq4_NL       | WT 10m  |             4.35 | 6.455593 ±   0.039630  | 0.223959 ±   0.005201  | 0.035742 ±   0.000288 | -0.590 ± 0.016 %  | 5.998 ± 0.054 %  |
+| iq4_XS       | WT 10m  |             4.14 | 6.459705 ±   0.039595  | 0.228071 ±   0.005207  | 0.036334 ±   0.000284 | -0.668 ± 0.016 %  | 6.044 ± 0.054 %  |
+| q4_K_S       | None    |             4.37 | 6.500529 ±   0.039778  | 0.268895 ±   0.005638  | 0.043136 ±   0.000314 | -0.927 ± 0.017 %  | 6.562 ± 0.055 %  |
+| q4_1         | None    |             4.78 | 6.682737 ±   0.041285  | 0.451103 ±   0.008030  | 0.071683 ±   0.000505 | -0.927 ± 0.017 %  | 8.512 ± 0.063 %  |
+| q4_0         | None    |             4.34 | 6.700147 ±   0.041226  | 0.468514 ±   0.007951  | 0.071940 ±   0.000491 | -1.588 ± 0.022 %  | 8.434 ± 0.061 %  |
+| q3_K_L       | WT 10m  |             4.03 | 6.671223 ±   0.041427  | 0.439590 ±   0.008154  | 0.073077 ±   0.000529 | -0.940 ± 0.023 %  | 8.662 ± 0.064 %  |
+| q3_K_M       | WT 10m  |             3.74 | 6.734255 ±   0.041838  | 0.502622 ±   0.008901  | 0.084358 ±   0.000588 | -1.198 ± 0.024 %  | 9.292 ± 0.065 %  |
+| q3_K_L       | None    |             4.03 | 6.787876 ±   0.042104  | 0.556242 ±   0.009171  | 0.087176 ±   0.000614 | -1.532 ± 0.025 %  | 9.432 ± 0.067 %  |
+| q3_K_M       | None    |             3.74 | 6.888498 ±   0.042669  | 0.656864 ±   0.010071  | 0.101913 ±   0.000677 | -1.990 ± 0.026 %  | 10.203 ± 0.068 % |
+| iq3_M        | WT 10m  |             3.53 | 6.898327 ±   0.041643  | 0.666694 ±   0.009449  | 0.102534 ±   0.000663 | -3.178 ± 0.026 %  | 10.513 ± 0.066 % |
+| iq3_S        | WT 10m  |             3.42 | 6.965501 ±   0.042406  | 0.733867 ±   0.010245  | 0.111278 ±   0.000710 | -3.066 ± 0.027 %  | 10.845 ± 0.068 % |
+| iq3_XS       | WT 10m  |             3.28 | 7.163043 ±   0.043772  | 0.931409 ±   0.012084  | 0.138693 ±   0.000857 | -3.667 ± 0.031 %  | 12.148 ± 0.070 % |
+| iq3_XXS      | WT 10m  |             3.05 | 7.458436 ±   0.046404  | 1.226803 ±   0.015234  | 0.183625 ±   0.001042 | -3.918 ± 0.035 %  | 13.836 ± 0.074 % |
+| q3_K_S       | WT 10m  |             3.41 | 7.602878 ±   0.046848  | 1.371244 ±   0.015688  | 0.199821 ±   0.001008 | -5.046 ± 0.037 %  | 14.980 ± 0.070 % |
+| q3_K_S       | None    |             3.41 | 7.863786 ±   0.048885  | 1.632152 ±   0.017733  | 0.228217 ±   0.001079 | -5.604 ± 0.038 %  | 15.541 ± 0.070 % |
+| iq2_M        | WT 10m  |             2.74 | 8.600799 ±   0.055124  | 2.369166 ±   0.025244  | 0.325989 ±   0.00160  | -6.463 ± 0.046 %  | 18.519 ± 0.080 % |
+| q2_K         | WT 10k  |             2.96 | 8.652290 ±   0.055572  | 2.420657 ±   0.025587  | 0.331393 ±   0.001562 | -6.606 ± 0.046 %  | 18.790 ± 0.078 % |
+| q2_K         | WT 100k |             2.96 | 8.641993 ±   0.055406  | 2.410359 ±   0.025495  | 0.331672 ±   0.001569 | -6.628 ± 0.047 %  | 18.856 ± 0.078 % |
+| q2_K         | WT 10m  |             2.96 | 8.647825 ±   0.055610  | 2.416191 ±   0.025683  | 0.332223 ±   0.001572 | -6.500 ± 0.047 %  | 18.881 ± 0.078 % |
+| q2_K         | WT 1m   |             2.96 | 8.674365 ±   0.055743  | 2.442732 ±   0.025843  | 0.335308 ±   0.001576 | -6.634 ± 0.047 %  | 19.009 ± 0.079 % |
+| q2_K         | WT 1k   |             2.96 | 8.682605 ±   0.055916  | 2.450972 ±   0.026069  | 0.337093 ±   0.001596 | -6.596 ± 0.047 %  | 18.977 ± 0.079 % |
+| q2_K_S       | WT 10m  |             2.96 | 9.323778 ±   0.061551  | 3.092145 ±   0.031914  | 0.403360 ±   0.001787 | -7.131 ± 0.049 %  | 20.050 ± 0.081 % |
+| q2_K_S       | WT 1m   |             2.96 | 9.329321 ±   0.061378  | 3.097688 ±   0.031816  | 0.403590 ±   0.001797 | -7.289 ± 0.049 %  | 20.123 ± 0.081 % |
+| q2_K_S       | WT 100k |             2.96 | 9.362973 ±   0.061740  | 3.131339 ±   0.032169  | 0.408367 ±   0.001802 | -7.198 ± 0.050 %  | 20.132 ± 0.081 % |
+| q2_K_S       | WT 10k  |             2.96 | 9.376479 ±   0.062045  | 3.144846 ±   0.032464  | 0.408662 ±   0.001819 | -7.141 ± 0.050 %  | 20.120 ± 0.081 % |
+| q2_K_S       | WT 1k   |             2.96 | 9.415200 ±   0.062475  | 3.183567 ±   0.032993  | 0.415865 ±   0.001846 | -7.153 ± 0.050 %  | 20.311 ± 0.082 % |
+| iq2_S        | WT 10m  |             2.56 | 9.650781 ±   0.063209  | 3.419148 ±   0.034017  | 0.439197 ±   0.001976 | -8.319 ± 0.052 %  | 21.491 ± 0.083 % |
+| q2_K         | None    |             2.96 | 9.751568 ±   0.063312  | 3.519934 ±   0.033863  | 0.445132 ±   0.001835 | -9.123 ± 0.051 %  | 21.421 ± 0.079 % |
+| iq2_XS       | WT 10m  |             2.43 | 10.761424 ±   0.071056 | 4.529791 ±   0.042229  | 0.546290 ±   0.002133 | -10.576 ± 0.056 % | 23.872 ± 0.082 % |
+| iq2_XXS      | WT 10m  |             2.24 | 14.091782 ±   0.098396 | 7.860148 ±   0.070752  | 0.812022 ±   0.002741 | -14.363 ± 0.065 % | 28.576 ± 0.084 % |
+| iq1_M        | WT 10m  |             2.01 | 25.493722 ±   0.177903 | 19.262089 ±   0.152396 | 1.393084 ±   0.003529 | -24.672 ± 0.077 % | 38.287 ± 0.084 % |
+| iq1_S        | WT 1m   |             1.88 | 58.097760 ±   0.438604 | 51.866126 ±   0.416604 | 2.211278 ±   0.004688 | -32.471 ± 0.087 % | 46.418 ± 0.085 % |
+| iq1_S        | WT 1k   |             1.88 | 58.267851 ±   0.446208 | 52.036218 ±   0.424373 | 2.214858 ±   0.004778 | -31.880 ± 0.089 % | 46.330 ± 0.086 % |
+| iq1_S        | WT 100k |             1.88 | 58.581498 ±   0.453145 | 52.349864 ±   0.431360 | 2.220834 ±   0.004818 | -32.261 ± 0.089 % | 46.002 ± 0.086 % |
+| iq1_S        | WT 10m  |             1.88 | 60.694593 ±   0.471290 | 54.462959 ±   0.449644 | 2.254554 ±   0.004868 | -31.973 ± 0.088 % | 46.271 ± 0.086 % |
+| iq1_S        | WT 10k  |             1.88 | 63.221324 ±   0.493077 | 56.989691 ±   0.471423 | 2.293527 ±   0.004885 | -32.261 ± 0.089 % | 46.562 ± 0.086 % |
+
+There seems to be no consistent improvement from using more Wikitext tokens for the importance matrix.
+K-quants score better on mean Δp than the legacy quants than e.g. KL divergence would suggest.
+
+## LLaMA 2 vs. LLaMA 3 Quantization comparison
+
+| Revision | f364eb6f           |
+|:---------|:-------------------|
+| Backend  | CUDA               |
+| CPU      | AMD Epyc 7742      |
+| GPU      | 1x NVIDIA RTX 4090 |
+
+| Metric          |          L2 7b q2_K |          L3 8b q2_K |        L2 7b q4_K_M |        L3 8b q4_K_M |          L2 7b q6_K |          L3 8b q6_K |          L2 7b q8_0 |          L3 8b q8_0 |
+|-----------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------|
+| Mean PPL        | 5.794552 ± 0.032298 | 9.751568 ± 0.063312 | 5.877078 ± 0.032781 | 6.407115 ± 0.039119 | 5.808494 ± 0.032425 | 6.253382 ± 0.038078 | 5.798542 ± 0.032366 | 6.234284 ± 0.037878 |
+| Mean PPL ratio  | 1.107955 ± 0.001427 | 1.564849 ± 0.004525 | 1.014242 ± 0.000432 | 1.028160 ± 0.000723 | 1.002406 ± 0.000191 | 1.003490 ± 0.000296 | 1.000689 ± 0.000107 | 1.000425 ± 0.000161 |
+| Mean ΔPPL       | 0.625552 ± 0.008725 | 3.519934 ± 0.033863 | 0.082526 ± 0.002530 | 0.175482 ± 0.004620 | 0.013941 ± 0.001110 | 0.021748 ± 0.001852 | 0.003990 ± 0.000624 | 0.002650 ± 0.001006 |
+| PPL correlation |              97.36% |              89.62% |              99.71% |              99.34% |              99.94% |              99.88% |              99.98% |              99.96% |
+| Mean KLD        | 0.108903 ± 0.000645 | 0.445132 ± 0.001835 | 0.012686 ± 0.000079 | 0.031273 ± 0.000238 | 0.002098 ± 0.000014 | 0.005452 ± 0.000035 | 0.000369 ± 0.000007 | 0.001355 ± 0.000006 |
+| Mean Δp         |    -2.710 ± 0.023 % |    -9.123 ± 0.051 % |    -0.416 ± 0.008 % |    -0.596 ± 0.014 % |    -0.035 ± 0.003 % |    -0.007 ± 0.006 % |    -0.005 ± 0.002 % |    -0.019 ± 0.003 % |
+| Maximum Δp      |             85.136% |             94.268% |             45.209% |             95.054% |             23.593% |             53.601% |             43.925% |             28.734% |
+| 99.9% Δp        |             37.184% |             50.003% |             17.461% |             27.084% |              7.798% |             13.613% |              3.387% |              6.402% |
+| 99.0% Δp        |             18.131% |             25.875% |              7.798% |             12.084% |              3.838% |              6.407% |              1.867% |              3.544% |
+| Median Δp       |             -0.391% |             -2.476% |             -0.026% |             -0.024% |             -0.001% |              0.000% |             -0.000% |             -0.000% |
+| 1.0% Δp         |            -39.762% |            -87.173% |            -11.433% |            -19.567% |             -4.222% |             -6.767% |             -1.862% |             -3.698% |
+| 0.1% Δp         |            -79.002% |            -98.897% |            -26.433% |            -56.054% |             -9.091% |            -16.584% |             -3.252% |             -6.579% |
+| Minimum Δp      |            -99.915% |            -99.965% |            -83.383% |            -98.699% |            -43.142% |            -68.487% |             -9.343% |            -24.301% |
+| RMS Δp          |     9.762 ± 0.053 % |    21.421 ± 0.079 % |     3.252 ± 0.024 % |     5.519 ± 0.050 % |     1.339 ± 0.010 % |     2.295 ± 0.019 % |     0.618 ± 0.011 % |     1.198 ± 0.007 % |
+| Same top p      |    85.584 ± 0.086 % |    71.138 ± 0.119 % |    94.665 ± 0.055 % |    91.901 ± 0.072 % |    97.520 ± 0.038 % |    96.031 ± 0.051 % |    98.846 ± 0.026 % |    97.674 ± 0.040 % |
+
+## LLaMA 3 BF16 vs. FP16 comparison
+
+| Revision | 83330d8c      |
+|:---------|:--------------|
+| Backend  | CPU           |
+| CPU      | AMD Epyc 7742 |
+| GPU      | N/A           |
+
+Results were calculated with LLaMA 3 8b BF16 as `--kl-divergence-base` and LLaMA 3 8b FP16 as the `--model` for comparison.
+
+| Metric                         |                    Value |
+|--------------------------------|--------------------------|
+| Mean PPL(Q)                    |      6.227711 ± 0.037833 |
+| Mean PPL(base)                 |      6.225194 ± 0.037771 |
+| Cor(ln(PPL(Q)), ln(PPL(base))) |                  99.990% |
+| Mean ln(PPL(Q)/PPL(base))      |      0.000404 ± 0.000086 |
+| Mean PPL(Q)/PPL(base)          |      1.000404 ± 0.000086 |
+| Mean PPL(Q)-PPL(base)          |      0.002517 ± 0.000536 |
+| Mean    KLD                    |  0.00002515 ± 0.00000020 |
+| Maximum KLD                    |                 0.012206 |
+| 99.9%   KLD                    |                 0.000799 |
+| 99.0%   KLD                    |                 0.000222 |
+| 99.0%   KLD                    |                 0.000222 |
+| Median  KLD                    |                 0.000013 |
+| 10.0%   KLD                    |                -0.000002 |
+| 5.0%   KLD                     |                -0.000008 |
+| 1.0%   KLD                     |                -0.000023 |
+| Minimum KLD                    |                -0.000059 |
+| Mean    Δp                     | -0.0000745 ± 0.0003952 % |
+| Maximum Δp                     |                   4.186% |
+| 99.9%   Δp                     |                   1.049% |
+| 99.0%   Δp                     |                   0.439% |
+| 95.0%   Δp                     |                   0.207% |
+| 90.0%   Δp                     |                   0.125% |
+| 75.0%   Δp                     |                   0.029% |
+| Median  Δp                     |                   0.000% |
+| 25.0%   Δp                     |                  -0.030% |
+| 10.0%   Δp                     |                  -0.126% |
+| 5.0%   Δp                      |                  -0.207% |
+| 1.0%   Δp                      |                  -0.434% |
+| 0.1%   Δp                      |                  -1.016% |
+| Minimum Δp                     |                  -4.672% |
+| RMS Δp                         |          0.150 ± 0.001 % |
+| Same top p                     |         99.739 ± 0.013 % |
+
+## Old Numbers
+
+<details>
+<summary>Llama 2 70B Scoreboard</summary>
+
+| Quantization | Model size (GiB) | Perplexity | Delta to fp16 |
+|--------------|------------------|------------|---------------|
+| Q4_0         | 36.20            | 3.5550     | 3.61%         |
+| Q4_1         | 40.20            | 3.5125     | 2.37%         |
+| Q5_0         | 44.20            | 3.4744     | 1.26%         |
+| Q2_K         | 27.27            | 3.7339     | 8.82%         |
+| Q3_K_S       | 27.86            | 3.7019     | 7.89%         |
+| Q3_K_M       | 30.83            | 3.5932     | 4.72%         |
+| Q3_K_L       | 33.67            | 3.5617     | 3.80%         |
+| Q4_K_S       | 36.39            | 3.4852     | 1.57%         |
+| Q4_K_M       | 38.54            | 3.4725     | 1.20%         |
+| Q5_K_S       | 44.20            | 3.4483     | 0.50%         |
+| Q5_K_M       | 45.41            | 3.4451     | 0.40%         |
+| Q6_K         | 52.70            | 3.4367     | 0.16%         |
+| fp16         | 128.5            | 3.4313     | -             |
+
+</details>
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
index 9ec989389..db6e0949d 100644
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -216,17 +216,22 @@ static void process_logits(std::ostream& out, int n_vocab, const float * logits,
 }
 
 struct kl_divergence_result {
-    double sum_nll  = 0;
-    double sum_nll2 = 0;
-    double sum_kld  = 0;
-    double sum_kld2 = 0;
-    double sum_nll_diff  = 0;
-    double sum_nll_diff2 = 0;
-    size_t n_same_top = 0;
-    size_t count = 0;
+    double sum_nll          = 0.0;
+    double sum_nll2         = 0.0;
+    double sum_nll_base     = 0.0;
+    double sum_nll_base2    = 0.0;
+    double sum_nll_nll_base = 0.0;
+    double sum_kld          = 0.0;
+    double sum_kld2         = 0.0;
+    double sum_p_diff       = 0.0;
+    double sum_p_diff2      = 0.0;
+    double sum_p_diff4      = 0.0;
+    float  max_p_diff       = 0.0f;
+    size_t n_same_top       = 0.0;
+    size_t count            = 0.0;
 };
 
-static double log_softmax(int n_vocab, const float * logits, const uint16_t * base_log_prob, int tok, kl_divergence_result & kld) {
+static std::pair<double, float> log_softmax(int n_vocab, const float * logits, const uint16_t * base_log_prob, int tok, kl_divergence_result & kld) {
     float max_logit = logits[0];
     int imax = 0;
     for (int i = 1; i < n_vocab; ++i) {
@@ -244,12 +249,17 @@ static double log_softmax(int n_vocab, const float * logits, const uint16_t * ba
     const float scale = d[0];
     const float min_log_prob = d[1];
     base_log_prob += 4;
-    float nll = max_logit + log_sum_exp - logits[tok];
+
+    const float nll = max_logit + log_sum_exp - logits[tok];
     kld.sum_nll  += nll;
     kld.sum_nll2 += nll*nll;
-    nll += (scale*base_log_prob[tok] + min_log_prob);
-    kld.sum_nll_diff  += nll;
-    kld.sum_nll_diff2 += nll*nll;
+
+    const float nll_base = -(scale*base_log_prob[tok] + min_log_prob);
+    kld.sum_nll_base  += nll_base;
+    kld.sum_nll_base2 += nll_base*nll_base;
+
+    kld.sum_nll_nll_base += nll*nll_base;
+
     max_logit += log_sum_exp;
     double sum = 0;
     int imax_base = -1;
@@ -269,34 +279,50 @@ static double log_softmax(int n_vocab, const float * logits, const uint16_t * ba
     kld.sum_kld2 += sum*sum;
     ++kld.count;
     if (imax == imax_base) ++kld.n_same_top;
-    return sum;
+
+    const float p_base = expf(-nll_base);
+    const float p = expf(-nll);
+    const float p_diff = p - p_base;
+    kld.sum_p_diff  += p_diff;
+    const double p_diff2 = p_diff*p_diff;
+    kld.sum_p_diff2 += p_diff2;
+    kld.sum_p_diff4 += p_diff2*p_diff2;
+    kld.max_p_diff = std::max(kld.max_p_diff, std::fabs(p_diff));
+
+    return std::make_pair(sum, p_diff);
 }
 
 static void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token,
         std::vector<std::thread> & workers, const std::vector<uint16_t> & base_log_probs, kl_divergence_result & kld,
-        float * kld_values) {
+        float * kld_values, float * p_diff_values) {
     std::mutex mutex;
     const int nv = 2*((n_vocab + 1)/2) + 4;
     int counter = 0;
-    auto compute = [&mutex, &counter, &base_log_probs, &kld, n_vocab, logits, tokens, n_token, nv, kld_values] () {
+    auto compute = [&mutex, &counter, &base_log_probs, &kld, n_vocab, logits, tokens, n_token, nv, kld_values, p_diff_values] () {
         kl_divergence_result local_kld;
         while (true) {
             std::unique_lock<std::mutex> lock(mutex);
             int i = counter++;
             if (i >= n_token) {
-                kld.sum_nll  += local_kld.sum_nll;
-                kld.sum_nll2 += local_kld.sum_nll2;
-                kld.sum_kld  += local_kld.sum_kld;
-                kld.sum_kld2 += local_kld.sum_kld2;
-                kld.sum_nll_diff  += local_kld.sum_nll_diff;
-                kld.sum_nll_diff2 += local_kld.sum_nll_diff2;
-                kld.n_same_top += local_kld.n_same_top;
-                kld.count += local_kld.count;
+                kld.sum_nll          += local_kld.sum_nll;
+                kld.sum_nll2         += local_kld.sum_nll2;
+                kld.sum_nll_base     += local_kld.sum_nll_base;
+                kld.sum_nll_base2    += local_kld.sum_nll_base2;
+                kld.sum_nll_nll_base += local_kld.sum_nll_nll_base;
+                kld.sum_kld          += local_kld.sum_kld;
+                kld.sum_kld2         += local_kld.sum_kld2;
+                kld.sum_p_diff       += local_kld.sum_p_diff;
+                kld.sum_p_diff2      += local_kld.sum_p_diff2;
+                kld.sum_p_diff4      += local_kld.sum_p_diff4;
+                kld.n_same_top       += local_kld.n_same_top;
+                kld.max_p_diff        = std::max(kld.max_p_diff, local_kld.max_p_diff);
+                kld.count            += local_kld.count;
                 break;
             }
             lock.unlock();
-            double v = log_softmax(n_vocab, logits + i*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld);
-            kld_values[i] = (float)v;
+            std::pair<double, float> v = log_softmax(n_vocab, logits + i*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld);
+            kld_values[i]    = (float)v.first;
+            p_diff_values[i] = v.second;
         }
     };
     for (auto & w : workers) {
@@ -315,10 +341,11 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
     // BOS tokens will be added for each chunk before eval
 
     const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
+    GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1);
 
     fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
 
-    std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
+    std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, true);
 
     const int n_ctx = llama_n_ctx(ctx);
 
@@ -380,6 +407,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
             const int batch_size  = std::min(end - batch_start, n_batch);
 
             //fprintf(stderr, "    Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch);
+            // TODO: use llama_batch.logits instead of relying on logits_all == true
             if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
                 //fprintf(stderr, "%s : failed to eval\n", __func__);
                 return {tokens, -1, logit_history, prob_history};
@@ -442,7 +470,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
     return {tokens, std::exp(nll / count), logit_history, prob_history};
 }
 
-static results_perplexity perplexity(llama_context * ctx, const gpt_params & params) {
+static results_perplexity perplexity(llama_context * ctx, const gpt_params & params, const int32_t n_ctx) {
     if (params.ppl_stride > 0) {
         return perplexity_v2(ctx, params);
     }
@@ -453,7 +481,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
     // BOS tokens will be added for each chunk before eval
 
     const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
-    const int n_ctx = llama_n_ctx(ctx);
+    GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1);
 
     std::ofstream logits_stream;
     if (!params.logits_file.empty()) {
@@ -470,7 +498,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
     auto tim1 = std::chrono::high_resolution_clock::now();
     fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
 
-    std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
+    std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, true);
 
     auto tim2 = std::chrono::high_resolution_clock::now();
     fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
@@ -499,13 +527,19 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
     double nll2 = 0.0;
 
     const int num_batches = (n_ctx + n_batch - 1) / n_batch;
+    const int n_seq = std::max(1, n_batch / n_ctx);
+
+    GGML_ASSERT(n_batch < n_ctx || n_batch % n_ctx == 0);
+    GGML_ASSERT(params.n_ctx == n_seq * n_ctx);
+
+    llama_batch batch = llama_batch_init(std::min(n_batch, n_ctx*n_seq), 0, 1);
 
     std::vector<float> logits;
     if (num_batches > 1) {
         logits.reserve((size_t)n_ctx * n_vocab);
     }
 
-    fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch);
+    fprintf(stderr, "%s: calculating perplexity over %d chunks, n_ctx=%d, batch_size=%d, n_seq=%d\n", __func__, n_chunk, n_ctx, n_batch, n_seq);
 
     std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
 
@@ -518,10 +552,26 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
         log_probs.resize(n_ctx * nv);
     }
 
-    for (int i = 0; i < n_chunk; ++i) {
+    // We get the logits for all the tokens in the context window (params.n_ctx)
+    // from llama_eval above.  Now, based on https://huggingface.co/docs/transformers/perplexity,
+    // calculate the perplexity over the last half of the window (so the model always has
+    // some context to predict the token).
+    //
+    // We rely on the fact that attention in the forward pass only looks at previous
+    // tokens here, so the logits returned for each token are an accurate representation
+    // of what the model would have predicted at that point.
+    //
+    // Example, we have a context window of 512, we will compute perplexity for each of the
+    // last 256 tokens.  Then, we split the input up into context window size chunks to
+    // process the entire prompt.
+    const int first = n_ctx/2;
+
+    for (int i = 0; i < n_chunk; i += n_seq) {
         const int start =     i * n_ctx;
         const int end   = start + n_ctx;
 
+        const int n_seq_batch = std::min(n_seq, n_chunk - i);
+
         const auto t_start = std::chrono::high_resolution_clock::now();
 
         // clear the KV cache
@@ -531,34 +581,54 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
             const int batch_start = start + j * n_batch;
             const int batch_size  = std::min(end - batch_start, n_batch);
 
-            // save original token and restore it after eval
-            const auto token_org = tokens[batch_start];
+            int n_outputs = 0;
 
-            // add BOS token for the first batch of each chunk
-            if (add_bos && j == 0) {
-                tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
+            batch.n_tokens = 0;
+            for (int seq = 0; seq < n_seq_batch; seq++) {
+                int seq_start = batch_start + seq*n_ctx;
+
+                // save original token and restore it after eval
+                const auto token_org = tokens[seq_start];
+
+                // add BOS token for the first batch of each chunk
+                if (add_bos && j == 0) {
+                    tokens[seq_start] = llama_token_bos(llama_get_model(ctx));
+                }
+
+                for (int k = 0; k < batch_size; ++k) {
+                    const int idx = seq*n_ctx + k;
+                    batch.token   [idx]    = tokens[seq_start + k];
+                    batch.pos     [idx]    = j*n_batch + k;
+                    batch.n_seq_id[idx]    = 1;
+                    batch.seq_id  [idx][0] = seq;
+                    batch.logits  [idx]    = batch.pos[idx] >= first ? 1 : 0;
+
+                    n_outputs += batch.logits[idx] != 0;
+                }
+                batch.n_tokens += batch_size;
+
+                // restore the original token in case it was set to BOS
+                tokens[seq_start] = token_org;
             }
 
-            if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
+            if (llama_decode(ctx, batch)) {
                 fprintf(stderr, "%s : failed to eval\n", __func__);
                 return {tokens, -1, logit_history, prob_history};
             }
 
-            // restore the original token in case it was set to BOS
-            tokens[batch_start] = token_org;
-
-            if (num_batches > 1) {
+            if (num_batches > 1 && n_outputs > 0) {
                 const auto * batch_logits = llama_get_logits(ctx);
-                logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
+                logits.insert(logits.end(), batch_logits, batch_logits + n_outputs * n_vocab);
             }
         }
 
-        const auto t_end = std::chrono::high_resolution_clock::now();
 
         if (i == 0) {
+            llama_synchronize(ctx);
+            const auto t_end = std::chrono::high_resolution_clock::now();
             const float t_total = std::chrono::duration<float>(t_end - t_start).count();
             fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total);
-            int total_seconds = (int)(t_total * n_chunk);
+            int total_seconds = (int)(t_total*n_chunk/n_seq);
             if (total_seconds >= 60*60) {
                 fprintf(stderr, "%d hours ", total_seconds / (60*60));
                 total_seconds = total_seconds % (60*60);
@@ -566,37 +636,32 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
             fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
         }
 
-        // We get the logits for all the tokens in the context window (params.n_ctx)
-        // from llama_eval above.  Now, based on https://huggingface.co/docs/transformers/perplexity,
-        // calculate the perplexity over the last half of the window (so the model always has
-        // some context to predict the token).
-        //
-        // We rely on the fact that attention in the forward pass only looks at previous
-        // tokens here, so the logits returned for each token are an accurate representation
-        // of what the model would have predicted at that point.
-        //
-        // Example, we have a context window of 512, we will compute perplexity for each of the
-        // last 256 tokens.  Then, we split the input up into context window size chunks to
-        // process the entire prompt.
-        const int first = n_ctx/2;
-        const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
-        if (!params.logits_file.empty()) {
-            process_logits(logits_stream, n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
-                    workers, log_probs, nll, nll2);
-        } else {
-            process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
-                    workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);
-        }
-        count += n_ctx - first - 1;
+        for (int seq = 0; seq < n_seq_batch; seq++) {
+            const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits_ith(ctx, seq*n_ctx + first);
 
-        // perplexity is e^(average negative log-likelihood)
-        if (params.ppl_output_type == 0) {
-            printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
-        } else {
-            double av = nll/count;
-            double av2 = nll2/count - av*av;
-            if (av2 > 0) av2 = sqrt(av2/(count-1));
-            printf("%8d  %.4lf  %4lf  %4lf\n", i*n_ctx, std::exp(nll / count), av, av2);
+            llama_token * tokens_data = tokens.data() + start + seq*n_ctx + first;
+            if (!params.logits_file.empty()) {
+                process_logits(logits_stream, n_vocab, all_logits,
+                        tokens_data, n_ctx - 1 - first,
+                        workers, log_probs, nll, nll2);
+            } else {
+                process_logits(n_vocab, all_logits,
+                        tokens_data, n_ctx - 1 - first,
+                        workers, nll, nll2,
+                        logit_history.data() + start + seq*n_ctx + first,
+                        prob_history.data()  + start + seq*n_ctx + first);
+            }
+            count += n_ctx - first - 1;
+
+            // perplexity is e^(average negative log-likelihood)
+            if (params.ppl_output_type == 0) {
+                printf("[%d]%.4lf,", i + seq + 1, std::exp(nll / count));
+            } else {
+                double av = nll/count;
+                double av2 = nll2/count - av*av;
+                if (av2 > 0) av2 = sqrt(av2/(count-1));
+                printf("%8d  %.4lf  %4lf  %4lf\n", i*n_ctx, std::exp(nll / count), av, av2);
+            }
         }
         fflush(stdout);
 
@@ -615,10 +680,13 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
         printf("Unexpected negative standard deviation of log(prob)\n");
     }
 
+    llama_batch_free(batch);
+
     return {tokens, ppl, logit_history, prob_history};
 }
 
 static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<float> & batch_logits, int32_t n_batch, int32_t n_vocab) {
+    int prev_outputs = 0;
     for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
         const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
 
@@ -639,7 +707,14 @@ static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<
             return false;
         }
 
-        memcpy(batch_logits.data() + i*n_vocab, llama_get_logits(ctx), n_tokens*n_vocab*sizeof(float));
+        int n_outputs = 0;
+        for (int i = 0; i < n_tokens; ++i) {
+            n_outputs += batch_view.logits[i] != 0;
+        }
+
+        memcpy(batch_logits.data() + prev_outputs*n_vocab, llama_get_logits(ctx), n_outputs*n_vocab*sizeof(float));
+
+        prev_outputs += n_outputs;
     }
 
     return true;
@@ -724,9 +799,6 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
     const bool is_spm = llama_vocab_type(llama_get_model(ctx)) == LLAMA_VOCAB_TYPE_SPM;
     fprintf(stderr, "================================= is_spm = %d\n", is_spm);
 
-    // This is needed as usual for LLaMA models
-    const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
-
     // The tasks should be randomized so the score stabilizes quickly.
     bool randomize_tasks = true;
 
@@ -746,7 +818,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
         size_t ending_logprob_count[4];
         double ending_logprob[4];
 
-        size_t i_batch;         // starting index in the llama_batch
+        size_t i_logits;        // starting index of logits in the llama_batch
         size_t common_prefix;   // max number of initial tokens that are the same in all sentences
         size_t required_tokens; // needed number of tokens to evaluate all 4 endings
         std::vector<llama_token> seq_tokens[4];
@@ -771,7 +843,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
         hs_cur.gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
         for (size_t j = 0; j < 4; j++) {
             hs_cur.ending[j] = prompt_lines[idx*6+2+j];
-            hs_cur.seq_tokens[j] = ::llama_tokenize(ctx, hs_cur.context + " " + hs_cur.ending[j], add_bos);
+            hs_cur.seq_tokens[j] = ::llama_tokenize(ctx, hs_cur.context + " " + hs_cur.ending[j], true);
         }
 
         // determine the common prefix of the endings
@@ -790,7 +862,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
             hs_cur.seq_tokens[2].size() - hs_cur.common_prefix +
             hs_cur.seq_tokens[3].size() - hs_cur.common_prefix;
 
-        //GGML_ASSERT(hs_cur.common_prefix >= ::llama_tokenize(ctx, hs_cur.context, add_bos).size());
+        //GGML_ASSERT(hs_cur.common_prefix >= ::llama_tokenize(ctx, hs_cur.context, true).size());
 
         // Delete the selected random example from the prompt
         if (randomize_tasks) {
@@ -809,11 +881,12 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
     const int n_batch = params.n_batch;
 
     const int max_tasks_per_batch = 32;
-    const int max_seq = 4*max_tasks_per_batch;
+    const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
 
-    llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
+    llama_batch batch = llama_batch_init(n_ctx, 0, 4);
 
     std::vector<float> tok_logits(n_vocab);
+    // TODO: this could be made smaller; it's currently the worst-case size
     std::vector<float> batch_logits(n_vocab*n_ctx);
 
     std::vector<std::pair<size_t, llama_token>> eval_pairs;
@@ -824,16 +897,17 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
         int n_cur = 0;
 
         size_t i1 = i0;
-        size_t i_batch = 0; // this tells us where in `llama_batch` we are currently
+        size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch
 
         llama_batch_clear(batch);
 
         // batch as much tasks as possible into the available context
-        // each task has 4 unique seuqnce ids - one for each ending
+        // each task has 4 unique sequence ids - one for each ending
         // the common prefix is shared among the 4 sequences to save tokens
         // we extract logits only from the last common token and from all ending tokens of each sequence
         while (n_cur + (int) hs_data[i1].required_tokens <= n_ctx) {
             auto & hs_cur = hs_data[i1];
+            int n_logits = 0;
 
             const int s0 = 4*(i1 - i0);
             if (s0 + 4 > max_seq) {
@@ -841,18 +915,23 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
             }
 
             for (size_t i = 0; i < hs_cur.common_prefix; ++i) {
-                llama_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3}, false);
+                llama_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3 }, false);
             }
             batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
+            n_logits += 1;
 
             for (int s = 0; s < 4; ++s) {
-                for (size_t i = hs_cur.common_prefix; i < hs_cur.seq_tokens[s].size(); ++i) {
-                    llama_batch_add(batch, hs_cur.seq_tokens[s][i], i, { s0 + s }, true);
+                const size_t seq_tokens_size = hs_cur.seq_tokens[s].size();
+                // TODO: don't evaluate the last token of each sequence
+                for (size_t i = hs_cur.common_prefix; i < seq_tokens_size; ++i) {
+                    const bool needs_logits = i < seq_tokens_size - 1;
+                    llama_batch_add(batch, hs_cur.seq_tokens[s][i], i, { s0 + s }, needs_logits);
+                    n_logits += needs_logits;
                 }
             }
 
-            hs_cur.i_batch = i_batch;
-            i_batch += hs_cur.required_tokens;
+            hs_cur.i_logits = i_logits;
+            i_logits += n_logits;
 
             n_cur += hs_data[i1].required_tokens;
             if (++i1 == hs_task_count) {
@@ -878,12 +957,11 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
         eval_pairs.clear();
         for (size_t i = i0; i < i1; ++i) {
             auto & hs_cur = hs_data[i];
-            size_t li = hs_cur.common_prefix;
+            size_t li = 1; // skip the last logit of the common prefix (computed separately below)
             for (int s = 0; s < 4; ++s) {
                 for (size_t j = hs_cur.common_prefix; j < hs_cur.seq_tokens[s].size() - 1; j++) {
-                    eval_pairs.emplace_back(hs_cur.i_batch + li++, hs_cur.seq_tokens[s][j + 1]);
+                    eval_pairs.emplace_back(hs_cur.i_logits + li++, hs_cur.seq_tokens[s][j + 1]);
                 }
-                ++li;
             }
         }
         // Then we do the actual calculation
@@ -895,7 +973,8 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
         for (size_t i = i0; i < i1; ++i) {
             auto & hs_cur = hs_data[i];
 
-            std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*(hs_cur.i_batch + hs_cur.common_prefix - 1), n_vocab*sizeof(float));
+            // get the logits of the last token of the common prefix
+            std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*hs_cur.i_logits, n_vocab*sizeof(float));
 
             const auto first_probs = softmax(tok_logits);
 
@@ -945,7 +1024,7 @@ struct winogrande_entry {
     std::array<std::string, 2> choices;
     int answer;
 
-    size_t i_batch;
+    size_t i_logits;
     size_t common_prefix;
     size_t required_tokens;
     size_t n_base1; // number of tokens for context + choice 1
@@ -1056,12 +1135,9 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
 
     fprintf(stderr, "%s : tokenizing selected tasks\n", __func__);
 
-    // This is needed as usual for LLaMA models
-    const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
-
     for (auto & task : data) {
-        task.seq_tokens[0] = ::llama_tokenize(ctx, task.first + task.choices[0] + task.second, add_bos);
-        task.seq_tokens[1] = ::llama_tokenize(ctx, task.first + task.choices[1] + task.second, add_bos);
+        task.seq_tokens[0] = ::llama_tokenize(ctx, task.first + task.choices[0] + task.second, true);
+        task.seq_tokens[1] = ::llama_tokenize(ctx, task.first + task.choices[1] + task.second, true);
 
         task.common_prefix = 0;
         for (size_t k = 0; k < task.seq_tokens[0].size(); k++) {
@@ -1071,12 +1147,13 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
             task.common_prefix++;
         }
 
+        // TODO: the last token of each of the sequences don't need to be evaluated
         task.required_tokens = task.common_prefix +
             task.seq_tokens[0].size() - task.common_prefix +
             task.seq_tokens[1].size() - task.common_prefix;
 
-        task.n_base1 = ::llama_tokenize(ctx, task.first + task.choices[0], add_bos).size();
-        task.n_base2 = ::llama_tokenize(ctx, task.first + task.choices[1], add_bos).size();
+        task.n_base1 = ::llama_tokenize(ctx, task.first + task.choices[0], true).size();
+        task.n_base2 = ::llama_tokenize(ctx, task.first + task.choices[1], true).size();
     }
 
     fprintf(stderr, "%s : calculating winogrande score over selected tasks.\n", __func__);
@@ -1086,11 +1163,12 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
     const int n_batch = params.n_batch;
 
     const int max_tasks_per_batch = 128;
-    const int max_seq = 2*max_tasks_per_batch;
+    const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
 
-    llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
+    llama_batch batch = llama_batch_init(n_ctx, 0, 2);
 
     std::vector<float> tok_logits(n_vocab);
+    // TODO: this could be made smaller; it's currently the worst-case size
     std::vector<float> batch_logits(n_vocab*n_ctx);
 
     std::vector<std::pair<size_t, llama_token>> eval_pairs;
@@ -1104,29 +1182,33 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
         int n_cur = 0;
 
         size_t i1 = i0;
-        size_t i_batch = 0;
+        size_t i_logits = 0;
 
         llama_batch_clear(batch);
 
         while (n_cur + (int) data[i1].required_tokens <= n_ctx) {
+            int n_logits = 0;
             const int s0 = 2*(i1 - i0);
             if (s0 + 2 > max_seq) {
                 break;
             }
 
             for (size_t i = 0; i < data[i1].common_prefix; ++i) {
-                llama_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1}, false);
+                llama_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1 }, false);
             }
             batch.logits[batch.n_tokens - 1] = true;
+            n_logits += 1;
 
             for (int s = 0; s < 2; ++s) {
+                // TODO: end before the last token, no need to predict past the end of the sequences
                 for (size_t i = data[i1].common_prefix; i < data[i1].seq_tokens[s].size(); ++i) {
                     llama_batch_add(batch, data[i1].seq_tokens[s][i], i, { s0 + s }, true);
+                    n_logits += 1;
                 }
             }
 
-            data[i1].i_batch = i_batch;
-            i_batch += data[i1].required_tokens;
+            data[i1].i_logits = i_logits;
+            i_logits += n_logits;
 
             n_cur += data[i1].required_tokens;
             if (++i1 == data.size()) {
@@ -1157,15 +1239,16 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
 
             const auto& n_base1 = skip_choice ? task.n_base1 : task.common_prefix;
             const int last_1st = task.seq_tokens[0].size() - n_base1 > 1 ? 1 : 0;
-            size_t li = n_base1 - 1;
+            size_t li = n_base1 - task.common_prefix;
             for (size_t j = n_base1-1; j < task.seq_tokens[0].size()-1-last_1st; ++j) {
-                eval_pairs.emplace_back(task.i_batch + li++, task.seq_tokens[0][j+1]);
+                eval_pairs.emplace_back(task.i_logits + li++, task.seq_tokens[0][j+1]);
             }
             const auto& n_base2 = skip_choice ? task.n_base2 : task.common_prefix;
             const int last_2nd = task.seq_tokens[1].size() - n_base2 > 1 ? 1 : 0;
-            li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - 1;
+            // FIXME: this uses the wrong first logits when not skipping the choice word
+            li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - task.common_prefix;
             for (size_t j = n_base2-1; j < task.seq_tokens[1].size()-1-last_2nd; ++j) {
-                eval_pairs.emplace_back(task.i_batch + li++, task.seq_tokens[1][j+1]);
+                eval_pairs.emplace_back(task.i_logits + li++, task.seq_tokens[1][j+1]);
             }
         }
         compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results);
@@ -1254,14 +1337,14 @@ struct multiple_choice_task {
     }
 
     // For evaluation
-    size_t i_batch;         // starting index in the llama_batch
+    size_t i_logits;        // starting index of logits in the llama_batch
     size_t common_prefix;   // max number of initial tokens that are the same in all sentences
     size_t required_tokens; // needed number of tokens to evaluate all answers
     std::vector<std::vector<llama_token>> seq_tokens;
     std::vector<float> log_probs;
 };
 
-static bool multiple_choice_prepare_one_task(llama_context * ctx, bool add_bos, multiple_choice_task& task, bool log_error) {
+static bool multiple_choice_prepare_one_task(llama_context * ctx, multiple_choice_task& task, bool log_error) {
     if (task.question.empty() || task.mc1.answers.empty()) {
         if (log_error) {
             printf("%s: found bad task with empty question and/or answers\n", __func__);
@@ -1276,7 +1359,7 @@ static bool multiple_choice_prepare_one_task(llama_context * ctx, bool add_bos,
             }
             return false;
         }
-        task.seq_tokens.emplace_back(::llama_tokenize(ctx, task.question + " " + answer, add_bos));
+        task.seq_tokens.emplace_back(::llama_tokenize(ctx, task.question + " " + answer, true));
     }
     auto min_len = task.seq_tokens.front().size();
     for (auto& seq : task.seq_tokens) {
@@ -1333,7 +1416,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
     std::vector<uint32_t> task_pos(n_task);
     strstream.read((char *)task_pos.data(), task_pos.size()*sizeof(uint32_t));
     if (strstream.fail()) {
-        printf("%s: failed to raad task positions from prompt\n", __func__);
+        printf("%s: failed to read task positions from prompt\n", __func__);
         return;
     }
 
@@ -1375,9 +1458,6 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
         n_task = params.multiple_choice_tasks;
     }
 
-    // This is needed as usual for LLaMA models
-    const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
-
     printf("%s: preparing task data", __func__);
     fflush(stdout);
     if (n_task > 500) {
@@ -1385,7 +1465,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
         fflush(stdout);
         std::atomic<int> counter(0);
         std::atomic<int> n_bad(0);
-        auto prepare = [&counter, &n_bad, &tasks, ctx, add_bos] () {
+        auto prepare = [&counter, &n_bad, &tasks, ctx] () {
             int num_tasks = tasks.size();
             int n_bad_local = 0;
             while (true) {
@@ -1396,7 +1476,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
                 }
                 int last = std::min(first + K_TOKEN_CHUNK, num_tasks);
                 for (int i = first; i < last; ++i) {
-                    if (!multiple_choice_prepare_one_task(ctx, add_bos, tasks[i], false)) ++n_bad_local;
+                    if (!multiple_choice_prepare_one_task(ctx, tasks[i], false)) ++n_bad_local;
                 }
             }
         };
@@ -1414,11 +1494,11 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
             return;
         }
     } else {
-        int n_dot = n_task/100;
+        int n_dot = std::max((int) n_task/100, 1);
         int i_task = 0;
         for (auto& task : tasks) {
             ++i_task;
-            if (!multiple_choice_prepare_one_task(ctx, add_bos, task, true)) {
+            if (!multiple_choice_prepare_one_task(ctx, task, true)) {
                 return;
             }
             if (i_task%n_dot == 0) {
@@ -1438,7 +1518,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
     const int n_batch = params.n_batch;
 
     const int max_tasks_per_batch = 32;
-    const int max_seq = 4*max_tasks_per_batch;
+    const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
 
     llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
 
@@ -1458,17 +1538,18 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
         int n_cur = 0;
 
         size_t i1 = i0;
-        size_t i_batch = 0; // this tells us where in `llama_batch` we are currently
+        size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch
 
         llama_batch_clear(batch);
 
         // batch as much tasks as possible into the available context
-        // each task has 4 unique seuqnce ids - one for each ending
+        // each task has 4 unique sequence ids - one for each ending
         // the common prefix is shared among the 4 sequences to save tokens
         // we extract logits only from the last common token and from all ending tokens of each sequence
         int s0 = 0;
         while (n_cur + (int) tasks[i1].required_tokens <= n_ctx) {
             auto& cur_task = tasks[i1];
+            int n_logits = 0;
 
             int num_answers = cur_task.seq_tokens.size();
             if (s0 + num_answers > max_seq) {
@@ -1485,17 +1566,22 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
                 llama_batch_add(batch, cur_task.seq_tokens[0][i], i, batch_indeces, false);
             }
             batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
+            n_logits += 1;
 
             for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
-                for (size_t i = cur_task.common_prefix; i < cur_task.seq_tokens[s].size(); ++i) {
-                    llama_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, true);
+                const size_t seq_tokens_size = cur_task.seq_tokens[s].size();
+                // TODO: don't evaluate the last token of each sequence
+                for (size_t i = cur_task.common_prefix; i < seq_tokens_size; ++i) {
+                    const bool needs_logits = i < seq_tokens_size - 1;
+                    llama_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, needs_logits);
+                    n_logits += needs_logits;
                 }
             }
 
             s0 += num_answers;
 
-            cur_task.i_batch = i_batch;
-            i_batch += cur_task.required_tokens;
+            cur_task.i_logits = i_logits;
+            i_logits += n_logits;
 
             n_cur += cur_task.required_tokens;
             if (++i1 == tasks.size()) {
@@ -1521,12 +1607,11 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
         eval_pairs.clear();
         for (size_t i = i0; i < i1; ++i) {
             auto& cur_task = tasks[i];
-            size_t li = cur_task.common_prefix;
+            size_t li = 1; // skip the last logit of the common prefix (computed separately below)
             for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
                 for (size_t j = cur_task.common_prefix; j < cur_task.seq_tokens[s].size() - 1; j++) {
-                    eval_pairs.emplace_back(cur_task.i_batch + li++, cur_task.seq_tokens[s][j + 1]);
+                    eval_pairs.emplace_back(cur_task.i_logits + li++, cur_task.seq_tokens[s][j + 1]);
                 }
-                ++li;
             }
         }
         // Then we do the actual calculation
@@ -1545,7 +1630,8 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
             //}
             //printf("\n    common_prefix: %zu\n", cur_task.common_prefix);
 
-            std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*(cur_task.i_batch + cur_task.common_prefix - 1), n_vocab*sizeof(float));
+            // get the logits of the last token of the common prefix
+            std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*cur_task.i_logits, n_vocab*sizeof(float));
 
             const auto first_probs = softmax(tok_logits);
 
@@ -1648,9 +1734,11 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
     const int num_batches = (n_ctx + n_batch - 1)/n_batch;
     const int nv = 2*((n_vocab + 1)/2) + 4;
     const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
+    GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1);
 
     std::vector<uint16_t> log_probs_uint16(size_t(n_ctx - 1 - n_ctx/2) * nv);
-    std::vector<float> kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk);
+    std::vector<float>    kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk);
+    std::vector<float> p_diff_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk);
     std::vector<float> logits;
     if (num_batches > 1) {
         logits.reserve(n_ctx * n_vocab);
@@ -1667,9 +1755,18 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
         df = df > 0 && count > 10 ? sqrt(df/(count-1)) : 0.;
         return std::make_pair(f, df);
     };
+    auto covariance = [] (double suma, double sumb, double sumab, size_t count) {
+        if (count < 10) {
+            return 0.0;
+        }
+        double var = sumab/count - (suma/count)*(sumb/count);
+        var /= count - 1;
+        return var;
+    };
 
     kl_divergence_result kld;
-    auto kld_ptr = kld_values.data();
+    auto    kld_ptr =    kld_values.data();
+    auto p_diff_ptr = p_diff_values.data();
 
     for (int i = 0; i < n_chunk; ++i) {
         const int start =     i * n_ctx;
@@ -1697,6 +1794,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
                 tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
             }
 
+            // TODO: use llama_batch.logits instead of relying on logits_all == true
             if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
                 fprintf(stderr, "%s : failed to eval\n", __func__);
                 return;
@@ -1723,24 +1821,42 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
             }
             fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
 
-            printf("\nchunk        PPL          ln(PPL(Q)/PPL(base))          KL-Divergence           Same top\n");
+            printf("\nchunk             PPL               ln(PPL(Q)/PPL(base))          KL Divergence              Δp RMS            Same top p\n");
         }
 
         const int first = n_ctx/2;
         const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
         process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
-                workers, log_probs_uint16, kld, kld_ptr);
-        kld_ptr += n_ctx - 1 - first;
+                workers, log_probs_uint16, kld, kld_ptr, p_diff_ptr);
+        p_diff_ptr += n_ctx - 1 - first;
+        kld_ptr    += n_ctx - 1 - first;
 
-        auto ppl           = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count);
-        auto log_ppl_ratio = mean_and_uncertainty(kld.sum_nll_diff, kld.sum_nll_diff2, kld.count);
-        auto kl_div        = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count);
-        auto p_top = 1.*kld.n_same_top/kld.count;
-        auto d_p_top = sqrt(p_top*(1 - p_top)/(kld.count - 1));
+        printf("%4d", i+1);
 
-        printf("%4d    %10.4lf    %10.5lf ± %10.5f    %10.5f ± %10.5lf    %.5f ± %.5f\n", i+1, exp(ppl.first),
-                log_ppl_ratio.first, log_ppl_ratio.second, kl_div.first, kl_div.second,
-                p_top, d_p_top);
+        auto log_ppl = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count);
+        const double ppl_val = exp(log_ppl.first);
+        const double ppl_unc = ppl_val * log_ppl.second; // ppl_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl.second ** 2 )
+        printf("    %9.4lf ± %9.4lf", ppl_val, ppl_unc);
+
+        auto log_ppl_base = mean_and_uncertainty(kld.sum_nll_base, kld.sum_nll_base2, kld.count);
+        const double log_ppl_cov = covariance(kld.sum_nll, kld.sum_nll_base, kld.sum_nll_nll_base, kld.count);
+        const double log_ppl_ratio_val = log_ppl.first - log_ppl_base.first;
+        const double log_ppl_ratio_unc = sqrt(log_ppl.second*log_ppl.second + log_ppl_base.second*log_ppl_base.second - 2.0*log_ppl_cov);
+        printf("    %10.5lf ± %10.5lf", log_ppl_ratio_val, log_ppl_ratio_unc);
+
+        auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count);
+        printf("    %10.5lf ± %10.5lf", kl_div.first, kl_div.second);
+
+        auto p_diff_mse   = mean_and_uncertainty(kld.sum_p_diff2, kld.sum_p_diff4, kld.count);
+        const double p_diff_rms_val = sqrt(p_diff_mse.first);
+        const double p_diff_rms_unc = 0.5/p_diff_rms_val * p_diff_mse.second;
+        printf("    %6.3lf ± %6.3lf %%", 100.0*p_diff_rms_val, 100.0*p_diff_rms_unc);
+
+        double p_top_val = 1.*kld.n_same_top/kld.count;
+        double p_top_unc = sqrt(p_top_val*(1 - p_top_val)/(kld.count - 1));
+        printf("    %6.3lf ± %6.3lf %%", 100.0*p_top_val, 100.0*p_top_unc);
+
+        printf("\n");
 
         fflush(stdout);
 
@@ -1751,44 +1867,129 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
     if (kld.count < 100) return; // we do not wish to do statistics on so few values
 
     std::sort(kld_values.begin(), kld_values.end());
+    std::sort(p_diff_values.begin(), p_diff_values.end());
 
-    printf("===== KL-divergence statistics\n");
+    printf("====== Perplexity statistics ======\n");
+
+    auto log_ppl = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count);
+    const double ppl_val = exp(log_ppl.first);
+    const double ppl_unc = ppl_val * log_ppl.second; // ppl_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl.second ** 2 )
+    printf("Mean PPL(Q)                   : %10.6lf ± %10.6lf\n", ppl_val, ppl_unc);
+
+    auto log_ppl_base = mean_and_uncertainty(kld.sum_nll_base, kld.sum_nll_base2, kld.count);
+    const double ppl_base_val = exp(log_ppl_base.first);
+    const double ppl_base_unc = ppl_base_val * log_ppl_base.second; // ppl_base_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl_base.second ** 2 )
+    printf("Mean PPL(base)                : %10.6lf ± %10.6lf\n", ppl_base_val, ppl_base_unc);
+
+    const double log_ppl_cov = covariance(kld.sum_nll, kld.sum_nll_base, kld.sum_nll_nll_base, kld.count);
+    // printf("Cov(ln(PPL(Q)), ln(PPL(base))): %10.6lf\n", log_ppl_cov);
+    const double log_ppl_cor = log_ppl_cov / (log_ppl.second*log_ppl_base.second);
+    printf("Cor(ln(PPL(Q)), ln(PPL(base))): %6.2lf%%\n", 100.0*log_ppl_cor);
+
+    const double log_ppl_ratio_val = log_ppl.first - log_ppl_base.first;
+    const double log_ppl_ratio_unc = sqrt(log_ppl.second*log_ppl.second + log_ppl_base.second*log_ppl_base.second - 2.0*log_ppl_cov);
+    printf("Mean ln(PPL(Q)/PPL(base))     : %10.6lf ± %10.6lf\n", log_ppl_ratio_val, log_ppl_ratio_unc);
+
+    const double ppl_ratio_val = exp(log_ppl_ratio_val);
+    const double ppl_ratio_unc = ppl_ratio_val * log_ppl_ratio_unc; // ppl_ratio_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl_ratio.second ** 2 )
+    printf("Mean PPL(Q)/PPL(base)         : %10.6lf ± %10.6lf\n", ppl_ratio_val, ppl_ratio_unc);
+
+    const double ppl_cov = ppl_val * ppl_base_val * log_ppl_cov;
+    const double ppl_diff_val = ppl_val - ppl_base_val;
+    const double ppl_diff_unc = sqrt(ppl_unc*ppl_unc + ppl_base_unc*ppl_base_unc - 2.0*ppl_cov);
+    printf("Mean PPL(Q)-PPL(base)         : %10.6lf ± %10.6lf\n", ppl_diff_val, ppl_diff_unc);
+
+    printf("\n");
+
+    printf("====== KL divergence statistics ======\n");
     auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count);
-    printf("Average: %10.6f ±%10.6lf\n", kl_div.first, kl_div.second);
+    printf("Mean    KLD: %10.6lf ± %10.6lf\n", kl_div.first, kl_div.second);
     auto kld_median = kld_values.size()%2 == 0 ? 0.5f*(kld_values[kld_values.size()/2] + kld_values[kld_values.size()/2-1])
                                                : kld_values[kld_values.size()/2];
-    printf("Median : %10.6f\n", kld_median);
 
-    auto percentile = [&kld_values] (float fraction) {
-        if (fraction <= 0) return kld_values.front();
-        if (fraction >= 1) return kld_values.back();
-        float p = fraction*(kld_values.size() - 1);
+    auto percentile = [] (std::vector<float> values, float fraction) {
+        if (fraction <= 0) return values.front();
+        if (fraction >= 1) return values.back();
+        float p = fraction*(values.size() - 1);
         size_t ip = size_t(p); p -= ip;
-        return (1 - p)*kld_values[ip] + p*kld_values[std::min(ip+1, kld_values.size()-1)];
+        return (1 - p)*values[ip] + p*values[std::min(ip+1, values.size()-1)];
     };
 
-    printf("Maximum: %10.6f\n", kld_values.back());
-    printf("KLD_99 : %10.6f\n", percentile(0.99f));
-    printf("KLD_95 : %10.6f\n", percentile(0.95f));
-    printf("KLD_90 : %10.6f\n", percentile(0.90f));
+    printf("Maximum KLD: %10.6f\n", kld_values.back());
+    printf("99.9%%   KLD: %10.6f\n", percentile(kld_values, 0.999f));
+    printf("99.0%%   KLD: %10.6f\n", percentile(kld_values, 0.990f));
+    printf("99.0%%   KLD: %10.6f\n", percentile(kld_values, 0.990f));
+    printf("Median  KLD: %10.6f\n", kld_median);
+    printf("10.0%%   KLD: %10.6f\n", percentile(kld_values, 0.100f));
+    printf(" 5.0%%   KLD: %10.6f\n", percentile(kld_values, 0.050f));
+    printf(" 1.0%%   KLD: %10.6f\n", percentile(kld_values, 0.010f));
+    printf("Minimum KLD: %10.6f\n", kld_values.front());
 
-    printf("Minimum: %10.6f\n", kld_values.front());
-    printf("KLD_01 : %10.6f\n", percentile(0.01f));
-    printf("KLD_05 : %10.6f\n", percentile(0.05f));
-    printf("KLD_10 : %10.6f\n", percentile(0.10f));
+    printf("\n");
+
+    printf("====== Token probability statistics ======\n");
+
+    auto p_diff = mean_and_uncertainty(kld.sum_p_diff, kld.sum_p_diff2, kld.count);
+    printf("Mean    Δp: %6.3lf ± %5.3lf %%\n",  100.0*p_diff.first, 100.0*p_diff.second);
+
+    auto p_diff_median = p_diff_values.size()%2 == 0 ? 0.5f*(p_diff_values[p_diff_values.size()/2] + p_diff_values[p_diff_values.size()/2-1])
+                                               : p_diff_values[p_diff_values.size()/2];
+
+    printf("Maximum Δp: %6.3lf%%\n",  100.0*p_diff_values.back());
+    printf("99.9%%   Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.999f));
+    printf("99.0%%   Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.990f));
+    printf("95.0%%   Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.950f));
+    printf("90.0%%   Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.900f));
+    printf("75.0%%   Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.750f));
+    printf("Median  Δp: %6.3lf%%\n",  100.0*p_diff_median);
+    printf("25.0%%   Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.250f));
+    printf("10.0%%   Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.100f));
+    printf(" 5.0%%   Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.050f));
+    printf(" 1.0%%   Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.010f));
+    printf(" 0.1%%   Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.001f));
+    printf("Minimum Δp: %6.3lf%%\n",  100.0*p_diff_values.front());
+
+    auto p_diff_mse = mean_and_uncertainty(kld.sum_p_diff2, kld.sum_p_diff4, kld.count);
+    // printf("MSE Δp    : %10.6lf ± %10.6lf\n", p_diff_mse.first, p_diff_mse.second);
+
+    const double p_diff_rms_val = sqrt(p_diff_mse.first);
+    const double p_diff_rms_unc = 0.5/p_diff_rms_val * p_diff_mse.second;
+    printf("RMS Δp    : %6.3lf ± %5.3lf %%\n", 100.0*p_diff_rms_val, 100.0*p_diff_rms_unc);
+
+    const double same_top_p = 1.0*kld.n_same_top/kld.count;
+    printf("Same top p: %6.3lf ± %5.3lf %%\n", 100.0*same_top_p, 100.0*sqrt(same_top_p*(1.0 - same_top_p)/(kld.count - 1)));
 
 }
 
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    params.n_batch = 512;
     if (!gpt_params_parse(argc, argv, params)) {
         return 1;
     }
 
     params.logits_all = true;
-    params.n_batch = std::min(params.n_batch, params.n_ctx);
+
+    const int32_t n_ctx = params.n_ctx;
+
+    if (n_ctx <= 0) {
+        fprintf(stderr, "%s: perplexity tool requires '--ctx-size' > 0\n", __func__);
+        return 1;
+    }
+
+    const bool ppl = !params.hellaswag && !params.winogrande && !params.multiple_choice && !params.kl_divergence;
+
+    if (ppl) {
+        const int32_t n_seq = std::max(1, params.n_batch / n_ctx);
+        const int32_t n_kv = n_seq * n_ctx;
+
+        params.n_parallel = n_seq;
+        params.n_ctx      = n_kv;
+
+        params.n_batch = std::min(params.n_batch, n_kv);
+    } else {
+        params.n_batch = std::min(params.n_batch, params.n_ctx);
+    }
 
     if (params.ppl_stride > 0) {
         fprintf(stderr, "Will perform strided perplexity calculation -> adjusting context size from %d to %d\n",
@@ -1815,6 +2016,9 @@ int main(int argc, char ** argv) {
     llama_model * model;
     llama_context * ctx;
 
+    // ensure there's at least enough seq_ids for HellaSwag
+    params.n_parallel = std::max(4, params.n_parallel);
+
     // load the model and apply lora adapter, if any
     std::tie(model, ctx) = llama_init_from_gpt_params(params);
     if (model == NULL) {
@@ -1844,7 +2048,7 @@ int main(int argc, char ** argv) {
     } else if (params.kl_divergence) {
         kl_divergence(ctx, params);
     } else {
-        results = perplexity(ctx, params);
+        results = perplexity(ctx, params, n_ctx);
     }
 
     llama_print_timings(ctx);
diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp
index 1d05f1391..746df8446 100644
--- a/examples/quantize-stats/quantize-stats.cpp
+++ b/examples/quantize-stats/quantize-stats.cpp
@@ -23,7 +23,7 @@
 #endif
 
 struct quantize_stats_params {
-    std::string model = "models/7B/ggml-model-f16.gguf";
+    std::string model = DEFAULT_MODEL_PATH;
     bool verbose = false;
     bool per_layer_stats = false;
     bool print_histogram = false;
diff --git a/examples/quantize/CMakeLists.txt b/examples/quantize/CMakeLists.txt
index 6f374a2bd..6b977fde8 100644
--- a/examples/quantize/CMakeLists.txt
+++ b/examples/quantize/CMakeLists.txt
@@ -1,6 +1,6 @@
 set(TARGET quantize)
 add_executable(${TARGET} quantize.cpp)
 install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE llama common ${CMAKE_THREAD_LIBS_INIT})
 target_include_directories(${TARGET} PRIVATE ../../common)
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/quantize/README.md b/examples/quantize/README.md
index c8b9a27a0..8a10365c0 100644
--- a/examples/quantize/README.md
+++ b/examples/quantize/README.md
@@ -4,17 +4,17 @@ TODO
 
 ## Llama 2 7B
 
-Quantization | Bits per Weight (BPW)
--- | --
-Q2_K | 3.35
-Q3_K_S | 3.50
-Q3_K_M | 3.91
-Q3_K_L | 4.27
-Q4_K_S | 4.58
-Q4_K_M | 4.84
-Q5_K_S | 5.52
-Q5_K_M | 5.68
-Q6_K | 6.56
+| Quantization | Bits per Weight (BPW) |
+|--------------|-----------------------|
+| Q2_K         | 3.35                  |
+| Q3_K_S       | 3.50                  |
+| Q3_K_M       | 3.91                  |
+| Q3_K_L       | 4.27                  |
+| Q4_K_S       | 4.58                  |
+| Q4_K_M       | 4.84                  |
+| Q5_K_S       | 5.52                  |
+| Q5_K_M       | 5.68                  |
+| Q6_K         | 6.56                  |
 
 ## Llama 2 13B
 Quantization | Bits per Weight (BPW)
diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp
index ea7ba50c9..909eab283 100644
--- a/examples/quantize/quantize.cpp
+++ b/examples/quantize/quantize.cpp
@@ -8,7 +8,6 @@
 #include <unordered_map>
 #include <fstream>
 #include <cmath>
-#include <algorithm>
 
 struct quant_option {
     std::string name;
@@ -23,15 +22,22 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
     { "Q5_1",   LLAMA_FTYPE_MOSTLY_Q5_1,   " 4.70G, +0.0349 ppl @ LLaMA-v1-7B", },
     { "IQ2_XXS",LLAMA_FTYPE_MOSTLY_IQ2_XXS," 2.06 bpw quantization",            },
     { "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS, " 2.31 bpw quantization",            },
+    { "IQ2_S",  LLAMA_FTYPE_MOSTLY_IQ2_S,  " 2.5  bpw quantization",            },
+    { "IQ2_M",  LLAMA_FTYPE_MOSTLY_IQ2_M,  " 2.7  bpw quantization",            },
     { "IQ1_S",  LLAMA_FTYPE_MOSTLY_IQ1_S,  " 1.56 bpw quantization",            },
+    { "IQ1_M",  LLAMA_FTYPE_MOSTLY_IQ1_M,  " 1.75 bpw quantization",            },
     { "Q2_K",   LLAMA_FTYPE_MOSTLY_Q2_K,   " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", },
     { "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", },
     { "IQ3_XXS",LLAMA_FTYPE_MOSTLY_IQ3_XXS," 3.06 bpw quantization",            },
+    { "IQ3_S",  LLAMA_FTYPE_MOSTLY_IQ3_S,  " 3.44 bpw quantization",            },
+    { "IQ3_M",  LLAMA_FTYPE_MOSTLY_IQ3_M,  " 3.66 bpw quantization mix",        },
     { "Q3_K",   LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" },
-    { "Q3_K_XS",LLAMA_FTYPE_MOSTLY_Q3_K_XS,"3-bit extra small quantization"   , },
+    { "IQ3_XS", LLAMA_FTYPE_MOSTLY_IQ3_XS, " 3.3 bpw quantization"   ,          },
     { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", },
     { "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.07G, +0.2496 ppl @ LLaMA-v1-7B", },
     { "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1764 ppl @ LLaMA-v1-7B", },
+    { "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.50 bpw non-linear quantization", },
+    { "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS, " 4.25 bpw non-linear quantization", },
     { "Q4_K",   LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", },
     { "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 3.59G, +0.0992 ppl @ LLaMA-v1-7B", },
     { "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 3.80G, +0.0532 ppl @ LLaMA-v1-7B", },
@@ -40,12 +46,17 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
     { "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 4.45G, +0.0122 ppl @ LLaMA-v1-7B", },
     { "Q6_K",   LLAMA_FTYPE_MOSTLY_Q6_K,   " 5.15G, +0.0008 ppl @ LLaMA-v1-7B", },
     { "Q8_0",   LLAMA_FTYPE_MOSTLY_Q8_0,   " 6.70G, +0.0004 ppl @ LLaMA-v1-7B", },
-    { "F16",    LLAMA_FTYPE_MOSTLY_F16,    "13.00G              @ 7B", },
+    { "F16",    LLAMA_FTYPE_MOSTLY_F16,    "14.00G, -0.0020 ppl @ Mistral-7B", },
+    { "BF16",   LLAMA_FTYPE_MOSTLY_BF16,   "14.00G, -0.0050 ppl @ Mistral-7B", },
     { "F32",    LLAMA_FTYPE_ALL_F32,       "26.00G              @ 7B", },
     // Note: Ensure COPY comes after F32 to avoid ftype 0 from matching.
     { "COPY",   LLAMA_FTYPE_ALL_F32,       "only copy tensors, no quantizing", },
 };
 
+static const char * const LLM_KV_QUANTIZE_IMATRIX_FILE       = "quantize.imatrix.file";
+static const char * const LLM_KV_QUANTIZE_IMATRIX_DATASET    = "quantize.imatrix.dataset";
+static const char * const LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES  = "quantize.imatrix.entries_count";
+static const char * const LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS   = "quantize.imatrix.chunks_count";
 
 static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std::string & ftype_str_out) {
     std::string ftype_str;
@@ -81,13 +92,18 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp
 //
 [[noreturn]]
 static void usage(const char * executable) {
-    printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
+    printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
     printf("  --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
     printf("  --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
     printf("  --pure: Disable k-quant mixtures and quantize all tensors to the same type\n");
     printf("  --imatrix file_name: use data in file_name as importance matrix for quant optimizations\n");
     printf("  --include-weights tensor_name: use importance matrix for this/these tensor(s)\n");
     printf("  --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n");
+    printf("  --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor\n");
+    printf("  --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n");
+    printf("  --keep-split: will generate quatized model in the same shards as input");
+    printf("  --override-kv KEY=TYPE:VALUE\n");
+    printf("      Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n");
     printf("Note: --include-weights and --exclude-weights cannot be used together\n");
     printf("\nAllowed quantization types:\n");
     for (auto & it : QUANT_OPTIONS) {
@@ -101,61 +117,80 @@ static void usage(const char * executable) {
     exit(1);
 }
 
-static void load_imatrix(const std::string& imatrix_file, std::unordered_map<std::string, std::vector<float>>& imatrix_data) {
+static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_dataset, std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
     std::ifstream in(imatrix_file.c_str(), std::ios::binary);
     if (!in) {
-        printf("%s: failed to open %s\n",__func__,imatrix_file.c_str());
-        return;
+        printf("%s: failed to open %s\n",__func__, imatrix_file.c_str());
+        exit(1);
     }
     int n_entries;
-    in.read((char*)&n_entries, sizeof(n_entries));
+    in.read((char *)&n_entries, sizeof(n_entries));
     if (in.fail() || n_entries < 1) {
         printf("%s: no data in file %s\n", __func__, imatrix_file.c_str());
-        return;
+        exit(1);
     }
     for (int i = 0; i < n_entries; ++i) {
         int len; in.read((char *)&len, sizeof(len));
         std::vector<char> name_as_vec(len+1);
         in.read((char *)name_as_vec.data(), len);
         if (in.fail()) {
-            printf("%s: failed reading name for entry %d from %s\n",__func__,i+1,imatrix_file.c_str());
-            return;
+            printf("%s: failed reading name for entry %d from %s\n", __func__, i+1, imatrix_file.c_str());
+            exit(1);
         }
         name_as_vec[len] = 0;
         std::string name{name_as_vec.data()};
-        auto& e = imatrix_data[std::move(name)];
+        auto & e = imatrix_data[name];
         int ncall;
-        in.read((char*)&ncall, sizeof(ncall));
+        in.read((char *)&ncall, sizeof(ncall));
         int nval;
         in.read((char *)&nval, sizeof(nval));
         if (in.fail() || nval < 1) {
-            printf("%s: failed reading number of values for entry %d\n",__func__,i);
+            printf("%s: failed reading number of values for entry %d\n", __func__, i);
             imatrix_data = {};
-            return;
+            exit(1);
         }
         e.resize(nval);
-        in.read((char*)e.data(), nval*sizeof(float));
+        in.read((char *)e.data(), nval*sizeof(float));
         if (in.fail()) {
-            printf("%s: failed reading data for entry %d\n",__func__,i);
+            printf("%s: failed reading data for entry %d\n", __func__, i);
             imatrix_data = {};
-            return;
+            exit(1);
         }
         if (ncall > 0) {
             for (auto& v : e) v /= ncall;
         }
+
+        if (getenv("LLAMA_TRACE")) {
+            printf("%s: loaded data (size = %6d, ncall = %6d) for '%s'\n", __func__, int(e.size()), ncall, name.c_str());
+        }
     }
-    printf("%s: loaded %d importance matrix entries from %s\n",__func__,int(imatrix_data.size()),imatrix_file.c_str());
+
+    // latest imatrix version contains the dataset filename at the end of the file
+    int m_last_call = 0;
+    if (in.peek() != EOF) {
+        in.read((char *)&m_last_call, sizeof(m_last_call));
+        int dataset_len;
+        in.read((char *)&dataset_len, sizeof(dataset_len));
+        std::vector<char> dataset_as_vec(dataset_len);
+        in.read(dataset_as_vec.data(), dataset_len);
+        imatrix_dataset.assign(dataset_as_vec.begin(), dataset_as_vec.end());
+        printf("%s: imatrix dataset='%s'\n", __func__, imatrix_dataset.c_str());
+    }
+    printf("%s: loaded %d importance matrix entries from %s computed on %d chunks\n", __func__, int(imatrix_data.size()), imatrix_file.c_str(), m_last_call);
+    return m_last_call;
 }
 
-static void prepare_imatrix(const std::string& imatrix_file,
-        const std::vector<std::string>& included_weights,
-        const std::vector<std::string>& excluded_weights,
-        std::unordered_map<std::string, std::vector<float>>& imatrix_data) {
+static int prepare_imatrix(const std::string & imatrix_file,
+        std::string & imatrix_dataset,
+        const std::vector<std::string> & included_weights,
+        const std::vector<std::string> & excluded_weights,
+        std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
+    int m_last_call = -1;
     if (!imatrix_file.empty()) {
-        load_imatrix(imatrix_file, imatrix_data);
+        m_last_call = load_imatrix(imatrix_file, imatrix_dataset, imatrix_data);
     }
     if (imatrix_data.empty()) {
-        return;
+        return m_last_call;
     }
     if (!excluded_weights.empty()) {
         for (auto& name : excluded_weights) {
@@ -181,6 +216,19 @@ static void prepare_imatrix(const std::string& imatrix_file,
     if (!imatrix_data.empty()) {
         printf("%s: have %d importance matrix entries\n", __func__, int(imatrix_data.size()));
     }
+    return m_last_call;
+}
+
+static ggml_type parse_ggml_type(const char * arg) {
+    ggml_type result = GGML_TYPE_COUNT;
+    for (int j = 0; j < GGML_TYPE_COUNT; ++j) {
+        auto type = ggml_type(j);
+        const auto * name = ggml_type_name(type);
+        if (name && strcmp(arg, name) == 0) {
+            result = type; break;
+        }
+    }
+    return result;
 }
 
 int main(int argc, char ** argv) {
@@ -193,10 +241,27 @@ int main(int argc, char ** argv) {
     int arg_idx = 1;
     std::string imatrix_file;
     std::vector<std::string> included_weights, excluded_weights;
+    std::vector<llama_model_kv_override> kv_overrides;
 
     for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) {
         if (strcmp(argv[arg_idx], "--leave-output-tensor") == 0) {
             params.quantize_output_tensor = false;
+        } else if (strcmp(argv[arg_idx], "--output-tensor-type") == 0) {
+            if (arg_idx < argc-1) {
+                params.output_tensor_type = parse_ggml_type(argv[++arg_idx]);
+            } else {
+                usage(argv[0]);
+            }
+        } else if (strcmp(argv[arg_idx], "--token-embedding-type") == 0) {
+            if (arg_idx < argc-1) {
+                params.token_embedding_type = parse_ggml_type(argv[++arg_idx]);
+            } else {
+                usage(argv[0]);
+            }
+        } else if (strcmp(argv[arg_idx], "--override-kv") == 0) {
+            if (arg_idx == argc-1 || !parse_kv_override(argv[++arg_idx], kv_overrides)) {
+                usage(argv[0]);
+            }
         } else if (strcmp(argv[arg_idx], "--allow-requantize") == 0) {
             params.allow_requantize = true;
         } else if (strcmp(argv[arg_idx], "--pure") == 0) {
@@ -219,6 +284,8 @@ int main(int argc, char ** argv) {
             } else {
                 usage(argv[0]);
             }
+        } else if (strcmp(argv[arg_idx], "--keep-split")) {
+            params.keep_split = true;
         } else {
             usage(argv[0]);
         }
@@ -232,10 +299,48 @@ int main(int argc, char ** argv) {
         usage(argv[0]);
     }
 
+    std::string imatrix_dataset;
     std::unordered_map<std::string, std::vector<float>> imatrix_data;
-    prepare_imatrix(imatrix_file, included_weights, excluded_weights, imatrix_data);
+    int m_last_call = prepare_imatrix(imatrix_file, imatrix_dataset, included_weights, excluded_weights, imatrix_data);
     if (!imatrix_data.empty()) {
         params.imatrix = &imatrix_data;
+        {
+            llama_model_kv_override kvo;
+            std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_FILE);
+            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
+            strncpy(kvo.val_str, imatrix_file.c_str(), 127);
+            kvo.val_str[127] = '\0';
+            kv_overrides.emplace_back(std::move(kvo));
+        }
+        if (!imatrix_dataset.empty()) {
+            llama_model_kv_override kvo;
+            std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_DATASET);
+            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
+            strncpy(kvo.val_str, imatrix_dataset.c_str(), 127);
+            kvo.val_str[127] = '\0';
+            kv_overrides.emplace_back(std::move(kvo));
+        }
+
+        {
+            llama_model_kv_override kvo;
+            std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES);
+            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
+            kvo.val_i64 = imatrix_data.size();
+            kv_overrides.emplace_back(std::move(kvo));
+        }
+
+        if (m_last_call > 0) {
+            llama_model_kv_override kvo;
+            std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS);
+            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
+            kvo.val_i64 = m_last_call;
+            kv_overrides.emplace_back(std::move(kvo));
+        }
+    }
+    if (!kv_overrides.empty()) {
+        kv_overrides.emplace_back();
+        kv_overrides.back().key[0] = 0;
+        params.kv_overrides = &kv_overrides;
     }
 
     llama_backend_init();
@@ -246,21 +351,28 @@ int main(int argc, char ** argv) {
     std::string fname_out;
 
     std::string ftype_str;
+    std::string suffix = ".gguf";
     if (try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) {
         std::string fpath;
         const size_t pos = fname_inp.find_last_of("/\\");
         if (pos != std::string::npos) {
             fpath = fname_inp.substr(0, pos + 1);
         }
-        // export as [inp path]/ggml-model-[ftype].gguf
-        fname_out = fpath + "ggml-model-" + ftype_str + ".gguf";
+
+        // export as [inp path]/ggml-model-[ftype]. Only add extension if there is no splitting
+        fname_out = fpath + "ggml-model-" + ftype_str;
+        if (!params.keep_split) {
+            fname_out += suffix;
+        }
         arg_idx++;
         if (ftype_str == "COPY") {
             params.only_copy = true;
         }
-    }
-    else {
+    } else {
         fname_out = argv[arg_idx];
+        if (params.keep_split && fname_out.find(suffix) != std::string::npos) {
+            fname_out = fname_out.substr(0, fname_out.length() - suffix.length());
+        }
         arg_idx++;
 
         if (argc <= arg_idx) {
@@ -289,10 +401,13 @@ int main(int argc, char ** argv) {
     }
 
     if ((params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS ||
-         params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) && imatrix_data.empty()) {
-        fprintf(stderr, "\n===============================================================================================\n");
-        fprintf(stderr, "Please do not use IQ1_S, IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n");
-        fprintf(stderr, "===============================================================================================\n\n\n");
+         params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_S  ||
+         params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S ||
+         params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S  ||
+         params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) && imatrix_data.empty()) {
+        fprintf(stderr, "\n==========================================================================================================\n");
+        fprintf(stderr, "Please do not use IQ1_S, IQ1_M, IQ2_S, IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n");
+        fprintf(stderr, "==========================================================================================================\n\n\n");
         return 1;
     }
 
diff --git a/examples/quantize/tests.sh b/examples/quantize/tests.sh
new file mode 100644
index 000000000..160c12bee
--- /dev/null
+++ b/examples/quantize/tests.sh
@@ -0,0 +1,65 @@
+#!/bin/bash
+
+set -eu
+
+if [ $# -lt 1 ]
+then
+    echo "usage:   $0 path_to_build_binary [path_to_temp_folder]"
+    echo "example: $0 ../../build/bin ../../tmp"
+    exit 1
+fi
+
+if [ $# -gt 1 ]
+then
+    TMP_DIR=$2
+else
+    TMP_DIR=/tmp
+fi
+
+set -x
+
+SPLIT=$1/gguf-split
+QUANTIZE=$1/quantize
+MAIN=$1/main
+WORK_PATH=$TMP_DIR/quantize
+ROOT_DIR=$(realpath $(dirname $0)/../../)
+
+mkdir -p "$WORK_PATH"
+
+# Clean up in case of previously failed test
+rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-requant*.gguf
+
+# 1. Get a model
+(
+cd $WORK_PATH
+"$ROOT_DIR"/scripts/hf.sh --repo ggml-org/gemma-1.1-2b-it-Q8_0-GGUF --file gemma-1.1-2b-it.Q8_0.gguf
+)
+echo PASS
+
+# 2. Split model
+$SPLIT --split-max-tensors 28  $WORK_PATH/gemma-1.1-2b-it.Q8_0.gguf $WORK_PATH/ggml-model-split
+echo PASS
+echo
+
+# 3. Requant model with '--keep_split'
+$QUANTIZE --allow-requantize --keep_split $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant.gguf Q4_K
+echo PASS
+echo
+
+# 3a. Test the requanted model is loading properly
+$MAIN --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --random-prompt --n-predict 32
+echo PASS
+echo
+
+# 4. Requant mode without '--keep_split'
+$QUANTIZE --allow-requantize $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant-merge.gguf Q4_K
+echo PASS
+echo
+
+# 4b. Test the requanted model is loading properly
+$MAIN --model $WORK_PATH/ggml-model-requant-merge.gguf --random-prompt --n-predict 32
+echo PASS
+echo
+
+# Clean up
+rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-requant*.gguf
diff --git a/examples/regex-to-grammar.py b/examples/regex-to-grammar.py
new file mode 100644
index 000000000..5cd9210a4
--- /dev/null
+++ b/examples/regex-to-grammar.py
@@ -0,0 +1,20 @@
+import json, subprocess, sys, os
+
+assert len(sys.argv) >= 2
+[_, pattern, *rest] = sys.argv
+
+print(subprocess.check_output(
+    [
+        "python",
+        os.path.join(
+        os.path.dirname(os.path.realpath(__file__)),
+        "json_schema_to_grammar.py"),
+        *rest,
+        "-",
+        "--raw-pattern",
+    ],
+    text=True,
+    input=json.dumps({
+        "type": "string",
+        "pattern": pattern,
+    }, indent=2)))
diff --git a/examples/retrieval/CMakeLists.txt b/examples/retrieval/CMakeLists.txt
new file mode 100644
index 000000000..eaabae08d
--- /dev/null
+++ b/examples/retrieval/CMakeLists.txt
@@ -0,0 +1,5 @@
+set(TARGET retrieval)
+add_executable(${TARGET} retrieval.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/retrieval/README.md b/examples/retrieval/README.md
new file mode 100644
index 000000000..2b2595c46
--- /dev/null
+++ b/examples/retrieval/README.md
@@ -0,0 +1,69 @@
+# llama.cpp/examples/retrieval
+
+Demonstration of simple retrieval technique based on cosine similarity
+
+More info:
+https://github.com/ggerganov/llama.cpp/pull/6193
+
+### How to use
+
+`retieval.cpp` has parameters of its own:
+- `--context-file`: file to be embedded - state this option multiple times to embed multiple files
+- `--chunk-size`: minimum size of each text chunk to be embedded
+- `--chunk-separator`: STRING to divide chunks by. newline by default
+
+`retrieval` example can be tested as follows:
+
+```bash
+make -j && ./retrieval --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .
+```
+
+This chunks and embeds all given files and starts a loop requesting query inputs:
+
+```
+Enter query:
+```
+
+On each query input, top k chunks are shown along with file name, chunk position within file and original text:
+
+```
+Enter query: describe the mit license
+batch_decode: n_tokens = 6, n_seq = 1
+Top 3 similar chunks:
+filename: README.md
+filepos: 119
+similarity: 0.762334
+textdata:
+png)
+
+[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
+
+[Roadmap](https://github.
+--------------------
+filename: License
+filepos: 0
+similarity: 0.725146
+textdata:
+MIT License
+
+Copyright (c) 2023 Georgi Gerganov
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+--------------------
+filename: README.md
+filepos: 9178
+similarity: 0.621722
+textdata:
+com/cztomsik/ava) (MIT)
+- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal)
+- [pythops/tenere](https://github.
+--------------------
+```
diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp
new file mode 100644
index 000000000..5ba71e76a
--- /dev/null
+++ b/examples/retrieval/retrieval.cpp
@@ -0,0 +1,350 @@
+#include "common.h"
+#include "llama.h"
+
+#include <algorithm>
+#include <fstream>
+
+struct retrieval_params {
+    std::vector<std::string> context_files; // context files to embed
+    int32_t chunk_size            = 64;     // chunk size for context embedding
+    std::string chunk_separator   = "\n";   // chunk separator for context embedding
+};
+
+static void retrieval_params_print_usage(int argc, char ** argv, gpt_params & gpt_params, retrieval_params & params) {
+    gpt_print_usage(argc, argv, gpt_params);
+    printf("retrieval options:\n");
+    printf("  --context-file FNAME  file containing context to embed.\n");
+    printf("                        specify multiple files by providing --context-file option multiple times.\n");
+    printf("  --chunk-size N        minimum length of embedded text chunk (default:%d)\n", params.chunk_size);
+    printf("  --chunk-separator STRING\n");
+    printf("                        string to separate chunks (default: \"\\n\")\n");
+    printf("\n");
+}
+
+static void retrieval_params_parse(int argc, char ** argv, gpt_params & gpt_params, retrieval_params & retrieval_params) {
+    int i = 1;
+    std::string arg;
+    while (i < argc) {
+        arg = argv[i];
+        bool invalid_gpt_param = false;
+        if(gpt_params_find_arg(argc, argv, argv[i], gpt_params, i, invalid_gpt_param)) {
+            if (invalid_gpt_param) {
+                fprintf(stderr, "error: invalid argument: %s\n", arg.c_str());
+                retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
+                exit(1);
+            }
+            // option was parsed by gpt_params_find_arg
+        } else if (arg == "--context-file") {
+            if (++i >= argc) {
+                fprintf(stderr, "error: missing argument for --context-file\n");
+                retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
+                exit(1);
+            }
+            std::ifstream file(argv[i]);
+            if (!file) {
+                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
+                retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
+                exit(1);
+            }
+            // store the external file name in params
+            retrieval_params.context_files.push_back(argv[i]);
+        } else if (arg == "--chunk-size") {
+            if (++i >= argc) {
+                fprintf(stderr, "error: missing argument for --chunk-size\n");
+                retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
+                exit(1);
+            }
+            retrieval_params.chunk_size = std::stoi(argv[i]);
+        } else if (arg == "--chunk-separator") {
+            if (++i >= argc) {
+                fprintf(stderr, "error: missing argument for --chunk-separator\n");
+                retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
+                exit(1);
+            }
+            retrieval_params.chunk_separator = argv[i];
+        } else {
+            // unknown argument
+            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
+            retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
+            exit(1);
+        }
+        i++;
+    }
+}
+
+struct chunk {
+    // filename
+    std::string filename;
+    // original file position
+    size_t filepos;
+    // original text data
+    std::string textdata = "";
+    // tokenized text data
+    std::vector<llama_token> tokens;
+    // embedding
+    std::vector<float> embedding;
+};
+
+// chunk file data to chunks of size >= chunk_size
+// chunk_separator is the separator between chunks
+static std::vector<chunk> chunk_file(const std::string & filename, int chunk_size, const std::string & chunk_separator) {
+    std::vector<chunk> chunks;
+    std::ifstream f(filename.c_str());
+
+    if (!f.is_open()) {
+        fprintf(stderr, "Error: could not open file %s\n", filename.c_str());
+        return chunks;
+    }
+
+    chunk current_chunk;
+    char buffer[1024];
+    int64_t filepos = 0;
+    std::string current = "";
+    while (f.read(buffer, 1024)) {
+        current += std::string(buffer, f.gcount());
+        size_t pos;
+        while ((pos = current.find(chunk_separator)) != std::string::npos) {
+            current_chunk.textdata += current.substr(0, pos + chunk_separator.size());
+            if ((int) current_chunk.textdata.size() > chunk_size) {
+                // save chunk
+                current_chunk.filepos = filepos;
+                current_chunk.filename = filename;
+                chunks.push_back(current_chunk);
+                // update filepos
+                filepos += (int) current_chunk.textdata.size();
+                // reset current_chunk
+                current_chunk = chunk();
+            }
+            current = current.substr(pos + chunk_separator.size());
+        }
+
+    }
+    // add leftover data to last chunk
+    if (current_chunk.textdata.size() > 0) {
+        if (chunks.empty()) {
+            current_chunk.filepos = filepos;
+            current_chunk.filename = filename;
+            chunks.push_back(current_chunk);
+        } else {
+            chunks.back().textdata += current_chunk.textdata;
+        }
+    }
+    f.close();
+    return chunks;
+}
+
+static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, int seq_id) {
+    for (size_t i = 0; i < tokens.size(); i++) {
+        llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
+    }
+}
+
+static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) {
+    // clear previous kv_cache values (irrelevant for embeddings)
+    llama_kv_cache_clear(ctx);
+
+    // run model
+    fprintf(stderr, "%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
+    if (llama_decode(ctx, batch) < 0) {
+        fprintf(stderr, "%s : failed to decode\n", __func__);
+    }
+
+    for (int i = 0; i < batch.n_tokens; i++) {
+        if (!batch.logits[i]) {
+            continue;
+        }
+
+        // try to get sequence embeddings - supported only when pooling_type is not NONE
+        const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
+        if (embd == NULL) {
+            embd = llama_get_embeddings_ith(ctx, i);
+            if (embd == NULL) {
+                fprintf(stderr, "%s: failed to get embeddings for token %d\n", __func__, i);
+                continue;
+            }
+        }
+
+        float * out = output + batch.seq_id[i][0] * n_embd;
+        llama_embd_normalize(embd, out, n_embd);
+    }
+}
+
+int main(int argc, char ** argv) {
+    gpt_params params;
+    retrieval_params retrieval_params;
+
+    retrieval_params_parse(argc, argv, params, retrieval_params);
+
+    // For BERT models, batch size must be equal to ubatch size
+    params.n_ubatch = params.n_batch;
+
+    if (retrieval_params.chunk_size <= 0) {
+        fprintf(stderr, "chunk_size must be positive\n");
+        return 1;
+    }
+    if (retrieval_params.context_files.empty()) {
+        fprintf(stderr, "context_files must be specified\n");
+        return 1;
+    }
+    params.embedding = true;
+
+    print_build_info();
+
+    printf("processing files:\n");
+    for (auto & context_file : retrieval_params.context_files) {
+        printf("%s\n", context_file.c_str());
+    }
+
+    std::vector<chunk> chunks;
+    for (auto & context_file : retrieval_params.context_files) {
+        std::vector<chunk> file_chunk = chunk_file(context_file, retrieval_params.chunk_size, retrieval_params.chunk_separator);
+        chunks.insert(chunks.end(), file_chunk.begin(), file_chunk.end());
+    }
+    printf("Number of chunks: %ld\n", chunks.size());
+
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    llama_model * model;
+    llama_context * ctx;
+
+    // load the model
+    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    if (model == NULL) {
+        fprintf(stderr, "%s: error: unable to load model\n", __func__);
+        return 1;
+    }
+
+    const int n_ctx_train = llama_n_ctx_train(model);
+    const int n_ctx = llama_n_ctx(ctx);
+
+    if (n_ctx > n_ctx_train) {
+        fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
+                __func__, n_ctx_train, n_ctx);
+    }
+
+    // print system information
+    {
+        fprintf(stderr, "\n");
+        fprintf(stderr, "%s\n", get_system_info(params).c_str());
+    }
+
+    // max batch size
+    const uint64_t n_batch = params.n_batch;
+    GGML_ASSERT(params.n_batch >= params.n_ctx);
+
+    // tokenize the prompts and trim
+    for (auto & chunk : chunks) {
+        auto inp = ::llama_tokenize(ctx, chunk.textdata, true, false);
+        if (inp.size() > n_batch) {
+            fprintf(stderr, "%s: error: chunk size (%lld) exceeds batch size (%lld), increase batch size and re-run\n",
+                    __func__, (long long int) inp.size(), (long long int) n_batch);
+            return 1;
+        }
+        // add eos if not present
+        if (inp.empty() || inp.back() != llama_token_eos(model)) {
+            inp.push_back(llama_token_eos(model));
+        }
+        chunk.tokens = inp;
+    }
+
+    // tokenization stats
+    if (params.verbose_prompt) {
+        for (int i = 0; i < (int) chunks.size(); i++) {
+            fprintf(stderr, "%s: prompt %d: '%s'\n", __func__, i, chunks[i].textdata.c_str());
+            fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, chunks[i].tokens.size());
+            for (int j = 0; j < (int) chunks[i].tokens.size(); j++) {
+                fprintf(stderr, "%6d -> '%s'\n", chunks[i].tokens[j], llama_token_to_piece(ctx, chunks[i].tokens[j]).c_str());
+            }
+            fprintf(stderr, "\n\n");
+        }
+    }
+
+    // initialize batch
+    const int n_chunks = chunks.size();
+    struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
+
+    // allocate output
+    const int n_embd = llama_n_embd(model);
+    std::vector<float> embeddings(n_chunks * n_embd, 0);
+    float * emb = embeddings.data();
+
+    // break into batches
+    int p = 0; // number of prompts processed already
+    int s = 0; // number of prompts in current batch
+    for (int k = 0; k < n_chunks; k++) {
+        // clamp to n_batch tokens
+        auto & inp = chunks[k].tokens;
+
+        const uint64_t n_toks = inp.size();
+
+        // encode if at capacity
+        if (batch.n_tokens + n_toks > n_batch) {
+            float * out = emb + p * n_embd;
+            batch_decode(ctx, batch, out, s, n_embd);
+            llama_batch_clear(batch);
+            p += s;
+            s = 0;
+        }
+
+        // add to batch
+        batch_add_seq(batch, inp, s);
+        s += 1;
+    }
+
+    // final batch
+    float * out = emb + p * n_embd;
+    batch_decode(ctx, batch, out, s, n_embd);
+
+    // save embeddings to chunks
+    for (int i = 0; i < n_chunks; i++) {
+        chunks[i].embedding = std::vector<float>(emb + i * n_embd, emb + (i + 1) * n_embd);
+        // clear tokens as they are no longer needed
+        chunks[i].tokens.clear();
+    }
+
+    // start loop, receive query and return top k similar chunks based on cosine similarity
+    std::string query;
+    while (true) {
+        printf("Enter query: ");
+        std::getline(std::cin, query);
+        std::vector<int32_t> query_tokens = llama_tokenize(ctx, query, true);
+
+        struct llama_batch query_batch = llama_batch_init(n_batch, 0, 1);
+        batch_add_seq(query_batch, query_tokens, 0);
+
+        std::vector<float> query_emb(n_embd, 0);
+        batch_decode(ctx, query_batch, query_emb.data(), 1, n_embd);
+
+        llama_batch_clear(query_batch);
+
+        // compute cosine similarities
+        {
+            std::vector<std::pair<int, float>> similarities;
+            for (int i = 0; i < n_chunks; i++) {
+                float sim = llama_embd_similarity_cos(chunks[i].embedding.data(), query_emb.data(), n_embd);
+                similarities.push_back(std::make_pair(i, sim));
+            }
+
+            // sort similarities
+            std::sort(similarities.begin(), similarities.end(), [](const std::pair<int, float> & a, const std::pair<int, float> & b) {
+                return a.second > b.second;
+            });
+
+            printf("Top %d similar chunks:\n", params.sparams.top_k);
+            for (int i = 0; i < std::min(params.sparams.top_k, (int) chunks.size()); i++) {
+                printf("filename: %s\n", chunks[similarities[i].first].filename.c_str());
+                printf("filepos: %lld\n", (long long int) chunks[similarities[i].first].filepos);
+                printf("similarity: %f\n", similarities[i].second);
+                printf("textdata:\n%s\n", chunks[similarities[i].first].textdata.c_str());
+                printf("--------------------\n");
+            }
+        }
+    }
+
+    // clean up
+    llama_print_timings(ctx);
+    llama_free(ctx);
+    llama_free_model(model);
+    llama_backend_free();
+}
diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp
index ef952e2bd..c3b766882 100644
--- a/examples/save-load-state/save-load-state.cpp
+++ b/examples/save-load-state/save-load-state.cpp
@@ -24,6 +24,7 @@ int main(int argc, char ** argv) {
 
     std::string result0;
     std::string result1;
+    std::string result2;
 
     // init
     llama_model * model;
@@ -44,8 +45,8 @@ int main(int argc, char ** argv) {
 
     // save state (rng, logits, embedding and kv_cache) to file
     {
-        std::vector<uint8_t> state_mem(llama_get_state_size(ctx));
-        const size_t written = llama_copy_state_data(ctx, state_mem.data());
+        std::vector<uint8_t> state_mem(llama_state_get_size(ctx));
+        const size_t written = llama_state_get_data(ctx, state_mem.data());
 
         FILE *fp_write = fopen("dump_state.bin", "wb");
         fwrite(state_mem.data(), 1, written, fp_write);
@@ -97,13 +98,13 @@ int main(int argc, char ** argv) {
 
     // load state (rng, logits, embedding and kv_cache) from file
     {
-        std::vector<uint8_t> state_mem(llama_get_state_size(ctx2));
+        std::vector<uint8_t> state_mem(llama_state_get_size(ctx2));
 
         FILE * fp_read = fopen("dump_state.bin", "rb");
         const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read);
         fclose(fp_read);
 
-        if (read != llama_set_state_data(ctx2, state_mem.data())) {
+        if (read != llama_state_set_data(ctx2, state_mem.data())) {
             fprintf(stderr, "\n%s : failed to read state\n", __func__);
             llama_free(ctx2);
             llama_free_model(model);
@@ -141,16 +142,104 @@ int main(int argc, char ** argv) {
         n_past += 1;
     }
 
-    printf("\n");
+    printf("\n\n");
 
     llama_free(ctx2);
-    llama_free_model(model);
 
     if (result0 != result1) {
         fprintf(stderr, "\n%s : error : the 2 generations are different\n", __func__);
         return 1;
     }
 
+    // make new context
+    auto* ctx3 = llama_new_context_with_model(model, llama_context_params_from_gpt_params(params));
+
+    printf("\nsingle seq run: %s", params.prompt.c_str());
+
+    // load state (rng, logits, embedding and kv_cache) from file
+    {
+        std::vector<uint8_t> state_mem(llama_state_get_size(ctx3));
+
+        FILE * fp_read = fopen("dump_state.bin", "rb");
+        const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read);
+        fclose(fp_read);
+
+        if (read != llama_state_set_data(ctx3, state_mem.data())) {
+            fprintf(stderr, "\n%s : failed to read state\n", __func__);
+            llama_free(ctx3);
+            llama_free_model(model);
+            return 1;
+        }
+
+        fprintf(stderr, "%s : deserialized state from %zd out of a maximum of %zd bytes\n", __func__, read, state_mem.size());
+    }
+
+    // restore state (last tokens)
+    n_past = n_past_saved;
+
+    // save seq 0 and load into seq 1
+    {
+        // save kv of seq 0
+        std::vector<uint8_t> seq_store(llama_state_seq_get_size(ctx3, 0));
+        const size_t ncopy = llama_state_seq_get_data(ctx3, seq_store.data(), 0);
+        if (ncopy != seq_store.size()) {
+            fprintf(stderr, "\n%s : seq copy data length %zd does not match expected length %zd\n", __func__, ncopy, seq_store.size());
+            llama_free(ctx3);
+            llama_free_model(model);
+            return 1;
+        }
+        fprintf(stderr, "%s : seq 0 copied, %zd bytes\n", __func__, ncopy);
+
+        // erase whole kv
+        llama_kv_cache_clear(ctx3);
+        fprintf(stderr, "%s : kv cache cleared\n", __func__);
+
+        // restore kv into seq 1
+        const size_t nset = llama_state_seq_set_data(ctx3, seq_store.data(), 1);
+        if (nset != seq_store.size()) {
+            fprintf(stderr, "\n%s : seq set data length %zd does not match expected length %zd\n", __func__, nset, seq_store.size());
+            llama_free(ctx3);
+            llama_free_model(model);
+            return 1;
+        }
+        fprintf(stderr, "%s : seq 1 restored, %zd bytes\n", __func__, nset);
+    }
+
+    // third run with seq 1 instead of 0
+    for (auto i = 0; i < params.n_predict; i++) {
+        auto * logits = llama_get_logits(ctx3);
+        auto n_vocab = llama_n_vocab(model);
+        std::vector<llama_token_data> candidates;
+        candidates.reserve(n_vocab);
+        for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+            candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
+        }
+        llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+        auto next_token = llama_sample_token(ctx3, &candidates_p);
+        auto next_token_str = llama_token_to_piece(ctx3, next_token);
+
+        printf("%s", next_token_str.c_str());
+        result2 += next_token_str;
+
+        if (llama_decode(ctx3, llama_batch_get_one(&next_token, 1, n_past, 1))) {
+            fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
+            llama_free(ctx3);
+            llama_free_model(model);
+            return 1;
+        }
+        n_past += 1;
+    }
+
+    printf("\n");
+
+    llama_free(ctx3);
+    llama_free_model(model);
+
+    if (result0 != result2) {
+        fprintf(stderr, "\n%s : error : the seq restore generation is different\n", __func__);
+        return 1;
+    }
+
     fprintf(stderr, "\n%s : success\n", __func__);
 
     return 0;
diff --git a/examples/server-embd.py b/examples/server-embd.py
new file mode 100644
index 000000000..118e04271
--- /dev/null
+++ b/examples/server-embd.py
@@ -0,0 +1,34 @@
+import asyncio
+import requests
+import numpy as np
+
+n = 8
+
+result = []
+
+async def requests_post_async(*args, **kwargs):
+    return await asyncio.to_thread(requests.post, *args, **kwargs)
+
+async def main():
+    model_url = "http://127.0.0.1:6900"
+    responses: list[requests.Response] = await asyncio.gather(*[requests_post_async(
+        url= f"{model_url}/embedding",
+        json= {"content": str(0)*1024}
+    ) for i in range(n)])
+
+    for response in responses:
+        embedding = response.json()["embedding"]
+        print(embedding[-8:])
+        result.append(embedding)
+
+asyncio.run(main())
+
+# compute cosine similarity
+
+for i in range(n-1):
+    for j in range(i+1, n):
+        embedding1 = np.array(result[i])
+        embedding2 = np.array(result[j])
+        similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
+        print(f"Similarity between {i} and {j}: {similarity:.2f}")
+
diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt
index cc13b2d63..4b89c5302 100644
--- a/examples/server/CMakeLists.txt
+++ b/examples/server/CMakeLists.txt
@@ -1,12 +1,39 @@
 set(TARGET server)
 option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
-include_directories(${CMAKE_CURRENT_SOURCE_DIR})
-add_executable(${TARGET} server.cpp oai.hpp utils.hpp json.hpp httplib.h)
+option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF)
+include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR})
+set(TARGET_SRCS
+    server.cpp
+    utils.hpp
+    httplib.h
+)
+set(PUBLIC_ASSETS
+    index.html
+    index.js
+    completion.js
+    json-schema-to-grammar.mjs
+)
+foreach(asset ${PUBLIC_ASSETS})
+    set(input "${CMAKE_CURRENT_SOURCE_DIR}/public/${asset}")
+    set(output "${CMAKE_CURRENT_BINARY_DIR}/${asset}.hpp")
+    list(APPEND TARGET_SRCS ${output})
+    add_custom_command(
+        DEPENDS "${input}"
+        OUTPUT "${output}"
+        COMMAND "${CMAKE_COMMAND}" "-DINPUT=${input}" "-DOUTPUT=${output}" -P "${PROJECT_SOURCE_DIR}/scripts/xxd.cmake"
+    )
+endforeach()
+add_executable(${TARGET} ${TARGET_SRCS})
 install(TARGETS ${TARGET} RUNTIME)
 target_compile_definitions(${TARGET} PRIVATE
     SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
 )
-target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT})
+if (LLAMA_SERVER_SSL)
+    find_package(OpenSSL REQUIRED)
+    target_link_libraries(${TARGET} PRIVATE OpenSSL::SSL OpenSSL::Crypto)
+    target_compile_definitions(${TARGET} PRIVATE CPPHTTPLIB_OPENSSL_SUPPORT)
+endif()
 if (WIN32)
     TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
 endif()
diff --git a/examples/server/README.md b/examples/server/README.md
index 809e2d37c..650317991 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -1,61 +1,121 @@
-# llama.cpp/example/server
+# LLaMA.cpp HTTP Server
 
-This example demonstrates a simple HTTP API server and a simple web front end to interact with llama.cpp.
+Fast, lightweight, pure C/C++ HTTP server based on [httplib](https://github.com/yhirose/cpp-httplib), [nlohmann::json](https://github.com/nlohmann/json) and **llama.cpp**.
 
-Command line options:
+Set of LLM REST APIs and a simple web front end to interact with llama.cpp.
 
-- `--threads N`, `-t N`: Set the number of threads to use during generation.
-- `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation.
+**Features:**
+ * LLM inference of F16 and quantum models on GPU and CPU
+ * [OpenAI API](https://github.com/openai/openai-openapi) compatible chat completions and embeddings routes
+ * Parallel decoding with multi-user support
+ * Continuous batching
+ * Multimodal (wip)
+ * Monitoring endpoints
+ * Schema-constrained JSON response format
+
+The project is under active development, and we are [looking for feedback and contributors](https://github.com/ggerganov/llama.cpp/issues/4216).
+
+**Command line options:**
+
+- `--threads N`, `-t N`: Set the number of threads to use during generation. Not used if model layers are offloaded to GPU. The server is using batching. This parameter is used only if one token is to be processed on CPU backend.
+- `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. Not used if model layers are offloaded to GPU.
+- `--threads-http N`: Number of threads in the http server pool to process requests. Default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)`
 - `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`).
+- `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file. Default: unused
+- `-hfr REPO, --hf-repo REPO`: Hugging Face model repository. Default: unused
+- `-hff FILE, --hf-file FILE`: Hugging Face model file. Default: unused
 - `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
-- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096.
-- `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
-- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.
-- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS.
-- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `512`.
-- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. Not recommended.
+- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is `512`, but LLaMA models were built with a context of `2048`, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of `4096`.
+- `-ngl N`, `--n-gpu-layers N`: When compiled with GPU support, this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
+- `-mg i, --main-gpu i`: When using multiple GPUs, this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default, GPU `0` is used.
+- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs, this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default, the data is split in proportion to VRAM, but this may not be optimal for performance.
+- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `2048`
+- `-ub N`, `--ubatch-size N`: Physical maximum batch size. Default: `512`
 - `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped.
 - `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed.
-- `--numa STRATEGY`: Attempt one of the below optimization strategies  that help on some NUMA systems
+- `--numa STRATEGY`: Attempt one of the below optimization strategies that may help on some NUMA systems
 - `--numa distribute`: Spread execution evenly over all nodes
 - `--numa isolate`: Only spawn threads on CPUs on the node that execution started on
-- `--numa numactl`: Use the CPU map provided by numactl
-if run without this previously, it is recommended to drop the system page cache before using this
-see https://github.com/ggerganov/llama.cpp/issues/1437
+- `--numa numactl`: Use the CPU map provided by numactl. If run without this previously, it is recommended to drop the system
+page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/1437
 
-- `--numa`: Attempt optimizations that help on some NUMA systems.
+- `--numa`: Attempt optimizations that may help on some NUMA systems.
 - `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
 - `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.
-- `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`.
-- `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`.
-- `--port`: Set the port to listen. Default: `8080`.
-- `--path`: path from which to serve static files (default examples/server/public)
-- `--api-key`: Set an api key for request authorization. By default the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. May be used multiple times to enable multiple valid keys.
-- `--api-key-file`: path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access. May be used in conjunction with `--api-key`'s.
-- `--embedding`: Enable embedding extraction, Default: disabled.
-- `-np N`, `--parallel N`: Set the number of slots for process requests (default: 1)
-- `-cb`, `--cont-batching`: enable continuous batching (a.k.a dynamic batching) (default: disabled)
-- `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load "a system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
+- `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`
+- `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`
+- `--port`: Set the port to listen. Default: `8080`
+- `--path`: Path from which to serve static files. Default: disabled
+- `--api-key`: Set an api key for request authorization. By default, the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. May be used multiple times to enable multiple valid keys.
+- `--api-key-file`: Path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access. May be used in conjunction with `--api-key`s.
+- `--embedding`: Enable embedding extraction. Default: disabled
+- `-np N`, `--parallel N`: Set the number of slots for process requests. Default: `1`
+- `-cb`, `--cont-batching`: Enable continuous batching (a.k.a dynamic batching).  Default: disabled
+- `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load a system prompt (initial prompt of all slots). This is useful for chat applications. [See more](#change-system-prompt-on-runtime)
 - `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA.
-- `--grp-attn-n`: Set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`
-- `--grp-attn-w`: Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`
-- `-n, --n-predict`: Set the maximum tokens to predict (default: -1)
+- `--grp-attn-n`: Set the group attention factor to extend context size through self-extend. Used together with group attention width `--grp-attn-w`. Default: `1`, which is disabled.
+- `--grp-attn-w`: Set the group attention width to extend context size through self-extend.  Used together with group attention factor `--grp-attn-n`. Default: `512`
+- `-n N, --n-predict N`: Set the maximum tokens to predict. Default: `-1`
 - `--slots-endpoint-disable`: To disable slots state monitoring endpoint. Slots state may contain user data, prompts included.
+- `--metrics`: enable prometheus `/metrics` compatible endpoint. Default: disabled
+- `--slot-save-path PATH`: Specifies the path where the state of slots (the prompt cache) can be stored. If not provided, the slot management endpoints will be disabled.
+- `--chat-template JINJA_TEMPLATE`: Set custom jinja chat template. This parameter accepts a string, not a file name.  Default: template taken from model's metadata. We only support [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
+- `--log-disable`: Output logs to stdout only, not to `llama.log`. Default: enabled
+- `--log-format FORMAT`: Define the log output to FORMAT: json or text Default: `json`
+- `--rope-scaling` : RoPE scaling method. Defaults to linear unless otherwise specified by the model. Options are `none`, `linear`, `yarn`
+- `--rope-freq-base N` : RoPE frequency base (default: loaded from model)
+- `--rope-freq-scale N`: RoPE frequency scaling factor, expands context by a factor of 1/N (e.g. 0.25)
+- `--yarn-ext-factor N` : YaRN: extrapolation mix factor (Default: 1.0, 0.0 = full interpolation)
+- `--yarn-attn-factor N` : YaRN: scale sqrt(t) or attention magnitude (default: 1.0)
+- `--yarn-beta-slow N`: YaRN: High correction dim or alpha (default: 1.0)
+- `--yarn-beta-fast N`: YaRN: low correction dim or beta (default: 32.0)
+- `--pooling` : Pooling type for embeddings, use model default if unspecified. Options are `none`, `mean`, `cls`
+- `-dt N`, `--defrag-thold N`: KV cache defragmentation threshold (default: -1.0, < 0 = disabled)
+- `-fa`, `--flash-attn` : enable flash attention (default: disabled).
+- `-ctk TYPE`, `--cache-type-k TYPE` : KV cache data type for K (default: `f16`, options `f32`, `f16`, `q8_0`, `q4_0`, `q4_1`, `iq4_nl`, `q5_0`, or `q5_1`)
+- `-ctv TYPE`, `--cache-type-v TYPE` : KV cache type for V (default `f16`, see `-ctk` for options)
+
+**If compiled with `LLAMA_SERVER_SSL=ON`**
+- `--ssl-key-file FNAME`: path to file a PEM-encoded SSL private key
+- `--ssl-cert-file FNAME`: path to file a PEM-encoded SSL certificate
 
 ## Build
 
-server is build alongside everything else from the root of the project
+`server` is built alongside everything else from the root of the project
 
 - Using `make`:
 
   ```bash
-  make
+  make server
   ```
 
 - Using `CMake`:
 
   ```bash
-  cmake --build . --config Release
+  cmake -B build
+  cmake --build build --config Release -t server
+  ```
+
+  Binary is at `./build/bin/server`
+
+## Build with SSL
+
+`server` can also be built with SSL support using OpenSSL 3
+
+- Using `make`:
+
+  ```bash
+  # NOTE: For non-system openssl, use the following:
+  #   CXXFLAGS="-I /path/to/openssl/include"
+  #   LDFLAGS="-L /path/to/openssl/lib"
+  make LLAMA_SERVER_SSL=true server
+  ```
+
+- Using `CMake`:
+
+  ```bash
+  cmake -B build -DLLAMA_SERVER_SSL=ON
+  cmake --build build --config Release -t server
   ```
 
 ## Quick Start
@@ -80,15 +140,15 @@ You can consume the endpoints with Postman or NodeJS with axios library. You can
 ### Docker
 
 ```bash
-docker run -p 8080:8080 -v /path/to/models:/models ggerganov/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080
+docker run -p 8080:8080 -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080
 
 # or, with CUDA:
-docker run -p 8080:8080 -v /path/to/models:/models --gpus all ggerganov/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99
+docker run -p 8080:8080 -v /path/to/models:/models --gpus all ghcr.io/ggerganov/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99
 ```
 
 ## Testing with CURL
 
-Using [curl](https://curl.se/). On Windows `curl.exe` should be available in the base OS.
+Using [curl](https://curl.se/). On Windows, `curl.exe` should be available in the base OS.
 
 ```sh
 curl --request POST \
@@ -97,6 +157,12 @@ curl --request POST \
     --data '{"prompt": "Building a website can be done in 10 simple steps:","n_predict": 128}'
 ```
 
+## Advanced testing
+
+We implemented a [server test framework](./tests/README.md) using human-readable scenario.
+
+*Before submitting an issue, please try to reproduce it with this format.*
+
 ## Node JS Test
 
 You need to have [Node.js](https://nodejs.org/en) installed.
@@ -106,7 +172,7 @@ mkdir llama-client
 cd llama-client
 ```
 
-Create a index.js file and put inside this:
+Create a index.js file and put this inside:
 
 ```javascript
 const prompt = `Building a website can be done in 10 simple steps:`;
@@ -134,86 +200,95 @@ node index.js
 ## API Endpoints
 
 - **GET** `/health`: Returns the current state of the server:
-  - `{"status": "loading model"}` if the model is still being loaded.
-  - `{"status": "error"}` if the model failed to load.
-  - `{"status": "ok"}` if the model is successfully loaded and the server is ready for further requests mentioned below.
-  - `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if no slot are currently available
+  - 503 -> `{"status": "loading model"}` if the model is still being loaded.
+  - 500 -> `{"status": "error"}` if the model failed to load.
+  - 200 -> `{"status": "ok", "slots_idle": 1, "slots_processing": 2 }` if the model is successfully loaded and the server is ready for further requests mentioned below.
+  - 200 -> `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if no slots are currently available.
+  - 503 -> `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if the query parameter `fail_on_no_slot` is provided and no slots are currently available.
+
+  If the query parameter `include_slots` is passed, `slots` field will contain internal slots data except if `--slots-endpoint-disable` is set.
 
 - **POST** `/completion`: Given a `prompt`, it returns the predicted completion.
 
     *Options:*
 
-    `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. If the prompt is a string or an array with the first element given as a string, a `bos` token is inserted in the front like `main` does.
+    `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, if `cache_prompt` is `true`, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. A `BOS` token is inserted at the start, if all of the following conditions are true:
 
-    `temperature`: Adjust the randomness of the generated text (default: 0.8).
+      - The prompt is a string or an array with the first element given as a string
+      - The model's `tokenizer.ggml.add_bos_token` metadata is `true`
+      - The system prompt is empty
 
-    `dynatemp_range`: Dynamic temperature range (default: 0.0, 0.0 = disabled).
+    `temperature`: Adjust the randomness of the generated text. Default: `0.8`
 
-    `dynatemp_exponent`: Dynamic temperature exponent (default: 1.0).
+    `dynatemp_range`: Dynamic temperature range. The final temperature will be in the range of `[temperature - dynatemp_range; temperature + dynatemp_range]` Default: `0.0`, which is disabled.
 
-    `top_k`: Limit the next token selection to the K most probable tokens (default: 40).
+    `dynatemp_exponent`: Dynamic temperature exponent. Default: `1.0`
 
-    `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.95).
+    `top_k`: Limit the next token selection to the K most probable tokens.  Default: `40`
 
-    `min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token (default: 0.05).
+    `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P. Default: `0.95`
 
-    `n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. (default: -1, -1 = infinity).
+    `min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token. Default: `0.05`
+
+    `n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. Default: `-1`, where `-1` is infinity.
 
     `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded.
-    By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the prompt.
+    By default, this value is set to `0`, meaning no tokens are kept. Use `-1` to retain all tokens from the prompt.
 
     `stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`.
 
     `stop`: Specify a JSON array of stopping strings.
-    These words will not be included in the completion, so make sure to add them to the prompt for the next iteration (default: []).
+    These words will not be included in the completion, so make sure to add them to the prompt for the next iteration. Default: `[]`
 
-    `tfs_z`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled).
+    `tfs_z`: Enable tail free sampling with parameter z. Default: `1.0`, which is disabled.
 
-    `typical_p`: Enable locally typical sampling with parameter p (default: 1.0, 1.0 = disabled).
+    `typical_p`: Enable locally typical sampling with parameter p. Default: `1.0`, which is disabled.
 
-    `repeat_penalty`: Control the repetition of token sequences in the generated text (default: 1.1).
+    `repeat_penalty`: Control the repetition of token sequences in the generated text. Default: `1.1`
 
-    `repeat_last_n`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx-size).
+    `repeat_last_n`: Last n tokens to consider for penalizing repetition. Default: `64`, where `0` is disabled and `-1` is ctx-size.
 
-    `penalize_nl`: Penalize newline tokens when applying the repeat penalty (default: true).
+    `penalize_nl`: Penalize newline tokens when applying the repeat penalty. Default: `true`
 
-    `presence_penalty`: Repeat alpha presence penalty (default: 0.0, 0.0 = disabled).
+    `presence_penalty`: Repeat alpha presence penalty. Default: `0.0`, which is disabled.
 
-    `frequency_penalty`: Repeat alpha frequency penalty (default: 0.0, 0.0 = disabled);
+    `frequency_penalty`: Repeat alpha frequency penalty. Default: `0.0`, which is disabled.
 
-    `penalty_prompt`: This will replace the `prompt` for the purpose of the penalty evaluation. Can be either `null`, a string or an array of numbers representing tokens (default: `null` = use the original `prompt`).
+    `penalty_prompt`: This will replace the `prompt` for the purpose of the penalty evaluation. Can be either `null`, a string or an array of numbers representing tokens. Default: `null`, which is to use the original `prompt`.
 
-    `mirostat`: Enable Mirostat sampling, controlling perplexity during text generation (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0).
+    `mirostat`: Enable Mirostat sampling, controlling perplexity during text generation. Default: `0`, where `0` is disabled, `1` is Mirostat, and `2` is Mirostat 2.0.
 
-    `mirostat_tau`: Set the Mirostat target entropy, parameter tau (default: 5.0).
+    `mirostat_tau`: Set the Mirostat target entropy, parameter tau. Default: `5.0`
 
-    `mirostat_eta`: Set the Mirostat learning rate, parameter eta (default: 0.1).
+    `mirostat_eta`: Set the Mirostat learning rate, parameter eta.  Default: `0.1`
 
-    `grammar`: Set grammar for grammar-based sampling (default: no grammar)
+    `grammar`: Set grammar for grammar-based sampling.  Default: no grammar
 
-    `seed`: Set the random number generator (RNG) seed (default: -1, -1 = random seed).
+    `json_schema`: Set a JSON schema for grammar-based sampling (e.g. `{"items": {"type": "string"}, "minItems": 10, "maxItems": 100}` of a list of strings, or `{}` for any JSON). See [tests](../../tests/test-json-schema-to-grammar.cpp) for supported features.  Default: no JSON schema.
 
-    `ignore_eos`: Ignore end of stream token and continue generating (default: false).
+    `seed`: Set the random number generator (RNG) seed.  Default: `-1`, which is a random seed.
 
-    `logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced. The tokens can also be represented as strings, e.g. `[["Hello, World!",-0.5]]` will reduce the likelihood of all the individual tokens that represent the string `Hello, World!`, just like the `presence_penalty` does. (default: []).
+    `ignore_eos`: Ignore end of stream token and continue generating.  Default: `false`
 
-    `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0)
+    `logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced. The tokens can also be represented as strings, e.g. `[["Hello, World!",-0.5]]` will reduce the likelihood of all the individual tokens that represent the string `Hello, World!`, just like the `presence_penalty` does. Default: `[]`
 
-    `min_keep`: If greater than 0, force samplers to return N possible tokens at minimum (default: 0)
+    `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token given the sampling settings. Note that for temperature < 0 the tokens are sampled greedily but token probabilities are still being calculated via a simple softmax of the logits without considering any other sampler settings. Default: `0`
+
+    `min_keep`: If greater than 0, force samplers to return N possible tokens at minimum. Default: `0`
 
     `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.
 
-    `slot_id`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1)
+    `id_slot`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot.  Default: `-1`
 
-    `cache_prompt`: Save the prompt and generation for avoid reprocess entire prompt if a part of this isn't change (default: false)
+    `cache_prompt`: Re-use previously cached prompt from the last request if possible. This may prevent re-caching the prompt from scratch.  Default: `false`
 
     `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
 
-    `samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. (default: `["top_k", "tfs_z", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values)
+    `samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["top_k", "tfs_z", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values.
 
 ### Result JSON
 
-- Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion.
+- Note: When using streaming mode (`stream`), only `content` and `stop` will be returned until end of completion.
 
 - `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has the following structure:
 
@@ -227,7 +302,7 @@ node index.js
     },
     {
       "prob": float,
-      "tok_str": "<second most likely tonen>"
+      "tok_str": "<second most likely token>"
     },
     ...
   ]
@@ -238,7 +313,7 @@ Notice that each `probs` is an array of length `n_probs`.
 
 - `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string.
 - `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options)
-- `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`
+- `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`. These options may differ from the original ones in some way (e.g. bad values filtered out, strings converted to tokens, etc.).
 - `model`: The path to the model loaded with `-m`
 - `prompt`: The provided `prompt`
 - `stopped_eos`: Indicating whether the completion has stopped because it encountered the EOS token
@@ -256,7 +331,7 @@ Notice that each `probs` is an array of length `n_probs`.
 
     `content`: Set the text to tokenize.
 
-    Note that the special `BOS` token is not added in front of the text and also a space character is not inserted automatically as it is for `/completion`.
+    `add_special`: Boolean indicating if special tokens, i.e. `BOS`, should be inserted.  Default: `false`
 
 - **POST** `/detokenize`: Convert tokens to text.
 
@@ -297,14 +372,16 @@ Notice that each `probs` is an array of length `n_probs`.
 
 - `assistant_name` - the required assistant name to generate the prompt in case you have specified a system prompt for all slots.
 - `user_name` - the required anti-prompt to generate the prompt in case you have specified a system prompt for all slots.
-- `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint.
+- `default_generation_settings` - the default generation settings for the `/completion` endpoint, which has the same fields as the `generation_settings` response object from the `/completion` endpoint.
 - `total_slots` - the total number of slots for process requests (defined by `--parallel` option)
 
-- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served.
+- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only model with [supported chat template](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, ChatML template will be used.
 
     *Options:*
 
-    See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such are `mirostat` are supported.
+    See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such as `mirostat` are supported.
+
+    The `response_format` parameter supports both plain JSON output (e.g. `{"type": "json_object"}`) and schema-constrained JSON (e.g. `{"type": "json_object", "schema": {"type": "string", "minLength": 10, "maxLength": 100}}`), similar to other OpenAI-inspired API providers.
 
     *Examples:*
 
@@ -410,7 +487,7 @@ Notice that each `probs` is an array of length `n_probs`.
         "next_token": {
             "has_next_token": true,
             "n_remain": -1,
-            "num_tokens_predicted": 0,
+            "n_decoded": 0,
             "stopped_eos": false,
             "stopped_limit": false,
             "stopped_word": false,
@@ -447,11 +524,74 @@ Notice that each `probs` is an array of length `n_probs`.
 ]
 ```
 
+- **GET** `/metrics`: [Prometheus](https://prometheus.io/) compatible metrics exporter endpoint if `--metrics` is enabled:
+
+Available metrics:
+- `llamacpp:prompt_tokens_total`: Number of prompt tokens processed.
+- `llamacpp:tokens_predicted_total`: Number of generation tokens processed.
+- `llamacpp:prompt_tokens_seconds`: Average prompt throughput in tokens/s.
+- `llamacpp:predicted_tokens_seconds`: Average generation throughput in tokens/s.
+- `llamacpp:kv_cache_usage_ratio`: KV-cache usage. `1` means 100 percent usage.
+- `llamacpp:kv_cache_tokens`: KV-cache tokens.
+- `llamacpp:requests_processing`: Number of requests processing.
+- `llamacpp:requests_deferred`: Number of requests deferred.
+
+- **POST** `/slots/{id_slot}?action=save`: Save the prompt cache of the specified slot to a file.
+
+    *Options:*
+
+    `filename`: Name of the file to save the slot's prompt cache. The file will be saved in the directory specified by the `--slot-save-path` server parameter.
+
+### Result JSON
+
+```json
+{
+    "id_slot": 0,
+    "filename": "slot_save_file.bin",
+    "n_saved": 1745,
+    "n_written": 14309796,
+    "timings": {
+        "save_ms": 49.865
+    }
+}
+```
+
+- **POST** `/slots/{id_slot}?action=restore`: Restore the prompt cache of the specified slot from a file.
+
+    *Options:*
+
+    `filename`: Name of the file to restore the slot's prompt cache from. The file should be located in the directory specified by the `--slot-save-path` server parameter.
+
+### Result JSON
+
+```json
+{
+    "id_slot": 0,
+    "filename": "slot_save_file.bin",
+    "n_restored": 1745,
+    "n_read": 14309796,
+    "timings": {
+        "restore_ms": 42.937
+    }
+}
+```
+
+- **POST** `/slots/{id_slot}?action=erase`: Erase the prompt cache of the specified slot.
+
+### Result JSON
+
+```json
+{
+    "id_slot": 0,
+    "n_erased": 1745
+}
+```
+
 ## More examples
 
 ### Change system prompt on runtime
 
-To use the server example to serve multiple chat-type clients while keeping the same system prompt, you can utilize the option `system_prompt` to achieve that. This only needs to be done once to establish it.
+To use the server example to serve multiple chat-type clients while keeping the same system prompt, you can utilize the option `system_prompt`. This only needs to be used once.
 
 `prompt`: Specify a context that you want all connecting clients to respect.
 
@@ -488,26 +628,55 @@ Run with bash:
 bash chat.sh
 ```
 
-### API like OAI
+### OAI-like API
 
-API example using Python Flask: [api_like_OAI.py](api_like_OAI.py)
-This example must be used with server.cpp
+The HTTP `server` supports an OAI-like API: https://github.com/openai/openai-openapi
 
-```sh
-python api_like_OAI.py
+### API errors
+
+`server` returns errors in the same format as OAI: https://github.com/openai/openai-openapi
+
+Example of an error:
+
+```json
+{
+    "error": {
+        "code": 401,
+        "message": "Invalid API Key",
+        "type": "authentication_error"
+    }
+}
 ```
 
-After running the API server, you can use it in Python by setting the API base URL.
+Apart from error types supported by OAI, we also have custom types that are specific to functionalities of llama.cpp:
 
-```python
-openai.api_base = "http://<Your api-server IP>:port"
+**When /metrics or /slots endpoint is disabled**
+
+```json
+{
+    "error": {
+        "code": 501,
+        "message": "This server does not support metrics endpoint.",
+        "type": "not_supported_error"
+    }
+}
 ```
 
-Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API
+**When the server receives invalid grammar via */completions endpoint**
+
+```json
+{
+    "error": {
+        "code": 400,
+        "message": "Failed to parse grammar",
+        "type": "invalid_request_error"
+    }
+}
+```
 
 ### Extending or building alternative Web Front End
 
-The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method.
+You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method.
 
 Read the documentation in `/completion.js` to see convenient ways to access llama.
 
diff --git a/examples/server/api_like_OAI.py b/examples/server/api_like_OAI.py
deleted file mode 100755
index 607fe49d3..000000000
--- a/examples/server/api_like_OAI.py
+++ /dev/null
@@ -1,228 +0,0 @@
-#!/usr/bin/env python3
-import argparse
-from flask import Flask, jsonify, request, Response
-import urllib.parse
-import requests
-import time
-import json
-
-
-app = Flask(__name__)
-slot_id = -1
-
-parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.")
-parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.')
-parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: 'USER: ')", default="USER: ")
-parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: 'ASSISTANT: ')", default="ASSISTANT: ")
-parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: 'ASSISTANT's RULE: ')", default="ASSISTANT's RULE: ")
-parser.add_argument("--stop", type=str, help="the end of response in chat completions(default: '</s>')", default="</s>")
-parser.add_argument("--llama-api", type=str, help="Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)", default='http://127.0.0.1:8080')
-parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="")
-parser.add_argument("--host", type=str, help="Set the ip address to listen.(default: 127.0.0.1)", default='127.0.0.1')
-parser.add_argument("--port", type=int, help="Set the port to listen.(default: 8081)", default=8081)
-
-args = parser.parse_args()
-
-def is_present(json, key):
-    try:
-        buf = json[key]
-    except KeyError:
-        return False
-    if json[key] == None:
-        return False
-    return True
-
-#convert chat to prompt
-def convert_chat(messages):
-
-    system_n = args.system_name
-    user_n = args.user_name
-    ai_n = args.ai_name
-    stop = args.stop
-
-    prompt = "" + args.chat_prompt + stop
-
-    for line in messages:
-        if (line["role"] == "system"):
-            prompt += f"{system_n}{line['content']}{stop}"
-        if (line["role"] == "user"):
-            prompt += f"{user_n}{line['content']}{stop}"
-        if (line["role"] == "assistant"):
-            prompt += f"{ai_n}{line['content']}{stop}"
-    prompt += ai_n.rstrip()
-
-    return prompt
-
-def make_postData(body, chat=False, stream=False):
-    postData = {}
-    if (chat):
-        postData["prompt"] = convert_chat(body["messages"])
-    else:
-        postData["prompt"] = body["prompt"]
-    if(is_present(body, "temperature")): postData["temperature"] = body["temperature"]
-    if(is_present(body, "top_k")): postData["top_k"] = body["top_k"]
-    if(is_present(body, "top_p")): postData["top_p"] = body["top_p"]
-    if(is_present(body, "max_tokens")): postData["n_predict"] = body["max_tokens"]
-    if(is_present(body, "presence_penalty")): postData["presence_penalty"] = body["presence_penalty"]
-    if(is_present(body, "frequency_penalty")): postData["frequency_penalty"] = body["frequency_penalty"]
-    if(is_present(body, "repeat_penalty")): postData["repeat_penalty"] = body["repeat_penalty"]
-    if(is_present(body, "mirostat")): postData["mirostat"] = body["mirostat"]
-    if(is_present(body, "mirostat_tau")): postData["mirostat_tau"] = body["mirostat_tau"]
-    if(is_present(body, "mirostat_eta")): postData["mirostat_eta"] = body["mirostat_eta"]
-    if(is_present(body, "seed")): postData["seed"] = body["seed"]
-    if(is_present(body, "grammar")): postData["grammar"] = body["grammar"]
-    if(is_present(body, "logit_bias")): postData["logit_bias"] = [[int(token), body["logit_bias"][token]] for token in body["logit_bias"].keys()]
-    if (args.stop != ""):
-        postData["stop"] = [args.stop]
-    else:
-        postData["stop"] = []
-    if(is_present(body, "stop")): postData["stop"] += body["stop"]
-    postData["n_keep"] = -1
-    postData["stream"] = stream
-    postData["cache_prompt"] = True
-    postData["slot_id"] = slot_id
-    return postData
-
-def make_resData(data, chat=False, promptToken=[]):
-    resData = {
-        "id": "chatcmpl" if (chat) else "cmpl",
-        "object": "chat.completion" if (chat) else "text_completion",
-        "created": int(time.time()),
-        "truncated": data["truncated"],
-        "model": "LLaMA_CPP",
-        "usage": {
-            "prompt_tokens": data["tokens_evaluated"],
-            "completion_tokens": data["tokens_predicted"],
-            "total_tokens": data["tokens_evaluated"] + data["tokens_predicted"]
-        }
-    }
-    if (len(promptToken) != 0):
-        resData["promptToken"] = promptToken
-    if (chat):
-        #only one choice is supported
-        resData["choices"] = [{
-            "index": 0,
-            "message": {
-                "role": "assistant",
-                "content": data["content"],
-            },
-            "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
-        }]
-    else:
-        #only one choice is supported
-        resData["choices"] = [{
-            "text": data["content"],
-            "index": 0,
-            "logprobs": None,
-            "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
-        }]
-    return resData
-
-def make_resData_stream(data, chat=False, time_now = 0, start=False):
-    resData = {
-        "id": "chatcmpl" if (chat) else "cmpl",
-        "object": "chat.completion.chunk" if (chat) else "text_completion.chunk",
-        "created": time_now,
-        "model": "LLaMA_CPP",
-        "choices": [
-            {
-                "finish_reason": None,
-                "index": 0
-            }
-        ]
-    }
-    slot_id = data.get("slot_id")
-    if (chat):
-        if (start):
-            resData["choices"][0]["delta"] =  {
-                "role": "assistant"
-            }
-        else:
-            resData["choices"][0]["delta"] =  {
-                "content": data["content"]
-            }
-            if (data["stop"]):
-                resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
-    else:
-        resData["choices"][0]["text"] = data["content"]
-        if (data["stop"]):
-            resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
-
-    return resData
-
-
-@app.route('/chat/completions', methods=['POST', 'OPTIONS'])
-@app.route('/v1/chat/completions', methods=['POST', 'OPTIONS'])
-def chat_completions():
-    if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
-        return Response(status=403)
-    if request.method == 'OPTIONS':
-        return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
-    body = request.get_json()
-    stream = False
-    tokenize = False
-    if(is_present(body, "stream")): stream = body["stream"]
-    if(is_present(body, "tokenize")): tokenize = body["tokenize"]
-    postData = make_postData(body, chat=True, stream=stream)
-
-    promptToken = []
-    if (tokenize):
-        tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json()
-        promptToken = tokenData["tokens"]
-
-    if (not stream):
-        data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData))
-        print(data.json())
-        resData = make_resData(data.json(), chat=True, promptToken=promptToken)
-        return jsonify(resData)
-    else:
-        def generate():
-            data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
-            time_now = int(time.time())
-            resData = make_resData_stream({}, chat=True, time_now=time_now, start=True)
-            yield 'data: {}\n\n'.format(json.dumps(resData))
-            for line in data.iter_lines():
-                if line:
-                    decoded_line = line.decode('utf-8')
-                    resData = make_resData_stream(json.loads(decoded_line[6:]), chat=True, time_now=time_now)
-                    yield 'data: {}\n\n'.format(json.dumps(resData))
-        return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
-
-
-@app.route('/completions', methods=['POST', 'OPTIONS'])
-@app.route('/v1/completions', methods=['POST', 'OPTIONS'])
-def completion():
-    if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
-        return Response(status=403)
-    if request.method == 'OPTIONS':
-        return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
-    body = request.get_json()
-    stream = False
-    tokenize = False
-    if(is_present(body, "stream")): stream = body["stream"]
-    if(is_present(body, "tokenize")): tokenize = body["tokenize"]
-    postData = make_postData(body, chat=False, stream=stream)
-
-    promptToken = []
-    if (tokenize):
-        tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json()
-        promptToken = tokenData["tokens"]
-
-    if (not stream):
-        data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData))
-        print(data.json())
-        resData = make_resData(data.json(), chat=False, promptToken=promptToken)
-        return jsonify(resData)
-    else:
-        def generate():
-            data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
-            time_now = int(time.time())
-            for line in data.iter_lines():
-                if line:
-                    decoded_line = line.decode('utf-8')
-                    resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now)
-                    yield 'data: {}\n\n'.format(json.dumps(resData))
-        return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
-
-if __name__ == '__main__':
-    app.run(args.host, port=args.port)
diff --git a/examples/server/bench/README.md b/examples/server/bench/README.md
new file mode 100644
index 000000000..23a3ec975
--- /dev/null
+++ b/examples/server/bench/README.md
@@ -0,0 +1,120 @@
+### Server benchmark tools
+
+Benchmark is using [k6](https://k6.io/).
+
+##### Install k6 and sse extension
+
+SSE is not supported by default in k6, you have to build k6 with the [xk6-sse](https://github.com/phymbert/xk6-sse) extension.
+
+Example:
+```shell
+go install go.k6.io/xk6/cmd/xk6@latest
+xk6 build master \
+--with github.com/phymbert/xk6-sse
+```
+
+#### Download a dataset
+
+This dataset was originally proposed in [vLLM benchmarks](https://github.com/vllm-project/vllm/blob/main/benchmarks/README.md).
+
+```shell
+wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+```
+
+#### Download a model
+Example for PHI-2
+
+```shell
+../../../scripts/hf.sh --repo ggml-org/models --file phi-2/ggml-model-q4_0.gguf
+```
+
+#### Start the server
+The server must answer OAI Chat completion requests on `http://localhost:8080/v1` or according to the environment variable `SERVER_BENCH_URL`.
+
+Example:
+```shell
+server --host localhost --port 8080 \
+  --model ggml-model-q4_0.gguf \
+  --cont-batching \
+  --metrics \
+  --parallel 8 \
+  --batch-size 512 \
+  --ctx-size 4096 \
+  --log-format text \
+  -ngl 33
+```
+
+#### Run the benchmark
+
+For 500 chat completions request with 8 concurrent users during maximum 10 minutes, run:
+```shell
+./k6 run script.js --duration 10m --iterations 500 --vus 8
+```
+
+The benchmark values can be overridden with:
+- `SERVER_BENCH_URL` server url prefix for chat completions, default `http://localhost:8080/v1`
+- `SERVER_BENCH_N_PROMPTS` total prompts to randomly select in the benchmark, default `480`
+- `SERVER_BENCH_MODEL_ALIAS` model alias to pass in the completion request, default `my-model`
+- `SERVER_BENCH_MAX_TOKENS` max tokens to predict, default: `512`
+- `SERVER_BENCH_DATASET` path to the benchmark dataset file
+- `SERVER_BENCH_MAX_PROMPT_TOKENS` maximum prompt tokens to filter out in the dataset: default `1024`
+- `SERVER_BENCH_MAX_CONTEXT` maximum context size of the completions request to filter out in the dataset: prompt + predicted tokens, default `2048`
+
+Note: the local tokenizer is just a string space split, real number of tokens will differ.
+
+Or with [k6 options](https://k6.io/docs/using-k6/k6-options/reference/):
+
+```shell
+SERVER_BENCH_N_PROMPTS=500 k6 run script.js --duration 10m --iterations 500 --vus 8
+```
+
+To [debug http request](https://k6.io/docs/using-k6/http-debugging/) use `--http-debug="full"`.
+
+#### Metrics
+
+Following metrics are available computed from the OAI chat completions response `usage`:
+- `llamacpp_tokens_second` Trend of `usage.total_tokens / request duration`
+- `llamacpp_prompt_tokens` Trend of `usage.prompt_tokens`
+- `llamacpp_prompt_tokens_total_counter` Counter of `usage.prompt_tokens`
+- `llamacpp_completion_tokens` Trend of `usage.completion_tokens`
+- `llamacpp_completion_tokens_total_counter` Counter of `usage.completion_tokens`
+- `llamacpp_completions_truncated_rate` Rate of completions truncated, i.e. if `finish_reason === 'length'`
+- `llamacpp_completions_stop_rate` Rate of completions stopped by the model, i.e. if `finish_reason === 'stop'`
+
+The script will fail if too many completions are truncated, see `llamacpp_completions_truncated_rate`.
+
+K6 metrics might be compared against [server metrics](../README.md), with:
+
+```shell
+curl http://localhost:8080/metrics
+```
+
+### Using the CI python script
+The `bench.py` script does several steps:
+- start the server
+- define good variable for k6
+- run k6 script
+- extract metrics from prometheus
+
+It aims to be used in the CI, but you can run it manually:
+
+```shell
+LLAMA_SERVER_BIN_PATH=../../../cmake-build-release/bin/server python bench.py \
+              --runner-label local \
+              --name local \
+              --branch `git rev-parse --abbrev-ref HEAD` \
+              --commit `git rev-parse HEAD` \
+              --scenario script.js \
+              --duration 5m \
+              --hf-repo ggml-org/models	 \
+              --hf-file phi-2/ggml-model-q4_0.gguf \
+              --model-path-prefix models \
+              --parallel 4 \
+              -ngl 33 \
+              --batch-size 2048 \
+              --ubatch-size	256 \
+              --ctx-size 4096 \
+              --n-prompts 200 \
+              --max-prompt-tokens 256 \
+              --max-tokens 256
+```
diff --git a/examples/server/bench/bench.py b/examples/server/bench/bench.py
new file mode 100644
index 000000000..86c5de101
--- /dev/null
+++ b/examples/server/bench/bench.py
@@ -0,0 +1,309 @@
+import argparse
+import json
+import os
+import re
+import signal
+import socket
+import subprocess
+import sys
+import threading
+import time
+import traceback
+from contextlib import closing
+from datetime import datetime
+
+import matplotlib
+import matplotlib.dates
+import matplotlib.pyplot as plt
+import requests
+from statistics import mean
+
+
+def main(args_in: list[str] | None = None) -> None:
+    parser = argparse.ArgumentParser(description="Start server benchmark scenario")
+    parser.add_argument("--name", type=str, help="Bench name", required=True)
+    parser.add_argument("--runner-label", type=str, help="Runner label", required=True)
+    parser.add_argument("--branch", type=str, help="Branch name", default="detached")
+    parser.add_argument("--commit", type=str, help="Commit name", default="dirty")
+    parser.add_argument("--host", type=str, help="Server listen host", default="0.0.0.0")
+    parser.add_argument("--port", type=int, help="Server listen host", default="8080")
+    parser.add_argument("--model-path-prefix", type=str, help="Prefix where to store the model files", default="models")
+    parser.add_argument("--n-prompts", type=int,
+                        help="SERVER_BENCH_N_PROMPTS: total prompts to randomly select in the benchmark", required=True)
+    parser.add_argument("--max-prompt-tokens", type=int,
+                        help="SERVER_BENCH_MAX_PROMPT_TOKENS: maximum prompt tokens to filter out in the dataset",
+                        required=True)
+    parser.add_argument("--max-tokens", type=int,
+                        help="SERVER_BENCH_MAX_CONTEXT: maximum context size of the completions request to filter out in the dataset: prompt + predicted tokens",
+                        required=True)
+    parser.add_argument("--hf-repo", type=str, help="Hugging Face model repository", required=True)
+    parser.add_argument("--hf-file", type=str, help="Hugging Face model file", required=True)
+    parser.add_argument("-ngl", "--n-gpu-layers", type=int, help="layers to the GPU for computation", required=True)
+    parser.add_argument("--ctx-size", type=int, help="Set the size of the prompt context", required=True)
+    parser.add_argument("--parallel", type=int, help="Set the number of slots for process requests", required=True)
+    parser.add_argument("--batch-size", type=int, help="Set the batch size for prompt processing", required=True)
+    parser.add_argument("--ubatch-size", type=int, help="physical maximum batch size", required=True)
+    parser.add_argument("--scenario", type=str, help="Scenario to run", required=True)
+    parser.add_argument("--duration", type=str, help="Bench scenario", required=True)
+
+    args = parser.parse_args(args_in)
+
+    start_time = time.time()
+
+    # Start the server and performance scenario
+    try:
+        server_process = start_server(args)
+    except Exception:
+        print("bench: server start error :")
+        traceback.print_exc(file=sys.stdout)
+        sys.exit(1)
+
+    # start the benchmark
+    try:
+        start_benchmark(args)
+
+        iterations = 0
+        with open("results.github.env", 'w') as github_env:
+            # parse output
+            with open('k6-results.json', 'r') as bench_results:
+                # Load JSON data from file
+                data = json.load(bench_results)
+                for metric_name in data['metrics']:
+                    for metric_metric in data['metrics'][metric_name]:
+                        value = data['metrics'][metric_name][metric_metric]
+                        if isinstance(value, float) or isinstance(value, int):
+                            value = round(value, 2)
+                            data['metrics'][metric_name][metric_metric]=value
+                            github_env.write(
+                                f"{escape_metric_name(metric_name)}_{escape_metric_name(metric_metric)}={value}\n")
+                iterations = data['root_group']['checks']['success completion']['passes']
+
+    except Exception:
+        print("bench: error :")
+        traceback.print_exc(file=sys.stdout)
+
+    # Stop the server
+    if server_process:
+        try:
+            print(f"bench: shutting down server pid={server_process.pid} ...")
+            if os.name == 'nt':
+                interrupt = signal.CTRL_C_EVENT
+            else:
+                interrupt = signal.SIGINT
+            server_process.send_signal(interrupt)
+            server_process.wait(0.5)
+
+        except subprocess.TimeoutExpired:
+            print(f"server still alive after 500ms, force-killing pid={server_process.pid} ...")
+            server_process.kill()  # SIGKILL
+            server_process.wait()
+
+        while is_server_listening(args.host, args.port):
+            time.sleep(0.1)
+
+    title = (f"llama.cpp {args.name} on {args.runner_label}\n "
+             f"duration={args.duration} {iterations} iterations")
+    xlabel = (f"{args.hf_repo}/{args.hf_file}\n"
+              f"parallel={args.parallel} ctx-size={args.ctx_size} ngl={args.n_gpu_layers} batch-size={args.batch_size} ubatch-size={args.ubatch_size} pp={args.max_prompt_tokens} pp+tg={args.max_tokens}\n"
+              f"branch={args.branch} commit={args.commit}")
+
+    # Prometheus
+    end_time = time.time()
+    prometheus_metrics = {}
+    if is_server_listening("0.0.0.0", 9090):
+        metrics = ['prompt_tokens_seconds', 'predicted_tokens_seconds',
+                   'kv_cache_usage_ratio', 'requests_processing', 'requests_deferred']
+
+        for metric in metrics:
+            resp = requests.get(f"http://localhost:9090/api/v1/query_range",
+                                params={'query': 'llamacpp:' + metric, 'start': start_time, 'end': end_time, 'step': 2})
+
+            with open(f"{metric}.json", 'w') as metric_json:
+                metric_json.write(resp.text)
+
+            if resp.status_code != 200:
+                print(f"bench: unable to extract prometheus metric {metric}: {resp.text}")
+            else:
+                metric_data = resp.json()
+                values = metric_data['data']['result'][0]['values']
+                timestamps, metric_values = zip(*values)
+                metric_values = [float(value) for value in metric_values]
+                prometheus_metrics[metric] = metric_values
+                timestamps_dt = [datetime.fromtimestamp(int(ts)) for ts in timestamps]
+                plt.figure(figsize=(16, 10), dpi=80)
+                plt.plot(timestamps_dt, metric_values, label=metric)
+                plt.xticks(rotation=0, fontsize=14, horizontalalignment='center', alpha=.7)
+                plt.yticks(fontsize=12, alpha=.7)
+
+                ylabel = f"llamacpp:{metric}"
+                plt.title(title,
+                          fontsize=14, wrap=True)
+                plt.grid(axis='both', alpha=.3)
+                plt.ylabel(ylabel, fontsize=22)
+                plt.xlabel(xlabel, fontsize=14, wrap=True)
+                plt.gca().xaxis.set_major_locator(matplotlib.dates.MinuteLocator())
+                plt.gca().xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%Y-%m-%d %H:%M:%S"))
+                plt.gcf().autofmt_xdate()
+
+                # Remove borders
+                plt.gca().spines["top"].set_alpha(0.0)
+                plt.gca().spines["bottom"].set_alpha(0.3)
+                plt.gca().spines["right"].set_alpha(0.0)
+                plt.gca().spines["left"].set_alpha(0.3)
+
+                # Save the plot as a jpg image
+                plt.savefig(f'{metric}.jpg', dpi=60)
+                plt.close()
+
+                # Mermaid format in case images upload failed
+                with (open(f"{metric}.mermaid", 'w') as mermaid_f):
+                    mermaid = (
+                    f"""---
+config:
+    xyChart:
+        titleFontSize: 12
+        width: 900
+        height: 600
+    themeVariables:
+        xyChart:
+            titleColor: "#000000"
+---
+xychart-beta
+    title "{title}"
+    y-axis "llamacpp:{metric}"
+    x-axis "llamacpp:{metric}" {int(min(timestamps))} --> {int(max(timestamps))}
+    line [{', '.join([str(round(float(value), 2)) for value in metric_values])}]
+                    """)
+                    mermaid_f.write(mermaid)
+
+    # 140 chars max for commit status description
+    bench_results = {
+        "i": iterations,
+        "req": {
+            "p95": round(data['metrics']["http_req_duration"]["p(95)"], 2),
+            "avg": round(data['metrics']["http_req_duration"]["avg"], 2),
+        },
+        "pp": {
+            "p95": round(data['metrics']["llamacpp_prompt_processing_second"]["p(95)"], 2),
+            "avg": round(data['metrics']["llamacpp_prompt_processing_second"]["avg"], 2),
+            "0": round(mean(prometheus_metrics['prompt_tokens_seconds']), 2),
+        },
+        "tg": {
+            "p95": round(data['metrics']["llamacpp_tokens_second"]["p(95)"], 2),
+            "avg": round(data['metrics']["llamacpp_tokens_second"]["avg"], 2),
+            "0": round(mean(prometheus_metrics['predicted_tokens_seconds']), 2),
+        },
+    }
+    with open("results.github.env", 'a') as github_env:
+        github_env.write(f"BENCH_RESULTS={json.dumps(bench_results, indent=None, separators=(',', ':') )}\n")
+        github_env.write(f"BENCH_ITERATIONS={iterations}\n")
+
+        title = title.replace('\n', ' ')
+        xlabel = xlabel.replace('\n', ' ')
+        github_env.write(f"BENCH_GRAPH_TITLE={title}\n")
+        github_env.write(f"BENCH_GRAPH_XLABEL={xlabel}\n")
+
+
+def start_benchmark(args):
+    k6_path = './k6'
+    if 'BENCH_K6_BIN_PATH' in os.environ:
+        k6_path = os.environ['BENCH_K6_BIN_PATH']
+    k6_args = [
+        'run', args.scenario,
+        '--no-color',
+    ]
+    k6_args.extend(['--duration', args.duration])
+    k6_args.extend(['--iterations', args.n_prompts])
+    k6_args.extend(['--vus', args.parallel])
+    k6_args.extend(['--summary-export', 'k6-results.json'])
+    args = f"SERVER_BENCH_N_PROMPTS={args.n_prompts} SERVER_BENCH_MAX_PROMPT_TOKENS={args.max_prompt_tokens} SERVER_BENCH_MAX_CONTEXT={args.max_tokens} "
+    args = args + ' '.join([str(arg) for arg in [k6_path, *k6_args]])
+    print(f"bench: starting k6 with: {args}")
+    k6_completed = subprocess.run(args, shell=True, stdout=sys.stdout, stderr=sys.stderr)
+    if k6_completed.returncode != 0:
+        raise Exception("bench: unable to run k6")
+
+
+def start_server(args):
+    server_process = start_server_background(args)
+
+    attempts = 0
+    max_attempts = 20
+    if 'GITHUB_ACTIONS' in os.environ:
+        max_attempts *= 2
+
+    while not is_server_listening(args.host, args.port):
+        attempts += 1
+        if attempts > max_attempts:
+            assert False, "server not started"
+        print(f"bench:     waiting for server to start ...")
+        time.sleep(0.5)
+
+    print("bench: server started.")
+    return server_process
+
+
+def start_server_background(args):
+    # Start the server
+    server_path = '../../../build/bin/server'
+    if 'LLAMA_SERVER_BIN_PATH' in os.environ:
+        server_path = os.environ['LLAMA_SERVER_BIN_PATH']
+    server_args = [
+        '--host', args.host,
+        '--port', args.port,
+    ]
+    model_file = args.model_path_prefix + os.path.sep + args.hf_file
+    model_dir  = os.path.dirname(model_file)
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+    server_args.extend(['--model', model_file])
+    server_args.extend(['--hf-repo', args.hf_repo])
+    server_args.extend(['--hf-file', args.hf_file])
+    server_args.extend(['--n-gpu-layers', args.n_gpu_layers])
+    server_args.extend(['--ctx-size', args.ctx_size])
+    server_args.extend(['--parallel', args.parallel])
+    server_args.extend(['--batch-size', args.batch_size])
+    server_args.extend(['--ubatch-size', args.ubatch_size])
+    server_args.extend(['--n-predict', args.max_tokens * 2])
+    server_args.extend(['--defrag-thold', "0.1"])
+    server_args.append('--cont-batching')
+    server_args.append('--metrics')
+    server_args.append('--flash-attn')
+    server_args.extend(['--log-format', "text"])
+    args = [str(arg) for arg in [server_path, *server_args]]
+    print(f"bench: starting server with: {' '.join(args)}")
+    pkwargs = {
+        'stdout': subprocess.PIPE,
+        'stderr': subprocess.PIPE
+    }
+    server_process = subprocess.Popen(
+        args,
+        **pkwargs)
+
+    def server_log(in_stream, out_stream):
+        for line in iter(in_stream.readline, b''):
+            print(line.decode('utf-8'), end='', file=out_stream)
+
+    thread_stdout = threading.Thread(target=server_log, args=(server_process.stdout, sys.stdout))
+    thread_stdout.start()
+    thread_stderr = threading.Thread(target=server_log, args=(server_process.stderr, sys.stderr))
+    thread_stderr.start()
+
+    return server_process
+
+
+def is_server_listening(server_fqdn, server_port):
+    with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
+        result = sock.connect_ex((server_fqdn, server_port))
+        _is_server_listening = result == 0
+        if _is_server_listening:
+            print(f"server is listening on {server_fqdn}:{server_port}...")
+        return _is_server_listening
+
+
+def escape_metric_name(metric_name):
+    return re.sub('[^A-Z0-9]', '_', metric_name.upper())
+
+
+if __name__ == '__main__':
+    main()
diff --git a/examples/server/bench/prometheus.yml b/examples/server/bench/prometheus.yml
new file mode 100644
index 000000000..b15ee5244
--- /dev/null
+++ b/examples/server/bench/prometheus.yml
@@ -0,0 +1,9 @@
+global:
+  scrape_interval:     10s
+  external_labels:
+    llamacpp: 'server'
+
+scrape_configs:
+  - job_name: 'llama.cpp server'
+    static_configs:
+      - targets: ['localhost:8080']
diff --git a/examples/server/bench/requirements.txt b/examples/server/bench/requirements.txt
new file mode 100644
index 000000000..66ed226ed
--- /dev/null
+++ b/examples/server/bench/requirements.txt
@@ -0,0 +1,2 @@
+matplotlib
+requests
diff --git a/examples/server/bench/script.js b/examples/server/bench/script.js
new file mode 100644
index 000000000..bdf4f5abc
--- /dev/null
+++ b/examples/server/bench/script.js
@@ -0,0 +1,150 @@
+import sse from 'k6/x/sse'
+import {check, sleep} from 'k6'
+import {SharedArray} from 'k6/data'
+import {Counter, Rate, Trend} from 'k6/metrics'
+import exec from 'k6/execution';
+
+// Server chat completions prefix
+const server_url = __ENV.SERVER_BENCH_URL ? __ENV.SERVER_BENCH_URL : 'http://localhost:8080/v1'
+
+// Number of total prompts in the dataset - default 10m / 10 seconds/request * number of users
+const n_prompt = __ENV.SERVER_BENCH_N_PROMPTS ? parseInt(__ENV.SERVER_BENCH_N_PROMPTS) : 600 / 10 * 8
+
+// Model name to request
+const model = __ENV.SERVER_BENCH_MODEL_ALIAS ? __ENV.SERVER_BENCH_MODEL_ALIAS : 'my-model'
+
+// Dataset path
+const dataset_path = __ENV.SERVER_BENCH_DATASET ? __ENV.SERVER_BENCH_DATASET : './ShareGPT_V3_unfiltered_cleaned_split.json'
+
+// Max tokens to predict
+const max_tokens = __ENV.SERVER_BENCH_MAX_TOKENS ? parseInt(__ENV.SERVER_BENCH_MAX_TOKENS) : 512
+
+// Max prompt tokens
+const n_prompt_tokens = __ENV.SERVER_BENCH_MAX_PROMPT_TOKENS ? parseInt(__ENV.SERVER_BENCH_MAX_PROMPT_TOKENS) : 1024
+
+// Max slot context
+const n_ctx_slot = __ENV.SERVER_BENCH_MAX_CONTEXT ? parseInt(__ENV.SERVER_BENCH_MAX_CONTEXT) : 2048
+
+export function setup() {
+    console.info(`Benchmark config: server_url=${server_url} n_prompt=${n_prompt} model=${model} dataset_path=${dataset_path} max_tokens=${max_tokens}`)
+}
+
+const data = new SharedArray('conversations', function () {
+    const tokenizer = (message) => message.split(/[\s,'".?]/)
+
+    return JSON.parse(open(dataset_path))
+        // Filter out the conversations with less than 2 turns.
+        .filter(data => data["conversations"].length >= 2)
+        .filter(data => data["conversations"][0]["from"] === "human")
+        .map(data => {
+            return {
+                prompt: data["conversations"][0]["value"],
+                n_prompt_tokens: tokenizer(data["conversations"][0]["value"]).length,
+                n_completion_tokens: tokenizer(data["conversations"][1]["value"]).length,
+            }
+        })
+        // Filter out too short sequences
+        .filter(conv => conv.n_prompt_tokens >= 4 && conv.n_completion_tokens >= 4)
+        // Filter out too long sequences.
+        .filter(conv => conv.n_prompt_tokens <= n_prompt_tokens && conv.n_prompt_tokens + conv.n_completion_tokens <= n_ctx_slot)
+        // Keep only first n prompts
+        .slice(0, n_prompt)
+})
+
+const llamacpp_prompt_tokens = new Trend('llamacpp_prompt_tokens')
+const llamacpp_completion_tokens = new Trend('llamacpp_completion_tokens')
+
+const llamacpp_tokens_second = new Trend('llamacpp_tokens_second')
+const llamacpp_prompt_processing_second = new Trend('llamacpp_prompt_processing_second')
+
+const llamacpp_prompt_tokens_total_counter = new Counter('llamacpp_prompt_tokens_total_counter')
+const llamacpp_completion_tokens_total_counter = new Counter('llamacpp_completion_tokens_total_counter')
+
+const llamacpp_completions_truncated_rate = new Rate('llamacpp_completions_truncated_rate')
+const llamacpp_completions_stop_rate = new Rate('llamacpp_completions_stop_rate')
+
+export const options = {
+    thresholds: {
+        llamacpp_completions_truncated_rate: [
+            // more than 80% of truncated input will abort the test
+            {threshold: 'rate < 0.8', abortOnFail: true, delayAbortEval: '1m'},
+        ],
+    },
+    duration: '10m',
+    vus: 8,
+}
+
+export default function () {
+    const conversation = data[exec.scenario.iterationInInstance % data.length]
+    const payload = {
+        "messages": [
+            {
+                "role": "system",
+                "content": "You are ChatGPT, an AI assistant.",
+            },
+            {
+                "role": "user",
+                "content": conversation.prompt,
+            }
+        ],
+        "model": model,
+        "stream": true,
+        "seed": 42,
+        "max_tokens": max_tokens,
+        "stop": ["<|im_end|>"] // This is temporary for phi-2 base (i.e. not instructed) since the server expects that the model always to emit BOS
+    }
+
+    const params = {method: 'POST', body: JSON.stringify(payload)};
+
+    const startTime = new Date()
+    let promptEvalEndTime = null
+    let prompt_tokens = 0
+    let completions_tokens = 0
+    let finish_reason = null
+    const res = sse.open(`${server_url}/chat/completions`, params, function (client) {
+        client.on('event', function (event) {
+            if (promptEvalEndTime == null) {
+                promptEvalEndTime = new Date()
+            }
+
+            let chunk = JSON.parse(event.data)
+            let choice = chunk.choices[0]
+            if (choice.finish_reason) {
+                finish_reason = choice.finish_reason
+            }
+
+            if (chunk.usage) {
+                prompt_tokens = chunk.usage.prompt_tokens
+                llamacpp_prompt_tokens.add(prompt_tokens)
+                llamacpp_prompt_tokens_total_counter.add(prompt_tokens)
+
+                completions_tokens = chunk.usage.completion_tokens
+                llamacpp_completion_tokens.add(completions_tokens)
+                llamacpp_completion_tokens_total_counter.add(completions_tokens)
+            }
+        })
+
+        client.on('error', function (e) {
+            console.log('An unexpected error occurred: ', e.error());
+            throw e;
+        })
+    })
+
+    check(res, {'success completion': (r) => r.status === 200})
+
+    const endTime = new Date()
+
+    const promptEvalTime = promptEvalEndTime - startTime
+    if (promptEvalTime > 0) {
+        llamacpp_prompt_processing_second.add(prompt_tokens / (promptEvalEndTime - startTime) * 1.e3)
+    }
+
+    const completion_time = endTime - promptEvalEndTime
+    if (completions_tokens > 0 && completion_time > 0) {
+        llamacpp_tokens_second.add(completions_tokens / completion_time * 1.e3)
+    }
+    llamacpp_completions_truncated_rate.add(finish_reason === 'length')
+    llamacpp_completions_stop_rate.add(finish_reason === 'stop')
+
+    sleep(0.3)
+}
diff --git a/examples/server/chat.mjs b/examples/server/chat.mjs
index 219ebb51a..a79c8a3cd 100644
--- a/examples/server/chat.mjs
+++ b/examples/server/chat.mjs
@@ -26,8 +26,9 @@ const propOrder = grammarJsonSchemaPropOrder
 
 let grammar = null
 if (grammarJsonSchemaFile) {
-    const schema = JSON.parse(readFileSync(grammarJsonSchemaFile, 'utf-8'))
-    const converter = new SchemaConverter(propOrder)
+    let schema = JSON.parse(readFileSync(grammarJsonSchemaFile, 'utf-8'))
+    const converter = new SchemaConverter({prop_order: propOrder, allow_fetch: true})
+    schema = await converter.resolveRefs(schema, grammarJsonSchemaFile)
     converter.visit(schema, '')
     grammar = converter.formatGrammar()
 }
diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp
deleted file mode 100644
index f5e696e17..000000000
--- a/examples/server/completion.js.hpp
+++ /dev/null
@@ -1,485 +0,0 @@
-unsigned char completion_js[] = {
-  0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x44,
-  0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x73, 0x20, 0x3d, 0x20, 0x7b, 0x0a,
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-  0x72, 0x6f, 0x70, 0x73, 0x2e, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74,
-  0x5f, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f,
-  0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x20, 0x20,
-  0x7d, 0x0a, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x67,
-  0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65,
-  0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x7d, 0x0a
-};
-unsigned int completion_js_len = 5782;
diff --git a/examples/server/deps.sh b/examples/server/deps.sh
index ea23e6450..d28378901 100755
--- a/examples/server/deps.sh
+++ b/examples/server/deps.sh
@@ -8,13 +8,3 @@ PUBLIC=$DIR/public
 echo "download js bundle files"
 curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js
 echo >> $PUBLIC/index.js # add newline
-
-FILES=$(ls $PUBLIC)
-
-cd $PUBLIC
-for FILE in $FILES; do
-  echo "generate $FILE.hpp"
-
-  # use simple flag for old version of xxd
-  xxd -i $FILE > $DIR/$FILE.hpp
-done
diff --git a/examples/server/httplib.h b/examples/server/httplib.h
index 28746000c..f360bd93e 100644
--- a/examples/server/httplib.h
+++ b/examples/server/httplib.h
@@ -1,14 +1,14 @@
 //
 //  httplib.h
 //
-//  Copyright (c) 2023 Yuji Hirose. All rights reserved.
+//  Copyright (c) 2024 Yuji Hirose. All rights reserved.
 //  MIT License
 //
 
 #ifndef CPPHTTPLIB_HTTPLIB_H
 #define CPPHTTPLIB_HTTPLIB_H
 
-#define CPPHTTPLIB_VERSION "0.12.2"
+#define CPPHTTPLIB_VERSION "0.15.3"
 
 /*
  * Configuration
@@ -82,6 +82,10 @@
 #define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 8192
 #endif
 
+#ifndef CPPHTTPLIB_RANGE_MAX_COUNT
+#define CPPHTTPLIB_RANGE_MAX_COUNT 1024
+#endif
+
 #ifndef CPPHTTPLIB_TCP_NODELAY
 #define CPPHTTPLIB_TCP_NODELAY false
 #endif
@@ -160,10 +164,6 @@ using ssize_t = long;
 #define WSA_FLAG_NO_HANDLE_INHERIT 0x80
 #endif
 
-#ifndef strcasecmp
-#define strcasecmp _stricmp
-#endif // strcasecmp
-
 using socket_t = SOCKET;
 #ifdef CPPHTTPLIB_USE_POLL
 #define poll(fds, nfds, timeout) WSAPoll(fds, nfds, timeout)
@@ -172,9 +172,15 @@ using socket_t = SOCKET;
 #else // not _WIN32
 
 #include <arpa/inet.h>
-#ifndef _AIX
+#if !defined(_AIX) && !defined(__MVS__)
 #include <ifaddrs.h>
 #endif
+#ifdef __MVS__
+#include <strings.h>
+#ifndef NI_MAXHOST
+#define NI_MAXHOST 1025
+#endif
+#endif
 #include <net/if.h>
 #include <netdb.h>
 #include <netinet/in.h>
@@ -187,6 +193,7 @@ using socket_t = SOCKET;
 #endif
 #include <csignal>
 #include <pthread.h>
+#include <sys/mman.h>
 #include <sys/select.h>
 #include <sys/socket.h>
 #include <sys/un.h>
@@ -207,6 +214,7 @@ using socket_t = int;
 #include <condition_variable>
 #include <cstring>
 #include <errno.h>
+#include <exception>
 #include <fcntl.h>
 #include <fstream>
 #include <functional>
@@ -223,6 +231,9 @@ using socket_t = int;
 #include <string>
 #include <sys/stat.h>
 #include <thread>
+#include <unordered_map>
+#include <unordered_set>
+#include <utility>
 
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
 #ifdef _WIN32
@@ -237,7 +248,6 @@ using socket_t = int;
 
 #ifdef _MSC_VER
 #pragma comment(lib, "crypt32.lib")
-#pragma comment(lib, "cryptui.lib")
 #endif
 #elif defined(CPPHTTPLIB_USE_CERTS_FROM_MACOSX_KEYCHAIN) && defined(__APPLE__)
 #include <TargetConditionals.h>
@@ -259,10 +269,8 @@ using socket_t = int;
 #include <iostream>
 #include <sstream>
 
-#if OPENSSL_VERSION_NUMBER < 0x1010100fL
-#error Sorry, OpenSSL versions prior to 1.1.1 are not supported
-#elif OPENSSL_VERSION_NUMBER < 0x30000000L
-#define SSL_get1_peer_certificate SSL_get_peer_certificate
+#if OPENSSL_VERSION_NUMBER < 0x30000000L
+#error Sorry, OpenSSL versions prior to 3.0.0 are not supported
 #endif
 
 #endif
@@ -321,7 +329,7 @@ struct scope_exit {
   explicit scope_exit(std::function<void(void)> &&f)
       : exit_function(std::move(f)), execute_on_destruction{true} {}
 
-  scope_exit(scope_exit &&rhs)
+  scope_exit(scope_exit &&rhs) noexcept
       : exit_function(std::move(rhs.exit_function)),
         execute_on_destruction{rhs.execute_on_destruction} {
     rhs.release();
@@ -344,6 +352,81 @@ private:
 
 } // namespace detail
 
+enum StatusCode {
+  // Information responses
+  Continue_100 = 100,
+  SwitchingProtocol_101 = 101,
+  Processing_102 = 102,
+  EarlyHints_103 = 103,
+
+  // Successful responses
+  OK_200 = 200,
+  Created_201 = 201,
+  Accepted_202 = 202,
+  NonAuthoritativeInformation_203 = 203,
+  NoContent_204 = 204,
+  ResetContent_205 = 205,
+  PartialContent_206 = 206,
+  MultiStatus_207 = 207,
+  AlreadyReported_208 = 208,
+  IMUsed_226 = 226,
+
+  // Redirection messages
+  MultipleChoices_300 = 300,
+  MovedPermanently_301 = 301,
+  Found_302 = 302,
+  SeeOther_303 = 303,
+  NotModified_304 = 304,
+  UseProxy_305 = 305,
+  unused_306 = 306,
+  TemporaryRedirect_307 = 307,
+  PermanentRedirect_308 = 308,
+
+  // Client error responses
+  BadRequest_400 = 400,
+  Unauthorized_401 = 401,
+  PaymentRequired_402 = 402,
+  Forbidden_403 = 403,
+  NotFound_404 = 404,
+  MethodNotAllowed_405 = 405,
+  NotAcceptable_406 = 406,
+  ProxyAuthenticationRequired_407 = 407,
+  RequestTimeout_408 = 408,
+  Conflict_409 = 409,
+  Gone_410 = 410,
+  LengthRequired_411 = 411,
+  PreconditionFailed_412 = 412,
+  PayloadTooLarge_413 = 413,
+  UriTooLong_414 = 414,
+  UnsupportedMediaType_415 = 415,
+  RangeNotSatisfiable_416 = 416,
+  ExpectationFailed_417 = 417,
+  ImATeapot_418 = 418,
+  MisdirectedRequest_421 = 421,
+  UnprocessableContent_422 = 422,
+  Locked_423 = 423,
+  FailedDependency_424 = 424,
+  TooEarly_425 = 425,
+  UpgradeRequired_426 = 426,
+  PreconditionRequired_428 = 428,
+  TooManyRequests_429 = 429,
+  RequestHeaderFieldsTooLarge_431 = 431,
+  UnavailableForLegalReasons_451 = 451,
+
+  // Server error responses
+  InternalServerError_500 = 500,
+  NotImplemented_501 = 501,
+  BadGateway_502 = 502,
+  ServiceUnavailable_503 = 503,
+  GatewayTimeout_504 = 504,
+  HttpVersionNotSupported_505 = 505,
+  VariantAlsoNegotiates_506 = 506,
+  InsufficientStorage_507 = 507,
+  LoopDetected_508 = 508,
+  NotExtended_510 = 510,
+  NetworkAuthenticationRequired_511 = 511,
+};
+
 using Headers = std::multimap<std::string, std::string, detail::ci>;
 
 using Params = std::multimap<std::string, std::string>;
@@ -373,17 +456,18 @@ public:
   DataSink &operator=(DataSink &&) = delete;
 
   std::function<bool(const char *data, size_t data_len)> write;
+  std::function<bool()> is_writable;
   std::function<void()> done;
   std::function<void(const Headers &trailer)> done_with_trailer;
   std::ostream os;
 
 private:
-  class data_sink_streambuf : public std::streambuf {
+  class data_sink_streambuf final : public std::streambuf {
   public:
     explicit data_sink_streambuf(DataSink &sink) : sink_(sink) {}
 
   protected:
-    std::streamsize xsputn(const char *s, std::streamsize n) {
+    std::streamsize xsputn(const char *s, std::streamsize n) override {
       sink_.write(s, static_cast<size_t>(n));
       return n;
     }
@@ -465,6 +549,7 @@ struct Request {
   MultipartFormDataMap files;
   Ranges ranges;
   Match matches;
+  std::unordered_map<std::string, std::string> path_params;
 
   // for client
   ResponseHandler response_handler;
@@ -476,8 +561,7 @@ struct Request {
 
   bool has_header(const std::string &key) const;
   std::string get_header_value(const std::string &key, size_t id = 0) const;
-  template <typename T>
-  T get_header_value(const std::string &key, size_t id = 0) const;
+  uint64_t get_header_value_u64(const std::string &key, size_t id = 0) const;
   size_t get_header_value_count(const std::string &key) const;
   void set_header(const std::string &key, const std::string &val);
 
@@ -509,14 +593,14 @@ struct Response {
 
   bool has_header(const std::string &key) const;
   std::string get_header_value(const std::string &key, size_t id = 0) const;
-  template <typename T>
-  T get_header_value(const std::string &key, size_t id = 0) const;
+  uint64_t get_header_value_u64(const std::string &key, size_t id = 0) const;
   size_t get_header_value_count(const std::string &key) const;
   void set_header(const std::string &key, const std::string &val);
 
-  void set_redirect(const std::string &url, int status = 302);
+  void set_redirect(const std::string &url, int status = StatusCode::Found_302);
   void set_content(const char *s, size_t n, const std::string &content_type);
   void set_content(const std::string &s, const std::string &content_type);
+  void set_content(std::string &&s, const std::string &content_type);
 
   void set_content_provider(
       size_t length, const std::string &content_type, ContentProvider provider,
@@ -573,15 +657,16 @@ public:
   TaskQueue() = default;
   virtual ~TaskQueue() = default;
 
-  virtual void enqueue(std::function<void()> fn) = 0;
+  virtual bool enqueue(std::function<void()> fn) = 0;
   virtual void shutdown() = 0;
 
   virtual void on_idle() {}
 };
 
-class ThreadPool : public TaskQueue {
+class ThreadPool final : public TaskQueue {
 public:
-  explicit ThreadPool(size_t n) : shutdown_(false) {
+  explicit ThreadPool(size_t n, size_t mqr = 0)
+      : shutdown_(false), max_queued_requests_(mqr) {
     while (n) {
       threads_.emplace_back(worker(*this));
       n--;
@@ -591,13 +676,17 @@ public:
   ThreadPool(const ThreadPool &) = delete;
   ~ThreadPool() override = default;
 
-  void enqueue(std::function<void()> fn) override {
+  bool enqueue(std::function<void()> fn) override {
     {
       std::unique_lock<std::mutex> lock(mutex_);
+      if (max_queued_requests_ > 0 && jobs_.size() >= max_queued_requests_) {
+        return false;
+      }
       jobs_.push_back(std::move(fn));
     }
 
     cond_.notify_one();
+    return true;
   }
 
   void shutdown() override {
@@ -647,6 +736,7 @@ private:
   std::list<std::function<void()>> jobs_;
 
   bool shutdown_;
+  size_t max_queued_requests_ = 0;
 
   std::condition_variable cond_;
   std::mutex mutex_;
@@ -658,6 +748,82 @@ using SocketOptions = std::function<void(socket_t sock)>;
 
 void default_socket_options(socket_t sock);
 
+const char *status_message(int status);
+
+std::string get_bearer_token_auth(const Request &req);
+
+namespace detail {
+
+class MatcherBase {
+public:
+  virtual ~MatcherBase() = default;
+
+  // Match request path and populate its matches and
+  virtual bool match(Request &request) const = 0;
+};
+
+/**
+ * Captures parameters in request path and stores them in Request::path_params
+ *
+ * Capture name is a substring of a pattern from : to /.
+ * The rest of the pattern is matched agains the request path directly
+ * Parameters are captured starting from the next character after
+ * the end of the last matched static pattern fragment until the next /.
+ *
+ * Example pattern:
+ * "/path/fragments/:capture/more/fragments/:second_capture"
+ * Static fragments:
+ * "/path/fragments/", "more/fragments/"
+ *
+ * Given the following request path:
+ * "/path/fragments/:1/more/fragments/:2"
+ * the resulting capture will be
+ * {{"capture", "1"}, {"second_capture", "2"}}
+ */
+class PathParamsMatcher final : public MatcherBase {
+public:
+  PathParamsMatcher(const std::string &pattern);
+
+  bool match(Request &request) const override;
+
+private:
+  static constexpr char marker = ':';
+  // Treat segment separators as the end of path parameter capture
+  // Does not need to handle query parameters as they are parsed before path
+  // matching
+  static constexpr char separator = '/';
+
+  // Contains static path fragments to match against, excluding the '/' after
+  // path params
+  // Fragments are separated by path params
+  std::vector<std::string> static_fragments_;
+  // Stores the names of the path parameters to be used as keys in the
+  // Request::path_params map
+  std::vector<std::string> param_names_;
+};
+
+/**
+ * Performs std::regex_match on request path
+ * and stores the result in Request::matches
+ *
+ * Note that regex match is performed directly on the whole request.
+ * This means that wildcard patterns may match multiple path segments with /:
+ * "/begin/(.*)/end" will match both "/begin/middle/end" and "/begin/1/2/end".
+ */
+class RegexMatcher final : public MatcherBase {
+public:
+  RegexMatcher(const std::string &pattern) : regex_(pattern) {}
+
+  bool match(Request &request) const override;
+
+private:
+  std::regex regex_;
+};
+
+ssize_t write_headers(Stream &strm, const Headers &headers);
+
+} // namespace detail
+
 class Server {
 public:
   using Handler = std::function<void(const Request &, Response &)>;
@@ -702,6 +868,7 @@ public:
   bool remove_mount_point(const std::string &mount_point);
   Server &set_file_extension_and_mimetype_mapping(const std::string &ext,
                                                   const std::string &mime);
+  Server &set_default_file_mimetype(const std::string &mime);
   Server &set_file_request_handler(Handler handler);
 
   Server &set_error_handler(HandlerWithResponse handler);
@@ -718,6 +885,8 @@ public:
   Server &set_socket_options(SocketOptions socket_options);
 
   Server &set_default_headers(Headers headers);
+  Server &
+  set_header_writer(std::function<ssize_t(Stream &, Headers &)> const &writer);
 
   Server &set_keep_alive_max_count(size_t count);
   Server &set_keep_alive_timeout(time_t sec);
@@ -765,9 +934,14 @@ protected:
   size_t payload_max_length_ = CPPHTTPLIB_PAYLOAD_MAX_LENGTH;
 
 private:
-  using Handlers = std::vector<std::pair<std::regex, Handler>>;
+  using Handlers =
+      std::vector<std::pair<std::unique_ptr<detail::MatcherBase>, Handler>>;
   using HandlersForContentReader =
-      std::vector<std::pair<std::regex, HandlerWithContentReader>>;
+      std::vector<std::pair<std::unique_ptr<detail::MatcherBase>,
+                            HandlerWithContentReader>>;
+
+  static std::unique_ptr<detail::MatcherBase>
+  make_matcher(const std::string &pattern);
 
   socket_t create_server_socket(const std::string &host, int port,
                                 int socket_flags,
@@ -778,16 +952,16 @@ private:
   bool routing(Request &req, Response &res, Stream &strm);
   bool handle_file_request(const Request &req, Response &res,
                            bool head = false);
-  bool dispatch_request(Request &req, Response &res, const Handlers &handlers);
-  bool
-  dispatch_request_for_content_reader(Request &req, Response &res,
-                                      ContentReader content_reader,
-                                      const HandlersForContentReader &handlers);
+  bool dispatch_request(Request &req, Response &res,
+                        const Handlers &handlers) const;
+  bool dispatch_request_for_content_reader(
+      Request &req, Response &res, ContentReader content_reader,
+      const HandlersForContentReader &handlers) const;
 
-  bool parse_request_line(const char *s, Request &req);
+  bool parse_request_line(const char *s, Request &req) const;
   void apply_ranges(const Request &req, Response &res,
-                    std::string &content_type, std::string &boundary);
-  bool write_response(Stream &strm, bool close_connection, const Request &req,
+                    std::string &content_type, std::string &boundary) const;
+  bool write_response(Stream &strm, bool close_connection, Request &req,
                       Response &res);
   bool write_response_with_content(Stream &strm, bool close_connection,
                                    const Request &req, Response &res);
@@ -806,21 +980,23 @@ private:
   bool read_content_core(Stream &strm, Request &req, Response &res,
                          ContentReceiver receiver,
                          MultipartContentHeader multipart_header,
-                         ContentReceiver multipart_receiver);
+                         ContentReceiver multipart_receiver) const;
 
   virtual bool process_and_close_socket(socket_t sock);
 
+  std::atomic<bool> is_running_{false};
+  std::atomic<bool> done_{false};
+
   struct MountPointEntry {
     std::string mount_point;
     std::string base_dir;
     Headers headers;
   };
   std::vector<MountPointEntry> base_dirs_;
-
-  std::atomic<bool> is_running_{false};
-  std::atomic<bool> done_{false};
   std::map<std::string, std::string> file_extension_and_mimetype_map_;
+  std::string default_file_mimetype_ = "application/octet-stream";
   Handler file_request_handler_;
+
   Handlers get_handlers_;
   Handlers post_handlers_;
   HandlersForContentReader post_handlers_for_content_reader_;
@@ -831,18 +1007,22 @@ private:
   Handlers delete_handlers_;
   HandlersForContentReader delete_handlers_for_content_reader_;
   Handlers options_handlers_;
+
   HandlerWithResponse error_handler_;
   ExceptionHandler exception_handler_;
   HandlerWithResponse pre_routing_handler_;
   Handler post_routing_handler_;
-  Logger logger_;
   Expect100ContinueHandler expect_100_continue_handler_;
 
+  Logger logger_;
+
   int address_family_ = AF_UNSPEC;
   bool tcp_nodelay_ = CPPHTTPLIB_TCP_NODELAY;
   SocketOptions socket_options_ = default_socket_options;
 
   Headers default_headers_;
+  std::function<ssize_t(Stream &, Headers &)> header_writer_ =
+      detail::write_headers;
 };
 
 enum class Error {
@@ -860,17 +1040,19 @@ enum class Error {
   UnsupportedMultipartBoundaryChars,
   Compression,
   ConnectionTimeout,
+  ProxyConnection,
 
   // For internal use only
   SSLPeerCouldBeClosed_,
 };
 
-std::string to_string(const Error error);
+std::string to_string(Error error);
 
 std::ostream &operator<<(std::ostream &os, const Error &obj);
 
 class Result {
 public:
+  Result() = default;
   Result(std::unique_ptr<Response> &&res, Error err,
          Headers &&request_headers = Headers{})
       : res_(std::move(res)), err_(err),
@@ -893,13 +1075,13 @@ public:
   bool has_request_header(const std::string &key) const;
   std::string get_request_header_value(const std::string &key,
                                        size_t id = 0) const;
-  template <typename T>
-  T get_request_header_value(const std::string &key, size_t id = 0) const;
+  uint64_t get_request_header_value_u64(const std::string &key,
+                                        size_t id = 0) const;
   size_t get_request_header_value_count(const std::string &key) const;
 
 private:
   std::unique_ptr<Response> res_;
-  Error err_;
+  Error err_ = Error::Unknown;
   Headers request_headers_;
 };
 
@@ -1059,16 +1241,21 @@ public:
   bool send(Request &req, Response &res, Error &error);
   Result send(const Request &req);
 
-  size_t is_socket_open() const;
-
-  socket_t socket() const;
-
   void stop();
 
+  std::string host() const;
+  int port() const;
+
+  size_t is_socket_open() const;
+  socket_t socket() const;
+
   void set_hostname_addr_map(std::map<std::string, std::string> addr_map);
 
   void set_default_headers(Headers headers);
 
+  void
+  set_header_writer(std::function<ssize_t(Stream &, Headers &)> const &writer);
+
   void set_address_family(int family);
   void set_tcp_nodelay(bool on);
   void set_socket_options(SocketOptions socket_options);
@@ -1117,6 +1304,7 @@ public:
   void set_ca_cert_path(const std::string &ca_cert_file_path,
                         const std::string &ca_cert_dir_path = std::string());
   void set_ca_cert_store(X509_STORE *ca_cert_store);
+  X509_STORE *create_ca_cert_store(const char *ca_cert, std::size_t size) const;
 #endif
 
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
@@ -1145,14 +1333,14 @@ protected:
   // Also, shutdown_ssl and close_socket should also NOT be called concurrently
   // with a DIFFERENT thread sending requests using that socket.
   virtual void shutdown_ssl(Socket &socket, bool shutdown_gracefully);
-  void shutdown_socket(Socket &socket);
+  void shutdown_socket(Socket &socket) const;
   void close_socket(Socket &socket);
 
   bool process_request(Stream &strm, Request &req, Response &res,
                        bool close_connection, Error &error);
 
   bool write_content_with_provider(Stream &strm, const Request &req,
-                                   Error &error);
+                                   Error &error) const;
 
   void copy_settings(const ClientImpl &rhs);
 
@@ -1177,6 +1365,10 @@ protected:
   // Default headers
   Headers default_headers_;
 
+  // Header writer
+  std::function<ssize_t(Stream &, Headers &)> header_writer_ =
+      detail::write_headers;
+
   // Settings
   std::string client_cert_path_;
   std::string client_key_path_;
@@ -1239,7 +1431,8 @@ private:
   Result send_(Request &&req);
 
   socket_t create_client_socket(Error &error) const;
-  bool read_response_line(Stream &strm, const Request &req, Response &res);
+  bool read_response_line(Stream &strm, const Request &req,
+                          Response &res) const;
   bool write_request(Stream &strm, Request &req, bool close_connection,
                      Error &error);
   bool redirect(Request &req, Response &res, Error &error);
@@ -1258,7 +1451,7 @@ private:
       const std::string &content_type);
   ContentProviderWithoutLength get_multipart_content_provider(
       const std::string &boundary, const MultipartFormDataItems &items,
-      const MultipartFormDataProviderItems &provider_items);
+      const MultipartFormDataProviderItems &provider_items) const;
 
   std::string adjust_host_string(const std::string &host) const;
 
@@ -1431,16 +1624,21 @@ public:
   bool send(Request &req, Response &res, Error &error);
   Result send(const Request &req);
 
-  size_t is_socket_open() const;
-
-  socket_t socket() const;
-
   void stop();
 
+  std::string host() const;
+  int port() const;
+
+  size_t is_socket_open() const;
+  socket_t socket() const;
+
   void set_hostname_addr_map(std::map<std::string, std::string> addr_map);
 
   void set_default_headers(Headers headers);
 
+  void
+  set_header_writer(std::function<ssize_t(Stream &, Headers &)> const &writer);
+
   void set_address_family(int family);
   void set_tcp_nodelay(bool on);
   void set_socket_options(SocketOptions socket_options);
@@ -1497,6 +1695,7 @@ public:
                         const std::string &ca_cert_dir_path = std::string());
 
   void set_ca_cert_store(X509_STORE *ca_cert_store);
+  void load_ca_cert_store(const char *ca_cert, std::size_t size);
 
   long get_openssl_verify_result() const;
 
@@ -1538,7 +1737,7 @@ private:
   std::mutex ctx_mutex_;
 };
 
-class SSLClient : public ClientImpl {
+class SSLClient final : public ClientImpl {
 public:
   explicit SSLClient(const std::string &host);
 
@@ -1546,16 +1745,19 @@ public:
 
   explicit SSLClient(const std::string &host, int port,
                      const std::string &client_cert_path,
-                     const std::string &client_key_path);
+                     const std::string &client_key_path,
+                     const std::string &private_key_password = std::string());
 
   explicit SSLClient(const std::string &host, int port, X509 *client_cert,
-                     EVP_PKEY *client_key);
+                     EVP_PKEY *client_key,
+                     const std::string &private_key_password = std::string());
 
   ~SSLClient() override;
 
   bool is_valid() const override;
 
   void set_ca_cert_store(X509_STORE *ca_cert_store);
+  void load_ca_cert_store(const char *ca_cert, std::size_t size);
 
   long get_openssl_verify_result() const;
 
@@ -1564,7 +1766,7 @@ public:
 private:
   bool create_and_connect_socket(Socket &socket, Error &error) override;
   void shutdown_ssl(Socket &socket, bool shutdown_gracefully) override;
-  void shutdown_ssl_impl(Socket &socket, bool shutdown_socket);
+  void shutdown_ssl_impl(Socket &socket, bool shutdown_gracefully);
 
   bool process_socket(const Socket &socket,
                       std::function<bool(Stream &strm)> callback) override;
@@ -1608,15 +1810,9 @@ inline void duration_to_sec_and_usec(const T &duration, U callback) {
   callback(static_cast<time_t>(sec), static_cast<time_t>(usec));
 }
 
-template <typename T>
-inline T get_header_value(const Headers & /*headers*/,
-                          const std::string & /*key*/, size_t /*id*/ = 0,
-                          uint64_t /*def*/ = 0) {}
-
-template <>
-inline uint64_t get_header_value<uint64_t>(const Headers &headers,
-                                           const std::string &key, size_t id,
-                                           uint64_t def) {
+inline uint64_t get_header_value_u64(const Headers &headers,
+                                     const std::string &key, size_t id,
+                                     uint64_t def) {
   auto rng = headers.equal_range(key);
   auto it = rng.first;
   std::advance(it, static_cast<ssize_t>(id));
@@ -1628,14 +1824,14 @@ inline uint64_t get_header_value<uint64_t>(const Headers &headers,
 
 } // namespace detail
 
-template <typename T>
-inline T Request::get_header_value(const std::string &key, size_t id) const {
-  return detail::get_header_value<T>(headers, key, id, 0);
+inline uint64_t Request::get_header_value_u64(const std::string &key,
+                                              size_t id) const {
+  return detail::get_header_value_u64(headers, key, id, 0);
 }
 
-template <typename T>
-inline T Response::get_header_value(const std::string &key, size_t id) const {
-  return detail::get_header_value<T>(headers, key, id, 0);
+inline uint64_t Response::get_header_value_u64(const std::string &key,
+                                               size_t id) const {
+  return detail::get_header_value_u64(headers, key, id, 0);
 }
 
 template <typename... Args>
@@ -1665,21 +1861,106 @@ inline ssize_t Stream::write_format(const char *fmt, const Args &...args) {
 inline void default_socket_options(socket_t sock) {
   int yes = 1;
 #ifdef _WIN32
-  setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, reinterpret_cast<char *>(&yes),
-             sizeof(yes));
+  setsockopt(sock, SOL_SOCKET, SO_REUSEADDR,
+             reinterpret_cast<const char *>(&yes), sizeof(yes));
   setsockopt(sock, SOL_SOCKET, SO_EXCLUSIVEADDRUSE,
-             reinterpret_cast<char *>(&yes), sizeof(yes));
+             reinterpret_cast<const char *>(&yes), sizeof(yes));
 #else
 #ifdef SO_REUSEPORT
-  setsockopt(sock, SOL_SOCKET, SO_REUSEPORT, reinterpret_cast<void *>(&yes),
-             sizeof(yes));
+  setsockopt(sock, SOL_SOCKET, SO_REUSEPORT,
+             reinterpret_cast<const void *>(&yes), sizeof(yes));
 #else
-  setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, reinterpret_cast<void *>(&yes),
-             sizeof(yes));
+  setsockopt(sock, SOL_SOCKET, SO_REUSEADDR,
+             reinterpret_cast<const void *>(&yes), sizeof(yes));
 #endif
 #endif
 }
 
+inline const char *status_message(int status) {
+  switch (status) {
+  case StatusCode::Continue_100: return "Continue";
+  case StatusCode::SwitchingProtocol_101: return "Switching Protocol";
+  case StatusCode::Processing_102: return "Processing";
+  case StatusCode::EarlyHints_103: return "Early Hints";
+  case StatusCode::OK_200: return "OK";
+  case StatusCode::Created_201: return "Created";
+  case StatusCode::Accepted_202: return "Accepted";
+  case StatusCode::NonAuthoritativeInformation_203:
+    return "Non-Authoritative Information";
+  case StatusCode::NoContent_204: return "No Content";
+  case StatusCode::ResetContent_205: return "Reset Content";
+  case StatusCode::PartialContent_206: return "Partial Content";
+  case StatusCode::MultiStatus_207: return "Multi-Status";
+  case StatusCode::AlreadyReported_208: return "Already Reported";
+  case StatusCode::IMUsed_226: return "IM Used";
+  case StatusCode::MultipleChoices_300: return "Multiple Choices";
+  case StatusCode::MovedPermanently_301: return "Moved Permanently";
+  case StatusCode::Found_302: return "Found";
+  case StatusCode::SeeOther_303: return "See Other";
+  case StatusCode::NotModified_304: return "Not Modified";
+  case StatusCode::UseProxy_305: return "Use Proxy";
+  case StatusCode::unused_306: return "unused";
+  case StatusCode::TemporaryRedirect_307: return "Temporary Redirect";
+  case StatusCode::PermanentRedirect_308: return "Permanent Redirect";
+  case StatusCode::BadRequest_400: return "Bad Request";
+  case StatusCode::Unauthorized_401: return "Unauthorized";
+  case StatusCode::PaymentRequired_402: return "Payment Required";
+  case StatusCode::Forbidden_403: return "Forbidden";
+  case StatusCode::NotFound_404: return "Not Found";
+  case StatusCode::MethodNotAllowed_405: return "Method Not Allowed";
+  case StatusCode::NotAcceptable_406: return "Not Acceptable";
+  case StatusCode::ProxyAuthenticationRequired_407:
+    return "Proxy Authentication Required";
+  case StatusCode::RequestTimeout_408: return "Request Timeout";
+  case StatusCode::Conflict_409: return "Conflict";
+  case StatusCode::Gone_410: return "Gone";
+  case StatusCode::LengthRequired_411: return "Length Required";
+  case StatusCode::PreconditionFailed_412: return "Precondition Failed";
+  case StatusCode::PayloadTooLarge_413: return "Payload Too Large";
+  case StatusCode::UriTooLong_414: return "URI Too Long";
+  case StatusCode::UnsupportedMediaType_415: return "Unsupported Media Type";
+  case StatusCode::RangeNotSatisfiable_416: return "Range Not Satisfiable";
+  case StatusCode::ExpectationFailed_417: return "Expectation Failed";
+  case StatusCode::ImATeapot_418: return "I'm a teapot";
+  case StatusCode::MisdirectedRequest_421: return "Misdirected Request";
+  case StatusCode::UnprocessableContent_422: return "Unprocessable Content";
+  case StatusCode::Locked_423: return "Locked";
+  case StatusCode::FailedDependency_424: return "Failed Dependency";
+  case StatusCode::TooEarly_425: return "Too Early";
+  case StatusCode::UpgradeRequired_426: return "Upgrade Required";
+  case StatusCode::PreconditionRequired_428: return "Precondition Required";
+  case StatusCode::TooManyRequests_429: return "Too Many Requests";
+  case StatusCode::RequestHeaderFieldsTooLarge_431:
+    return "Request Header Fields Too Large";
+  case StatusCode::UnavailableForLegalReasons_451:
+    return "Unavailable For Legal Reasons";
+  case StatusCode::NotImplemented_501: return "Not Implemented";
+  case StatusCode::BadGateway_502: return "Bad Gateway";
+  case StatusCode::ServiceUnavailable_503: return "Service Unavailable";
+  case StatusCode::GatewayTimeout_504: return "Gateway Timeout";
+  case StatusCode::HttpVersionNotSupported_505:
+    return "HTTP Version Not Supported";
+  case StatusCode::VariantAlsoNegotiates_506: return "Variant Also Negotiates";
+  case StatusCode::InsufficientStorage_507: return "Insufficient Storage";
+  case StatusCode::LoopDetected_508: return "Loop Detected";
+  case StatusCode::NotExtended_510: return "Not Extended";
+  case StatusCode::NetworkAuthenticationRequired_511:
+    return "Network Authentication Required";
+
+  default:
+  case StatusCode::InternalServerError_500: return "Internal Server Error";
+  }
+}
+
+inline std::string get_bearer_token_auth(const Request &req) {
+  if (req.has_header("Authorization")) {
+    static std::string BearerHeaderPrefix = "Bearer ";
+    return req.get_header_value("Authorization")
+        .substr(BearerHeaderPrefix.length());
+  }
+  return "";
+}
+
 template <class Rep, class Period>
 inline Server &
 Server::set_read_timeout(const std::chrono::duration<Rep, Period> &duration) {
@@ -1720,6 +2001,7 @@ inline std::string to_string(const Error error) {
     return "Unsupported HTTP multipart boundary characters";
   case Error::Compression: return "Compression failed";
   case Error::ConnectionTimeout: return "Connection timed out";
+  case Error::ProxyConnection: return "Proxy connection failed";
   case Error::Unknown: return "Unknown";
   default: break;
   }
@@ -1733,10 +2015,9 @@ inline std::ostream &operator<<(std::ostream &os, const Error &obj) {
   return os;
 }
 
-template <typename T>
-inline T Result::get_request_header_value(const std::string &key,
-                                          size_t id) const {
-  return detail::get_header_value<T>(request_headers_, key, id, 0);
+inline uint64_t Result::get_request_header_value_u64(const std::string &key,
+                                                     size_t id) const {
+  return detail::get_header_value_u64(request_headers_, key, id, 0);
 }
 
 template <class Rep, class Period>
@@ -1790,7 +2071,7 @@ void hosted_at(const std::string &hostname, std::vector<std::string> &addrs);
 
 std::string append_query_params(const std::string &path, const Params &params);
 
-std::pair<std::string, std::string> make_range_header(Ranges ranges);
+std::pair<std::string, std::string> make_range_header(const Ranges &ranges);
 
 std::pair<std::string, std::string>
 make_basic_authentication_header(const std::string &username,
@@ -1810,6 +2091,9 @@ std::string trim_copy(const std::string &s);
 void split(const char *b, const char *e, char d,
            std::function<void(const char *, const char *)> fn);
 
+void split(const char *b, const char *e, char d, size_t m,
+           std::function<void(const char *, const char *)> fn);
+
 bool process_client_socket(socket_t sock, time_t read_timeout_sec,
                            time_t read_timeout_usec, time_t write_timeout_sec,
                            time_t write_timeout_usec,
@@ -1844,7 +2128,7 @@ enum class EncodingType { None = 0, Gzip, Brotli };
 
 EncodingType encoding_type(const Request &req, const Response &res);
 
-class BufferStream : public Stream {
+class BufferStream final : public Stream {
 public:
   BufferStream() = default;
   ~BufferStream() override = default;
@@ -1884,19 +2168,19 @@ public:
                           Callback callback) = 0;
 };
 
-class nocompressor : public compressor {
+class nocompressor final : public compressor {
 public:
-  virtual ~nocompressor() = default;
+  ~nocompressor() override = default;
 
   bool compress(const char *data, size_t data_length, bool /*last*/,
                 Callback callback) override;
 };
 
 #ifdef CPPHTTPLIB_ZLIB_SUPPORT
-class gzip_compressor : public compressor {
+class gzip_compressor final : public compressor {
 public:
   gzip_compressor();
-  ~gzip_compressor();
+  ~gzip_compressor() override;
 
   bool compress(const char *data, size_t data_length, bool last,
                 Callback callback) override;
@@ -1906,10 +2190,10 @@ private:
   z_stream strm_;
 };
 
-class gzip_decompressor : public decompressor {
+class gzip_decompressor final : public decompressor {
 public:
   gzip_decompressor();
-  ~gzip_decompressor();
+  ~gzip_decompressor() override;
 
   bool is_valid() const override;
 
@@ -1923,7 +2207,7 @@ private:
 #endif
 
 #ifdef CPPHTTPLIB_BROTLI_SUPPORT
-class brotli_compressor : public compressor {
+class brotli_compressor final : public compressor {
 public:
   brotli_compressor();
   ~brotli_compressor();
@@ -1935,7 +2219,7 @@ private:
   BrotliEncoderState *state_ = nullptr;
 };
 
-class brotli_decompressor : public decompressor {
+class brotli_decompressor final : public decompressor {
 public:
   brotli_decompressor();
   ~brotli_decompressor();
@@ -1972,6 +2256,29 @@ private:
   std::string glowable_buffer_;
 };
 
+class mmap {
+public:
+  mmap(const char *path);
+  ~mmap();
+
+  bool open(const char *path);
+  void close();
+
+  bool is_open() const;
+  size_t size() const;
+  const char *data() const;
+
+private:
+#if defined(_WIN32)
+  HANDLE hFile_;
+  HANDLE hMapping_;
+#else
+  int fd_;
+#endif
+  size_t size_;
+  void *addr_;
+};
+
 } // namespace detail
 
 // ----------------------------------------------------------------------------
@@ -2003,7 +2310,7 @@ inline bool from_hex_to_i(const std::string &s, size_t i, size_t cnt,
   val = 0;
   for (; cnt; i++, cnt--) {
     if (!s[i]) { return false; }
-    int v = 0;
+    auto v = 0;
     if (is_hex(s[i], v)) {
       val = val * 16 + v;
     } else {
@@ -2014,7 +2321,7 @@ inline bool from_hex_to_i(const std::string &s, size_t i, size_t cnt,
 }
 
 inline std::string from_i_to_hex(size_t n) {
-  const char *charset = "0123456789abcdef";
+  static const auto charset = "0123456789abcdef";
   std::string ret;
   do {
     ret = charset[n & 15] + ret;
@@ -2025,7 +2332,7 @@ inline std::string from_i_to_hex(size_t n) {
 
 inline size_t to_utf8(int code, char *buff) {
   if (code < 0x0080) {
-    buff[0] = (code & 0x7F);
+    buff[0] = static_cast<char>(code & 0x7F);
     return 1;
   } else if (code < 0x0800) {
     buff[0] = static_cast<char>(0xC0 | ((code >> 6) & 0x1F));
@@ -2064,8 +2371,8 @@ inline std::string base64_encode(const std::string &in) {
   std::string out;
   out.reserve(in.size());
 
-  int val = 0;
-  int valb = -6;
+  auto val = 0;
+  auto valb = -6;
 
   for (auto c : in) {
     val = (val << 8) + static_cast<uint8_t>(c);
@@ -2112,6 +2419,11 @@ inline bool is_valid_path(const std::string &path) {
     // Read component
     auto beg = i;
     while (i < path.size() && path[i] != '/') {
+      if (path[i] == '\0') {
+        return false;
+      } else if (path[i] == '\\') {
+        return false;
+      }
       i++;
     }
 
@@ -2196,7 +2508,7 @@ inline std::string decode_url(const std::string &s,
   for (size_t i = 0; i < s.size(); i++) {
     if (s[i] == '%' && i + 1 < s.size()) {
       if (s[i + 1] == 'u') {
-        int val = 0;
+        auto val = 0;
         if (from_hex_to_i(s, i + 2, 4, val)) {
           // 4 digits Unicode codes
           char buff[4];
@@ -2207,7 +2519,7 @@ inline std::string decode_url(const std::string &s,
           result += s[i];
         }
       } else {
-        int val = 0;
+        auto val = 0;
         if (from_hex_to_i(s, i + 1, 2, val)) {
           // 2 digits hex codes
           result += static_cast<char>(val);
@@ -2260,16 +2572,30 @@ inline std::string trim_copy(const std::string &s) {
   return s.substr(r.first, r.second - r.first);
 }
 
+inline std::string trim_double_quotes_copy(const std::string &s) {
+  if (s.length() >= 2 && s.front() == '"' && s.back() == '"') {
+    return s.substr(1, s.size() - 2);
+  }
+  return s;
+}
+
 inline void split(const char *b, const char *e, char d,
                   std::function<void(const char *, const char *)> fn) {
+  return split(b, e, d, (std::numeric_limits<size_t>::max)(), std::move(fn));
+}
+
+inline void split(const char *b, const char *e, char d, size_t m,
+                  std::function<void(const char *, const char *)> fn) {
   size_t i = 0;
   size_t beg = 0;
+  size_t count = 1;
 
   while (e ? (b + i < e) : (b[i] != '\0')) {
-    if (b[i] == d) {
+    if (b[i] == d && count < m) {
       auto r = trim(b, e, beg, i);
       if (r.first < r.second) { fn(&b[r.first], &b[r.second]); }
       beg = i + 1;
+      count++;
     }
     i++;
   }
@@ -2345,6 +2671,105 @@ inline void stream_line_reader::append(char c) {
   }
 }
 
+inline mmap::mmap(const char *path)
+#if defined(_WIN32)
+    : hFile_(NULL), hMapping_(NULL)
+#else
+    : fd_(-1)
+#endif
+      ,
+      size_(0), addr_(nullptr) {
+  open(path);
+}
+
+inline mmap::~mmap() { close(); }
+
+inline bool mmap::open(const char *path) {
+  close();
+
+#if defined(_WIN32)
+  std::wstring wpath;
+  for (size_t i = 0; i < strlen(path); i++) {
+    wpath += path[i];
+  }
+
+  hFile_ = ::CreateFile2(wpath.c_str(), GENERIC_READ, FILE_SHARE_READ,
+                         OPEN_EXISTING, NULL);
+
+  if (hFile_ == INVALID_HANDLE_VALUE) { return false; }
+
+  LARGE_INTEGER size{};
+  if (!::GetFileSizeEx(hFile_, &size)) { return false; }
+  size_ = static_cast<size_t>(size.QuadPart);
+
+  hMapping_ =
+      ::CreateFileMappingFromApp(hFile_, NULL, PAGE_READONLY, size_, NULL);
+
+  if (hMapping_ == NULL) {
+    close();
+    return false;
+  }
+
+  addr_ = ::MapViewOfFileFromApp(hMapping_, FILE_MAP_READ, 0, 0);
+#else
+  fd_ = ::open(path, O_RDONLY);
+  if (fd_ == -1) { return false; }
+
+  struct stat sb;
+  if (fstat(fd_, &sb) == -1) {
+    close();
+    return false;
+  }
+  size_ = static_cast<size_t>(sb.st_size);
+
+  addr_ = ::mmap(NULL, size_, PROT_READ, MAP_PRIVATE, fd_, 0);
+#endif
+
+  if (addr_ == nullptr) {
+    close();
+    return false;
+  }
+
+  return true;
+}
+
+inline bool mmap::is_open() const { return addr_ != nullptr; }
+
+inline size_t mmap::size() const { return size_; }
+
+inline const char *mmap::data() const {
+  return static_cast<const char *>(addr_);
+}
+
+inline void mmap::close() {
+#if defined(_WIN32)
+  if (addr_) {
+    ::UnmapViewOfFile(addr_);
+    addr_ = nullptr;
+  }
+
+  if (hMapping_) {
+    ::CloseHandle(hMapping_);
+    hMapping_ = NULL;
+  }
+
+  if (hFile_ != INVALID_HANDLE_VALUE) {
+    ::CloseHandle(hFile_);
+    hFile_ = INVALID_HANDLE_VALUE;
+  }
+#else
+  if (addr_ != nullptr) {
+    munmap(addr_, size_);
+    addr_ = nullptr;
+  }
+
+  if (fd_ != -1) {
+    ::close(fd_);
+    fd_ = -1;
+  }
+#endif
+  size_ = 0;
+}
 inline int close_socket(socket_t sock) {
 #ifdef _WIN32
   return closesocket(sock);
@@ -2354,7 +2779,7 @@ inline int close_socket(socket_t sock) {
 }
 
 template <typename T> inline ssize_t handle_EINTR(T fn) {
-  ssize_t res = false;
+  ssize_t res = 0;
   while (true) {
     res = fn();
     if (res < 0 && errno == EINTR) { continue; }
@@ -2399,7 +2824,7 @@ inline ssize_t select_read(socket_t sock, time_t sec, time_t usec) {
   return handle_EINTR([&]() { return poll(&pfd_read, 1, timeout); });
 #else
 #ifndef _WIN32
-  if (sock >= FD_SETSIZE) { return 1; }
+  if (sock >= FD_SETSIZE) { return -1; }
 #endif
 
   fd_set fds;
@@ -2427,7 +2852,7 @@ inline ssize_t select_write(socket_t sock, time_t sec, time_t usec) {
   return handle_EINTR([&]() { return poll(&pfd_read, 1, timeout); });
 #else
 #ifndef _WIN32
-  if (sock >= FD_SETSIZE) { return 1; }
+  if (sock >= FD_SETSIZE) { return -1; }
 #endif
 
   fd_set fds;
@@ -2458,7 +2883,7 @@ inline Error wait_until_socket_is_ready(socket_t sock, time_t sec,
   if (poll_res == 0) { return Error::ConnectionTimeout; }
 
   if (poll_res > 0 && pfd_read.revents & (POLLIN | POLLOUT)) {
-    int error = 0;
+    auto error = 0;
     socklen_t len = sizeof(error);
     auto res = getsockopt(sock, SOL_SOCKET, SO_ERROR,
                           reinterpret_cast<char *>(&error), &len);
@@ -2490,7 +2915,7 @@ inline Error wait_until_socket_is_ready(socket_t sock, time_t sec,
   if (ret == 0) { return Error::ConnectionTimeout; }
 
   if (ret > 0 && (FD_ISSET(sock, &fdsr) || FD_ISSET(sock, &fdsw))) {
-    int error = 0;
+    auto error = 0;
     socklen_t len = sizeof(error);
     auto res = getsockopt(sock, SOL_SOCKET, SO_ERROR,
                           reinterpret_cast<char *>(&error), &len);
@@ -2512,7 +2937,7 @@ inline bool is_socket_alive(socket_t sock) {
   return detail::read_socket(sock, &buf[0], sizeof(buf), MSG_PEEK) > 0;
 }
 
-class SocketStream : public Stream {
+class SocketStream final : public Stream {
 public:
   SocketStream(socket_t sock, time_t read_timeout_sec, time_t read_timeout_usec,
                time_t write_timeout_sec, time_t write_timeout_usec);
@@ -2537,11 +2962,11 @@ private:
   size_t read_buff_off_ = 0;
   size_t read_buff_content_size_ = 0;
 
-  static const size_t read_buff_size_ = 1024 * 4;
+  static const size_t read_buff_size_ = 1024l * 4;
 };
 
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
-class SSLSocketStream : public Stream {
+class SSLSocketStream final : public Stream {
 public:
   SSLSocketStream(socket_t sock, SSL *ssl, time_t read_timeout_sec,
                   time_t read_timeout_usec, time_t write_timeout_sec,
@@ -2666,7 +3091,7 @@ socket_t create_socket(const std::string &host, const std::string &ip, int port,
 #ifndef _WIN32
   if (hints.ai_family == AF_UNIX) {
     const auto addrlen = host.length();
-    if (addrlen > sizeof(sockaddr_un::sun_path)) return INVALID_SOCKET;
+    if (addrlen > sizeof(sockaddr_un::sun_path)) { return INVALID_SOCKET; }
 
     auto sock = socket(hints.ai_family, hints.ai_socktype, hints.ai_protocol);
     if (sock != INVALID_SOCKET) {
@@ -2735,17 +3160,27 @@ socket_t create_socket(const std::string &host, const std::string &ip, int port,
 #endif
 
     if (tcp_nodelay) {
-      int yes = 1;
-      setsockopt(sock, IPPROTO_TCP, TCP_NODELAY, reinterpret_cast<char *>(&yes),
-                 sizeof(yes));
+      auto yes = 1;
+#ifdef _WIN32
+      setsockopt(sock, IPPROTO_TCP, TCP_NODELAY,
+                 reinterpret_cast<const char *>(&yes), sizeof(yes));
+#else
+      setsockopt(sock, IPPROTO_TCP, TCP_NODELAY,
+                 reinterpret_cast<const void *>(&yes), sizeof(yes));
+#endif
     }
 
     if (socket_options) { socket_options(sock); }
 
     if (rp->ai_family == AF_INET6) {
-      int no = 0;
-      setsockopt(sock, IPPROTO_IPV6, IPV6_V6ONLY, reinterpret_cast<char *>(&no),
-                 sizeof(no));
+      auto no = 0;
+#ifdef _WIN32
+      setsockopt(sock, IPPROTO_IPV6, IPV6_V6ONLY,
+                 reinterpret_cast<const char *>(&no), sizeof(no));
+#else
+      setsockopt(sock, IPPROTO_IPV6, IPV6_V6ONLY,
+                 reinterpret_cast<const void *>(&no), sizeof(no));
+#endif
     }
 
     // bind or connect
@@ -2804,7 +3239,7 @@ inline bool bind_ip_address(socket_t sock, const std::string &host) {
   return ret;
 }
 
-#if !defined _WIN32 && !defined ANDROID && !defined _AIX
+#if !defined _WIN32 && !defined ANDROID && !defined _AIX && !defined __MVS__
 #define USE_IF2IP
 #endif
 
@@ -2860,7 +3295,7 @@ inline socket_t create_client_socket(
 #ifdef USE_IF2IP
           auto ip_from_if = if2ip(address_family, intf);
           if (ip_from_if.empty()) { ip_from_if = intf; }
-          if (!bind_ip_address(sock2, ip_from_if.c_str())) {
+          if (!bind_ip_address(sock2, ip_from_if)) {
             error = Error::BindIPAddress;
             return false;
           }
@@ -2888,13 +3323,14 @@ inline socket_t create_client_socket(
 #ifdef _WIN32
           auto timeout = static_cast<uint32_t>(read_timeout_sec * 1000 +
                                                read_timeout_usec / 1000);
-          setsockopt(sock2, SOL_SOCKET, SO_RCVTIMEO, (char *)&timeout,
-                     sizeof(timeout));
+          setsockopt(sock2, SOL_SOCKET, SO_RCVTIMEO,
+                     reinterpret_cast<const char *>(&timeout), sizeof(timeout));
 #else
           timeval tv;
           tv.tv_sec = static_cast<long>(read_timeout_sec);
           tv.tv_usec = static_cast<decltype(tv.tv_usec)>(read_timeout_usec);
-          setsockopt(sock2, SOL_SOCKET, SO_RCVTIMEO, (char *)&tv, sizeof(tv));
+          setsockopt(sock2, SOL_SOCKET, SO_RCVTIMEO,
+                     reinterpret_cast<const void *>(&tv), sizeof(tv));
 #endif
         }
         {
@@ -2902,13 +3338,14 @@ inline socket_t create_client_socket(
 #ifdef _WIN32
           auto timeout = static_cast<uint32_t>(write_timeout_sec * 1000 +
                                                write_timeout_usec / 1000);
-          setsockopt(sock2, SOL_SOCKET, SO_SNDTIMEO, (char *)&timeout,
-                     sizeof(timeout));
+          setsockopt(sock2, SOL_SOCKET, SO_SNDTIMEO,
+                     reinterpret_cast<const char *>(&timeout), sizeof(timeout));
 #else
           timeval tv;
           tv.tv_sec = static_cast<long>(write_timeout_sec);
           tv.tv_usec = static_cast<decltype(tv.tv_usec)>(write_timeout_usec);
-          setsockopt(sock2, SOL_SOCKET, SO_SNDTIMEO, (char *)&tv, sizeof(tv));
+          setsockopt(sock2, SOL_SOCKET, SO_SNDTIMEO,
+                     reinterpret_cast<const void *>(&tv), sizeof(tv));
 #endif
         }
 
@@ -3008,18 +3445,20 @@ inline constexpr unsigned int operator"" _t(const char *s, size_t l) {
 
 } // namespace udl
 
-inline const char *
+inline std::string
 find_content_type(const std::string &path,
-                  const std::map<std::string, std::string> &user_data) {
+                  const std::map<std::string, std::string> &user_data,
+                  const std::string &default_content_type) {
   auto ext = file_extension(path);
 
   auto it = user_data.find(ext);
-  if (it != user_data.end()) { return it->second.c_str(); }
+  if (it != user_data.end()) { return it->second; }
 
   using udl::operator""_t;
 
   switch (str2tag(ext)) {
-  default: return nullptr;
+  default: return default_content_type;
+
   case "css"_t: return "text/css";
   case "csv"_t: return "text/csv";
   case "htm"_t:
@@ -3072,76 +3511,6 @@ find_content_type(const std::string &path,
   }
 }
 
-inline const char *status_message(int status) {
-  switch (status) {
-  case 100: return "Continue";
-  case 101: return "Switching Protocol";
-  case 102: return "Processing";
-  case 103: return "Early Hints";
-  case 200: return "OK";
-  case 201: return "Created";
-  case 202: return "Accepted";
-  case 203: return "Non-Authoritative Information";
-  case 204: return "No Content";
-  case 205: return "Reset Content";
-  case 206: return "Partial Content";
-  case 207: return "Multi-Status";
-  case 208: return "Already Reported";
-  case 226: return "IM Used";
-  case 300: return "Multiple Choice";
-  case 301: return "Moved Permanently";
-  case 302: return "Found";
-  case 303: return "See Other";
-  case 304: return "Not Modified";
-  case 305: return "Use Proxy";
-  case 306: return "unused";
-  case 307: return "Temporary Redirect";
-  case 308: return "Permanent Redirect";
-  case 400: return "Bad Request";
-  case 401: return "Unauthorized";
-  case 402: return "Payment Required";
-  case 403: return "Forbidden";
-  case 404: return "Not Found";
-  case 405: return "Method Not Allowed";
-  case 406: return "Not Acceptable";
-  case 407: return "Proxy Authentication Required";
-  case 408: return "Request Timeout";
-  case 409: return "Conflict";
-  case 410: return "Gone";
-  case 411: return "Length Required";
-  case 412: return "Precondition Failed";
-  case 413: return "Payload Too Large";
-  case 414: return "URI Too Long";
-  case 415: return "Unsupported Media Type";
-  case 416: return "Range Not Satisfiable";
-  case 417: return "Expectation Failed";
-  case 418: return "I'm a teapot";
-  case 421: return "Misdirected Request";
-  case 422: return "Unprocessable Entity";
-  case 423: return "Locked";
-  case 424: return "Failed Dependency";
-  case 425: return "Too Early";
-  case 426: return "Upgrade Required";
-  case 428: return "Precondition Required";
-  case 429: return "Too Many Requests";
-  case 431: return "Request Header Fields Too Large";
-  case 451: return "Unavailable For Legal Reasons";
-  case 501: return "Not Implemented";
-  case 502: return "Bad Gateway";
-  case 503: return "Service Unavailable";
-  case 504: return "Gateway Timeout";
-  case 505: return "HTTP Version Not Supported";
-  case 506: return "Variant Also Negotiates";
-  case 507: return "Insufficient Storage";
-  case 508: return "Loop Detected";
-  case 510: return "Not Extended";
-  case 511: return "Network Authentication Required";
-
-  default:
-  case 500: return "Internal Server Error";
-  }
-}
-
 inline bool can_compress_content_type(const std::string &content_type) {
   using udl::operator""_t;
 
@@ -3218,7 +3587,7 @@ inline bool gzip_compressor::compress(const char *data, size_t data_length,
     data += strm_.avail_in;
 
     auto flush = (last && data_length == 0) ? Z_FINISH : Z_NO_FLUSH;
-    int ret = Z_OK;
+    auto ret = Z_OK;
 
     std::array<char, CPPHTTPLIB_COMPRESSION_BUFSIZ> buff{};
     do {
@@ -3262,7 +3631,7 @@ inline bool gzip_decompressor::decompress(const char *data, size_t data_length,
                                           Callback callback) {
   assert(is_valid_);
 
-  int ret = Z_OK;
+  auto ret = Z_OK;
 
   do {
     constexpr size_t max_avail_in =
@@ -3276,16 +3645,12 @@ inline bool gzip_decompressor::decompress(const char *data, size_t data_length,
     data += strm_.avail_in;
 
     std::array<char, CPPHTTPLIB_COMPRESSION_BUFSIZ> buff{};
-    while (strm_.avail_in > 0) {
+    while (strm_.avail_in > 0 && ret == Z_OK) {
       strm_.avail_out = static_cast<uInt>(buff.size());
       strm_.next_out = reinterpret_cast<Bytef *>(buff.data());
 
-      auto prev_avail_in = strm_.avail_in;
-
       ret = inflate(&strm_, Z_NO_FLUSH);
 
-      if (prev_avail_in - strm_.avail_in == 0) { return false; }
-
       assert(ret != Z_STREAM_ERROR);
       switch (ret) {
       case Z_NEED_DICT:
@@ -3298,7 +3663,7 @@ inline bool gzip_decompressor::decompress(const char *data, size_t data_length,
       }
     }
 
-    if (ret != Z_OK && ret != Z_STREAM_END) return false;
+    if (ret != Z_OK && ret != Z_STREAM_END) { return false; }
 
   } while (data_length > 0);
 
@@ -3367,7 +3732,7 @@ inline bool brotli_decompressor::decompress(const char *data,
     return 0;
   }
 
-  const uint8_t *next_in = (const uint8_t *)data;
+  auto next_in = reinterpret_cast<const uint8_t *>(data);
   size_t avail_in = data_length;
   size_t total_out;
 
@@ -3437,6 +3802,9 @@ inline bool parse_header(const char *beg, const char *end, T fn) {
   }
 
   if (p < end) {
+    auto key_len = key_end - beg;
+    if (!key_len) { return false; }
+
     auto key = std::string(beg, key_end);
     auto val = compare_case_ignore(key, "Location")
                    ? std::string(p, end)
@@ -3526,11 +3894,7 @@ inline bool read_content_without_length(Stream &strm,
   uint64_t r = 0;
   for (;;) {
     auto n = strm.read(buf, CPPHTTPLIB_RECV_BUFSIZ);
-    if (n < 0) {
-      return false;
-    } else if (n == 0) {
-      return true;
-    }
+    if (n <= 0) { return true; }
 
     if (!out(buf, static_cast<size_t>(n), r, 0)) { return false; }
     r += static_cast<uint64_t>(n);
@@ -3566,7 +3930,7 @@ inline bool read_content_chunked(Stream &strm, T &x,
 
     if (!line_reader.getline()) { return false; }
 
-    if (strcmp(line_reader.ptr(), "\r\n")) { return false; }
+    if (strcmp(line_reader.ptr(), "\r\n") != 0) { return false; }
 
     if (!line_reader.getline()) { return false; }
   }
@@ -3576,7 +3940,7 @@ inline bool read_content_chunked(Stream &strm, T &x,
   // Trailer
   if (!line_reader.getline()) { return false; }
 
-  while (strcmp(line_reader.ptr(), "\r\n")) {
+  while (strcmp(line_reader.ptr(), "\r\n") != 0) {
     if (line_reader.size() > CPPHTTPLIB_HEADER_MAX_LENGTH) { return false; }
 
     // Exclude line terminator
@@ -3595,8 +3959,8 @@ inline bool read_content_chunked(Stream &strm, T &x,
 }
 
 inline bool is_chunked_transfer_encoding(const Headers &headers) {
-  return !strcasecmp(get_header_value(headers, "Transfer-Encoding", 0, ""),
-                     "chunked");
+  return compare_case_ignore(
+      get_header_value(headers, "Transfer-Encoding", 0, ""), "chunked");
 }
 
 template <typename T, typename U>
@@ -3611,14 +3975,14 @@ bool prepare_content_receiver(T &x, int &status,
 #ifdef CPPHTTPLIB_ZLIB_SUPPORT
       decompressor = detail::make_unique<gzip_decompressor>();
 #else
-      status = 415;
+      status = StatusCode::UnsupportedMediaType_415;
       return false;
 #endif
     } else if (encoding.find("br") != std::string::npos) {
 #ifdef CPPHTTPLIB_BROTLI_SUPPORT
       decompressor = detail::make_unique<brotli_decompressor>();
 #else
-      status = 415;
+      status = StatusCode::UnsupportedMediaType_415;
       return false;
 #endif
     }
@@ -3634,7 +3998,7 @@ bool prepare_content_receiver(T &x, int &status,
         };
         return callback(std::move(out));
       } else {
-        status = 500;
+        status = StatusCode::InternalServerError_500;
         return false;
       }
     }
@@ -3662,7 +4026,7 @@ bool read_content(Stream &strm, T &x, size_t payload_max_length, int &status,
         } else if (!has_header(x.headers, "Content-Length")) {
           ret = read_content_without_length(strm, out);
         } else {
-          auto len = get_header_value<uint64_t>(x.headers, "Content-Length");
+          auto len = get_header_value_u64(x.headers, "Content-Length", 0, 0);
           if (len > payload_max_length) {
             exceed_payload_max_length = true;
             skip_content_with_length(strm, len);
@@ -3672,7 +4036,10 @@ bool read_content(Stream &strm, T &x, size_t payload_max_length, int &status,
           }
         }
 
-        if (!ret) { status = exceed_payload_max_length ? 413 : 400; }
+        if (!ret) {
+          status = exceed_payload_max_length ? StatusCode::PayloadTooLarge_413
+                                             : StatusCode::BadRequest_400;
+        }
         return ret;
       });
 } // namespace detail
@@ -3720,6 +4087,8 @@ inline bool write_content(Stream &strm, const ContentProvider &content_provider,
     return ok;
   };
 
+  data_sink.is_writable = [&]() -> bool { return strm.is_writable(); };
+
   while (offset < end_offset && !is_shutting_down()) {
     if (!strm.is_writable()) {
       error = Error::Write;
@@ -3764,6 +4133,8 @@ write_content_without_length(Stream &strm,
     return ok;
   };
 
+  data_sink.is_writable = [&]() -> bool { return strm.is_writable(); };
+
   data_sink.done = [&](void) { data_available = false; };
 
   while (data_available && !is_shutting_down()) {
@@ -3814,6 +4185,8 @@ write_content_chunked(Stream &strm, const ContentProvider &content_provider,
     return ok;
   };
 
+  data_sink.is_writable = [&]() -> bool { return strm.is_writable(); };
+
   auto done_with_trailer = [&](const Headers *trailer) {
     if (!ok) { return; }
 
@@ -3898,7 +4271,8 @@ inline bool redirect(T &cli, Request &req, Response &res,
   new_req.path = path;
   new_req.redirect_count_ -= 1;
 
-  if (res.status == 303 && (req.method != "GET" && req.method != "HEAD")) {
+  if (res.status == StatusCode::SeeOther_303 &&
+      (req.method != "GET" && req.method != "HEAD")) {
     new_req.method = "GET";
     new_req.body.clear();
     new_req.headers.clear();
@@ -3910,7 +4284,8 @@ inline bool redirect(T &cli, Request &req, Response &res,
   if (ret) {
     req = new_req;
     res = new_res;
-    res.location = location;
+
+    if (res.location.empty()) { res.location = location; }
   }
   return ret;
 }
@@ -3957,14 +4332,34 @@ inline bool parse_multipart_boundary(const std::string &content_type,
   if (pos == std::string::npos) { return false; }
   auto end = content_type.find(';', pos);
   auto beg = pos + strlen(boundary_keyword);
-  boundary = content_type.substr(beg, end - beg);
-  if (boundary.length() >= 2 && boundary.front() == '"' &&
-      boundary.back() == '"') {
-    boundary = boundary.substr(1, boundary.size() - 2);
-  }
+  boundary = trim_double_quotes_copy(content_type.substr(beg, end - beg));
   return !boundary.empty();
 }
 
+inline void parse_disposition_params(const std::string &s, Params &params) {
+  std::set<std::string> cache;
+  split(s.data(), s.data() + s.size(), ';', [&](const char *b, const char *e) {
+    std::string kv(b, e);
+    if (cache.find(kv) != cache.end()) { return; }
+    cache.insert(kv);
+
+    std::string key;
+    std::string val;
+    split(b, e, '=', [&](const char *b2, const char *e2) {
+      if (key.empty()) {
+        key.assign(b2, e2);
+      } else {
+        val.assign(b2, e2);
+      }
+    });
+
+    if (!key.empty()) {
+      params.emplace(trim_double_quotes_copy((key)),
+                     trim_double_quotes_copy((val)));
+    }
+  });
+}
+
 #ifdef CPPHTTPLIB_NO_EXCEPTIONS
 inline bool parse_range_header(const std::string &s, Ranges &ranges) {
 #else
@@ -3975,9 +4370,9 @@ inline bool parse_range_header(const std::string &s, Ranges &ranges) try {
   if (std::regex_match(s, m, re_first_range)) {
     auto pos = static_cast<size_t>(m.position(1));
     auto len = static_cast<size_t>(m.length(1));
-    bool all_valid_ranges = true;
+    auto all_valid_ranges = true;
     split(&s[pos], &s[pos + len], ',', [&](const char *b, const char *e) {
-      if (!all_valid_ranges) return;
+      if (!all_valid_ranges) { return; }
       static auto re_another_range = std::regex(R"(\s*(\d*)-(\d*))");
       std::cmatch cm;
       if (std::regex_match(b, e, cm, re_another_range)) {
@@ -4022,11 +4417,6 @@ public:
   bool parse(const char *buf, size_t n, const ContentReceiver &content_callback,
              const MultipartContentHeader &header_callback) {
 
-    // TODO: support 'filename*'
-    static const std::regex re_content_disposition(
-        R"~(^Content-Disposition:\s*form-data;\s*name="(.*?)"(?:;\s*filename="(.*?)")?(?:;\s*filename\*=\S+)?\s*$)~",
-        std::regex_constants::icase);
-
     buf_append(buf, n);
 
     while (buf_size() > 0) {
@@ -4059,18 +4449,54 @@ public:
             break;
           }
 
-          static const std::string header_name = "content-type:";
           const auto header = buf_head(pos);
-          if (start_with_case_ignore(header, header_name)) {
-            file_.content_type = trim_copy(header.substr(header_name.size()));
+
+          if (!parse_header(header.data(), header.data() + header.size(),
+                            [&](std::string &&, std::string &&) {})) {
+            is_valid_ = false;
+            return false;
+          }
+
+          static const std::string header_content_type = "Content-Type:";
+
+          if (start_with_case_ignore(header, header_content_type)) {
+            file_.content_type =
+                trim_copy(header.substr(header_content_type.size()));
           } else {
+            static const std::regex re_content_disposition(
+                R"~(^Content-Disposition:\s*form-data;\s*(.*)$)~",
+                std::regex_constants::icase);
+
             std::smatch m;
             if (std::regex_match(header, m, re_content_disposition)) {
-              file_.name = m[1];
-              file_.filename = m[2];
-            } else {
-              is_valid_ = false;
-              return false;
+              Params params;
+              parse_disposition_params(m[1], params);
+
+              auto it = params.find("name");
+              if (it != params.end()) {
+                file_.name = it->second;
+              } else {
+                is_valid_ = false;
+                return false;
+              }
+
+              it = params.find("filename");
+              if (it != params.end()) { file_.filename = it->second; }
+
+              it = params.find("filename*");
+              if (it != params.end()) {
+                // Only allow UTF-8 enconnding...
+                static const std::regex re_rfc5987_encoding(
+                    R"~(^UTF-8''(.+?)$)~", std::regex_constants::icase);
+
+                std::smatch m2;
+                if (std::regex_match(it->second, m2, re_rfc5987_encoding)) {
+                  file_.filename = decode_url(m2[1], false); // override...
+                } else {
+                  is_valid_ = false;
+                  return false;
+                }
+              }
             }
           }
           buf_erase(pos + crlf_.size());
@@ -4108,9 +4534,9 @@ public:
           buf_erase(crlf_.size());
           state_ = 1;
         } else {
-          if (dash_crlf_.size() > buf_size()) { return true; }
-          if (buf_start_with(dash_crlf_)) {
-            buf_erase(dash_crlf_.size());
+          if (dash_.size() > buf_size()) { return true; }
+          if (buf_start_with(dash_)) {
+            buf_erase(dash_.size());
             is_valid_ = true;
             buf_erase(buf_size()); // Remove epilogue
           } else {
@@ -4143,7 +4569,6 @@ private:
 
   const std::string dash_ = "--";
   const std::string crlf_ = "\r\n";
-  const std::string dash_crlf_ = "--\r\n";
   std::string boundary_;
   std::string dash_boundary_crlf_;
   std::string crlf_dash_boundary_;
@@ -4230,28 +4655,32 @@ inline std::string to_lower(const char *beg, const char *end) {
   return out;
 }
 
-inline std::string make_multipart_data_boundary() {
+inline std::string random_string(size_t length) {
   static const char data[] =
       "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
 
   // std::random_device might actually be deterministic on some
   // platforms, but due to lack of support in the c++ standard library,
   // doing better requires either some ugly hacks or breaking portability.
-  std::random_device seed_gen;
+  static std::random_device seed_gen;
 
   // Request 128 bits of entropy for initialization
-  std::seed_seq seed_sequence{seed_gen(), seed_gen(), seed_gen(), seed_gen()};
-  std::mt19937 engine(seed_sequence);
+  static std::seed_seq seed_sequence{seed_gen(), seed_gen(), seed_gen(),
+                                     seed_gen()};
 
-  std::string result = "--cpp-httplib-multipart-data-";
+  static std::mt19937 engine(seed_sequence);
 
-  for (auto i = 0; i < 16; i++) {
+  std::string result;
+  for (size_t i = 0; i < length; i++) {
     result += data[engine() % (sizeof(data) - 1)];
   }
-
   return result;
 }
 
+inline std::string make_multipart_data_boundary() {
+  return "--cpp-httplib-multipart-data-" + detail::random_string(16);
+}
+
 inline bool is_multipart_boundary_chars_valid(const std::string &boundary) {
   auto valid = true;
   for (size_t i = 0; i < boundary.size(); i++) {
@@ -4304,48 +4733,97 @@ serialize_multipart_formdata(const MultipartFormDataItems &items,
     body += item.content + serialize_multipart_formdata_item_end();
   }
 
-  if (finish) body += serialize_multipart_formdata_finish(boundary);
+  if (finish) { body += serialize_multipart_formdata_finish(boundary); }
 
   return body;
 }
 
+inline bool range_error(Request &req, Response &res) {
+  if (!req.ranges.empty() && 200 <= res.status && res.status < 300) {
+    ssize_t contant_len = static_cast<ssize_t>(
+        res.content_length_ ? res.content_length_ : res.body.size());
+
+    ssize_t prev_first_pos = -1;
+    ssize_t prev_last_pos = -1;
+    size_t overwrapping_count = 0;
+
+    // NOTE: The following Range check is based on '14.2. Range' in RFC 9110
+    // 'HTTP Semantics' to avoid potential denial-of-service attacks.
+    // https://www.rfc-editor.org/rfc/rfc9110#section-14.2
+
+    // Too many ranges
+    if (req.ranges.size() > CPPHTTPLIB_RANGE_MAX_COUNT) { return true; }
+
+    for (auto &r : req.ranges) {
+      auto &first_pos = r.first;
+      auto &last_pos = r.second;
+
+      if (first_pos == -1 && last_pos == -1) {
+        first_pos = 0;
+        last_pos = contant_len;
+      }
+
+      if (first_pos == -1) {
+        first_pos = contant_len - last_pos;
+        last_pos = contant_len - 1;
+      }
+
+      if (last_pos == -1) { last_pos = contant_len - 1; }
+
+      // Range must be within content length
+      if (!(0 <= first_pos && first_pos <= last_pos &&
+            last_pos <= contant_len - 1)) {
+        return true;
+      }
+
+      // Ranges must be in ascending order
+      if (first_pos <= prev_first_pos) { return true; }
+
+      // Request must not have more than two overlapping ranges
+      if (first_pos <= prev_last_pos) {
+        overwrapping_count++;
+        if (overwrapping_count > 2) { return true; }
+      }
+
+      prev_first_pos = (std::max)(prev_first_pos, first_pos);
+      prev_last_pos = (std::max)(prev_last_pos, last_pos);
+    }
+  }
+
+  return false;
+}
+
 inline std::pair<size_t, size_t>
-get_range_offset_and_length(const Request &req, size_t content_length,
-                            size_t index) {
-  auto r = req.ranges[index];
+get_range_offset_and_length(Range r, size_t content_length) {
+  (void)(content_length); // patch to get rid of "unused parameter" on release build
+  assert(r.first != -1 && r.second != -1);
+  assert(0 <= r.first && r.first < static_cast<ssize_t>(content_length));
+  assert(r.first <= r.second &&
+         r.second < static_cast<ssize_t>(content_length));
 
-  if (r.first == -1 && r.second == -1) {
-    return std::make_pair(0, content_length);
-  }
-
-  auto slen = static_cast<ssize_t>(content_length);
-
-  if (r.first == -1) {
-    r.first = (std::max)(static_cast<ssize_t>(0), slen - r.second);
-    r.second = slen - 1;
-  }
-
-  if (r.second == -1) { r.second = slen - 1; }
   return std::make_pair(r.first, static_cast<size_t>(r.second - r.first) + 1);
 }
 
-inline std::string make_content_range_header_field(size_t offset, size_t length,
-                                                   size_t content_length) {
+inline std::string make_content_range_header_field(
+    const std::pair<size_t, size_t> &offset_and_length, size_t content_length) {
+  auto st = offset_and_length.first;
+  auto ed = st + offset_and_length.second - 1;
+
   std::string field = "bytes ";
-  field += std::to_string(offset);
+  field += std::to_string(st);
   field += "-";
-  field += std::to_string(offset + length - 1);
+  field += std::to_string(ed);
   field += "/";
   field += std::to_string(content_length);
   return field;
 }
 
 template <typename SToken, typename CToken, typename Content>
-bool process_multipart_ranges_data(const Request &req, Response &res,
+bool process_multipart_ranges_data(const Request &req,
                                    const std::string &boundary,
                                    const std::string &content_type,
-                                   SToken stoken, CToken ctoken,
-                                   Content content) {
+                                   size_t content_length, SToken stoken,
+                                   CToken ctoken, Content content) {
   for (size_t i = 0; i < req.ranges.size(); i++) {
     ctoken("--");
     stoken(boundary);
@@ -4356,50 +4834,51 @@ bool process_multipart_ranges_data(const Request &req, Response &res,
       ctoken("\r\n");
     }
 
-    auto offsets = get_range_offset_and_length(req, res.body.size(), i);
-    auto offset = offsets.first;
-    auto length = offsets.second;
+    auto offset_and_length =
+        get_range_offset_and_length(req.ranges[i], content_length);
 
     ctoken("Content-Range: ");
-    stoken(make_content_range_header_field(offset, length, res.body.size()));
+    stoken(make_content_range_header_field(offset_and_length, content_length));
     ctoken("\r\n");
     ctoken("\r\n");
-    if (!content(offset, length)) { return false; }
+
+    if (!content(offset_and_length.first, offset_and_length.second)) {
+      return false;
+    }
     ctoken("\r\n");
   }
 
   ctoken("--");
   stoken(boundary);
-  ctoken("--\r\n");
+  ctoken("--");
 
   return true;
 }
 
-inline bool make_multipart_ranges_data(const Request &req, Response &res,
+inline void make_multipart_ranges_data(const Request &req, Response &res,
                                        const std::string &boundary,
                                        const std::string &content_type,
+                                       size_t content_length,
                                        std::string &data) {
-  return process_multipart_ranges_data(
-      req, res, boundary, content_type,
+  process_multipart_ranges_data(
+      req, boundary, content_type, content_length,
       [&](const std::string &token) { data += token; },
       [&](const std::string &token) { data += token; },
       [&](size_t offset, size_t length) {
-        if (offset < res.body.size()) {
-          data += res.body.substr(offset, length);
-          return true;
-        }
-        return false;
+        assert(offset + length <= content_length);
+        data += res.body.substr(offset, length);
+        return true;
       });
 }
 
-inline size_t
-get_multipart_ranges_data_length(const Request &req, Response &res,
-                                 const std::string &boundary,
-                                 const std::string &content_type) {
+inline size_t get_multipart_ranges_data_length(const Request &req,
+                                               const std::string &boundary,
+                                               const std::string &content_type,
+                                               size_t content_length) {
   size_t data_length = 0;
 
   process_multipart_ranges_data(
-      req, res, boundary, content_type,
+      req, boundary, content_type, content_length,
       [&](const std::string &token) { data_length += token.size(); },
       [&](const std::string &token) { data_length += token.size(); },
       [&](size_t /*offset*/, size_t length) {
@@ -4411,13 +4890,13 @@ get_multipart_ranges_data_length(const Request &req, Response &res,
 }
 
 template <typename T>
-inline bool write_multipart_ranges_data(Stream &strm, const Request &req,
-                                        Response &res,
-                                        const std::string &boundary,
-                                        const std::string &content_type,
-                                        const T &is_shutting_down) {
+inline bool
+write_multipart_ranges_data(Stream &strm, const Request &req, Response &res,
+                            const std::string &boundary,
+                            const std::string &content_type,
+                            size_t content_length, const T &is_shutting_down) {
   return process_multipart_ranges_data(
-      req, res, boundary, content_type,
+      req, boundary, content_type, content_length,
       [&](const std::string &token) { strm.write(token); },
       [&](const std::string &token) { strm.write(token); },
       [&](size_t offset, size_t length) {
@@ -4426,18 +4905,6 @@ inline bool write_multipart_ranges_data(Stream &strm, const Request &req,
       });
 }
 
-inline std::pair<size_t, size_t>
-get_range_offset_and_length(const Request &req, const Response &res,
-                            size_t index) {
-  auto r = req.ranges[index];
-
-  if (r.second == -1) {
-    r.second = static_cast<ssize_t>(res.content_length_) - 1;
-  }
-
-  return std::make_pair(r.first, r.second - r.first + 1);
-}
-
 inline bool expect_content(const Request &req) {
   if (req.method == "POST" || req.method == "PUT" || req.method == "PATCH" ||
       req.method == "PRI" || req.method == "DELETE") {
@@ -4471,7 +4938,7 @@ inline std::string message_digest(const std::string &s, const EVP_MD *algo) {
   std::stringstream ss;
   for (auto i = 0u; i < hash_length; ++i) {
     ss << std::hex << std::setw(2) << std::setfill('0')
-       << (unsigned int)hash[i];
+       << static_cast<unsigned int>(hash[i]);
   }
 
   return ss.str();
@@ -4564,7 +5031,7 @@ inline bool retrieve_root_certs_from_keychain(CFObjectPtr<CFArrayRef> &certs) {
 
 inline bool add_certs_to_x509_store(CFArrayRef certs, X509_STORE *store) {
   auto result = false;
-  for (int i = 0; i < CFArrayGetCount(certs); ++i) {
+  for (auto i = 0; i < CFArrayGetCount(certs); ++i) {
     const auto cert = reinterpret_cast<const __SecCertificate *>(
         CFArrayGetValueAtIndex(certs, i));
 
@@ -4707,7 +5174,7 @@ inline bool parse_www_authenticate(const Response &res,
         s = s.substr(pos + 1);
         auto beg = std::sregex_iterator(s.begin(), s.end(), re);
         for (auto i = beg; i != std::sregex_iterator(); ++i) {
-          auto m = *i;
+          const auto &m = *i;
           auto key = s.substr(static_cast<size_t>(m.position(1)),
                               static_cast<size_t>(m.length(1)));
           auto val = m.length(2) > 0
@@ -4724,20 +5191,6 @@ inline bool parse_www_authenticate(const Response &res,
   return false;
 }
 
-// https://stackoverflow.com/questions/440133/how-do-i-create-a-random-alpha-numeric-string-in-c/440240#answer-440240
-inline std::string random_string(size_t length) {
-  auto randchar = []() -> char {
-    const char charset[] = "0123456789"
-                           "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
-                           "abcdefghijklmnopqrstuvwxyz";
-    const size_t max_index = (sizeof(charset) - 1);
-    return charset[static_cast<size_t>(std::rand()) % max_index];
-  };
-  std::string str(length, 0);
-  std::generate_n(str.begin(), length, randchar);
-  return str;
-}
-
 class ContentProviderAdapter {
 public:
   explicit ContentProviderAdapter(
@@ -4782,7 +5235,7 @@ inline void hosted_at(const std::string &hostname,
     const auto &addr =
         *reinterpret_cast<struct sockaddr_storage *>(rp->ai_addr);
     std::string ip;
-    int dummy = -1;
+    auto dummy = -1;
     if (detail::get_ip_and_port(addr, sizeof(struct sockaddr_storage), ip,
                                 dummy)) {
       addrs.push_back(ip);
@@ -4802,10 +5255,11 @@ inline std::string append_query_params(const std::string &path,
 }
 
 // Header utilities
-inline std::pair<std::string, std::string> make_range_header(Ranges ranges) {
+inline std::pair<std::string, std::string>
+make_range_header(const Ranges &ranges) {
   std::string field = "bytes=";
   auto i = 0;
-  for (auto r : ranges) {
+  for (const auto &r : ranges) {
     if (i != 0) { field += ", "; }
     if (r.first != -1) { field += std::to_string(r.first); }
     field += '-';
@@ -4924,7 +5378,7 @@ inline void Response::set_redirect(const std::string &url, int stat) {
     if (300 <= stat && stat < 400) {
       this->status = stat;
     } else {
-      this->status = 302;
+      this->status = StatusCode::Found_302;
     }
   }
 }
@@ -4943,13 +5397,22 @@ inline void Response::set_content(const std::string &s,
   set_content(s.data(), s.size(), content_type);
 }
 
+inline void Response::set_content(std::string &&s,
+                                  const std::string &content_type) {
+  body = std::move(s);
+
+  auto rng = headers.equal_range("Content-Type");
+  headers.erase(rng.first, rng.second);
+  set_header("Content-Type", content_type);
+}
+
 inline void Response::set_content_provider(
     size_t in_length, const std::string &content_type, ContentProvider provider,
     ContentProviderResourceReleaser resource_releaser) {
   set_header("Content-Type", content_type);
   content_length_ = in_length;
   if (in_length > 0) { content_provider_ = std::move(provider); }
-  content_provider_resource_releaser_ = resource_releaser;
+  content_provider_resource_releaser_ = std::move(resource_releaser);
   is_chunked_content_provider_ = false;
 }
 
@@ -4959,7 +5422,7 @@ inline void Response::set_content_provider(
   set_header("Content-Type", content_type);
   content_length_ = 0;
   content_provider_ = detail::ContentProviderAdapter(std::move(provider));
-  content_provider_resource_releaser_ = resource_releaser;
+  content_provider_resource_releaser_ = std::move(resource_releaser);
   is_chunked_content_provider_ = false;
 }
 
@@ -4969,7 +5432,7 @@ inline void Response::set_chunked_content_provider(
   set_header("Content-Type", content_type);
   content_length_ = 0;
   content_provider_ = detail::ContentProviderAdapter(std::move(provider));
-  content_provider_resource_releaser_ = resource_releaser;
+  content_provider_resource_releaser_ = std::move(resource_releaser);
   is_chunked_content_provider_ = true;
 }
 
@@ -5010,7 +5473,7 @@ inline SocketStream::SocketStream(socket_t sock, time_t read_timeout_sec,
       write_timeout_sec_(write_timeout_sec),
       write_timeout_usec_(write_timeout_usec), read_buff_(read_buff_size_, 0) {}
 
-inline SocketStream::~SocketStream() {}
+inline SocketStream::~SocketStream() = default;
 
 inline bool SocketStream::is_readable() const {
   return select_read(sock_, read_timeout_sec_, read_timeout_usec_) > 0;
@@ -5120,6 +5583,99 @@ inline socket_t BufferStream::socket() const { return 0; }
 
 inline const std::string &BufferStream::get_buffer() const { return buffer; }
 
+inline PathParamsMatcher::PathParamsMatcher(const std::string &pattern) {
+  // One past the last ending position of a path param substring
+  std::size_t last_param_end = 0;
+
+#ifndef CPPHTTPLIB_NO_EXCEPTIONS
+  // Needed to ensure that parameter names are unique during matcher
+  // construction
+  // If exceptions are disabled, only last duplicate path
+  // parameter will be set
+  std::unordered_set<std::string> param_name_set;
+#endif
+
+  while (true) {
+    const auto marker_pos = pattern.find(marker, last_param_end);
+    if (marker_pos == std::string::npos) { break; }
+
+    static_fragments_.push_back(
+        pattern.substr(last_param_end, marker_pos - last_param_end));
+
+    const auto param_name_start = marker_pos + 1;
+
+    auto sep_pos = pattern.find(separator, param_name_start);
+    if (sep_pos == std::string::npos) { sep_pos = pattern.length(); }
+
+    auto param_name =
+        pattern.substr(param_name_start, sep_pos - param_name_start);
+
+#ifndef CPPHTTPLIB_NO_EXCEPTIONS
+    if (param_name_set.find(param_name) != param_name_set.cend()) {
+      std::string msg = "Encountered path parameter '" + param_name +
+                        "' multiple times in route pattern '" + pattern + "'.";
+      throw std::invalid_argument(msg);
+    }
+#endif
+
+    param_names_.push_back(std::move(param_name));
+
+    last_param_end = sep_pos + 1;
+  }
+
+  if (last_param_end < pattern.length()) {
+    static_fragments_.push_back(pattern.substr(last_param_end));
+  }
+}
+
+inline bool PathParamsMatcher::match(Request &request) const {
+  request.matches = std::smatch();
+  request.path_params.clear();
+  request.path_params.reserve(param_names_.size());
+
+  // One past the position at which the path matched the pattern last time
+  std::size_t starting_pos = 0;
+  for (size_t i = 0; i < static_fragments_.size(); ++i) {
+    const auto &fragment = static_fragments_[i];
+
+    if (starting_pos + fragment.length() > request.path.length()) {
+      return false;
+    }
+
+    // Avoid unnecessary allocation by using strncmp instead of substr +
+    // comparison
+    if (std::strncmp(request.path.c_str() + starting_pos, fragment.c_str(),
+                     fragment.length()) != 0) {
+      return false;
+    }
+
+    starting_pos += fragment.length();
+
+    // Should only happen when we have a static fragment after a param
+    // Example: '/users/:id/subscriptions'
+    // The 'subscriptions' fragment here does not have a corresponding param
+    if (i >= param_names_.size()) { continue; }
+
+    auto sep_pos = request.path.find(separator, starting_pos);
+    if (sep_pos == std::string::npos) { sep_pos = request.path.length(); }
+
+    const auto &param_name = param_names_[i];
+
+    request.path_params.emplace(
+        param_name, request.path.substr(starting_pos, sep_pos - starting_pos));
+
+    // Mark everythin up to '/' as matched
+    starting_pos = sep_pos + 1;
+  }
+  // Returns false if the path is longer than the pattern
+  return starting_pos >= request.path.length();
+}
+
+inline bool RegexMatcher::match(Request &request) const {
+  request.path_params.clear();
+  return std::regex_match(request.path, request.matches, regex_);
+}
+
 } // namespace detail
 
 // HTTP server implementation
@@ -5131,69 +5687,72 @@ inline Server::Server()
 #endif
 }
 
-inline Server::~Server() {}
+inline Server::~Server() = default;
+
+inline std::unique_ptr<detail::MatcherBase>
+Server::make_matcher(const std::string &pattern) {
+  if (pattern.find("/:") != std::string::npos) {
+    return detail::make_unique<detail::PathParamsMatcher>(pattern);
+  } else {
+    return detail::make_unique<detail::RegexMatcher>(pattern);
+  }
+}
 
 inline Server &Server::Get(const std::string &pattern, Handler handler) {
-  get_handlers_.push_back(
-      std::make_pair(std::regex(pattern), std::move(handler)));
+  get_handlers_.emplace_back(make_matcher(pattern), std::move(handler));
   return *this;
 }
 
 inline Server &Server::Post(const std::string &pattern, Handler handler) {
-  post_handlers_.push_back(
-      std::make_pair(std::regex(pattern), std::move(handler)));
+  post_handlers_.emplace_back(make_matcher(pattern), std::move(handler));
   return *this;
 }
 
 inline Server &Server::Post(const std::string &pattern,
                             HandlerWithContentReader handler) {
-  post_handlers_for_content_reader_.push_back(
-      std::make_pair(std::regex(pattern), std::move(handler)));
+  post_handlers_for_content_reader_.emplace_back(make_matcher(pattern),
+                                                 std::move(handler));
   return *this;
 }
 
 inline Server &Server::Put(const std::string &pattern, Handler handler) {
-  put_handlers_.push_back(
-      std::make_pair(std::regex(pattern), std::move(handler)));
+  put_handlers_.emplace_back(make_matcher(pattern), std::move(handler));
   return *this;
 }
 
 inline Server &Server::Put(const std::string &pattern,
                            HandlerWithContentReader handler) {
-  put_handlers_for_content_reader_.push_back(
-      std::make_pair(std::regex(pattern), std::move(handler)));
+  put_handlers_for_content_reader_.emplace_back(make_matcher(pattern),
+                                                std::move(handler));
   return *this;
 }
 
 inline Server &Server::Patch(const std::string &pattern, Handler handler) {
-  patch_handlers_.push_back(
-      std::make_pair(std::regex(pattern), std::move(handler)));
+  patch_handlers_.emplace_back(make_matcher(pattern), std::move(handler));
   return *this;
 }
 
 inline Server &Server::Patch(const std::string &pattern,
                              HandlerWithContentReader handler) {
-  patch_handlers_for_content_reader_.push_back(
-      std::make_pair(std::regex(pattern), std::move(handler)));
+  patch_handlers_for_content_reader_.emplace_back(make_matcher(pattern),
+                                                  std::move(handler));
   return *this;
 }
 
 inline Server &Server::Delete(const std::string &pattern, Handler handler) {
-  delete_handlers_.push_back(
-      std::make_pair(std::regex(pattern), std::move(handler)));
+  delete_handlers_.emplace_back(make_matcher(pattern), std::move(handler));
   return *this;
 }
 
 inline Server &Server::Delete(const std::string &pattern,
                               HandlerWithContentReader handler) {
-  delete_handlers_for_content_reader_.push_back(
-      std::make_pair(std::regex(pattern), std::move(handler)));
+  delete_handlers_for_content_reader_.emplace_back(make_matcher(pattern),
+                                                   std::move(handler));
   return *this;
 }
 
 inline Server &Server::Options(const std::string &pattern, Handler handler) {
-  options_handlers_.push_back(
-      std::make_pair(std::regex(pattern), std::move(handler)));
+  options_handlers_.emplace_back(make_matcher(pattern), std::move(handler));
   return *this;
 }
 
@@ -5231,6 +5790,11 @@ Server::set_file_extension_and_mimetype_mapping(const std::string &ext,
   return *this;
 }
 
+inline Server &Server::set_default_file_mimetype(const std::string &mime) {
+  default_file_mimetype_ = mime;
+  return *this;
+}
+
 inline Server &Server::set_file_request_handler(Handler handler) {
   file_request_handler_ = std::move(handler);
   return *this;
@@ -5272,7 +5836,6 @@ inline Server &Server::set_logger(Logger logger) {
 inline Server &
 Server::set_expect_100_continue_handler(Expect100ContinueHandler handler) {
   expect_100_continue_handler_ = std::move(handler);
-
   return *this;
 }
 
@@ -5296,6 +5859,12 @@ inline Server &Server::set_default_headers(Headers headers) {
   return *this;
 }
 
+inline Server &Server::set_header_writer(
+    std::function<ssize_t(Stream &, Headers &)> const &writer) {
+  header_writer_ = writer;
+  return *this;
+}
+
 inline Server &Server::set_keep_alive_max_count(size_t count) {
   keep_alive_max_count_ = count;
   return *this;
@@ -5331,8 +5900,7 @@ inline Server &Server::set_payload_max_length(size_t length) {
 
 inline bool Server::bind_to_port(const std::string &host, int port,
                                  int socket_flags) {
-  if (bind_internal(host, port, socket_flags) < 0) return false;
-  return true;
+  return bind_internal(host, port, socket_flags) >= 0;
 }
 inline int Server::bind_to_any_port(const std::string &host, int socket_flags) {
   return bind_internal(host, 0, socket_flags);
@@ -5366,7 +5934,7 @@ inline void Server::stop() {
   }
 }
 
-inline bool Server::parse_request_line(const char *s, Request &req) {
+inline bool Server::parse_request_line(const char *s, Request &req) const {
   auto len = strlen(s);
   if (len < 2 || s[len - 2] != '\r' || s[len - 1] != '\n') { return false; }
   len -= 2;
@@ -5407,7 +5975,7 @@ inline bool Server::parse_request_line(const char *s, Request &req) {
     size_t count = 0;
 
     detail::split(req.target.data(), req.target.data() + req.target.size(), '?',
-                  [&](const char *b, const char *e) {
+                  2, [&](const char *b, const char *e) {
                     switch (count) {
                     case 0:
                       req.path = detail::decode_url(std::string(b, e), false);
@@ -5430,7 +5998,10 @@ inline bool Server::parse_request_line(const char *s, Request &req) {
 }
 
 inline bool Server::write_response(Stream &strm, bool close_connection,
-                                   const Request &req, Response &res) {
+                                   Request &req, Response &res) {
+  // NOTE: `req.ranges` should be empty, otherwise it will be applied
+  // incorrectly to the error content.
+  req.ranges.clear();
   return write_response_core(strm, close_connection, req, res, false);
 }
 
@@ -5486,11 +6057,11 @@ inline bool Server::write_response_core(Stream &strm, bool close_connection,
     detail::BufferStream bstrm;
 
     if (!bstrm.write_format("HTTP/1.1 %d %s\r\n", res.status,
-                            detail::status_message(res.status))) {
+                            status_message(res.status))) {
       return false;
     }
 
-    if (!detail::write_headers(bstrm, res.headers)) { return false; }
+    if (!header_writer_(bstrm, res.headers)) { return false; }
 
     // Flush buffer
     auto &data = bstrm.get_buffer();
@@ -5508,7 +6079,6 @@ inline bool Server::write_response_core(Stream &strm, bool close_connection,
       if (write_content_with_provider(strm, req, res, boundary, content_type)) {
         res.content_provider_success_ = true;
       } else {
-        res.content_provider_success_ = false;
         ret = false;
       }
     }
@@ -5533,15 +6103,16 @@ Server::write_content_with_provider(Stream &strm, const Request &req,
       return detail::write_content(strm, res.content_provider_, 0,
                                    res.content_length_, is_shutting_down);
     } else if (req.ranges.size() == 1) {
-      auto offsets =
-          detail::get_range_offset_and_length(req, res.content_length_, 0);
-      auto offset = offsets.first;
-      auto length = offsets.second;
-      return detail::write_content(strm, res.content_provider_, offset, length,
-                                   is_shutting_down);
+      auto offset_and_length = detail::get_range_offset_and_length(
+          req.ranges[0], res.content_length_);
+
+      return detail::write_content(strm, res.content_provider_,
+                                   offset_and_length.first,
+                                   offset_and_length.second, is_shutting_down);
     } else {
       return detail::write_multipart_ranges_data(
-          strm, req, res, boundary, content_type, is_shutting_down);
+          strm, req, res, boundary, content_type, res.content_length_,
+          is_shutting_down);
     }
   } else {
     if (res.is_chunked_content_provider_) {
@@ -5598,7 +6169,7 @@ inline bool Server::read_content(Stream &strm, Request &req, Response &res) {
     const auto &content_type = req.get_header_value("Content-Type");
     if (!content_type.find("application/x-www-form-urlencoded")) {
       if (req.body.size() > CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH) {
-        res.status = 413; // NOTE: should be 414?
+        res.status = StatusCode::PayloadTooLarge_413; // NOTE: should be 414?
         return false;
       }
       detail::parse_query_text(req.body, req.params);
@@ -5617,10 +6188,11 @@ inline bool Server::read_content_with_content_receiver(
                            std::move(multipart_receiver));
 }
 
-inline bool Server::read_content_core(Stream &strm, Request &req, Response &res,
-                                      ContentReceiver receiver,
-                                      MultipartContentHeader multipart_header,
-                                      ContentReceiver multipart_receiver) {
+inline bool
+Server::read_content_core(Stream &strm, Request &req, Response &res,
+                          ContentReceiver receiver,
+                          MultipartContentHeader multipart_header,
+                          ContentReceiver multipart_receiver) const {
   detail::MultipartFormDataParser multipart_form_data_parser;
   ContentReceiverWithProgress out;
 
@@ -5628,7 +6200,7 @@ inline bool Server::read_content_core(Stream &strm, Request &req, Response &res,
     const auto &content_type = req.get_header_value("Content-Type");
     std::string boundary;
     if (!detail::parse_multipart_boundary(content_type, boundary)) {
-      res.status = 400;
+      res.status = StatusCode::BadRequest_400;
       return false;
     }
 
@@ -5664,7 +6236,7 @@ inline bool Server::read_content_core(Stream &strm, Request &req, Response &res,
 
   if (req.is_multipart_form_data()) {
     if (!multipart_form_data_parser.is_valid()) {
-      res.status = 400;
+      res.status = StatusCode::BadRequest_400;
       return false;
     }
   }
@@ -5683,17 +6255,26 @@ inline bool Server::handle_file_request(const Request &req, Response &res,
         if (path.back() == '/') { path += "index.html"; }
 
         if (detail::is_file(path)) {
-          detail::read_file(path, res.body);
-          auto type =
-              detail::find_content_type(path, file_extension_and_mimetype_map_);
-          if (type) { res.set_header("Content-Type", type); }
           for (const auto &kv : entry.headers) {
-            res.set_header(kv.first.c_str(), kv.second);
+            res.set_header(kv.first, kv.second);
           }
-          res.status = req.has_header("Range") ? 206 : 200;
+
+          auto mm = std::make_shared<detail::mmap>(path.c_str());
+          if (!mm->is_open()) { return false; }
+
+          res.set_content_provider(
+              mm->size(),
+              detail::find_content_type(path, file_extension_and_mimetype_map_,
+                                        default_file_mimetype_),
+              [mm](size_t offset, size_t length, DataSink &sink) -> bool {
+                sink.write(mm->data() + offset, length);
+                return true;
+              });
+
           if (!head && file_request_handler_) {
             file_request_handler_(req, res);
           }
+
           return true;
         }
       }
@@ -5789,13 +6370,14 @@ inline bool Server::listen_internal() {
 #ifdef _WIN32
         auto timeout = static_cast<uint32_t>(read_timeout_sec_ * 1000 +
                                              read_timeout_usec_ / 1000);
-        setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO, (char *)&timeout,
-                   sizeof(timeout));
+        setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO,
+                   reinterpret_cast<const char *>(&timeout), sizeof(timeout));
 #else
         timeval tv;
         tv.tv_sec = static_cast<long>(read_timeout_sec_);
         tv.tv_usec = static_cast<decltype(tv.tv_usec)>(read_timeout_usec_);
-        setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO, (char *)&tv, sizeof(tv));
+        setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO,
+                   reinterpret_cast<const void *>(&tv), sizeof(tv));
 #endif
       }
       {
@@ -5803,17 +6385,22 @@ inline bool Server::listen_internal() {
 #ifdef _WIN32
         auto timeout = static_cast<uint32_t>(write_timeout_sec_ * 1000 +
                                              write_timeout_usec_ / 1000);
-        setsockopt(sock, SOL_SOCKET, SO_SNDTIMEO, (char *)&timeout,
-                   sizeof(timeout));
+        setsockopt(sock, SOL_SOCKET, SO_SNDTIMEO,
+                   reinterpret_cast<const char *>(&timeout), sizeof(timeout));
 #else
         timeval tv;
         tv.tv_sec = static_cast<long>(write_timeout_sec_);
         tv.tv_usec = static_cast<decltype(tv.tv_usec)>(write_timeout_usec_);
-        setsockopt(sock, SOL_SOCKET, SO_SNDTIMEO, (char *)&tv, sizeof(tv));
+        setsockopt(sock, SOL_SOCKET, SO_SNDTIMEO,
+                   reinterpret_cast<const void *>(&tv), sizeof(tv));
 #endif
       }
 
-      task_queue->enqueue([this, sock]() { process_and_close_socket(sock); });
+      if (!task_queue->enqueue(
+              [this, sock]() { process_and_close_socket(sock); })) {
+        detail::shutdown_socket(sock);
+        detail::close_socket(sock);
+      }
     }
 
     task_queue->shutdown();
@@ -5829,7 +6416,7 @@ inline bool Server::routing(Request &req, Response &res, Stream &strm) {
   }
 
   // File handler
-  bool is_head_request = req.method == "HEAD";
+  auto is_head_request = req.method == "HEAD";
   if ((req.method == "GET" || is_head_request) &&
       handle_file_request(req, res, is_head_request)) {
     return true;
@@ -5895,17 +6482,17 @@ inline bool Server::routing(Request &req, Response &res, Stream &strm) {
     return dispatch_request(req, res, patch_handlers_);
   }
 
-  res.status = 400;
+  res.status = StatusCode::BadRequest_400;
   return false;
 }
 
 inline bool Server::dispatch_request(Request &req, Response &res,
-                                     const Handlers &handlers) {
+                                     const Handlers &handlers) const {
   for (const auto &x : handlers) {
-    const auto &pattern = x.first;
+    const auto &matcher = x.first;
     const auto &handler = x.second;
 
-    if (std::regex_match(req.path, req.matches, pattern)) {
+    if (matcher->match(req)) {
       handler(req, res);
       return true;
     }
@@ -5915,18 +6502,18 @@ inline bool Server::dispatch_request(Request &req, Response &res,
 
 inline void Server::apply_ranges(const Request &req, Response &res,
                                  std::string &content_type,
-                                 std::string &boundary) {
+                                 std::string &boundary) const {
   if (req.ranges.size() > 1) {
-    boundary = detail::make_multipart_data_boundary();
-
     auto it = res.headers.find("Content-Type");
     if (it != res.headers.end()) {
       content_type = it->second;
       res.headers.erase(it);
     }
 
-    res.headers.emplace("Content-Type",
-                        "multipart/byteranges; boundary=" + boundary);
+    boundary = detail::make_multipart_data_boundary();
+
+    res.set_header("Content-Type",
+                   "multipart/byteranges; boundary=" + boundary);
   }
 
   auto type = detail::encoding_type(req, res);
@@ -5937,16 +6524,17 @@ inline void Server::apply_ranges(const Request &req, Response &res,
       if (req.ranges.empty()) {
         length = res.content_length_;
       } else if (req.ranges.size() == 1) {
-        auto offsets =
-            detail::get_range_offset_and_length(req, res.content_length_, 0);
-        auto offset = offsets.first;
-        length = offsets.second;
+        auto offset_and_length = detail::get_range_offset_and_length(
+            req.ranges[0], res.content_length_);
+
+        length = offset_and_length.second;
+
         auto content_range = detail::make_content_range_header_field(
-            offset, length, res.content_length_);
+            offset_and_length, res.content_length_);
         res.set_header("Content-Range", content_range);
       } else {
-        length = detail::get_multipart_ranges_data_length(req, res, boundary,
-                                                          content_type);
+        length = detail::get_multipart_ranges_data_length(
+            req, boundary, content_type, res.content_length_);
       }
       res.set_header("Content-Length", std::to_string(length));
     } else {
@@ -5965,28 +6553,22 @@ inline void Server::apply_ranges(const Request &req, Response &res,
     if (req.ranges.empty()) {
       ;
     } else if (req.ranges.size() == 1) {
-      auto offsets =
-          detail::get_range_offset_and_length(req, res.body.size(), 0);
-      auto offset = offsets.first;
-      auto length = offsets.second;
+      auto offset_and_length =
+          detail::get_range_offset_and_length(req.ranges[0], res.body.size());
+      auto offset = offset_and_length.first;
+      auto length = offset_and_length.second;
+
       auto content_range = detail::make_content_range_header_field(
-          offset, length, res.body.size());
+          offset_and_length, res.body.size());
       res.set_header("Content-Range", content_range);
-      if (offset < res.body.size()) {
-        res.body = res.body.substr(offset, length);
-      } else {
-        res.body.clear();
-        res.status = 416;
-      }
+
+      assert(offset + length <= res.body.size());
+      res.body = res.body.substr(offset, length);
     } else {
       std::string data;
-      if (detail::make_multipart_ranges_data(req, res, boundary, content_type,
-                                             data)) {
-        res.body.swap(data);
-      } else {
-        res.body.clear();
-        res.status = 416;
-      }
+      detail::make_multipart_ranges_data(req, res, boundary, content_type,
+                                         res.body.size(), data);
+      res.body.swap(data);
     }
 
     if (type != detail::EncodingType::None) {
@@ -6025,12 +6607,12 @@ inline void Server::apply_ranges(const Request &req, Response &res,
 
 inline bool Server::dispatch_request_for_content_reader(
     Request &req, Response &res, ContentReader content_reader,
-    const HandlersForContentReader &handlers) {
+    const HandlersForContentReader &handlers) const {
   for (const auto &x : handlers) {
-    const auto &pattern = x.first;
+    const auto &matcher = x.first;
     const auto &handler = x.second;
 
-    if (std::regex_match(req.path, req.matches, pattern)) {
+    if (matcher->match(req)) {
       handler(req, res, content_reader);
       return true;
     }
@@ -6050,15 +6632,10 @@ Server::process_request(Stream &strm, bool close_connection,
   if (!line_reader.getline()) { return false; }
 
   Request req;
+
   Response res;
-
   res.version = "HTTP/1.1";
-
-  for (const auto &header : default_headers_) {
-    if (res.headers.find(header.first) == res.headers.end()) {
-      res.headers.insert(header);
-    }
-  }
+  res.headers = default_headers_;
 
 #ifdef _WIN32
   // TODO: Increase FD_SETSIZE statically (libzmq), dynamically (MySQL).
@@ -6068,7 +6645,7 @@ Server::process_request(Stream &strm, bool close_connection,
   if (strm.socket() >= FD_SETSIZE) {
     Headers dummy;
     detail::read_headers(strm, dummy);
-    res.status = 500;
+    res.status = StatusCode::InternalServerError_500;
     return write_response(strm, close_connection, req, res);
   }
 #endif
@@ -6078,14 +6655,14 @@ Server::process_request(Stream &strm, bool close_connection,
   if (line_reader.size() > CPPHTTPLIB_REQUEST_URI_MAX_LENGTH) {
     Headers dummy;
     detail::read_headers(strm, dummy);
-    res.status = 414;
+    res.status = StatusCode::UriTooLong_414;
     return write_response(strm, close_connection, req, res);
   }
 
   // Request line and headers
   if (!parse_request_line(line_reader.ptr(), req) ||
       !detail::read_headers(strm, req.headers)) {
-    res.status = 400;
+    res.status = StatusCode::BadRequest_400;
     return write_response(strm, close_connection, req, res);
   }
 
@@ -6109,7 +6686,7 @@ Server::process_request(Stream &strm, bool close_connection,
   if (req.has_header("Range")) {
     const auto &range_header_value = req.get_header_value("Range");
     if (!detail::parse_range_header(range_header_value, req.ranges)) {
-      res.status = 416;
+      res.status = StatusCode::RangeNotSatisfiable_416;
       return write_response(strm, close_connection, req, res);
     }
   }
@@ -6117,22 +6694,22 @@ Server::process_request(Stream &strm, bool close_connection,
   if (setup_request) { setup_request(req); }
 
   if (req.get_header_value("Expect") == "100-continue") {
-    auto status = 100;
+    int status = StatusCode::Continue_100;
     if (expect_100_continue_handler_) {
       status = expect_100_continue_handler_(req, res);
     }
     switch (status) {
-    case 100:
-    case 417:
+    case StatusCode::Continue_100:
+    case StatusCode::ExpectationFailed_417:
       strm.write_format("HTTP/1.1 %d %s\r\n\r\n", status,
-                        detail::status_message(status));
+                        status_message(status));
       break;
     default: return write_response(strm, close_connection, req, res);
     }
   }
 
-  // Rounting
-  bool routed = false;
+  // Routing
+  auto routed = false;
 #ifdef CPPHTTPLIB_NO_EXCEPTIONS
   routed = routing(req, res, strm);
 #else
@@ -6144,7 +6721,7 @@ Server::process_request(Stream &strm, bool close_connection,
       exception_handler_(req, res, ep);
       routed = true;
     } else {
-      res.status = 500;
+      res.status = StatusCode::InternalServerError_500;
       std::string val;
       auto s = e.what();
       for (size_t i = 0; s[i]; i++) {
@@ -6162,17 +6739,29 @@ Server::process_request(Stream &strm, bool close_connection,
       exception_handler_(req, res, ep);
       routed = true;
     } else {
-      res.status = 500;
+      res.status = StatusCode::InternalServerError_500;
       res.set_header("EXCEPTION_WHAT", "UNKNOWN");
     }
   }
 #endif
-
   if (routed) {
-    if (res.status == -1) { res.status = req.ranges.empty() ? 200 : 206; }
+    if (res.status == -1) {
+      res.status = req.ranges.empty() ? StatusCode::OK_200
+                                      : StatusCode::PartialContent_206;
+    }
+
+    if (detail::range_error(req, res)) {
+      res.body.clear();
+      res.content_length_ = 0;
+      res.content_provider_ = nullptr;
+      res.status = StatusCode::RangeNotSatisfiable_416;
+      return write_response(strm, close_connection, req, res);
+    }
+
     return write_response_with_content(strm, close_connection, req, res);
   } else {
-    if (res.status == -1) { res.status = 404; }
+    if (res.status == -1) { res.status = StatusCode::NotFound_404; }
+
     return write_response(strm, close_connection, req, res);
   }
 }
@@ -6272,7 +6861,7 @@ inline socket_t ClientImpl::create_client_socket(Error &error) const {
   // Check is custom IP specified for host_
   std::string ip;
   auto it = addr_map_.find(host_);
-  if (it != addr_map_.end()) ip = it->second;
+  if (it != addr_map_.end()) { ip = it->second; }
 
   return detail::create_client_socket(
       host_, ip, port_, address_family_, tcp_nodelay_, socket_options_,
@@ -6297,7 +6886,7 @@ inline void ClientImpl::shutdown_ssl(Socket & /*socket*/,
          socket_requests_are_from_thread_ == std::this_thread::get_id());
 }
 
-inline void ClientImpl::shutdown_socket(Socket &socket) {
+inline void ClientImpl::shutdown_socket(Socket &socket) const {
   if (socket.sock == INVALID_SOCKET) { return; }
   detail::shutdown_socket(socket.sock);
 }
@@ -6322,7 +6911,7 @@ inline void ClientImpl::close_socket(Socket &socket) {
 }
 
 inline bool ClientImpl::read_response_line(Stream &strm, const Request &req,
-                                           Response &res) {
+                                           Response &res) const {
   std::array<char, 2048> buf{};
 
   detail::stream_line_reader line_reader(strm, buf.data(), buf.size());
@@ -6330,9 +6919,9 @@ inline bool ClientImpl::read_response_line(Stream &strm, const Request &req,
   if (!line_reader.getline()) { return false; }
 
 #ifdef CPPHTTPLIB_ALLOW_LF_AS_LINE_TERMINATOR
-  const static std::regex re("(HTTP/1\\.[01]) (\\d{3})(?: (.*?))?\r\n");
-#else
   const static std::regex re("(HTTP/1\\.[01]) (\\d{3})(?: (.*?))?\r?\n");
+#else
+  const static std::regex re("(HTTP/1\\.[01]) (\\d{3})(?: (.*?))?\r\n");
 #endif
 
   std::cmatch m;
@@ -6344,7 +6933,7 @@ inline bool ClientImpl::read_response_line(Stream &strm, const Request &req,
   res.reason = std::string(m[3]);
 
   // Ignore '100 Continue'
-  while (res.status == 100) {
+  while (res.status == StatusCode::Continue_100) {
     if (!line_reader.getline()) { return false; } // CRLF
     if (!line_reader.getline()) { return false; } // next response line
 
@@ -6492,15 +7081,31 @@ inline bool ClientImpl::handle_request(Stream &strm, Request &req,
 
   if (!ret) { return false; }
 
+  if (res.get_header_value("Connection") == "close" ||
+      (res.version == "HTTP/1.0" && res.reason != "Connection established")) {
+    // TODO this requires a not-entirely-obvious chain of calls to be correct
+    // for this to be safe.
+
+    // This is safe to call because handle_request is only called by send_
+    // which locks the request mutex during the process. It would be a bug
+    // to call it from a different thread since it's a thread-safety issue
+    // to do these things to the socket if another thread is using the socket.
+    std::lock_guard<std::mutex> guard(socket_mutex_);
+    shutdown_ssl(socket_, true);
+    shutdown_socket(socket_);
+    close_socket(socket_);
+  }
+
   if (300 < res.status && res.status < 400 && follow_location_) {
     req = req_save;
     ret = redirect(req, res, error);
   }
 
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
-  if ((res.status == 401 || res.status == 407) &&
+  if ((res.status == StatusCode::Unauthorized_401 ||
+       res.status == StatusCode::ProxyAuthenticationRequired_407) &&
       req.authorization_count_ < 5) {
-    auto is_proxy = res.status == 407;
+    auto is_proxy = res.status == StatusCode::ProxyAuthenticationRequired_407;
     const auto &username =
         is_proxy ? proxy_digest_auth_username_ : digest_auth_username_;
     const auto &password =
@@ -6571,7 +7176,7 @@ inline bool ClientImpl::redirect(Request &req, Response &res, Error &error) {
   } else {
     if (next_scheme == "https") {
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
-      SSLClient cli(next_host.c_str(), next_port);
+      SSLClient cli(next_host, next_port);
       cli.copy_settings(*this);
       if (ca_cert_store_) { cli.set_ca_cert_store(ca_cert_store_); }
       return detail::redirect(cli, req, res, path, location, error);
@@ -6579,7 +7184,7 @@ inline bool ClientImpl::redirect(Request &req, Response &res, Error &error) {
       return false;
 #endif
     } else {
-      ClientImpl cli(next_host.c_str(), next_port);
+      ClientImpl cli(next_host, next_port);
       cli.copy_settings(*this);
       return detail::redirect(cli, req, res, path, location, error);
     }
@@ -6588,7 +7193,7 @@ inline bool ClientImpl::redirect(Request &req, Response &res, Error &error) {
 
 inline bool ClientImpl::write_content_with_provider(Stream &strm,
                                                     const Request &req,
-                                                    Error &error) {
+                                                    Error &error) const {
   auto is_shutting_down = []() { return false; };
 
   if (req.is_chunked_content_provider_) {
@@ -6616,32 +7221,32 @@ inline bool ClientImpl::write_request(Stream &strm, Request &req,
   // Prepare additional headers
   if (close_connection) {
     if (!req.has_header("Connection")) {
-      req.headers.emplace("Connection", "close");
+      req.set_header("Connection", "close");
     }
   }
 
   if (!req.has_header("Host")) {
     if (is_ssl()) {
       if (port_ == 443) {
-        req.headers.emplace("Host", host_);
+        req.set_header("Host", host_);
       } else {
-        req.headers.emplace("Host", host_and_port_);
+        req.set_header("Host", host_and_port_);
       }
     } else {
       if (port_ == 80) {
-        req.headers.emplace("Host", host_);
+        req.set_header("Host", host_);
       } else {
-        req.headers.emplace("Host", host_and_port_);
+        req.set_header("Host", host_and_port_);
       }
     }
   }
 
-  if (!req.has_header("Accept")) { req.headers.emplace("Accept", "*/*"); }
+  if (!req.has_header("Accept")) { req.set_header("Accept", "*/*"); }
 
 #ifndef CPPHTTPLIB_NO_DEFAULT_USER_AGENT
   if (!req.has_header("User-Agent")) {
     auto agent = std::string("cpp-httplib/") + CPPHTTPLIB_VERSION;
-    req.headers.emplace("User-Agent", agent);
+    req.set_header("User-Agent", agent);
   }
 #endif
 
@@ -6650,23 +7255,23 @@ inline bool ClientImpl::write_request(Stream &strm, Request &req,
       if (!req.is_chunked_content_provider_) {
         if (!req.has_header("Content-Length")) {
           auto length = std::to_string(req.content_length_);
-          req.headers.emplace("Content-Length", length);
+          req.set_header("Content-Length", length);
         }
       }
     } else {
       if (req.method == "POST" || req.method == "PUT" ||
           req.method == "PATCH") {
-        req.headers.emplace("Content-Length", "0");
+        req.set_header("Content-Length", "0");
       }
     }
   } else {
     if (!req.has_header("Content-Type")) {
-      req.headers.emplace("Content-Type", "text/plain");
+      req.set_header("Content-Type", "text/plain");
     }
 
     if (!req.has_header("Content-Length")) {
       auto length = std::to_string(req.body.size());
-      req.headers.emplace("Content-Length", length);
+      req.set_header("Content-Length", length);
     }
   }
 
@@ -6706,7 +7311,7 @@ inline bool ClientImpl::write_request(Stream &strm, Request &req,
     const auto &path = url_encode_ ? detail::encode_url(req.path) : req.path;
     bstrm.write_format("%s %s HTTP/1.1\r\n", req.method.c_str(), path.c_str());
 
-    detail::write_headers(bstrm, req.headers);
+    header_writer_(bstrm, req.headers);
 
     // Flush buffer
     auto &data = bstrm.get_buffer();
@@ -6734,12 +7339,10 @@ inline std::unique_ptr<Response> ClientImpl::send_with_content_provider(
     ContentProvider content_provider,
     ContentProviderWithoutLength content_provider_without_length,
     const std::string &content_type, Error &error) {
-  if (!content_type.empty()) {
-    req.headers.emplace("Content-Type", content_type);
-  }
+  if (!content_type.empty()) { req.set_header("Content-Type", content_type); }
 
 #ifdef CPPHTTPLIB_ZLIB_SUPPORT
-  if (compress_) { req.headers.emplace("Content-Encoding", "gzip"); }
+  if (compress_) { req.set_header("Content-Encoding", "gzip"); }
 #endif
 
 #ifdef CPPHTTPLIB_ZLIB_SUPPORT
@@ -6800,10 +7403,9 @@ inline std::unique_ptr<Response> ClientImpl::send_with_content_provider(
       req.content_provider_ = detail::ContentProviderAdapter(
           std::move(content_provider_without_length));
       req.is_chunked_content_provider_ = true;
-      req.headers.emplace("Transfer-Encoding", "chunked");
+      req.set_header("Transfer-Encoding", "chunked");
     } else {
       req.body.assign(body, content_length);
-      ;
     }
   }
 
@@ -6864,7 +7466,8 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req,
   }
 
   // Body
-  if ((res.status != 204) && req.method != "HEAD" && req.method != "CONNECT") {
+  if ((res.status != StatusCode::NoContent_204) && req.method != "HEAD" &&
+      req.method != "CONNECT") {
     auto redirect = 300 < res.status && res.status < 400 && follow_location_;
 
     if (req.response_handler && !redirect) {
@@ -6909,24 +7512,6 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req,
     }
   }
 
-  if (res.get_header_value("Connection") == "close" ||
-      (res.version == "HTTP/1.0" && res.reason != "Connection established")) {
-    // TODO this requires a not-entirely-obvious chain of calls to be correct
-    // for this to be safe. Maybe a code refactor (such as moving this out to
-    // the send function and getting rid of the recursiveness of the mutex)
-    // could make this more obvious.
-
-    // This is safe to call because process_request is only called by
-    // handle_request which is only called by send, which locks the request
-    // mutex during the process. It would be a bug to call it from a different
-    // thread since it's a thread-safety issue to do these things to the socket
-    // if another thread is using the socket.
-    std::lock_guard<std::mutex> guard(socket_mutex_);
-    shutdown_ssl(socket_, true);
-    shutdown_socket(socket_);
-    close_socket(socket_);
-  }
-
   // Log
   if (logger_) { logger_(req, res); }
 
@@ -6935,13 +7520,14 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req,
 
 inline ContentProviderWithoutLength ClientImpl::get_multipart_content_provider(
     const std::string &boundary, const MultipartFormDataItems &items,
-    const MultipartFormDataProviderItems &provider_items) {
-  size_t cur_item = 0, cur_start = 0;
+    const MultipartFormDataProviderItems &provider_items) const {
+  size_t cur_item = 0;
+  size_t cur_start = 0;
   // cur_item and cur_start are copied to within the std::function and maintain
   // state between successive calls
   return [&, cur_item, cur_start](size_t offset,
                                   DataSink &sink) mutable -> bool {
-    if (!offset && items.size()) {
+    if (!offset && !items.empty()) {
       sink.os << detail::serialize_multipart_formdata(items, boundary, false);
       return true;
     } else if (cur_item < provider_items.size()) {
@@ -6954,12 +7540,13 @@ inline ContentProviderWithoutLength ClientImpl::get_multipart_content_provider(
       }
 
       DataSink cur_sink;
-      bool has_data = true;
+      auto has_data = true;
       cur_sink.write = sink.write;
       cur_sink.done = [&]() { has_data = false; };
 
-      if (!provider_items[cur_item].provider(offset - cur_start, cur_sink))
+      if (!provider_items[cur_item].provider(offset - cur_start, cur_sink)) {
         return false;
+      }
 
       if (!has_data) {
         sink.os << detail::serialize_multipart_formdata_item_end();
@@ -7078,14 +7665,15 @@ inline Result ClientImpl::Get(const std::string &path, const Params &params,
   if (params.empty()) { return Get(path, headers); }
 
   std::string path_with_query = append_query_params(path, params);
-  return Get(path_with_query.c_str(), headers, progress);
+  return Get(path_with_query, headers, std::move(progress));
 }
 
 inline Result ClientImpl::Get(const std::string &path, const Params &params,
                               const Headers &headers,
                               ContentReceiver content_receiver,
                               Progress progress) {
-  return Get(path, params, headers, nullptr, content_receiver, progress);
+  return Get(path, params, headers, nullptr, std::move(content_receiver),
+             std::move(progress));
 }
 
 inline Result ClientImpl::Get(const std::string &path, const Params &params,
@@ -7094,12 +7682,13 @@ inline Result ClientImpl::Get(const std::string &path, const Params &params,
                               ContentReceiver content_receiver,
                               Progress progress) {
   if (params.empty()) {
-    return Get(path, headers, response_handler, content_receiver, progress);
+    return Get(path, headers, std::move(response_handler),
+               std::move(content_receiver), std::move(progress));
   }
 
   std::string path_with_query = append_query_params(path, params);
-  return Get(path_with_query.c_str(), headers, response_handler,
-             content_receiver, progress);
+  return Get(path_with_query, headers, std::move(response_handler),
+             std::move(content_receiver), std::move(progress));
 }
 
 inline Result ClientImpl::Head(const std::string &path) {
@@ -7201,7 +7790,7 @@ inline Result ClientImpl::Post(const std::string &path, const Headers &headers,
   const auto &content_type =
       detail::serialize_multipart_formdata_get_content_type(boundary);
   const auto &body = detail::serialize_multipart_formdata(items, boundary);
-  return Post(path, headers, body, content_type.c_str());
+  return Post(path, headers, body, content_type);
 }
 
 inline Result ClientImpl::Post(const std::string &path, const Headers &headers,
@@ -7214,7 +7803,7 @@ inline Result ClientImpl::Post(const std::string &path, const Headers &headers,
   const auto &content_type =
       detail::serialize_multipart_formdata_get_content_type(boundary);
   const auto &body = detail::serialize_multipart_formdata(items, boundary);
-  return Post(path, headers, body, content_type.c_str());
+  return Post(path, headers, body, content_type);
 }
 
 inline Result
@@ -7423,9 +8012,7 @@ inline Result ClientImpl::Delete(const std::string &path,
   req.headers = headers;
   req.path = path;
 
-  if (!content_type.empty()) {
-    req.headers.emplace("Content-Type", content_type);
-  }
+  if (!content_type.empty()) { req.set_header("Content-Type", content_type); }
   req.body.assign(body, content_length);
 
   return send_(std::move(req));
@@ -7458,13 +8045,6 @@ inline Result ClientImpl::Options(const std::string &path,
   return send_(std::move(req));
 }
 
-inline size_t ClientImpl::is_socket_open() const {
-  std::lock_guard<std::mutex> guard(socket_mutex_);
-  return socket_.is_open();
-}
-
-inline socket_t ClientImpl::socket() const { return socket_.sock; }
-
 inline void ClientImpl::stop() {
   std::lock_guard<std::mutex> guard(socket_mutex_);
 
@@ -7488,6 +8068,17 @@ inline void ClientImpl::stop() {
   close_socket(socket_);
 }
 
+inline std::string ClientImpl::host() const { return host_; }
+
+inline int ClientImpl::port() const { return port_; }
+
+inline size_t ClientImpl::is_socket_open() const {
+  std::lock_guard<std::mutex> guard(socket_mutex_);
+  return socket_.is_open();
+}
+
+inline socket_t ClientImpl::socket() const { return socket_.sock; }
+
 inline void ClientImpl::set_connection_timeout(time_t sec, time_t usec) {
   connection_timeout_sec_ = sec;
   connection_timeout_usec_ = usec;
@@ -7536,6 +8127,11 @@ inline void ClientImpl::set_default_headers(Headers headers) {
   default_headers_ = std::move(headers);
 }
 
+inline void ClientImpl::set_header_writer(
+    std::function<ssize_t(Stream &, Headers &)> const &writer) {
+  header_writer_ = writer;
+}
+
 inline void ClientImpl::set_address_family(int family) {
   address_family_ = family;
 }
@@ -7575,9 +8171,7 @@ inline void ClientImpl::set_proxy_digest_auth(const std::string &username,
   proxy_digest_auth_username_ = username;
   proxy_digest_auth_password_ = password;
 }
-#endif
 
-#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
 inline void ClientImpl::set_ca_cert_path(const std::string &ca_cert_file_path,
                                          const std::string &ca_cert_dir_path) {
   ca_cert_file_path_ = ca_cert_file_path;
@@ -7589,9 +8183,34 @@ inline void ClientImpl::set_ca_cert_store(X509_STORE *ca_cert_store) {
     ca_cert_store_ = ca_cert_store;
   }
 }
-#endif
 
-#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
+inline X509_STORE *ClientImpl::create_ca_cert_store(const char *ca_cert,
+                                                    std::size_t size) const {
+  auto mem = BIO_new_mem_buf(ca_cert, static_cast<int>(size));
+  if (!mem) { return nullptr; }
+
+  auto inf = PEM_X509_INFO_read_bio(mem, nullptr, nullptr, nullptr);
+  if (!inf) {
+    BIO_free_all(mem);
+    return nullptr;
+  }
+
+  auto cts = X509_STORE_new();
+  if (cts) {
+    for (auto i = 0; i < static_cast<int>(sk_X509_INFO_num(inf)); i++) {
+      auto itmp = sk_X509_INFO_value(inf, i);
+      if (!itmp) { continue; }
+
+      if (itmp->x509) { X509_STORE_add_cert(cts, itmp->x509); }
+      if (itmp->crl) { X509_STORE_add_crl(cts, itmp->crl); }
+    }
+  }
+
+  sk_X509_INFO_pop_free(inf, X509_INFO_free);
+  BIO_free_all(mem);
+  return cts;
+}
+
 inline void ClientImpl::enable_server_certificate_verification(bool enabled) {
   server_certificate_verification_ = enabled;
 }
@@ -7655,7 +8274,7 @@ bool ssl_connect_or_accept_nonblocking(socket_t sock, SSL *ssl,
                                        U ssl_connect_or_accept,
                                        time_t timeout_sec,
                                        time_t timeout_usec) {
-  int res = 0;
+  auto res = 0;
   while ((res = ssl_connect_or_accept(ssl)) != 1) {
     auto err = SSL_get_error(ssl, res);
     switch (err) {
@@ -7718,7 +8337,7 @@ inline SSLSocketStream::SSLSocketStream(socket_t sock, SSL *ssl,
   SSL_clear_mode(ssl, SSL_MODE_AUTO_RETRY);
 }
 
-inline SSLSocketStream::~SSLSocketStream() {}
+inline SSLSocketStream::~SSLSocketStream() = default;
 
 inline bool SSLSocketStream::is_readable() const {
   return detail::select_read(sock_, read_timeout_sec_, read_timeout_usec_) > 0;
@@ -7736,7 +8355,7 @@ inline ssize_t SSLSocketStream::read(char *ptr, size_t size) {
     auto ret = SSL_read(ssl_, ptr, static_cast<int>(size));
     if (ret < 0) {
       auto err = SSL_get_error(ssl_, ret);
-      int n = 1000;
+      auto n = 1000;
 #ifdef _WIN32
       while (--n >= 0 && (err == SSL_ERROR_WANT_READ ||
                           (err == SSL_ERROR_SYSCALL &&
@@ -7769,7 +8388,7 @@ inline ssize_t SSLSocketStream::write(const char *ptr, size_t size) {
     auto ret = SSL_write(ssl_, ptr, static_cast<int>(handle_size));
     if (ret < 0) {
       auto err = SSL_get_error(ssl_, ret);
-      int n = 1000;
+      auto n = 1000;
 #ifdef _WIN32
       while (--n >= 0 && (err == SSL_ERROR_WANT_WRITE ||
                           (err == SSL_ERROR_SYSCALL &&
@@ -7822,10 +8441,10 @@ inline SSLServer::SSLServer(const char *cert_path, const char *private_key_path,
 
     SSL_CTX_set_min_proto_version(ctx_, TLS1_1_VERSION);
 
-    // add default password callback before opening encrypted private key
     if (private_key_password != nullptr && (private_key_password[0] != '\0')) {
-      SSL_CTX_set_default_passwd_cb_userdata(ctx_,
-                                             (char *)private_key_password);
+      SSL_CTX_set_default_passwd_cb_userdata(
+          ctx_,
+          reinterpret_cast<void *>(const_cast<char *>(private_key_password)));
     }
 
     if (SSL_CTX_use_certificate_chain_file(ctx_, cert_path) != 1 ||
@@ -7927,16 +8546,23 @@ inline SSLClient::SSLClient(const std::string &host, int port)
 
 inline SSLClient::SSLClient(const std::string &host, int port,
                             const std::string &client_cert_path,
-                            const std::string &client_key_path)
+                            const std::string &client_key_path,
+                            const std::string &private_key_password)
     : ClientImpl(host, port, client_cert_path, client_key_path) {
   ctx_ = SSL_CTX_new(TLS_client_method());
 
   detail::split(&host_[0], &host_[host_.size()], '.',
                 [&](const char *b, const char *e) {
-                  host_components_.emplace_back(std::string(b, e));
+                  host_components_.emplace_back(b, e);
                 });
 
   if (!client_cert_path.empty() && !client_key_path.empty()) {
+    if (!private_key_password.empty()) {
+      SSL_CTX_set_default_passwd_cb_userdata(
+          ctx_, reinterpret_cast<void *>(
+                    const_cast<char *>(private_key_password.c_str())));
+    }
+
     if (SSL_CTX_use_certificate_file(ctx_, client_cert_path.c_str(),
                                      SSL_FILETYPE_PEM) != 1 ||
         SSL_CTX_use_PrivateKey_file(ctx_, client_key_path.c_str(),
@@ -7948,16 +8574,23 @@ inline SSLClient::SSLClient(const std::string &host, int port,
 }
 
 inline SSLClient::SSLClient(const std::string &host, int port,
-                            X509 *client_cert, EVP_PKEY *client_key)
+                            X509 *client_cert, EVP_PKEY *client_key,
+                            const std::string &private_key_password)
     : ClientImpl(host, port) {
   ctx_ = SSL_CTX_new(TLS_client_method());
 
   detail::split(&host_[0], &host_[host_.size()], '.',
                 [&](const char *b, const char *e) {
-                  host_components_.emplace_back(std::string(b, e));
+                  host_components_.emplace_back(b, e);
                 });
 
   if (client_cert != nullptr && client_key != nullptr) {
+    if (!private_key_password.empty()) {
+      SSL_CTX_set_default_passwd_cb_userdata(
+          ctx_, reinterpret_cast<void *>(
+                    const_cast<char *>(private_key_password.c_str())));
+    }
+
     if (SSL_CTX_use_certificate(ctx_, client_cert) != 1 ||
         SSL_CTX_use_PrivateKey(ctx_, client_key) != 1) {
       SSL_CTX_free(ctx_);
@@ -7989,6 +8622,11 @@ inline void SSLClient::set_ca_cert_store(X509_STORE *ca_cert_store) {
   }
 }
 
+inline void SSLClient::load_ca_cert_store(const char *ca_cert,
+                                          std::size_t size) {
+  set_ca_cert_store(ClientImpl::create_ca_cert_store(ca_cert, size));
+}
+
 inline long SSLClient::get_openssl_verify_result() const {
   return verify_result_;
 }
@@ -8003,14 +8641,14 @@ inline bool SSLClient::create_and_connect_socket(Socket &socket, Error &error) {
 inline bool SSLClient::connect_with_proxy(Socket &socket, Response &res,
                                           bool &success, Error &error) {
   success = true;
-  Response res2;
+  Response proxy_res;
   if (!detail::process_client_socket(
           socket.sock, read_timeout_sec_, read_timeout_usec_,
           write_timeout_sec_, write_timeout_usec_, [&](Stream &strm) {
             Request req2;
             req2.method = "CONNECT";
             req2.path = host_and_port_;
-            return process_request(strm, req2, res2, false, error);
+            return process_request(strm, req2, proxy_res, false, error);
           })) {
     // Thread-safe to close everything because we are assuming there are no
     // requests in flight
@@ -8021,12 +8659,12 @@ inline bool SSLClient::connect_with_proxy(Socket &socket, Response &res,
     return false;
   }
 
-  if (res2.status == 407) {
+  if (proxy_res.status == StatusCode::ProxyAuthenticationRequired_407) {
     if (!proxy_digest_auth_username_.empty() &&
         !proxy_digest_auth_password_.empty()) {
       std::map<std::string, std::string> auth;
-      if (detail::parse_www_authenticate(res2, auth, true)) {
-        Response res3;
+      if (detail::parse_www_authenticate(proxy_res, auth, true)) {
+        proxy_res = Response();
         if (!detail::process_client_socket(
                 socket.sock, read_timeout_sec_, read_timeout_usec_,
                 write_timeout_sec_, write_timeout_usec_, [&](Stream &strm) {
@@ -8037,7 +8675,7 @@ inline bool SSLClient::connect_with_proxy(Socket &socket, Response &res,
                       req3, auth, 1, detail::random_string(10),
                       proxy_digest_auth_username_, proxy_digest_auth_password_,
                       true));
-                  return process_request(strm, req3, res3, false, error);
+                  return process_request(strm, req3, proxy_res, false, error);
                 })) {
           // Thread-safe to close everything because we are assuming there are
           // no requests in flight
@@ -8048,17 +8686,28 @@ inline bool SSLClient::connect_with_proxy(Socket &socket, Response &res,
           return false;
         }
       }
-    } else {
-      res = res2;
-      return false;
     }
   }
 
+  // If status code is not 200, proxy request is failed.
+  // Set error to ProxyConnection and return proxy response
+  // as the response of the request
+  if (proxy_res.status != StatusCode::OK_200) {
+    error = Error::ProxyConnection;
+    res = std::move(proxy_res);
+    // Thread-safe to close everything because we are assuming there are
+    // no requests in flight
+    shutdown_ssl(socket, true);
+    shutdown_socket(socket);
+    close_socket(socket);
+    return false;
+  }
+
   return true;
 }
 
 inline bool SSLClient::load_certs() {
-  bool ret = true;
+  auto ret = true;
 
   std::call_once(initialize_cert_, [&]() {
     std::lock_guard<std::mutex> guard(ctx_mutex_);
@@ -8134,7 +8783,11 @@ inline bool SSLClient::initialize_ssl(Socket &socket, Error &error) {
         return true;
       },
       [&](SSL *ssl2) {
-        SSL_set_tlsext_host_name(ssl2, host_.c_str());
+        // NOTE: Direct call instead of using the OpenSSL macro to suppress
+        // -Wold-style-cast warning
+        // SSL_set_tlsext_host_name(ssl2, host_.c_str());
+        SSL_ctrl(ssl2, SSL_CTRL_SET_TLSEXT_HOSTNAME, TLSEXT_NAMETYPE_host_name,
+                 static_cast<void *>(const_cast<char *>(host_.c_str())));
         return true;
       });
 
@@ -8208,8 +8861,8 @@ SSLClient::verify_host_with_subject_alt_name(X509 *server_cert) const {
 
   auto type = GEN_DNS;
 
-  struct in6_addr addr6;
-  struct in_addr addr;
+  struct in6_addr addr6 {};
+  struct in_addr addr {};
   size_t addr_len = 0;
 
 #ifndef __MINGW32__
@@ -8234,8 +8887,9 @@ SSLClient::verify_host_with_subject_alt_name(X509 *server_cert) const {
     for (decltype(count) i = 0; i < count && !dsn_matched; i++) {
       auto val = sk_GENERAL_NAME_value(alt_names, i);
       if (val->type == type) {
-        auto name = (const char *)ASN1_STRING_get0_data(val->d.ia5);
-        auto name_len = (size_t)ASN1_STRING_length(val->d.ia5);
+        auto name =
+            reinterpret_cast<const char *>(ASN1_STRING_get0_data(val->d.ia5));
+        auto name_len = static_cast<size_t>(ASN1_STRING_length(val->d.ia5));
 
         switch (type) {
         case GEN_DNS: dsn_matched = check_host_name(name, name_len); break;
@@ -8253,7 +8907,8 @@ SSLClient::verify_host_with_subject_alt_name(X509 *server_cert) const {
     if (dsn_matched || ip_matched) { ret = true; }
   }
 
-  GENERAL_NAMES_free((STACK_OF(GENERAL_NAME) *)alt_names);
+  GENERAL_NAMES_free(const_cast<STACK_OF(GENERAL_NAME) *>(
+      reinterpret_cast<const STACK_OF(GENERAL_NAME) *>(alt_names)));
   return ret;
 }
 
@@ -8282,7 +8937,7 @@ inline bool SSLClient::check_host_name(const char *pattern,
   std::vector<std::string> pattern_components;
   detail::split(&pattern[0], &pattern[pattern_len], '.',
                 [&](const char *b, const char *e) {
-                  pattern_components.emplace_back(std::string(b, e));
+                  pattern_components.emplace_back(b, e);
                 });
 
   if (host_components_.size() != pattern_components.size()) { return false; }
@@ -8361,7 +9016,7 @@ inline Client::Client(const std::string &host, int port,
     : cli_(detail::make_unique<ClientImpl>(host, port, client_cert_path,
                                            client_key_path)) {}
 
-inline Client::~Client() {}
+inline Client::~Client() = default;
 
 inline bool Client::is_valid() const {
   return cli_ != nullptr && cli_->is_valid();
@@ -8421,19 +9076,20 @@ inline Result Client::Get(const std::string &path, const Headers &headers,
 }
 inline Result Client::Get(const std::string &path, const Params &params,
                           const Headers &headers, Progress progress) {
-  return cli_->Get(path, params, headers, progress);
+  return cli_->Get(path, params, headers, std::move(progress));
 }
 inline Result Client::Get(const std::string &path, const Params &params,
                           const Headers &headers,
                           ContentReceiver content_receiver, Progress progress) {
-  return cli_->Get(path, params, headers, content_receiver, progress);
+  return cli_->Get(path, params, headers, std::move(content_receiver),
+                   std::move(progress));
 }
 inline Result Client::Get(const std::string &path, const Params &params,
                           const Headers &headers,
                           ResponseHandler response_handler,
                           ContentReceiver content_receiver, Progress progress) {
-  return cli_->Get(path, params, headers, response_handler, content_receiver,
-                   progress);
+  return cli_->Get(path, params, headers, std::move(response_handler),
+                   std::move(content_receiver), std::move(progress));
 }
 
 inline Result Client::Head(const std::string &path) { return cli_->Head(path); }
@@ -8665,12 +9321,16 @@ inline bool Client::send(Request &req, Response &res, Error &error) {
 
 inline Result Client::send(const Request &req) { return cli_->send(req); }
 
+inline void Client::stop() { cli_->stop(); }
+
+inline std::string Client::host() const { return cli_->host(); }
+
+inline int Client::port() const { return cli_->port(); }
+
 inline size_t Client::is_socket_open() const { return cli_->is_socket_open(); }
 
 inline socket_t Client::socket() const { return cli_->socket(); }
 
-inline void Client::stop() { cli_->stop(); }
-
 inline void
 Client::set_hostname_addr_map(std::map<std::string, std::string> addr_map) {
   cli_->set_hostname_addr_map(std::move(addr_map));
@@ -8680,6 +9340,11 @@ inline void Client::set_default_headers(Headers headers) {
   cli_->set_default_headers(std::move(headers));
 }
 
+inline void Client::set_header_writer(
+    std::function<ssize_t(Stream &, Headers &)> const &writer) {
+  cli_->set_header_writer(writer);
+}
+
 inline void Client::set_address_family(int family) {
   cli_->set_address_family(family);
 }
@@ -8754,7 +9419,9 @@ inline void Client::enable_server_certificate_verification(bool enabled) {
 }
 #endif
 
-inline void Client::set_logger(Logger logger) { cli_->set_logger(logger); }
+inline void Client::set_logger(Logger logger) {
+  cli_->set_logger(std::move(logger));
+}
 
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
 inline void Client::set_ca_cert_path(const std::string &ca_cert_file_path,
@@ -8770,6 +9437,10 @@ inline void Client::set_ca_cert_store(X509_STORE *ca_cert_store) {
   }
 }
 
+inline void Client::load_ca_cert_store(const char *ca_cert, std::size_t size) {
+  set_ca_cert_store(cli_->create_ca_cert_store(ca_cert, size));
+}
+
 inline long Client::get_openssl_verify_result() const {
   if (is_ssl_) {
     return static_cast<SSLClient &>(*cli_).get_openssl_verify_result();
diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp
deleted file mode 100644
index 20551520e..000000000
--- a/examples/server/index.html.hpp
+++ /dev/null
@@ -1,2791 +0,0 @@
-unsigned char index_html[] = {
-  0x3c, 0x68, 0x74, 0x6d, 0x6c, 0x3e, 0x0a, 0x0a, 0x3c, 0x68, 0x65, 0x61,
-  0x64, 0x3e, 0x0a, 0x20, 0x20, 0x3c, 0x6d, 0x65, 0x74, 0x61, 0x20, 0x63,
-  0x68, 0x61, 0x72, 0x73, 0x65, 0x74, 0x3d, 0x22, 0x55, 0x54, 0x46, 0x2d,
-  0x38, 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x3c, 0x6d, 0x65, 0x74, 0x61, 0x20,
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-unsigned int index_html_len = 33456;
diff --git a/examples/server/index.js.hpp b/examples/server/index.js.hpp
deleted file mode 100644
index e09b3c8c5..000000000
--- a/examples/server/index.js.hpp
+++ /dev/null
@@ -1,1903 +0,0 @@
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-unsigned int index_js_len = 22800;
diff --git a/examples/server/json-schema-to-grammar.mjs.hpp b/examples/server/json-schema-to-grammar.mjs.hpp
deleted file mode 100644
index 0a05c369d..000000000
--- a/examples/server/json-schema-to-grammar.mjs.hpp
+++ /dev/null
@@ -1,311 +0,0 @@
-unsigned char json_schema_to_grammar_mjs[] = {
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-};
-unsigned int json_schema_to_grammar_mjs_len = 3695;
diff --git a/examples/server/oai.hpp b/examples/server/oai.hpp
deleted file mode 100644
index 2eca8a9fb..000000000
--- a/examples/server/oai.hpp
+++ /dev/null
@@ -1,227 +0,0 @@
-#pragma once
-
-#include <string>
-#include <vector>
-#include <set>
-#include <mutex>
-#include <condition_variable>
-#include <unordered_map>
-
-#include "json.hpp"
-#include "utils.hpp"
-
-#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
-
-using json = nlohmann::json;
-
-inline static json oaicompat_completion_params_parse(
-    const json &body, /* openai api json semantics */
-    const std::string &chat_template)
-{
-    json llama_params;
-    std::string formatted_prompt = chat_template == "chatml"
-        ? format_chatml(body["messages"])  // OpenAI 'messages' to chatml (with <|im_start|>,...)
-        : format_llama2(body["messages"]); // OpenAI 'messages' to llama2 (with [INST],...)
-
-    llama_params["__oaicompat"] = true;
-
-    // Map OpenAI parameters to llama.cpp parameters
-    //
-    // For parameters that are defined by the OpenAI documentation (e.g.
-    // temperature), we explicitly specify OpenAI's intended default; we
-    // need to do that because sometimes OpenAI disagrees with llama.cpp
-    //
-    // https://platform.openai.com/docs/api-reference/chat/create
-    llama_sampling_params default_sparams;
-    llama_params["model"]             = json_value(body, "model", std::string("unknown"));
-    llama_params["prompt"]            = formatted_prompt;
-    llama_params["cache_prompt"]      = json_value(body, "cache_prompt", false);
-    llama_params["temperature"]       = json_value(body, "temperature", 0.0);
-    llama_params["top_k"]             = json_value(body, "top_k", default_sparams.top_k);
-    llama_params["top_p"]             = json_value(body, "top_p", 1.0);
-    llama_params["n_predict"]         = json_value(body, "max_tokens", -1);
-    llama_params["logit_bias"]        = json_value(body, "logit_bias",json::object());
-    llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0);
-    llama_params["presence_penalty"]  = json_value(body, "presence_penalty", 0.0);
-    llama_params["seed"]              = json_value(body, "seed", LLAMA_DEFAULT_SEED);
-    llama_params["stream"]            = json_value(body, "stream", false);
-    llama_params["mirostat"]          = json_value(body, "mirostat", default_sparams.mirostat);
-    llama_params["mirostat_tau"]      = json_value(body, "mirostat_tau", default_sparams.mirostat_tau);
-    llama_params["mirostat_eta"]      = json_value(body, "mirostat_eta", default_sparams.mirostat_eta);
-    llama_params["penalize_nl"]       = json_value(body, "penalize_nl", default_sparams.penalize_nl);
-    llama_params["typical_p"]         = json_value(body, "typical_p", default_sparams.typical_p);
-    llama_params["repeat_last_n"]     = json_value(body, "repeat_last_n", default_sparams.penalty_last_n);
-    llama_params["ignore_eos"]        = json_value(body, "ignore_eos", false);
-    llama_params["tfs_z"]             = json_value(body, "tfs_z", default_sparams.tfs_z);
-
-    if (body.count("grammar") != 0) {
-        llama_params["grammar"] = json_value(body, "grammar", json::object());
-    }
-
-    // Handle 'stop' field
-    if (body.contains("stop") && body["stop"].is_string()) {
-        llama_params["stop"] = json::array({body["stop"].get<std::string>()});
-    } else {
-        llama_params["stop"] = json_value(body, "stop", json::array());
-    }
-
-    // Ensure there is ChatML-specific end sequence among stop words
-    llama_params["stop"].push_back("<|im_end|>");
-
-    return llama_params;
-}
-
-inline static json format_final_response_oaicompat(const json &request, const task_result &response, bool streaming = false)
-{
-    json result = response.result_json;
-
-    bool stopped_word        = result.count("stopped_word") != 0;
-    bool stopped_eos         = json_value(result, "stopped_eos", false);
-    int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
-    int num_prompt_tokens    = json_value(result, "tokens_evaluated", 0);
-    std::string content      = json_value(result, "content", std::string(""));
-
-    std::string finish_reason = "length";
-    if (stopped_word || stopped_eos) {
-        finish_reason = "stop";
-    }
-
-    json choices =
-        streaming ? json::array({json{{"finish_reason", finish_reason},
-                                        {"index", 0},
-                                        {"delta", json::object()}}})
-                  : json::array({json{{"finish_reason", finish_reason},
-                                        {"index", 0},
-                                        {"message", json{{"content", content},
-                                                         {"role", "assistant"}}}}});
-
-    std::time_t t = std::time(0);
-
-    json res =
-        json{{"choices", choices},
-            {"created", t},
-            {"model",
-                json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
-            {"object", streaming ? "chat.completion.chunk" : "chat.completion"},
-            {"usage",
-                json{{"completion_tokens", num_tokens_predicted},
-                     {"prompt_tokens",     num_prompt_tokens},
-                     {"total_tokens",      num_tokens_predicted + num_prompt_tokens}}},
-            {"id", gen_chatcmplid()}};
-
-    if (server_verbose) {
-        res["__verbose"] = result;
-    }
-
-    if (result.contains("completion_probabilities")) {
-        res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array());
-    }
-
-    return res;
-}
-
-// return value is vector as there is one case where we might need to generate two responses
-inline static std::vector<json> format_partial_response_oaicompat(const task_result &response) {
-    json result = response.result_json;
-
-    if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
-        return std::vector<json>({response.result_json});
-    }
-
-    bool first = json_value(result, "oaicompat_token_ctr", 0) == 0;
-    std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
-
-    bool stopped_word   = json_value(result, "stopped_word", false);
-    bool stopped_eos    = json_value(result, "stopped_eos", false);
-    bool stopped_limit  = json_value(result, "stopped_limit", false);
-    std::string content = json_value(result, "content", std::string(""));
-
-    std::string finish_reason;
-    if (stopped_word || stopped_eos) {
-        finish_reason = "stop";
-    }
-    if (stopped_limit) {
-        finish_reason = "length";
-    }
-
-    std::time_t t = std::time(0);
-
-    json choices;
-
-    if (!finish_reason.empty()) {
-        choices = json::array({json{{"finish_reason", finish_reason},
-                                    {"index", 0},
-                                    {"delta", json::object()}}});
-    } else {
-        if (first) {
-            if (content.empty()) {
-                choices = json::array({json{{"finish_reason", nullptr},
-                                            {"index", 0},
-                                            {"delta", json{{"role", "assistant"}}}}});
-            } else {
-                // We have to send this as two updates to conform to openai behavior
-                json initial_ret = json{{"choices", json::array({json{
-                                        {"finish_reason", nullptr},
-                                        {"index", 0},
-                                        {"delta", json{
-                                            {"role", "assistant"}
-                                        }}}})},
-                            {"created", t},
-                            {"id", gen_chatcmplid()},
-                            {"model", modelname},
-                            {"object", "chat.completion.chunk"}};
-
-                json second_ret = json{
-                            {"choices", json::array({json{{"finish_reason", nullptr},
-                                                            {"index", 0},
-                                                            {"delta", json{
-                                                            {"content", content}}}
-                                                            }})},
-                            {"created", t},
-                            {"id", gen_chatcmplid()},
-                            {"model", modelname},
-                            {"object", "chat.completion.chunk"}};
-
-                return std::vector<json>({initial_ret, second_ret});
-            }
-        } else {
-            // Some idiosyncrasy in task processing logic makes several trailing calls
-            // with empty content, we ignore these at the calee site.
-            if (content.empty()) {
-                return std::vector<json>({json::object()});
-            }
-
-            choices = json::array({json{
-                {"finish_reason", nullptr},
-                {"index", 0},
-                {"delta",
-                json{
-                    {"content", content},
-                }},
-            }});
-        }
-    }
-
-    json ret = json{{"choices", choices},
-                    {"created", t},
-                    {"id", gen_chatcmplid()},
-                    {"model", modelname},
-                    {"object", "chat.completion.chunk"}};
-
-    return std::vector<json>({ret});
-}
-
-inline static json format_embeddings_response_oaicompat(const json &request, const json &embeddings)
-{
-    json res =
-        json{
-            {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
-            {"object", "list"},
-            {"usage",
-                json{{"prompt_tokens", 0},
-                     {"total_tokens", 0}}},
-            {"data", embeddings}
-        };
-    return res;
-}
-
diff --git a/examples/server/public/completion.js b/examples/server/public/completion.js
index ab38a7b40..987b9a3b8 100644
--- a/examples/server/public/completion.js
+++ b/examples/server/public/completion.js
@@ -21,6 +21,7 @@ let generation_settings = null;
 //
 export async function* llama(prompt, params = {}, config = {}) {
   let controller = config.controller;
+  const api_url = config.api_url || "";
 
   if (!controller) {
     controller = new AbortController();
@@ -28,7 +29,7 @@ export async function* llama(prompt, params = {}, config = {}) {
 
   const completionParams = { ...paramDefaults, ...params, prompt };
 
-  const response = await fetch("/completion", {
+  const response = await fetch(`${api_url}/completion`, {
     method: 'POST',
     body: JSON.stringify(completionParams),
     headers: {
@@ -96,18 +97,18 @@ export async function* llama(prompt, params = {}, config = {}) {
             }
           }
           if (result.error) {
-            result.error = JSON.parse(result.error);
-            if (result.error.content.includes('slot unavailable')) {
-              // Throw an error to be caught by upstream callers
-              throw new Error('slot unavailable');
-            } else {
-              console.error(`llama.cpp error: ${result.error.content}`);
+            try {
+              result.error = JSON.parse(result.error);
+              if (result.error.message.includes('slot unavailable')) {
+                // Throw an error to be caught by upstream callers
+                throw new Error('slot unavailable');
+              } else {
+                console.error(`llama.cpp error [${result.error.code} - ${result.error.type}]: ${result.error.message}`);
+              }
+            } catch(e) {
+              console.error(`llama.cpp error ${result.error}`)
             }
           }
-          if (result.error) {
-            result.error = JSON.parse(result.error);
-            console.error(`llama.cpp error: ${result.error.content}`);
-          }
         }
       }
     }
@@ -193,9 +194,10 @@ export const llamaComplete = async (params, controller, callback) => {
 }
 
 // Get the model info from the server. This is useful for getting the context window and so on.
-export const llamaModelInfo = async () => {
+export const llamaModelInfo = async (config = {}) => {
   if (!generation_settings) {
-    const props = await fetch("/props").then(r => r.json());
+    const api_url = config.api_url || "";
+    const props = await fetch(`${api_url}/props`).then(r => r.json());
     generation_settings = props.default_generation_settings;
   }
   return generation_settings;
diff --git a/examples/server/public/favicon.ico b/examples/server/public/favicon.ico
new file mode 100644
index 000000000..89e154a0a
Binary files /dev/null and b/examples/server/public/favicon.ico differ
diff --git a/examples/server/public/index.html b/examples/server/public/index.html
index 84038ddce..2961999f2 100644
--- a/examples/server/public/index.html
+++ b/examples/server/public/index.html
@@ -199,10 +199,10 @@
   <script type="module">
     import {
       html, h, signal, effect, computed, render, useSignal, useEffect, useRef, Component
-    } from '/index.js';
+    } from './index.js';
 
-    import { llama } from '/completion.js';
-    import { SchemaConverter } from '/json-schema-to-grammar.mjs';
+    import { llama } from './completion.js';
+    import { SchemaConverter } from './json-schema-to-grammar.mjs';
     let selected_image = false;
     var slot_id = -1;
 
@@ -222,6 +222,7 @@
       temperature: 0.7,
       repeat_last_n: 256, // 0 = disable penalty, -1 = context size
       repeat_penalty: 1.18, // 1.0 = disabled
+      penalize_nl: false,
       top_k: 40, // <= 0 to use vocab size
       top_p: 0.95, // 1.0 = disabled
       min_p: 0.05, // 0 = disabled
@@ -405,7 +406,7 @@
         throw new Error("already running");
       }
       controller.value = new AbortController();
-      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) {
+      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: location.pathname.replace(/\/+$/, '') })) {
         const data = chunk.data;
 
         if (data.stop) {
@@ -627,17 +628,20 @@
       const updateParams = (el) => params.value = { ...params.value, [el.target.name]: el.target.value }
       const updateParamsFloat = (el) => params.value = { ...params.value, [el.target.name]: parseFloat(el.target.value) }
       const updateParamsInt = (el) => params.value = { ...params.value, [el.target.name]: Math.floor(parseFloat(el.target.value)) }
+      const updateParamsBool = (el) => params.value = { ...params.value, [el.target.name]: el.target.checked }
 
       const grammarJsonSchemaPropOrder = signal('')
       const updateGrammarJsonSchemaPropOrder = (el) => grammarJsonSchemaPropOrder.value = el.target.value
-      const convertJSONSchemaGrammar = () => {
+      const convertJSONSchemaGrammar = async () => {
         try {
-          const schema = JSON.parse(params.value.grammar)
-          const converter = new SchemaConverter(
-            grammarJsonSchemaPropOrder.value
+          let schema = JSON.parse(params.value.grammar)
+          const converter = new SchemaConverter({
+            prop_order: grammarJsonSchemaPropOrder.value
               .split(',')
-              .reduce((acc, cur, i) => ({ ...acc, [cur.trim()]: i }), {})
-          )
+              .reduce((acc, cur, i) => ({ ...acc, [cur.trim()]: i }), {}),
+            allow_fetch: true,
+          })
+          schema = await converter.resolveRefs(schema, 'input')
           converter.visit(schema, '')
           params.value = {
             ...params.value,
@@ -668,6 +672,15 @@
         `
       };
 
+      const BoolField = ({ label, name, value }) => {
+        return html`
+          <div>
+            <label for="${name}">${label}</label>
+            <input type="checkbox" id="${name}" name="${name}" checked="${value}" onclick=${updateParamsBool} />
+          </div>
+        `
+      };
+
       const userTemplateReset = (e) => {
         e.preventDefault();
         userTemplateResetToDefaultAndApply()
@@ -767,6 +780,7 @@
             ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
             ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
             ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
+            ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
             ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
             ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
             ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}
@@ -867,11 +881,11 @@
         .replace(/&/g, '&amp;')
         .replace(/</g, '&lt;')
         .replace(/>/g, '&gt;')
-        .replace(/^#{1,6} (.*)$/gim, '<h3>$1</h3>')
-        .replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>')
-        .replace(/__(.*?)__/g, '<strong>$1</strong>')
-        .replace(/\*(.*?)\*/g, '<em>$1</em>')
-        .replace(/_(.*?)_/g, '<em>$1</em>')
+        .replace(/(^|\n)#{1,6} ([^\n]*)(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1<h3>$2</h3>')
+        .replace(/\*\*(.*?)\*\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
+        .replace(/__(.*?)__(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
+        .replace(/\*(.*?)\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
+        .replace(/_(.*?)_(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
         .replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
         .replace(/`(.*?)`/g, '<code>$1</code>')
         .replace(/\n/gim, '<br />');
@@ -1001,6 +1015,10 @@
     }
 
     function App(props) {
+      useEffect(() => {
+        const query = new URLSearchParams(location.search).get("q");
+        if (query) chat(query);
+      }, []);
 
       return html`
         <div class="mode-${session.value.type}">
diff --git a/examples/server/public/index.js b/examples/server/public/index.js
index 9db5a9d9f..695aec256 100644
--- a/examples/server/public/index.js
+++ b/examples/server/public/index.js
@@ -1 +1 @@
-function t(){throw new Error("Cycle detected")}function n(){if(u>1){u--;return}let t,n=!1;while(void 0!==_){let i=_;_=void 0;f++;while(void 0!==i){const _=i.o;i.o=void 0;i.f&=-3;if(!(8&i.f)&&a(i))try{i.c()}catch(e){if(!n){t=e;n=!0}}i=_}}f=0;u--;if(n)throw t}function e(t){if(u>0)return t();u++;try{return t()}finally{n()}}let i,_,o=0;function r(t){if(o>0)return t();const n=i;i=void 0;o++;try{return t()}finally{o--;i=n}}let u=0,f=0,l=0;function s(t){if(void 0===i)return;let n=t.n;if(void 0===n||n.t!==i){n={i:0,S:t,p:i.s,n:void 0,t:i,e:void 0,x:void 0,r:n};if(void 0!==i.s)i.s.n=n;i.s=n;t.n=n;if(32&i.f)t.S(n);return n}else if(-1===n.i){n.i=0;if(void 0!==n.n){n.n.p=n.p;if(void 0!==n.p)n.p.n=n.n;n.p=i.s;n.n=void 0;i.s.n=n;i.s=n}return n}}function c(t){this.v=t;this.i=0;this.n=void 0;this.t=void 0}c.prototype.h=function(){return!0};c.prototype.S=function(t){if(this.t!==t&&void 0===t.e){t.x=this.t;if(void 0!==this.t)this.t.e=t;this.t=t}};c.prototype.U=function(t){if(void 0!==this.t){const n=t.e,e=t.x;if(void 0!==n){n.x=e;t.e=void 0}if(void 0!==e){e.e=n;t.x=void 0}if(t===this.t)this.t=e}};c.prototype.subscribe=function(t){const n=this;return S((function(){const e=n.value,i=32&this.f;this.f&=-33;try{t(e)}finally{this.f|=i}}))};c.prototype.valueOf=function(){return this.value};c.prototype.toString=function(){return this.value+""};c.prototype.toJSON=function(){return this.value};c.prototype.peek=function(){return this.v};Object.defineProperty(c.prototype,"value",{get(){const t=s(this);if(void 0!==t)t.i=this.i;return this.v},set(e){if(i instanceof v)!function(){throw new Error("Computed cannot have side-effects")}();if(e!==this.v){if(f>100)t();this.v=e;this.i++;l++;u++;try{for(let t=this.t;void 0!==t;t=t.x)t.t.N()}finally{n()}}}});function h(t){return new c(t)}function a(t){for(let n=t.s;void 0!==n;n=n.n)if(n.S.i!==n.i||!n.S.h()||n.S.i!==n.i)return!0;return!1}function p(t){for(let n=t.s;void 0!==n;n=n.n){const e=n.S.n;if(void 0!==e)n.r=e;n.S.n=n;n.i=-1;if(void 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diff --git a/examples/server/public/json-schema-to-grammar.mjs b/examples/server/public/json-schema-to-grammar.mjs
index 3f1b255c2..8e0be1b40 100644
--- a/examples/server/public/json-schema-to-grammar.mjs
+++ b/examples/server/public/json-schema-to-grammar.mjs
@@ -1,112 +1,594 @@
+// WARNING: This file was ported from json_schema_to_grammar.py, please fix bugs / add features there first.
 const SPACE_RULE = '" "?';
 
+function _buildRepetition(itemRule, minItems, maxItems, opts={}) {
+  const separatorRule = opts.separatorRule ?? '';
+  const itemRuleIsLiteral = opts.itemRuleIsLiteral ?? false
+
+  if (separatorRule === '') {
+    if (minItems === 0 && maxItems === 1) {
+      return `${itemRule}?`;
+    } else if (minItems === 1 && maxItems === undefined) {
+      return `${itemRule}+`;
+    }
+  }
+
+  let result = '';
+  if (minItems > 0) {
+    if (itemRuleIsLiteral && separatorRule === '') {
+      result = `"${itemRule.slice(1, -1).repeat(minItems)}"`;
+    } else {
+      result = Array.from({ length: minItems }, () => itemRule)
+        .join(separatorRule !== '' ? ` ${separatorRule} ` : ' ');
+    }
+  }
+
+  const optRepetitions = (upToN, prefixWithSep=false) => {
+    const content = separatorRule !== '' && prefixWithSep ? `${separatorRule} ${itemRule}` : itemRule;
+    if (upToN === 0) {
+      return '';
+    } else if (upToN === 1) {
+      return `(${content})?`;
+    } else if (separatorRule !== '' && !prefixWithSep) {
+      return `(${content} ${optRepetitions(upToN - 1, true)})?`;
+    } else {
+      return Array.from({ length: upToN }, () => `(${content}`).join(' ').trim() + Array.from({ length: upToN }, () => ')?').join('');
+    }
+  };
+
+  if (minItems > 0 && maxItems !== minItems) {
+    result += ' ';
+  }
+
+  if (maxItems !== undefined) {
+    result += optRepetitions(maxItems - minItems, minItems > 0);
+  } else {
+    const itemOperator = `(${separatorRule !== '' ? separatorRule + ' ' : ''}${itemRule})`;
+
+    if (minItems === 0 && separatorRule !== '') {
+      result = `(${itemRule} ${itemOperator}*)?`;
+    } else {
+      result += `${itemOperator}*`;
+    }
+  }
+
+  return result;
+}
+
+class BuiltinRule {
+  constructor(content, deps) {
+    this.content = content;
+    this.deps = deps || [];
+  }
+}
+
+const UP_TO_15_DIGITS = _buildRepetition('[0-9]', 0, 15);
+
 const PRIMITIVE_RULES = {
-  boolean: '("true" | "false") space',
-  number: '("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space',
-  integer: '("-"? ([0-9] | [1-9] [0-9]*)) space',
-  string: ` "\\"" (
-        [^"\\\\] |
-        "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
-      )* "\\"" space`,
-  null: '"null" space',
+  boolean        : new BuiltinRule('("true" | "false") space', []),
+  'decimal-part' : new BuiltinRule('[0-9] ' + UP_TO_15_DIGITS, []),
+  'integral-part': new BuiltinRule('[0-9] | [1-9] ' + UP_TO_15_DIGITS, []),
+  number         : new BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']),
+  integer        : new BuiltinRule('("-"? integral-part) space', ['integral-part']),
+  value          : new BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']),
+  object         : new BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']),
+  array          : new BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']),
+  uuid           : new BuiltinRule('"\\"" ' + [8, 4, 4, 4, 12].map(n => [...new Array(n)].map(_ => '[0-9a-fA-F]').join('')).join(' "-" ') + ' "\\"" space', []),
+  char           : new BuiltinRule(`[^"\\\\] | "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])`, []),
+  string         : new BuiltinRule(`"\\"" char* "\\"" space`, ['char']),
+  null           : new BuiltinRule('"null" space', []),
 };
 
+// TODO: support "uri", "email" string formats
+const STRING_FORMAT_RULES = {
+  'date'            : new BuiltinRule('[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []),
+  'time'            : new BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []),
+  'date-time'       : new BuiltinRule('date "T" time', ['date', 'time']),
+  'date-string'     : new BuiltinRule('"\\"" date "\\"" space', ['date']),
+  'time-string'     : new BuiltinRule('"\\"" time "\\"" space', ['time']),
+  'date-time-string': new BuiltinRule('"\\"" date-time "\\"" space', ['date-time']),
+}
+
+const RESERVED_NAMES = {'root': true, ...PRIMITIVE_RULES, ...STRING_FORMAT_RULES};
+
 const INVALID_RULE_CHARS_RE = /[^\dA-Za-z-]+/g;
 const GRAMMAR_LITERAL_ESCAPE_RE = /[\n\r"]/g;
-const GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"'};
+const GRAMMAR_RANGE_LITERAL_ESCAPE_RE = /[\n\r"\]\-\\]/g;
+const GRAMMAR_LITERAL_ESCAPES = { '\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']': '\\]' };
+
+const NON_LITERAL_SET = new Set('|.()[]{}*+?');
+const ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = new Set('[]()|{}*+?');
 
 export class SchemaConverter {
-  constructor(propOrder) {
-    this._propOrder = propOrder || {};
-    this._rules = new Map();
-    this._rules.set('space', SPACE_RULE);
+  constructor(options) {
+    this._propOrder = options.prop_order || {};
+    this._allowFetch = options.allow_fetch || false;
+    this._dotall = options.dotall || false;
+    this._rules = {'space': SPACE_RULE};
+    this._refs = {};
+    this._refsBeingResolved = new Set();
   }
 
   _formatLiteral(literal) {
-    const escaped = JSON.stringify(literal).replace(
+    const escaped = literal.replace(
       GRAMMAR_LITERAL_ESCAPE_RE,
       m => GRAMMAR_LITERAL_ESCAPES[m]
     );
     return `"${escaped}"`;
   }
 
+  _formatRangeChar(literal) {
+    return JSON.stringify(literal).slice(1, -1).replace(
+      GRAMMAR_RANGE_LITERAL_ESCAPE_RE,
+      m => GRAMMAR_LITERAL_ESCAPES[m]
+    );
+  }
+
   _addRule(name, rule) {
     let escName = name.replace(INVALID_RULE_CHARS_RE, '-');
     let key = escName;
 
-    if (this._rules.has(escName)) {
-      if (this._rules.get(escName) === rule) {
+    if (escName in this._rules) {
+      if (this._rules[escName] === rule) {
         return key;
       }
 
       let i = 0;
-      while (this._rules.has(`${escName}${i}`)) {
+      while ((`${escName}${i}` in this._rules) && (this._rules[`${escName}${i}`] !== rule)) {
         i += 1;
       }
       key = `${escName}${i}`;
     }
 
-    this._rules.set(key, rule);
+    this._rules[key] = rule;
     return key;
   }
 
+  async resolveRefs(schema, url) {
+    const visit = async (n) => {
+      if (Array.isArray(n)) {
+        return Promise.all(n.map(visit));
+      } else if (typeof n === 'object' && n !== null) {
+        let ref = n.$ref;
+        let target;
+        if (ref !== undefined && !this._refs[ref]) {
+          if (ref.startsWith('https://')) {
+            if (!this._allowFetch) {
+              throw new Error('Fetching remote schemas is not allowed (use --allow-fetch for force)');
+            }
+            const fetch = (await import('node-fetch')).default;
+
+            const fragSplit = ref.split('#');
+            const baseUrl = fragSplit[0];
+
+            target = this._refs[baseUrl];
+            if (!target) {
+              target = await this.resolveRefs(await fetch(ref).then(res => res.json()), baseUrl);
+              this._refs[baseUrl] = target;
+            }
+
+            if (fragSplit.length === 1 || fragSplit[fragSplit.length - 1] === '') {
+              return target;
+            }
+          } else if (ref.startsWith('#/')) {
+            target = schema;
+            ref = `${url}${ref}`;
+            n.$ref = ref;
+          } else {
+            throw new Error(`Unsupported ref ${ref}`);
+          }
+
+          const selectors = ref.split('#')[1].split('/').slice(1);
+          for (const sel of selectors) {
+            if (!target || !(sel in target)) {
+              throw new Error(`Error resolving ref ${ref}: ${sel} not in ${JSON.stringify(target)}`);
+            }
+            target = target[sel];
+          }
+
+          this._refs[ref] = target;
+        } else {
+          await Promise.all(Object.values(n).map(visit));
+        }
+      }
+
+      return n;
+    };
+
+    return visit(schema);
+  }
+
+  _generateUnionRule(name, altSchemas) {
+    return altSchemas
+      .map((altSchema, i) => this.visit(altSchema, `${name ?? ''}${name ? '-' : 'alternative-'}${i}`))
+      .join(' | ');
+  }
+
+  _visitPattern(pattern, name) {
+    if (!pattern.startsWith('^') || !pattern.endsWith('$')) {
+      throw new Error('Pattern must start with "^" and end with "$"');
+    }
+    pattern = pattern.slice(1, -1);
+    const subRuleIds = {};
+
+    let i = 0;
+    const length = pattern.length;
+
+    const getDot = () => {
+      let rule;
+      if (this._dotall) {
+        rule = '[\\U00000000-\\U0010FFFF]';
+      } else {
+        // Accept any character... except \n and \r line break chars (\x0A and \xOD)
+        rule = '[^\\x0A\\x0D]';
+      }
+      return this._addRule('dot', rule);
+    };
+
+
+    const toRule = ([s, isLiteral]) => isLiteral ? "\"" + s + "\"" : s;
+
+    const transform = () => {
+      const start = i;
+      // For each component of this sequence, store its string representation and whether it's a literal.
+      // We only need a flat structure here to apply repetition operators to the last item, and
+      // to merge literals at the and (we're parsing grouped ( sequences ) recursively and don't treat '|' specially
+      // (GBNF's syntax is luckily very close to regular expressions!)
+      const seq = [];
+
+      const joinSeq = () => {
+        const ret = [];
+        for (const [isLiteral, g] of groupBy(seq, x => x[1])) {
+          if (isLiteral) {
+            ret.push([[...g].map(x => x[0]).join(''), true]);
+          } else {
+            ret.push(...g);
+          }
+        }
+        if (ret.length === 1) {
+          return ret[0];
+        }
+        return [ret.map(x => toRule(x)).join(' '), false];
+      };
+
+      while (i < length) {
+        const c = pattern[i];
+        if (c === '.') {
+          seq.push([getDot(), false]);
+          i += 1;
+        } else if (c === '(') {
+          i += 1;
+          if (i < length) {
+            if (pattern[i] === '?') {
+              throw new Error(`Unsupported pattern syntax "${pattern[i]}" at index ${i} of /${pattern}/`);
+            }
+          }
+          seq.push([`(${toRule(transform())})`, false]);
+        } else if (c === ')') {
+          i += 1;
+          if (start <= 0 || pattern[start - 1] !== '(') {
+            throw new Error(`Unbalanced parentheses; start = ${start}, i = ${i}, pattern = ${pattern}`);
+          }
+          return joinSeq();
+        } else if (c === '[') {
+          let squareBrackets = c;
+          i += 1;
+          while (i < length && pattern[i] !== ']') {
+            if (pattern[i] === '\\') {
+              squareBrackets += pattern.slice(i, i + 2);
+              i += 2;
+            } else {
+              squareBrackets += pattern[i];
+              i += 1;
+            }
+          }
+          if (i >= length) {
+            throw new Error(`Unbalanced square brackets; start = ${start}, i = ${i}, pattern = ${pattern}`);
+          }
+          squareBrackets += ']';
+          i += 1;
+          seq.push([squareBrackets, false]);
+        } else if (c === '|') {
+          seq.push(['|', false]);
+          i += 1;
+        } else if (c === '*' || c === '+' || c === '?') {
+          seq[seq.length - 1] = [toRule(seq[seq.length - 1]) + c, false];
+          i += 1;
+        } else if (c === '{') {
+          let curlyBrackets = c;
+          i += 1;
+          while (i < length && pattern[i] !== '}') {
+            curlyBrackets += pattern[i];
+            i += 1;
+          }
+          if (i >= length) {
+            throw new Error(`Unbalanced curly brackets; start = ${start}, i = ${i}, pattern = ${pattern}`);
+          }
+          curlyBrackets += '}';
+          i += 1;
+          const nums = curlyBrackets.slice(1, -1).split(',').map(s => s.trim());
+          let minTimes, maxTimes;
+          if (nums.length === 1) {
+            minTimes = parseInt(nums[0], 10);
+            maxTimes = minTimes;
+          } else {
+            if (nums.length !== 2) {
+              throw new Error(`Invalid quantifier ${curlyBrackets}`);
+            }
+            minTimes = nums[0] ? parseInt(nums[0], 10) : 0;
+            maxTimes = nums[1] ? parseInt(nums[1], 10) : Infinity;
+          }
+
+          let [sub, subIsLiteral] = seq[seq.length - 1];
+
+          if (!subIsLiteral) {
+            let id = subRuleIds[sub];
+            if (id === undefined) {
+              id = this._addRule(`${name}-${Object.keys(subRuleIds).length + 1}`, sub);
+              subRuleIds[sub] = id;
+            }
+            sub = id;
+          }
+
+          seq[seq.length - 1] = [
+            _buildRepetition(subIsLiteral ? `"${sub}"` : sub, minTimes, maxTimes, {itemRuleIsLiteral: subIsLiteral}),
+            false
+          ];
+        } else {
+          let literal = '';
+          while (i < length) {
+            if (pattern[i] === '\\' && i < length - 1) {
+              const next = pattern[i + 1];
+              if (ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS.has(next)) {
+                i += 1;
+                literal += pattern[i];
+                i += 1;
+              } else {
+                literal += pattern.slice(i, i + 2);
+                i += 2;
+              }
+            } else if (pattern[i] === '"') {
+              literal += '\\"';
+              i += 1;
+            } else if (!NON_LITERAL_SET.has(pattern[i]) &&
+                (i === length - 1 || literal === '' || pattern[i + 1] === '.' || !NON_LITERAL_SET.has(pattern[i+1]))) {
+              literal += pattern[i];
+              i += 1;
+            } else {
+              break;
+            }
+          }
+          if (literal !== '') {
+            seq.push([literal, true]);
+          }
+        }
+      }
+
+      return joinSeq();
+    };
+
+    return this._addRule(name, "\"\\\"\" " + toRule(transform()) + " \"\\\"\" space")
+  }
+
+  _resolveRef(ref) {
+    let refName = ref.split('/').pop();
+    if (!(refName in this._rules) && !this._refsBeingResolved.has(ref)) {
+      this._refsBeingResolved.add(ref);
+      const resolved = this._refs[ref];
+      refName = this.visit(resolved, refName);
+      this._refsBeingResolved.delete(ref);
+    }
+    return refName;
+  }
+
+  _generateConstantRule(value) {
+    return this._formatLiteral(JSON.stringify(value));
+  }
+
   visit(schema, name) {
     const schemaType = schema.type;
-    const ruleName = name || 'root';
+    const schemaFormat = schema.format;
+    const ruleName = name in RESERVED_NAMES ? name + '-' : name == '' ? 'root' : name;
 
-    if (schema.oneOf || schema.anyOf) {
-      const rule = (schema.oneOf || schema.anyOf).map((altSchema, i) =>
-        this.visit(altSchema, `${name}${name ? "-" : ""}${i}`)
-      ).join(' | ');
-
-      return this._addRule(ruleName, rule);
+    const ref = schema.$ref;
+    if (ref !== undefined) {
+      return this._addRule(ruleName, this._resolveRef(ref));
+    } else if (schema.oneOf || schema.anyOf) {
+      return this._addRule(ruleName, this._generateUnionRule(name, schema.oneOf || schema.anyOf));
+    } else if (Array.isArray(schemaType)) {
+      return this._addRule(ruleName, this._generateUnionRule(name, schemaType.map(t => ({ type: t }))));
     } else if ('const' in schema) {
-      return this._addRule(ruleName, this._formatLiteral(schema.const));
+      return this._addRule(ruleName, this._generateConstantRule(schema.const));
     } else if ('enum' in schema) {
-      const rule = schema.enum.map(v => this._formatLiteral(v)).join(' | ');
+      const rule = schema.enum.map(v => this._generateConstantRule(v)).join(' | ');
       return this._addRule(ruleName, rule);
-    } else if (schemaType === 'object' && 'properties' in schema) {
-      // TODO: `required` keyword (from python implementation)
-      const propOrder = this._propOrder;
-      const propPairs = Object.entries(schema.properties).sort((a, b) => {
-        // sort by position in prop_order (if specified) then by key
-        const orderA = typeof propOrder[a[0]] === 'number' ? propOrder[a[0]] : Infinity;
-        const orderB = typeof propOrder[b[0]] === 'number' ? propOrder[b[0]] : Infinity;
-        return orderA - orderB || a[0].localeCompare(b[0]);
-      });
-
-      let rule = '"{" space';
-      propPairs.forEach(([propName, propSchema], i) => {
-        const propRuleName = this.visit(propSchema, `${name}${name ? "-" : ""}${propName}`);
-        if (i > 0) {
-          rule += ' "," space';
+    } else if ((schemaType === undefined || schemaType === 'object') &&
+               ('properties' in schema ||
+                ('additionalProperties' in schema && schema.additionalProperties !== true))) {
+      const required = new Set(schema.required || []);
+      const properties = Object.entries(schema.properties ?? {});
+      return this._addRule(ruleName, this._buildObjectRule(properties, required, name, schema.additionalProperties));
+    } else if ((schemaType === undefined || schemaType === 'object') && 'allOf' in schema) {
+      const required = new Set();
+      const properties = [];
+      const addComponent = (compSchema, isRequired) => {
+        const ref = compSchema.$ref;
+        if (ref !== undefined) {
+          compSchema = this._refs[ref];
         }
-        rule += ` ${this._formatLiteral(propName)} space ":" space ${propRuleName}`;
-      });
-      rule += ' "}" space';
 
-      return this._addRule(ruleName, rule);
-    } else if (schemaType === 'array' && 'items' in schema) {
-      // TODO `prefixItems` keyword (from python implementation)
-      const itemRuleName = this.visit(schema.items, `${name}${name ? "-" : ""}item`);
-      const rule = `"[" space (${itemRuleName} ("," space ${itemRuleName})*)? "]" space`;
-      return this._addRule(ruleName, rule);
+        if ('properties' in compSchema) {
+          for (const [propName, propSchema] of Object.entries(compSchema.properties)) {
+            properties.push([propName, propSchema]);
+            if (isRequired) {
+              required.add(propName);
+            }
+          }
+        }
+      };
+
+      for (const t of schema.allOf) {
+        if ('anyOf' in t) {
+          for (const tt of t.anyOf) {
+            addComponent(tt, false);
+          }
+        } else {
+          addComponent(t, true);
+        }
+      }
+
+      return this._addRule(ruleName, this._buildObjectRule(properties, required, name, /* additionalProperties= */ false));
+    } else if ((schemaType === undefined || schemaType === 'array') && ('items' in schema || 'prefixItems' in schema)) {
+      const items = schema.items ?? schema.prefixItems;
+      if (Array.isArray(items)) {
+        return this._addRule(
+          ruleName,
+          '"[" space ' +
+            items.map((item, i) => this.visit(item, `${name ?? ''}${name ? '-' : ''}tuple-${i}`)).join(' "," space ') +
+            ' "]" space'
+        );
+      } else {
+        const itemRuleName = this.visit(items, `${name ?? ''}${name ? '-' : ''}item`);
+        const minItems = schema.minItems || 0;
+        const maxItems = schema.maxItems;
+        return this._addRule(ruleName, '"[" space ' + _buildRepetition(itemRuleName, minItems, maxItems, {separatorRule: '"," space'}) + ' "]" space');
+      }
+    } else if ((schemaType === undefined || schemaType === 'string') && 'pattern' in schema) {
+      return this._visitPattern(schema.pattern, ruleName);
+    } else if ((schemaType === undefined || schemaType === 'string') && /^uuid[1-5]?$/.test(schema.format || '')) {
+      return this._addPrimitive(
+        ruleName === 'root' ? 'root' : schemaFormat,
+        PRIMITIVE_RULES['uuid']
+      );
+    } else if ((schemaType === undefined || schemaType === 'string') && `${schema.format}-string` in STRING_FORMAT_RULES) {
+      const primName = `${schema.format}-string`
+      return this._addRule(ruleName, this._addPrimitive(primName, STRING_FORMAT_RULES[primName]));
+    } else if (schemaType === 'string' && ('minLength' in schema || 'maxLength' in schema)) {
+      const charRuleName = this._addPrimitive('char', PRIMITIVE_RULES['char']);
+      const minLen = schema.minLength || 0;
+      const maxLen = schema.maxLength;
+      return this._addRule(ruleName, '"\\\"" ' + _buildRepetition(charRuleName, minLen, maxLen) + ' "\\\"" space');
+    } else if ((schemaType === 'object') || (Object.keys(schema).length === 0)) {
+      return this._addRule(ruleName, this._addPrimitive('object', PRIMITIVE_RULES['object']));
     } else {
-      if (!PRIMITIVE_RULES[schemaType]) {
+      if (!(schemaType in PRIMITIVE_RULES)) {
         throw new Error(`Unrecognized schema: ${JSON.stringify(schema)}`);
       }
-      return this._addRule(
-        ruleName === 'root' ? 'root' : schemaType,
-        PRIMITIVE_RULES[schemaType]
+      // TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero
+      return this._addPrimitive(ruleName === 'root' ? 'root' : schemaType, PRIMITIVE_RULES[schemaType]);
+    }
+  }
+
+  _addPrimitive(name, rule) {
+    let n = this._addRule(name, rule.content);
+    for (const dep of rule.deps) {
+      const depRule = PRIMITIVE_RULES[dep] || STRING_FORMAT_RULES[dep];
+      if (!depRule) {
+        throw new Error(`Rule ${dep} not known`);
+      }
+      if (!(dep in this._rules)) {
+        this._addPrimitive(dep, depRule);
+      }
+    }
+    return n;
+  }
+
+  _buildObjectRule(properties, required, name, additionalProperties) {
+    const propOrder = this._propOrder;
+    // sort by position in prop_order (if specified) then by original order
+    const sortedProps = properties.map(([k]) => k).sort((a, b) => {
+      const orderA = propOrder[a] || Infinity;
+      const orderB = propOrder[b] || Infinity;
+      return orderA - orderB || properties.findIndex(([k]) => k === a) - properties.findIndex(([k]) => k === b);
+    });
+
+    const propKvRuleNames = {};
+    for (const [propName, propSchema] of properties) {
+      const propRuleName = this.visit(propSchema, `${name ?? ''}${name ? '-' : ''}${propName}`);
+      propKvRuleNames[propName] = this._addRule(
+        `${name ?? ''}${name ? '-' : ''}${propName}-kv`,
+        `${this._formatLiteral(JSON.stringify(propName))} space ":" space ${propRuleName}`
       );
     }
+    const requiredProps = sortedProps.filter(k => required.has(k));
+    const optionalProps = sortedProps.filter(k => !required.has(k));
+
+    if (typeof additionalProperties === 'object' || additionalProperties === true) {
+      const subName = `${name ?? ''}${name ? '-' : ''}additional`;
+      const valueRule = this.visit(additionalProperties === true ? {} : additionalProperties, `${subName}-value`);
+      propKvRuleNames['*'] = this._addRule(
+        `${subName}-kv`,
+        `${this._addPrimitive('string', PRIMITIVE_RULES['string'])} ":" space ${valueRule}`);
+      optionalProps.push('*');
+    }
+
+    let rule = '"{" space ';
+    rule += requiredProps.map(k => propKvRuleNames[k]).join(' "," space ');
+
+    if (optionalProps.length > 0) {
+      rule += ' (';
+      if (requiredProps.length > 0) {
+        rule += ' "," space ( ';
+      }
+
+      const getRecursiveRefs = (ks, firstIsOptional) => {
+        const [k, ...rest] = ks;
+        const kvRuleName = propKvRuleNames[k];
+        let res;
+        if (k === '*') {
+            res = this._addRule(
+                `${name ?? ''}${name ? '-' : ''}additional-kvs`,
+                `${kvRuleName} ( "," space ` + kvRuleName + ` )*`
+            )
+        } else if (firstIsOptional) {
+          res = `( "," space ${kvRuleName} )?`;
+        } else {
+          res = kvRuleName;
+        }
+        if (rest.length > 0) {
+          res += ' ' + this._addRule(
+            `${name ?? ''}${name ? '-' : ''}${k}-rest`,
+            getRecursiveRefs(rest, true)
+          );
+        }
+        return res;
+      };
+
+      rule += optionalProps.map((_, i) => getRecursiveRefs(optionalProps.slice(i), false)).join(' | ');
+      if (requiredProps.length > 0) {
+        rule += ' )';
+      }
+      rule += ' )?';
+    }
+
+    rule += ' "}" space';
+
+    return rule;
   }
 
   formatGrammar() {
     let grammar = '';
-    this._rules.forEach((rule, name) => {
+    for (const [name, rule] of Object.entries(this._rules).sort(([a], [b]) => a.localeCompare(b))) {
       grammar += `${name} ::= ${rule}\n`;
-    });
+    }
     return grammar;
   }
 }
+
+// Helper function to group elements by a key function
+function* groupBy(iterable, keyFn) {
+  let lastKey = null;
+  let group = [];
+  for (const element of iterable) {
+    const key = keyFn(element);
+    if (lastKey !== null && key !== lastKey) {
+      yield [lastKey, group];
+      group = [];
+    }
+    group.push(element);
+    lastKey = key;
+  }
+  if (group.length > 0) {
+    yield [lastKey, group];
+  }
+}
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 22c344dd4..ceaeb1f76 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1,12 +1,9 @@
+#include "utils.hpp"
+
 #include "common.h"
+#include "json-schema-to-grammar.h"
 #include "llama.h"
 #include "grammar-parser.h"
-#include "utils.hpp"
-#include "oai.hpp"
-
-#include "../llava/clip.h"
-
-#include "stb_image.h"
 
 #ifndef NDEBUG
 // crash the server in debug mode, otherwise send an http 500 error
@@ -15,6 +12,8 @@
 // increase max payload length to allow use of larger context size
 #define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576
 #include "httplib.h"
+// Change JSON_ASSERT from assert() to GGML_ASSERT:
+#define JSON_ASSERT GGML_ASSERT
 #include "json.hpp"
 
 // auto generated files (update with ./deps.sh)
@@ -23,133 +22,129 @@
 #include "completion.js.hpp"
 #include "json-schema-to-grammar.mjs.hpp"
 
-#include <cstddef>
-#include <thread>
+#include <atomic>
 #include <chrono>
 #include <condition_variable>
-#include <atomic>
+#include <cstddef>
+#include <set>
+#include <mutex>
+#include <thread>
 #include <signal.h>
+#include <memory>
 
-using json = nlohmann::json;
-
-struct server_params
-{
-    std::string hostname = "127.0.0.1";
-    std::vector<std::string> api_keys;
-    std::string public_path = "examples/server/public";
-    std::string chat_template = "chatml";
-    int32_t port = 8080;
-    int32_t read_timeout = 600;
-    int32_t write_timeout = 600;
-    bool slots_endpoint = true;
-};
+using json = nlohmann::ordered_json;
 
 bool server_verbose = false;
+bool server_log_json = true;
 
-static size_t common_part(const std::vector<llama_token> &a, const std::vector<llama_token> &b)
-{
-    size_t i;
-    for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++)
-    {
-    }
-    return i;
-}
-
-enum stop_type
-{
-    STOP_FULL,
-    STOP_PARTIAL,
+enum stop_type {
+    STOP_TYPE_FULL,
+    STOP_TYPE_PARTIAL,
 };
 
-static bool ends_with(const std::string &str, const std::string &suffix)
-{
-    return str.size() >= suffix.size() &&
-           0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix);
-}
+enum slot_state {
+    SLOT_STATE_IDLE,
+    SLOT_STATE_PROCESSING,
+};
 
-static size_t find_partial_stop_string(const std::string &stop,
-                                       const std::string &text)
-{
-    if (!text.empty() && !stop.empty())
-    {
-        const char text_last_char = text.back();
-        for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--)
-        {
-            if (stop[char_index] == text_last_char)
-            {
-                const std::string current_partial = stop.substr(0, char_index + 1);
-                if (ends_with(text, current_partial))
-                {
-                    return text.size() - char_index - 1;
-                }
-            }
-        }
-    }
-    return std::string::npos;
-}
+enum slot_command {
+    SLOT_COMMAND_NONE,
+    SLOT_COMMAND_LOAD_PROMPT,
+    SLOT_COMMAND_RELEASE,
+};
 
-// TODO: reuse llama_detokenize
-template <class Iter>
-static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end)
-{
-    std::string ret;
-    for (; begin != end; ++begin)
-    {
-        ret += llama_token_to_piece(ctx, *begin);
-    }
-    return ret;
-}
+enum server_state {
+    SERVER_STATE_LOADING_MODEL,  // Server is starting up, model not fully loaded yet
+    SERVER_STATE_READY,          // Server is ready and model is loaded
+    SERVER_STATE_ERROR           // An error occurred, load_model failed
+};
 
-// format incomplete utf-8 multibyte character for output
-static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token)
-{
-    std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token);
-    // if the size is 1 and first bit is 1, meaning it's a partial character
-    //   (size > 1 meaning it's already a known token)
-    if (out.size() == 1 && (out[0] & 0x80) == 0x80)
-    {
-        std::stringstream ss;
-        ss << std::hex << (out[0] & 0xff);
-        std::string res(ss.str());
-        out = "byte: \\x" + res;
-    }
-    return out;
-}
+enum server_task_type {
+    SERVER_TASK_TYPE_COMPLETION,
+    SERVER_TASK_TYPE_CANCEL,
+    SERVER_TASK_TYPE_NEXT_RESPONSE,
+    SERVER_TASK_TYPE_METRICS,
+    SERVER_TASK_TYPE_SLOT_SAVE,
+    SERVER_TASK_TYPE_SLOT_RESTORE,
+    SERVER_TASK_TYPE_SLOT_ERASE,
+};
 
-// convert a vector of completion_token_output to json
-static json probs_vector_to_json(const llama_context *ctx, const std::vector<completion_token_output> &probs)
-{
-    json out = json::array();
-    for (const auto &prob : probs)
-    {
-        json probs_for_token = json::array();
-        for (const auto &p : prob.probs)
-        {
-            std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok);
-            probs_for_token.push_back(json
-            {
-                {"tok_str", tok_str},
-                {"prob",    p.prob},
-            });
-        }
-        std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok);
-        out.push_back(json{
-            {"content", tok_str},
-            {"probs",   probs_for_token},
-        });
-    }
-    return out;
-}
+struct server_task {
+    int id        = -1; // to be filled by server_queue
+    int id_multi  = -1;
+    int id_target = -1;
 
-struct llama_client_slot
-{
+    server_task_type type;
+    json data;
+
+    bool infill    = false;
+    bool embedding = false;
+};
+
+struct server_task_result {
+    int id       = -1;
+    int id_multi = -1;
+
+    json data;
+
+    bool stop;
+    bool error;
+};
+
+struct server_task_multi {
+    int id = -1;
+
+    std::set<int> subtasks_remaining;
+    std::vector<server_task_result> results;
+};
+
+struct slot_params {
+    bool stream       = true;
+    bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt
+
+    uint32_t seed      = -1; // RNG seed
+    int32_t  n_keep    =  0; // number of tokens to keep from initial prompt
+    int32_t  n_discard =  0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half
+    int32_t  n_predict = -1; // new tokens to predict
+
+    std::vector<std::string> antiprompt;
+
+    json input_prefix;
+    json input_suffix;
+};
+
+struct server_params {
+    int32_t port           = 8080;
+    int32_t read_timeout   = 600;
+    int32_t write_timeout  = 600;
+    int32_t n_threads_http = -1;
+
+    std::string hostname      = "127.0.0.1";
+    std::string public_path   = "";
+    std::string chat_template = "";
+    std::string system_prompt = "";
+
+    std::vector<std::string> api_keys;
+
+#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
+    std::string ssl_key_file = "";
+    std::string ssl_cert_file = "";
+#endif
+
+    bool slots_endpoint   = true;
+    bool metrics_endpoint = false;
+    std::string slot_save_path;
+};
+
+struct server_slot {
     int id;
-    int task_id = -1;
+    int id_task = -1;
+    int id_multi = -1;
 
     struct slot_params params;
 
-    slot_state state = IDLE;
-    slot_command command = NONE;
+    slot_state state = SLOT_STATE_IDLE;
+    slot_command command = SLOT_COMMAND_NONE;
 
     // used to determine the slot that has been used the longest
     int64_t t_last_used = -1;
@@ -160,33 +155,38 @@ struct llama_client_slot
     int32_t n_decoded   = 0;
     int32_t n_remaining = -1;
     int32_t i_batch     = -1;
-    int32_t n_predict   = -1;
+    int32_t n_predict   = -1; // TODO: disambiguate from params.n_predict
 
-    int32_t num_prompt_tokens           = 0;
-    int32_t num_prompt_tokens_processed = 0;
+    int32_t n_prompt_tokens           = 0;
+    int32_t n_prompt_tokens_processed = 0;
 
     json prompt;
+
+    // when a task is submitted, we first tokenize the prompt and store it here
+    std::vector<llama_token> prompt_tokens;
+
     std::string generated_text;
-    llama_token sampled;
     std::vector<llama_token> cache_tokens;
     std::vector<completion_token_output> generated_token_probs;
 
-    bool infill = false;
-    bool embedding = false;
+    bool infill         = false;
+    bool embedding      = false;
     bool has_next_token = true;
-    bool truncated = false;
-    bool stopped_eos = false;
-    bool stopped_word = false;
-    bool stopped_limit = false;
+    bool truncated      = false;
+    bool stopped_eos    = false;
+    bool stopped_word   = false;
+    bool stopped_limit  = false;
 
     bool oaicompat = false;
-    std::string oaicompat_model;
 
+    std::string oaicompat_model;
     std::string stopping_word;
 
     // sampling
+    llama_token sampled;
     struct llama_sampling_params sparams;
-    llama_sampling_context *ctx_sampling = nullptr;
+    llama_sampling_context * ctx_sampling = nullptr;
+    json json_schema;
 
     int32_t ga_i = 0;   // group-attention state
     int32_t ga_n = 1;   // group-attention factor
@@ -194,65 +194,44 @@ struct llama_client_slot
 
     int32_t n_past_se = 0; // self-extend
 
-    // multimodal
-    std::vector<slot_image> images;
-
     // stats
-    size_t sent_count = 0;
-    size_t sent_token_probs_index = 0;
+    size_t n_sent_text = 0; // number of sent text character
+    size_t n_sent_token_probs = 0;
 
     int64_t t_start_process_prompt;
-    int64_t t_start_genereration;
+    int64_t t_start_generation;
 
     double t_prompt_processing; // ms
     double t_token_generation; // ms
 
-    // multitasks
-    int multitask_id = -1;
-
     void reset() {
-        num_prompt_tokens      = 0;
-        generated_text         = "";
-        truncated              = false;
-        stopped_eos            = false;
-        stopped_word           = false;
-        stopped_limit          = false;
-        stopping_word          = "";
-        n_past                 = 0;
-        sent_count             = 0;
-        sent_token_probs_index = 0;
-        infill                 = false;
-        ga_i                   = 0;
-        n_past_se              = 0;
+        n_prompt_tokens    = 0;
+        generated_text     = "";
+        truncated          = false;
+        stopped_eos        = false;
+        stopped_word       = false;
+        stopped_limit      = false;
+        stopping_word      = "";
+        n_past             = 0;
+        n_sent_text        = 0;
+        n_sent_token_probs = 0;
+        infill             = false;
+        ga_i               = 0;
+        n_past_se          = 0;
 
         generated_token_probs.clear();
-
-        for (slot_image & img : images)
-        {
-            free(img.image_embedding);
-            if (img.img_data) {
-                clip_image_u8_free(img.img_data);
-            }
-            img.prefix_prompt = "";
-        }
-
-        images.clear();
     }
 
     bool has_budget(gpt_params &global_params) {
-        if (params.n_predict == -1 && global_params.n_predict == -1)
-        {
+        if (params.n_predict == -1 && global_params.n_predict == -1) {
             return true; // limitless
         }
 
         n_remaining = -1;
 
-        if (params.n_predict != -1)
-        {
+        if (params.n_predict != -1) {
             n_remaining = params.n_predict - n_decoded;
-        }
-        else if (global_params.n_predict != -1)
-        {
+        } else if (global_params.n_predict != -1) {
             n_remaining = global_params.n_predict - n_decoded;
         }
 
@@ -260,37 +239,33 @@ struct llama_client_slot
     }
 
     bool available() const {
-        return state == IDLE && command == NONE;
+        return state == SLOT_STATE_IDLE && command == SLOT_COMMAND_NONE;
     }
 
     bool is_processing() const {
-        return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING;
+        return (state == SLOT_STATE_IDLE && command == SLOT_COMMAND_LOAD_PROMPT) || state == SLOT_STATE_PROCESSING;
     }
 
-    void add_token_string(const completion_token_output &token) {
-        if (command == RELEASE)
-        {
+    void add_token_string(const completion_token_output & token) {
+        if (command == SLOT_COMMAND_RELEASE) {
             return;
         }
-        cache_tokens.push_back(token.tok);
         generated_token_probs.push_back(token);
     }
 
     void release() {
-        if (state == PROCESSING)
-        {
-            t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3;
-            command = RELEASE;
+        if (state == SLOT_STATE_PROCESSING) {
+            t_token_generation = (ggml_time_us() - t_start_generation) / 1e3;
+            command = SLOT_COMMAND_RELEASE;
         }
     }
 
-    json get_formated_timings() {
-        return json
-        {
-            {"prompt_n",               num_prompt_tokens_processed},
+    json get_formated_timings() const {
+        return json {
+            {"prompt_n",               n_prompt_tokens_processed},
             {"prompt_ms",              t_prompt_processing},
-            {"prompt_per_token_ms",    t_prompt_processing / num_prompt_tokens_processed},
-            {"prompt_per_second",      1e3 / t_prompt_processing * num_prompt_tokens_processed},
+            {"prompt_per_token_ms",    t_prompt_processing / n_prompt_tokens_processed},
+            {"prompt_per_second",      1e3 / t_prompt_processing * n_prompt_tokens_processed},
 
             {"predicted_n",            n_decoded},
             {"predicted_ms",           t_token_generation},
@@ -299,33 +274,376 @@ struct llama_client_slot
         };
     }
 
+    size_t find_stopping_strings(const std::string & text, const size_t last_token_size, const stop_type type) {
+        size_t stop_pos = std::string::npos;
+
+        for (const std::string & word : params.antiprompt) {
+            size_t pos;
+
+            if (type == STOP_TYPE_FULL) {
+                const size_t tmp      = word.size() + last_token_size;
+                const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0;
+
+                pos = text.find(word, from_pos);
+            } else {
+                pos = find_partial_stop_string(word, text);
+            }
+
+            if (pos != std::string::npos && (stop_pos == std::string::npos || pos < stop_pos)) {
+                if (type == STOP_TYPE_FULL) {
+                    stopped_word   = true;
+                    stopping_word  = word;
+                    has_next_token = false;
+                }
+                stop_pos = pos;
+            }
+        }
+
+        return stop_pos;
+    }
+
     void print_timings() const {
-        LOG_TEE("\n");
-        LOG_TEE("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
-            __func__, t_prompt_processing, num_prompt_tokens_processed, t_prompt_processing / num_prompt_tokens_processed, 1e3 / t_prompt_processing * num_prompt_tokens_processed);
-        LOG_TEE("%s:        eval time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
-            __func__, t_token_generation, n_decoded,t_token_generation / n_decoded, 1e3 / t_token_generation * n_decoded);
-        LOG_TEE("%s:       total time = %10.2f ms\n", __func__, t_prompt_processing + t_token_generation);
+        char buffer[512];
+
+        double t_token = t_prompt_processing / n_prompt_tokens_processed;
+        double n_tokens_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed;
+
+        snprintf(buffer, 512, "prompt eval time     = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)",
+                t_prompt_processing, n_prompt_tokens_processed,
+                t_token, n_tokens_second);
+
+        LOG_INFO(buffer, {
+            {"id_slot",                   id},
+            {"id_task",                   id_task},
+            {"t_prompt_processing",       t_prompt_processing},
+            {"n_prompt_tokens_processed", n_prompt_tokens_processed},
+            {"t_token",                   t_token},
+            {"n_tokens_second",           n_tokens_second},
+        });
+
+        t_token = t_token_generation / n_decoded;
+        n_tokens_second = 1e3 / t_token_generation * n_decoded;
+
+        snprintf(buffer, 512, "generation eval time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)",
+                t_token_generation, n_decoded,
+                t_token, n_tokens_second);
+
+        LOG_INFO(buffer, {
+            {"id_slot",            id},
+            {"id_task",            id_task},
+            {"t_token_generation", t_token_generation},
+            {"n_decoded",          n_decoded},
+            {"t_token",            t_token},
+            {"n_tokens_second",    n_tokens_second},
+        });
+
+        snprintf(buffer, 512, "          total time = %10.2f ms", t_prompt_processing + t_token_generation);
+
+        LOG_INFO(buffer, {
+            {"id_slot",             id},
+            {"id_task",             id_task},
+            {"t_prompt_processing", t_prompt_processing},
+            {"t_token_generation",  t_token_generation},
+            {"t_total",             t_prompt_processing + t_token_generation},
+        });
     }
 };
 
-struct llama_server_context
-{
-    llama_model *model = nullptr;
-    llama_context *ctx = nullptr;
+struct server_metrics {
+    int64_t t_start = 0;
 
-    clip_ctx *clp_ctx = nullptr;
+    uint64_t n_prompt_tokens_processed_total = 0;
+    uint64_t t_prompt_processing_total       = 0;
+    uint64_t n_tokens_predicted_total        = 0;
+    uint64_t t_tokens_generation_total       = 0;
+
+    uint64_t n_prompt_tokens_processed = 0;
+    uint64_t t_prompt_processing       = 0;
+
+    uint64_t n_tokens_predicted  = 0;
+    uint64_t t_tokens_generation = 0;
+
+    void init() {
+        t_start = ggml_time_us();
+    }
+
+    void on_prompt_eval(const server_slot & slot) {
+        n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed;
+        n_prompt_tokens_processed       += slot.n_prompt_tokens_processed;
+        t_prompt_processing             += slot.t_prompt_processing;
+        t_prompt_processing_total       += slot.t_prompt_processing;
+    }
+
+    void on_prediction(const server_slot & slot) {
+        n_tokens_predicted_total   += slot.n_decoded;
+        n_tokens_predicted         += slot.n_decoded;
+        t_tokens_generation        += slot.t_token_generation;
+        t_tokens_generation_total  += slot.t_token_generation;
+    }
+
+    void reset_bucket() {
+        n_prompt_tokens_processed = 0;
+        t_prompt_processing       = 0;
+        n_tokens_predicted        = 0;
+        t_tokens_generation       = 0;
+    }
+};
+
+struct server_queue {
+    int id = 0;
+    bool running;
+
+    // queues
+    std::vector<server_task> queue_tasks;
+    std::vector<server_task> queue_tasks_deferred;
+
+    std::vector<server_task_multi> queue_multitasks;
+
+    std::mutex mutex_tasks;
+    std::condition_variable condition_tasks;
+
+    // callback functions
+    std::function<void(server_task       &)> callback_new_task;
+    std::function<void(server_task_multi &)> callback_finish_multitask;
+    std::function<void(void)>                callback_update_slots;
+
+    // Add a new task to the end of the queue
+    int post(server_task task) {
+        std::unique_lock<std::mutex> lock(mutex_tasks);
+        if (task.id == -1) {
+            task.id = id++;
+            LOG_VERBOSE("new task id", {{"new_id", task.id}});
+        }
+        queue_tasks.push_back(std::move(task));
+        condition_tasks.notify_one();
+        return task.id;
+    }
+
+    // Add a new task, but defer until one slot is available
+    void defer(server_task task) {
+        std::unique_lock<std::mutex> lock(mutex_tasks);
+        queue_tasks_deferred.push_back(std::move(task));
+    }
+
+    // Get the next id for creating anew task
+    int get_new_id() {
+        std::unique_lock<std::mutex> lock(mutex_tasks);
+        int new_id = id++;
+        LOG_VERBOSE("new task id", {{"new_id", new_id}});
+        return new_id;
+    }
+
+    // Register function to process a new task
+    void on_new_task(std::function<void(server_task &)> callback) {
+        callback_new_task = std::move(callback);
+    }
+
+    // Register function to process a multitask when it is finished
+    void on_finish_multitask(std::function<void(server_task_multi&)> callback) {
+        callback_finish_multitask = std::move(callback);
+    }
+
+    // Register the function to be called when all slots data is ready to be processed
+    void on_update_slots(std::function<void(void)> callback) {
+        callback_update_slots = std::move(callback);
+    }
+
+    // Call when the state of one slot is changed
+    void notify_slot_changed() {
+        // move deferred tasks back to main loop
+        std::unique_lock<std::mutex> lock(mutex_tasks);
+        for (auto & task : queue_tasks_deferred) {
+            queue_tasks.push_back(std::move(task));
+        }
+        queue_tasks_deferred.clear();
+    }
+
+    // end the start_loop routine
+    void terminate() {
+        std::unique_lock<std::mutex> lock(mutex_tasks);
+        running = false;
+        condition_tasks.notify_all();
+    }
+
+    /**
+     * Main loop consists of these steps:
+     * - Wait until a new task arrives
+     * - Process the task (i.e. maybe copy data into slot)
+     * - Check if multitask is finished
+     * - Update all slots
+     */
+    void start_loop() {
+        running = true;
+
+        while (true) {
+            LOG_VERBOSE("new task may arrive", {});
+
+            while (true) {
+                std::unique_lock<std::mutex> lock(mutex_tasks);
+                if (queue_tasks.empty()) {
+                    lock.unlock();
+                    break;
+                }
+                server_task task = queue_tasks.front();
+                queue_tasks.erase(queue_tasks.begin());
+                lock.unlock();
+                LOG_VERBOSE("callback_new_task", {{"id_task", task.id}});
+                callback_new_task(task);
+            }
+
+            LOG_VERBOSE("update_multitasks", {});
+
+            // check if we have any finished multitasks
+            auto queue_iterator = queue_multitasks.begin();
+            while (queue_iterator != queue_multitasks.end()) {
+                if (queue_iterator->subtasks_remaining.empty()) {
+                    // all subtasks done == multitask is done
+                    server_task_multi current_multitask = *queue_iterator;
+                    callback_finish_multitask(current_multitask);
+                    // remove this multitask
+                    queue_iterator = queue_multitasks.erase(queue_iterator);
+                } else {
+                    ++queue_iterator;
+                }
+            }
+
+            // all tasks in the current loop is processed, slots data is now ready
+            LOG_VERBOSE("callback_update_slots", {});
+
+            callback_update_slots();
+
+            LOG_VERBOSE("wait for new task", {});
+            {
+                std::unique_lock<std::mutex> lock(mutex_tasks);
+                if (queue_tasks.empty()) {
+                    if (!running) {
+                        LOG_VERBOSE("ending start_loop", {});
+                        return;
+                    }
+                    condition_tasks.wait(lock, [&]{
+                        return (!queue_tasks.empty() || !running);
+                    });
+                }
+            }
+        }
+    }
+
+    //
+    // functions to manage multitasks
+    //
+
+    // add a multitask by specifying the id of all subtask (subtask is a server_task)
+    void add_multitask(int id_multi, std::vector<int> & sub_ids) {
+        std::lock_guard<std::mutex> lock(mutex_tasks);
+        server_task_multi multi;
+        multi.id = id_multi;
+        std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end()));
+        queue_multitasks.push_back(multi);
+    }
+
+    // updatethe remaining subtasks, while appending results to multitask
+    void update_multitask(int id_multi, int id_sub, server_task_result & result) {
+        std::lock_guard<std::mutex> lock(mutex_tasks);
+        for (auto & multitask : queue_multitasks) {
+            if (multitask.id == id_multi) {
+                multitask.subtasks_remaining.erase(id_sub);
+                multitask.results.push_back(result);
+            }
+        }
+    }
+};
+
+struct server_response {
+    typedef std::function<void(int, int, server_task_result &)> callback_multitask_t;
+    callback_multitask_t callback_update_multitask;
+
+    // for keeping track of all tasks waiting for the result
+    std::set<int> waiting_task_ids;
+
+    // the main result queue
+    std::vector<server_task_result> queue_results;
+
+    std::mutex mutex_results;
+    std::condition_variable condition_results;
+
+    // add the id_task to the list of tasks waiting for response
+    void add_waiting_task_id(int id_task) {
+        LOG_VERBOSE("waiting for task id", {{"id_task", id_task}});
+
+        std::unique_lock<std::mutex> lock(mutex_results);
+        waiting_task_ids.insert(id_task);
+    }
+
+    // when the request is finished, we can remove task associated with it
+    void remove_waiting_task_id(int id_task) {
+        LOG_VERBOSE("remove waiting for task id", {{"id_task", id_task}});
+
+        std::unique_lock<std::mutex> lock(mutex_results);
+        waiting_task_ids.erase(id_task);
+    }
+
+    // This function blocks the thread until there is a response for this id_task
+    server_task_result recv(int id_task) {
+        while (true) {
+            std::unique_lock<std::mutex> lock(mutex_results);
+            condition_results.wait(lock, [&]{
+                return !queue_results.empty();
+            });
+
+            for (int i = 0; i < (int) queue_results.size(); i++) {
+                if (queue_results[i].id == id_task) {
+                    assert(queue_results[i].id_multi == -1);
+                    server_task_result res = queue_results[i];
+                    queue_results.erase(queue_results.begin() + i);
+                    return res;
+                }
+            }
+        }
+
+        // should never reach here
+    }
+
+    // Register the function to update multitask
+    void on_multitask_update(callback_multitask_t callback) {
+        callback_update_multitask = std::move(callback);
+    }
+
+    // Send a new result to a waiting id_task
+    void send(server_task_result result) {
+        LOG_VERBOSE("send new result", {{"id_task", result.id}});
+
+        std::unique_lock<std::mutex> lock(mutex_results);
+        for (const auto & id_task : waiting_task_ids) {
+            // LOG_TEE("waiting task id %i \n", id_task);
+            // for now, tasks that have associated parent multitasks just get erased once multitask picks up the result
+            if (result.id_multi == id_task) {
+                LOG_VERBOSE("callback_update_multitask", {{"id_task", id_task}});
+                callback_update_multitask(id_task, result.id, result);
+                continue;
+            }
+
+            if (result.id == id_task) {
+                LOG_VERBOSE("queue_results.push_back", {{"id_task", id_task}});
+                queue_results.push_back(result);
+                condition_results.notify_all();
+                return;
+            }
+        }
+    }
+};
+
+struct server_context {
+    llama_model * model = nullptr;
+    llama_context * ctx = nullptr;
 
     gpt_params params;
 
     llama_batch batch;
 
-    bool multimodal         = false;
-    bool clean_kv_cache     = true;
-    bool all_slots_are_idle = false;
-    bool add_bos_token      = true;
+    bool clean_kv_cache = true;
+    bool add_bos_token  = true;
 
-    int32_t n_ctx;  // total context for all clients / slots
+    int32_t n_ctx; // total context for all clients / slots
 
     // system prompt
     bool system_need_update = false;
@@ -333,98 +651,89 @@ struct llama_server_context
     std::string              system_prompt;
     std::vector<llama_token> system_tokens;
 
-    std::string name_user;      // this should be the antiprompt
-    std::string name_assistant;
-
     // slots / clients
-    std::vector<llama_client_slot> slots;
+    std::vector<server_slot> slots;
     json default_generation_settings_for_props;
 
-    llama_server_queue queue_tasks;
-    llama_server_response queue_results;
+    server_queue    queue_tasks;
+    server_response queue_results;
 
-    ~llama_server_context()
-    {
-        if (ctx)
-        {
+    server_metrics metrics;
+
+    ~server_context() {
+        if (ctx) {
             llama_free(ctx);
             ctx = nullptr;
         }
-        if (model)
-        {
+
+        if (model) {
             llama_free_model(model);
             model = nullptr;
         }
+
+        llama_batch_free(batch);
     }
 
-    bool load_model(const gpt_params &params_)
-    {
+    bool load_model(const gpt_params & params_) {
         params = params_;
-        if (!params.mmproj.empty()) {
-            multimodal = true;
-            LOG_TEE("Multi Modal Mode Enabled");
-            clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1);
-            if(clp_ctx == nullptr) {
-                LOG_ERROR("unable to load clip model", {{"model", params.mmproj}});
-                return false;
-            }
 
-            if (params.n_ctx < 2048) { // request larger context for the image embedding
-                params.n_ctx = 2048;
-            }
-        }
+        // dedicate one sequence to the system prompt
+        params.n_parallel += 1;
 
         std::tie(model, ctx) = llama_init_from_gpt_params(params);
-        if (model == nullptr)
-        {
+        params.n_parallel -= 1; // but be sneaky about it
+        if (model == nullptr) {
             LOG_ERROR("unable to load model", {{"model", params.model}});
             return false;
         }
 
-        if (multimodal) {
-            const int n_embd_clip = clip_n_mmproj_embd(clp_ctx);
-            const int n_embd_llm  = llama_n_embd(model);
-            if (n_embd_clip != n_embd_llm) {
-                LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_embd_clip, n_embd_llm);
-                llama_free(ctx);
-                llama_free_model(model);
-                return false;
-            }
-        }
-
         n_ctx = llama_n_ctx(ctx);
 
         add_bos_token = llama_should_add_bos_token(model);
+        GGML_ASSERT(llama_add_eos_token(model) != 1);
 
         return true;
     }
 
-    void initialize() {
-        // create slots
-        all_slots_are_idle = true;
+    bool validate_model_chat_template() const {
+        llama_chat_message chat[] = {{"user", "test"}};
 
+        const int res = llama_chat_apply_template(model, nullptr, chat, 1, true, nullptr, 0);
+
+        return res > 0;
+    }
+
+    void init() {
         const int32_t n_ctx_slot = n_ctx / params.n_parallel;
 
-        LOG_TEE("Available slots:\n");
-        for (int i = 0; i < params.n_parallel; i++)
-        {
-            llama_client_slot slot;
+        LOG_INFO("initializing slots", {{"n_slots", params.n_parallel}});
+
+        for (int i = 0; i < params.n_parallel; i++) {
+            server_slot slot;
 
             slot.id = i;
             slot.n_ctx = n_ctx_slot;
             slot.n_predict = params.n_predict;
 
-            LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, n_ctx_slot);
+            LOG_INFO("new slot", {
+                {"id_slot",    slot.id},
+                {"n_ctx_slot", slot.n_ctx}
+            });
 
             const int ga_n = params.grp_attn_n;
             const int ga_w = params.grp_attn_w;
 
             if (ga_n != 1) {
-                GGML_ASSERT(ga_n > 0                    && "ga_n must be positive");                     // NOLINT
-                GGML_ASSERT(ga_w % ga_n == 0            && "ga_w must be a multiple of ga_n");     // NOLINT
+                GGML_ASSERT(ga_n > 0                    && "ga_n must be positive");                       // NOLINT
+                GGML_ASSERT(ga_w % ga_n == 0            && "ga_w must be a multiple of ga_n");             // NOLINT
                 //GGML_ASSERT(n_ctx_train % ga_w == 0     && "n_ctx_train must be a multiple of ga_w");    // NOLINT
                 //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT
-                LOG_TEE(" -> Slot %i - self-extend: ga_n = %d, ga_w = %d\n", slot.id, ga_n, ga_w);
+
+                LOG_INFO("slot self-extend", {
+                    {"id_slot", slot.id},
+                    {"ga_n",    ga_n},
+                    {"ga_w",    ga_w}
+                });
             }
 
             slot.ga_i = 0;
@@ -439,11 +748,19 @@ struct llama_server_context
         default_generation_settings_for_props = get_formated_generation(slots.front());
         default_generation_settings_for_props["seed"] = -1;
 
-        batch = llama_batch_init(n_ctx, 0, params.n_parallel);
+        // the update_slots() logic will always submit a maximum of n_batch tokens
+        // note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used)
+        {
+            const int32_t n_batch = llama_n_batch(ctx);
+
+            // only a single seq_id per token is needed
+            batch = llama_batch_init(n_batch, 0, 1);
+        }
+
+        metrics.init();
     }
 
-    std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const
-    {
+    std::vector<llama_token> tokenize(const json & json_prompt, bool add_special) const {
         // TODO: currently, we tokenize using special tokens by default
         //       this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216)
         //       but it's better compared to completely ignoring ChatML and other chat templates
@@ -453,58 +770,49 @@ struct llama_server_context
         // or the first element of the json_prompt array is a string.
         std::vector<llama_token> prompt_tokens;
 
-        if (json_prompt.is_array())
-        {
+        if (json_prompt.is_array()) {
             bool first = true;
-            for (const auto& p : json_prompt)
-            {
-                if (p.is_string())
-                {
+            for (const auto & p : json_prompt) {
+                if (p.is_string()) {
                     auto s = p.template get<std::string>();
+
                     std::vector<llama_token> p;
-                    if (first)
-                    {
-                        p = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
+                    if (first) {
+                        p = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
                         first = false;
-                    }
-                    else
-                    {
+                    } else {
                         p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL);
                     }
+
                     prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
-                }
-                else
-                {
-                    if (first)
-                    {
+                } else {
+                    if (first) {
                         first = false;
                     }
+
                     prompt_tokens.push_back(p.template get<llama_token>());
                 }
             }
-        }
-        else
-        {
+        } else {
             auto s = json_prompt.template get<std::string>();
-            prompt_tokens = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
+            prompt_tokens = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
         }
 
         return prompt_tokens;
     }
 
-    llama_client_slot* get_slot(int id) {
+    server_slot * get_slot(int id) {
         int64_t t_last = ggml_time_us();
-        llama_client_slot *last_used = nullptr;
 
-        for (llama_client_slot & slot : slots)
-        {
-            if (slot.id == id && slot.available())
-            {
+        server_slot * last_used = nullptr;
+
+        for (server_slot & slot : slots) {
+            if (slot.id == id && slot.available()) {
                 return &slot;
             }
 
-            if (slot.available() && slot.t_last_used < t_last)
-            {
+            // among all available slots, find the one that has been least recently used
+            if (slot.available() && slot.t_last_used < t_last) {
                 last_used = &slot;
                 t_last = slot.t_last_used;
             }
@@ -513,406 +821,327 @@ struct llama_server_context
         return last_used;
     }
 
-    bool launch_slot_with_data(llama_client_slot* &slot, json data) {
+    bool launch_slot_with_task(server_slot & slot, const server_task & task) {
         slot_params default_params;
         llama_sampling_params default_sparams;
+        auto & data = task.data;
 
         if (data.count("__oaicompat") != 0) {
-            slot->oaicompat = true;
-            slot->oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
+            slot.oaicompat = true;
+            slot.oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
         } else {
-            slot->oaicompat = false;
-            slot->oaicompat_model = "";
+            slot.oaicompat = false;
+            slot.oaicompat_model = "";
         }
 
-        slot->params.stream             = json_value(data, "stream",            false);
-        slot->params.cache_prompt       = json_value(data, "cache_prompt",      false);
-        slot->params.n_predict          = json_value(data, "n_predict",         default_params.n_predict);
-        slot->sparams.top_k             = json_value(data, "top_k",             default_sparams.top_k);
-        slot->sparams.top_p             = json_value(data, "top_p",             default_sparams.top_p);
-        slot->sparams.min_p             = json_value(data, "min_p",             default_sparams.min_p);
-        slot->sparams.tfs_z             = json_value(data, "tfs_z",             default_sparams.tfs_z);
-        slot->sparams.typical_p         = json_value(data, "typical_p",         default_sparams.typical_p);
-        slot->sparams.temp              = json_value(data, "temperature",       default_sparams.temp);
-        slot->sparams.dynatemp_range    = json_value(data, "dynatemp_range",    default_sparams.dynatemp_range);
-        slot->sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent);
-        slot->sparams.penalty_last_n    = json_value(data, "repeat_last_n",     default_sparams.penalty_last_n);
-        slot->sparams.penalty_repeat    = json_value(data, "repeat_penalty",    default_sparams.penalty_repeat);
-        slot->sparams.penalty_freq      = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
-        slot->sparams.penalty_present   = json_value(data, "presence_penalty",  default_sparams.penalty_present);
-        slot->sparams.mirostat          = json_value(data, "mirostat",          default_sparams.mirostat);
-        slot->sparams.mirostat_tau      = json_value(data, "mirostat_tau",      default_sparams.mirostat_tau);
-        slot->sparams.mirostat_eta      = json_value(data, "mirostat_eta",      default_sparams.mirostat_eta);
-        slot->sparams.penalize_nl       = json_value(data, "penalize_nl",       default_sparams.penalize_nl);
-        slot->params.n_keep             = json_value(data, "n_keep",            slot->params.n_keep);
-        slot->params.seed               = json_value(data, "seed",              default_params.seed);
-        slot->sparams.grammar           = json_value(data, "grammar",           default_sparams.grammar);
-        slot->sparams.n_probs           = json_value(data, "n_probs",           default_sparams.n_probs);
-        slot->sparams.min_keep          = json_value(data, "min_keep",          default_sparams.min_keep);
+        slot.params.stream             = json_value(data, "stream",            false);
+        slot.params.cache_prompt       = json_value(data, "cache_prompt",      false);
+        slot.params.n_predict          = json_value(data, "n_predict",         default_params.n_predict);
+        slot.sparams.top_k             = json_value(data, "top_k",             default_sparams.top_k);
+        slot.sparams.top_p             = json_value(data, "top_p",             default_sparams.top_p);
+        slot.sparams.min_p             = json_value(data, "min_p",             default_sparams.min_p);
+        slot.sparams.tfs_z             = json_value(data, "tfs_z",             default_sparams.tfs_z);
+        slot.sparams.typical_p         = json_value(data, "typical_p",         default_sparams.typical_p);
+        slot.sparams.temp              = json_value(data, "temperature",       default_sparams.temp);
+        slot.sparams.dynatemp_range    = json_value(data, "dynatemp_range",    default_sparams.dynatemp_range);
+        slot.sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent);
+        slot.sparams.penalty_last_n    = json_value(data, "repeat_last_n",     default_sparams.penalty_last_n);
+        slot.sparams.penalty_repeat    = json_value(data, "repeat_penalty",    default_sparams.penalty_repeat);
+        slot.sparams.penalty_freq      = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
+        slot.sparams.penalty_present   = json_value(data, "presence_penalty",  default_sparams.penalty_present);
+        slot.sparams.mirostat          = json_value(data, "mirostat",          default_sparams.mirostat);
+        slot.sparams.mirostat_tau      = json_value(data, "mirostat_tau",      default_sparams.mirostat_tau);
+        slot.sparams.mirostat_eta      = json_value(data, "mirostat_eta",      default_sparams.mirostat_eta);
+        slot.sparams.penalize_nl       = json_value(data, "penalize_nl",       default_sparams.penalize_nl);
+        slot.params.n_keep             = json_value(data, "n_keep",            slot.params.n_keep);
+        slot.params.n_discard          = json_value(data, "n_discard",         default_params.n_discard);
+        slot.sparams.seed              = json_value(data, "seed",              default_sparams.seed);
+        slot.sparams.n_probs           = json_value(data, "n_probs",           default_sparams.n_probs);
+        slot.sparams.min_keep          = json_value(data, "min_keep",          default_sparams.min_keep);
 
-        if (slot->n_predict > 0 && slot->params.n_predict > slot->n_predict) {
+        // process "json_schema" and "grammar"
+        if (data.contains("json_schema") && !data.at("json_schema").is_null() && data.contains("grammar") && !data.at("grammar").is_null()) {
+            send_error(task, "Either \"json_schema\" or \"grammar\" can be specified, but not both", ERROR_TYPE_INVALID_REQUEST);
+            return false;
+        } else if (data.contains("json_schema") && !data.contains("grammar")) {
+            try {
+                auto schema                = json_value(data, "json_schema", json::object());
+                slot.sparams.grammar       = json_schema_to_grammar(schema);
+            } catch (const std::exception & e) {
+                send_error(task, std::string("\"json_schema\": ") + e.what(), ERROR_TYPE_INVALID_REQUEST);
+                return false;
+            }
+        } else {
+            slot.sparams.grammar       = json_value(data, "grammar",           default_sparams.grammar);
+        }
+
+        if (slot.params.cache_prompt && slot.ga_n != 1) {
+            LOG_WARNING("cache_prompt is not supported with group-attention", {});
+            slot.params.cache_prompt = false;
+        }
+
+        if (slot.n_predict > 0 && slot.params.n_predict > slot.n_predict) {
             // Might be better to reject the request with a 400 ?
             LOG_WARNING("Max tokens to predict exceeds server configuration", {
-                {"params.n_predict", slot->params.n_predict},
-                {"slot.n_predict", slot->n_predict},
+                {"params.n_predict", slot.params.n_predict},
+                {"slot.n_predict",   slot.n_predict},
             });
-            slot->params.n_predict = slot->n_predict;
+            slot.params.n_predict = slot.n_predict;
         }
 
         // infill
-        if (data.count("input_prefix") != 0)
-        {
-            slot->params.input_prefix = data["input_prefix"];
-        }
-        else
-        {
-            slot->params.input_prefix = "";
-        }
+        slot.params.input_prefix = json_value(data, "input_prefix", default_params.input_prefix);
+        slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix);
 
-        if (data.count("input_suffix") != 0)
+        // get prompt
         {
-            slot->params.input_suffix = data["input_suffix"];
-        }
-        else
-        {
-            slot->params.input_suffix = "";
-        }
-
-        if (data.count("prompt") != 0)
-        {
-            slot->prompt = data["prompt"];
-        }
-        else
-        {
-            slot->prompt = "";
-        }
-
-        slot->sparams.penalty_prompt_tokens.clear();
-        slot->sparams.use_penalty_prompt_tokens = false;
-        const auto &penalty_prompt = data.find("penalty_prompt");
-        if (penalty_prompt != data.end())
-        {
-            if (penalty_prompt->is_string())
-            {
-                const auto penalty_prompt_string = penalty_prompt->get<std::string>();
-                auto penalty_tokens = llama_tokenize(model, penalty_prompt_string, false);
-                slot->sparams.penalty_prompt_tokens.swap(penalty_tokens);
-                if (slot->params.n_predict > 0)
-                {
-                    slot->sparams.penalty_prompt_tokens.reserve(slot->sparams.penalty_prompt_tokens.size() + slot->params.n_predict);
-                }
-                slot->sparams.use_penalty_prompt_tokens = true;
+            const auto & prompt = data.find("prompt");
+            if (prompt == data.end()) {
+                send_error(task, "Either \"prompt\" or \"messages\" must be provided", ERROR_TYPE_INVALID_REQUEST);
+                return false;
+            } else {
+                slot.prompt = *prompt;
             }
-            else if (penalty_prompt->is_array())
-            {
-                const auto n_tokens = penalty_prompt->size();
-                slot->sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot->params.n_predict));
-                const int n_vocab = llama_n_vocab(model);
-                for (const auto &penalty_token : *penalty_prompt)
-                {
-                    if (penalty_token.is_number_integer())
-                    {
-                        const auto tok = penalty_token.get<llama_token>();
-                        if (tok >= 0 && tok < n_vocab)
-                        {
-                            slot->sparams.penalty_prompt_tokens.push_back(tok);
-                        }
-                    }
-                }
-                slot->sparams.use_penalty_prompt_tokens = true;
+            if (slot.prompt.is_array() && slot.prompt.size() == 0) {
+                send_error(task, "\"prompt\" cannot be an empty array", ERROR_TYPE_INVALID_REQUEST);
+                return false;
             }
         }
 
-        slot->sparams.logit_bias.clear();
-
-        if (json_value(data, "ignore_eos", false))
+        // penalize user-provided tokens
         {
-            slot->sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
-        }
+            slot.sparams.penalty_prompt_tokens.clear();
+            slot.sparams.use_penalty_prompt_tokens = false;
 
-        const auto &logit_bias = data.find("logit_bias");
-        if (logit_bias != data.end() && logit_bias->is_array())
-        {
-            const int n_vocab = llama_n_vocab(model);
-            for (const auto &el : *logit_bias)
-            {
-                if (el.is_array() && el.size() == 2)
-                {
-                    float bias;
-                    if (el[1].is_number())
-                    {
-                        bias = el[1].get<float>();
-                    }
-                    else if (el[1].is_boolean() && !el[1].get<bool>())
-                    {
-                        bias = -INFINITY;
-                    }
-                    else
-                    {
-                        continue;
-                    }
+            const auto & penalty_prompt = data.find("penalty_prompt");
 
-                    if (el[0].is_number_integer())
-                    {
-                        llama_token tok = el[0].get<llama_token>();
-                        if (tok >= 0 && tok < n_vocab)
-                        {
-                            slot->sparams.logit_bias[tok] = bias;
-                        }
-                    }
-                    else if (el[0].is_string())
-                    {
-                        auto toks = llama_tokenize(model, el[0].get<std::string>(), false);
-                        for (auto tok : toks)
-                        {
-                            slot->sparams.logit_bias[tok] = bias;
-                        }
+            if (penalty_prompt != data.end()) {
+                if (penalty_prompt->is_string()) {
+                    const auto penalty_prompt_string = penalty_prompt->get<std::string>();
+                    slot.sparams.penalty_prompt_tokens = llama_tokenize(model, penalty_prompt_string, false);
+
+                    if (slot.params.n_predict > 0) {
+                        slot.sparams.penalty_prompt_tokens.reserve(slot.sparams.penalty_prompt_tokens.size() + slot.params.n_predict);
                     }
+                    slot.sparams.use_penalty_prompt_tokens = true;
+
+                    LOG_VERBOSE("penalty_prompt_tokens", {
+                        {"id_slot", slot.id},
+                        {"tokens",  slot.sparams.penalty_prompt_tokens},
+                    });
                 }
-            }
-        }
+                else if (penalty_prompt->is_array()) {
+                    const auto n_tokens = penalty_prompt->size();
+                    slot.sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot.params.n_predict));
 
-        slot->params.antiprompt.clear();
-
-        const auto &stop = data.find("stop");
-        if (stop != data.end() && stop->is_array())
-        {
-            for (const auto &word : *stop)
-            {
-                if (!word.empty())
-                {
-                    slot->params.antiprompt.push_back(word);
-                }
-            }
-        }
-
-        const auto &samplers_sequence = data.find("samplers");
-        if (samplers_sequence != data.end() && samplers_sequence->is_array())
-        {
-            std::vector<std::string> sampler_names;
-            for (const auto &sampler_name : *samplers_sequence)
-            {
-                if (sampler_name.is_string())
-                {
-                    sampler_names.emplace_back(sampler_name);
-                }
-            }
-            slot->sparams.samplers_sequence = sampler_types_from_names(sampler_names, false);
-        }
-        else
-        {
-            slot->sparams.samplers_sequence = default_sparams.samplers_sequence;
-        }
-
-        if (multimodal)
-        {
-            const auto &images_data = data.find("image_data");
-            if (images_data != data.end() && images_data->is_array())
-            {
-                for (const auto &img : *images_data)
-                {
-                    const std::vector<uint8_t> image_buffer = base64_decode(img["data"].get<std::string>());
-
-                    slot_image img_sl;
-                    img_sl.id = img.count("id") != 0 ? img["id"].get<int>() : slot->images.size();
-                    img_sl.img_data = clip_image_u8_init();
-                    if (!clip_image_load_from_bytes(image_buffer.data(), image_buffer.size(), img_sl.img_data))
-                    {
-                        LOG_TEE("slot %i - failed to load image [id: %i]\n", slot->id, img_sl.id);
-                        return false;
-                    }
-                    LOG_TEE("slot %i - loaded image\n", slot->id);
-                    img_sl.request_encode_image = true;
-                    slot->images.push_back(img_sl);
-                }
-                // process prompt
-                // example: system prompt [img-102] user [img-103] describe [img-134] -> [{id: 102, prefix: 'system prompt '}, {id: 103, prefix: ' user '}, {id: 134, prefix: ' describe '}]}
-                if (slot->images.size() > 0 && !slot->prompt.is_array())
-                {
-                    std::string prompt = slot->prompt.get<std::string>();
-                    size_t pos = 0, begin_prefix = 0;
-                    std::string pattern = "[img-";
-                    while ((pos = prompt.find(pattern, pos)) != std::string::npos) {
-                        size_t end_prefix = pos;
-                        pos += pattern.length();
-                        size_t end_pos = prompt.find(']', pos);
-                        if (end_pos != std::string::npos)
-                        {
-                            std::string image_id = prompt.substr(pos, end_pos - pos);
-                            try
-                            {
-                                int img_id = std::stoi(image_id);
-                                bool found = false;
-                                for (slot_image &img : slot->images)
-                                {
-                                    if (img.id == img_id) {
-                                        found = true;
-                                        img.prefix_prompt = prompt.substr(begin_prefix, end_prefix - begin_prefix);
-                                        begin_prefix = end_pos + 1;
-                                        break;
-                                    }
-                                }
-                                if (!found) {
-                                    LOG_TEE("ERROR: Image with id: %i, not found.\n", img_id);
-                                    slot->images.clear();
-                                    return false;
-                                }
-                            } catch (const std::invalid_argument& e) {
-                                LOG_TEE("Invalid image number id in prompt\n");
-                                slot->images.clear();
-                                return false;
+                    const int n_vocab = llama_n_vocab(model);
+                    for (const auto & penalty_token : *penalty_prompt) {
+                        if (penalty_token.is_number_integer()) {
+                            const auto tok = penalty_token.get<llama_token>();
+                            if (tok >= 0 && tok < n_vocab) {
+                                slot.sparams.penalty_prompt_tokens.push_back(tok);
                             }
                         }
                     }
-                    slot->prompt = "";
-                    slot->params.input_suffix = prompt.substr(begin_prefix);
-                    slot->params.cache_prompt = false; // multimodal doesn't support cache prompt
+                    slot.sparams.use_penalty_prompt_tokens = true;
+
+                    LOG_VERBOSE("penalty_prompt_tokens", {
+                        {"id_slot", slot.id},
+                        {"tokens",  slot.sparams.penalty_prompt_tokens},
+                    });
                 }
             }
         }
 
-        if (slot->ctx_sampling != nullptr)
         {
-            llama_sampling_free(slot->ctx_sampling);
+            slot.sparams.logit_bias.clear();
+
+            if (json_value(data, "ignore_eos", false)) {
+                slot.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
+            }
+
+            const auto & logit_bias = data.find("logit_bias");
+            if (logit_bias != data.end() && logit_bias->is_array()) {
+                const int n_vocab = llama_n_vocab(model);
+                for (const auto & el : *logit_bias) {
+                    // TODO: we may want to throw errors here, in case "el" is incorrect
+                    if (el.is_array() && el.size() == 2) {
+                        float bias;
+                        if (el[1].is_number()) {
+                            bias = el[1].get<float>();
+                        } else if (el[1].is_boolean() && !el[1].get<bool>()) {
+                            bias = -INFINITY;
+                        } else {
+                            continue;
+                        }
+
+                        if (el[0].is_number_integer()) {
+                            llama_token tok = el[0].get<llama_token>();
+                            if (tok >= 0 && tok < n_vocab) {
+                                slot.sparams.logit_bias[tok] = bias;
+                            }
+                        } else if (el[0].is_string()) {
+                            auto toks = llama_tokenize(model, el[0].get<std::string>(), false);
+                            for (auto tok : toks) {
+                                slot.sparams.logit_bias[tok] = bias;
+                            }
+                        }
+                    }
+                }
+            }
         }
-        slot->ctx_sampling = llama_sampling_init(slot->sparams);
-        llama_set_rng_seed(ctx, slot->params.seed);
-        slot->command = LOAD_PROMPT;
 
-        all_slots_are_idle = false;
+        {
+            slot.params.antiprompt.clear();
 
-        LOG_TEE("slot %i is processing [task id: %i]\n", slot->id, slot->task_id);
+            const auto & stop = data.find("stop");
+            if (stop != data.end() && stop->is_array()) {
+                for (const auto & word : *stop) {
+                    if (!word.empty()) {
+                        slot.params.antiprompt.push_back(word);
+                    }
+                }
+            }
+        }
+
+        {
+            const auto & samplers_sequence = data.find("samplers");
+            if (samplers_sequence != data.end() && samplers_sequence->is_array()) {
+                std::vector<std::string> sampler_names;
+                for (const auto & sampler_name : *samplers_sequence) {
+                    if (sampler_name.is_string()) {
+                        sampler_names.emplace_back(sampler_name);
+                    }
+                }
+                slot.sparams.samplers_sequence = sampler_types_from_names(sampler_names, false);
+            } else {
+                slot.sparams.samplers_sequence = default_sparams.samplers_sequence;
+            }
+        }
+
+        {
+            if (slot.ctx_sampling != nullptr) {
+                llama_sampling_free(slot.ctx_sampling);
+            }
+            slot.ctx_sampling = llama_sampling_init(slot.sparams);
+            if (slot.ctx_sampling == nullptr) {
+                // for now, the only error that may happen here is invalid grammar
+                send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST);
+                return false;
+            }
+        }
+
+        slot.command = SLOT_COMMAND_LOAD_PROMPT;
+        slot.prompt_tokens.clear();
+
+        LOG_INFO("slot is processing task", {
+            {"id_slot", slot.id},
+            {"id_task", slot.id_task},
+        });
 
         return true;
     }
 
     void kv_cache_clear() {
+        LOG_VERBOSE("clearing KV cache", {});
+
         // clear the entire KV cache
         llama_kv_cache_clear(ctx);
         clean_kv_cache = false;
     }
 
-    void update_system_prompt() {
+    void system_prompt_update() {
+        LOG_VERBOSE("system prompt update", {
+            {"system_prompt", system_prompt},
+        });
+
         kv_cache_clear();
         system_tokens.clear();
 
         if (!system_prompt.empty()) {
-            system_tokens = ::llama_tokenize(ctx, system_prompt, add_bos_token);
+            system_tokens = ::llama_tokenize(ctx, system_prompt, true);
 
             llama_batch_clear(batch);
 
-            for (int i = 0; i < (int)system_tokens.size(); ++i)
-            {
+            for (int i = 0; i < (int)system_tokens.size(); ++i) {
                 llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
             }
 
-            if (llama_decode(ctx, batch) != 0)
-            {
-                LOG_TEE("%s: llama_decode() failed\n", __func__);
-                return;
+            const int32_t n_batch = llama_n_batch(ctx);
+
+            for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
+                const int32_t n_tokens = std::min(params.n_batch, batch.n_tokens - i);
+                llama_batch batch_view = {
+                    n_tokens,
+                    batch.token    + i,
+                    nullptr,
+                    batch.pos      + i,
+                    batch.n_seq_id + i,
+                    batch.seq_id   + i,
+                    batch.logits   + i,
+                    0, 0, 0, // unused
+                };
+
+                if (llama_decode(ctx, batch_view) != 0) {
+                    LOG_ERROR("llama_decode() failed", {});
+                    return;
+                }
             }
 
             // assign the system KV cache to all parallel sequences
-            for (int32_t i = 1; i < params.n_parallel; ++i)
-            {
-                llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size());
+            for (int32_t i = 1; i <= params.n_parallel; ++i) {
+                llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
             }
         }
 
-        LOG_TEE("system prompt updated\n");
         system_need_update = false;
     }
 
-    void notify_system_prompt_changed() {
+    bool system_prompt_set(const std::string & sys_prompt) {
+        system_prompt = sys_prompt;
+
+        LOG_VERBOSE("system prompt process", {
+            {"system_prompt",  system_prompt},
+        });
+
         // release all slots
-        for (llama_client_slot &slot : slots)
-        {
+        for (server_slot & slot : slots) {
             slot.release();
         }
 
         system_need_update = true;
+        return true;
     }
 
-    void process_system_prompt_data(const json &sys_props) {
-        system_prompt  = sys_props.value("prompt", "");
-        name_user      = sys_props.value("anti_prompt", "");
-        name_assistant = sys_props.value("assistant_name", "");
-
-
-        notify_system_prompt_changed();
-    }
-
-    static size_t find_stopping_strings(const std::string &text, const size_t last_token_size,
-                                        const stop_type type, llama_client_slot &slot)
-    {
-        size_t stop_pos = std::string::npos;
-
-        for (const std::string &word : slot.params.antiprompt)
-        {
-            size_t pos;
-            if (type == STOP_FULL)
-            {
-                const size_t tmp = word.size() + last_token_size;
-                const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0;
-                pos = text.find(word, from_pos);
-            }
-            else
-            {
-                pos = find_partial_stop_string(word, text);
-            }
-            if (pos != std::string::npos &&
-                (stop_pos == std::string::npos || pos < stop_pos))
-            {
-                if (type == STOP_FULL)
-                {
-                    slot.stopped_word = true;
-                    slot.stopping_word = word;
-                    slot.has_next_token = false;
-                }
-                stop_pos = pos;
-            }
-        }
-
-        return stop_pos;
-    }
-
-    bool process_token(completion_token_output &result, llama_client_slot &slot) {
+    bool process_token(completion_token_output & result, server_slot & slot) {
         // remember which tokens were sampled - used for repetition penalties during sampling
-        const std::string token_str = llama_token_to_piece(ctx, result.tok);
+        const std::string token_str = llama_token_to_piece(ctx, result.tok, false);
         slot.sampled = result.tok;
 
         // search stop word and delete it
         slot.generated_text += token_str;
         slot.has_next_token = true;
 
-        if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1)
-        {
+        if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1) {
             // we can change penalty_prompt_tokens because it is always created from scratch each request
             slot.ctx_sampling->params.penalty_prompt_tokens.push_back(result.tok);
         }
 
         // check if there is incomplete UTF-8 character at the end
         bool incomplete = false;
-        for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i)
-        {
+        for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i) {
             unsigned char c = slot.generated_text[slot.generated_text.size() - i];
-            if ((c & 0xC0) == 0x80)
-            {
+            if ((c & 0xC0) == 0x80) {
                 // continuation byte: 10xxxxxx
                 continue;
             }
-            if ((c & 0xE0) == 0xC0)
-            {
+            if ((c & 0xE0) == 0xC0) {
                 // 2-byte character: 110xxxxx ...
                 incomplete = i < 2;
-            }
-            else if ((c & 0xF0) == 0xE0)
-            {
+            } else if ((c & 0xF0) == 0xE0) {
                 // 3-byte character: 1110xxxx ...
                 incomplete = i < 3;
-            }
-            else if ((c & 0xF8) == 0xF0)
-            {
+            } else if ((c & 0xF8) == 0xF0) {
                 // 4-byte character: 11110xxx ...
                 incomplete = i < 4;
             }
@@ -920,241 +1149,222 @@ struct llama_server_context
             break;
         }
 
-        if (!incomplete)
-        {
-            size_t pos = std::min(slot.sent_count, slot.generated_text.size());
+        if (!incomplete) {
+            size_t pos = std::min(slot.n_sent_text, slot.generated_text.size());
+
             const std::string str_test = slot.generated_text.substr(pos);
             bool is_stop_full = false;
-            size_t stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_FULL, slot);
-            if (stop_pos != std::string::npos)
-            {
+
+            size_t stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_FULL);
+            if (stop_pos != std::string::npos) {
                 is_stop_full = true;
                 slot.generated_text.erase(
                     slot.generated_text.begin() + pos + stop_pos,
                     slot.generated_text.end());
-                pos = std::min(slot.sent_count, slot.generated_text.size());
-            }
-            else
-            {
+                pos = std::min(slot.n_sent_text, slot.generated_text.size());
+            } else {
                 is_stop_full = false;
-                stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_PARTIAL, slot);
+                stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_PARTIAL);
             }
 
             // check if there is any token to predict
-            if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0))
-            {
+            if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0)) {
                 // no send the stop word in the response
                 result.text_to_send = slot.generated_text.substr(pos, std::string::npos);
-                slot.sent_count += result.text_to_send.size();
+                slot.n_sent_text += result.text_to_send.size();
                 // add the token to slot queue and cache
             }
+
             slot.add_token_string(result);
-            if (slot.params.stream)
-            {
+            if (slot.params.stream) {
                 send_partial_response(slot, result);
             }
         }
 
-        if (incomplete)
-        {
+        if (incomplete) {
             slot.has_next_token = true;
         }
 
         // check the limits
-        if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params))
-        {
-            slot.stopped_limit = true;
+        if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params)) {
+            slot.stopped_limit  = true;
             slot.has_next_token = false;
+
+            LOG_VERBOSE("stopped by limit", {
+                {"id_slot",   slot.id},
+                {"id_task",   slot.id_task},
+                {"n_decoded", slot.n_decoded},
+                {"n_predict", slot.params.n_predict},
+            });
         }
 
-        if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(model))
-        {
-            slot.stopped_eos = true;
+        if (llama_token_is_eog(model, result.tok)) {
+            slot.stopped_eos    = true;
             slot.has_next_token = false;
+
             LOG_VERBOSE("eos token found", {});
         }
 
+        auto n_ctx_train = llama_n_ctx_train(model);
+        if (slot.params.n_predict < 1 && slot.n_predict < 1 && slot.ga_n == 1
+                    && slot.n_prompt_tokens + slot.n_decoded >= n_ctx_train) {
+            LOG_WARNING("n_predict is not set and self-context extend is disabled."
+                        " Limiting generated tokens to n_ctx_train to avoid EOS-less generation infinite loop", {
+                    { "id_slot",              slot.id },
+                    { "params.n_predict",     slot.params.n_predict },
+                    { "slot.n_prompt_tokens", slot.n_prompt_tokens },
+                    { "slot.n_decoded",       slot.n_decoded },
+                    { "slot.n_predict",       slot.n_predict },
+                    { "n_slots",              params.n_parallel },
+                    { "slot.n_ctx",           slot.n_ctx },
+                    { "n_ctx",                n_ctx },
+                    { "n_ctx_train",          n_ctx_train },
+                    { "ga_n",                 slot.ga_n },
+                });
+            slot.truncated      = true;
+            slot.stopped_limit  = true;
+            slot.has_next_token = false; // stop prediction
+        }
+
         LOG_VERBOSE("next token", {
-                                      {"token", result.tok},
-                                      {"token_text", tokens_to_output_formatted_string(ctx, result.tok)},
-                                      {"has_next_token", slot.has_next_token},
-                                      {"n_remain", slot.n_remaining},
-                                      {"num_tokens_predicted", slot.n_decoded},
-                                      {"stopped_eos", slot.stopped_eos},
-                                      {"stopped_word", slot.stopped_word},
-                                      {"stopped_limit", slot.stopped_limit},
-                                      {"stopping_word", slot.stopping_word},
-                                  });
+            {"id_slot",        slot.id},
+            {"id_task",        slot.id_task},
+            {"token",          result.tok},
+            {"token_text",     tokens_to_output_formatted_string(ctx, result.tok)},
+            {"has_next_token", slot.has_next_token},
+            {"n_remain",       slot.n_remaining},
+            {"n_decoded",      slot.n_decoded},
+            {"stopped_eos",    slot.stopped_eos},
+            {"stopped_word",   slot.stopped_word},
+            {"stopped_limit",  slot.stopped_limit},
+            {"stopping_word",  slot.stopping_word},
+        });
 
         return slot.has_next_token; // continue
     }
 
-    bool process_images(llama_client_slot &slot) const
-    {
-        for (slot_image &img : slot.images)
-        {
-            if (!img.request_encode_image)
-            {
-                continue;
-            }
-            clip_image_f32_batch img_res_v;
-            img_res_v.size = 0;
-            img_res_v.data = nullptr;
-            if (!clip_image_preprocess(clp_ctx, img.img_data, img_res_v))
-            {
-                LOG_TEE("Error processing the given image");
-                clip_free(clp_ctx);
-                clip_image_f32_batch_free(img_res_v);
-                return false;
-            }
-            if (img_res_v.size == 0)
-            {
-                LOG_TEE("Error processing the given image");
-                return false;
-            }
-
-            // note: assumes only one image was returned by clip_image_preprocess
-            clip_image_f32 * img_res = img_res_v.data;
-
-            img.image_tokens = clip_n_patches(clp_ctx);
-            img.image_embedding = (float *)malloc(clip_embd_nbytes(clp_ctx));
-            if (!img.image_embedding)
-            {
-                LOG_TEE("Unable to allocate memory for image embeddings\n");
-                clip_image_f32_batch_free(img_res_v);
-                clip_free(clp_ctx);
-                return false;
-            }
-            LOG_TEE("slot %i - encoding image [id: %i]\n", slot.id, img.id);
-            if (!clip_image_encode(clp_ctx, params.n_threads, img_res, img.image_embedding))
-            {
-                LOG_TEE("Unable to encode image\n");
-                clip_image_f32_batch_free(img_res_v);
-                return false;
-            }
-
-            clip_image_f32_batch_free(img_res_v);
-
-            img.request_encode_image = false;
-        }
-
-        return slot.images.size() > 0;
-    }
-
-    void send_error(task_server& task, const std::string &error)
-    {
-        LOG_TEE("task %i - error: %s\n", task.id, error.c_str());
-        task_result res;
-        res.id = task.id;
-        res.multitask_id = task.multitask_id;
-        res.stop = false;
-        res.error = true;
-        res.result_json = { { "content", error } };
-        queue_results.send(res);
-    }
-
-    json get_formated_generation(llama_client_slot &slot)
-    {
+    json get_formated_generation(const server_slot & slot) const {
         const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
-        const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() &&
-                                eos_bias->second < 0.0f && std::isinf(eos_bias->second);
+        const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && eos_bias->second < 0.0f && std::isinf(eos_bias->second);
+
         std::vector<std::string> samplers_sequence;
-        for (const auto &sampler_type : slot.sparams.samplers_sequence)
-        {
+        samplers_sequence.reserve(slot.sparams.samplers_sequence.size());
+        for (const auto & sampler_type : slot.sparams.samplers_sequence) {
             samplers_sequence.emplace_back(sampler_type_to_name_string(sampler_type));
         }
 
         return json {
-            {"n_ctx",             slot.n_ctx},
-            {"n_predict",         slot.n_predict},
-            {"model",             params.model_alias},
-            {"seed",              slot.params.seed},
-            {"temperature",       slot.sparams.temp},
-            {"dynatemp_range",    slot.sparams.dynatemp_range},
-            {"dynatemp_exponent", slot.sparams.dynatemp_exponent},
-            {"top_k",             slot.sparams.top_k},
-            {"top_p",             slot.sparams.top_p},
-            {"min_p",             slot.sparams.min_p},
-            {"tfs_z",             slot.sparams.tfs_z},
-            {"typical_p",         slot.sparams.typical_p},
-            {"repeat_last_n",     slot.sparams.penalty_last_n},
-            {"repeat_penalty",    slot.sparams.penalty_repeat},
-            {"presence_penalty",  slot.sparams.penalty_present},
-            {"frequency_penalty", slot.sparams.penalty_freq},
-            {"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens},
+            {"n_ctx",                     slot.n_ctx},
+            {"n_predict",                 slot.n_predict},
+            {"model",                     params.model_alias},
+            {"seed",                      slot.params.seed},
+            {"temperature",               slot.sparams.temp},
+            {"dynatemp_range",            slot.sparams.dynatemp_range},
+            {"dynatemp_exponent",         slot.sparams.dynatemp_exponent},
+            {"top_k",                     slot.sparams.top_k},
+            {"top_p",                     slot.sparams.top_p},
+            {"min_p",                     slot.sparams.min_p},
+            {"tfs_z",                     slot.sparams.tfs_z},
+            {"typical_p",                 slot.sparams.typical_p},
+            {"repeat_last_n",             slot.sparams.penalty_last_n},
+            {"repeat_penalty",            slot.sparams.penalty_repeat},
+            {"presence_penalty",          slot.sparams.penalty_present},
+            {"frequency_penalty",         slot.sparams.penalty_freq},
+            {"penalty_prompt_tokens",     slot.sparams.penalty_prompt_tokens},
             {"use_penalty_prompt_tokens", slot.sparams.use_penalty_prompt_tokens},
-            {"mirostat",          slot.sparams.mirostat},
-            {"mirostat_tau",      slot.sparams.mirostat_tau},
-            {"mirostat_eta",      slot.sparams.mirostat_eta},
-            {"penalize_nl",       slot.sparams.penalize_nl},
-            {"stop",              slot.params.antiprompt},
-            {"n_predict",         slot.params.n_predict},
-            {"n_keep",            params.n_keep},
-            {"ignore_eos",        ignore_eos},
-            {"stream",            slot.params.stream},
-            {"logit_bias",        slot.sparams.logit_bias},
-            {"n_probs",           slot.sparams.n_probs},
-            {"min_keep",          slot.sparams.min_keep},
-            {"grammar",           slot.sparams.grammar},
-            {"samplers",          samplers_sequence}
+            {"mirostat",                  slot.sparams.mirostat},
+            {"mirostat_tau",              slot.sparams.mirostat_tau},
+            {"mirostat_eta",              slot.sparams.mirostat_eta},
+            {"penalize_nl",               slot.sparams.penalize_nl},
+            {"stop",                      slot.params.antiprompt},
+            {"n_predict",                 slot.params.n_predict}, // TODO: fix duplicate key n_predict
+            {"n_keep",                    slot.params.n_keep},
+            {"n_discard",                 slot.params.n_discard},
+            {"ignore_eos",                ignore_eos},
+            {"stream",                    slot.params.stream},
+            {"logit_bias",                slot.sparams.logit_bias},
+            {"n_probs",                   slot.sparams.n_probs},
+            {"min_keep",                  slot.sparams.min_keep},
+            {"grammar",                   slot.sparams.grammar},
+            {"samplers",                  samplers_sequence}
         };
     }
 
-    void send_partial_response(llama_client_slot &slot, completion_token_output tkn)
-    {
-        task_result res;
-        res.id = slot.task_id;
-        res.multitask_id = slot.multitask_id;
-        res.error = false;
-        res.stop = false;
+    void send_error(const server_task & task, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
+        send_error(task.id, task.id_multi, error, type);
+    }
 
-        res.result_json = json
-        {
+    void send_error(const server_slot & slot, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
+        send_error(slot.id_task, slot.id_multi, error, type);
+    }
+
+    void send_error(const int id_task, const int id_multi, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
+        LOG_ERROR("task error", {
+            {"id_multi", id_multi},
+            {"id_task", id_task},
+            {"error", error},
+        });
+
+        server_task_result res;
+        res.id       = id_task;
+        res.id_multi = id_multi;
+        res.stop     = false;
+        res.error    = true;
+        res.data     = format_error_response(error, type);
+
+        queue_results.send(res);
+    }
+
+    void send_partial_response(server_slot & slot, completion_token_output tkn) {
+        server_task_result res;
+        res.id       = slot.id_task;
+        res.id_multi = slot.id_multi;
+        res.error    = false;
+        res.stop     = false;
+        res.data     = json {
             {"content",    tkn.text_to_send},
             {"stop",       false},
-            {"slot_id",    slot.id},
-            {"multimodal", multimodal}
+            {"id_slot",    slot.id},
+            {"multimodal", false}
         };
 
-        if (slot.sparams.n_probs > 0)
-        {
-            std::vector<completion_token_output> probs_output = {};
+        if (slot.sparams.n_probs > 0) {
             const std::vector<llama_token> to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false);
-            size_t probs_pos      = std::min(slot.sent_token_probs_index,                       slot.generated_token_probs.size());
-            size_t probs_stop_pos = std::min(slot.sent_token_probs_index + to_send_toks.size(), slot.generated_token_probs.size());
-            if (probs_pos < probs_stop_pos)
-            {
-                probs_output = std::vector<completion_token_output>(slot.generated_token_probs.begin() + probs_pos, slot.generated_token_probs.begin() + probs_stop_pos);
+            const size_t probs_pos      = std::min(slot.n_sent_token_probs,                       slot.generated_token_probs.size());
+            const size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size());
+
+            std::vector<completion_token_output> probs_output;
+            if (probs_pos < probs_stop_pos) {
+                probs_output = std::vector<completion_token_output>(
+                        slot.generated_token_probs.begin() + probs_pos,
+                        slot.generated_token_probs.begin() + probs_stop_pos);
             }
-            slot.sent_token_probs_index = probs_stop_pos;
-            res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output);
+            slot.n_sent_token_probs = probs_stop_pos;
+
+            res.data["completion_probabilities"] = probs_vector_to_json(ctx, probs_output);
         }
 
-        if (slot.oaicompat)
-        {
-            res.result_json["oaicompat_token_ctr"] = slot.n_decoded;
-            res.result_json["model"] = slot.oaicompat_model;
+        if (slot.oaicompat) {
+            res.data["oaicompat_token_ctr"] = slot.n_decoded;
+            res.data["model"] = slot.oaicompat_model;
         }
 
         queue_results.send(res);
     }
 
-    void send_final_response(llama_client_slot &slot)
-    {
-        task_result res;
-        res.id = slot.task_id;
-        res.multitask_id = slot.multitask_id;
-        res.error = false;
-        res.stop = true;
-
-        res.result_json = json
-        {
+    void send_final_response(const server_slot & slot) {
+        server_task_result res;
+        res.id       = slot.id_task;
+        res.id_multi = slot.id_multi;
+        res.error    = false;
+        res.stop     = true;
+        res.data     = json {
             {"content",             !slot.params.stream ? slot.generated_text : ""},
-            {"slot_id",             slot.id},
+            {"id_slot",             slot.id},
             {"stop",                true},
             {"model",               params.model_alias},
             {"tokens_predicted",    slot.n_decoded},
-            {"tokens_evaluated",    slot.num_prompt_tokens},
+            {"tokens_evaluated",    slot.n_prompt_tokens},
             {"generation_settings", get_formated_generation(slot)},
             {"prompt",              slot.prompt},
             {"truncated",           slot.truncated},
@@ -1166,80 +1376,92 @@ struct llama_server_context
             {"timings",             slot.get_formated_timings()}
         };
 
-        if (slot.sparams.n_probs > 0)
-        {
-            std::vector<completion_token_output> probs = {};
-            if (!slot.params.stream && slot.stopped_word)
-            {
+        if (slot.sparams.n_probs > 0) {
+            std::vector<completion_token_output> probs;
+            if (!slot.params.stream && slot.stopped_word) {
                 const std::vector<llama_token> stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false);
-                probs = std::vector<completion_token_output>(slot.generated_token_probs.begin(), slot.generated_token_probs.end() - stop_word_toks.size());
-            }
-            else
-            {
+
+                size_t safe_offset = std::min(slot.generated_token_probs.size(), stop_word_toks.size());
                 probs = std::vector<completion_token_output>(
-                                    slot.generated_token_probs.begin(),
-                                    slot.generated_token_probs.end());
+                        slot.generated_token_probs.begin(),
+                        slot.generated_token_probs.end() - safe_offset);
+            } else {
+                probs = std::vector<completion_token_output>(
+                        slot.generated_token_probs.begin(),
+                        slot.generated_token_probs.end());
             }
-            res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs);
+
+            res.data["completion_probabilities"] = probs_vector_to_json(ctx, probs);
         }
 
-        if (slot.oaicompat)
-        {
-            res.result_json["oaicompat_token_ctr"] = slot.n_decoded;
-            res.result_json["model"] = slot.oaicompat_model;
+        if (slot.oaicompat) {
+            res.data["oaicompat_token_ctr"] = slot.n_decoded;
+            res.data["model"] = slot.oaicompat_model;
         }
 
         queue_results.send(res);
     }
 
-    void send_embedding(llama_client_slot &slot)
-    {
-        task_result res;
-        res.id = slot.task_id;
-        res.multitask_id = slot.multitask_id;
-        res.error = false;
-        res.stop = true;
+    void send_embedding(const server_slot & slot, const llama_batch & batch) {
+        server_task_result res;
+        res.id       = slot.id_task;
+        res.id_multi = slot.id_multi;
+        res.error    = false;
+        res.stop     = true;
 
         const int n_embd = llama_n_embd(model);
-        if (!params.embedding)
-        {
-            LOG_WARNING("embedding disabled", {
-                                                  {"params.embedding", params.embedding},
-                                              });
-            res.result_json = json
-            {
-                {"embedding", std::vector<float>(n_embd, 0.0f)},
-            };
-        }
-        else
-        {
-            const float *data = llama_get_embeddings(ctx);
-            std::vector<float> embedding(data, data + n_embd);
-            res.result_json = json
-            {
-                {"embedding", embedding },
+
+        std::vector<float> embd_res(n_embd, 0.0f);
+
+        for (int i = 0; i < batch.n_tokens; ++i) {
+            if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) {
+                continue;
+            }
+
+            const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
+            if (embd == NULL) {
+                embd = llama_get_embeddings_ith(ctx, i);
+            }
+
+            if (embd == NULL) {
+                LOG_ERROR("failed to get embeddings", {
+                    {"token",  batch.token [i]},
+                        {"seq_id", batch.seq_id[i][0]}
+                });
+
+                res.data = json {
+                    {"embedding", std::vector<float>(n_embd, 0.0f)},
+                };
+
+                continue;
+            }
+
+            llama_embd_normalize(embd, embd_res.data(), n_embd);
+
+            res.data = json {
+                {"embedding", embd_res},
             };
         }
+
         queue_results.send(res);
     }
 
-    void request_completion(int task_id, json data, bool infill, bool embedding, int multitask_id)
-    {
-        task_server task;
-        task.id = task_id;
-        task.target_id = 0;
-        task.data = std::move(data);
-        task.infill_mode = infill;
-        task.embedding_mode = embedding;
-        task.type = TASK_TYPE_COMPLETION;
-        task.multitask_id = multitask_id;
+    void request_completion(int id_task, int id_multi, json data, bool infill, bool embedding) {
+        server_task task;
+        task.id        = id_task;
+        task.id_multi  = id_multi;
+        task.id_target = 0;
+        task.data      = std::move(data);
+        task.infill    = infill;
+        task.embedding = embedding;
+        task.type      = SERVER_TASK_TYPE_COMPLETION;
 
         // when a completion task's prompt array is not a singleton, we split it into multiple requests
         // otherwise, it's a single-prompt task, we actually queue it
         // if there's numbers in the prompt array it will be treated as an array of tokens
         if (task.data.count("prompt") != 0 && task.data.at("prompt").size() > 1) {
             bool numbers = false;
-            for (const auto& e : task.data.at("prompt")) {
+            for (const auto & e : task.data.at("prompt")) {
                 if (e.is_number()) {
                     numbers = true;
                     break;
@@ -1254,92 +1476,23 @@ struct llama_server_context
             if (numbers) {
                 queue_tasks.post(task);
             } else {
-                split_multiprompt_task(task_id, task);
+                split_multiprompt_task(id_task, task);
             }
         } else {
             queue_tasks.post(task);
         }
     }
 
-    // for multiple images processing
-    bool ingest_images(llama_client_slot &slot, int n_batch)
-    {
-        int image_idx = 0;
+    void request_cancel(int id_task) {
+        server_task task;
+        task.type      = SERVER_TASK_TYPE_CANCEL;
+        task.id_target = id_task;
 
-        while (image_idx < (int) slot.images.size())
-        {
-            slot_image &img = slot.images[image_idx];
-
-            // process prefix prompt
-            for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch)
-            {
-                const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
-                llama_batch batch_view = {
-                    n_tokens,
-                    batch.token    + i,
-                    nullptr,
-                    batch.pos      + i,
-                    batch.n_seq_id + i,
-                    batch.seq_id   + i,
-                    batch.logits   + i,
-                    0, 0, 0, // unused
-                };
-                if (llama_decode(ctx, batch_view))
-                {
-                    LOG_TEE("%s : failed to eval\n", __func__);
-                    return false;
-                }
-            }
-
-            // process image with llm
-            for (int i = 0; i < img.image_tokens; i += n_batch)
-            {
-                int n_eval = img.image_tokens - i;
-                if (n_eval > n_batch)
-                {
-                    n_eval = n_batch;
-                }
-
-                const int n_embd = llama_n_embd(model);
-                llama_batch batch_img = { n_eval, nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, nullptr, slot.n_past, 1, 0, };
-                if (llama_decode(ctx, batch_img))
-                {
-                    LOG_TEE("%s : failed to eval image\n", __func__);
-                    return false;
-                }
-                slot.n_past += n_eval;
-            }
-            image_idx++;
-
-            llama_batch_clear(batch);
-
-            // append prefix of next image
-            const auto json_prompt = (image_idx >= (int) slot.images.size()) ?
-                slot.params.input_suffix : // no more images, then process suffix prompt
-                (json)(slot.images[image_idx].prefix_prompt);
-
-            std::vector<llama_token> append_tokens = tokenize(json_prompt, false); // has next image
-            for (int i = 0; i < (int) append_tokens.size(); ++i)
-            {
-                llama_batch_add(batch, append_tokens[i], system_tokens.size() + slot.n_past, { slot.id }, true);
-                slot.n_past += 1;
-            }
-        }
-
-        return true;
-    }
-
-    void request_cancel(int task_id)
-    {
-        task_server task;
-        task.type = TASK_TYPE_CANCEL;
-        task.target_id = task_id;
         queue_tasks.post(task);
     }
 
-    void split_multiprompt_task(int multitask_id, task_server& multiprompt_task)
-    {
-        int prompt_count = multiprompt_task.data.at("prompt").size();
+    void split_multiprompt_task(int id_multi, const server_task & multiprompt_task) {
+        const int prompt_count = multiprompt_task.data.at("prompt").size();
         if (prompt_count <= 1) {
             send_error(multiprompt_task, "error while handling multiple prompts");
             return;
@@ -1347,181 +1500,374 @@ struct llama_server_context
 
         // generate all the ID for subtask
         std::vector<int> subtask_ids(prompt_count);
-        for (int i = 0; i < prompt_count; i++)
-        {
+        for (int i = 0; i < prompt_count; i++) {
             subtask_ids[i] = queue_tasks.get_new_id();
         }
 
         // queue up the multitask so we can track its subtask progression
-        queue_tasks.add_multitask(multitask_id, subtask_ids);
+        queue_tasks.add_multitask(id_multi, subtask_ids);
 
         // add subtasks
-        for (int i = 0; i < prompt_count; i++)
-        {
+        for (int i = 0; i < prompt_count; i++) {
             json subtask_data = multiprompt_task.data;
-            subtask_data["prompt"] = subtask_data["prompt"][i];
+            subtask_data["prompt"] = subtask_data.at("prompt")[i];
 
             // subtasks inherit everything else (infill mode, embedding mode, etc.)
-            request_completion(subtask_ids[i], subtask_data, multiprompt_task.infill_mode, multiprompt_task.embedding_mode, multitask_id);
+            request_completion(subtask_ids[i], id_multi, subtask_data, multiprompt_task.infill, multiprompt_task.embedding);
         }
     }
 
-    void process_single_task(task_server& task)
-    {
-        switch (task.type)
-        {
-            case TASK_TYPE_COMPLETION: {
-                llama_client_slot *slot = get_slot(json_value(task.data, "slot_id", -1));
-                if (slot == nullptr)
+    void process_single_task(const server_task & task) {
+        switch (task.type) {
+            case SERVER_TASK_TYPE_COMPLETION:
                 {
-                    // if no slot is available, we defer this task for processing later
-                    LOG_VERBOSE("no slot is available", {});
-                    queue_tasks.defer(task);
-                    break;
-                }
-
-                if (task.data.contains("system_prompt"))
-                {
-                    if (!all_slots_are_idle) {
-                        send_error(task, "system prompt can only be updated when all slots are idle");
+                    server_slot * slot = get_slot(json_value(task.data, "id_slot", -1));
+                    if (slot == nullptr) {
+                        // if no slot is available, we defer this task for processing later
+                        LOG_VERBOSE("no slot is available", {{"id_task", task.id}});
+                        queue_tasks.defer(task);
                         break;
                     }
-                    process_system_prompt_data(task.data["system_prompt"]);
 
-                    // reset cache_tokens for all slots
-                    for (llama_client_slot &slot : slots)
-                    {
-                        slot.cache_tokens.clear();
-                        slot.n_past    = 0;
-                        slot.n_past_se = 0;
+                    if (task.data.contains("system_prompt")) {
+                        std::string sys_prompt = json_value(task.data, "system_prompt", std::string());
+                        system_prompt_set(sys_prompt);
+
+                        for (server_slot & slot : slots) {
+                            slot.n_past    = 0;
+                            slot.n_past_se = 0;
+                        }
                     }
-                }
 
-                slot->reset();
+                    slot->reset();
 
-                slot->infill       = task.infill_mode;
-                slot->embedding    = task.embedding_mode;
-                slot->task_id      = task.id;
-                slot->multitask_id = task.multitask_id;
+                    slot->id_task   = task.id;
+                    slot->id_multi  = task.id_multi;
+                    slot->infill    = task.infill;
+                    slot->embedding = task.embedding;
 
-                if (!launch_slot_with_data(slot, task.data))
-                {
-                    // send error result
-                    send_error(task, "internal_error");
-                    break;
-                }
-            } break;
-            case TASK_TYPE_CANCEL: { // release slot linked with the task id
-                for (auto & slot : slots)
-                {
-                    if (slot.task_id == task.target_id)
-                    {
-                        slot.release();
+                    if (!launch_slot_with_task(*slot, task)) {
+                        LOG_ERROR("error while launching slot", task.data);
                         break;
                     }
-                }
-            } break;
-            case TASK_TYPE_NEXT_RESPONSE: {
-                // do nothing
-            } break;
+                } break;
+            case SERVER_TASK_TYPE_CANCEL:
+                {
+                    // release slot linked with the task id
+                    for (auto & slot : slots) {
+                        if (slot.id_task == task.id_target) {
+                            slot.release();
+                            break;
+                        }
+                    }
+                } break;
+            case SERVER_TASK_TYPE_NEXT_RESPONSE:
+                {
+                    // do nothing
+                } break;
+            case SERVER_TASK_TYPE_METRICS:
+                {
+                    json slots_data = json::array();
+
+                    int n_idle_slots       = 0;
+                    int n_processing_slots = 0;
+
+                    for (server_slot & slot : slots) {
+                        json slot_data = get_formated_generation(slot);
+                        slot_data["id"]         = slot.id;
+                        slot_data["id_task"]    = slot.id_task;
+                        slot_data["state"]      = slot.state;
+                        slot_data["prompt"]     = slot.prompt;
+                        slot_data["next_token"] = {
+                            {"has_next_token", slot.has_next_token},
+                            {"n_remain",       slot.n_remaining},
+                            {"n_decoded",      slot.n_decoded},
+                            {"stopped_eos",    slot.stopped_eos},
+                            {"stopped_word",   slot.stopped_word},
+                            {"stopped_limit",  slot.stopped_limit},
+                            {"stopping_word",  slot.stopping_word},
+                        };
+
+                        if (slot_data["state"] == SLOT_STATE_IDLE) {
+                            n_idle_slots++;
+                        } else {
+                            n_processing_slots++;
+                        }
+
+                        slots_data.push_back(slot_data);
+                    }
+                    LOG_INFO("slot data", {
+                        {"id_task",            task.id},
+                        {"n_idle_slots",       n_idle_slots},
+                        {"n_processing_slots", n_processing_slots}
+                    });
+
+                    LOG_VERBOSE("slot data", {
+                        {"id_task",            task.id},
+                        {"n_idle_slots",       n_idle_slots},
+                        {"n_processing_slots", n_processing_slots},
+                        {"slots",              slots_data}
+                    });
+
+                    server_task_result res;
+                    res.id       = task.id;
+                    res.id_multi = task.id_multi;
+                    res.stop     = true;
+                    res.error    = false;
+                    res.data     = {
+                        { "idle",                            n_idle_slots       },
+                        { "processing",                      n_processing_slots },
+                        { "deferred",                        queue_tasks.queue_tasks_deferred.size() },
+                        { "t_start",                         metrics.t_start},
+
+                        { "n_prompt_tokens_processed_total", metrics.n_prompt_tokens_processed_total},
+                        { "t_tokens_generation_total",       metrics.t_tokens_generation_total},
+                        { "n_tokens_predicted_total",        metrics.n_tokens_predicted_total},
+                        { "t_prompt_processing_total",       metrics.t_prompt_processing_total},
+
+                        { "n_prompt_tokens_processed",       metrics.n_prompt_tokens_processed},
+                        { "t_prompt_processing",             metrics.t_prompt_processing},
+                        { "n_tokens_predicted",              metrics.n_tokens_predicted},
+                        { "t_tokens_generation",             metrics.t_tokens_generation},
+
+                        { "kv_cache_tokens_count",           llama_get_kv_cache_token_count(ctx)},
+                        { "kv_cache_used_cells",             llama_get_kv_cache_used_cells(ctx)},
+
+                        { "slots",                           slots_data },
+                    };
+
+                    if (json_value(task.data, "reset_bucket", false)) {
+                        metrics.reset_bucket();
+                    }
+                    queue_results.send(res);
+                } break;
+            case SERVER_TASK_TYPE_SLOT_SAVE:
+                {
+                    int id_slot = task.data.at("id_slot");
+                    server_slot * slot = get_slot(id_slot);
+                    if (slot == nullptr) {
+                        send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
+                        break;
+                    }
+
+                    const size_t token_count = slot->cache_tokens.size();
+                    const int64_t t_start = ggml_time_us();
+
+                    std::string filename = task.data.at("filename");
+                    std::string filepath = task.data.at("filepath");
+
+                    const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), token_count);
+
+                    const int64_t t_end = ggml_time_us();
+                    const double t_save_ms = (t_end - t_start) / 1000.0;
+
+                    server_task_result result;
+                    result.id = task.id;
+                    result.stop = true;
+                    result.error = false;
+                    result.data = json {
+                        { "id_slot",   id_slot },
+                        { "filename",  filename },
+                        { "n_saved",   token_count }, // tokens saved
+                        { "n_written", nwrite },      // bytes written
+                        { "timings", {
+                            { "save_ms", t_save_ms }
+                        } }
+                    };
+                    queue_results.send(result);
+                } break;
+            case SERVER_TASK_TYPE_SLOT_RESTORE:
+                {
+                    int id_slot = task.data.at("id_slot");
+                    server_slot * slot = get_slot(id_slot);
+                    if (slot == nullptr) {
+                        send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
+                        break;
+                    }
+
+                    const int64_t t_start = ggml_time_us();
+
+                    std::string filename = task.data.at("filename");
+                    std::string filepath = task.data.at("filepath");
+
+                    slot->cache_tokens.resize(slot->n_ctx);
+                    size_t token_count = 0;
+                    size_t nread = llama_state_seq_load_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), slot->cache_tokens.size(), &token_count);
+                    if (nread == 0) {
+                        slot->cache_tokens.resize(0);
+                        send_error(task, "Unable to restore slot, no available space in KV cache or invalid slot save file", ERROR_TYPE_INVALID_REQUEST);
+                        break;
+                    }
+                    slot->cache_tokens.resize(token_count);
+
+                    const int64_t t_end = ggml_time_us();
+                    const double t_restore_ms = (t_end - t_start) / 1000.0;
+
+                    server_task_result result;
+                    result.id = task.id;
+                    result.stop = true;
+                    result.error = false;
+                    result.data = json {
+                        { "id_slot",    id_slot },
+                        { "filename",   filename },
+                        { "n_restored", token_count }, // tokens restored
+                        { "n_read",     nread },       // bytes read
+                        { "timings", {
+                            { "restore_ms", t_restore_ms }
+                        } }
+                    };
+                    queue_results.send(result);
+                } break;
+            case SERVER_TASK_TYPE_SLOT_ERASE:
+                {
+                    int id_slot = task.data.at("id_slot");
+                    server_slot * slot = get_slot(id_slot);
+                    if (slot == nullptr) {
+                        send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
+                        break;
+                    }
+
+                    // Erase token cache
+                    const size_t n_erased = slot->cache_tokens.size();
+                    llama_kv_cache_seq_rm(ctx, slot->id + 1, -1, -1);
+                    slot->cache_tokens.clear();
+
+                    server_task_result result;
+                    result.id = task.id;
+                    result.stop = true;
+                    result.error = false;
+                    result.data = json {
+                        { "id_slot",  id_slot },
+                        { "n_erased", n_erased }
+                    };
+                    queue_results.send(result);
+                } break;
         }
     }
 
-    void on_finish_multitask(task_multi& multitask)
-    {
+    void on_finish_multitask(const server_task_multi & multitask) {
         // all subtasks done == multitask is done
-        task_result result;
-        result.id = multitask.id;
-        result.stop = true;
+        server_task_result result;
+        result.id    = multitask.id;
+        result.stop  = true;
         result.error = false;
 
         // collect json results into one json result
         std::vector<json> result_jsons;
-        for (auto& subres : multitask.results)
-        {
-            result_jsons.push_back(subres.result_json);
+        for (const auto & subres : multitask.results) {
+            result_jsons.push_back(subres.data);
             result.error = result.error && subres.error;
         }
-        result.result_json = json{ { "results", result_jsons } };
+        result.data = json {
+            { "results", result_jsons }
+        };
+
         queue_results.send(result);
     }
 
-    bool update_slots() {
-        if (system_need_update)
-        {
-            LOG_TEE("updating system prompt\n");
-            update_system_prompt();
+    void update_slots() {
+        if (system_need_update) {
+            system_prompt_update();
         }
 
-        llama_batch_clear(batch);
+        // release slots
+        for (auto & slot : slots) {
+            if (slot.command == SLOT_COMMAND_RELEASE) {
+                slot.state       = SLOT_STATE_IDLE;
+                slot.command     = SLOT_COMMAND_NONE;
+                slot.t_last_used = ggml_time_us();
 
-        if (all_slots_are_idle)
-        {
-            if (system_prompt.empty() && clean_kv_cache)
-            {
-                LOG_TEE("all slots are idle and system prompt is empty, clear the KV cache\n");
-                kv_cache_clear();
+                LOG_INFO("slot released", {
+                    {"id_slot",         slot.id},
+                    {"id_task",         slot.id_task},
+                    {"n_ctx",           n_ctx},
+                    {"n_past",          slot.n_past},
+                    {"n_system_tokens", system_tokens.size()},
+                    {"n_cache_tokens",  slot.cache_tokens.size()},
+                    {"truncated",       slot.truncated}
+                });
+
+                queue_tasks.notify_slot_changed();
             }
-            return true;
         }
 
-        task_server task;
-        task.type = TASK_TYPE_NEXT_RESPONSE;
-        task.target_id = -1;
-        queue_tasks.post(task);
-
-        for (llama_client_slot &slot : slots)
+        // check if all slots are idle
         {
-            if (slot.ga_n == 1)
-            {
-                if (slot.is_processing() && system_tokens.size() + slot.cache_tokens.size() >= (size_t) slot.n_ctx)
-                {
+            bool all_idle = true;
+
+            for (auto & slot : slots) {
+                if (slot.state != SLOT_STATE_IDLE || slot.command != SLOT_COMMAND_NONE) {
+                    all_idle = false;
+                    break;
+                }
+            }
+
+            if (all_idle) {
+                LOG_INFO("all slots are idle", {});
+                if (system_prompt.empty() && clean_kv_cache) {
+                    kv_cache_clear();
+                }
+
+                return;
+            }
+        }
+
+        {
+            LOG_VERBOSE("posting NEXT_RESPONSE", {});
+
+            server_task task;
+            task.type      = SERVER_TASK_TYPE_NEXT_RESPONSE;
+            task.id_target = -1;
+
+            queue_tasks.post(task);
+        }
+
+        // apply context-shift if needed
+        // TODO: simplify and improve
+        for (server_slot & slot : slots) {
+            if (slot.ga_n == 1) {
+                if (slot.is_processing() && (int) system_tokens.size() + slot.n_past >= slot.n_ctx - 1) {
                     // Shift context
-                    const int n_left    = system_tokens.size() + slot.n_past - slot.params.n_keep - 1;
-                    const int n_discard = n_left / 2;
+                    const int n_keep    = slot.params.n_keep + add_bos_token;
+                    const int n_left    = (int) system_tokens.size() + slot.n_past - n_keep;
+                    const int n_discard = slot.params.n_discard ? slot.params.n_discard : (n_left / 2);
 
-                    LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, slot.params.n_keep, n_left, n_discard);
-                    llama_kv_cache_seq_rm   (ctx, slot.id, slot.params.n_keep + 1            , slot.params.n_keep + n_discard + 1);
-                    llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, system_tokens.size() + slot.n_past, -n_discard);
+                    LOG_INFO("slot context shift", {
+                        {"id_slot",         slot.id},
+                        {"id_task",         slot.id_task},
+                        {"n_keep",          n_keep},
+                        {"n_left",          n_left},
+                        {"n_discard",       n_discard},
+                        {"n_ctx",           n_ctx},
+                        {"n_past",          slot.n_past},
+                        {"n_system_tokens", system_tokens.size()},
+                        {"n_cache_tokens",  slot.cache_tokens.size()}
+                    });
 
-                    for (size_t i = slot.params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++)
-                    {
-                        slot.cache_tokens[i - n_discard] = slot.cache_tokens[i];
+                    llama_kv_cache_seq_rm (ctx, slot.id + 1, n_keep            , n_keep + n_discard);
+                    llama_kv_cache_seq_add(ctx, slot.id + 1, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard);
+
+                    if (slot.params.cache_prompt) {
+                        for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) {
+                            slot.cache_tokens[i - n_discard] = slot.cache_tokens[i];
+                        }
+
+                        slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard);
                     }
 
-                    slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard);
-
                     slot.n_past -= n_discard;
 
                     slot.truncated = true;
-
-                    LOG_VERBOSE("context shift", {
-                        { "n_ctx", n_ctx },
-                        { "n_keep", params.n_keep },
-                        { "n_left", n_left },
-                    });
                 }
             }
         }
 
-        // decode any currently ongoing sequences
-        for (auto & slot : slots)
-        {
-            // release the slot
-            if (slot.command == RELEASE)
-            {
-                slot.state = IDLE;
-                slot.command = NONE;
-                slot.t_last_used = ggml_time_us();
+        // start populating the batch for this iteration
+        llama_batch_clear(batch);
 
-                LOG_TEE("slot %d released (%d tokens in cache)\n", slot.id, (int) slot.cache_tokens.size());
-                queue_tasks.notify_slot_changed();
-
-                continue;
-            }
-
-            if (slot.state == IDLE)
-            {
+        // frist, add sampled tokens from any ongoing sequences
+        for (auto & slot : slots) {
+            if (slot.state == SLOT_STATE_IDLE) {
                 continue;
             }
 
@@ -1531,167 +1877,217 @@ struct llama_server_context
 
             // TODO: we always have to take into account the "system_tokens"
             //       this is not great and needs to be improved somehow
-            llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id }, true);
+            llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id + 1 }, true);
+
             slot.n_past += 1;
+
+            if (slot.params.cache_prompt) {
+                slot.cache_tokens.push_back(slot.sampled);
+            }
+
+            LOG_VERBOSE("slot decode token", {
+                {"id_slot",         slot.id},
+                {"id_task",         slot.id_task},
+                {"n_ctx",           n_ctx},
+                {"n_past",          slot.n_past},
+                {"n_system_tokens", system_tokens.size()},
+                {"n_cache_tokens",  slot.cache_tokens.size()},
+                {"truncated",       slot.truncated}
+            });
         }
 
         // process in chunks of params.n_batch
-        int32_t n_batch = params.n_batch;
+        int32_t n_batch  = llama_n_batch(ctx);
+        int32_t n_ubatch = llama_n_ubatch(ctx);
 
-        // assign workload to the slots
-        if (params.cont_batching || batch.n_tokens == 0)
-        {
-            for (auto & slot : slots)
-            {
-                const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get<std::string>().empty()) || !slot.images.empty();
+        // next, batch any pending prompts without exceeding n_batch
+        if (params.cont_batching || batch.n_tokens == 0) {
+            for (auto & slot : slots) {
+                // this slot still has a prompt to be processed
+                if (slot.state == SLOT_STATE_IDLE && slot.command == SLOT_COMMAND_LOAD_PROMPT) {
+                    auto & prompt_tokens = slot.prompt_tokens;
 
-                // empty prompt passed -> release the slot and send empty response
-                // note: infill mode allows empty prompt
-                if (slot.state == IDLE && slot.command == LOAD_PROMPT && !has_prompt && !slot.infill)
-                {
-                    slot.release();
-                    slot.print_timings();
-                    send_final_response(slot);
-                    continue;
-                }
-
-                // need process the prompt
-                if (slot.state == IDLE && slot.command == LOAD_PROMPT)
-                {
-                    slot.state = PROCESSING;
-                    slot.command = NONE;
-                    std::vector<llama_token> prompt_tokens;
-                    slot.t_start_process_prompt = ggml_time_us();
-                    slot.t_start_genereration = 0;
-
-                    if (slot.infill)
-                    {
-                        bool suff_rm_leading_spc = true;
-                        if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1)
-                        {
-                            params.input_suffix.erase(0, 1);
-                            suff_rm_leading_spc = false;
-                        }
-                        auto prefix_tokens = tokenize(slot.params.input_prefix, false);
-                        auto suffix_tokens = tokenize(slot.params.input_suffix, false);
-
-                        const int space_token = 29871; // TODO: this should not be hardcoded
-                        if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) {
-                            suffix_tokens.erase(suffix_tokens.begin());
-                        }
-
-                        prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model));
-                        prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS
-                        prefix_tokens.insert(prefix_tokens.end(),   llama_token_suffix(model));
-                        prefix_tokens.insert(prefix_tokens.end(),   suffix_tokens.begin(), suffix_tokens.end());
-                        prefix_tokens.push_back(llama_token_middle(model));
-                        prompt_tokens = prefix_tokens;
-                    }
-                    else
-                    {
-                        prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_token);  // add BOS if there isn't system prompt
-                    }
-
-                    slot.num_prompt_tokens = prompt_tokens.size();
-
-                    if (slot.params.n_keep < 0)
-                    {
-                        slot.params.n_keep = slot.num_prompt_tokens;
-                    }
-                    slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep);
-
-                    // if input prompt is too big, truncate it
-                    if (slot.num_prompt_tokens >= slot.n_ctx)
-                    {
-                        const int n_left = slot.n_ctx - slot.params.n_keep;
-                        const int n_block_size = n_left / 2;
-                        const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size;
-
-                        std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep);
-                        new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end());
-
-                        LOG_VERBOSE("input truncated", {
-                            {"n_ctx",  slot.n_ctx},
-                            {"n_keep", slot.params.n_keep},
-                            {"n_left", n_left},
-                            {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
+                    // we haven't tokenized the prompt yet - do it now:
+                    if (prompt_tokens.empty()) {
+                        LOG_VERBOSE("tokenizing prompt", {
+                            {"id_slot", slot.id},
+                            {"id_task", slot.id_task}
                         });
-                        slot.truncated = true;
-                        prompt_tokens = new_tokens;
 
-                        slot.num_prompt_tokens = prompt_tokens.size();
-                        GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx);
+                        slot.t_start_process_prompt = ggml_time_us();
+                        slot.t_start_generation = 0;
+
+                        if (slot.infill) {
+                            bool suff_rm_leading_spc = true;
+                            if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) {
+                                params.input_suffix.erase(0, 1);
+                                suff_rm_leading_spc = false;
+                            }
+
+                            auto prefix_tokens = tokenize(slot.params.input_prefix, false);
+                            auto suffix_tokens = tokenize(slot.params.input_suffix, false);
+
+                            const int space_token = 29871; // TODO: this should not be hardcoded
+                            if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) {
+                                suffix_tokens.erase(suffix_tokens.begin());
+                            }
+
+                            prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model));
+                            prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS
+                            prefix_tokens.insert(prefix_tokens.end(),   llama_token_suffix(model));
+                            prefix_tokens.insert(prefix_tokens.end(),   suffix_tokens.begin(), suffix_tokens.end());
+                            prefix_tokens.push_back(llama_token_middle(model));
+                            prompt_tokens = prefix_tokens;
+                        } else {
+                            prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt
+                        }
+
+                        slot.n_past = 0;
+                        slot.n_prompt_tokens = prompt_tokens.size();
+
+                        LOG_VERBOSE("prompt tokenized", {
+                            {"id_slot",         slot.id},
+                            {"id_task",         slot.id_task},
+                            {"n_ctx",           slot.n_ctx},
+                            {"n_keep",          slot.params.n_keep},
+                            {"n_prompt_tokens", slot.n_prompt_tokens},
+                            {"prompt_tokens",   tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())},
+                        });
+
+                        // empty prompt passed -> release the slot and send empty response
+                        if (prompt_tokens.empty()) {
+                            LOG_INFO("empty prompt - releasing slot", {
+                                {"id_slot", slot.id},
+                                {"id_task", slot.id_task}
+                            });
+
+                            slot.state = SLOT_STATE_PROCESSING;
+                            slot.command = SLOT_COMMAND_NONE;
+                            slot.release();
+                            slot.print_timings();
+                            send_final_response(slot);
+                            continue;
+                        }
+
+                        if (slot.embedding) {
+                            // this prompt is too large to process - discard it
+                            if (slot.n_prompt_tokens > n_ubatch) {
+                                slot.state = SLOT_STATE_PROCESSING;
+                                slot.command = SLOT_COMMAND_NONE;
+                                slot.release();
+                                slot.print_timings();
+                                send_final_response(slot);
+                                continue;
+                            }
+                        } else {
+                            if (slot.params.n_keep < 0) {
+                                slot.params.n_keep = slot.n_prompt_tokens;
+                            }
+                            slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep);
+
+                            // if input prompt is too big, truncate it (if group attention self-extend is disabled)
+                            if (slot.ga_n == 1 && slot.n_prompt_tokens >= slot.n_ctx) {
+                                const int n_left = slot.n_ctx - slot.params.n_keep;
+
+                                const int n_block_size = n_left / 2;
+                                const int erased_blocks = (slot.n_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size;
+
+                                std::vector<llama_token> new_tokens(
+                                        prompt_tokens.begin(),
+                                        prompt_tokens.begin() + slot.params.n_keep);
+
+                                new_tokens.insert(
+                                        new_tokens.end(),
+                                        prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size,
+                                        prompt_tokens.end());
+
+                                prompt_tokens = std::move(new_tokens);
+
+                                slot.truncated = true;
+                                slot.n_prompt_tokens = prompt_tokens.size();
+
+                                LOG_VERBOSE("input truncated", {
+                                    {"id_slot",         slot.id},
+                                    {"id_task",         slot.id_task},
+                                    {"n_ctx",           slot.n_ctx},
+                                    {"n_keep",          slot.params.n_keep},
+                                    {"n_left",          n_left},
+                                    {"n_prompt_tokens", slot.n_prompt_tokens},
+                                    {"prompt_tokens",   tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())},
+                                });
+
+                                GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx);
+                            }
+
+                            llama_sampling_reset(slot.ctx_sampling);
+
+                            if (!slot.params.cache_prompt) {
+                                slot.n_past_se = 0;
+                                slot.ga_i      = 0;
+                            } else {
+                                GGML_ASSERT(slot.ga_n == 1);
+
+                                // reuse any previously computed tokens that are common with the new prompt
+                                slot.n_past = common_part(slot.cache_tokens, prompt_tokens);
+
+                                // push the prompt into the sampling context (do not apply grammar)
+                                for (int i = 0; i < slot.n_past; ++i) {
+                                    llama_sampling_accept(slot.ctx_sampling, ctx, slot.cache_tokens[i], false);
+                                }
+                            }
+                        }
+
+                        if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0) {
+                            // we have to evaluate at least 1 token to generate logits.
+                            LOG_INFO("we have to evaluate at least 1 token to generate logits", {
+                                { "id_slot", slot.id },
+                                { "id_task", slot.id_task }
+                            });
+
+                            slot.n_past--;
+                            if (slot.ga_i > 0) {
+                                slot.n_past_se--;
+                            }
+                        }
+
+                        slot.n_prompt_tokens_processed = 0;
                     }
 
-                    if (!slot.params.cache_prompt)
-                    {
-                        llama_sampling_reset(slot.ctx_sampling);
+                    if (slot.embedding) {
+                        // cannot fit the prompt in the current batch - will try next iter
+                        if (batch.n_tokens + slot.n_prompt_tokens > n_batch) {
+                            continue;
+                        }
+                    }
 
+                    // keep only the common part
+                    int p0 = (int) system_tokens.size() + slot.n_past;
+                    if (!llama_kv_cache_seq_rm(ctx, slot.id + 1, p0, -1)) {
+                        // could not partially delete (likely using a non-Transformer model)
+                        llama_kv_cache_seq_rm(ctx, slot.id + 1, -1, -1);
+
+                        p0 = (int) system_tokens.size();
+                        if (p0 != 0) {
+                            // copy over the system prompt when there is one
+                            llama_kv_cache_seq_cp(ctx, 0, slot.id + 1, -1, -1);
+                        }
+
+                        // there is no common part left (except for the system prompt)
                         slot.n_past = 0;
                         slot.n_past_se = 0;
                         slot.ga_i = 0;
-                        slot.num_prompt_tokens_processed = slot.num_prompt_tokens;
-                    }
-                    else
-                    {
-                        // push the prompt into the sampling context (do not apply grammar)
-                        for (auto &token : prompt_tokens)
-                        {
-                            llama_sampling_accept(slot.ctx_sampling, ctx, token, false);
-                        }
-
-                        slot.n_past = common_part(slot.cache_tokens, prompt_tokens);
-                        slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past;
-
-                        if (slot.ga_n != 1)
-                        {
-                            int ga_i = 0;
-                            int32_t ga_n = slot.ga_n;
-                            int32_t ga_w = slot.ga_w;
-                            int32_t slot_npast = 0;
-                            for (int k = 0; k < slot.n_past; ++k)
-                            {
-                                while (slot_npast >= ga_i + ga_w) {
-                                    const int bd = (ga_w/ga_n)*(ga_n - 1);
-                                    slot_npast -= bd;
-                                    ga_i += ga_w/ga_n;
-                                }
-                                slot_npast++;
-                            }
-                            slot.n_past_se = slot_npast;
-                            slot.ga_i = ga_i;
-                        }
-
-                        LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed);
+                        // TODO: is the system prompt ever in the sampling context?
+                        llama_sampling_reset(slot.ctx_sampling);
                     }
 
-                    slot.cache_tokens = prompt_tokens;
+                    // remove the non-common part from the cache
+                    slot.cache_tokens.resize(slot.n_past);
 
-                    if (slot.n_past == slot.num_prompt_tokens && slot.n_past > 0)
-                    {
-                        // we have to evaluate at least 1 token to generate logits.
-                        LOG_TEE("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id);
-                        slot.n_past--;
-                        if (slot.ga_i > 0)
-                        {
-                            slot.n_past_se--;
-                        }
-                    }
-
-                    LOG_TEE("slot %d : kv cache rm - [%d, end)\n", slot.id, (int) system_tokens.size() + slot.n_past);
-
-                    llama_kv_cache_seq_rm(ctx, slot.id, system_tokens.size() + slot.n_past, -1);
-
-                    LOG_VERBOSE("prompt ingested", {
-                                                    {"n_past",  slot.n_past},
-                                                    {"cached",  tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)},
-                                                    {"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())},
-                                                });
-
-                    const bool has_images = process_images(slot);
-
-                    // process the prefix of first image
-                    std::vector<llama_token> prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens;
+                    LOG_INFO("kv cache rm [p0, end)", {
+                        { "id_slot", slot.id },
+                        { "id_task", slot.id_task },
+                        { "p0",      p0 }
+                    });
 
                     int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
 
@@ -1699,55 +2095,81 @@ struct llama_server_context
                     int32_t ga_n = slot.ga_n;
                     int32_t ga_w = slot.ga_w;
 
-                    for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past)
-                    {
-                        if (slot.ga_n != 1)
-                        {
+                    // add prompt tokens for processing in the current batch
+                    // TODO: the self-extend stuff here is a mess - simplify and/or abstract it somehow
+                    for (; slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch; ++slot.n_past) {
+                        if (slot.ga_n != 1) {
                             while (slot_npast >= ga_i + ga_w) {
                                 const int bd = (ga_w/ga_n)*(ga_n - 1);
                                 slot_npast -= bd;
                                 ga_i += ga_w/ga_n;
                             }
                         }
-                        llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot_npast, {slot.id }, false);
+
+                        llama_batch_add(batch, prompt_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id + 1 }, false);
+
+                        if (slot.params.cache_prompt) {
+                            slot.cache_tokens.push_back(prompt_tokens[slot.n_past]);
+                        }
+
+                        slot.n_prompt_tokens_processed++;
                         slot_npast++;
                     }
 
-                    if (has_images && !ingest_images(slot, n_batch))
-                    {
-                        LOG_TEE("failed processing images\n");
-                        return false;
-                    }
+                    LOG_VERBOSE("prompt processing progress", {
+                        {"id_slot",  slot.id},
+                        {"n_past",   slot.n_past},
+                        {"n_ctx",    n_ctx},
+                        {"n_tokens", batch.n_tokens},
+                        {"progress", (float) slot.n_prompt_tokens_processed / slot.n_prompt_tokens},
+                    });
 
-                    // extract the logits only for the last token
-                    if (batch.n_tokens > 0)
-                    {
+                    // entire prompt has been processed - start decoding new tokens
+                    if (slot.n_past == slot.n_prompt_tokens) {
+                        slot.state   = SLOT_STATE_PROCESSING;
+                        slot.command = SLOT_COMMAND_NONE;
+
+                        GGML_ASSERT(batch.n_tokens > 0);
+
+                        // extract the logits only for the last token
                         batch.logits[batch.n_tokens - 1] = true;
-                    }
 
-                    slot.n_decoded = 0;
-                    slot.i_batch   = batch.n_tokens - 1;
+                        slot.n_decoded = 0;
+                        slot.i_batch   = batch.n_tokens - 1;
+
+                        LOG_VERBOSE("prompt done", {
+                            {"id_slot",  slot.id},
+                            {"n_past",   slot.n_past},
+                            {"n_ctx",    n_ctx},
+                            {"n_tokens", batch.n_tokens},
+                        });
+                    }
+                }
+
+                if (batch.n_tokens >= n_batch) {
+                    break;
                 }
             }
         }
 
-        if (batch.n_tokens == 0)
-        {
-            all_slots_are_idle = true;
-            return true;
+        if (batch.n_tokens == 0) {
+            LOG_VERBOSE("no tokens to decode", {});
+            return;
         }
 
-        for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch)
-        {
-            const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
+        LOG_VERBOSE("decoding batch", {
+            {"n_tokens", batch.n_tokens},
+        });
 
-            for (auto & slot : slots)
-            {
-                if (slot.ga_n != 1)
-                {
+        // process the created batch of tokens
+        for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
+            const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i);
+
+            for (auto & slot : slots) {
+                if (slot.ga_n != 1) {
                     // context extension via Self-Extend
-                    while (slot.n_past_se >= slot.ga_i + slot.ga_w)
-                    {
+                    // TODO: simplify and/or abstract this
+                    while (slot.n_past_se >= slot.ga_i + slot.ga_w) {
                         const int ib = (slot.ga_n * slot.ga_i) / slot.ga_w;
                         const int bd = (slot.ga_w / slot.ga_n) * (slot.ga_n - 1);
                         const int dd = (slot.ga_w / slot.ga_n) - ib * bd - slot.ga_w;
@@ -1757,9 +2179,9 @@ struct llama_server_context
                         LOG_TEE("div:   [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n);
                         LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd);
 
-                        llama_kv_cache_seq_shift(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd);
-                        llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w,slot.ga_n);
-                        llama_kv_cache_seq_shift(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w,slot.n_past_se + ib * bd, dd);
+                        llama_kv_cache_seq_add(ctx, slot.id + 1, slot.ga_i, slot.n_past_se, ib * bd);
+                        llama_kv_cache_seq_div(ctx, slot.id + 1, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n);
+                        llama_kv_cache_seq_add(ctx, slot.id + 1, slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd);
 
                         slot.n_past_se -= bd;
 
@@ -1767,12 +2189,12 @@ struct llama_server_context
 
                         LOG_TEE("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", slot.n_past_se + bd, slot.n_past_se, slot.ga_i);
                     }
+
                     slot.n_past_se += n_tokens;
                 }
             }
 
-            llama_batch batch_view =
-            {
+            llama_batch batch_view = {
                 n_tokens,
                 batch.token    + i,
                 nullptr,
@@ -1785,37 +2207,47 @@ struct llama_server_context
 
             const int ret = llama_decode(ctx, batch_view);
 
-            if (ret != 0)
-            {
-                if (n_batch == 1 || ret < 0)
-                {
+            if (ret != 0) {
+                if (n_batch == 1 || ret < 0) {
                     // if you get here, it means the KV cache is full - try increasing it via the context size
-                    LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret);
-                    return false;
+                    LOG_ERROR("failed to decode the batch: KV cache is full - try increasing it via the context size", {
+                        {"i",   i},
+                        {"n_batch",  ret},
+                        {"ret",   ret},
+                    });
+                    for (auto & slot : slots) {
+                        slot.state = SLOT_STATE_PROCESSING;
+                        slot.command = SLOT_COMMAND_NONE;
+                        slot.release();
+                        send_error(slot, "Input prompt is too big compared to KV size. Please try increasing KV size.");
+                    }
+                    break; // break loop of n_batch
                 }
 
-                LOG_TEE("%s : failed to find free space in the KV cache, retrying with smaller n_batch = %d\n", __func__, n_batch / 2);
-
                 // retry with half the batch size to try to find a free slot in the KV cache
                 n_batch /= 2;
                 i -= n_batch;
-                continue;
+
+                LOG_WARNING("failed to find free space in the KV cache, retrying with smaller batch size - try increasing it via the context size or enable defragmentation", {
+                    {"i",   i},
+                    {"n_batch",  n_batch},
+                    {"ret",   ret},
+                });
+
+                continue; // continue loop of n_batch
             }
 
-            for (auto & slot : slots)
-            {
-                if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens))
-                {
-                    continue;
+            for (auto & slot : slots) {
+                if (slot.state != SLOT_STATE_PROCESSING || slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) {
+                    continue; // continue loop of slots
                 }
 
                 // prompt evaluated for embedding
-                if (slot.embedding)
-                {
-                    send_embedding(slot);
+                if (slot.embedding) {
+                    send_embedding(slot, batch_view);
                     slot.release();
                     slot.i_batch = -1;
-                    return true;
+                    continue; // continue loop of slots
                 }
 
                 completion_token_output result;
@@ -1824,48 +2256,69 @@ struct llama_server_context
                 llama_sampling_accept(slot.ctx_sampling, ctx, id, true);
 
                 slot.n_decoded += 1;
-                if (slot.n_decoded == 1)
-                {
-                    slot.t_start_genereration = ggml_time_us();
-                    slot.t_prompt_processing = (slot.t_start_genereration - slot.t_start_process_prompt) / 1e3;
+                if (slot.n_decoded == 1) {
+                    slot.t_start_generation = ggml_time_us();
+                    slot.t_prompt_processing = (slot.t_start_generation - slot.t_start_process_prompt) / 1e3;
+                    metrics.on_prompt_eval(slot);
                 }
 
                 llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false };
                 result.tok = id;
 
-                const int32_t n_probs = slot.sparams.n_probs;
-                if (slot.sparams.temp <= 0 && n_probs > 0)
-                {
-                    // for llama_sample_token_greedy we need to sort candidates
-                    llama_sample_softmax(ctx, &cur_p);
+                const size_t n_probs = std::min(cur_p.size, (size_t) slot.sparams.n_probs);
+                if (n_probs > 0) {
+                    const size_t n_valid = slot.ctx_sampling->n_valid;
+
+                    // Make sure at least n_probs top tokens are at the front of the vector:
+                    if (slot.sparams.temp == 0.0f && n_probs > n_valid) {
+                        llama_sample_top_k(ctx, &cur_p, n_probs, 0);
+                    }
+
+                    if (slot.sparams.temp == 0.0f) {
+                        // With greedy sampling the probabilities have possibly not been calculated.
+                        for (size_t i = 0; i < n_probs; ++i) {
+                            result.probs.push_back({
+                                cur_p.data[i].id,
+                                i == 0 ? 1.0f : 0.0f
+                            });
+                        }
+                    } else {
+                        for (size_t i = 0; i < n_probs; ++i) {
+                            result.probs.push_back({
+                                cur_p.data[i].id,
+                                i >= n_valid ? 0.0f : cur_p.data[i].p // Tokens filtered out due to e.g. top_k have 0 probability.
+                            });
+                        }
+                    }
                 }
 
-                for (size_t i = 0; i < std::min(cur_p.size, (size_t)n_probs); ++i)
-                {
-                    result.probs.push_back({cur_p.data[i].id, cur_p.data[i].p});
-                }
-
-                if (!process_token(result, slot))
-                {
+                if (!process_token(result, slot)) {
                     slot.release();
                     slot.print_timings();
                     send_final_response(slot);
+                    metrics.on_prediction(slot);
                 }
 
                 slot.i_batch = -1;
             }
         }
-        return true;
+
+        LOG_VERBOSE("run slots completed", {});
     }
 
-    void run_on_all_tasks_finished() {
-        update_slots();
+    json model_meta() const {
+        return json {
+            {"vocab_type",  llama_vocab_type    (model)},
+            {"n_vocab",     llama_n_vocab       (model)},
+            {"n_ctx_train", llama_n_ctx_train   (model)},
+            {"n_embd",      llama_n_embd        (model)},
+            {"n_params",    llama_model_n_params(model)},
+            {"size",        llama_model_size    (model)},
+        };
     }
 };
 
-static void server_print_usage(const char *argv0, const gpt_params &params,
-                               const server_params &sparams)
-{
+static void server_print_usage(const char * argv0, const gpt_params & params, const server_params & sparams) {
     printf("usage: %s [options]\n", argv0);
     printf("\n");
     printf("options:\n");
@@ -1873,6 +2326,7 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     printf("  -v, --verbose             verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
     printf("  -t N, --threads N         number of threads to use during computation (default: %d)\n", params.n_threads);
     printf("  -tb N, --threads-batch N  number of threads to use during batch and prompt processing (default: same as --threads)\n");
+    printf("  --threads-http N          number of threads in the http server pool to process requests (default: max(hardware concurrency - 1, --parallel N + 2))\n");
     printf("  -c N, --ctx-size N        size of the prompt context (default: %d)\n", params.n_ctx);
     printf("  --rope-scaling {none,linear,yarn}\n");
     printf("                            RoPE frequency scaling method, defaults to linear unless specified by the model\n");
@@ -1882,15 +2336,15 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     printf("  --yarn-attn-factor N      YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n");
     printf("  --yarn-beta-slow N        YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow);
     printf("  --yarn-beta-fast N        YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast);
-    printf("  -b N, --batch-size N      batch size for prompt processing (default: %d)\n", params.n_batch);
-    printf("  --memory-f32              use f32 instead of f16 for memory key+value (default: disabled)\n");
-    printf("                            not recommended: doubles context memory required and no measurable increase in quality\n");
-    if (llama_supports_mlock())
-    {
+    printf("  --pooling {none,mean,cls} pooling type for embeddings, use model default if unspecified\n");
+    printf("  -dt N, --defrag-thold N\n");
+    printf("                            KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold);
+    printf("  -b N, --batch-size N      logical maximum batch size (default: %d)\n", params.n_batch);
+    printf("  -ub N, --ubatch-size N    physical maximum batch size (default: %d)\n", params.n_ubatch);
+    if (llama_supports_mlock()) {
         printf("  --mlock                   force system to keep model in RAM rather than swapping or compressing\n");
     }
-    if (llama_supports_mmap())
-    {
+    if (llama_supports_mmap()) {
         printf("  --no-mmap                 do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
     }
     printf("  --numa TYPE               attempt optimizations that help on some NUMA systems\n");
@@ -1909,90 +2363,95 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
         printf("                            fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n");
         printf("  -mg i, --main-gpu i       the GPU to use for the model (with split-mode = none),\n");
         printf("                            or for intermediate results and KV (with split-mode = row)\n");
+        printf("  -nkvo, --no-kv-offload\n");
+        printf("                            disable KV offload\n");
     }
     printf("  -m FNAME, --model FNAME\n");
-    printf("                            model path (default: %s)\n", params.model.c_str());
+    printf("                            model path (default: models/$filename with filename from --hf-file or --model-url if set, otherwise %s)\n", DEFAULT_MODEL_PATH);
+    printf("  -mu MODEL_URL, --model-url MODEL_URL\n");
+    printf("                            model download url (default: unused)\n");
+    printf("  -hfr REPO, --hf-repo REPO\n");
+    printf("                            Hugging Face model repository (default: unused)\n");
+    printf("  -hff FILE, --hf-file FILE\n");
+    printf("                            Hugging Face model file (default: unused)\n");
     printf("  -a ALIAS, --alias ALIAS\n");
     printf("                            set an alias for the model, will be added as `model` field in completion response\n");
     printf("  --lora FNAME              apply LoRA adapter (implies --no-mmap)\n");
     printf("  --lora-base FNAME         optional model to use as a base for the layers modified by the LoRA adapter\n");
     printf("  --host                    ip address to listen (default  (default: %s)\n", sparams.hostname.c_str());
     printf("  --port PORT               port to listen (default  (default: %d)\n", sparams.port);
-    printf("  --path PUBLIC_PATH        path from which to serve static files (default %s)\n", sparams.public_path.c_str());
+    printf("  --path PUBLIC_PATH        path from which to serve static files (default: disabled)\n");
     printf("  --api-key API_KEY         optional api key to enhance server security. If set, requests must include this key for access.\n");
     printf("  --api-key-file FNAME      path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n");
+#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
+    printf("  --ssl-key-file FNAME      path to file a PEM-encoded SSL private key\n");
+    printf("  --ssl-cert-file FNAME     path to file a PEM-encoded SSL certificate\n");
+#endif
     printf("  -to N, --timeout N        server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
-    printf("  --embedding               enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
+    printf("  --embeddings              enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
     printf("  -np N, --parallel N       number of slots for process requests (default: %d)\n", params.n_parallel);
-    printf("  -cb, --cont-batching      enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
+    printf("  -cb, --cont-batching      enable continuous batching (a.k.a dynamic batching) (default: enabled)\n");
+    printf("  -fa, --flash-attn         enable Flash Attention (default: %s)\n", params.flash_attn ? "enabled" : "disabled");
     printf("  -spf FNAME, --system-prompt-file FNAME\n");
     printf("                            set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n");
-    printf("  --mmproj MMPROJ_FILE      path to a multimodal projector file for LLaVA.\n");
+    printf("  -ctk TYPE, --cache-type-k TYPE\n");
+    printf("                            KV cache data type for K (default: f16)\n");
+    printf("  -ctv TYPE, --cache-type-v TYPE\n");
+    printf("                            KV cache data type for V (default: f16)\n");
+    printf("  --log-format              log output format: json or text (default: json)\n");
     printf("  --log-disable             disables logging to a file.\n");
     printf("  --slots-endpoint-disable  disables slots monitoring endpoint.\n");
+    printf("  --metrics                 enable prometheus compatible metrics endpoint (default: %s).\n", sparams.metrics_endpoint ? "enabled" : "disabled");
+    printf("  --slot-save-path PATH     path to save slot kv cache (default: disabled)\n");
     printf("\n");
     printf("  -n, --n-predict           maximum tokens to predict (default: %d)\n", params.n_predict);
     printf("  --override-kv KEY=TYPE:VALUE\n");
     printf("                            advanced option to override model metadata by key. may be specified multiple times.\n");
-    printf("                            types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
-    printf("  -gan N, --grp-attn-n N    set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`");
-    printf("  -gaw N, --grp-attn-w N    set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`");
-    printf("  --chat-template FORMAT_NAME");
-    printf("                            set chat template, possible value is: llama2, chatml (default %s)", sparams.chat_template.c_str());
+    printf("                            types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
+    printf("  -gan N, --grp-attn-n N    set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`\n");
+    printf("  -gaw N, --grp-attn-w N    set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n");
+    printf("  --chat-template JINJA_TEMPLATE\n");
+    printf("                            set custom jinja chat template (default: template taken from model's metadata)\n");
+    printf("                            only commonly used templates are accepted:\n");
+    printf("                            https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template\n");
     printf("\n");
 }
 
-static void server_params_parse(int argc, char **argv, server_params &sparams,
-                                gpt_params &params, llama_server_context& llama)
-{
-    gpt_params default_params;
+static void server_params_parse(int argc, char ** argv, server_params & sparams, gpt_params & params) {
+    gpt_params    default_params;
     server_params default_sparams;
+
     std::string arg;
     bool invalid_param = false;
 
-    for (int i = 1; i < argc; i++)
-    {
+    for (int i = 1; i < argc; i++) {
         arg = argv[i];
-        if (arg == "--port")
-        {
-            if (++i >= argc)
-            {
+        if (arg == "--port") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             sparams.port = std::stoi(argv[i]);
-        }
-        else if (arg == "--host")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--host") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             sparams.hostname = argv[i];
-        }
-        else if (arg == "--path")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--path") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             sparams.public_path = argv[i];
-        }
-        else if (arg == "--api-key")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--api-key") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            sparams.api_keys.emplace_back(argv[i]);
-        }
-        else if (arg == "--api-key-file")
-        {
-            if (++i >= argc)
-            {
+            sparams.api_keys.push_back(argv[i]);
+        } else if (arg == "--api-key-file") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
@@ -2009,209 +2468,217 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
                }
             }
             key_file.close();
+
         }
-        else if (arg == "--timeout" || arg == "-to")
-        {
-            if (++i >= argc)
-            {
+#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
+        else if (arg == "--ssl-key-file") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            sparams.ssl_key_file = argv[i];
+        } else if (arg == "--ssl-cert-file") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            sparams.ssl_cert_file = argv[i];
+        }
+#endif
+        else if (arg == "--timeout" || arg == "-to") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             sparams.read_timeout = std::stoi(argv[i]);
             sparams.write_timeout = std::stoi(argv[i]);
-        }
-        else if (arg == "-m" || arg == "--model")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "-m" || arg == "--model") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.model = argv[i];
-        }
-        else if (arg == "-a" || arg == "--alias")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "-mu" || arg == "--model-url") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.model_url = argv[i];
+        } else if (arg == "-hfr" || arg == "--hf-repo") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.hf_repo = argv[i];
+        } else if (arg == "-hff" || arg == "--hf-file") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.hf_file = argv[i];
+        } else if (arg == "-a" || arg == "--alias") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.model_alias = argv[i];
-        }
-        else if (arg == "-h" || arg == "--help")
-        {
+        } else if (arg == "-h" || arg == "--help") {
             server_print_usage(argv[0], default_params, default_sparams);
             exit(0);
-        }
-        else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.n_ctx = std::stoi(argv[i]);
-        }
-        else if (arg == "--rope-scaling")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--rope-scaling") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             std::string value(argv[i]);
-            /**/ if (value == "none")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; }
-            else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; }
-            else if (value == "yarn")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; }
+            /**/ if (value == "none")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
+            else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
+            else if (value == "yarn")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
             else { invalid_param = true; break; }
-        }
-        else if (arg == "--rope-freq-base")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--rope-freq-base") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.rope_freq_base = std::stof(argv[i]);
-        }
-        else if (arg == "--rope-freq-scale")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--rope-freq-scale") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.rope_freq_scale = std::stof(argv[i]);
-        }
-        else if (arg == "--yarn-ext-factor")
-        {
+        } else if (arg == "--yarn-ext-factor") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.yarn_ext_factor = std::stof(argv[i]);
         }
-        else if (arg == "--yarn-attn-factor")
-        {
+        else if (arg == "--yarn-attn-factor") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.yarn_attn_factor = std::stof(argv[i]);
-        }
-        else if (arg == "--yarn-beta-fast")
-        {
+        } else if (arg == "--yarn-beta-fast") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.yarn_beta_fast = std::stof(argv[i]);
-        }
-        else if (arg == "--yarn-beta-slow")
-        {
+        } else if (arg == "--yarn-beta-slow") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.yarn_beta_slow = std::stof(argv[i]);
-        }
-        else if (arg == "--threads" || arg == "-t")
-        {
+        } else if (arg == "--pooling") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            std::string value(argv[i]);
+            /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
+            else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
+            else if (value == "cls")  { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
+            else { invalid_param = true; break; }
+        } else if (arg == "--defrag-thold" || arg == "-dt") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.defrag_thold = std::stof(argv[i]);
+        } else if (arg == "--threads" || arg == "-t") {
             if (++i >= argc)
             {
                 invalid_param = true;
                 break;
             }
             params.n_threads = std::stoi(argv[i]);
-        }
-        else if (arg == "--grp-attn-n" || arg == "-gan")
-        {
+        } else if (arg == "--grp-attn-n" || arg == "-gan") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
 
             params.grp_attn_n = std::stoi(argv[i]);
-        }
-        else if (arg == "--grp-attn-w" || arg == "-gaw")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--grp-attn-w" || arg == "-gaw") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
 
             params.grp_attn_w = std::stoi(argv[i]);
-        }
-        else if (arg == "--threads-batch" || arg == "-tb")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--threads-batch" || arg == "-tb") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.n_threads_batch = std::stoi(argv[i]);
-        }
-        else if (arg == "-b" || arg == "--batch-size")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--threads-http") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            sparams.n_threads_http = std::stoi(argv[i]);
+        } else if (arg == "-b" || arg == "--batch-size") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.n_batch = std::stoi(argv[i]);
-            params.n_batch = std::min(512, params.n_batch);
-        }
-        else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "-ub" || arg == "--ubatch-size") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.n_ubatch = std::stoi(argv[i]);
+        } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             if (llama_supports_gpu_offload()) {
                 params.n_gpu_layers = std::stoi(argv[i]);
             } else {
-                LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. "
-                        "See main README.md for information on enabling GPU BLAS support",
-                        {{"n_gpu_layers", params.n_gpu_layers}});
+                LOG_WARNING(
+                    "Not compiled with GPU offload support, --n-gpu-layers option will be ignored. "
+                    "See main README.md for information on enabling GPU BLAS support",
+                    {{"n_gpu_layers", params.n_gpu_layers}});
             }
-        }
-        else if (arg == "--split-mode" || arg == "-sm")
-        {
+        } else if (arg == "-nkvo" || arg == "--no-kv-offload") {
+            params.no_kv_offload = true;
+        } else if (arg == "--split-mode" || arg == "-sm") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             std::string arg_next = argv[i];
-            if (arg_next == "none")
-            {
-                params.split_mode = LLAMA_SPLIT_NONE;
-            }
-            else if (arg_next == "layer")
-            {
-                params.split_mode = LLAMA_SPLIT_LAYER;
-            }
-            else if (arg_next == "row")
-            {
-                params.split_mode = LLAMA_SPLIT_ROW;
-            }
-            else {
+            if (arg_next == "none") {
+                params.split_mode = LLAMA_SPLIT_MODE_NONE;
+            } else if (arg_next == "layer") {
+                params.split_mode = LLAMA_SPLIT_MODE_LAYER;
+            } else if (arg_next == "row") {
+                params.split_mode = LLAMA_SPLIT_MODE_ROW;
+            } else {
                 invalid_param = true;
                 break;
             }
-#ifndef GGML_USE_CUBLAS
-            fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting the split mode has no effect.\n");
-#endif // GGML_USE_CUBLAS
-        }
-        else if (arg == "--tensor-split" || arg == "-ts")
-        {
-            if (++i >= argc)
-            {
+#ifndef GGML_USE_CUDA
+            fprintf(stderr, "warning: llama.cpp was compiled without CUDA. Setting the split mode has no effect.\n");
+#endif // GGML_USE_CUDA
+        } else if (arg == "--tensor-split" || arg == "-ts") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)
+#if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)
             std::string arg_next = argv[i];
 
             // split string by , and /
@@ -2220,133 +2687,92 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
             std::vector<std::string> split_arg{it, {}};
             GGML_ASSERT(split_arg.size() <= llama_max_devices());
 
-            for (size_t i_device = 0; i_device < llama_max_devices(); ++i_device)
-            {
-                if (i_device < split_arg.size())
-                {
+            for (size_t i_device = 0; i_device < llama_max_devices(); ++i_device) {
+                if (i_device < split_arg.size()) {
                     params.tensor_split[i_device] = std::stof(split_arg[i_device]);
-                }
-                else
-                {
+                } else {
                     params.tensor_split[i_device] = 0.0f;
                 }
             }
 #else
-            LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {});
-#endif // GGML_USE_CUBLAS
-        }
-        else if (arg == "--no-mul-mat-q" || arg == "-nommq")
-        {
-#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)
-            params.mul_mat_q = false;
-#else
-            LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n", {});
-#endif // GGML_USE_CUBLAS
-        }
-        else if (arg == "--main-gpu" || arg == "-mg")
-        {
-            if (++i >= argc)
-            {
+            LOG_WARNING("llama.cpp was compiled without CUDA. It is not possible to set a tensor split.\n", {});
+#endif // GGML_USE_CUDA
+        } else if (arg == "--main-gpu" || arg == "-mg") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)
+#if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)
             params.main_gpu = std::stoi(argv[i]);
 #else
-            LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {});
+            LOG_WARNING("llama.cpp was compiled without CUDA. It is not possible to set a main GPU.", {});
 #endif
-        }
-        else if (arg == "--lora")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--lora") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.lora_adapter.emplace_back(argv[i], 1.0f);
             params.use_mmap = false;
-        }
-        else if (arg == "--lora-scaled")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--lora-scaled") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             const char * lora_adapter = argv[i];
-            if (++i >= argc)
-            {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i]));
             params.use_mmap = false;
-        }
-        else if (arg == "--lora-base")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--lora-base") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.lora_base = argv[i];
-        }
-        else if (arg == "-v" || arg == "--verbose")
-        {
+        } else if (arg == "-v" || arg == "--verbose") {
 #if SERVER_VERBOSE != 1
             LOG_WARNING("server.cpp is not built with verbose logging.", {});
 #else
             server_verbose = true;
 #endif
-        }
-        else if (arg == "--mlock")
-        {
+        } else if (arg == "--mlock") {
             params.use_mlock = true;
-        }
-        else if (arg == "--no-mmap")
-        {
+        } else if (arg == "--no-mmap") {
             params.use_mmap = false;
-        }
-        else if (arg == "--numa") {
+        } else if (arg == "--numa") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             } else {
                 std::string value(argv[i]);
                 /**/ if (value == "distribute" || value == "" ) { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
-                else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
-                else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
+                else if (value == "isolate")                    { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
+                else if (value == "numactl")                    { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
                 else { invalid_param = true; break; }
             }
-        }
-        else if (arg == "--embedding")
-        {
+        } else if (arg == "--embedding" || arg == "--embeddings") {
             params.embedding = true;
-        }
-        else if (arg == "-cb" || arg == "--cont-batching")
-        {
+        } else if (arg == "-cb" || arg == "--cont-batching") {
             params.cont_batching = true;
-        }
-        else if (arg == "-np" || arg == "--parallel")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "-fa" || arg == "--flash-attn") {
+            params.flash_attn = true;
+        } else if (arg == "-np" || arg == "--parallel") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.n_parallel = std::stoi(argv[i]);
-        } else if (arg == "-n" || arg == "--n-predict")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "-n" || arg == "--n-predict") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.n_predict = std::stoi(argv[i]);
-        } else if (arg == "-spf" || arg == "--system-prompt-file")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "-spf" || arg == "--system-prompt-file") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
@@ -2356,401 +2782,311 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
                 invalid_param = true;
                 break;
             }
-            std::string systm_content;
+            std::string system_prompt;
             std::copy(
                 std::istreambuf_iterator<char>(file),
                 std::istreambuf_iterator<char>(),
-                std::back_inserter(systm_content)
+                std::back_inserter(system_prompt)
             );
-            llama.process_system_prompt_data(json::parse(systm_content));
-        }
-        else if(arg == "--mmproj")
-        {
-            if (++i >= argc)
-            {
-                invalid_param = true;
-                break;
-            }
-            params.mmproj = argv[i];
-        }
-        else if (arg == "--log-disable")
-        {
-            log_set_target(stdout);
-            LOG_INFO("logging to file is disabled.", {});
-        }
-        else if (arg == "--slots-endpoint-disable")
-        {
-            sparams.slots_endpoint = false;
-        }
-        else if (arg == "--chat-template")
-        {
-            if (++i >= argc)
-            {
-                invalid_param = true;
-                break;
-            }
-            std::string value(argv[i]);
-            if (value != "chatml" && value != "llama2") {
-                fprintf(stderr, "error: chat template can be \"llama2\" or \"chatml\", but got: %s\n", value.c_str());
-                invalid_param = true;
-                break;
-            }
-            sparams.chat_template = value;
-        }
-        else if (arg == "--override-kv")
-        {
+            sparams.system_prompt = system_prompt;
+        } else if (arg == "-ctk" || arg == "--cache-type-k") {
+            params.cache_type_k = argv[++i];
+        } else if (arg == "-ctv" || arg == "--cache-type-v") {
+            params.cache_type_v = argv[++i];
+        } else if (arg == "--log-format") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            char * sep = strchr(argv[i], '=');
-            if (sep == nullptr || sep - argv[i] >= 128) {
-                fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
+            if (std::strcmp(argv[i], "json") == 0) {
+                server_log_json = true;
+            } else if (std::strcmp(argv[i], "text") == 0) {
+                server_log_json = false;
+            } else {
                 invalid_param = true;
                 break;
             }
-            struct llama_model_kv_override kvo;
-            std::strncpy(kvo.key, argv[i], sep - argv[i]);
-            kvo.key[sep - argv[i]] = 0;
-            sep++;
-            if (strncmp(sep, "int:", 4) == 0) {
-                sep += 4;
-                kvo.tag = LLAMA_KV_OVERRIDE_INT;
-                kvo.int_value = std::atol(sep);
-            } else if (strncmp(sep, "float:", 6) == 0) {
-                sep += 6;
-                kvo.tag = LLAMA_KV_OVERRIDE_FLOAT;
-                kvo.float_value = std::atof(sep);
-            } else if (strncmp(sep, "bool:", 5) == 0) {
-                sep += 5;
-                kvo.tag = LLAMA_KV_OVERRIDE_BOOL;
-                if (std::strcmp(sep, "true") == 0) {
-                    kvo.bool_value = true;
-                } else if (std::strcmp(sep, "false") == 0) {
-                    kvo.bool_value = false;
-                } else {
-                    fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
-                    invalid_param = true;
-                    break;
-                }
-            } else {
+        } else if (arg == "--log-disable") {
+            log_set_target(stdout);
+            LOG_INFO("logging to file is disabled.", {});
+        } else if (arg == "--slots-endpoint-disable") {
+            sparams.slots_endpoint = false;
+        } else if (arg == "--metrics") {
+            sparams.metrics_endpoint = true;
+        } else if (arg == "--slot-save-path") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            sparams.slot_save_path = argv[i];
+            // if doesn't end with DIRECTORY_SEPARATOR, add it
+            if (!sparams.slot_save_path.empty() && sparams.slot_save_path[sparams.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) {
+                sparams.slot_save_path += DIRECTORY_SEPARATOR;
+            }
+        } else if (arg == "--chat-template") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            if (!verify_custom_template(argv[i])) {
+                fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]);
+                fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n");
+                invalid_param = true;
+                break;
+            }
+            sparams.chat_template = argv[i];
+        } else if (arg == "--override-kv") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            if (!parse_kv_override(argv[i], params.kv_overrides)) {
                 fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
                 invalid_param = true;
                 break;
             }
-            params.kv_overrides.push_back(kvo);
-        }
-        else
-        {
+        } else {
             fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
             server_print_usage(argv[0], default_params, default_sparams);
             exit(1);
         }
     }
+
+    gpt_params_handle_model_default(params);
+
     if (!params.kv_overrides.empty()) {
         params.kv_overrides.emplace_back();
         params.kv_overrides.back().key[0] = 0;
     }
 
-    if (invalid_param)
-    {
+    if (invalid_param) {
         fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
         server_print_usage(argv[0], default_params, default_sparams);
         exit(1);
     }
 }
 
-/* llama.cpp completion api semantics */
-static json format_partial_response(
-    llama_server_context &llama, llama_client_slot *slot, const std::string &content, const std::vector<completion_token_output> &probs
-) {
-    json res = json
-    {
-        {"content",    content },
-        {"stop",       false},
-        {"slot_id",    slot->id },
-        {"multimodal", llama.multimodal }
-    };
-
-    if (slot->sparams.n_probs > 0)
-    {
-        res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs);
+static void log_server_request(const httplib::Request & req, const httplib::Response & res) {
+    // skip GH copilot requests when using default port
+    if (req.path == "/v1/health" || req.path == "/v1/completions") {
+        return;
     }
 
-    return res;
-}
-
-static json format_tokenizer_response(const std::vector<llama_token> &tokens)
-{
-    return json{
-        {"tokens", tokens}};
-}
-
-static json format_detokenized_response(std::string content)
-{
-    return json{
-        {"content", content}};
-}
-
-
-static void log_server_request(const httplib::Request &req, const httplib::Response &res)
-{
     LOG_INFO("request", {
-                            {"remote_addr", req.remote_addr},
-                            {"remote_port", req.remote_port},
-                            {"status", res.status},
-                            {"method", req.method},
-                            {"path", req.path},
-                            {"params", req.params},
-                        });
+        {"remote_addr", req.remote_addr},
+        {"remote_port", req.remote_port},
+        {"status",      res.status},
+        {"method",      req.method},
+        {"path",        req.path},
+        {"params",      req.params},
+    });
 
     LOG_VERBOSE("request", {
-                               {"request", req.body},
-                               {"response", res.body},
-                           });
-}
-
-struct token_translator
-{
-    llama_context * ctx;
-    std::string operator()(llama_token tok)                    const { return llama_token_to_piece(ctx, tok); }
-    std::string operator()(const completion_token_output &cto) const { return (*this)(cto.tok); }
-};
-
-static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama, llama_client_slot *slot)
-{
-    auto & gtps = slot->generated_token_probs;
-    auto translator = token_translator{llama.ctx};
-    auto add_strlen = [=](size_t sum, const completion_token_output & cto) { return sum + translator(cto).size(); };
-    const size_t len = std::accumulate(gtps.begin(), gtps.end(), size_t(0), add_strlen);
-    if (slot->generated_text.capacity() < slot->generated_text.size() + len)
-    {
-        slot->generated_text.reserve(slot->generated_text.size() + len);
-    }
-    for (const completion_token_output & cto : gtps)
-    {
-        slot->generated_text += translator(cto);
-    }
+        {"request",  req.body},
+        {"response", res.body},
+    });
 }
 
 std::function<void(int)> shutdown_handler;
-inline void signal_handler(int signal) { shutdown_handler(signal); }
+std::atomic_flag is_terminating = ATOMIC_FLAG_INIT;
 
-int main(int argc, char **argv)
-{
+inline void signal_handler(int signal) {
+    if (is_terminating.test_and_set()) {
+        // in case it hangs, we can force terminate the server by hitting Ctrl+C twice
+        // this is for better developer experience, we can remove when the server is stable enough
+        fprintf(stderr, "Received second interrupt, terminating immediately.\n");
+        exit(1);
+    }
+
+    shutdown_handler(signal);
+}
+
+int main(int argc, char ** argv) {
 #if SERVER_VERBOSE != 1
     log_disable();
 #endif
     // own arguments required by this example
-    gpt_params params;
+    gpt_params    params;
     server_params sparams;
 
     // struct that contains llama context and inference
-    llama_server_context llama;
+    server_context ctx_server;
 
-    server_params_parse(argc, argv, sparams, params, llama);
+    server_params_parse(argc, argv, sparams, params);
 
-    if (params.model_alias == "unknown")
-    {
+    if (!sparams.system_prompt.empty()) {
+        ctx_server.system_prompt_set(sparams.system_prompt);
+    }
+
+    if (params.model_alias == "unknown") {
         params.model_alias = params.model;
     }
 
     llama_backend_init();
     llama_numa_init(params.numa);
 
-    LOG_INFO("build info", {{"build", LLAMA_BUILD_NUMBER},
-                            {"commit", LLAMA_COMMIT}});
+    LOG_INFO("build info", {
+        {"build",  LLAMA_BUILD_NUMBER},
+        {"commit", LLAMA_COMMIT}
+    });
 
     LOG_INFO("system info", {
-                                {"n_threads", params.n_threads},
-                                {"n_threads_batch", params.n_threads_batch},
-                                {"total_threads", std::thread::hardware_concurrency()},
-                                {"system_info", llama_print_system_info()},
-                            });
+        {"n_threads",       params.n_threads},
+        {"n_threads_batch", params.n_threads_batch},
+        {"total_threads",   std::thread::hardware_concurrency()},
+        {"system_info",     llama_print_system_info()},
+    });
 
-    httplib::Server svr;
+    std::unique_ptr<httplib::Server> svr;
+#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
+    if (sparams.ssl_key_file != "" && sparams.ssl_cert_file != "") {
+        LOG_INFO("Running with SSL", {{"key", sparams.ssl_key_file}, {"cert", sparams.ssl_cert_file}});
+        svr.reset(
+            new httplib::SSLServer(sparams.ssl_cert_file.c_str(), sparams.ssl_key_file.c_str())
+        );
+    } else {
+        LOG_INFO("Running without SSL", {});
+        svr.reset(new httplib::Server());
+    }
+#else
+    svr.reset(new httplib::Server());
+#endif
 
     std::atomic<server_state> state{SERVER_STATE_LOADING_MODEL};
 
-    svr.set_default_headers({{"Server", "llama.cpp"}});
+    svr->set_default_headers({{"Server", "llama.cpp"}});
 
     // CORS preflight
-    svr.Options(R"(.*)", [](const httplib::Request &req, httplib::Response &res) {
-        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+    svr->Options(R"(.*)", [](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin",      req.get_header_value("Origin"));
         res.set_header("Access-Control-Allow-Credentials", "true");
-        res.set_header("Access-Control-Allow-Methods", "POST");
-        res.set_header("Access-Control-Allow-Headers", "*");
+        res.set_header("Access-Control-Allow-Methods",     "POST");
+        res.set_header("Access-Control-Allow-Headers",     "*");
+        return res.set_content("", "application/json; charset=utf-8");
     });
 
-    svr.Get("/health", [&](const httplib::Request&, httplib::Response& res) {
-        server_state current_state = state.load();
-        switch(current_state) {
-            case SERVER_STATE_READY:
-                if (llama.all_slots_are_idle) {
-                    res.set_content(R"({"status": "ok"})", "application/json");
-                    res.status = 200; // HTTP OK
-                } else {
-                    int available_slots = 0;
-                    int processing_slots = 0;
-                    for (llama_client_slot & slot : llama.slots) {
-                        if (slot.available()) {
-                            available_slots++;
-                        } else {
-                            processing_slots++;
-                        }
-                    }
-                    if (available_slots > 0) {
-                        json health = {
-                                {"status",           "ok"},
-                                {"slots_idle",       available_slots},
-                                {"slots_processing", processing_slots}};
-                        res.set_content(health.dump(), "application/json");
-                        res.status = 200; // HTTP OK
-                    } else {
-                        json health = {
-                                {"status",           "no slot available"},
-                                {"slots_idle",       available_slots},
-                                {"slots_processing", processing_slots}};
-                        res.set_content(health.dump(), "application/json");
-                        res.status = 503; // HTTP Service Unavailable
-                    }
-                }
-                break;
-            case SERVER_STATE_LOADING_MODEL:
-                res.set_content(R"({"status": "loading model"})", "application/json");
-                res.status = 503; // HTTP Service Unavailable
-                break;
-            case SERVER_STATE_ERROR:
-                res.set_content(R"({"status": "error", "error": "Model failed to load"})", "application/json");
-                res.status = 500; // HTTP Internal Server Error
-                break;
+    svr->set_logger(log_server_request);
+
+    auto res_error = [](httplib::Response & res, json error_data) {
+        json final_response {{"error", error_data}};
+        res.set_content(final_response.dump(), "application/json; charset=utf-8");
+        res.status = json_value(error_data, "code", 500);
+    };
+
+    svr->set_exception_handler([&res_error](const httplib::Request &, httplib::Response & res, std::exception_ptr ep) {
+        std::string message;
+        try {
+            std::rethrow_exception(std::move(ep));
+        } catch (std::exception & e) {
+            message = e.what();
+        } catch (...) {
+            message = "Unknown Exception";
         }
+
+        json formatted_error = format_error_response(message, ERROR_TYPE_SERVER);
+        LOG_VERBOSE("Got exception", formatted_error);
+        res_error(res, formatted_error);
     });
 
-    if (sparams.slots_endpoint) {
-        svr.Get("/slots", [&](const httplib::Request&, httplib::Response& res) {
-            json slots;
-            for (llama_client_slot & slot : llama.slots) {
-                json slot_data = llama.get_formated_generation(slot);
-                slot_data["id"] = slot.id;
-                slot_data["task_id"] = slot.task_id;
-                slot_data["state"] = slot.state;
-                slot_data["prompt"] = slot.prompt;
-                slot_data["next_token"] = {
-                        {"has_next_token", slot.has_next_token},
-                        {"n_remain", slot.n_remaining},
-                        {"num_tokens_predicted", slot.n_decoded},
-                        {"stopped_eos", slot.stopped_eos},
-                        {"stopped_word", slot.stopped_word},
-                        {"stopped_limit", slot.stopped_limit},
-                        {"stopping_word", slot.stopping_word},
-                };
-
-                slots.push_back(slot_data);
-            }
-            res.set_content(slots.dump(), "application/json");
-            res.status = 200; // HTTP OK
-        });
-    }
-
-    svr.set_logger(log_server_request);
-
-    svr.set_exception_handler([](const httplib::Request &, httplib::Response &res, std::exception_ptr ep)
-            {
-                const char fmt[] = "500 Internal Server Error\n%s";
-                char buf[BUFSIZ];
-                try
-                {
-                    std::rethrow_exception(std::move(ep));
-                }
-                catch (std::exception &e)
-                {
-                    snprintf(buf, sizeof(buf), fmt, e.what());
-                }
-                catch (...)
-                {
-                    snprintf(buf, sizeof(buf), fmt, "Unknown Exception");
-                }
-                res.set_content(buf, "text/plain; charset=utf-8");
-                res.status = 500;
-            });
-
-    svr.set_error_handler([](const httplib::Request &, httplib::Response &res)
-            {
-                if (res.status == 401)
-                {
-                    res.set_content("Unauthorized", "text/plain; charset=utf-8");
-                }
-                if (res.status == 400)
-                {
-                    res.set_content("Invalid request", "text/plain; charset=utf-8");
-                }
-                else if (res.status == 404)
-                {
-                    res.set_content("File Not Found", "text/plain; charset=utf-8");
-                    res.status = 404;
-                }
-            });
+    svr->set_error_handler([&res_error](const httplib::Request &, httplib::Response & res) {
+        if (res.status == 404) {
+            res_error(res, format_error_response("File Not Found", ERROR_TYPE_NOT_FOUND));
+        }
+        // for other error codes, we skip processing here because it's already done by res_error()
+    });
 
     // set timeouts and change hostname and port
-    svr.set_read_timeout (sparams.read_timeout);
-    svr.set_write_timeout(sparams.write_timeout);
+    svr->set_read_timeout (sparams.read_timeout);
+    svr->set_write_timeout(sparams.write_timeout);
 
-    if (!svr.bind_to_port(sparams.hostname, sparams.port))
-    {
+    if (!svr->bind_to_port(sparams.hostname, sparams.port)) {
         fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port);
         return 1;
     }
 
-    // Set the base directory for serving static files
-    svr.set_base_dir(sparams.public_path);
-
-    // to make it ctrl+clickable:
-    LOG_TEE("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port);
-
     std::unordered_map<std::string, std::string> log_data;
+
     log_data["hostname"] = sparams.hostname;
-    log_data["port"] = std::to_string(sparams.port);
+    log_data["port"]     = std::to_string(sparams.port);
 
     if (sparams.api_keys.size() == 1) {
-        log_data["api_key"] = "api_key: ****" + sparams.api_keys[0].substr(sparams.api_keys[0].length() - 4);
+        auto key = sparams.api_keys[0];
+        log_data["api_key"] = "api_key: ****" + key.substr(std::max((int)(key.length() - 4), 0));
     } else if (sparams.api_keys.size() > 1) {
         log_data["api_key"] = "api_key: " + std::to_string(sparams.api_keys.size()) + " keys loaded";
     }
 
-    LOG_INFO("HTTP server listening", log_data);
-    // run the HTTP server in a thread - see comment below
-    std::thread t([&]()
-            {
-                if (!svr.listen_after_bind())
-                {
-                    state.store(SERVER_STATE_ERROR);
-                    return 1;
-                }
-
-                return 0;
-            });
-
     // load the model
-    if (!llama.load_model(params))
-    {
+    if (!ctx_server.load_model(params)) {
         state.store(SERVER_STATE_ERROR);
         return 1;
     } else {
-        llama.initialize();
+        ctx_server.init();
         state.store(SERVER_STATE_READY);
-        LOG_INFO("model loaded", {});
     }
 
-    // Middleware for API key validation
-    auto validate_api_key = [&sparams](const httplib::Request &req, httplib::Response &res) -> bool {
+    LOG_INFO("model loaded", {});
+
+    const auto model_meta = ctx_server.model_meta();
+
+    // if a custom chat template is not supplied, we will use the one that comes with the model (if any)
+    if (sparams.chat_template.empty()) {
+        if (!ctx_server.validate_model_chat_template()) {
+            LOG_ERROR("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {});
+            sparams.chat_template = "chatml";
+        }
+    }
+
+    // print sample chat example to make it clear which template is used
+    {
+        json chat;
+        chat.push_back({{"role", "system"},    {"content", "You are a helpful assistant"}});
+        chat.push_back({{"role", "user"},      {"content", "Hello"}});
+        chat.push_back({{"role", "assistant"}, {"content", "Hi there"}});
+        chat.push_back({{"role", "user"},      {"content", "How are you?"}});
+
+        const std::string chat_example = format_chat(ctx_server.model, sparams.chat_template, chat);
+
+        LOG_INFO("chat template", {
+            {"chat_example", chat_example},
+            {"built_in", sparams.chat_template.empty()},
+        });
+    }
+
+    //
+    // Middlewares
+    //
+
+    auto middleware_validate_api_key = [&sparams, &res_error](const httplib::Request & req, httplib::Response & res) {
+        // TODO: should we apply API key to all endpoints, including "/health" and "/models"?
+        static const std::set<std::string> protected_endpoints = {
+            "/props",
+            "/completion",
+            "/completions",
+            "/v1/completions",
+            "/chat/completions",
+            "/v1/chat/completions",
+            "/infill",
+            "/tokenize",
+            "/detokenize",
+            "/embedding",
+            "/embeddings",
+            "/v1/embeddings",
+        };
+
         // If API key is not set, skip validation
         if (sparams.api_keys.empty()) {
             return true;
         }
 
+        // If path is not in protected_endpoints list, skip validation
+        if (protected_endpoints.find(req.path) == protected_endpoints.end()) {
+            return true;
+        }
+
         // Check for API key in the header
         auto auth_header = req.get_header_value("Authorization");
+
         std::string prefix = "Bearer ";
         if (auth_header.substr(0, prefix.size()) == prefix) {
             std::string received_api_key = auth_header.substr(prefix.size());
@@ -2760,455 +3096,716 @@ int main(int argc, char **argv)
         }
 
         // API key is invalid or not provided
-        res.set_content("Unauthorized: Invalid API Key", "text/plain; charset=utf-8");
-        res.status = 401; // Unauthorized
+        // TODO: make another middleware for CORS related logic
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+        res_error(res, format_error_response("Invalid API Key", ERROR_TYPE_AUTHENTICATION));
 
         LOG_WARNING("Unauthorized: Invalid API Key", {});
 
         return false;
     };
 
-    // this is only called if no index.html is found in the public --path
-    svr.Get("/", [](const httplib::Request &, httplib::Response &res)
-            {
-                res.set_content(reinterpret_cast<const char*>(&index_html), index_html_len, "text/html; charset=utf-8");
-                return false;
-            });
+    // register server middlewares
+    svr->set_pre_routing_handler([&middleware_validate_api_key](const httplib::Request & req, httplib::Response & res) {
+        if (!middleware_validate_api_key(req, res)) {
+            return httplib::Server::HandlerResponse::Handled;
+        }
+        return httplib::Server::HandlerResponse::Unhandled;
+    });
 
-    // this is only called if no index.js is found in the public --path
-    svr.Get("/index.js", [](const httplib::Request &, httplib::Response &res)
-            {
-                res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript; charset=utf-8");
-                return false;
-            });
+    //
+    // Route handlers (or controllers)
+    //
 
-    // this is only called if no index.html is found in the public --path
-    svr.Get("/completion.js", [](const httplib::Request &, httplib::Response &res)
-            {
-                res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript; charset=utf-8");
-                return false;
-            });
+    const auto handle_health = [&](const httplib::Request & req, httplib::Response & res) {
+        server_state current_state = state.load();
+        switch (current_state) {
+            case SERVER_STATE_READY:
+                {
+                    // request slots data using task queue
+                    server_task task;
+                    task.id   = ctx_server.queue_tasks.get_new_id();
+                    task.type = SERVER_TASK_TYPE_METRICS;
+                    task.id_target = -1;
 
-    // this is only called if no index.html is found in the public --path
-    svr.Get("/json-schema-to-grammar.mjs", [](const httplib::Request &, httplib::Response &res)
-            {
-                res.set_content(reinterpret_cast<const char*>(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript; charset=utf-8");
-                return false;
-            });
+                    ctx_server.queue_results.add_waiting_task_id(task.id);
+                    ctx_server.queue_tasks.post(task);
 
-    svr.Get("/props", [&llama](const httplib::Request & req, httplib::Response &res)
-            {
-                res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
-                json data = {
-                    { "user_name",      llama.name_user.c_str() },
-                    { "assistant_name", llama.name_assistant.c_str() },
-                    { "default_generation_settings", llama.default_generation_settings_for_props },
-                    { "total_slots",    llama.params.n_parallel }
-                };
-                res.set_content(data.dump(), "application/json; charset=utf-8");
-            });
+                    // get the result
+                    server_task_result result = ctx_server.queue_results.recv(task.id);
+                    ctx_server.queue_results.remove_waiting_task_id(task.id);
 
-    svr.Post("/completion", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
-            {
-                res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
-                if (!validate_api_key(req, res)) {
-                    return;
+                    const int n_idle_slots       = result.data.at("idle");
+                    const int n_processing_slots = result.data.at("processing");
+
+                    json health = {
+                        {"status",           "ok"},
+                        {"slots_idle",       n_idle_slots},
+                        {"slots_processing", n_processing_slots}
+                    };
+
+                    res.status = 200; // HTTP OK
+                    if (sparams.slots_endpoint && req.has_param("include_slots")) {
+                        health["slots"] = result.data.at("slots");
+                    }
+
+                    if (n_idle_slots == 0) {
+                        health["status"] = "no slot available";
+                        if (req.has_param("fail_on_no_slot")) {
+                            res.status = 503; // HTTP Service Unavailable
+                        }
+                    }
+
+                    res.set_content(health.dump(), "application/json");
+                    break;
                 }
-                json data = json::parse(req.body);
-                const int task_id = llama.queue_tasks.get_new_id();
-                llama.queue_results.add_waiting_task_id(task_id);
-                llama.request_completion(task_id, data, false, false, -1);
-                if (!json_value(data, "stream", false)) {
-                    std::string completion_text;
-                    task_result result = llama.queue_results.recv(task_id);
-                    if (!result.error && result.stop) {
-                        res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
-                    }
-                    else
-                    {
-                        res.status = 404;
-                        res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
-                    }
-                    llama.queue_results.remove_waiting_task_id(task_id);
-                } else {
-                    const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink)
-                    {
-                        while (true)
-                        {
-                            task_result result = llama.queue_results.recv(task_id);
-                            if (!result.error) {
-                                const std::string str =
-                                    "data: " +
-                                    result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
-                                    "\n\n";
-                                LOG_VERBOSE("data stream", {
-                                    { "to_send", str }
-                                });
-                                if (!sink.write(str.c_str(), str.size()))
-                                {
-                                    llama.queue_results.remove_waiting_task_id(task_id);
-                                    return false;
-                                }
-                                if (result.stop) {
-                                    break;
-                                }
-                            } else {
-                                const std::string str =
-                                    "error: " +
-                                    result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
-                                    "\n\n";
-                                LOG_VERBOSE("data stream", {
-                                    { "to_send", str }
-                                });
-                                if (!sink.write(str.c_str(), str.size()))
-                                {
-                                    llama.queue_results.remove_waiting_task_id(task_id);
-                                    return false;
-                                }
-                                break;
-                            }
+            case SERVER_STATE_LOADING_MODEL:
+                {
+                    res_error(res, format_error_response("Loading model", ERROR_TYPE_UNAVAILABLE));
+                } break;
+            case SERVER_STATE_ERROR:
+                {
+                    res_error(res, format_error_response("Model failed to load", ERROR_TYPE_SERVER));
+                } break;
+        }
+    };
+
+    const auto handle_slots = [&](const httplib::Request &, httplib::Response & res) {
+        if (!sparams.slots_endpoint) {
+            res_error(res, format_error_response("This server does not support slots endpoint.", ERROR_TYPE_NOT_SUPPORTED));
+            return;
+        }
+
+        // request slots data using task queue
+        server_task task;
+        task.id = ctx_server.queue_tasks.get_new_id();
+        task.id_multi  = -1;
+        task.id_target = -1;
+        task.type = SERVER_TASK_TYPE_METRICS;
+
+        ctx_server.queue_results.add_waiting_task_id(task.id);
+        ctx_server.queue_tasks.post(task);
+
+        // get the result
+        server_task_result result = ctx_server.queue_results.recv(task.id);
+        ctx_server.queue_results.remove_waiting_task_id(task.id);
+
+        res.set_content(result.data.at("slots").dump(), "application/json");
+        res.status = 200; // HTTP OK
+    };
+
+    const auto handle_metrics = [&](const httplib::Request &, httplib::Response & res) {
+        if (!sparams.metrics_endpoint) {
+            res_error(res, format_error_response("This server does not support metrics endpoint.", ERROR_TYPE_NOT_SUPPORTED));
+            return;
+        }
+
+        // request slots data using task queue
+        server_task task;
+        task.id = ctx_server.queue_tasks.get_new_id();
+        task.id_multi  = -1;
+        task.id_target = -1;
+        task.type = SERVER_TASK_TYPE_METRICS;
+        task.data.push_back({{"reset_bucket", true}});
+
+        ctx_server.queue_results.add_waiting_task_id(task.id);
+        ctx_server.queue_tasks.post(task);
+
+        // get the result
+        server_task_result result = ctx_server.queue_results.recv(task.id);
+        ctx_server.queue_results.remove_waiting_task_id(task.id);
+
+        json data = result.data;
+
+        const uint64_t n_prompt_tokens_processed = data.at("n_prompt_tokens_processed");
+        const uint64_t t_prompt_processing       = data.at("t_prompt_processing");
+
+        const uint64_t n_tokens_predicted  = data.at("n_tokens_predicted");
+        const uint64_t t_tokens_generation = data.at("t_tokens_generation");
+
+        const int32_t kv_cache_used_cells = data.at("kv_cache_used_cells");
+
+        // metrics definition: https://prometheus.io/docs/practices/naming/#metric-names
+        json all_metrics_def = json {
+            {"counter", {{
+                    {"name",  "prompt_tokens_total"},
+                    {"help",  "Number of prompt tokens processed."},
+                    {"value",  (uint64_t) data.at("n_prompt_tokens_processed_total")}
+            }, {
+                    {"name",  "prompt_seconds_total"},
+                    {"help",  "Prompt process time"},
+                    {"value",  (uint64_t) data.at("t_prompt_processing_total") / 1.e3}
+            }, {
+                    {"name",  "tokens_predicted_total"},
+                    {"help",  "Number of generation tokens processed."},
+                    {"value",  (uint64_t) data.at("n_tokens_predicted_total")}
+            }, {
+                    {"name",  "tokens_predicted_seconds_total"},
+                    {"help",  "Predict process time"},
+                    {"value",  (uint64_t) data.at("t_tokens_generation_total") / 1.e3}
+            }}},
+            {"gauge", {{
+                    {"name",  "prompt_tokens_seconds"},
+                    {"help",  "Average prompt throughput in tokens/s."},
+                    {"value",  n_prompt_tokens_processed ? 1.e3 / t_prompt_processing * n_prompt_tokens_processed : 0.}
+            },{
+                    {"name",  "predicted_tokens_seconds"},
+                    {"help",  "Average generation throughput in tokens/s."},
+                    {"value",  n_tokens_predicted ? 1.e3 / t_tokens_generation * n_tokens_predicted : 0.}
+            },{
+                    {"name",  "kv_cache_usage_ratio"},
+                    {"help",  "KV-cache usage. 1 means 100 percent usage."},
+                    {"value",  1. * kv_cache_used_cells / params.n_ctx}
+            },{
+                    {"name",  "kv_cache_tokens"},
+                    {"help",  "KV-cache tokens."},
+                    {"value",  (uint64_t) data.at("kv_cache_tokens_count")}
+            },{
+                    {"name",  "requests_processing"},
+                    {"help",  "Number of request processing."},
+                    {"value",  (uint64_t) data.at("processing")}
+            },{
+                    {"name",  "requests_deferred"},
+                    {"help",  "Number of request deferred."},
+                    {"value",  (uint64_t) data.at("deferred")}
+            }}}
+        };
+
+        std::stringstream prometheus;
+
+        for (const auto & el : all_metrics_def.items()) {
+            const auto & type        = el.key();
+            const auto & metrics_def = el.value();
+
+            for (const auto & metric_def : metrics_def) {
+                const std::string name = metric_def.at("name");
+                const std::string help = metric_def.at("help");
+
+                auto value = json_value(metric_def, "value", 0.);
+                prometheus << "# HELP llamacpp:" << name << " " << help  << "\n"
+                            << "# TYPE llamacpp:" << name << " " << type  << "\n"
+                            << "llamacpp:"        << name << " " << value << "\n";
+            }
+        }
+
+        const int64_t t_start = data.at("t_start");
+        res.set_header("Process-Start-Time-Unix", std::to_string(t_start));
+
+        res.set_content(prometheus.str(), "text/plain; version=0.0.4");
+        res.status = 200; // HTTP OK
+    };
+
+    const auto handle_slots_save = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) {
+        json request_data = json::parse(req.body);
+        std::string filename = request_data.at("filename");
+        if (!validate_file_name(filename)) {
+            res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST));
+            return;
+        }
+        std::string filepath = sparams.slot_save_path + filename;
+
+        server_task task;
+        task.type = SERVER_TASK_TYPE_SLOT_SAVE;
+        task.data = {
+            { "id_slot", id_slot },
+            { "filename", filename },
+            { "filepath", filepath }
+        };
+
+        const int id_task = ctx_server.queue_tasks.post(task);
+        ctx_server.queue_results.add_waiting_task_id(id_task);
+
+        server_task_result result = ctx_server.queue_results.recv(id_task);
+        ctx_server.queue_results.remove_waiting_task_id(id_task);
+
+        if (result.error) {
+            res_error(res, result.data);
+        } else {
+            res.set_content(result.data.dump(), "application/json");
+        }
+    };
+
+    const auto handle_slots_restore = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) {
+        json request_data = json::parse(req.body);
+        std::string filename = request_data.at("filename");
+        if (!validate_file_name(filename)) {
+            res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST));
+            return;
+        }
+        std::string filepath = sparams.slot_save_path + filename;
+
+        server_task task;
+        task.type = SERVER_TASK_TYPE_SLOT_RESTORE;
+        task.data = {
+            { "id_slot", id_slot },
+            { "filename", filename },
+            { "filepath", filepath }
+        };
+
+        const int id_task = ctx_server.queue_tasks.post(task);
+        ctx_server.queue_results.add_waiting_task_id(id_task);
+
+        server_task_result result = ctx_server.queue_results.recv(id_task);
+        ctx_server.queue_results.remove_waiting_task_id(id_task);
+
+        if (result.error) {
+            res_error(res, result.data);
+        } else {
+            res.set_content(result.data.dump(), "application/json");
+        }
+    };
+
+    const auto handle_slots_erase = [&ctx_server, &res_error](const httplib::Request & /* req */, httplib::Response & res, int id_slot) {
+        server_task task;
+        task.type = SERVER_TASK_TYPE_SLOT_ERASE;
+        task.data = {
+            { "id_slot", id_slot },
+        };
+
+        const int id_task = ctx_server.queue_tasks.post(task);
+        ctx_server.queue_results.add_waiting_task_id(id_task);
+
+        server_task_result result = ctx_server.queue_results.recv(id_task);
+        ctx_server.queue_results.remove_waiting_task_id(id_task);
+
+        if (result.error) {
+            res_error(res, result.data);
+        } else {
+            res.set_content(result.data.dump(), "application/json");
+        }
+    };
+
+    const auto handle_slots_action = [&res_error, &handle_slots_save, &handle_slots_restore, &handle_slots_erase](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+
+        std::string id_slot_str = req.path_params.at("id_slot");
+        int id_slot;
+
+        try {
+            id_slot = std::stoi(id_slot_str);
+        } catch (const std::exception &) {
+            res_error(res, format_error_response("Invalid slot ID", ERROR_TYPE_INVALID_REQUEST));
+            return;
+        }
+
+        std::string action = req.get_param_value("action");
+
+        if (action == "save") {
+            handle_slots_save(req, res, id_slot);
+        } else if (action == "restore") {
+            handle_slots_restore(req, res, id_slot);
+        } else if (action == "erase") {
+            handle_slots_erase(req, res, id_slot);
+        } else {
+            res_error(res, format_error_response("Invalid action", ERROR_TYPE_INVALID_REQUEST));
+        }
+    };
+
+    const auto handle_props = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+        json data = {
+            { "system_prompt",               ctx_server.system_prompt.c_str() },
+            { "default_generation_settings", ctx_server.default_generation_settings_for_props },
+            { "total_slots",                 ctx_server.params.n_parallel }
+        };
+
+        res.set_content(data.dump(), "application/json; charset=utf-8");
+    };
+
+    const auto handle_completions = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+
+        json data = json::parse(req.body);
+
+        const int id_task = ctx_server.queue_tasks.get_new_id();
+
+        ctx_server.queue_results.add_waiting_task_id(id_task);
+        ctx_server.request_completion(id_task, -1, data, false, false);
+
+        if (!json_value(data, "stream", false)) {
+            server_task_result result = ctx_server.queue_results.recv(id_task);
+            if (!result.error && result.stop) {
+                res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
+            } else {
+                res_error(res, result.data);
+            }
+
+            ctx_server.queue_results.remove_waiting_task_id(id_task);
+        } else {
+            const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) {
+                while (true) {
+                    server_task_result result = ctx_server.queue_results.recv(id_task);
+                    if (!result.error) {
+                        const std::string str =
+                            "data: " +
+                            result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
+                            "\n\n";
+
+                        LOG_VERBOSE("data stream", {
+                            { "to_send", str }
+                        });
+
+                        if (!sink.write(str.c_str(), str.size())) {
+                            ctx_server.queue_results.remove_waiting_task_id(id_task);
+                            return false;
                         }
 
-                        llama.queue_results.remove_waiting_task_id(task_id);
-                        sink.done();
-                        return true;
-                    };
-
-                    auto on_complete = [task_id, &llama] (bool)
-                    {
-                        // cancel
-                        llama.request_cancel(task_id);
-                        llama.queue_results.remove_waiting_task_id(task_id);
-                    };
-
-                    res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
-                }
-            });
-
-    svr.Get("/v1/models", [&params](const httplib::Request& req, httplib::Response& res)
-            {
-                res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
-                std::time_t t = std::time(0);
-
-                json models = {
-                    {"object", "list"},
-                    {"data", {
-                        {
-                            {"id", params.model_alias},
-                            {"object", "model"},
-                            {"created", t},
-                            {"owned_by", "llamacpp"}
-                        },
-                    }}
-                };
-
-                res.set_content(models.dump(), "application/json; charset=utf-8");
-            });
-
-
-    // TODO: add mount point without "/v1" prefix -- how?
-    svr.Post("/v1/chat/completions", [&llama, &validate_api_key, &sparams](const httplib::Request &req, httplib::Response &res)
-            {
-                res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
-                if (!validate_api_key(req, res)) {
-                    return;
-                }
-                json data = oaicompat_completion_params_parse(json::parse(req.body), sparams.chat_template);
-
-                const int task_id = llama.queue_tasks.get_new_id();
-                llama.queue_results.add_waiting_task_id(task_id);
-                llama.request_completion(task_id, data, false, false, -1);
-
-                if (!json_value(data, "stream", false)) {
-                    std::string completion_text;
-                    task_result result = llama.queue_results.recv(task_id);
-
-                    if (!result.error && result.stop) {
-                        json oaicompat_result = format_final_response_oaicompat(data, result);
-
-                        res.set_content(oaicompat_result.dump(-1, ' ', false,
-                                            json::error_handler_t::replace),
-                                            "application/json; charset=utf-8");
+                        if (result.stop) {
+                            break;
+                        }
                     } else {
-                        res.status = 500;
-                        res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
-                    }
-                    llama.queue_results.remove_waiting_task_id(task_id);
-                } else {
-                    const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink &sink) {
-                        while (true) {
-                            task_result llama_result = llama.queue_results.recv(task_id);
-                            if (!llama_result.error) {
-                                std::vector<json> result_array = format_partial_response_oaicompat( llama_result);
+                        const std::string str =
+                            "error: " +
+                            result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
+                            "\n\n";
 
-                                for (auto it = result_array.begin(); it != result_array.end(); ++it)
-                                {
-                                    if (!it->empty()) {
-                                        const std::string str =
-                                            "data: " +
-                                            it->dump(-1, ' ', false, json::error_handler_t::replace) +
-                                            "\n\n";
-                                        LOG_VERBOSE("data stream", {{"to_send", str}});
-                                        if (!sink.write(str.c_str(), str.size())) {
-                                            llama.queue_results.remove_waiting_task_id(task_id);
-                                            return false;
-                                        }
-                                    }
-                                }
-                                if (llama_result.stop) {
-                                    break;
-                                }
-                            } else {
+                        LOG_VERBOSE("data stream", {
+                            { "to_send", str }
+                        });
+
+                        if (!sink.write(str.c_str(), str.size())) {
+                            ctx_server.queue_results.remove_waiting_task_id(id_task);
+                            return false;
+                        }
+
+                        break;
+                    }
+                }
+
+                ctx_server.queue_results.remove_waiting_task_id(id_task);
+                sink.done();
+
+                return true;
+            };
+
+            auto on_complete = [id_task, &ctx_server] (bool) {
+                // cancel
+                ctx_server.request_cancel(id_task);
+                ctx_server.queue_results.remove_waiting_task_id(id_task);
+            };
+
+            res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
+        }
+    };
+
+    const auto handle_models = [&params, &model_meta](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+
+        json models = {
+            {"object", "list"},
+            {"data", {
+                 {
+                     {"id",       params.model_alias},
+                     {"object",   "model"},
+                     {"created",  std::time(0)},
+                     {"owned_by", "llamacpp"},
+                     {"meta",     model_meta}
+                 },
+             }}
+        };
+
+        res.set_content(models.dump(), "application/json; charset=utf-8");
+    };
+
+    const auto handle_chat_completions = [&ctx_server, &sparams, &res_error](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+        json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), sparams.chat_template);
+
+        const int id_task = ctx_server.queue_tasks.get_new_id();
+
+        ctx_server.queue_results.add_waiting_task_id(id_task);
+        ctx_server.request_completion(id_task, -1, data, false, false);
+
+        const auto completion_id = gen_chatcmplid();
+        if (!json_value(data, "stream", false)) {
+            server_task_result result = ctx_server.queue_results.recv(id_task);
+
+            if (!result.error && result.stop) {
+                json result_oai = format_final_response_oaicompat(data, result.data, completion_id);
+
+                res.set_content(result_oai.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
+            } else {
+                res_error(res, result.data);
+            }
+            ctx_server.queue_results.remove_waiting_task_id(id_task);
+        } else {
+            const auto chunked_content_provider = [id_task, &ctx_server, completion_id](size_t, httplib::DataSink & sink) {
+                while (true) {
+                    server_task_result result = ctx_server.queue_results.recv(id_task);
+                    if (!result.error) {
+                        std::vector<json> result_array = format_partial_response_oaicompat(result.data, completion_id);
+
+                        for (auto it = result_array.begin(); it != result_array.end(); ++it) {
+                            if (!it->empty()) {
                                 const std::string str =
-                                    "error: " +
-                                    llama_result.result_json.dump(-1, ' ', false,
-                                            json::error_handler_t::replace) +
+                                    "data: " +
+                                    it->dump(-1, ' ', false, json::error_handler_t::replace) +
                                     "\n\n";
                                 LOG_VERBOSE("data stream", {{"to_send", str}});
                                 if (!sink.write(str.c_str(), str.size())) {
-                                    llama.queue_results.remove_waiting_task_id(task_id);
+                                    ctx_server.queue_results.remove_waiting_task_id(id_task);
                                     return false;
                                 }
-                                break;
                             }
                         }
-                        sink.done();
-                        llama.queue_results.remove_waiting_task_id(task_id);
-                        return true;
-                    };
-
-                    auto on_complete = [task_id, &llama](bool) {
-                        // cancel request
-                        llama.request_cancel(task_id);
-                        llama.queue_results.remove_waiting_task_id(task_id);
-                    };
-
-                    res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
-                }
-            });
-
-    svr.Post("/infill", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
-            {
-                res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
-                if (!validate_api_key(req, res)) {
-                    return;
-                }
-                json data = json::parse(req.body);
-                const int task_id = llama.queue_tasks.get_new_id();
-                llama.queue_results.add_waiting_task_id(task_id);
-                llama.request_completion(task_id, data, true, false, -1);
-                if (!json_value(data, "stream", false)) {
-                    std::string completion_text;
-                    task_result result = llama.queue_results.recv(task_id);
-                    if (!result.error && result.stop)
-                    {
-                        res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
-                    }
-                    else
-                    {
-                        res.status = 404;
-                        res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
-                    }
-                    llama.queue_results.remove_waiting_task_id(task_id);
-                } else {
-                    const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) {
-                        while (true)
-                        {
-                            task_result result = llama.queue_results.recv(task_id);
-                            if (!result.error) {
-                                const std::string str =
-                                "data: " +
-                                result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
-                                "\n\n";
-                                LOG_VERBOSE("data stream", {
-                                    { "to_send", str }
-                                });
-                                if (!sink.write(str.c_str(), str.size()))
-                                {
-                                    llama.queue_results.remove_waiting_task_id(task_id);
-                                    return false;
-                                }
-                                if (result.stop)
-                                {
-                                    break;
-                                }
-                            }
-                            else
-                            {
-                                break;
-                            }
+                        if (result.stop) {
+                            break;
                         }
-
-                        llama.queue_results.remove_waiting_task_id(task_id);
-                        sink.done();
-                        return true;
-                    };
-
-                    auto on_complete = [task_id, &llama] (bool)
-                    {
-                        // cancel
-                        llama.request_cancel(task_id);
-                    };
-
-                    res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
-                }
-            });
-
-    svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res)
-                { return res.set_content("", "application/json; charset=utf-8"); });
-
-    svr.Post("/tokenize", [&llama](const httplib::Request &req, httplib::Response &res)
-            {
-                res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
-                const json body = json::parse(req.body);
-                std::vector<llama_token> tokens;
-                if (body.count("content") != 0)
-                {
-                    tokens = llama.tokenize(body["content"], false);
-                }
-                const json data = format_tokenizer_response(tokens);
-                return res.set_content(data.dump(), "application/json; charset=utf-8");
-            });
-
-    svr.Post("/detokenize", [&llama](const httplib::Request &req, httplib::Response &res)
-            {
-                res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
-                const json body = json::parse(req.body);
-                std::string content;
-                if (body.count("tokens") != 0)
-                {
-                    const std::vector<llama_token> tokens = body["tokens"];
-                    content = tokens_to_str(llama.ctx, tokens.cbegin(), tokens.cend());
-                }
-
-                const json data = format_detokenized_response(content);
-                return res.set_content(data.dump(), "application/json; charset=utf-8");
-            });
-
-    svr.Post("/embedding", [&llama](const httplib::Request &req, httplib::Response &res)
-            {
-                res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
-                const json body = json::parse(req.body);
-                json prompt;
-                if (body.count("content") != 0)
-                {
-                    prompt = body["content"];
-                }
-                else
-                {
-                    prompt = "";
-                }
-
-                json image_data;
-                if (body.count("image_data") != 0) {
-                    image_data = body["image_data"];
-                }
-                else
-                {
-                    image_data = "";
-                }
-
-                // create and queue the task
-                const int task_id = llama.queue_tasks.get_new_id();
-                llama.queue_results.add_waiting_task_id(task_id);
-                llama.request_completion(task_id, { {"prompt", prompt}, { "n_predict", 0}, {"image_data", image_data} }, false, true, -1);
-
-                // get the result
-                task_result result = llama.queue_results.recv(task_id);
-                llama.queue_results.remove_waiting_task_id(task_id);
-
-                // send the result
-                return res.set_content(result.result_json.dump(), "application/json; charset=utf-8");
-            });
-
-    svr.Post("/v1/embeddings", [&llama](const httplib::Request &req, httplib::Response &res)
-            {
-                res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
-                const json body = json::parse(req.body);
-
-                json prompt;
-                if (body.count("input") != 0)
-                {
-                    prompt = body["input"];
-                    // batch
-                    if(prompt.is_array()) {
-                        json data = json::array();
-                        int i = 0;
-                        for (const json &elem : prompt) {
-                            const int task_id = llama.queue_tasks.get_new_id();
-                            llama.queue_results.add_waiting_task_id(task_id);
-                            llama.request_completion(task_id, { {"prompt", elem}, { "n_predict", 0} }, false, true, -1);
-
-                            // get the result
-                            task_result result = llama.queue_results.recv(task_id);
-                            llama.queue_results.remove_waiting_task_id(task_id);
-
-                            json embedding = json{
-                                {"embedding", json_value(result.result_json, "embedding", json::array())},
-                                {"index", i++},
-                                {"object", "embedding"}
-                            };
-                            data.push_back(embedding);
+                    } else {
+                        const std::string str =
+                            "error: " +
+                            result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
+                            "\n\n";
+                        LOG_VERBOSE("data stream", {{"to_send", str}});
+                        if (!sink.write(str.c_str(), str.size())) {
+                            ctx_server.queue_results.remove_waiting_task_id(id_task);
+                            return false;
                         }
-                        json result = format_embeddings_response_oaicompat(body, data);
-                        return res.set_content(result.dump(), "application/json; charset=utf-8");
+                        break;
                     }
                 }
-                else
-                {
-                    prompt = "";
-                }
+                sink.done();
+                ctx_server.queue_results.remove_waiting_task_id(id_task);
+                return true;
+            };
 
-                // create and queue the task
-                const int task_id = llama.queue_tasks.get_new_id();
-                llama.queue_results.add_waiting_task_id(task_id);
-                llama.request_completion(task_id, { {"prompt", prompt}, { "n_predict", 0}}, false, true, -1);
+            auto on_complete = [id_task, &ctx_server](bool) {
+                // cancel request
+                ctx_server.request_cancel(id_task);
+                ctx_server.queue_results.remove_waiting_task_id(id_task);
+            };
 
-                // get the result
-                task_result result = llama.queue_results.recv(task_id);
-                llama.queue_results.remove_waiting_task_id(task_id);
-
-                json data = json::array({json{
-                        {"embedding", json_value(result.result_json, "embedding", json::array())},
-                        {"index", 0},
-                        {"object", "embedding"}
-                    }}
-                );
-
-                json root = format_embeddings_response_oaicompat(body, data);
-
-                // send the result
-                return res.set_content(root.dump(), "application/json; charset=utf-8");
-            });
-
-    // GG: if I put the main loop inside a thread, it crashes on the first request when build in Debug!?
-    //     "Bus error: 10" - this is on macOS, it does not crash on Linux
-    //std::thread t2([&]()
-    /*{
-        bool running = true;
-        while (running)
-        {
-            running = llama.update_slots();
+            res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
         }
-    }*/
-    //);
+    };
 
-    llama.queue_tasks.on_new_task(std::bind(
-        &llama_server_context::process_single_task, &llama, std::placeholders::_1));
-    llama.queue_tasks.on_finish_multitask(std::bind(
-        &llama_server_context::on_finish_multitask, &llama, std::placeholders::_1));
-    llama.queue_tasks.on_all_tasks_finished(std::bind(
-        &llama_server_context::run_on_all_tasks_finished, &llama));
-    llama.queue_results.on_multitask_update(std::bind(
-        &llama_server_queue::update_multitask,
-        &llama.queue_tasks,
+    const auto handle_infill = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+
+        json data = json::parse(req.body);
+
+        const int id_task = ctx_server.queue_tasks.get_new_id();
+
+        ctx_server.queue_results.add_waiting_task_id(id_task);
+        ctx_server.request_completion(id_task, -1, data, true, false);
+
+        if (!json_value(data, "stream", false)) {
+            server_task_result result = ctx_server.queue_results.recv(id_task);
+            if (!result.error && result.stop) {
+                res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
+            } else {
+                res_error(res, result.data);
+            }
+
+            ctx_server.queue_results.remove_waiting_task_id(id_task);
+        } else {
+            const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) {
+                while (true) {
+                    server_task_result result = ctx_server.queue_results.recv(id_task);
+                    if (!result.error) {
+                        const std::string str =
+                            "data: " +
+                            result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
+                            "\n\n";
+
+                        LOG_VERBOSE("data stream", {
+                            { "to_send", str }
+                        });
+
+                        if (!sink.write(str.c_str(), str.size())) {
+                            ctx_server.queue_results.remove_waiting_task_id(id_task);
+                            return false;
+                        }
+
+                        if (result.stop) {
+                            break;
+                        }
+                    } else {
+                        break;
+                    }
+                }
+
+                ctx_server.queue_results.remove_waiting_task_id(id_task);
+                sink.done();
+
+                return true;
+            };
+
+            auto on_complete = [id_task, &ctx_server] (bool) {
+                ctx_server.request_cancel(id_task);
+            };
+
+            res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
+        }
+    };
+
+    const auto handle_tokenize = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+        const json body = json::parse(req.body);
+
+        std::vector<llama_token> tokens;
+        if (body.count("content") != 0) {
+            const bool add_special = json_value(body, "add_special", false);
+            tokens = ctx_server.tokenize(body.at("content"), add_special);
+        }
+        const json data = format_tokenizer_response(tokens);
+        return res.set_content(data.dump(), "application/json; charset=utf-8");
+    };
+
+    const auto handle_detokenize = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+        const json body = json::parse(req.body);
+
+        std::string content;
+        if (body.count("tokens") != 0) {
+            const std::vector<llama_token> tokens = body.at("tokens");
+            content = tokens_to_str(ctx_server.ctx, tokens.cbegin(), tokens.cend());
+        }
+
+        const json data = format_detokenized_response(content);
+        return res.set_content(data.dump(), "application/json; charset=utf-8");
+    };
+
+    const auto handle_embeddings = [&params, &ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+        if (!params.embedding) {
+            res.status = 501;
+            res.set_content("This server does not support embeddings. Start it with `--embeddings`", "text/plain; charset=utf-8");
+            return;
+        }
+
+        const json body = json::parse(req.body);
+        bool is_openai = false;
+
+        // an input prompt can be a string or a list of tokens (integer)
+        json prompt;
+        if (body.count("input") != 0) {
+            is_openai = true;
+            prompt = body.at("input");
+        } else if (body.count("content") != 0) {
+            // with "content", we only support single prompt
+            prompt = std::vector<std::string>{body.at("content")};
+        } else {
+            res_error(res, format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST));
+            return;
+        }
+
+        // create and queue the task
+        json responses;
+        {
+            const int id_task = ctx_server.queue_tasks.get_new_id();
+            ctx_server.queue_results.add_waiting_task_id(id_task);
+            ctx_server.request_completion(id_task, -1, {{"prompt", prompt}}, false, true);
+
+            // get the result
+            server_task_result result = ctx_server.queue_results.recv(id_task);
+            ctx_server.queue_results.remove_waiting_task_id(id_task);
+            if (!result.error) {
+                if (result.data.count("results")) {
+                    // result for multi-task
+                    responses = result.data.at("results");
+                } else {
+                    // result for single task
+                    responses = std::vector<json>{result.data};
+                }
+            } else {
+                // error received, ignore everything else
+                res_error(res, result.data);
+                return;
+            }
+        }
+
+        // write JSON response
+        json root = is_openai
+            ? format_embeddings_response_oaicompat(body, responses)
+            : responses[0];
+        return res.set_content(root.dump(), "application/json; charset=utf-8");
+    };
+
+    auto handle_static_file = [](unsigned char * content, size_t len, const char * mime_type) {
+        return [content, len, mime_type](const httplib::Request &, httplib::Response & res) {
+            res.set_content(reinterpret_cast<const char*>(content), len, mime_type);
+            return false;
+        };
+    };
+
+    //
+    // Router
+    //
+
+    // register static assets routes
+    if (!sparams.public_path.empty()) {
+        // Set the base directory for serving static files
+        svr->set_base_dir(sparams.public_path);
+    }
+
+    // using embedded static files
+    svr->Get("/", handle_static_file(index_html, index_html_len, "text/html; charset=utf-8"));
+    svr->Get("/index.js", handle_static_file(index_js, index_js_len, "text/javascript; charset=utf-8"));
+    svr->Get("/completion.js", handle_static_file(completion_js, completion_js_len, "text/javascript; charset=utf-8"));
+    svr->Get("/json-schema-to-grammar.mjs", handle_static_file(
+        json_schema_to_grammar_mjs, json_schema_to_grammar_mjs_len, "text/javascript; charset=utf-8"));
+
+    // register API routes
+    svr->Get ("/health",              handle_health);
+    svr->Get ("/slots",               handle_slots);
+    svr->Get ("/metrics",             handle_metrics);
+    svr->Get ("/props",               handle_props);
+    svr->Get ("/v1/models",           handle_models);
+    svr->Post("/completion",          handle_completions); // legacy
+    svr->Post("/completions",         handle_completions);
+    svr->Post("/v1/completions",      handle_completions);
+    svr->Post("/chat/completions",    handle_chat_completions);
+    svr->Post("/v1/chat/completions", handle_chat_completions);
+    svr->Post("/infill",              handle_infill);
+    svr->Post("/embedding",           handle_embeddings); // legacy
+    svr->Post("/embeddings",          handle_embeddings);
+    svr->Post("/v1/embeddings",       handle_embeddings);
+    svr->Post("/tokenize",            handle_tokenize);
+    svr->Post("/detokenize",          handle_detokenize);
+    if (!sparams.slot_save_path.empty()) {
+        // only enable slot endpoints if slot_save_path is set
+        svr->Post("/slots/:id_slot",  handle_slots_action);
+    }
+
+    //
+    // Start the server
+    //
+    if (sparams.n_threads_http < 1) {
+        // +2 threads for monitoring endpoints
+        sparams.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1);
+    }
+    log_data["n_threads_http"] =  std::to_string(sparams.n_threads_http);
+    svr->new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); };
+
+    LOG_INFO("HTTP server listening", log_data);
+
+    // run the HTTP server in a thread - see comment below
+    std::thread t([&]() {
+        if (!svr->listen_after_bind()) {
+            state.store(SERVER_STATE_ERROR);
+            return 1;
+        }
+
+        return 0;
+    });
+
+    ctx_server.queue_tasks.on_new_task(std::bind(
+        &server_context::process_single_task, &ctx_server, std::placeholders::_1));
+    ctx_server.queue_tasks.on_finish_multitask(std::bind(
+        &server_context::on_finish_multitask, &ctx_server, std::placeholders::_1));
+    ctx_server.queue_tasks.on_update_slots(std::bind(
+        &server_context::update_slots, &ctx_server));
+    ctx_server.queue_results.on_multitask_update(std::bind(
+        &server_queue::update_multitask,
+        &ctx_server.queue_tasks,
         std::placeholders::_1,
         std::placeholders::_2,
         std::placeholders::_3
     ));
 
     shutdown_handler = [&](int) {
-        llama.queue_tasks.terminate();
+        ctx_server.queue_tasks.terminate();
     };
 
 #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
@@ -3217,16 +3814,20 @@ int main(int argc, char **argv)
     sigemptyset (&sigint_action.sa_mask);
     sigint_action.sa_flags = 0;
     sigaction(SIGINT, &sigint_action, NULL);
+    sigaction(SIGTERM, &sigint_action, NULL);
 #elif defined (_WIN32)
     auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
         return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false;
     };
     SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
 #endif
-    llama.queue_tasks.start_loop();
-    svr.stop();
+
+    ctx_server.queue_tasks.start_loop();
+
+    svr->stop();
     t.join();
 
     llama_backend_free();
+
     return 0;
 }
diff --git a/examples/server/tests/README.md b/examples/server/tests/README.md
new file mode 100644
index 000000000..83c0208f3
--- /dev/null
+++ b/examples/server/tests/README.md
@@ -0,0 +1,67 @@
+# Server tests
+
+Python based server tests scenario using [BDD](https://en.wikipedia.org/wiki/Behavior-driven_development)
+and [behave](https://behave.readthedocs.io/en/latest/):
+
+* [issues.feature](./features/issues.feature) Pending issues scenario
+* [parallel.feature](./features/parallel.feature) Scenario involving multi slots and concurrent requests
+* [security.feature](./features/security.feature) Security, CORS and API Key
+* [server.feature](./features/server.feature) Server base scenario: completion, embedding, tokenization, etc...
+
+Tests target GitHub workflows job runners with 4 vCPU.
+
+Requests are
+using [aiohttp](https://docs.aiohttp.org/en/stable/client_reference.html), [asyncio](https://docs.python.org/fr/3/library/asyncio.html)
+based http client.
+
+Note: If the host architecture inference speed is faster than GitHub runners one, parallel scenario may randomly fail.
+To mitigate it, you can increase values in `n_predict`, `kv_size`.
+
+### Install dependencies
+
+`pip install -r requirements.txt`
+
+### Run tests
+
+1. Build the server
+
+```shell
+cd ../../..
+mkdir build
+cd build
+cmake -DLLAMA_CURL=ON ../
+cmake --build . --target server
+```
+
+2. Start the test: `./tests.sh`
+
+It's possible to override some scenario steps values with environment variables:
+
+| variable                 | description                                                                                    |
+|--------------------------|------------------------------------------------------------------------------------------------|
+| `PORT`                   | `context.server_port` to set the listening port of the server during scenario, default: `8080` |
+| `LLAMA_SERVER_BIN_PATH`  | to change the server binary path, default: `../../../build/bin/server`                         |
+| `DEBUG`                  | "ON" to enable steps and server verbose mode `--verbose`                                       |
+| `SERVER_LOG_FORMAT_JSON` | if set switch server logs to json format                                                       |
+| `N_GPU_LAYERS`           | number of model layers to offload to VRAM `-ngl --n-gpu-layers`                                |
+
+### Run @bug, @wip or @wrong_usage annotated scenario
+
+Feature or Scenario must be annotated with `@llama.cpp` to be included in the default scope.
+
+- `@bug` annotation aims to link a scenario with a GitHub issue.
+- `@wrong_usage` are meant to show user issue that are actually an expected behavior
+- `@wip` to focus on a scenario working in progress
+- `@slow` heavy test, disabled by default
+
+To run a scenario annotated with `@bug`, start:
+
+```shell
+DEBUG=ON ./tests.sh --no-skipped --tags bug --stop
+```
+
+After changing logic in `steps.py`, ensure that `@bug` and `@wrong_usage` scenario are updated.
+
+```shell
+./tests.sh --no-skipped --tags bug,wrong_usage || echo "should failed but compile"
+```
diff --git a/examples/server/tests/features/embeddings.feature b/examples/server/tests/features/embeddings.feature
new file mode 100644
index 000000000..6f163ce04
--- /dev/null
+++ b/examples/server/tests/features/embeddings.feature
@@ -0,0 +1,96 @@
+@llama.cpp
+@embeddings
+Feature: llama.cpp server
+
+  Background: Server startup
+    Given a server listening on localhost:8080
+    And   a model url https://huggingface.co/ggml-org/models/resolve/main/bert-bge-small/ggml-model-f16.gguf
+    And   a model file bert-bge-small.gguf
+    And   a model alias bert-bge-small
+    And   42 as server seed
+    And   2 slots
+    And   1024 as batch size
+    And   1024 as ubatch size
+    And   2048 KV cache size
+    And   embeddings extraction
+    Then  the server is starting
+    Then  the server is healthy
+
+  Scenario: Embedding
+    When embeddings are computed for:
+    """
+    What is the capital of Bulgaria ?
+    """
+    Then embeddings are generated
+
+  Scenario: OAI Embeddings compatibility
+    Given a model bert-bge-small
+    When an OAI compatible embeddings computation request for:
+    """
+    What is the capital of Spain ?
+    """
+    Then embeddings are generated
+
+  Scenario: OAI Embeddings compatibility with multiple inputs
+    Given a model bert-bge-small
+    Given a prompt:
+      """
+      In which country Paris is located ?
+      """
+    And a prompt:
+      """
+      Is Madrid the capital of Spain ?
+      """
+    When an OAI compatible embeddings computation request for multiple inputs
+    Then embeddings are generated
+
+  Scenario: Multi users embeddings
+    Given a prompt:
+      """
+      Write a very long story about AI.
+      """
+    And a prompt:
+      """
+      Write another very long music lyrics.
+      """
+    And a prompt:
+      """
+      Write a very long poem.
+      """
+    And a prompt:
+      """
+      Write a very long joke.
+      """
+    Given concurrent embedding requests
+    Then the server is busy
+    Then the server is idle
+    Then all embeddings are generated
+
+  Scenario: Multi users OAI compatibility embeddings
+    Given a prompt:
+      """
+      In which country Paris is located ?
+      """
+    And a prompt:
+      """
+      Is Madrid the capital of Spain ?
+      """
+    And a prompt:
+      """
+      What is the biggest US city ?
+      """
+    And a prompt:
+      """
+      What is the capital of Bulgaria ?
+      """
+    And   a model bert-bge-small
+    Given concurrent OAI embedding requests
+    Then the server is busy
+    Then the server is idle
+    Then all embeddings are generated
+
+  Scenario: All embeddings should be the same
+    Given 10 fixed prompts
+    And   a model bert-bge-small
+    Given concurrent OAI embedding requests
+    Then all embeddings are the same
diff --git a/examples/server/tests/features/environment.py b/examples/server/tests/features/environment.py
new file mode 100644
index 000000000..e7845dc2f
--- /dev/null
+++ b/examples/server/tests/features/environment.py
@@ -0,0 +1,71 @@
+import os
+import signal
+import socket
+import sys
+import time
+import traceback
+from contextlib import closing
+from subprocess import TimeoutExpired
+
+
+def before_scenario(context, scenario):
+    context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON'
+    if context.debug:
+        print("DEBUG=ON")
+    print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m")
+    port = 8080
+    if 'PORT' in os.environ:
+        port = int(os.environ['PORT'])
+    if is_server_listening("localhost", port):
+        assert False, "Server already started"
+
+
+def after_scenario(context, scenario):
+    try:
+        if 'server_process' not in context or context.server_process is None:
+            return
+        if scenario.status == "failed":
+            if 'GITHUB_ACTIONS' in os.environ:
+                print(f"\x1b[33;101mSCENARIO FAILED: {scenario.name} server logs:\x1b[0m\n")
+                if os.path.isfile('llama.log'):
+                    with closing(open('llama.log', 'r')) as f:
+                        for line in f:
+                            print(line)
+            if not is_server_listening(context.server_fqdn, context.server_port):
+                print("\x1b[33;101mERROR: Server stopped listening\x1b[0m")
+
+        if context.server_process.poll() is not None:
+            assert False, f"Server not running pid={context.server_process.pid} ..."
+
+        server_graceful_shutdown(context)  # SIGINT
+
+        try:
+            context.server_process.wait(0.5)
+        except TimeoutExpired:
+            print(f"server still alive after 500ms, force-killing pid={context.server_process.pid} ...")
+            context.server_process.kill()  # SIGKILL
+            context.server_process.wait()
+
+        while is_server_listening(context.server_fqdn, context.server_port):
+            time.sleep(0.1)
+    except Exception:
+        print("ignoring error in after_scenario:")
+        traceback.print_exc(file=sys.stdout)
+
+
+def server_graceful_shutdown(context):
+    print(f"shutting down server pid={context.server_process.pid} ...")
+    if os.name == 'nt':
+        interrupt = signal.CTRL_C_EVENT
+    else:
+        interrupt = signal.SIGINT
+    context.server_process.send_signal(interrupt)
+
+
+def is_server_listening(server_fqdn, server_port):
+    with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
+        result = sock.connect_ex((server_fqdn, server_port))
+        _is_server_listening = result == 0
+        if _is_server_listening:
+            print(f"server is listening on {server_fqdn}:{server_port}...")
+        return _is_server_listening
diff --git a/examples/server/tests/features/issues.feature b/examples/server/tests/features/issues.feature
new file mode 100644
index 000000000..7b13e44ca
--- /dev/null
+++ b/examples/server/tests/features/issues.feature
@@ -0,0 +1,5 @@
+# List of ongoing issues
+# run with: DEBUG=ON ./tests.sh --no-skipped --tags bug
+@bug
+Feature: Issues
+  # No confirmed issue at the moment
diff --git a/examples/server/tests/features/parallel.feature b/examples/server/tests/features/parallel.feature
new file mode 100644
index 000000000..6cd306a2b
--- /dev/null
+++ b/examples/server/tests/features/parallel.feature
@@ -0,0 +1,102 @@
+@llama.cpp
+@parallel
+Feature: Parallel
+
+  Background: Server startup
+    Given a server listening on localhost:8080
+    And   a model file tinyllamas/split/stories15M-00001-of-00003.gguf from HF repo ggml-org/models
+    And   a model file test-model-00001-of-00003.gguf
+    And   42 as server seed
+    And   128 as batch size
+    And   256 KV cache size
+    And   2 slots
+    And   continuous batching
+    Then  the server is starting
+    Then  the server is healthy
+
+  Scenario Outline: Multi users completion
+    Given a prompt:
+      """
+      Write a very long story about AI.
+      """
+    And a prompt:
+      """
+      Write another very long music lyrics.
+      """
+    And <n_predict> max tokens to predict
+    Given concurrent completion requests
+    Then the server is busy
+    Then the server is idle
+    And  all slots are idle
+    Then all prompts are predicted with <n_predict> tokens
+    Examples:
+      | n_predict |
+      | 128       |
+
+  Scenario Outline: Multi users OAI completions compatibility
+    Given a system prompt You are a writer.
+    And   a model tinyllama-2
+    Given a prompt:
+      """
+      Write a very long book.
+      """
+    And a prompt:
+      """
+      Write another a poem.
+      """
+    And <n_predict> max tokens to predict
+    And streaming is <streaming>
+    Given concurrent OAI completions requests
+    Then the server is busy
+    Then the server is idle
+    Then all prompts are predicted with <n_predict> tokens
+    Examples:
+      | streaming | n_predict |
+      | disabled  | 128       |
+      | enabled   | 64        |
+
+  Scenario Outline: Multi users OAI completions compatibility no v1
+    Given a system prompt You are a writer.
+    And   a model tinyllama-2
+    Given a prompt:
+      """
+      Write a very long book.
+      """
+    And a prompt:
+      """
+      Write another a poem.
+      """
+    And <n_predict> max tokens to predict
+    And streaming is <streaming>
+    Given concurrent OAI completions requests no v1
+    Then the server is busy
+    Then the server is idle
+    Then all prompts are predicted with <n_predict> tokens
+    Examples:
+      | streaming | n_predict |
+      | disabled  | 128       |
+      | enabled   | 64        |
+
+
+  Scenario:  Multi users with total number of tokens to predict exceeds the KV Cache size #3969
+    Given a prompt:
+      """
+      Write a very long story about AI.
+      """
+    And a prompt:
+      """
+      Write another very long music lyrics.
+      """
+    And a prompt:
+      """
+      Write a very long poem.
+      """
+    And a prompt:
+      """
+      Write a very long joke.
+      """
+    And 128 max tokens to predict
+    Given concurrent completion requests
+    Then the server is busy
+    Then the server is idle
+    Then all prompts are predicted
diff --git a/examples/server/tests/features/passkey.feature b/examples/server/tests/features/passkey.feature
new file mode 100644
index 000000000..1bde7aab8
--- /dev/null
+++ b/examples/server/tests/features/passkey.feature
@@ -0,0 +1,55 @@
+# run with: ./tests.sh --no-skipped --tags passkey
+@passkey
+@slow
+Feature: Passkey / Self-extend with context shift
+
+  Background: Server startup
+    Given a server listening on localhost:8080
+
+  # Generates a long text of junk and inserts a secret passkey number inside it.
+  # Then we query the LLM for the secret passkey.
+  # see #3856 and #4810
+  Scenario Outline: Passkey
+    Given a model file <hf_file> from HF repo <hf_repo>
+    And   <n_batch> as batch size
+    And   <n_junk> as number of junk
+    And   <n_predicted> server max tokens to predict
+    And   42 as seed
+    And   <n_ctx> KV cache size
+    And   1 slots
+    And   <n_ga> group attention factor to extend context size through self-extend
+    And   <n_ga_w> group attention width to extend context size through self-extend
+    # Can be override with N_GPU_LAYERS
+    And   <ngl> GPU offloaded layers
+    Then  the server is starting
+    Then  the server is healthy
+    Given available models
+    Then  model 0 is trained on <n_ctx_train> tokens context
+    Given a prefix prompt:
+    """
+    here is an important info hidden inside a lot of irrelevant text. Find it and memorize them. I will quiz you about the important information there.
+    """
+    And a passkey prompt template:
+    """
+    The pass key is <passkey> Remember it. <passkey> is the pass key.
+    """
+    And a junk suffix prompt:
+    """
+    The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.
+    """
+    And a suffix prompt:
+    """
+    What is the pass key? The pass key is
+    """
+    Given a "<passkey>" passkey challenge prompt with the passkey inserted every <i_pos> junk
+    And  a completion request with no api error
+    Then <n_predicted> tokens are predicted matching <re_content>
+
+    Examples:
+      | hf_repo                         | hf_file                     | n_ctx_train | ngl | n_ctx | n_batch | n_ga | n_ga_w | n_junk | i_pos | passkey | n_predicted | re_content     |
+      | TheBloke/phi-2-GGUF             | phi-2.Q4_K_M.gguf           | 2048        | 5   | 8192  | 512     | 4    | 512    | 250    | 50    | 42      | 1           | 42             |
+      | TheBloke/phi-2-GGUF             | phi-2.Q4_K_M.gguf           | 2048        | 5   | 8192  | 512     | 2    | 512    | 250    | 50    | 42      | 1           | \b((?!42)\w)+\b  |
+      #| TheBloke/Llama-2-7B-GGUF        | llama-2-7b.Q2_K.gguf        | 4096        | 3   | 16384 | 512     | 4    | 512    | 500    | 300   | 1234    | 5           | 1234           |
+      #| TheBloke/Mixtral-8x7B-v0.1-GGUF | mixtral-8x7b-v0.1.Q2_K.gguf | 32768       | 2   | 16384 | 512     | 4    | 512    | 500    | 100   | 0987    | 5           | 0
+      # 987           |
+
diff --git a/examples/server/tests/features/results.feature b/examples/server/tests/features/results.feature
new file mode 100644
index 000000000..aa0b8d0c6
--- /dev/null
+++ b/examples/server/tests/features/results.feature
@@ -0,0 +1,81 @@
+@llama.cpp
+@results
+Feature: Results
+
+  Background: Server startup
+    Given a server listening on localhost:8080
+    And   a model file tinyllamas/split/stories15M-00001-of-00003.gguf from HF repo ggml-org/models
+    And   a model file test-model-00001-of-00003.gguf
+    And   128 as batch size
+    And   1024 KV cache size
+    And   128 max tokens to predict
+    And   continuous batching
+
+  Scenario Outline: consistent results with same seed
+    Given <n_slots> slots
+    Then  the server is starting
+    Then  the server is healthy
+
+    Given 4 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 42
+
+    Given concurrent completion requests
+    Then the server is busy
+    Then the server is idle
+    And  all slots are idle
+    Then all predictions are equal
+    Examples:
+      | n_slots |
+      | 1       |
+      | 2       |
+
+  Scenario Outline: different results with different seed
+    Given <n_slots> slots
+    Then  the server is starting
+    Then  the server is healthy
+
+    Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 42
+    Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 43
+    Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 44
+    Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 45
+
+    Given concurrent completion requests
+    Then the server is busy
+    Then the server is idle
+    And  all slots are idle
+    Then all predictions are different
+    Examples:
+      | n_slots |
+      | 1       |
+      | 2       |
+
+  Scenario Outline: consistent results with same seed and varying batch size
+    Given 4 slots
+    And   <temp> temperature
+    # And   0 as draft
+    Then  the server is starting
+    Then  the server is healthy
+
+    Given 1 prompts "Write a very long story about AI." with seed 42
+    And   concurrent completion requests
+    # Then the server is busy # Not all slots will be utilized.
+    Then  the server is idle
+    And   all slots are idle
+
+    Given <n_parallel> prompts "Write a very long story about AI." with seed 42
+    And   concurrent completion requests
+    # Then the server is busy # Not all slots will be utilized.
+    Then the server is idle
+    And  all slots are idle
+
+    Then all predictions are equal
+    Examples:
+      | n_parallel | temp |
+      |  1         | 0.0  |
+      |  2         | 0.0  |
+      |  4         | 0.0  |
+      |  1         | 1.0  |
+      # FIXME: These tests fail on master. The problem seems to be the unified KV cache.
+      # See https://github.com/ggerganov/whisper.cpp/issues/1941#issuecomment-1986923227
+      # and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574 .
+      # |  2         | 1.0  |
+      # |  4         | 1.0  |
diff --git a/examples/server/tests/features/security.feature b/examples/server/tests/features/security.feature
new file mode 100644
index 000000000..eb82e7aca
--- /dev/null
+++ b/examples/server/tests/features/security.feature
@@ -0,0 +1,68 @@
+@llama.cpp
+@security
+Feature: Security
+
+  Background: Server startup with an api key defined
+    Given a server listening on localhost:8080
+    And   a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
+    And   a server api key llama.cpp
+    Then  the server is starting
+    Then  the server is healthy
+
+  Scenario Outline: Completion with some user api key
+    Given a prompt test
+    And   a user api key <api_key>
+    And   4 max tokens to predict
+    And   a completion request with <api_error> api error
+
+    Examples: Prompts
+      | api_key   | api_error |
+      | llama.cpp | no        |
+      | llama.cpp | no        |
+      | hackeme   | raised    |
+      |           | raised    |
+
+  Scenario Outline: OAI Compatibility
+    Given a system prompt test
+    And   a user prompt test
+    And   a model test
+    And   2 max tokens to predict
+    And   streaming is disabled
+    And   a user api key <api_key>
+    Given an OAI compatible chat completions request with <api_error> api error
+
+    Examples: Prompts
+      | api_key   | api_error |
+      | llama.cpp | no        |
+      | llama.cpp | no        |
+      | hackme    | raised    |
+
+  Scenario Outline: OAI Compatibility (invalid response formats)
+    Given a system prompt test
+    And   a user prompt test
+    And   a response format <response_format>
+    And   a model test
+    And   2 max tokens to predict
+    And   streaming is disabled
+    Given an OAI compatible chat completions request with raised api error
+
+    Examples: Prompts
+      | response_format                                       |
+      | {"type": "sound"}                                     |
+      | {"type": "json_object", "schema": 123}                |
+      | {"type": "json_object", "schema": {"type": 123}}      |
+      | {"type": "json_object", "schema": {"type": "hiccup"}} |
+
+
+  Scenario Outline: CORS Options
+    Given a user api key llama.cpp
+    When  an OPTIONS request is sent from <origin>
+    Then  CORS header <cors_header> is set to <cors_header_value>
+
+    Examples: Headers
+      | origin          | cors_header                      | cors_header_value |
+      | localhost       | Access-Control-Allow-Origin      | localhost         |
+      | web.mydomain.fr | Access-Control-Allow-Origin      | web.mydomain.fr   |
+      | origin          | Access-Control-Allow-Credentials | true              |
+      | web.mydomain.fr | Access-Control-Allow-Methods     | POST              |
+      | web.mydomain.fr | Access-Control-Allow-Headers     | *                 |
diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature
new file mode 100644
index 000000000..d21c09135
--- /dev/null
+++ b/examples/server/tests/features/server.feature
@@ -0,0 +1,112 @@
+@llama.cpp
+@server
+Feature: llama.cpp server
+
+  Background: Server startup
+    Given a server listening on localhost:8080
+    And   a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
+    And   a model file test-model.gguf
+    And   a model alias tinyllama-2
+    And   BOS token is 1
+    And   42 as server seed
+      # KV Cache corresponds to the total amount of tokens
+      # that can be stored across all independent sequences: #4130
+      # see --ctx-size and #5568
+    And   256 KV cache size
+    And   32 as batch size
+    And   2 slots
+    And   64 server max tokens to predict
+    And   prometheus compatible metrics exposed
+    Then  the server is starting
+    Then  the server is healthy
+
+  Scenario: Health
+    Then the server is ready
+    And  all slots are idle
+
+
+  Scenario Outline: Completion
+    Given a prompt <prompt>
+    And   <n_predict> max tokens to predict
+    And   a completion request with no api error
+    Then  <n_predicted> tokens are predicted matching <re_content>
+    And   the completion is <truncated> truncated
+    And   <n_prompt> prompt tokens are processed
+    And   prometheus metrics are exposed
+    And   metric llamacpp:tokens_predicted is <n_predicted>
+
+    Examples: Prompts
+      | prompt                                                                    | n_predict | re_content                                  | n_prompt | n_predicted | truncated |
+      | I believe the meaning of life is                                          | 8         | (read\|going)+                              | 18       | 8           | not       |
+      | Write a joke about AI from a very long prompt which will not be truncated | 256       | (princesses\|everyone\|kids\|Anna\|forest)+ | 46       | 64          | not       |
+
+  Scenario: Completion prompt truncated
+    Given a prompt:
+    """
+    Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
+    Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
+    Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
+    Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
+    """
+    And   a completion request with no api error
+    Then  64 tokens are predicted matching fun|Annaks|popcorns|pictry|bowl
+    And   the completion is  truncated
+    And   109 prompt tokens are processed
+
+
+  Scenario Outline: OAI Compatibility
+    Given a model <model>
+    And   a system prompt <system_prompt>
+    And   a user prompt <user_prompt>
+    And   <max_tokens> max tokens to predict
+    And   streaming is <enable_streaming>
+    Given an OAI compatible chat completions request with no api error
+    Then  <n_predicted> tokens are predicted matching <re_content>
+    And   <n_prompt> prompt tokens are processed
+    And   the completion is <truncated> truncated
+
+    Examples: Prompts
+      | model        | system_prompt               | user_prompt                          | max_tokens | re_content                        | n_prompt | n_predicted | enable_streaming | truncated |
+      | llama-2      | Book                        | What is the best book                | 8          | (Here\|what)+                     | 77       | 8           | disabled         | not       |
+      | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128        | (thanks\|happy\|bird\|Annabyear)+ | -1       | 64          | enabled          |           |
+
+
+  Scenario Outline: OAI Compatibility w/ response format
+    Given a model test
+    And   a system prompt test
+    And   a user prompt test
+    And   a response format <response_format>
+    And   10 max tokens to predict
+    Given an OAI compatible chat completions request with no api error
+    Then  <n_predicted> tokens are predicted matching <re_content>
+
+    Examples: Prompts
+      | response_format                                                     | n_predicted | re_content             |
+      | {"type": "json_object", "schema": {"const": "42"}}                  | 5           | "42"                   |
+      | {"type": "json_object", "schema": {"items": [{"type": "integer"}]}} | 10          | \[ -300 \]             |
+      | {"type": "json_object"}                                             | 10          | \{ " Jacky.            |
+
+
+  Scenario: Tokenize / Detokenize
+    When tokenizing:
+    """
+    What is the capital of France ?
+    """
+    Then tokens can be detokenized
+    And  tokens do not begin with BOS
+
+  Scenario: Tokenize w/ BOS
+    Given adding special tokens
+    When  tokenizing:
+    """
+    What is the capital of Germany?
+    """
+    Then  tokens begin with BOS
+    Given first token is removed
+    Then  tokens can be detokenized
+
+  Scenario: Models available
+    Given available models
+    Then  1 models are supported
+    Then  model 0 is identified by tinyllama-2
+    Then  model 0 is trained on 128 tokens context
diff --git a/examples/server/tests/features/slotsave.feature b/examples/server/tests/features/slotsave.feature
new file mode 100644
index 000000000..1c281c074
--- /dev/null
+++ b/examples/server/tests/features/slotsave.feature
@@ -0,0 +1,58 @@
+@llama.cpp
+@slotsave
+Feature: llama.cpp server slot management
+
+  Background: Server startup
+    Given a server listening on localhost:8080
+    And   a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
+    And   prompt caching is enabled
+    And   2 slots
+    And   . as slot save path
+    And   2048 KV cache size
+    And   42 as server seed
+    And   24 max tokens to predict
+    Then  the server is starting
+    Then  the server is healthy
+
+  Scenario: Save and Restore Slot
+    # First prompt in slot 1 should be fully processed
+    Given a user prompt "What is the capital of France?"
+    And   using slot id 1
+    And   a completion request with no api error
+    Then  24 tokens are predicted matching (Lily|cake)
+    And   22 prompt tokens are processed
+    When  the slot 1 is saved with filename "slot1.bin"
+    Then  the server responds with status code 200
+    # Since we have cache, this should only process the last tokens
+    Given a user prompt "What is the capital of Germany?"
+    And   a completion request with no api error
+    Then  24 tokens are predicted matching (Thank|special)
+    And   7 prompt tokens are processed
+    # Loading the original cache into slot 0,
+    # we should only be processing 1 prompt token and get the same output
+    When  the slot 0 is restored with filename "slot1.bin"
+    Then  the server responds with status code 200
+    Given a user prompt "What is the capital of France?"
+    And   using slot id 0
+    And   a completion request with no api error
+    Then  24 tokens are predicted matching (Lily|cake)
+    And   1 prompt tokens are processed
+    # For verification that slot 1 was not corrupted during slot 0 load, same thing
+    Given a user prompt "What is the capital of Germany?"
+    And   using slot id 1
+    And   a completion request with no api error
+    Then  24 tokens are predicted matching (Thank|special)
+    And   1 prompt tokens are processed
+
+  Scenario: Erase Slot
+    Given a user prompt "What is the capital of France?"
+    And   using slot id 1
+    And   a completion request with no api error
+    Then  24 tokens are predicted matching (Lily|cake)
+    And   22 prompt tokens are processed
+    When  the slot 1 is erased
+    Then  the server responds with status code 200
+    Given a user prompt "What is the capital of France?"
+    And   a completion request with no api error
+    Then  24 tokens are predicted matching (Lily|cake)
+    And   22 prompt tokens are processed
diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py
new file mode 100644
index 000000000..577b87af3
--- /dev/null
+++ b/examples/server/tests/features/steps/steps.py
@@ -0,0 +1,1323 @@
+import asyncio
+import collections
+import json
+import os
+import re
+import socket
+import subprocess
+import sys
+import threading
+import time
+from contextlib import closing
+from re import RegexFlag
+
+import aiohttp
+import numpy as np
+import openai
+from behave import step
+from behave.api.async_step import async_run_until_complete
+from prometheus_client import parser
+
+
+@step("a server listening on {server_fqdn}:{server_port}")
+def step_server_config(context, server_fqdn, server_port):
+    context.server_fqdn = server_fqdn
+    context.server_port = int(server_port)
+    context.n_gpu_layer = None
+    if 'PORT' in os.environ:
+        context.server_port = int(os.environ['PORT'])
+        print(f"$PORT set, overriding server port with to {context.server_port}")
+    if 'FQDN' in os.environ:
+        context.server_fqdn = os.environ['FQDN']
+        print(f"$FQDN set, overriding server fqdn with to {context.server_fqdn}")
+    if 'N_GPU_LAYERS' in os.environ:
+        context.n_gpu_layer = int(os.environ['N_GPU_LAYERS'])
+        print(f"$N_GPU_LAYERS set, overriding n_gpu_layer with to {context.n_gpu_layer}")
+
+    context.base_url = f'http://{context.server_fqdn}:{context.server_port}'
+
+    context.model_alias = None
+    context.model_file = None
+    context.model_hf_repo = None
+    context.model_hf_file = None
+    context.model_url = None
+    context.n_batch = None
+    context.n_ubatch = None
+    context.n_ctx = None
+    context.n_ga = None
+    context.n_ga_w = None
+    context.n_predict = None
+    context.n_prompts = 0
+    context.n_server_predict = None
+    context.slot_save_path = None
+    context.id_slot = None
+    context.cache_prompt = None
+    context.n_slots = None
+    context.prompt_prefix = None
+    context.prompt_suffix = None
+    context.server_api_key = None
+    context.server_continuous_batching = False
+    context.server_embeddings = False
+    context.server_metrics = False
+    context.server_process = None
+    context.seed = None
+    context.draft = None
+    context.server_seed = None
+    context.user_api_key = None
+    context.response_format = None
+    context.temperature = None
+
+    context.tasks_result = []
+    context.concurrent_tasks = []
+    context.prompts = []
+
+
+@step('a model file {hf_file} from HF repo {hf_repo}')
+def step_download_hf_model(context, hf_file, hf_repo):
+    context.model_hf_repo = hf_repo
+    context.model_hf_file = hf_file
+    context.model_file = os.path.basename(hf_file)
+
+
+@step('a model file {model_file}')
+def step_model_file(context, model_file):
+    context.model_file = model_file
+
+
+@step('a model url {model_url}')
+def step_model_url(context, model_url):
+    context.model_url = model_url
+
+
+@step('a model alias {model_alias}')
+def step_model_alias(context, model_alias):
+    context.model_alias = model_alias
+
+
+@step('{seed:d} as server seed')
+def step_seed(context, seed):
+    context.server_seed = seed
+
+
+@step('{ngl:d} GPU offloaded layers')
+def step_n_gpu_layer(context, ngl):
+    if 'N_GPU_LAYERS' in os.environ:
+        new_ngl = int(os.environ['N_GPU_LAYERS'])
+        if context.debug:
+            print(f"-ngl upgraded from {ngl} to {new_ngl}")
+        ngl = new_ngl
+    context.n_gpu_layer = ngl
+
+
+@step('{draft:d} as draft')
+def step_draft(context, draft):
+    context.draft = draft
+
+
+@step('{n_ctx:d} KV cache size')
+def step_n_ctx(context, n_ctx):
+    context.n_ctx = n_ctx
+
+
+@step('{n_slots:d} slots')
+def step_n_slots(context, n_slots):
+    context.n_slots = n_slots
+
+
+@step('{n_predict:d} server max tokens to predict')
+def step_server_n_predict(context, n_predict):
+    context.n_server_predict = n_predict
+
+
+@step('{slot_save_path} as slot save path')
+def step_slot_save_path(context, slot_save_path):
+    context.slot_save_path = slot_save_path
+
+
+@step('using slot id {id_slot:d}')
+def step_id_slot(context, id_slot):
+    context.id_slot = id_slot
+
+
+@step('prompt caching is enabled')
+def step_enable_prompt_cache(context):
+    context.cache_prompt = True
+
+
+@step('continuous batching')
+def step_server_continuous_batching(context):
+    context.server_continuous_batching = True
+
+
+@step('embeddings extraction')
+def step_server_embeddings(context):
+    context.server_embeddings = True
+
+
+@step('prometheus compatible metrics exposed')
+def step_server_metrics(context):
+    context.server_metrics = True
+
+
+@step("the server is starting")
+def step_start_server(context):
+    start_server_background(context)
+    attempts = 0
+    max_attempts = 20
+    if 'GITHUB_ACTIONS' in os.environ:
+        max_attempts *= 2
+
+    addrs = socket.getaddrinfo(context.server_fqdn, context.server_port, type=socket.SOCK_STREAM)
+    family, typ, proto, _, sockaddr = addrs[0]
+
+    while True:
+        with closing(socket.socket(family, typ, proto)) as sock:
+            result = sock.connect_ex(sockaddr)
+            if result == 0:
+                print("\x1b[33;46mserver started!\x1b[0m")
+                return
+            attempts += 1
+            if attempts > max_attempts:
+                assert False, "server not started"
+            print(f"waiting for server to start, connect error code = {result}...")
+            time.sleep(0.1)
+
+
+@step("the server is {expecting_status}")
+@async_run_until_complete
+async def step_wait_for_the_server_to_be_started(context, expecting_status):
+    match expecting_status:
+        case 'healthy':
+            await wait_for_health_status(context, context.base_url, 200, 'ok',
+                                         timeout=30)
+
+        case 'ready' | 'idle':
+            await wait_for_health_status(context, context.base_url, 200, 'ok',
+                                         timeout=10,
+                                         params={'fail_on_no_slot': 0, 'include_slots': 0},
+                                         slots_idle=context.n_slots,
+                                         slots_processing=0,
+                                         expected_slots=[{'id': slot_id, 'state': 0}
+                                                         for slot_id in
+                                                         range(context.n_slots if context.n_slots else 1)])
+        case 'busy':
+            await wait_for_health_status(context, context.base_url, 503,
+                                         'no slot available',
+                                         params={'fail_on_no_slot': 0, 'include_slots': 0},
+                                         slots_idle=0,
+                                         slots_processing=context.n_slots,
+                                         expected_slots=[{'id': slot_id, 'state': 1}
+                                                         for slot_id in
+                                                         range(context.n_slots if context.n_slots else 1)])
+        case _:
+            assert False, "unknown status"
+
+
+@step('all slots are {expected_slot_status_string}')
+@async_run_until_complete
+async def step_all_slots_status(context, expected_slot_status_string):
+    match expected_slot_status_string:
+        case 'idle':
+            expected_slot_status = 0
+        case 'busy':
+            expected_slot_status = 1
+        case _:
+            assert False, "unknown status"
+
+    expected_slots = [{'id': slot_id, 'state': expected_slot_status}
+                      for slot_id in range(context.n_slots)]
+    await request_slots_status(context, expected_slots)
+
+
+@step('a completion request with {api_error} api error')
+@async_run_until_complete
+async def step_request_completion(context, api_error):
+    expect_api_error = api_error == 'raised'
+    seeds = await completions_seed(context, num_seeds=1)
+    completion = await request_completion(context.prompts.pop(),
+                                          seeds[0] if seeds is not None else seeds,
+                                          context.base_url,
+                                          debug=context.debug,
+                                          n_predict=context.n_predict,
+                                          cache_prompt=context.cache_prompt,
+                                          id_slot=context.id_slot,
+                                          expect_api_error=expect_api_error,
+                                          user_api_key=context.user_api_key,
+                                          temperature=context.temperature)
+    context.tasks_result.append(completion)
+    if context.debug:
+        print(f"Completion response: {completion}")
+    if expect_api_error:
+        assert completion == 401, f"completion must be an 401 status code: {completion}"
+
+
+@step('{predicted_n:d} tokens are predicted matching {re_content}')
+def step_n_tokens_predicted_with_content(context, predicted_n, re_content):
+    context.completion = context.tasks_result.pop()
+    assert_n_tokens_predicted(context.completion, predicted_n, re_content)
+
+
+@step('{predicted_n:d} tokens are predicted')
+def step_n_tokens_predicted(context, predicted_n):
+    context.completion = context.tasks_result.pop()
+    assert_n_tokens_predicted(context.completion, predicted_n)
+
+
+@step('all predictions are equal')
+@async_run_until_complete
+async def step_predictions_equal(context):
+    n_completions = await gather_tasks_results(context)
+    assert n_completions >= 2, "need at least 2 completions"
+    assert_all_predictions_equal(context.tasks_result)
+    context.tasks_result = []
+
+
+@step('all predictions are different')
+@async_run_until_complete
+async def step_predictions_equal(context):
+    n_completions = await gather_tasks_results(context)
+    assert n_completions >= 2, "need at least 2 completions"
+    assert_all_predictions_different(context.tasks_result)
+    context.tasks_result = []
+
+
+@step('the completion is  truncated')
+def step_assert_completion_truncated(context):
+    step_assert_completion_truncated(context, '')
+
+
+@step('the completion is {truncated} truncated')
+def step_assert_completion_truncated(context, truncated):
+    truncated = truncated != "not"
+    assert context.completion['truncated'] == truncated, f'{context.completion}'
+
+
+@step('{n_prompt:d} prompt tokens are processed')
+def step_impl(context, n_prompt):
+    assert n_prompt < 0 or n_prompt == context.completion['timings']['prompt_n'], f"n_prompt={context.completion['timings']['prompt_n']}"
+
+
+@step('a user prompt {user_prompt}')
+def step_user_prompt(context, user_prompt):
+    context.prompts.append(user_prompt)
+    context.n_prompts = len(context.prompts)
+
+
+@step('a system prompt {system_prompt}')
+def step_system_prompt(context, system_prompt):
+    context.system_prompt = system_prompt
+
+
+@step('a model {model}')
+def step_model(context, model):
+    context.model = model
+
+
+@step('{max_tokens:d} max tokens to predict')
+def step_max_tokens(context, max_tokens):
+    context.n_predict = max_tokens
+
+
+@step('a response format {response_format}')
+def step_response_format(context, response_format):
+    context.response_format = json.loads(response_format)
+
+
+@step('{temperature:f} temperature')
+def step_temperature(context, temperature):
+    context.temperature = temperature
+
+
+@step('streaming is {enable_streaming}')
+def step_streaming(context, enable_streaming):
+    context.enable_streaming = enable_streaming == 'enabled'
+
+
+@step('a user api key {user_api_key}')
+def step_user_api_key(context, user_api_key):
+    context.user_api_key = user_api_key
+
+
+@step('no user api key')
+def step_no_user_api_key(context):
+    context.user_api_key = None
+
+
+@step('a user api key ')
+def step_no_user_api_key_space(context):
+    context.user_api_key = None
+
+
+@step('a server api key {server_api_key}')
+def step_server_api_key(context, server_api_key):
+    context.server_api_key = server_api_key
+
+
+@step('{n_junk:d} as number of junk')
+def step_n_junk(context, n_junk):
+    context.n_junk = n_junk
+
+
+@step('{n_batch:d} as batch size')
+def step_n_batch(context, n_batch):
+    context.n_batch = n_batch
+
+
+@step('{n_ubatch:d} as ubatch size')
+def step_n_ubatch(context, n_ubatch):
+    context.n_ubatch = n_ubatch
+
+
+@step('{seed:d} as seed')
+def step_seed(context, seed):
+    if context.seed is None:
+        context.seed = [seed]
+    else:
+        context.seed.append(seed)
+
+
+@step('BOS token is {bos:d}')
+def step_bos_token(context, bos):
+    context.bos = bos
+
+
+@step('a prefix prompt')
+def step_prompt_prefix(context):
+    context.prompt_prefix = context_text(context)
+
+
+@step('a junk suffix prompt')
+def step_prompt_junk_suffix(context):
+    context.prompt_junk_suffix = context_text(context)
+
+
+@step('a suffix prompt')
+def step_prompt_suffix(context):
+    context.prompt_suffix = context_text(context)
+
+
+@step('{n_ga:d} group attention factor'
+      ' to extend context size through self-extend')
+def step_impl(context, n_ga):
+    context.n_ga = n_ga
+
+
+@step('{n_ga_w:d} group attention width to extend context size through self-extend')
+def step_impl(context, n_ga_w):
+    context.n_ga_w = n_ga_w
+
+
+@step('a passkey prompt template')
+def step_prompt_passkey(context):
+    context.prompt_passkey = context_text(context)
+
+
+@step('{n_prompts:d} fixed prompts')
+def step_fixed_prompts(context, n_prompts):
+    context.prompts.extend([str(0)*(context.n_batch if context.n_batch is not None else 512) for i in range(n_prompts)])
+    context.n_prompts = n_prompts
+
+
+@step('a "{passkey}" passkey challenge prompt with the passkey inserted every {i_pos:d} junk')
+def step_prompt_passkey(context, passkey, i_pos):
+    prompt = ""
+    for i in range(context.n_junk):
+        if i % context.n_junk == i_pos:
+            prompt += context.prompt_passkey # the passkey is already substituted
+        prompt += context.prompt_junk_suffix
+    if context.debug:
+        passkey_highlight = "\x1b[33m" + passkey + "\x1b[0m"
+        print(f"Passkey challenge:\n```{prompt.replace(passkey, passkey_highlight)}```")
+    context.prompts.append(context.prompt_prefix + prompt + context.prompt_suffix)
+    context.n_prompts = len(context.prompts)
+
+
+@step('an OAI compatible chat completions request with {api_error} api error')
+@async_run_until_complete
+async def step_oai_chat_completions(context, api_error):
+    if context.debug:
+        print(f"Submitting OAI compatible completions request...")
+    expect_api_error = api_error == 'raised'
+    seeds = await completions_seed(context, num_seeds=1),
+    completion = await oai_chat_completions(context.prompts.pop(),
+                                            seeds[0] if seeds is not None else seeds,
+                                            context.system_prompt,
+                                            context.base_url,
+                                            '/v1/chat',
+                                            False,
+                                            model=context.model if hasattr(context, 'model') else None,
+
+                                            n_predict=context.n_predict
+                                            if hasattr(context, 'n_predict') else None,
+
+                                            enable_streaming=context.enable_streaming
+                                            if hasattr(context, 'enable_streaming') else None,
+
+                                            response_format=context.response_format
+                                            if hasattr(context, 'response_format') else None,
+
+                                            user_api_key=context.user_api_key
+                                            if hasattr(context, 'user_api_key') else None,
+
+                                            expect_api_error=expect_api_error)
+    context.tasks_result.append(completion)
+    if context.debug:
+        print(f"Completion response: {completion}")
+    if expect_api_error:
+        assert completion == 401, f"completion must be an 401 status code: {completion}"
+
+    if context.debug:
+        print(f"Completion response: {completion}")
+
+
+@step('a prompt')
+def step_a_prompt(context):
+    context.prompts.append(context_text(context))
+    context.n_prompts = len(context.prompts)
+
+
+@step('a prompt {prompt}')
+def step_a_prompt_prompt(context, prompt):
+    context.prompts.append(prompt)
+    context.n_prompts = len(context.prompts)
+
+
+@step('{num_prompts:d} prompts {prompt} with seed {seed:d}')
+def step_many_prompts(context, num_prompts, prompt, seed):
+    if context.seed is None:
+        context.seed = []
+    for _ in range(num_prompts):
+        context.seed.append(seed)
+        context.prompts.append(prompt)
+    context.n_prompts = len(context.prompts)
+
+
+@step('concurrent completion requests')
+@async_run_until_complete()
+async def step_concurrent_completion_requests(context):
+    await concurrent_requests(
+        context,
+        request_completion,
+        # prompt is inserted automatically
+        context.base_url,
+        debug=context.debug,
+        prompt_prefix=context.prompt_prefix,
+        prompt_suffix=context.prompt_suffix,
+        n_predict=context.n_predict if hasattr(context, 'n_predict') else None,
+        user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None,
+        temperature=context.temperature,
+    )
+
+
+@step('concurrent OAI completions requests')
+@async_run_until_complete
+async def step_oai_chat_completions(context):
+    await concurrent_requests(context, oai_chat_completions,
+                              # user_prompt is inserted automatically
+                              context.system_prompt,
+                              context.base_url,
+                              '/v1/chat/completions',
+                              True,  # async_client
+                              model=context.model
+                              if hasattr(context, 'model') else None,
+                              n_predict=context.n_predict
+                              if hasattr(context, 'n_predict') else None,
+                              enable_streaming=context.enable_streaming
+                              if hasattr(context, 'enable_streaming') else None,
+                              response_format=context.response_format
+                              if hasattr(context, 'response_format') else None,
+                              user_api_key=context.user_api_key
+                              if hasattr(context, 'user_api_key') else None)
+
+
+@step('concurrent OAI completions requests no v1')
+@async_run_until_complete
+async def step_oai_chat_completions(context):
+    await concurrent_requests(context, oai_chat_completions,
+                              # user_prompt is inserted automatically
+                              context.system_prompt,
+                              context.base_url,
+                              '/chat/completions',
+                              True,  # async_client
+                              model=context.model
+                              if hasattr(context, 'model') else None,
+                              n_predict=context.n_predict
+                              if hasattr(context, 'n_predict') else None,
+                              enable_streaming=context.enable_streaming
+                              if hasattr(context, 'enable_streaming') else None,
+                              response_format=context.response_format
+                              if hasattr(context, 'response_format') else None,
+                              user_api_key=context.user_api_key
+                              if hasattr(context, 'user_api_key') else None)
+
+
+@step('all prompts are predicted')
+@async_run_until_complete
+async def step_all_prompts_are_predicted(context):
+    await all_prompts_are_predicted(context)
+
+
+@step('all prompts are predicted with {n_expected_predicted:d} tokens')
+@async_run_until_complete
+async def step_all_prompts_are_predicted_with_n_tokens(context, n_expected_predicted):
+    await all_prompts_are_predicted(context, n_expected_predicted)
+
+
+async def all_prompts_are_predicted(context, expected_predicted_n=None):
+    n_completions = await gather_tasks_results(context)
+    assert n_completions > 0
+    for i in range(n_completions):
+        assert_n_tokens_predicted(context.tasks_result.pop(), expected_predicted_n=expected_predicted_n)
+    assert len(context.concurrent_tasks) == 0, f"{len(context.concurrent_tasks)} pending requests"
+
+
+@step('embeddings are computed for')
+@async_run_until_complete
+async def step_compute_embedding(context):
+    context.n_prompts = 1
+    context.embeddings = await request_embedding(context_text(context), None, base_url=context.base_url)
+
+
+@step('all embeddings are the same')
+@async_run_until_complete
+async def step_all_embeddings_are_the_same(context):
+    n_embedding_requests = await gather_tasks_results(context)
+    assert n_embedding_requests > 0
+    embeddings = []
+    for i in range(n_embedding_requests):
+        embedding = context.tasks_result.pop().pop()
+        embeddings.append(embedding)
+        assert_embeddings(embedding)
+    n = len(embeddings)
+    for i in range(n-1):
+        for j in range(i+1, n):
+            embedding1 = np.array(embeddings[i])
+            embedding2 = np.array(embeddings[j])
+            if context.debug:
+                print(f"embedding1: {embedding1[-8:]}")
+                print(f"embedding2: {embedding2[-8:]}")
+            similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
+            msg = f"Similarity between {i} and {j}: {similarity:.10f}"
+            if context.debug:
+                print(f"{msg}")
+            assert np.isclose(similarity, 1.0, rtol=1e-05, atol=1e-08, equal_nan=False), msg
+
+
+@step('embeddings are generated')
+def step_assert_embeddings(context):
+    assert context.n_prompts == len(context.embeddings), (f"unexpected response:\n"
+                                                             f"context.n_prompts={context.n_prompts}\n"
+                                                             f"context.embeddings={context.embeddings}")
+    for embedding in context.embeddings:
+        assert_embeddings(embedding)
+
+
+@step('an OAI compatible embeddings computation request for')
+@async_run_until_complete
+async def step_oai_compute_embeddings(context):
+    context.n_prompts = 1
+    context.embeddings = await request_oai_embeddings(context_text(context), None,
+                                                      base_url=context.base_url,
+                                                      user_api_key=context.user_api_key,
+                                                      model=context.model)
+
+
+@step('an OAI compatible embeddings computation request for multiple inputs')
+@async_run_until_complete
+async def step_oai_compute_embeddings_multiple_inputs(context):
+    context.embeddings = await request_oai_embeddings(context.prompts, None,
+                                                      base_url=context.base_url,
+                                                      user_api_key=context.user_api_key,
+                                                      model=context.model)
+    context.prompts.clear()
+
+
+@step('concurrent embedding requests')
+@async_run_until_complete()
+async def step_concurrent_embedding_requests(context):
+    await concurrent_requests(context,
+                              request_embedding,
+                              # prompt is inserted automatically
+                              base_url=context.base_url)
+
+
+@step('concurrent OAI embedding requests')
+@async_run_until_complete()
+async def step_concurrent_oai_embedding_requests(context):
+    await concurrent_requests(context,
+                              request_oai_embeddings,
+                              # prompt is inserted automatically
+                              base_url=context.base_url,
+                              async_client=True,
+                              model=context.model)
+
+
+@step('all embeddings are generated')
+@async_run_until_complete()
+async def all_embeddings_are_generated(context):
+    n_embedding_requests = await gather_tasks_results(context)
+    assert n_embedding_requests == context.n_prompts
+    for i in range(n_embedding_requests):
+        assert_embeddings(context.tasks_result.pop().pop())
+
+
+@step('adding special tokens')
+def step_tokenize_set_add_special(context):
+    context.tokenize_add_special = True
+
+
+@step('tokenizing')
+@async_run_until_complete
+async def step_tokenize(context):
+    context.tokenized_text = context_text(context)
+    async with aiohttp.ClientSession() as session:
+        tokenize_args = {
+            "content": context.tokenized_text,
+        }
+        if getattr(context, 'tokenize_add_special', None) is not None:
+            tokenize_args['add_special'] = context.tokenize_add_special
+        async with session.post(f'{context.base_url}/tokenize',
+                                json=tokenize_args) as response:
+            assert response.status == 200
+            tokenize_json = await response.json()
+            context.tokens = tokenize_json['tokens']
+
+
+@step('tokens can be detokenized')
+@async_run_until_complete
+async def step_detokenize(context):
+    assert len(context.tokens) > 0
+    async with aiohttp.ClientSession() as session:
+        async with session.post(f'{context.base_url}/detokenize',
+                                json={
+                                    "tokens": context.tokens,
+                                }) as response:
+            assert response.status == 200
+            detokenize_json = await response.json()
+            # SPM tokenizer adds a whitespace prefix: https://github.com/google/sentencepiece/issues/15
+            assert context.tokenized_text == detokenize_json['content'].strip()
+
+
+@step('tokens begin with BOS')
+def step_strings_for_tokenization(context):
+    assert context.tokens[0] == context.bos
+
+
+@step('tokens do not begin with BOS')
+def step_strings_for_tokenization(context):
+    assert context.tokens[0] != context.bos
+
+
+@step('first token is removed')
+def step_strings_for_tokenization(context):
+    context.tokens = context.tokens[1:]
+
+
+@step('an OPTIONS request is sent from {origin}')
+@async_run_until_complete
+async def step_options_request(context, origin):
+    async with aiohttp.ClientSession() as session:
+        headers = {'Authorization': f'Bearer {context.user_api_key}', 'Origin': origin}
+        async with session.options(f'{context.base_url}/v1/chat/completions',
+                                    headers=headers) as response:
+            assert response.status == 200
+            context.options_response = response
+
+
+@step('CORS header {cors_header} is set to {cors_header_value}')
+def step_check_options_header_value(context, cors_header, cors_header_value):
+    assert context.options_response.headers[cors_header] == cors_header_value
+
+
+@step('prometheus metrics are exposed')
+@async_run_until_complete
+async def step_prometheus_metrics_exported(context):
+    async with aiohttp.ClientSession() as session:
+        async with await session.get(f'{context.base_url}/metrics') as metrics_response:
+            assert metrics_response.status == 200
+            assert metrics_response.headers['Content-Type'] == "text/plain; version=0.0.4"
+            metrics_raw = await metrics_response.text()
+            metric_exported = False
+            if context.debug:
+                print(f"/metrics answer:\n{metrics_raw}")
+            context.metrics = {}
+            for metric in parser.text_string_to_metric_families(metrics_raw):
+                match metric.name:
+                    case "llamacpp:kv_cache_usage_ratio":
+                        assert len(metric.samples) > 0
+                        metric_exported = True
+                context.metrics[metric.name] = metric
+            assert int(metrics_response.headers["Process-Start-Time-Unix"]) > 0, "no header process start time"
+            assert metric_exported, "No metrics exported"
+
+
+@step('metric {metric_name} is {metric_value:d}')
+def step_assert_metric_value(context, metric_name, metric_value):
+    if metric_name not in context.metrics:
+        assert False, f"no metric {metric_name} in {context.metrics.keys()}"
+    assert context.metrics[metric_name].samples[0].value == metric_value, f"metric: {context.metrics[metric_name]}"
+
+
+@step('available models')
+def step_available_models(context):
+    # openai client always expects an api_key
+    openai.api_key = context.user_api_key if context.user_api_key is not None else 'nope'
+    openai.api_base = f'{context.base_url}/v1'
+    context.models = openai.Model.list().data
+
+
+@step('{n_model:d} models are supported')
+def step_supported_models(context, n_model):
+    if context.debug:
+        print("server models available:", context.models)
+    assert len(context.models) == n_model
+
+
+@step('model {i_model:d} is {param} {preposition} {param_value}')
+def step_supported_models(context, i_model, param, preposition, param_value):
+    assert i_model < len(context.models)
+    model = context.models[i_model]
+
+    param_value = param_value.split(' ', 1)[0]
+    match param:
+        case 'identified':
+            value = model.id
+        case 'trained':
+            value = str(model.meta.n_ctx_train)
+        case _:
+            assert False, "param {param} not supported"
+    assert param_value == value, f"model param {param} {value} != {param_value}"
+
+
+async def concurrent_requests(context, f_completion, *args, **kwargs):
+    context.n_prompts = len(context.prompts)
+    if context.debug:
+        print(f"starting {context.n_prompts} concurrent completion requests...")
+    assert context.n_prompts > 0
+    seeds = await completions_seed(context)
+    for prompt_no in range(context.n_prompts):
+        shifted_args = [context.prompts.pop(), seeds[prompt_no], *args]
+        context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs)))
+    await asyncio.sleep(0.1)
+
+
+@step('the slot {slot_id:d} is saved with filename "{filename}"')
+@async_run_until_complete
+async def step_save_slot(context, slot_id, filename):
+    async with aiohttp.ClientSession() as session:
+        async with session.post(f'{context.base_url}/slots/{slot_id}?action=save',
+                                json={"filename": filename},
+                                headers={"Content-Type": "application/json"}) as response:
+            context.response = response
+
+
+@step('the slot {slot_id:d} is restored with filename "{filename}"')
+@async_run_until_complete
+async def step_restore_slot(context, slot_id, filename):
+    async with aiohttp.ClientSession() as session:
+        async with session.post(f'{context.base_url}/slots/{slot_id}?action=restore',
+                                json={"filename": filename},
+                                headers={"Content-Type": "application/json"}) as response:
+            context.response = response
+
+
+@step('the slot {slot_id:d} is erased')
+@async_run_until_complete
+async def step_erase_slot(context, slot_id):
+    async with aiohttp.ClientSession() as session:
+        async with session.post(f'{context.base_url}/slots/{slot_id}?action=erase',
+                                headers={"Content-Type": "application/json"}) as response:
+            context.response = response
+
+
+@step('the server responds with status code {status_code:d}')
+def step_server_responds_with_status_code(context, status_code):
+    assert context.response.status == status_code
+
+
+async def request_completion(prompt,
+                             seed,
+                             base_url,
+                             debug=False,
+                             prompt_prefix=None,
+                             prompt_suffix=None,
+                             n_predict=None,
+                             cache_prompt=False,
+                             id_slot=None,
+                             expect_api_error=None,
+                             user_api_key=None,
+                             temperature=None):
+    if debug:
+        print(f"Sending completion request: {prompt}")
+    origin = "my.super.domain"
+    headers = {
+        'Origin': origin
+    }
+    if user_api_key is not None:
+        if debug:
+            print(f"Set user_api_key: {user_api_key}")
+        headers['Authorization'] = f'Bearer {user_api_key}'
+
+    async with aiohttp.ClientSession() as session:
+        async with session.post(f'{base_url}/completion',
+                                json={
+                                    "input_prefix": prompt_prefix,
+                                    "prompt": prompt,
+                                    "input_suffix": prompt_suffix,
+                                    "n_predict": n_predict if n_predict is not None else -1,
+                                    "cache_prompt": cache_prompt,
+                                    "id_slot": id_slot,
+                                    "seed": seed if seed is not None else 42,
+                                    "temperature": temperature if temperature is not None else "0.8f",
+                                },
+                                headers=headers,
+                                timeout=3600) as response:
+            if expect_api_error is None or not expect_api_error:
+                assert response.status == 200
+                assert response.headers['Access-Control-Allow-Origin'] == origin
+                return await response.json()
+            else:
+                return response.status
+
+
+async def oai_chat_completions(user_prompt,
+                               seed,
+                               system_prompt,
+                               base_url,
+                               base_path,
+                               async_client,
+                               debug=False,
+                               temperature=None,
+                               model=None,
+                               n_predict=None,
+                               enable_streaming=None,
+                               response_format=None,
+                               user_api_key=None,
+                               expect_api_error=None):
+    if debug:
+        print(f"Sending OAI Chat completions request: {user_prompt}")
+    # openai client always expects an api key
+    user_api_key = user_api_key if user_api_key is not None else 'nope'
+    seed = seed if seed is not None else 42
+    enable_streaming = enable_streaming if enable_streaming is not None else False
+    payload = {
+        "messages": [
+            {
+                "role": "system",
+                "content": system_prompt,
+            },
+            {
+                "role": "user",
+                "content": user_prompt,
+            }
+        ],
+        "model": model,
+        "max_tokens": n_predict,
+        "stream": enable_streaming,
+        "temperature": temperature if temperature is not None else 0.0,
+        "seed": seed,
+    }
+    if response_format is not None:
+        payload['response_format'] = response_format
+    completion_response = {
+        'content': '',
+        'timings': {
+            'predicted_n': 0,
+            'prompt_n': 0
+        }
+    }
+    if async_client:
+        origin = 'llama.cpp'
+        headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin}
+        async with aiohttp.ClientSession() as session:
+            async with session.post(f'{base_url}{base_path}',
+                                    json=payload,
+                                    headers=headers) as response:
+                if enable_streaming:
+                    assert response.status == 200
+                    assert response.headers['Access-Control-Allow-Origin'] == origin
+                    assert response.headers['Content-Type'] == "text/event-stream"
+                    event_received = True
+                    while event_received:
+                        event_received = False
+                        async for line_in_bytes in response.content:
+                            line = line_in_bytes.decode('utf-8')
+                            line = line.rstrip('\n').rstrip('\r')
+                            if line == '':
+                                continue
+                            event_data = line.split(': ', 1)
+                            assert event_data[0] == 'data', f'Bad event code received: ```{event_data}```'
+                            chunk_raw = event_data[1]
+
+                            chunk = json.loads(chunk_raw)
+                            assert len(chunk['choices']) == 1, f"no choices provided, line ```{line}```"
+                            delta = chunk['choices'][0]['delta']
+                            if 'content' in delta:
+                                completion_response['content'] += delta['content']
+                                completion_response['timings']['predicted_n'] += 1
+                else:
+                    if expect_api_error is None or not expect_api_error:
+                        assert response.status == 200
+                        assert response.headers['Access-Control-Allow-Origin'] == origin
+                        assert response.headers['Content-Type'] == "application/json; charset=utf-8"
+                        chat_completion_raw = await response.json()
+                        completion_response = {
+                            'content': chat_completion_raw['choices'][0]['message'],
+                            'timings': {
+                                'predicted_n': chat_completion_raw['usage']['completion_tokens'],
+                                'prompt_n': chat_completion_raw['usage']['prompt_tokens']
+                            }
+                        }
+                    else:
+                        return response.status
+    else:
+        try:
+            openai.api_key = user_api_key
+            openai.api_base = f'{base_url}{base_path}'
+            chat_completion = openai.Completion.create(
+                messages=payload['messages'],
+                model=model,
+                max_tokens=n_predict,
+                stream=enable_streaming,
+                response_format=payload.get('response_format'),
+                seed=seed,
+                temperature=payload['temperature']
+            )
+        except openai.error.AuthenticationError as e:
+            if expect_api_error is not None and expect_api_error:
+                return 401
+            else:
+                assert False, f'error raised: {e}'
+
+        if enable_streaming:
+            for chunk in chat_completion:
+                assert len(chunk.choices) == 1
+                delta = chunk.choices[0].delta
+                if 'content' in delta:
+                    completion_response['content'] += delta['content']
+                    completion_response['timings']['predicted_n'] += 1
+                completion_response['truncated'] = chunk.choices[0].finish_reason != 'stop'
+        else:
+            assert len(chat_completion.choices) == 1
+            completion_response = {
+                'content': chat_completion.choices[0].message.content,
+                'timings': {
+                    'predicted_n': chat_completion.usage.completion_tokens,
+                    'prompt_n': chat_completion.usage.prompt_tokens
+                    },
+                'truncated': chat_completion.choices[0].finish_reason != 'stop'
+            }
+    if debug:
+        print("OAI response formatted to llama.cpp:", completion_response)
+    return completion_response
+
+
+async def request_embedding(content, seed, base_url=None):
+    async with aiohttp.ClientSession() as session:
+        async with session.post(f'{base_url}/embedding',
+                                json={
+                                    "content": content,
+                                }) as response:
+            assert response.status == 200
+            response_json = await response.json()
+            return [response_json['embedding']]
+
+
+async def request_oai_embeddings(input, seed,
+                                 base_url=None, user_api_key=None,
+                                 model=None, async_client=False):
+    # openai client always expects an api_key
+    user_api_key = user_api_key if user_api_key is not None else 'nope'
+    if async_client:
+        origin = 'llama.cpp'
+        headers=[]
+        if user_api_key is not None:
+            headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin}
+        async with aiohttp.ClientSession() as session:
+            async with session.post(f'{base_url}/v1/embeddings',
+                                    json={
+                                        "input": input,
+                                        "model": model,
+                                    },
+                                    headers=headers,
+                                    timeout=3600) as response:
+                assert response.status == 200, f"received status code not expected: {response.status}"
+                assert response.headers['Access-Control-Allow-Origin'] == origin
+                assert response.headers['Content-Type'] == "application/json; charset=utf-8"
+                response_json = await response.json()
+                assert response_json['model'] == model, f"invalid model received: {response_json['model']}"
+                assert response_json['object'] == 'list'
+                if isinstance(input, collections.abc.Sequence):
+                    embeddings = []
+                    for an_oai_embeddings in response_json['data']:
+                        embeddings.append(an_oai_embeddings['embedding'])
+                else:
+                    embeddings = [response_json['data']['embedding']]
+                return embeddings
+    else:
+        openai.api_key = user_api_key
+        openai.api_base = f'{base_url}/v1'
+        oai_embeddings = openai.Embedding.create(
+            model=model,
+            input=input,
+        )
+
+        if isinstance(input, collections.abc.Sequence):
+            embeddings = []
+            for an_oai_embeddings in oai_embeddings.data:
+                embeddings.append(an_oai_embeddings.embedding)
+        else:
+            embeddings = [oai_embeddings.data.embedding]
+        return embeddings
+
+
+def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re_content=None):
+    content = completion_response['content']
+    n_predicted = completion_response['timings']['predicted_n']
+    assert len(content) > 0, "no token predicted"
+    if re_content is not None:
+        p = re.compile(re_content, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL)
+        matches = p.finditer(content)
+        last_match = 0
+        highlighted = ''
+        for match in matches:
+            start, end = match.span()
+            highlighted += content[last_match: start]
+            highlighted += '\x1b[33m'
+            highlighted += content[start: end]
+            highlighted += '\x1b[0m'
+            last_match = end
+        highlighted += content[last_match:]
+        if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
+          print(f"Checking completion response: {highlighted}")
+        assert last_match > 0, f'/{re_content}/ must match ```{highlighted}```'
+    if expected_predicted_n and expected_predicted_n > 0:
+        assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:'
+                                                     f' {n_predicted} <> {expected_predicted_n}')
+
+def assert_all_predictions_equal(completion_responses):
+    if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
+        for i, response_i in enumerate(completion_responses):
+            content_i = response_i['content']
+            print(f"content {i}: {content_i}")
+    for i, response_i in enumerate(completion_responses):
+        content_i = response_i['content']
+        for j, response_j in enumerate(completion_responses):
+            if i == j:
+                continue
+            content_j = response_j['content']
+        assert content_i == content_j, "contents not equal"
+
+
+def assert_all_predictions_different(completion_responses):
+    if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
+        for i, response_i in enumerate(completion_responses):
+            content_i = response_i['content']
+            print(f"content {i}: {content_i}")
+    for i, response_i in enumerate(completion_responses):
+        content_i = response_i['content']
+        for j, response_j in enumerate(completion_responses):
+            if i == j:
+                continue
+            content_j = response_j['content']
+        assert content_i != content_j, "contents not different"
+
+
+async def gather_tasks_results(context):
+    n_tasks = len(context.concurrent_tasks)
+    if context.debug:
+        print(f"Waiting for all {n_tasks} tasks results...")
+    for task_no in range(n_tasks):
+        context.tasks_result.append(await context.concurrent_tasks.pop())
+    n_completions = len(context.tasks_result)
+    return n_completions
+
+
+async def wait_for_health_status(context,
+                                 base_url,
+                                 expected_http_status_code,
+                                 expected_health_status,
+                                 timeout=3,
+                                 params=None,
+                                 slots_idle=None,
+                                 slots_processing=None,
+                                 expected_slots=None):
+    if context.debug:
+        print(f"Starting checking for health for expected_health_status={expected_health_status}")
+    interval = 0.5
+    counter = 0
+    if 'GITHUB_ACTIONS' in os.environ:
+        timeout *= 2
+
+    async with aiohttp.ClientSession() as session:
+        while True:
+            async with await session.get(f'{base_url}/health', params=params) as health_response:
+                status_code = health_response.status
+                health = await health_response.json()
+                if context.debug:
+                    print(f"HEALTH - response for expected health status='{expected_health_status}' on "
+                          f"'{base_url}/health'?{params} is {health}\n")
+                if (status_code == expected_http_status_code
+                        and health['status'] == expected_health_status
+                        and (slots_idle is None or health['slots_idle'] == slots_idle)
+                        and (slots_processing is None or health['slots_processing'] == slots_processing)):
+                    if expected_slots is not None:
+                        assert_slots_status(health['slots'], expected_slots)
+                    return
+                if (status_code == expected_http_status_code
+                        and health['status'] == expected_health_status
+                        and (slots_idle is None or health['slots_idle'] == slots_idle)
+                        and (slots_processing is None or health['slots_processing'] == slots_processing)):
+                    if expected_slots is not None:
+                        assert_slots_status(health['slots'], expected_slots)
+                    return
+            await asyncio.sleep(interval)
+
+            counter += interval
+            if counter >= timeout:
+                # Sometimes health requests are triggered after completions are predicted
+                if expected_http_status_code == 503:
+                    if len(context.tasks_result) == 0:
+                        print("\x1b[5;37;43mWARNING: forcing concurrent tasks,"
+                              " busy health check missed, probably too fast inference\x1b[0m\n")
+                        n_completions = await gather_tasks_results(context)
+                        if n_completions > 0:
+                            return
+
+                assert False, f'{expected_health_status} timeout exceeded {counter}s>={timeout}'
+
+
+def assert_embeddings(embeddings):
+    assert len(embeddings) > 0
+    embeddings_computed = False
+    for emb in embeddings:
+        if not isinstance(emb, float):
+            assert False, f"Bad embeddings: {embeddings}"
+        if emb != 0:
+            embeddings_computed = True
+    assert embeddings_computed, f"Embeddings: {embeddings}"
+
+
+async def request_slots_status(context, expected_slots):
+    async with aiohttp.ClientSession() as session:
+        async with await session.get(f'{context.base_url}/slots') as slots_response:
+            assert slots_response.status == 200
+            slots = await slots_response.json()
+            assert_slots_status(slots, expected_slots)
+
+
+def assert_slots_status(slots, expected_slots):
+    assert len(slots) == len(expected_slots)
+    for slot_id, (expected, slot) in enumerate(zip(expected_slots, slots)):
+        for key in expected:
+            assert expected[key] == slot[key], (f"invalid slot {slot_id}"
+                                                f" expected[{key}] != slot[{key}]"
+                                                f" = {expected[key]} != {slot[key]}")
+
+
+async def completions_seed(context, num_seeds=None):
+    if hasattr(context, "seed") and context.seed is not None:
+        assert len(context.seed) == context.n_prompts
+        if num_seeds is None:
+            num_seeds = context.n_prompts
+        assert num_seeds <= context.n_prompts
+        seeds = context.seed[:num_seeds]
+        context.seed = context.seed[num_seeds:] if num_seeds < context.n_prompts else None
+        return seeds
+
+    if hasattr(context, "server_seed") and context.server_seed is not None:
+        if num_seeds is None:
+            return [context.server_seed] * context.n_prompts
+        else:
+            return [context.server_seed] * num_seeds
+    return None
+
+
+def context_text(context):
+    return context.text.replace('\r', '')
+
+
+def start_server_background(context):
+    if os.name == 'nt':
+        context.server_path = '../../../build/bin/Release/server.exe'
+    else:
+        context.server_path = '../../../build/bin/server'
+    if 'LLAMA_SERVER_BIN_PATH' in os.environ:
+        context.server_path = os.environ['LLAMA_SERVER_BIN_PATH']
+    server_listen_addr = context.server_fqdn
+    server_args = [
+        '--host', server_listen_addr,
+        '--port', context.server_port,
+    ]
+    if context.model_file:
+        server_args.extend(['--model', context.model_file])
+    if context.model_url:
+        server_args.extend(['--model-url', context.model_url])
+    if context.model_hf_repo:
+        server_args.extend(['--hf-repo', context.model_hf_repo])
+    if context.model_hf_file:
+        server_args.extend(['--hf-file', context.model_hf_file])
+    if context.n_batch:
+        server_args.extend(['--batch-size', context.n_batch])
+    if context.n_ubatch:
+        server_args.extend(['--ubatch-size', context.n_ubatch])
+    if context.n_gpu_layer:
+        server_args.extend(['--n-gpu-layers', context.n_gpu_layer])
+    if context.draft is not None:
+        server_args.extend(['--draft', context.draft])
+    if context.server_continuous_batching:
+        server_args.append('--cont-batching')
+    if context.server_embeddings:
+        server_args.append('--embedding')
+    if context.server_metrics:
+        server_args.append('--metrics')
+    if context.model_alias:
+        server_args.extend(['--alias', context.model_alias])
+    if context.n_ctx:
+        server_args.extend(['--ctx-size', context.n_ctx])
+    if context.n_slots:
+        server_args.extend(['--parallel', context.n_slots])
+    if context.n_server_predict:
+        server_args.extend(['--n-predict', context.n_server_predict])
+    if context.slot_save_path:
+        server_args.extend(['--slot-save-path', context.slot_save_path])
+    if context.server_api_key:
+        server_args.extend(['--api-key', context.server_api_key])
+    if context.n_ga:
+        server_args.extend(['--grp-attn-n', context.n_ga])
+    if context.n_ga_w:
+        server_args.extend(['--grp-attn-w', context.n_ga_w])
+    if context.debug:
+        server_args.append('--verbose')
+    if 'SERVER_LOG_FORMAT_JSON' not in os.environ:
+        server_args.extend(['--log-format', "text"])
+
+    args = [str(arg) for arg in [context.server_path, *server_args]]
+    print(f"bench: starting server with: {' '.join(args)}")
+
+    flags = 0
+    if 'nt' == os.name:
+        flags |= subprocess.DETACHED_PROCESS
+        flags |= subprocess.CREATE_NEW_PROCESS_GROUP
+        flags |= subprocess.CREATE_NO_WINDOW
+
+    pkwargs = {
+        'creationflags': flags,
+        'stdout': subprocess.PIPE,
+        'stderr': subprocess.PIPE
+    }
+    context.server_process = subprocess.Popen(
+        [str(arg) for arg in [context.server_path, *server_args]],
+        **pkwargs)
+
+    def server_log(in_stream, out_stream):
+        for line in iter(in_stream.readline, b''):
+            print(line.decode('utf-8'), end='', file=out_stream)
+
+    thread_stdout = threading.Thread(target=server_log, args=(context.server_process.stdout, sys.stdout))
+    thread_stdout.start()
+
+    thread_stderr = threading.Thread(target=server_log, args=(context.server_process.stderr, sys.stderr))
+    thread_stderr.start()
+
+    print(f"server pid={context.server_process.pid}, behave pid={os.getpid()}")
diff --git a/examples/server/tests/features/wrong_usages.feature b/examples/server/tests/features/wrong_usages.feature
new file mode 100644
index 000000000..cf14b3b44
--- /dev/null
+++ b/examples/server/tests/features/wrong_usages.feature
@@ -0,0 +1,22 @@
+# run with: ./tests.sh --no-skipped --tags wrong_usage
+@wrong_usage
+Feature: Wrong usage of llama.cpp server
+
+  #3969 The user must always set --n-predict option
+  # to cap the number of tokens any completion request can generate
+  # or pass n_predict/max_tokens in the request.
+  Scenario: Infinite loop
+    Given a server listening on localhost:8080
+    And   a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
+    # Uncomment below to fix the issue
+    #And   64 server max tokens to predict
+    Then  the server is starting
+    Given a prompt:
+      """
+      Go to: infinite loop
+      """
+    # Uncomment below to fix the issue
+    #And   128 max tokens to predict
+    Given concurrent completion requests
+    Then the server is idle
+    Then all prompts are predicted
diff --git a/examples/server/tests/requirements.txt b/examples/server/tests/requirements.txt
new file mode 100644
index 000000000..2e4f42ad2
--- /dev/null
+++ b/examples/server/tests/requirements.txt
@@ -0,0 +1,6 @@
+aiohttp~=3.9.3
+behave~=1.2.6
+huggingface_hub~=0.20.3
+numpy~=1.24.4
+openai~=0.25.0
+prometheus-client~=0.20.0
diff --git a/examples/server/tests/tests.sh b/examples/server/tests/tests.sh
new file mode 100755
index 000000000..72a0fbad8
--- /dev/null
+++ b/examples/server/tests/tests.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+
+set -eu
+
+if [ $# -lt 1 ]
+then
+    # Start @llama.cpp scenario
+    behave --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp
+else
+    behave "$@"
+fi
diff --git a/examples/server/themes/README.md b/examples/server/themes/README.md
new file mode 100644
index 000000000..62e721a27
--- /dev/null
+++ b/examples/server/themes/README.md
@@ -0,0 +1,5 @@
+# LLaMA.cpp Server Wild Theme
+
+Simple themes directory of sample "public" directories. To try any of these add --path to your run like `server --path=wild`.
+
+![image](wild/wild.png)
diff --git a/examples/server/themes/buttons-top/README.md b/examples/server/themes/buttons-top/README.md
new file mode 100644
index 000000000..808c4cf81
--- /dev/null
+++ b/examples/server/themes/buttons-top/README.md
@@ -0,0 +1,7 @@
+# LLaMA.cpp Server Buttons Top Theme
+
+Simple tweaks to the UI. Chat buttons at the top of the page instead of bottom so you can hit Stop instead of chasing it down the page.
+
+To use simply run server with `--path=themes/buttons_top`
+
+![image](buttons_top.png)
diff --git a/examples/server/themes/buttons-top/buttons_top.png b/examples/server/themes/buttons-top/buttons_top.png
new file mode 100644
index 000000000..c54454519
Binary files /dev/null and b/examples/server/themes/buttons-top/buttons_top.png differ
diff --git a/examples/server/themes/buttons-top/favicon.ico b/examples/server/themes/buttons-top/favicon.ico
new file mode 100644
index 000000000..89e154a0a
Binary files /dev/null and b/examples/server/themes/buttons-top/favicon.ico differ
diff --git a/examples/server/themes/buttons-top/index.html b/examples/server/themes/buttons-top/index.html
new file mode 100644
index 000000000..6af30d307
--- /dev/null
+++ b/examples/server/themes/buttons-top/index.html
@@ -0,0 +1,1057 @@
+<html>
+
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
+  <meta name="color-scheme" content="light dark">
+  <title>llama.cpp - chat</title>
+
+  <style>
+    body {
+      font-family: system-ui;
+      font-size: 90%;
+    }
+
+    #container {
+      margin: 0em auto;
+      display: flex;
+      flex-direction: column;
+      justify-content: space-between;
+      height: 100%;
+    }
+
+    main {
+      margin: 3px;
+      display: flex;
+      flex-direction: column;
+      justify-content: space-between;
+      gap: 1em;
+
+      flex-grow: 1;
+      overflow-y: auto;
+
+      border: 1px solid #ccc;
+      border-radius: 5px;
+      padding: 0.5em;
+    }
+
+    body {
+      max-width: 600px;
+      min-width: 300px;
+      line-height: 1.2;
+      margin: 0 auto;
+      padding: 0 0.5em;
+    }
+
+    p {
+      overflow-wrap: break-word;
+      word-wrap: break-word;
+      hyphens: auto;
+      margin-top: 0.5em;
+      margin-bottom: 0.5em;
+    }
+
+    #write form {
+      margin: 1em 0 0 0;
+      display: flex;
+      flex-direction: column;
+      gap: 0.5em;
+      align-items: stretch;
+    }
+
+    .right {
+      display: flex;
+      flex-direction: row;
+      gap: 0.5em;
+      justify-content: flex-end;
+    }
+
+    fieldset {
+      border: none;
+      padding: 0;
+      margin: 0;
+    }
+
+    fieldset.two {
+      display: grid;
+      grid-template: "a a";
+      gap: 1em;
+    }
+
+    fieldset.three {
+      display: grid;
+      grid-template: "a a a";
+      gap: 1em;
+    }
+
+    details {
+      border: 1px solid #aaa;
+      border-radius: 4px;
+      padding: 0.5em 0.5em 0;
+      margin-top: 0.5em;
+    }
+
+    summary {
+      font-weight: bold;
+      margin: -0.5em -0.5em 0;
+      padding: 0.5em;
+      cursor: pointer;
+    }
+
+    details[open] {
+      padding: 0.5em;
+    }
+
+    .prob-set {
+      padding: 0.3em;
+      border-bottom: 1px solid #ccc;
+    }
+
+    .popover-content {
+      position: absolute;
+      background-color: white;
+      padding: 0.2em;
+      box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
+    }
+
+    textarea {
+      padding: 5px;
+      flex-grow: 1;
+      width: 100%;
+    }
+
+    pre code {
+      display: block;
+      background-color: #222;
+      color: #ddd;
+    }
+
+    code {
+      font-family: monospace;
+      padding: 0.1em 0.3em;
+      border-radius: 3px;
+    }
+
+    fieldset label {
+      margin: 0.5em 0;
+      display: block;
+    }
+
+    fieldset label.slim {
+      margin: 0 0.5em;
+      display: inline;
+    }
+
+    header,
+    footer {
+      text-align: center;
+    }
+
+    footer {
+      font-size: 80%;
+      color: #888;
+    }
+
+    .mode-chat textarea[name=prompt] {
+      height: 4.5em;
+    }
+
+    .mode-completion textarea[name=prompt] {
+      height: 10em;
+    }
+
+    [contenteditable] {
+      display: inline-block;
+      white-space: pre-wrap;
+      outline: 0px solid transparent;
+    }
+
+    @keyframes loading-bg-wipe {
+      0% {
+        background-position: 0%;
+      }
+
+      100% {
+        background-position: 100%;
+      }
+    }
+
+    .loading {
+      --loading-color-1: #eeeeee00;
+      --loading-color-2: #eeeeeeff;
+      background-size: 50% 100%;
+      background-image: linear-gradient(90deg, var(--loading-color-1), var(--loading-color-2), var(--loading-color-1));
+      animation: loading-bg-wipe 2s linear infinite;
+    }
+
+    @media (prefers-color-scheme: dark) {
+      .loading {
+        --loading-color-1: #22222200;
+        --loading-color-2: #222222ff;
+      }
+
+      .popover-content {
+        background-color: black;
+      }
+    }
+  </style>
+
+  <script type="module">
+    import {
+      html, h, signal, effect, computed, render, useSignal, useEffect, useRef, Component
+    } from './index.js';
+
+    import { llama } from './completion.js';
+    import { SchemaConverter } from './json-schema-to-grammar.mjs';
+    let selected_image = false;
+    var slot_id = -1;
+
+    const session = signal({
+      prompt: "This is a conversation between User and Llama, a friendly chatbot. Llama is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision.",
+      template: "{{prompt}}\n\n{{history}}\n{{char}}:",
+      historyTemplate: "{{name}}: {{message}}",
+      transcript: [],
+      type: "chat",  // "chat" | "completion"
+      char: "Llama",
+      user: "User",
+      image_selected: ''
+    })
+
+    const params = signal({
+      n_predict: 400,
+      temperature: 0.7,
+      repeat_last_n: 256, // 0 = disable penalty, -1 = context size
+      repeat_penalty: 1.18, // 1.0 = disabled
+      penalize_nl: false,
+      top_k: 40, // <= 0 to use vocab size
+      top_p: 0.95, // 1.0 = disabled
+      min_p: 0.05, // 0 = disabled
+      tfs_z: 1.0, // 1.0 = disabled
+      typical_p: 1.0, // 1.0 = disabled
+      presence_penalty: 0.0, // 0.0 = disabled
+      frequency_penalty: 0.0, // 0.0 = disabled
+      mirostat: 0, // 0/1/2
+      mirostat_tau: 5, // target entropy
+      mirostat_eta: 0.1, // learning rate
+      grammar: '',
+      n_probs: 0, // no completion_probabilities,
+      min_keep: 0, // min probs from each sampler,
+      image_data: [],
+      cache_prompt: true,
+      api_key: ''
+    })
+
+    /* START: Support for storing prompt templates and parameters in browsers LocalStorage */
+
+    const local_storage_storageKey = "llamacpp_server_local_storage";
+
+    function local_storage_setDataFromObject(tag, content) {
+      localStorage.setItem(local_storage_storageKey + '/' + tag, JSON.stringify(content));
+    }
+
+    function local_storage_setDataFromRawText(tag, content) {
+      localStorage.setItem(local_storage_storageKey + '/' + tag, content);
+    }
+
+    function local_storage_getDataAsObject(tag) {
+      const item = localStorage.getItem(local_storage_storageKey + '/' + tag);
+      if (!item) {
+        return null;
+      } else {
+        return JSON.parse(item);
+      }
+    }
+
+    function local_storage_getDataAsRawText(tag) {
+      const item = localStorage.getItem(local_storage_storageKey + '/' + tag);
+      if (!item) {
+        return null;
+      } else {
+        return item;
+      }
+    }
+
+    // create a container for user templates and settings
+
+    const savedUserTemplates = signal({})
+    const selectedUserTemplate = signal({ name: '', template: { session: {}, params: {} } })
+
+    // let's import locally saved templates and settings if there are any
+    // user templates and settings are stored in one object
+    // in form of { "templatename": "templatedata" } and { "settingstemplatename":"settingsdata" }
+
+    console.log('Importing saved templates')
+
+    let importedTemplates = local_storage_getDataAsObject('user_templates')
+
+    if (importedTemplates) {
+      // saved templates were successfully imported.
+
+      console.log('Processing saved templates and updating default template')
+      params.value = { ...params.value, image_data: [] };
+
+      //console.log(importedTemplates);
+      savedUserTemplates.value = importedTemplates;
+
+      //override default template
+      savedUserTemplates.value.default = { session: session.value, params: params.value }
+      local_storage_setDataFromObject('user_templates', savedUserTemplates.value)
+    } else {
+      // no saved templates detected.
+
+      console.log('Initializing LocalStorage and saving default template')
+
+      savedUserTemplates.value = { "default": { session: session.value, params: params.value } }
+      local_storage_setDataFromObject('user_templates', savedUserTemplates.value)
+    }
+
+    function userTemplateResetToDefault() {
+      console.log('Resetting template to default')
+      selectedUserTemplate.value.name = 'default';
+      selectedUserTemplate.value.data = savedUserTemplates.value['default'];
+    }
+
+    function userTemplateApply(t) {
+      session.value = t.data.session;
+      session.value = { ...session.value, image_selected: '' };
+      params.value = t.data.params;
+      params.value = { ...params.value, image_data: [] };
+    }
+
+    function userTemplateResetToDefaultAndApply() {
+      userTemplateResetToDefault()
+      userTemplateApply(selectedUserTemplate.value)
+    }
+
+    function userTemplateLoadAndApplyAutosaved() {
+      // get autosaved last used template
+      let lastUsedTemplate = local_storage_getDataAsObject('user_templates_last')
+
+      if (lastUsedTemplate) {
+
+        console.log('Autosaved template found, restoring')
+
+        selectedUserTemplate.value = lastUsedTemplate
+      }
+      else {
+
+        console.log('No autosaved template found, using default template')
+        // no autosaved last used template was found, so load from default.
+
+        userTemplateResetToDefault()
+      }
+
+      console.log('Applying template')
+      // and update internal data from templates
+
+      userTemplateApply(selectedUserTemplate.value)
+    }
+
+    //console.log(savedUserTemplates.value)
+    //console.log(selectedUserTemplate.value)
+
+    function userTemplateAutosave() {
+      console.log('Template Autosave...')
+      if (selectedUserTemplate.value.name == 'default') {
+        // we don't want to save over default template, so let's create a new one
+        let newTemplateName = 'UserTemplate-' + Date.now().toString()
+        let newTemplate = { 'name': newTemplateName, 'data': { 'session': session.value, 'params': params.value } }
+
+        console.log('Saving as ' + newTemplateName)
+
+        // save in the autosave slot
+        local_storage_setDataFromObject('user_templates_last', newTemplate)
+
+        // and load it back and apply
+        userTemplateLoadAndApplyAutosaved()
+      } else {
+        local_storage_setDataFromObject('user_templates_last', { 'name': selectedUserTemplate.value.name, 'data': { 'session': session.value, 'params': params.value } })
+      }
+    }
+
+    console.log('Checking for autosaved last used template')
+    userTemplateLoadAndApplyAutosaved()
+
+    /* END: Support for storing prompt templates and parameters in browsers LocalStorage */
+
+    const llamaStats = signal(null)
+    const controller = signal(null)
+
+    // currently generating a completion?
+    const generating = computed(() => controller.value != null)
+
+    // has the user started a chat?
+    const chatStarted = computed(() => session.value.transcript.length > 0)
+
+    const transcriptUpdate = (transcript) => {
+      session.value = {
+        ...session.value,
+        transcript
+      }
+    }
+
+    // simple template replace
+    const template = (str, extraSettings) => {
+      let settings = session.value;
+      if (extraSettings) {
+        settings = { ...settings, ...extraSettings };
+      }
+      return String(str).replaceAll(/\{\{(.*?)\}\}/g, (_, key) => template(settings[key]));
+    }
+
+    async function runLlama(prompt, llamaParams, char) {
+      const currentMessages = [];
+      const history = session.value.transcript;
+      if (controller.value) {
+        throw new Error("already running");
+      }
+      controller.value = new AbortController();
+      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: location.pathname.replace(/\/+$/, '') })) {
+        const data = chunk.data;
+
+        if (data.stop) {
+          while (
+            currentMessages.length > 0 &&
+            currentMessages[currentMessages.length - 1].content.match(/\n$/) != null
+          ) {
+            currentMessages.pop();
+          }
+          transcriptUpdate([...history, [char, currentMessages]])
+          console.log("Completion finished: '", currentMessages.map(msg => msg.content).join(''), "', summary: ", data);
+        } else {
+          currentMessages.push(data);
+          slot_id = data.slot_id;
+          if (selected_image && !data.multimodal) {
+            alert("The server was not compiled for multimodal or the model projector can't be loaded.");
+            return;
+          }
+          transcriptUpdate([...history, [char, currentMessages]])
+        }
+
+        if (data.timings) {
+          llamaStats.value = data;
+        }
+      }
+
+      controller.value = null;
+    }
+
+    // send message to server
+    const chat = async (msg) => {
+      if (controller.value) {
+        console.log('already running...');
+        return;
+      }
+
+      transcriptUpdate([...session.value.transcript, ["{{user}}", msg]])
+
+      let prompt = template(session.value.template, {
+        message: msg,
+        history: session.value.transcript.flatMap(
+          ([name, data]) =>
+            template(
+              session.value.historyTemplate,
+              {
+                name,
+                message: Array.isArray(data) ?
+                  data.map(msg => msg.content).join('').replace(/^\s/, '') :
+                  data,
+              }
+            )
+        ).join("\n"),
+      });
+      if (selected_image) {
+        prompt = `A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:[img-10]${msg}\nASSISTANT:`;
+      }
+      await runLlama(prompt, {
+        ...params.value,
+        slot_id: slot_id,
+        stop: ["</s>", template("{{char}}:"), template("{{user}}:")],
+      }, "{{char}}");
+    }
+
+    const runCompletion = () => {
+      if (controller.value) {
+        console.log('already running...');
+        return;
+      }
+      const { prompt } = session.value;
+      transcriptUpdate([...session.value.transcript, ["", prompt]]);
+      runLlama(prompt, {
+        ...params.value,
+        slot_id: slot_id,
+        stop: [],
+      }, "").finally(() => {
+        session.value.prompt = session.value.transcript.map(([_, data]) =>
+          Array.isArray(data) ? data.map(msg => msg.content).join('') : data
+        ).join('');
+        session.value.transcript = [];
+      })
+    }
+
+    const stop = (e) => {
+      e.preventDefault();
+      if (controller.value) {
+        controller.value.abort();
+        controller.value = null;
+      }
+    }
+
+    const reset = (e) => {
+      stop(e);
+      transcriptUpdate([]);
+    }
+
+    const uploadImage = (e) => {
+      e.preventDefault();
+      document.getElementById("fileInput").click();
+      document.getElementById("fileInput").addEventListener("change", function (event) {
+        const selectedFile = event.target.files[0];
+        if (selectedFile) {
+          const reader = new FileReader();
+          reader.onload = function () {
+            const image_data = reader.result;
+            session.value = { ...session.value, image_selected: image_data };
+            params.value = {
+              ...params.value, image_data: [
+                { data: image_data.replace(/data:image\/[^;]+;base64,/, ''), id: 10 }]
+            }
+          };
+          selected_image = true;
+          reader.readAsDataURL(selectedFile);
+        }
+      });
+    }
+
+    function MessageInput() {
+      const message = useSignal("")
+
+      const submit = (e) => {
+        stop(e);
+        chat(message.value);
+        message.value = "";
+      }
+
+      const enterSubmits = (event) => {
+        if (event.which === 13 && !event.shiftKey) {
+          submit(event);
+        }
+      }
+
+      return html`
+        <form onsubmit=${submit}>
+          <div>
+            <textarea
+               className=${generating.value ? "loading" : null}
+               oninput=${(e) => message.value = e.target.value}
+               onkeypress=${enterSubmits}
+               placeholder="Say something..."
+               rows=2
+               type="text"
+               value="${message}"
+            />
+          </div>
+          <div class="right">
+            <button type="submit" disabled=${generating.value}>Send</button>
+            <button onclick=${uploadImage}>Upload Image</button>
+            <button onclick=${stop} disabled=${!generating.value}>Stop</button>
+            <button onclick=${reset}>Reset</button>
+          </div>
+        </form>
+      `
+    }
+
+    function CompletionControls() {
+      const submit = (e) => {
+        stop(e);
+        runCompletion();
+      }
+      return html`
+        <div>
+          <button onclick=${submit} type="button" disabled=${generating.value}>Start</button>
+          <button onclick=${stop} disabled=${!generating.value}>Stop</button>
+          <button onclick=${reset}>Reset</button>
+        </div>`;
+    }
+
+    const ChatLog = (props) => {
+      const messages = session.value.transcript;
+      const container = useRef(null)
+
+      useEffect(() => {
+        // scroll to bottom (if needed)
+        const parent = container.current.parentElement;
+        if (parent && parent.scrollHeight <= parent.scrollTop + parent.offsetHeight + 300) {
+          parent.scrollTo(0, parent.scrollHeight)
+        }
+      }, [messages])
+
+      const isCompletionMode = session.value.type === 'completion'
+      const chatLine = ([user, data], index) => {
+        let message
+        const isArrayMessage = Array.isArray(data)
+        if (params.value.n_probs > 0 && isArrayMessage) {
+          message = html`<${Probabilities} data=${data} />`
+        } else {
+          const text = isArrayMessage ?
+            data.map(msg => msg.content).join('').replace(/^\s+/, '') :
+            data;
+          message = isCompletionMode ?
+            text :
+            html`<${Markdownish} text=${template(text)} />`
+        }
+        if (user) {
+          return html`<p key=${index}><strong>${template(user)}:</strong> ${message}</p>`
+        } else {
+          return isCompletionMode ?
+            html`<span key=${index}>${message}</span>` :
+            html`<p key=${index}>${message}</p>`
+        }
+      };
+
+      const handleCompletionEdit = (e) => {
+        session.value.prompt = e.target.innerText;
+        session.value.transcript = [];
+      }
+
+      return html`
+        <div id="chat" ref=${container} key=${messages.length}>
+          <img style="width: 60%;${!session.value.image_selected ? `display: none;` : ``}" src="${session.value.image_selected}"/>
+          <span contenteditable=${isCompletionMode} ref=${container} oninput=${handleCompletionEdit}>
+            ${messages.flatMap(chatLine)}
+          </span>
+        </div>`;
+    };
+
+    const ConfigForm = (props) => {
+      const updateSession = (el) => session.value = { ...session.value, [el.target.name]: el.target.value }
+      const updateParams = (el) => params.value = { ...params.value, [el.target.name]: el.target.value }
+      const updateParamsFloat = (el) => params.value = { ...params.value, [el.target.name]: parseFloat(el.target.value) }
+      const updateParamsInt = (el) => params.value = { ...params.value, [el.target.name]: Math.floor(parseFloat(el.target.value)) }
+      const updateParamsBool = (el) => params.value = { ...params.value, [el.target.name]: el.target.checked }
+
+      const grammarJsonSchemaPropOrder = signal('')
+      const updateGrammarJsonSchemaPropOrder = (el) => grammarJsonSchemaPropOrder.value = el.target.value
+      const convertJSONSchemaGrammar = async () => {
+        try {
+          let schema = JSON.parse(params.value.grammar)
+          const converter = new SchemaConverter({
+            prop_order: grammarJsonSchemaPropOrder.value
+              .split(',')
+              .reduce((acc, cur, i) => ({ ...acc, [cur.trim()]: i }), {}),
+            allow_fetch: true,
+          })
+          schema = await converter.resolveRefs(schema, 'input')
+          converter.visit(schema, '')
+          params.value = {
+            ...params.value,
+            grammar: converter.formatGrammar(),
+          }
+        } catch (e) {
+          alert(`Convert failed: ${e.message}`)
+        }
+      }
+
+      const FloatField = ({ label, max, min, name, step, value }) => {
+        return html`
+          <div>
+            <label for="${name}">${label}</label>
+            <input type="range" id="${name}" min="${min}" max="${max}" step="${step}" name="${name}" value="${value}" oninput=${updateParamsFloat} />
+            <span>${value}</span>
+          </div>
+        `
+      };
+
+      const IntField = ({ label, max, min, name, value }) => {
+        return html`
+          <div>
+            <label for="${name}">${label}</label>
+            <input type="range" id="${name}" min="${min}" max="${max}" name="${name}" value="${value}" oninput=${updateParamsInt} />
+            <span>${value}</span>
+          </div>
+        `
+      };
+
+      const BoolField = ({ label, name, value }) => {
+        return html`
+          <div>
+            <label for="${name}">${label}</label>
+            <input type="checkbox" id="${name}" name="${name}" checked="${value}" onclick=${updateParamsBool} />
+          </div>
+        `
+      };
+
+      const userTemplateReset = (e) => {
+        e.preventDefault();
+        userTemplateResetToDefaultAndApply()
+      }
+
+      const UserTemplateResetButton = () => {
+        if (selectedUserTemplate.value.name == 'default') {
+          return html`
+            <button disabled>Using default template</button>
+          `
+        }
+
+        return html`
+          <button onclick=${userTemplateReset}>Reset all to default</button>
+        `
+      };
+
+      useEffect(() => {
+        // autosave template on every change
+        userTemplateAutosave()
+      }, [session.value, params.value])
+
+      const GrammarControl = () => (
+        html`
+          <div>
+            <label for="template">Grammar</label>
+            <textarea id="grammar" name="grammar" placeholder="Use gbnf or JSON Schema+convert" value="${params.value.grammar}" rows=4 oninput=${updateParams}/>
+            <input type="text" name="prop-order" placeholder="order: prop1,prop2,prop3" oninput=${updateGrammarJsonSchemaPropOrder} />
+            <button type="button" onclick=${convertJSONSchemaGrammar}>Convert JSON Schema</button>
+          </div>
+          `
+      );
+
+      const PromptControlFieldSet = () => (
+        html`
+        <fieldset>
+          <div>
+            <label htmlFor="prompt">Prompt</label>
+            <textarea type="text" name="prompt" value="${session.value.prompt}" oninput=${updateSession}/>
+          </div>
+        </fieldset>
+        `
+      );
+
+      const ChatConfigForm = () => (
+        html`
+          ${PromptControlFieldSet()}
+
+          <fieldset class="two">
+            <div>
+              <label for="user">User name</label>
+              <input type="text" name="user" value="${session.value.user}" oninput=${updateSession} />
+            </div>
+
+            <div>
+              <label for="bot">Bot name</label>
+              <input type="text" name="char" value="${session.value.char}" oninput=${updateSession} />
+            </div>
+          </fieldset>
+
+          <fieldset>
+            <div>
+              <label for="template">Prompt template</label>
+              <textarea id="template" name="template" value="${session.value.template}" rows=4 oninput=${updateSession}/>
+            </div>
+
+            <div>
+              <label for="template">Chat history template</label>
+              <textarea id="template" name="historyTemplate" value="${session.value.historyTemplate}" rows=1 oninput=${updateSession}/>
+            </div>
+            ${GrammarControl()}
+          </fieldset>
+      `
+      );
+
+      const CompletionConfigForm = () => (
+        html`
+          ${PromptControlFieldSet()}
+          <fieldset>${GrammarControl()}</fieldset>
+        `
+      );
+
+      return html`
+        <form>
+          <fieldset class="two">
+            <${UserTemplateResetButton}/>
+            <div>
+              <label class="slim"><input type="radio" name="type" value="chat" checked=${session.value.type === "chat"} oninput=${updateSession} /> Chat</label>
+              <label class="slim"><input type="radio" name="type" value="completion" checked=${session.value.type === "completion"} oninput=${updateSession} /> Completion</label>
+            </div>
+          </fieldset>
+
+          ${session.value.type === 'chat' ? ChatConfigForm() : CompletionConfigForm()}
+
+          <fieldset class="two">
+            ${IntField({ label: "Predictions", max: 2048, min: -1, name: "n_predict", value: params.value.n_predict })}
+            ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
+            ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
+            ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
+            ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
+            ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
+            ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
+            ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}
+          </fieldset>
+          <details>
+            <summary>More options</summary>
+            <fieldset class="two">
+              ${FloatField({ label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })}
+              ${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })}
+              ${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })}
+              ${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })}
+            </fieldset>
+            <hr />
+            <fieldset class="three">
+              <div>
+                <label><input type="radio" name="mirostat" value="0" checked=${params.value.mirostat == 0} oninput=${updateParamsInt} /> no Mirostat</label>
+                <label><input type="radio" name="mirostat" value="1" checked=${params.value.mirostat == 1} oninput=${updateParamsInt} /> Mirostat v1</label>
+                <label><input type="radio" name="mirostat" value="2" checked=${params.value.mirostat == 2} oninput=${updateParamsInt} /> Mirostat v2</label>
+              </div>
+              ${FloatField({ label: "Mirostat tau", max: 10.0, min: 0.0, name: "mirostat_tau", step: 0.01, value: params.value.mirostat_tau })}
+              ${FloatField({ label: "Mirostat eta", max: 1.0, min: 0.0, name: "mirostat_eta", step: 0.01, value: params.value.mirostat_eta })}
+            </fieldset>
+            <fieldset>
+              ${IntField({ label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs })}
+            </fieldset>
+            <fieldset>
+              ${IntField({ label: "Min Probabilities from each Sampler", max: 10, min: 0, name: "min_keep", value: params.value.min_keep })}
+            </fieldset>
+            <fieldset>
+              <label for="api_key">API Key</label>
+              <input type="text" name="api_key" value="${params.value.api_key}" placeholder="Enter API key" oninput=${updateParams} />
+            </fieldset>
+          </details>
+        </form>
+      `
+    }
+
+    const probColor = (p) => {
+      const r = Math.floor(192 * (1 - p));
+      const g = Math.floor(192 * p);
+      return `rgba(${r},${g},0,0.3)`;
+    }
+
+    const Probabilities = (params) => {
+      return params.data.map(msg => {
+        const { completion_probabilities } = msg;
+        if (
+          !completion_probabilities ||
+          completion_probabilities.length === 0
+        ) return msg.content
+
+        if (completion_probabilities.length > 1) {
+          // Not for byte pair
+          if (completion_probabilities[0].content.startsWith('byte: \\')) return msg.content
+
+          const splitData = completion_probabilities.map(prob => ({
+            content: prob.content,
+            completion_probabilities: [prob]
+          }))
+          return html`<${Probabilities} data=${splitData} />`
+        }
+
+        const { probs, content } = completion_probabilities[0]
+        const found = probs.find(p => p.tok_str === msg.content)
+        const pColor = found ? probColor(found.prob) : 'transparent'
+
+        const popoverChildren = html`
+          <div class="prob-set">
+            ${probs.map((p, index) => {
+          return html`
+                <div
+                  key=${index}
+                  title=${`prob: ${p.prob}`}
+                  style=${{
+              padding: '0.3em',
+              backgroundColor: p.tok_str === content ? probColor(p.prob) : 'transparent'
+            }}
+                >
+                  <span>${p.tok_str}: </span>
+                  <span>${Math.floor(p.prob * 100)}%</span>
+                </div>
+              `
+        })}
+          </div>
+        `
+
+        return html`
+          <${Popover} style=${{ backgroundColor: pColor }} popoverChildren=${popoverChildren}>
+            ${msg.content.match(/\n/gim) ? html`<br />` : msg.content}
+          </>
+        `
+      });
+    }
+
+    // poor mans markdown replacement
+    const Markdownish = (params) => {
+      const md = params.text
+        .replace(/&/g, '&amp;')
+        .replace(/</g, '&lt;')
+        .replace(/>/g, '&gt;')
+        .replace(/^#{1,6} (.*)$/gim, '<h3>$1</h3>')
+        .replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>')
+        .replace(/__(.*?)__/g, '<strong>$1</strong>')
+        .replace(/\*(.*?)\*/g, '<em>$1</em>')
+        .replace(/_(.*?)_/g, '<em>$1</em>')
+        .replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
+        .replace(/`(.*?)`/g, '<code>$1</code>')
+        .replace(/\n/gim, '<br />');
+      return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`;
+    };
+
+    const ModelGenerationInfo = (params) => {
+      if (!llamaStats.value) {
+        return html`<span/>`
+      }
+      return html`
+        <span>
+          ${llamaStats.value.tokens_predicted} predicted, ${llamaStats.value.tokens_cached} cached, ${llamaStats.value.timings.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.timings.predicted_per_second.toFixed(2)} tokens per second
+        </span>
+      `
+    }
+
+    // simple popover impl
+    const Popover = (props) => {
+      const isOpen = useSignal(false);
+      const position = useSignal({ top: '0px', left: '0px' });
+      const buttonRef = useRef(null);
+      const popoverRef = useRef(null);
+
+      const togglePopover = () => {
+        if (buttonRef.current) {
+          const rect = buttonRef.current.getBoundingClientRect();
+          position.value = {
+            top: `${rect.bottom + window.scrollY}px`,
+            left: `${rect.left + window.scrollX}px`,
+          };
+        }
+        isOpen.value = !isOpen.value;
+      };
+
+      const handleClickOutside = (event) => {
+        if (popoverRef.current && !popoverRef.current.contains(event.target) && !buttonRef.current.contains(event.target)) {
+          isOpen.value = false;
+        }
+      };
+
+      useEffect(() => {
+        document.addEventListener('mousedown', handleClickOutside);
+        return () => {
+          document.removeEventListener('mousedown', handleClickOutside);
+        };
+      }, []);
+
+      return html`
+        <span style=${props.style} ref=${buttonRef} onClick=${togglePopover}>${props.children}</span>
+        ${isOpen.value && html`
+          <${Portal} into="#portal">
+            <div
+              ref=${popoverRef}
+              class="popover-content"
+              style=${{
+            top: position.value.top,
+            left: position.value.left,
+          }}
+            >
+              ${props.popoverChildren}
+            </div>
+          </${Portal}>
+        `}
+      `;
+    };
+
+    // Source: preact-portal (https://github.com/developit/preact-portal/blob/master/src/preact-portal.js)
+    /** Redirect rendering of descendants into the given CSS selector */
+    class Portal extends Component {
+      componentDidUpdate(props) {
+        for (let i in props) {
+          if (props[i] !== this.props[i]) {
+            return setTimeout(this.renderLayer);
+          }
+        }
+      }
+
+      componentDidMount() {
+        this.isMounted = true;
+        this.renderLayer = this.renderLayer.bind(this);
+        this.renderLayer();
+      }
+
+      componentWillUnmount() {
+        this.renderLayer(false);
+        this.isMounted = false;
+        if (this.remote && this.remote.parentNode) this.remote.parentNode.removeChild(this.remote);
+      }
+
+      findNode(node) {
+        return typeof node === 'string' ? document.querySelector(node) : node;
+      }
+
+      renderLayer(show = true) {
+        if (!this.isMounted) return;
+
+        // clean up old node if moving bases:
+        if (this.props.into !== this.intoPointer) {
+          this.intoPointer = this.props.into;
+          if (this.into && this.remote) {
+            this.remote = render(html`<${PortalProxy} />`, this.into, this.remote);
+          }
+          this.into = this.findNode(this.props.into);
+        }
+
+        this.remote = render(html`
+          <${PortalProxy} context=${this.context}>
+            ${show && this.props.children || null}
+          </${PortalProxy}>
+        `, this.into, this.remote);
+      }
+
+      render() {
+        return null;
+      }
+    }
+    // high-order component that renders its first child if it exists.
+    // used as a conditional rendering proxy.
+    class PortalProxy extends Component {
+      getChildContext() {
+        return this.props.context;
+      }
+      render({ children }) {
+        return children || null;
+      }
+    }
+
+    function App(props) {
+      useEffect(() => {
+        const query = new URLSearchParams(location.search).get("q");
+        if (query) chat(query);
+      }, []);
+
+      return html`
+        <div class="mode-${session.value.type}">
+          <header>
+            <h1>llama.cpp</h1>
+          </header>
+
+          <section id="write">
+            <${session.value.type === 'chat' ? MessageInput : CompletionControls} />
+          </section>
+
+          <main id="content">
+            <${chatStarted.value ? ChatLog : ConfigForm} />
+          </main>
+
+          <footer>
+            <p><${ModelGenerationInfo} /></p>
+            <p>Powered by <a href="https://github.com/ggerganov/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a>.</p>
+          </footer>
+        </div>
+      `;
+    }
+
+    render(h(App), document.querySelector('#container'));
+  </script>
+</head>
+
+<body>
+  <div id="container">
+    <input type="file" id="fileInput" accept="image/*" style="display: none;">
+  </div>
+  <div id="portal"></div>
+</body>
+
+</html>
+
diff --git a/examples/server/themes/wild/README.md b/examples/server/themes/wild/README.md
new file mode 100644
index 000000000..560bcc81b
--- /dev/null
+++ b/examples/server/themes/wild/README.md
@@ -0,0 +1,5 @@
+# LLaMA.cpp Server Wild Theme
+
+Simple tweaks to the UI. To use simply run server with `--path=themes/wild`
+
+![image](wild.png)
diff --git a/examples/server/themes/wild/favicon.ico b/examples/server/themes/wild/favicon.ico
new file mode 100644
index 000000000..89e154a0a
Binary files /dev/null and b/examples/server/themes/wild/favicon.ico differ
diff --git a/examples/server/themes/wild/index.html b/examples/server/themes/wild/index.html
new file mode 100644
index 000000000..772e716cd
--- /dev/null
+++ b/examples/server/themes/wild/index.html
@@ -0,0 +1,1061 @@
+<html>
+
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
+  <meta name="color-scheme" content="light dark">
+  <title>llama.cpp - chat</title>
+
+  <style>
+    body {
+      font-family: system-ui;
+      font-size: 90%;
+      background-image: url('llamapattern.png');
+    }
+
+    #container {
+      margin: 0em auto;
+      display: flex;
+      flex-direction: column;
+      justify-content: space-between;
+      height: 100%;
+    }
+
+    main {
+      margin: 3px;
+      display: flex;
+      flex-direction: column;
+      justify-content: space-between;
+      gap: 1em;
+
+      flex-grow: 1;
+      overflow-y: auto;
+
+      border: 1px solid #ccc;
+      border-radius: 5px;
+      padding: 0.5em;
+
+      background-color: rgba(255,255,255,0.9);
+    }
+
+    body {
+      max-width: 600px;
+      min-width: 300px;
+      line-height: 1.2;
+      margin: 0 auto;
+      padding: 0 0.5em;
+    }
+
+    p {
+      overflow-wrap: break-word;
+      word-wrap: break-word;
+      hyphens: auto;
+      margin-top: 0.5em;
+      margin-bottom: 0.5em;
+    }
+
+    #write form {
+      margin: 1em 0 0 0;
+      display: flex;
+      flex-direction: column;
+      gap: 0.5em;
+      align-items: stretch;
+    }
+
+    .right {
+      display: flex;
+      flex-direction: row;
+      gap: 0.5em;
+      justify-content: flex-end;
+    }
+
+    fieldset {
+      border: none;
+      padding: 0;
+      margin: 0;
+    }
+
+    fieldset.two {
+      display: grid;
+      grid-template: "a a";
+      gap: 1em;
+    }
+
+    fieldset.three {
+      display: grid;
+      grid-template: "a a a";
+      gap: 1em;
+    }
+
+    details {
+      border: 1px solid #aaa;
+      border-radius: 4px;
+      padding: 0.5em 0.5em 0;
+      margin-top: 0.5em;
+    }
+
+    summary {
+      font-weight: bold;
+      margin: -0.5em -0.5em 0;
+      padding: 0.5em;
+      cursor: pointer;
+    }
+
+    details[open] {
+      padding: 0.5em;
+    }
+
+    .prob-set {
+      padding: 0.3em;
+      border-bottom: 1px solid #ccc;
+    }
+
+    .popover-content {
+      position: absolute;
+      background-color: white;
+      padding: 0.2em;
+      box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
+    }
+
+    textarea {
+      padding: 5px;
+      flex-grow: 1;
+      width: 100%;
+    }
+
+    pre code {
+      display: block;
+      background-color: #222;
+      color: #ddd;
+    }
+
+    code {
+      font-family: monospace;
+      padding: 0.1em 0.3em;
+      border-radius: 3px;
+    }
+
+    fieldset label {
+      margin: 0.5em 0;
+      display: block;
+    }
+
+    fieldset label.slim {
+      margin: 0 0.5em;
+      display: inline;
+    }
+
+    header,
+    footer {
+      text-align: center;
+    }
+
+    footer {
+      font-size: 80%;
+      color: #888;
+    }
+
+    .mode-chat textarea[name=prompt] {
+      height: 4.5em;
+    }
+
+    .mode-completion textarea[name=prompt] {
+      height: 10em;
+    }
+
+    [contenteditable] {
+      display: inline-block;
+      white-space: pre-wrap;
+      outline: 0px solid transparent;
+    }
+
+    @keyframes loading-bg-wipe {
+      0% {
+        background-position: 0%;
+      }
+
+      100% {
+        background-position: 100%;
+      }
+    }
+
+    .loading {
+      --loading-color-1: #eeeeee00;
+      --loading-color-2: #eeeeeeff;
+      background-size: 50% 100%;
+      background-image: linear-gradient(90deg, var(--loading-color-1), var(--loading-color-2), var(--loading-color-1));
+      animation: loading-bg-wipe 2s linear infinite;
+    }
+
+    @media (prefers-color-scheme: dark) {
+      .loading {
+        --loading-color-1: #22222200;
+        --loading-color-2: #222222ff;
+      }
+
+      .popover-content {
+        background-color: black;
+      }
+    }
+  </style>
+
+  <script type="module">
+    import {
+      html, h, signal, effect, computed, render, useSignal, useEffect, useRef, Component
+    } from './index.js';
+
+    import { llama } from './completion.js';
+    import { SchemaConverter } from './json-schema-to-grammar.mjs';
+    let selected_image = false;
+    var slot_id = -1;
+
+    const session = signal({
+      prompt: "This is a conversation between User and Llama, a friendly chatbot. Llama is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision.",
+      template: "{{prompt}}\n\n{{history}}\n{{char}}:",
+      historyTemplate: "{{name}}: {{message}}",
+      transcript: [],
+      type: "chat",  // "chat" | "completion"
+      char: "Llama",
+      user: "User",
+      image_selected: ''
+    })
+
+    const params = signal({
+      n_predict: 400,
+      temperature: 0.7,
+      repeat_last_n: 256, // 0 = disable penalty, -1 = context size
+      repeat_penalty: 1.18, // 1.0 = disabled
+      penalize_nl: false,
+      top_k: 40, // <= 0 to use vocab size
+      top_p: 0.95, // 1.0 = disabled
+      min_p: 0.05, // 0 = disabled
+      tfs_z: 1.0, // 1.0 = disabled
+      typical_p: 1.0, // 1.0 = disabled
+      presence_penalty: 0.0, // 0.0 = disabled
+      frequency_penalty: 0.0, // 0.0 = disabled
+      mirostat: 0, // 0/1/2
+      mirostat_tau: 5, // target entropy
+      mirostat_eta: 0.1, // learning rate
+      grammar: '',
+      n_probs: 0, // no completion_probabilities,
+      min_keep: 0, // min probs from each sampler,
+      image_data: [],
+      cache_prompt: true,
+      api_key: ''
+    })
+
+    /* START: Support for storing prompt templates and parameters in browsers LocalStorage */
+
+    const local_storage_storageKey = "llamacpp_server_local_storage";
+
+    function local_storage_setDataFromObject(tag, content) {
+      localStorage.setItem(local_storage_storageKey + '/' + tag, JSON.stringify(content));
+    }
+
+    function local_storage_setDataFromRawText(tag, content) {
+      localStorage.setItem(local_storage_storageKey + '/' + tag, content);
+    }
+
+    function local_storage_getDataAsObject(tag) {
+      const item = localStorage.getItem(local_storage_storageKey + '/' + tag);
+      if (!item) {
+        return null;
+      } else {
+        return JSON.parse(item);
+      }
+    }
+
+    function local_storage_getDataAsRawText(tag) {
+      const item = localStorage.getItem(local_storage_storageKey + '/' + tag);
+      if (!item) {
+        return null;
+      } else {
+        return item;
+      }
+    }
+
+    // create a container for user templates and settings
+
+    const savedUserTemplates = signal({})
+    const selectedUserTemplate = signal({ name: '', template: { session: {}, params: {} } })
+
+    // let's import locally saved templates and settings if there are any
+    // user templates and settings are stored in one object
+    // in form of { "templatename": "templatedata" } and { "settingstemplatename":"settingsdata" }
+
+    console.log('Importing saved templates')
+
+    let importedTemplates = local_storage_getDataAsObject('user_templates')
+
+    if (importedTemplates) {
+      // saved templates were successfully imported.
+
+      console.log('Processing saved templates and updating default template')
+      params.value = { ...params.value, image_data: [] };
+
+      //console.log(importedTemplates);
+      savedUserTemplates.value = importedTemplates;
+
+      //override default template
+      savedUserTemplates.value.default = { session: session.value, params: params.value }
+      local_storage_setDataFromObject('user_templates', savedUserTemplates.value)
+    } else {
+      // no saved templates detected.
+
+      console.log('Initializing LocalStorage and saving default template')
+
+      savedUserTemplates.value = { "default": { session: session.value, params: params.value } }
+      local_storage_setDataFromObject('user_templates', savedUserTemplates.value)
+    }
+
+    function userTemplateResetToDefault() {
+      console.log('Resetting template to default')
+      selectedUserTemplate.value.name = 'default';
+      selectedUserTemplate.value.data = savedUserTemplates.value['default'];
+    }
+
+    function userTemplateApply(t) {
+      session.value = t.data.session;
+      session.value = { ...session.value, image_selected: '' };
+      params.value = t.data.params;
+      params.value = { ...params.value, image_data: [] };
+    }
+
+    function userTemplateResetToDefaultAndApply() {
+      userTemplateResetToDefault()
+      userTemplateApply(selectedUserTemplate.value)
+    }
+
+    function userTemplateLoadAndApplyAutosaved() {
+      // get autosaved last used template
+      let lastUsedTemplate = local_storage_getDataAsObject('user_templates_last')
+
+      if (lastUsedTemplate) {
+
+        console.log('Autosaved template found, restoring')
+
+        selectedUserTemplate.value = lastUsedTemplate
+      }
+      else {
+
+        console.log('No autosaved template found, using default template')
+        // no autosaved last used template was found, so load from default.
+
+        userTemplateResetToDefault()
+      }
+
+      console.log('Applying template')
+      // and update internal data from templates
+
+      userTemplateApply(selectedUserTemplate.value)
+    }
+
+    //console.log(savedUserTemplates.value)
+    //console.log(selectedUserTemplate.value)
+
+    function userTemplateAutosave() {
+      console.log('Template Autosave...')
+      if (selectedUserTemplate.value.name == 'default') {
+        // we don't want to save over default template, so let's create a new one
+        let newTemplateName = 'UserTemplate-' + Date.now().toString()
+        let newTemplate = { 'name': newTemplateName, 'data': { 'session': session.value, 'params': params.value } }
+
+        console.log('Saving as ' + newTemplateName)
+
+        // save in the autosave slot
+        local_storage_setDataFromObject('user_templates_last', newTemplate)
+
+        // and load it back and apply
+        userTemplateLoadAndApplyAutosaved()
+      } else {
+        local_storage_setDataFromObject('user_templates_last', { 'name': selectedUserTemplate.value.name, 'data': { 'session': session.value, 'params': params.value } })
+      }
+    }
+
+    console.log('Checking for autosaved last used template')
+    userTemplateLoadAndApplyAutosaved()
+
+    /* END: Support for storing prompt templates and parameters in browsers LocalStorage */
+
+    const llamaStats = signal(null)
+    const controller = signal(null)
+
+    // currently generating a completion?
+    const generating = computed(() => controller.value != null)
+
+    // has the user started a chat?
+    const chatStarted = computed(() => session.value.transcript.length > 0)
+
+    const transcriptUpdate = (transcript) => {
+      session.value = {
+        ...session.value,
+        transcript
+      }
+    }
+
+    // simple template replace
+    const template = (str, extraSettings) => {
+      let settings = session.value;
+      if (extraSettings) {
+        settings = { ...settings, ...extraSettings };
+      }
+      return String(str).replaceAll(/\{\{(.*?)\}\}/g, (_, key) => template(settings[key]));
+    }
+
+    async function runLlama(prompt, llamaParams, char) {
+      const currentMessages = [];
+      const history = session.value.transcript;
+      if (controller.value) {
+        throw new Error("already running");
+      }
+      controller.value = new AbortController();
+      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: location.pathname.replace(/\/+$/, '') })) {
+        const data = chunk.data;
+
+        if (data.stop) {
+          while (
+            currentMessages.length > 0 &&
+            currentMessages[currentMessages.length - 1].content.match(/\n$/) != null
+          ) {
+            currentMessages.pop();
+          }
+          transcriptUpdate([...history, [char, currentMessages]])
+          console.log("Completion finished: '", currentMessages.map(msg => msg.content).join(''), "', summary: ", data);
+        } else {
+          currentMessages.push(data);
+          slot_id = data.slot_id;
+          if (selected_image && !data.multimodal) {
+            alert("The server was not compiled for multimodal or the model projector can't be loaded.");
+            return;
+          }
+          transcriptUpdate([...history, [char, currentMessages]])
+        }
+
+        if (data.timings) {
+          llamaStats.value = data;
+        }
+      }
+
+      controller.value = null;
+    }
+
+    // send message to server
+    const chat = async (msg) => {
+      if (controller.value) {
+        console.log('already running...');
+        return;
+      }
+
+      transcriptUpdate([...session.value.transcript, ["{{user}}", msg]])
+
+      let prompt = template(session.value.template, {
+        message: msg,
+        history: session.value.transcript.flatMap(
+          ([name, data]) =>
+            template(
+              session.value.historyTemplate,
+              {
+                name,
+                message: Array.isArray(data) ?
+                  data.map(msg => msg.content).join('').replace(/^\s/, '') :
+                  data,
+              }
+            )
+        ).join("\n"),
+      });
+      if (selected_image) {
+        prompt = `A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:[img-10]${msg}\nASSISTANT:`;
+      }
+      await runLlama(prompt, {
+        ...params.value,
+        slot_id: slot_id,
+        stop: ["</s>", template("{{char}}:"), template("{{user}}:")],
+      }, "{{char}}");
+    }
+
+    const runCompletion = () => {
+      if (controller.value) {
+        console.log('already running...');
+        return;
+      }
+      const { prompt } = session.value;
+      transcriptUpdate([...session.value.transcript, ["", prompt]]);
+      runLlama(prompt, {
+        ...params.value,
+        slot_id: slot_id,
+        stop: [],
+      }, "").finally(() => {
+        session.value.prompt = session.value.transcript.map(([_, data]) =>
+          Array.isArray(data) ? data.map(msg => msg.content).join('') : data
+        ).join('');
+        session.value.transcript = [];
+      })
+    }
+
+    const stop = (e) => {
+      e.preventDefault();
+      if (controller.value) {
+        controller.value.abort();
+        controller.value = null;
+      }
+    }
+
+    const reset = (e) => {
+      stop(e);
+      transcriptUpdate([]);
+    }
+
+    const uploadImage = (e) => {
+      e.preventDefault();
+      document.getElementById("fileInput").click();
+      document.getElementById("fileInput").addEventListener("change", function (event) {
+        const selectedFile = event.target.files[0];
+        if (selectedFile) {
+          const reader = new FileReader();
+          reader.onload = function () {
+            const image_data = reader.result;
+            session.value = { ...session.value, image_selected: image_data };
+            params.value = {
+              ...params.value, image_data: [
+                { data: image_data.replace(/data:image\/[^;]+;base64,/, ''), id: 10 }]
+            }
+          };
+          selected_image = true;
+          reader.readAsDataURL(selectedFile);
+        }
+      });
+    }
+
+    function MessageInput() {
+      const message = useSignal("")
+
+      const submit = (e) => {
+        stop(e);
+        chat(message.value);
+        message.value = "";
+      }
+
+      const enterSubmits = (event) => {
+        if (event.which === 13 && !event.shiftKey) {
+          submit(event);
+        }
+      }
+
+      return html`
+        <form onsubmit=${submit}>
+          <div>
+            <textarea
+               className=${generating.value ? "loading" : null}
+               oninput=${(e) => message.value = e.target.value}
+               onkeypress=${enterSubmits}
+               placeholder="Say something..."
+               rows=2
+               type="text"
+               value="${message}"
+            />
+          </div>
+          <div class="right">
+            <button type="submit" disabled=${generating.value}>Send</button>
+            <button onclick=${uploadImage}>Upload Image</button>
+            <button onclick=${stop} disabled=${!generating.value}>Stop</button>
+            <button onclick=${reset}>Reset</button>
+          </div>
+        </form>
+      `
+    }
+
+    function CompletionControls() {
+      const submit = (e) => {
+        stop(e);
+        runCompletion();
+      }
+      return html`
+        <div>
+          <button onclick=${submit} type="button" disabled=${generating.value}>Start</button>
+          <button onclick=${stop} disabled=${!generating.value}>Stop</button>
+          <button onclick=${reset}>Reset</button>
+        </div>`;
+    }
+
+    const ChatLog = (props) => {
+      const messages = session.value.transcript;
+      const container = useRef(null)
+
+      useEffect(() => {
+        // scroll to bottom (if needed)
+        const parent = container.current.parentElement;
+        if (parent && parent.scrollHeight <= parent.scrollTop + parent.offsetHeight + 300) {
+          parent.scrollTo(0, parent.scrollHeight)
+        }
+      }, [messages])
+
+      const isCompletionMode = session.value.type === 'completion'
+      const chatLine = ([user, data], index) => {
+        let message
+        const isArrayMessage = Array.isArray(data)
+        if (params.value.n_probs > 0 && isArrayMessage) {
+          message = html`<${Probabilities} data=${data} />`
+        } else {
+          const text = isArrayMessage ?
+            data.map(msg => msg.content).join('').replace(/^\s+/, '') :
+            data;
+          message = isCompletionMode ?
+            text :
+            html`<${Markdownish} text=${template(text)} />`
+        }
+        if (user) {
+          return html`<p key=${index}><strong>${template(user)}:</strong> ${message}</p>`
+        } else {
+          return isCompletionMode ?
+            html`<span key=${index}>${message}</span>` :
+            html`<p key=${index}>${message}</p>`
+        }
+      };
+
+      const handleCompletionEdit = (e) => {
+        session.value.prompt = e.target.innerText;
+        session.value.transcript = [];
+      }
+
+      return html`
+        <div id="chat" ref=${container} key=${messages.length}>
+          <img style="width: 60%;${!session.value.image_selected ? `display: none;` : ``}" src="${session.value.image_selected}"/>
+          <span contenteditable=${isCompletionMode} ref=${container} oninput=${handleCompletionEdit}>
+            ${messages.flatMap(chatLine)}
+          </span>
+        </div>`;
+    };
+
+    const ConfigForm = (props) => {
+      const updateSession = (el) => session.value = { ...session.value, [el.target.name]: el.target.value }
+      const updateParams = (el) => params.value = { ...params.value, [el.target.name]: el.target.value }
+      const updateParamsFloat = (el) => params.value = { ...params.value, [el.target.name]: parseFloat(el.target.value) }
+      const updateParamsInt = (el) => params.value = { ...params.value, [el.target.name]: Math.floor(parseFloat(el.target.value)) }
+      const updateParamsBool = (el) => params.value = { ...params.value, [el.target.name]: el.target.checked }
+
+      const grammarJsonSchemaPropOrder = signal('')
+      const updateGrammarJsonSchemaPropOrder = (el) => grammarJsonSchemaPropOrder.value = el.target.value
+      const convertJSONSchemaGrammar = async () => {
+        try {
+          let schema = JSON.parse(params.value.grammar)
+          const converter = new SchemaConverter({
+            prop_order: grammarJsonSchemaPropOrder.value
+              .split(',')
+              .reduce((acc, cur, i) => ({ ...acc, [cur.trim()]: i }), {}),
+            allow_fetch: true,
+          })
+          schema = await converter.resolveRefs(schema, 'input')
+          converter.visit(schema, '')
+          params.value = {
+            ...params.value,
+            grammar: converter.formatGrammar(),
+          }
+        } catch (e) {
+          alert(`Convert failed: ${e.message}`)
+        }
+      }
+
+      const FloatField = ({ label, max, min, name, step, value }) => {
+        return html`
+          <div>
+            <label for="${name}">${label}</label>
+            <input type="range" id="${name}" min="${min}" max="${max}" step="${step}" name="${name}" value="${value}" oninput=${updateParamsFloat} />
+            <span>${value}</span>
+          </div>
+        `
+      };
+
+      const IntField = ({ label, max, min, name, value }) => {
+        return html`
+          <div>
+            <label for="${name}">${label}</label>
+            <input type="range" id="${name}" min="${min}" max="${max}" name="${name}" value="${value}" oninput=${updateParamsInt} />
+            <span>${value}</span>
+          </div>
+        `
+      };
+
+      const BoolField = ({ label, name, value }) => {
+        return html`
+          <div>
+            <label for="${name}">${label}</label>
+            <input type="checkbox" id="${name}" name="${name}" checked="${value}" onclick=${updateParamsBool} />
+          </div>
+        `
+      };
+
+      const userTemplateReset = (e) => {
+        e.preventDefault();
+        userTemplateResetToDefaultAndApply()
+      }
+
+      const UserTemplateResetButton = () => {
+        if (selectedUserTemplate.value.name == 'default') {
+          return html`
+            <button disabled>Using default template</button>
+          `
+        }
+
+        return html`
+          <button onclick=${userTemplateReset}>Reset all to default</button>
+        `
+      };
+
+      useEffect(() => {
+        // autosave template on every change
+        userTemplateAutosave()
+      }, [session.value, params.value])
+
+      const GrammarControl = () => (
+        html`
+          <div>
+            <label for="template">Grammar</label>
+            <textarea id="grammar" name="grammar" placeholder="Use gbnf or JSON Schema+convert" value="${params.value.grammar}" rows=4 oninput=${updateParams}/>
+            <input type="text" name="prop-order" placeholder="order: prop1,prop2,prop3" oninput=${updateGrammarJsonSchemaPropOrder} />
+            <button type="button" onclick=${convertJSONSchemaGrammar}>Convert JSON Schema</button>
+          </div>
+          `
+      );
+
+      const PromptControlFieldSet = () => (
+        html`
+        <fieldset>
+          <div>
+            <label htmlFor="prompt">Prompt</label>
+            <textarea type="text" name="prompt" value="${session.value.prompt}" oninput=${updateSession}/>
+          </div>
+        </fieldset>
+        `
+      );
+
+      const ChatConfigForm = () => (
+        html`
+          ${PromptControlFieldSet()}
+
+          <fieldset class="two">
+            <div>
+              <label for="user">User name</label>
+              <input type="text" name="user" value="${session.value.user}" oninput=${updateSession} />
+            </div>
+
+            <div>
+              <label for="bot">Bot name</label>
+              <input type="text" name="char" value="${session.value.char}" oninput=${updateSession} />
+            </div>
+          </fieldset>
+
+          <fieldset>
+            <div>
+              <label for="template">Prompt template</label>
+              <textarea id="template" name="template" value="${session.value.template}" rows=4 oninput=${updateSession}/>
+            </div>
+
+            <div>
+              <label for="template">Chat history template</label>
+              <textarea id="template" name="historyTemplate" value="${session.value.historyTemplate}" rows=1 oninput=${updateSession}/>
+            </div>
+            ${GrammarControl()}
+          </fieldset>
+      `
+      );
+
+      const CompletionConfigForm = () => (
+        html`
+          ${PromptControlFieldSet()}
+          <fieldset>${GrammarControl()}</fieldset>
+        `
+      );
+
+      return html`
+        <form>
+          <fieldset class="two">
+            <${UserTemplateResetButton}/>
+            <div>
+              <label class="slim"><input type="radio" name="type" value="chat" checked=${session.value.type === "chat"} oninput=${updateSession} /> Chat</label>
+              <label class="slim"><input type="radio" name="type" value="completion" checked=${session.value.type === "completion"} oninput=${updateSession} /> Completion</label>
+            </div>
+          </fieldset>
+
+          ${session.value.type === 'chat' ? ChatConfigForm() : CompletionConfigForm()}
+
+          <fieldset class="two">
+            ${IntField({ label: "Predictions", max: 2048, min: -1, name: "n_predict", value: params.value.n_predict })}
+            ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
+            ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
+            ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
+            ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
+            ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
+            ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
+            ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}
+          </fieldset>
+          <details>
+            <summary>More options</summary>
+            <fieldset class="two">
+              ${FloatField({ label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })}
+              ${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })}
+              ${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })}
+              ${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })}
+            </fieldset>
+            <hr />
+            <fieldset class="three">
+              <div>
+                <label><input type="radio" name="mirostat" value="0" checked=${params.value.mirostat == 0} oninput=${updateParamsInt} /> no Mirostat</label>
+                <label><input type="radio" name="mirostat" value="1" checked=${params.value.mirostat == 1} oninput=${updateParamsInt} /> Mirostat v1</label>
+                <label><input type="radio" name="mirostat" value="2" checked=${params.value.mirostat == 2} oninput=${updateParamsInt} /> Mirostat v2</label>
+              </div>
+              ${FloatField({ label: "Mirostat tau", max: 10.0, min: 0.0, name: "mirostat_tau", step: 0.01, value: params.value.mirostat_tau })}
+              ${FloatField({ label: "Mirostat eta", max: 1.0, min: 0.0, name: "mirostat_eta", step: 0.01, value: params.value.mirostat_eta })}
+            </fieldset>
+            <fieldset>
+              ${IntField({ label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs })}
+            </fieldset>
+            <fieldset>
+              ${IntField({ label: "Min Probabilities from each Sampler", max: 10, min: 0, name: "min_keep", value: params.value.min_keep })}
+            </fieldset>
+            <fieldset>
+              <label for="api_key">API Key</label>
+              <input type="text" name="api_key" value="${params.value.api_key}" placeholder="Enter API key" oninput=${updateParams} />
+            </fieldset>
+          </details>
+        </form>
+      `
+    }
+
+    const probColor = (p) => {
+      const r = Math.floor(192 * (1 - p));
+      const g = Math.floor(192 * p);
+      return `rgba(${r},${g},0,0.3)`;
+    }
+
+    const Probabilities = (params) => {
+      return params.data.map(msg => {
+        const { completion_probabilities } = msg;
+        if (
+          !completion_probabilities ||
+          completion_probabilities.length === 0
+        ) return msg.content
+
+        if (completion_probabilities.length > 1) {
+          // Not for byte pair
+          if (completion_probabilities[0].content.startsWith('byte: \\')) return msg.content
+
+          const splitData = completion_probabilities.map(prob => ({
+            content: prob.content,
+            completion_probabilities: [prob]
+          }))
+          return html`<${Probabilities} data=${splitData} />`
+        }
+
+        const { probs, content } = completion_probabilities[0]
+        const found = probs.find(p => p.tok_str === msg.content)
+        const pColor = found ? probColor(found.prob) : 'transparent'
+
+        const popoverChildren = html`
+          <div class="prob-set">
+            ${probs.map((p, index) => {
+          return html`
+                <div
+                  key=${index}
+                  title=${`prob: ${p.prob}`}
+                  style=${{
+              padding: '0.3em',
+              backgroundColor: p.tok_str === content ? probColor(p.prob) : 'transparent'
+            }}
+                >
+                  <span>${p.tok_str}: </span>
+                  <span>${Math.floor(p.prob * 100)}%</span>
+                </div>
+              `
+        })}
+          </div>
+        `
+
+        return html`
+          <${Popover} style=${{ backgroundColor: pColor }} popoverChildren=${popoverChildren}>
+            ${msg.content.match(/\n/gim) ? html`<br />` : msg.content}
+          </>
+        `
+      });
+    }
+
+    // poor mans markdown replacement
+    const Markdownish = (params) => {
+      const md = params.text
+        .replace(/&/g, '&amp;')
+        .replace(/</g, '&lt;')
+        .replace(/>/g, '&gt;')
+        .replace(/^#{1,6} (.*)$/gim, '<h3>$1</h3>')
+        .replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>')
+        .replace(/__(.*?)__/g, '<strong>$1</strong>')
+        .replace(/\*(.*?)\*/g, '<em>$1</em>')
+        .replace(/_(.*?)_/g, '<em>$1</em>')
+        .replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
+        .replace(/`(.*?)`/g, '<code>$1</code>')
+        .replace(/\n/gim, '<br />');
+      return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`;
+    };
+
+    const ModelGenerationInfo = (params) => {
+      if (!llamaStats.value) {
+        return html`<span/>`
+      }
+      return html`
+        <span>
+          ${llamaStats.value.tokens_predicted} predicted, ${llamaStats.value.tokens_cached} cached, ${llamaStats.value.timings.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.timings.predicted_per_second.toFixed(2)} tokens per second
+        </span>
+      `
+    }
+
+    // simple popover impl
+    const Popover = (props) => {
+      const isOpen = useSignal(false);
+      const position = useSignal({ top: '0px', left: '0px' });
+      const buttonRef = useRef(null);
+      const popoverRef = useRef(null);
+
+      const togglePopover = () => {
+        if (buttonRef.current) {
+          const rect = buttonRef.current.getBoundingClientRect();
+          position.value = {
+            top: `${rect.bottom + window.scrollY}px`,
+            left: `${rect.left + window.scrollX}px`,
+          };
+        }
+        isOpen.value = !isOpen.value;
+      };
+
+      const handleClickOutside = (event) => {
+        if (popoverRef.current && !popoverRef.current.contains(event.target) && !buttonRef.current.contains(event.target)) {
+          isOpen.value = false;
+        }
+      };
+
+      useEffect(() => {
+        document.addEventListener('mousedown', handleClickOutside);
+        return () => {
+          document.removeEventListener('mousedown', handleClickOutside);
+        };
+      }, []);
+
+      return html`
+        <span style=${props.style} ref=${buttonRef} onClick=${togglePopover}>${props.children}</span>
+        ${isOpen.value && html`
+          <${Portal} into="#portal">
+            <div
+              ref=${popoverRef}
+              class="popover-content"
+              style=${{
+            top: position.value.top,
+            left: position.value.left,
+          }}
+            >
+              ${props.popoverChildren}
+            </div>
+          </${Portal}>
+        `}
+      `;
+    };
+
+    // Source: preact-portal (https://github.com/developit/preact-portal/blob/master/src/preact-portal.js)
+    /** Redirect rendering of descendants into the given CSS selector */
+    class Portal extends Component {
+      componentDidUpdate(props) {
+        for (let i in props) {
+          if (props[i] !== this.props[i]) {
+            return setTimeout(this.renderLayer);
+          }
+        }
+      }
+
+      componentDidMount() {
+        this.isMounted = true;
+        this.renderLayer = this.renderLayer.bind(this);
+        this.renderLayer();
+      }
+
+      componentWillUnmount() {
+        this.renderLayer(false);
+        this.isMounted = false;
+        if (this.remote && this.remote.parentNode) this.remote.parentNode.removeChild(this.remote);
+      }
+
+      findNode(node) {
+        return typeof node === 'string' ? document.querySelector(node) : node;
+      }
+
+      renderLayer(show = true) {
+        if (!this.isMounted) return;
+
+        // clean up old node if moving bases:
+        if (this.props.into !== this.intoPointer) {
+          this.intoPointer = this.props.into;
+          if (this.into && this.remote) {
+            this.remote = render(html`<${PortalProxy} />`, this.into, this.remote);
+          }
+          this.into = this.findNode(this.props.into);
+        }
+
+        this.remote = render(html`
+          <${PortalProxy} context=${this.context}>
+            ${show && this.props.children || null}
+          </${PortalProxy}>
+        `, this.into, this.remote);
+      }
+
+      render() {
+        return null;
+      }
+    }
+    // high-order component that renders its first child if it exists.
+    // used as a conditional rendering proxy.
+    class PortalProxy extends Component {
+      getChildContext() {
+        return this.props.context;
+      }
+      render({ children }) {
+        return children || null;
+      }
+    }
+
+    function App(props) {
+      useEffect(() => {
+        const query = new URLSearchParams(location.search).get("q");
+        if (query) chat(query);
+      }, []);
+
+      return html`
+        <div class="mode-${session.value.type}">
+          <header>
+            <img src="llama_cpp.png" style="width:100%"/>
+          </header>
+
+          <section id="write">
+            <${session.value.type === 'chat' ? MessageInput : CompletionControls} />
+          </section>
+
+          <main id="content">
+            <${chatStarted.value ? ChatLog : ConfigForm} />
+          </main>
+
+
+          <footer>
+            <p><${ModelGenerationInfo} /></p>
+            <p>Powered by <a href="https://github.com/ggerganov/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a>.</p>
+          </footer>
+        </div>
+      `;
+    }
+
+    render(h(App), document.querySelector('#container'));
+  </script>
+</head>
+
+<body>
+  <div id="container">
+    <input type="file" id="fileInput" accept="image/*" style="display: none;">
+  </div>
+  <div id="portal"></div>
+</body>
+
+</html>
+
diff --git a/examples/server/themes/wild/llama_cpp.png b/examples/server/themes/wild/llama_cpp.png
new file mode 100644
index 000000000..bad1dc9fc
Binary files /dev/null and b/examples/server/themes/wild/llama_cpp.png differ
diff --git a/examples/server/themes/wild/llamapattern.png b/examples/server/themes/wild/llamapattern.png
new file mode 100644
index 000000000..2a159ce6a
Binary files /dev/null and b/examples/server/themes/wild/llamapattern.png differ
diff --git a/examples/server/themes/wild/wild.png b/examples/server/themes/wild/wild.png
new file mode 100644
index 000000000..46ffa0f3e
Binary files /dev/null and b/examples/server/themes/wild/wild.png differ
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index 0ee670dba..d8a2286e4 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -1,19 +1,34 @@
 #pragma once
 
+#include "llama.h"
+#include "common.h"
+
+// Change JSON_ASSERT from assert() to GGML_ASSERT:
+#define JSON_ASSERT GGML_ASSERT
+#include "json.hpp"
+
 #include <string>
 #include <vector>
-#include <set>
-#include <mutex>
-#include <condition_variable>
-#include <unordered_map>
+#include <sstream>
+#include <random>
 
-#include "json.hpp"
+#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
 
-#include "../llava/clip.h"
+using json = nlohmann::ordered_json;
 
-using json = nlohmann::json;
+// https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11
+enum error_type {
+    ERROR_TYPE_INVALID_REQUEST,
+    ERROR_TYPE_AUTHENTICATION,
+    ERROR_TYPE_SERVER,
+    ERROR_TYPE_NOT_FOUND,
+    ERROR_TYPE_PERMISSION,
+    ERROR_TYPE_UNAVAILABLE, // custom error
+    ERROR_TYPE_NOT_SUPPORTED, // custom error
+};
 
 extern bool server_verbose;
+extern bool server_log_json;
 
 #ifndef SERVER_VERBOSE
 #define SERVER_VERBOSE 1
@@ -27,429 +42,122 @@ extern bool server_verbose;
     {                                                                    \
         if (server_verbose)                                              \
         {                                                                \
-            server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \
+            server_log("VERB", __func__, __LINE__, MSG, __VA_ARGS__); \
         }                                                                \
     } while (0)
 #endif
 
-#define LOG_ERROR(  MSG, ...) server_log("ERROR",   __func__, __LINE__, MSG, __VA_ARGS__)
-#define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__)
-#define LOG_INFO(   MSG, ...) server_log("INFO",    __func__, __LINE__, MSG, __VA_ARGS__)
+#define LOG_ERROR(  MSG, ...) server_log("ERR",  __func__, __LINE__, MSG, __VA_ARGS__)
+#define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__)
+#define LOG_INFO(   MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
 
-//
-// parallel
-//
+static inline void server_log(const char * level, const char * function, int line, const char * message, const json & extra);
 
-enum server_state {
-    SERVER_STATE_LOADING_MODEL,  // Server is starting up, model not fully loaded yet
-    SERVER_STATE_READY,          // Server is ready and model is loaded
-    SERVER_STATE_ERROR           // An error occurred, load_model failed
-};
-
-enum task_type {
-    TASK_TYPE_COMPLETION,
-    TASK_TYPE_CANCEL,
-    TASK_TYPE_NEXT_RESPONSE
-};
-
-struct task_server {
-    int id = -1; // to be filled by llama_server_queue
-    int target_id;
-    task_type type;
-    json data;
-    bool infill_mode = false;
-    bool embedding_mode = false;
-    int multitask_id = -1;
-};
-
-struct task_result {
-    int id;
-    int multitask_id = -1;
-    bool stop;
-    bool error;
-    json result_json;
-};
-
-struct task_multi {
-    int id;
-    std::set<int> subtasks_remaining{};
-    std::vector<task_result> results{};
-};
-
-// TODO: can become bool if we can't find use of more states
-enum slot_state
-{
-    IDLE,
-    PROCESSING,
-};
-
-enum slot_command
-{
-    NONE,
-    LOAD_PROMPT,
-    RELEASE,
-};
-
-struct slot_params
-{
-    bool stream       = true;
-    bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt
-
-    uint32_t seed      = -1; // RNG seed
-    int32_t  n_keep    =  0; // number of tokens to keep from initial prompt
-    int32_t  n_predict = -1; // new tokens to predict
-
-    std::vector<std::string> antiprompt;
-
-    json input_prefix;
-    json input_suffix;
-};
-
-struct slot_image
-{
-    int32_t id;
-
-    bool request_encode_image = false;
-    float * image_embedding = nullptr;
-    int32_t image_tokens = 0;
-
-    clip_image_u8 * img_data;
-
-    std::string prefix_prompt; // before of this image
-};
-
-// completion token output with probabilities
-struct completion_token_output
-{
-    struct token_prob
-    {
-        llama_token tok;
-        float prob;
-    };
-
-    std::vector<token_prob> probs;
-    llama_token tok;
-    std::string text_to_send;
-};
-
-static inline void server_log(const char *level, const char *function, int line,
-                       const char *message, const nlohmann::ordered_json &extra)
-{
-    nlohmann::ordered_json log
-    {
-        {"timestamp", time(nullptr)},
-        {"level",     level},
-        {"function",  function},
-        {"line",      line},
-        {"message",   message},
-    };
-
-    if (!extra.empty())
-    {
-        log.merge_patch(extra);
+template <typename T>
+static T json_value(const json & body, const std::string & key, const T & default_value) {
+    // Fallback null to default value
+    if (body.contains(key) && !body.at(key).is_null()) {
+        try {
+            return body.at(key);
+        } catch (NLOHMANN_JSON_NAMESPACE::detail::type_error const &) {
+            std::stringstream ss;
+            ss << "Wrong type supplied for parameter '" << key << "'. Expected '" << json(default_value).type_name() << "', using default value.";
+            LOG_WARNING(ss.str().c_str(), body);
+            return default_value;
+        }
+    } else {
+        return default_value;
     }
+}
 
-    const std::string str = log.dump(-1, ' ', false, json::error_handler_t::replace);
-    printf("%.*s\n", (int)str.size(), str.data());
+static inline void server_log(const char * level, const char * function, int line, const char * message, const json & extra) {
+    std::stringstream ss_tid;
+    ss_tid << std::this_thread::get_id();
+    json log = json{
+        {"tid",       ss_tid.str()},
+        {"timestamp", time(nullptr)},
+    };
+
+    if (server_log_json) {
+        log.merge_patch({
+            {"level",    level},
+            {"function", function},
+            {"line",     line},
+            {"msg",      message},
+        });
+
+        if (!extra.empty()) {
+            log.merge_patch(extra);
+        }
+
+        printf("%s\n", log.dump(-1, ' ', false, json::error_handler_t::replace).c_str());
+    } else {
+        char buf[1024];
+        snprintf(buf, 1024, "%4s [%24s] %s", level, function, message);
+
+        if (!extra.empty()) {
+            log.merge_patch(extra);
+        }
+        std::stringstream ss;
+        ss << buf << " |";
+        for (const auto & el : log.items())
+        {
+            const std::string value = el.value().dump(-1, ' ', false, json::error_handler_t::replace);
+            ss << " " << el.key() << "=" << value;
+        }
+
+        const std::string str = ss.str();
+        printf("%.*s\n", (int)str.size(), str.data());
+    }
     fflush(stdout);
 }
 
 //
-// server utils
+// chat template utils
 //
 
-template <typename T>
-static T json_value(const json &body, const std::string &key, const T &default_value)
-{
-    // Fallback null to default value
-    return body.contains(key) && !body.at(key).is_null()
-        ? body.value(key, default_value)
-        : default_value;
+// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
+inline bool verify_custom_template(const std::string & tmpl) {
+    llama_chat_message chat[] = {{"user", "test"}};
+    int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
+    return res >= 0;
 }
 
-inline std::string format_llama2(std::vector<json> messages)
-{
-    std::ostringstream output;
-    bool is_inside_turn = false;
+// Format given chat. If tmpl is empty, we take the template from model metadata
+inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector<json> & messages) {
+    size_t alloc_size = 0;
+    // vector holding all allocated string to be passed to llama_chat_apply_template
+    std::vector<std::string> str(messages.size() * 2);
+    std::vector<llama_chat_message> chat(messages.size());
 
-    for (auto it = messages.begin(); it != messages.end(); ++it) {
-        if (!is_inside_turn) {
-            output << "[INST] ";
-        }
-        std::string role    = json_value(*it, "role", std::string("user"));
-        std::string content = json_value(*it, "content", std::string(""));
-        if (role == "system") {
-            output << "<<SYS>>\n" << content << "\n<<SYS>>\n\n";
-            is_inside_turn = true;
-        } else if (role == "user") {
-            output << content << " [/INST]";
-            is_inside_turn = true;
-        } else {
-            output << " " << content << " </s>";
-            is_inside_turn = false;
-        }
+    for (size_t i = 0; i < messages.size(); ++i) {
+        const auto & curr_msg = messages[i];
+        str[i*2 + 0]    = json_value(curr_msg, "role",    std::string(""));
+        str[i*2 + 1]    = json_value(curr_msg, "content", std::string(""));
+        alloc_size     += str[i*2 + 1].length();
+        chat[i].role    = str[i*2 + 0].c_str();
+        chat[i].content = str[i*2 + 1].c_str();
     }
 
-    LOG_VERBOSE("format_llama2", {{"text", output.str()}});
+    const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
+    std::vector<char> buf(alloc_size * 2);
 
-    return output.str();
+    // run the first time to get the total output length
+    int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size());
+
+    // if it turns out that our buffer is too small, we resize it
+    if ((size_t) res > buf.size()) {
+        buf.resize(res);
+        res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size());
+    }
+
+    const std::string formatted_chat(buf.data(), res);
+
+    LOG_VERBOSE("formatted_chat", {{"text", formatted_chat.c_str()}});
+
+    return formatted_chat;
 }
 
-inline std::string format_chatml(std::vector<json> messages)
-{
-    std::ostringstream chatml_msgs;
-
-    for (auto it = messages.begin(); it != messages.end(); ++it) {
-        chatml_msgs << "<|im_start|>"
-                    << json_value(*it, "role",    std::string("user")) << '\n';
-        chatml_msgs << json_value(*it, "content", std::string(""))
-                    << "<|im_end|>\n";
-    }
-
-    chatml_msgs << "<|im_start|>assistant" << '\n';
-
-    LOG_VERBOSE("format_chatml", {{"text", chatml_msgs.str()}});
-
-    return chatml_msgs.str();
-}
-
-//
-// work queue utils
-//
-
-struct llama_server_queue {
-    int id = 0;
-    std::mutex mutex_tasks;
-    bool running;
-    // queues
-    std::vector<task_server> queue_tasks;
-    std::vector<task_server> queue_tasks_deferred;
-    std::vector<task_multi> queue_multitasks;
-    std::condition_variable condition_tasks;
-    // callback functions
-    std::function<void(task_server&)> callback_new_task;
-    std::function<void(task_multi&)> callback_finish_multitask;
-    std::function<void(void)> callback_all_task_finished;
-
-    // Add a new task to the end of the queue
-    int post(task_server task) {
-        std::unique_lock<std::mutex> lock(mutex_tasks);
-        if (task.id == -1) {
-            task.id = id++;
-        }
-        queue_tasks.push_back(std::move(task));
-        condition_tasks.notify_one();
-        return task.id;
-    }
-
-    // Add a new task, but defer until one slot is available
-    void defer(task_server task) {
-        std::unique_lock<std::mutex> lock(mutex_tasks);
-        queue_tasks_deferred.push_back(std::move(task));
-    }
-
-    // Get the next id for creating anew task
-    int get_new_id() {
-        std::unique_lock<std::mutex> lock(mutex_tasks);
-        return id++;
-    }
-
-    // Register function to process a new task
-    void on_new_task(std::function<void(task_server&)> callback) {
-        callback_new_task = callback;
-    }
-
-    // Register function to process a multitask
-    void on_finish_multitask(std::function<void(task_multi&)> callback) {
-        callback_finish_multitask = callback;
-    }
-
-    // Register the function to be called when the batch of tasks is finished
-    void on_all_tasks_finished(std::function<void(void)> callback) {
-        callback_all_task_finished = callback;
-    }
-
-    // Call when the state of one slot is changed
-    void notify_slot_changed() {
-        // move deferred tasks back to main loop
-        std::unique_lock<std::mutex> lock(mutex_tasks);
-        for (auto & task : queue_tasks_deferred) {
-            queue_tasks.push_back(std::move(task));
-        }
-        queue_tasks_deferred.clear();
-    }
-
-    // end the start_loop routine
-    void terminate() {
-        {
-            std::unique_lock<std::mutex> lock(mutex_tasks);
-            running = false;
-        }
-        condition_tasks.notify_all();
-    }
-
-    // Start the main loop.
-    void start_loop() {
-        running = true;
-        while (true) {
-            // new task arrived
-            LOG_VERBOSE("have new task", {});
-            {
-                while (true)
-                {
-                    std::unique_lock<std::mutex> lock(mutex_tasks);
-                    if (queue_tasks.empty()) {
-                        lock.unlock();
-                        break;
-                    }
-                    task_server task = queue_tasks.front();
-                    queue_tasks.erase(queue_tasks.begin());
-                    lock.unlock();
-                    LOG_VERBOSE("callback_new_task", {});
-                    callback_new_task(task);
-                }
-                LOG_VERBOSE("callback_all_task_finished", {});
-                // process and update all the multitasks
-                auto queue_iterator = queue_multitasks.begin();
-                while (queue_iterator != queue_multitasks.end())
-                {
-                    if (queue_iterator->subtasks_remaining.empty())
-                    {
-                        // all subtasks done == multitask is done
-                        task_multi current_multitask = *queue_iterator;
-                        callback_finish_multitask(current_multitask);
-                        // remove this multitask
-                        queue_iterator = queue_multitasks.erase(queue_iterator);
-                    }
-                    else
-                    {
-                        ++queue_iterator;
-                    }
-                }
-                // all tasks in the current loop is finished
-                callback_all_task_finished();
-            }
-            LOG_VERBOSE("wait for new task", {});
-            // wait for new task
-            {
-                std::unique_lock<std::mutex> lock(mutex_tasks);
-                if (queue_tasks.empty()) {
-                    if (!running) {
-                        LOG_VERBOSE("ending start_loop", {});
-                        return;
-                    }
-                    condition_tasks.wait(lock, [&]{
-                        return (!queue_tasks.empty() || !running);
-                    });
-                }
-            }
-        }
-    }
-
-    //
-    // functions to manage multitasks
-    //
-
-    // add a multitask by specifying the id of all subtask (subtask is a task_server)
-    void add_multitask(int multitask_id, std::vector<int>& sub_ids)
-    {
-        std::lock_guard<std::mutex> lock(mutex_tasks);
-        task_multi multi;
-        multi.id = multitask_id;
-        std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end()));
-        queue_multitasks.push_back(multi);
-    }
-
-    // updatethe remaining subtasks, while appending results to multitask
-    void update_multitask(int multitask_id, int subtask_id, task_result& result)
-    {
-        std::lock_guard<std::mutex> lock(mutex_tasks);
-        for (auto& multitask : queue_multitasks)
-        {
-            if (multitask.id == multitask_id)
-            {
-                multitask.subtasks_remaining.erase(subtask_id);
-                multitask.results.push_back(result);
-            }
-        }
-    }
-};
-
-struct llama_server_response {
-    typedef std::function<void(int, int, task_result&)> callback_multitask_t;
-    callback_multitask_t callback_update_multitask;
-    // for keeping track of all tasks waiting for the result
-    std::set<int> waiting_task_ids;
-    // the main result queue
-    std::vector<task_result> queue_results;
-    std::mutex mutex_results;
-    std::condition_variable condition_results;
-
-    void add_waiting_task_id(int task_id) {
-        std::unique_lock<std::mutex> lock(mutex_results);
-        waiting_task_ids.insert(task_id);
-    }
-
-    void remove_waiting_task_id(int task_id) {
-        std::unique_lock<std::mutex> lock(mutex_results);
-        waiting_task_ids.erase(task_id);
-    }
-
-    // This function blocks the thread until there is a response for this task_id
-    task_result recv(int task_id) {
-        while (true)
-        {
-            std::unique_lock<std::mutex> lock(mutex_results);
-            condition_results.wait(lock, [&]{
-                return !queue_results.empty();
-            });
-            LOG_VERBOSE("condition_results unblock", {});
-
-            for (int i = 0; i < (int) queue_results.size(); i++)
-            {
-                if (queue_results[i].id == task_id)
-                {
-                    assert(queue_results[i].multitask_id == -1);
-                    task_result res = queue_results[i];
-                    queue_results.erase(queue_results.begin() + i);
-                    return res;
-                }
-            }
-        }
-
-        // should never reach here
-    }
-
-    // Register the function to update multitask
-    void on_multitask_update(callback_multitask_t callback) {
-        callback_update_multitask = callback;
-    }
-
-    // Send a new result to a waiting task_id
-    void send(task_result result) {
-        std::unique_lock<std::mutex> lock(mutex_results);
-        LOG_VERBOSE("send new result", {});
-        for (auto& task_id : waiting_task_ids) {
-            // LOG_TEE("waiting task id %i \n", task_id);
-            // for now, tasks that have associated parent multitasks just get erased once multitask picks up the result
-            if (result.multitask_id == task_id)
-            {
-                LOG_VERBOSE("callback_update_multitask", {});
-                callback_update_multitask(task_id, result.id, result);
-                continue;
-            }
-
-            if (result.id == task_id)
-            {
-                LOG_VERBOSE("queue_results.push_back", {});
-                queue_results.push_back(result);
-                condition_results.notify_one();
-                return;
-            }
-        }
-    }
-};
-
 //
 // base64 utils (TODO: move to common in the future)
 //
@@ -459,13 +167,11 @@ static const std::string base64_chars =
              "abcdefghijklmnopqrstuvwxyz"
              "0123456789+/";
 
-static inline bool is_base64(uint8_t c)
-{
+static inline bool is_base64(uint8_t c) {
     return (isalnum(c) || (c == '+') || (c == '/'));
 }
 
-static inline std::vector<uint8_t> base64_decode(const std::string & encoded_string)
-{
+static inline std::vector<uint8_t> base64_decode(const std::string & encoded_string) {
     int i = 0;
     int j = 0;
     int in_ = 0;
@@ -477,13 +183,10 @@ static inline std::vector<uint8_t> base64_decode(const std::string & encoded_str
 
     std::vector<uint8_t> ret;
 
-    while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_]))
-    {
+    while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) {
         char_array_4[i++] = encoded_string[in_]; in_++;
-        if (i == 4)
-        {
-            for (i = 0; i <4; i++)
-            {
+        if (i == 4) {
+            for (i = 0; i < 4; i++) {
                 char_array_4[i] = base64_chars.find(char_array_4[i]);
             }
 
@@ -491,23 +194,20 @@ static inline std::vector<uint8_t> base64_decode(const std::string & encoded_str
             char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
             char_array_3[2] = ((char_array_4[2] & 0x3) << 6) +   char_array_4[3];
 
-            for (i = 0; (i < 3); i++)
-            {
+            for (i = 0; (i < 3); i++) {
                 ret.push_back(char_array_3[i]);
             }
+
             i = 0;
         }
     }
 
-    if (i)
-    {
-        for (j = i; j <4; j++)
-        {
+    if (i) {
+        for (j = i; j < 4; j++) {
             char_array_4[j] = 0;
         }
 
-        for (j = 0; j <4; j++)
-        {
+        for (j = 0; j < 4; j++) {
             char_array_4[j] = base64_chars.find(char_array_4[j]);
         }
 
@@ -515,8 +215,7 @@ static inline std::vector<uint8_t> base64_decode(const std::string & encoded_str
         char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
         char_array_3[2] = ((char_array_4[2] & 0x3) << 6) +   char_array_4[3];
 
-        for (j = 0; (j < i - 1); j++)
-        {
+        for (j = 0; j < i - 1; j++) {
             ret.push_back(char_array_3[j]);
         }
     }
@@ -528,8 +227,7 @@ static inline std::vector<uint8_t> base64_decode(const std::string & encoded_str
 // random string / id
 //
 
-static std::string random_string()
-{
+static std::string random_string() {
     static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz");
 
     std::random_device rd;
@@ -544,9 +242,414 @@ static std::string random_string()
     return result;
 }
 
-static std::string gen_chatcmplid()
-{
+static std::string gen_chatcmplid() {
     std::stringstream chatcmplid;
     chatcmplid << "chatcmpl-" << random_string();
+
     return chatcmplid.str();
 }
+
+//
+// other common utils
+//
+
+static size_t common_part(const std::vector<llama_token> & a, const std::vector<llama_token> & b) {
+    size_t i;
+    for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {}
+
+    return i;
+}
+
+static bool ends_with(const std::string & str, const std::string & suffix) {
+    return str.size() >= suffix.size() && 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix);
+}
+
+static size_t find_partial_stop_string(const std::string &stop, const std::string &text) {
+    if (!text.empty() && !stop.empty()) {
+        const char text_last_char = text.back();
+        for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) {
+            if (stop[char_index] == text_last_char) {
+                const std::string current_partial = stop.substr(0, char_index + 1);
+                if (ends_with(text, current_partial)) {
+                    return text.size() - char_index - 1;
+                }
+            }
+        }
+    }
+
+    return std::string::npos;
+}
+
+// TODO: reuse llama_detokenize
+template <class Iter>
+static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
+    std::string ret;
+    for (; begin != end; ++begin) {
+        ret += llama_token_to_piece(ctx, *begin);
+    }
+
+    return ret;
+}
+
+// format incomplete utf-8 multibyte character for output
+static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) {
+    std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token);
+
+    // if the size is 1 and first bit is 1, meaning it's a partial character
+    //   (size > 1 meaning it's already a known token)
+    if (out.size() == 1 && (out[0] & 0x80) == 0x80) {
+        std::stringstream ss;
+        ss << std::hex << (out[0] & 0xff);
+        std::string res(ss.str());
+        out = "byte: \\x" + res;
+    }
+
+    return out;
+}
+
+struct completion_token_output {
+    llama_token tok;
+    std::string text_to_send;
+
+    struct token_prob {
+        llama_token tok;
+        float prob;
+    };
+
+    std::vector<token_prob> probs;
+};
+
+// convert a vector of completion_token_output to json
+static json probs_vector_to_json(const llama_context * ctx, const std::vector<completion_token_output> & probs) {
+    json out = json::array();
+
+    for (const auto & prob : probs) {
+        json probs_for_token = json::array();
+
+        for (const auto & p : prob.probs) {
+            const std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok);
+            probs_for_token.push_back(json {
+                {"tok_str", tok_str},
+                {"prob",    p.prob},
+            });
+        }
+
+        const std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok);
+        out.push_back(json {
+            {"content", tok_str},
+            {"probs",   probs_for_token},
+        });
+    }
+
+    return out;
+}
+
+//
+// OAI utils
+//
+
+static json oaicompat_completion_params_parse(
+    const struct llama_model * model,
+    const json & body, /* openai api json semantics */
+    const std::string & chat_template) {
+    json llama_params;
+
+    llama_params["__oaicompat"] = true;
+
+    // Map OpenAI parameters to llama.cpp parameters
+    //
+    // For parameters that are defined by the OpenAI documentation (e.g.
+    // temperature), we explicitly specify OpenAI's intended default; we
+    // need to do that because sometimes OpenAI disagrees with llama.cpp
+    //
+    // https://platform.openai.com/docs/api-reference/chat/create
+    llama_sampling_params default_sparams;
+    llama_params["model"]             = json_value(body,   "model",             std::string("unknown"));
+    llama_params["frequency_penalty"] = json_value(body,   "frequency_penalty", 0.0);
+    llama_params["logit_bias"]        = json_value(body,   "logit_bias",        json::object());
+    llama_params["n_predict"]         = json_value(body,   "max_tokens",        -1);
+    llama_params["presence_penalty"]  = json_value(body,   "presence_penalty",  0.0);
+    llama_params["seed"]              = json_value(body,   "seed",              LLAMA_DEFAULT_SEED);
+    llama_params["stream"]            = json_value(body,   "stream",            false);
+    llama_params["temperature"]       = json_value(body,   "temperature",       1.0);
+    llama_params["top_p"]             = json_value(body,   "top_p",             1.0);
+
+    // Apply chat template to the list of messages
+    llama_params["prompt"] = format_chat(model, chat_template, body.at("messages"));
+
+    // Handle "stop" field
+    if (body.contains("stop") && body.at("stop").is_string()) {
+        llama_params["stop"] = json::array({body.at("stop").get<std::string>()});
+    } else {
+        llama_params["stop"] = json_value(body, "stop", json::array());
+    }
+
+    // Handle "response_format" field
+    if (body.contains("response_format")) {
+        json response_format      = json_value(body, "response_format", json::object());
+        std::string response_type = json_value(response_format, "type", std::string());
+        if (response_type == "json_object") {
+            llama_params["json_schema"] = json_value(response_format, "schema", json::object());
+        } else if (!response_type.empty() && response_type != "text") {
+            throw std::runtime_error("response_format type must be one of \"text\" or \"json_object\", but got: " + response_type);
+        }
+    }
+
+    // Handle "n" field
+    int n_choices = json_value(body, "n", 1);
+    if (n_choices != 1) {
+        throw std::runtime_error("Only one completion choice is allowed");
+    }
+
+    // Handle "logprobs" field
+    // TODO: The response format of this option is not yet OAI-compatible, but seems like no one really using it; We may need to fix it in the future
+    if (body.contains("logprobs")) {
+        llama_params["n_probs"] = json_value(body, "top_logprobs", 20);
+    } else if (body.contains("top_logprobs")) {
+        throw std::runtime_error("top_logprobs requires logprobs to be set to true");
+    }
+
+    // Params supported by OAI but unsupported by llama.cpp
+    static const std::vector<std::string> unsupported_params { "tools", "tool_choice" };
+    for (auto & param : unsupported_params) {
+        if (body.contains(param)) {
+            throw std::runtime_error("Unsupported param: " + param);
+        }
+    }
+
+    // Copy remaining properties to llama_params
+    // This allows user to use llama.cpp-specific params like "mirostat", "tfs_z",... via OAI endpoint.
+    // See "launch_slot_with_task()" for a complete list of params supported by llama.cpp
+    for (const auto & item : body.items()) {
+        // Exception: if "n_predict" is present, we overwrite the value specified earlier by "max_tokens"
+        if (!llama_params.contains(item.key()) || item.key() == "n_predict") {
+            llama_params[item.key()] = item.value();
+        }
+    }
+
+    return llama_params;
+}
+
+static json format_final_response_oaicompat(const json & request, json result, const std::string & completion_id, bool streaming = false) {
+    bool stopped_word        = result.count("stopped_word") != 0;
+    bool stopped_eos         = json_value(result, "stopped_eos", false);
+    int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
+    int num_prompt_tokens    = json_value(result, "tokens_evaluated", 0);
+    std::string content      = json_value(result, "content", std::string(""));
+
+    std::string finish_reason = "length";
+    if (stopped_word || stopped_eos) {
+        finish_reason = "stop";
+    }
+
+    json choices =
+        streaming ? json::array({json{{"finish_reason", finish_reason},
+                                        {"index", 0},
+                                        {"delta", json::object()}}})
+                  : json::array({json{{"finish_reason", finish_reason},
+                                        {"index", 0},
+                                        {"message", json{{"content", content},
+                                                         {"role", "assistant"}}}}});
+
+    std::time_t t = std::time(0);
+
+    json res = json {
+        {"choices", choices},
+        {"created", t},
+        {"model",
+            json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
+        {"object", streaming ? "chat.completion.chunk" : "chat.completion"},
+        {"usage", json {
+            {"completion_tokens", num_tokens_predicted},
+            {"prompt_tokens",     num_prompt_tokens},
+            {"total_tokens",      num_tokens_predicted + num_prompt_tokens}
+        }},
+        {"id", completion_id}
+    };
+
+    if (server_verbose) {
+        res["__verbose"] = result;
+    }
+
+    if (result.contains("completion_probabilities")) {
+        res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array());
+    }
+
+    return res;
+}
+
+// return value is vector as there is one case where we might need to generate two responses
+static std::vector<json> format_partial_response_oaicompat(json result, const std::string & completion_id) {
+    if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
+        return std::vector<json>({result});
+    }
+
+    bool first = json_value(result, "oaicompat_token_ctr", 0) == 0;
+    std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
+
+    bool stopped_word   = json_value(result, "stopped_word",  false);
+    bool stopped_eos    = json_value(result, "stopped_eos",   false);
+    bool stopped_limit  = json_value(result, "stopped_limit", false);
+    std::string content = json_value(result, "content",       std::string(""));
+
+    std::string finish_reason;
+    if (stopped_word || stopped_eos) {
+        finish_reason = "stop";
+    }
+    if (stopped_limit) {
+        finish_reason = "length";
+    }
+
+    std::time_t t = std::time(0);
+
+    json choices;
+
+    if (!finish_reason.empty()) {
+        choices = json::array({json{{"finish_reason", finish_reason},
+                                    {"index", 0},
+                                    {"delta", json::object()}}});
+    } else {
+        if (first) {
+            if (content.empty()) {
+                choices = json::array({json{{"finish_reason", nullptr},
+                                            {"index", 0},
+                                            {"delta", json{{"role", "assistant"}}}}});
+            } else {
+                // We have to send this as two updates to conform to openai behavior
+                json initial_ret = json{{"choices", json::array({json{
+                                        {"finish_reason", nullptr},
+                                        {"index", 0},
+                                        {"delta", json{
+                                            {"role", "assistant"}
+                                        }}}})},
+                            {"created", t},
+                            {"id", completion_id},
+                            {"model", modelname},
+                            {"object", "chat.completion.chunk"}};
+
+                json second_ret = json{
+                            {"choices", json::array({json{{"finish_reason", nullptr},
+                                                            {"index", 0},
+                                                            {"delta", json{
+                                                            {"content", content}}}
+                                                            }})},
+                            {"created", t},
+                            {"id", completion_id},
+                            {"model", modelname},
+                            {"object", "chat.completion.chunk"}};
+
+                return std::vector<json>({initial_ret, second_ret});
+            }
+        } else {
+            // Some idiosyncrasy in task processing logic makes several trailing calls
+            // with empty content, we ignore these at the calee site.
+            if (content.empty()) {
+                return std::vector<json>({json::object()});
+            }
+
+            choices = json::array({json{
+                {"finish_reason", nullptr},
+                {"index", 0},
+                {"delta",
+                json{
+                    {"content", content},
+                }},
+            }});
+        }
+    }
+
+    json ret = json {
+        {"choices", choices},
+        {"created", t},
+        {"id",      completion_id},
+        {"model",   modelname},
+        {"object",  "chat.completion.chunk"}
+    };
+    if (!finish_reason.empty()) {
+        int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
+        int num_prompt_tokens    = json_value(result, "tokens_evaluated", 0);
+        ret.push_back({"usage", json {
+            {"completion_tokens", num_tokens_predicted},
+            {"prompt_tokens",     num_prompt_tokens},
+            {"total_tokens",      num_tokens_predicted + num_prompt_tokens}
+        }});
+    }
+
+    return std::vector<json>({ret});
+}
+
+static json format_embeddings_response_oaicompat(const json & request, const json & embeddings) {
+    json data = json::array();
+    int i = 0;
+    for (auto & elem : embeddings) {
+        data.push_back(json{
+            {"embedding", json_value(elem, "embedding", json::array())},
+            {"index",     i++},
+            {"object",    "embedding"}
+        });
+    }
+
+    json res = json {
+        {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
+        {"object", "list"},
+        {"usage", json {
+            {"prompt_tokens", 0},
+            {"total_tokens", 0}
+        }},
+        {"data", data}
+    };
+
+    return res;
+}
+
+static json format_tokenizer_response(const std::vector<llama_token> & tokens) {
+    return json {
+        {"tokens", tokens}
+    };
+}
+
+static json format_detokenized_response(const std::string & content) {
+    return json {
+        {"content", content}
+    };
+}
+
+static json format_error_response(const std::string & message, const enum error_type type) {
+    std::string type_str;
+    int code = 500;
+    switch (type) {
+        case ERROR_TYPE_INVALID_REQUEST:
+            type_str = "invalid_request_error";
+            code = 400;
+            break;
+        case ERROR_TYPE_AUTHENTICATION:
+            type_str = "authentication_error";
+            code = 401;
+            break;
+        case ERROR_TYPE_NOT_FOUND:
+            type_str = "not_found_error";
+            code = 404;
+            break;
+        case ERROR_TYPE_SERVER:
+            type_str = "server_error";
+            code = 500;
+            break;
+        case ERROR_TYPE_PERMISSION:
+            type_str = "permission_error";
+            code = 403;
+            break;
+        case ERROR_TYPE_NOT_SUPPORTED:
+            type_str = "not_supported_error";
+            code = 501;
+            break;
+        case ERROR_TYPE_UNAVAILABLE:
+            type_str = "unavailable_error";
+            code = 503;
+            break;
+    }
+    return json {
+        {"code", code},
+        {"message", message},
+        {"type", type_str},
+    };
+}
diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp
index 39e2d8ea4..b0f8e0fdc 100644
--- a/examples/simple/simple.cpp
+++ b/examples/simple/simple.cpp
@@ -133,8 +133,8 @@ int main(int argc, char ** argv) {
             // sample the most likely token
             const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
 
-            // is it an end of stream?
-            if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
+            // is it an end of generation?
+            if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
                 LOG_TEE("\n");
 
                 break;
diff --git a/examples/speculative/README.md b/examples/speculative/README.md
index 814efa592..a6608c5fe 100644
--- a/examples/speculative/README.md
+++ b/examples/speculative/README.md
@@ -6,3 +6,4 @@ More info:
 
 - https://github.com/ggerganov/llama.cpp/pull/2926
 - https://github.com/ggerganov/llama.cpp/pull/3624
+- https://github.com/ggerganov/llama.cpp/pull/5625
diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp
index 3848791d4..12e46fbc9 100644
--- a/examples/speculative/speculative.cpp
+++ b/examples/speculative/speculative.cpp
@@ -5,6 +5,7 @@
 #include <cstdio>
 #include <string>
 #include <vector>
+#include <set>
 
 #define SPEC_VOCAB_MAX_SIZE_DIFFERENCE  100
 #define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
@@ -18,6 +19,7 @@ struct seq_draft {
     std::vector<int> i_batch_tgt;
 
     std::vector<llama_token> tokens;
+    std::vector<std::vector<llama_token_data>> dists;
 
     struct llama_sampling_context * ctx_sampling;
 };
@@ -37,12 +39,15 @@ int main(int argc, char ** argv) {
     // max number of parallel drafting sequences (i.e. tree branches)
     const int n_seq_dft = params.n_parallel;
 
-    // probability threshold for accepting a token from the draft model
-    const float p_accept = params.p_accept;
-
     // probability threshold for splitting a draft branch (only for n_seq_dft > 1)
     const float p_split  = params.p_split;
 
+    if (params.seed == LLAMA_DEFAULT_SEED) {
+        params.seed = time(NULL);
+    }
+    std::default_random_engine rng(params.seed);
+    std::uniform_real_distribution<> u_dist;
+
 #ifndef LOG_DISABLE_LOGS
     log_set_target(log_filename_generator("speculative", "log"));
     LOG_TEE("Log start\n");
@@ -60,7 +65,6 @@ int main(int argc, char ** argv) {
     llama_context * ctx_dft = NULL;
 
     // load the target model
-    params.logits_all = true;
     std::tie(model_tgt, ctx_tgt) = llama_init_from_gpt_params(params);
 
     // load the draft model
@@ -72,6 +76,28 @@ int main(int argc, char ** argv) {
     params.n_threads_batch = params.n_threads_batch_draft;
     std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params);
 
+    const bool vocab_type_tgt = llama_vocab_type(model_tgt);
+    LOG("vocab_type tgt: %d\n", vocab_type_tgt);
+
+    const bool vocab_type_dft = llama_vocab_type(model_dft);
+    LOG("vocab_type dft: %d\n", vocab_type_dft);
+
+    if (vocab_type_tgt != vocab_type_dft) {
+        fprintf(stderr, "%s: error: draft model vocab type must match target model to use speculation but ", __func__);
+        fprintf(stderr, "vocab_type_dft = %d while vocab_type_tgt = %d\n", vocab_type_dft, vocab_type_tgt);
+        return 1;
+    }
+
+    if (
+        llama_add_bos_token(model_tgt) != llama_add_bos_token(model_dft) ||
+        llama_add_eos_token(model_tgt) != llama_add_eos_token(model_dft) ||
+        llama_token_bos(model_tgt) != llama_token_bos(model_dft) ||
+        llama_token_eos(model_tgt) != llama_token_eos(model_dft)
+    ) {
+        fprintf(stderr, "%s: error: draft model special tokens must match target model to use speculation\n", __func__);
+        return 1;
+    }
+
     {
         const int n_vocab_tgt = llama_n_vocab(model_tgt);
         const int n_vocab_dft = llama_n_vocab(model_dft);
@@ -101,20 +127,8 @@ int main(int argc, char ** argv) {
 
 
     // Tokenize the prompt
-    const bool add_bos_tgt = llama_should_add_bos_token(model_tgt);
-    LOG("add_bos tgt: %d\n", add_bos_tgt);
-
-    const bool add_bos_dft = llama_should_add_bos_token(model_dft);
-    LOG("add_bos dft: %d\n", add_bos_dft);
-
-    if (add_bos_tgt != add_bos_dft) {
-        fprintf(stderr, "%s: error: draft model add_bos must match target model to use speculation but ", __func__);
-        fprintf(stderr, "add_bos_dft = %d while add_bos_tgt = %d\n", add_bos_dft, add_bos_tgt);
-        return 1;
-    }
-
     std::vector<llama_token> inp;
-    inp = ::llama_tokenize(ctx_tgt, params.prompt, add_bos_tgt, true);
+    inp = ::llama_tokenize(ctx_tgt, params.prompt, true, true);
 
     const int max_context_size     = llama_n_ctx(ctx_tgt);
     const int max_tokens_list_size = max_context_size - 4;
@@ -166,7 +180,9 @@ int main(int argc, char ** argv) {
     std::vector<seq_draft> drafts(n_seq_dft);
 
     params.sparams.grammar.clear(); // the draft samplers will copy the target sampler's grammar
-    params.sparams.temp = -1.0f;    // force greedy sampling with probs for the draft model
+    if (params.sparams.temp == 0) {
+        params.sparams.temp = -1.0f; // force greedy sampling with probs for the draft model
+    }
 
     for (int s = 0; s < n_seq_dft; ++s) {
         drafts[s].ctx_sampling = llama_sampling_init(params.sparams);
@@ -182,12 +198,15 @@ int main(int argc, char ** argv) {
     drafts[0].i_batch_tgt[0] = 0;
 
     while (true) {
+        std::set<int> active_seqs = {};
+
         // print current draft sequences
         for (int s = 0; s < n_seq_dft; ++s) {
             if (!drafts[s].active) {
                 continue;
             }
 
+            active_seqs.insert(s);
             const auto & tokens = drafts[s].tokens;
 
             LOG("draft %d: %s\n", s, LOG_TOKENS_TOSTR_PRETTY(ctx_dft, tokens).c_str());
@@ -196,48 +215,157 @@ int main(int argc, char ** argv) {
         int i_dft  = 0;
         int s_keep = 0;
 
+        llama_token token_id;
+        std::string token_str;
+
+        // loop until we fail to accept a drafted token or we run out of drafted tokens
         while (true) {
-            LOG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]);
-
-            // sample from the target model
-            llama_token id = llama_sampling_sample(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]);
-
-            llama_sampling_accept(ctx_sampling, ctx_tgt, id, true);
-
-            //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, ctx_sampling->prev).c_str());
-
-            const std::string token_str = llama_token_to_piece(ctx_tgt, id);
-
-            if (!params.use_color) {
-                printf("%s", token_str.c_str());
-            }
-
-            if (id == llama_token_eos(model_tgt)) {
-                has_eos = true;
-            }
-
-            ++n_predict;
 
             // check if the target token matches any of the drafts
+            // for stochastic sampling, attempt to match the token with the drafted tokens
             {
-                bool matches = false;
+                bool accept = false;
+                if (params.sparams.temp > 0) {
+                    // stochastic verification
 
-                for (int s = 0; s < n_seq_dft; ++s) {
-                    if (!drafts[s].active) {
-                        continue;
+                    llama_token_data_array dist_tgt = llama_sampling_prepare(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft], true, NULL);
+                    llama_sample_softmax(ctx_tgt, &dist_tgt);
+                    float p_tgt = 0, p_dft = 0;
+
+                    // GGML_ASSERT(dist_tgt.size() == dist_dft.size());
+
+                    while (active_seqs.size() > 0) {
+                        // randomly select a sequence to verify from active sequences
+                        std::uniform_int_distribution<unsigned int> u_int_dist(0, active_seqs.size() - 1);
+                        int s = *std::next(active_seqs.begin(), u_int_dist(rng));
+                        if (i_dft >= (int) drafts[s].tokens.size()) {
+                            drafts[s].active = false;
+                            active_seqs.erase(s);
+                            continue;
+                        }
+                        if (accept) {
+                            // if we already accepted a token, we can skip the rest
+                            if (drafts[s].tokens[i_dft] != drafts[s_keep].tokens[i_dft]) {
+                                drafts[s].active = false;
+                                active_seqs.erase(s);
+                            }
+                            continue;
+                        }
+                        LOG("verifying sequence #%d at pos #%d from %d active sequence(s)\n", s, i_dft, (int) active_seqs.size());
+                        float r = u_dist(rng);
+                        llama_token_data_array dist_dft = { drafts[s].dists[i_dft].data() , drafts[s].dists[i_dft].size(), true };
+                        // acquire the token probabilities assigned by the draft and target models
+                        for (size_t i = 0; i < dist_tgt.size; i++) {
+                            if (dist_tgt.data[i].id == drafts[s].tokens[i_dft]) {
+                                p_tgt = dist_tgt.data[i].p;
+                            }
+                            if (dist_dft.data[i].id == drafts[s].tokens[i_dft]) {
+                                p_dft = dist_dft.data[i].p;
+                            }
+                            if (p_tgt && p_dft) {
+                                break;
+                            }
+                        }
+                        LOG("r = %f, p_dft = %f, p_tgt = %f\n", r, p_dft, p_tgt);
+                        if (r <= p_tgt / p_dft) {
+                            s_keep = s;
+                            accept = true;
+                            token_id = drafts[s].tokens[i_dft];
+                            token_str = llama_token_to_piece(ctx_tgt, token_id);
+                            llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true);
+
+                            LOG("draft token %d of sequence %d (%d, '%s') accepted\n", i_dft, s, token_id, token_str.c_str());
+                            break;
+                        } else {
+                            LOG("draft token %d of sequence %d (%d, '%s') rejected\n", i_dft, s, drafts[s].tokens[i_dft], llama_token_to_piece(ctx_tgt, drafts[s].tokens[i_dft]).c_str());
+                            drafts[s].active = false;
+
+                            // calculate residual probability
+                            GGML_ASSERT(dist_tgt.sorted);
+                            GGML_ASSERT(dist_dft.sorted);
+                            float sum_probs = 0.0f;
+
+                            // sort dist by id
+                            std::sort(dist_tgt.data, dist_tgt.data + dist_tgt.size, [](const llama_token_data &a, const llama_token_data &b) {
+                                return a.id < b.id;
+                            });
+                            std::sort(dist_dft.data, dist_dft.data + dist_dft.size, [](const llama_token_data &a, const llama_token_data &b) {
+                                return a.id < b.id;
+                            });
+
+                            for (size_t i = 0; i < dist_tgt.size; i++) {
+                                dist_tgt.data[i].p = std::max(0.0f, dist_tgt.data[i].p - dist_dft.data[i].p);
+                                sum_probs += dist_tgt.data[i].p;
+                            }
+                            for (size_t i = 0; i < dist_tgt.size; i++) {
+                                dist_tgt.data[i].p /= sum_probs;
+                            }
+
+                            // sort dist_tgt by p desc
+                            std::sort(dist_tgt.data, dist_tgt.data + dist_tgt.size, [](const llama_token_data &a, const llama_token_data &b) {
+                                return a.p > b.p;
+                            });
+                        }
+
+                        active_seqs.erase(s);
+                        for(int i = 0; i < n_seq_dft; i++) {
+                            if (i == s) {
+                                continue;
+                            }
+                            if (drafts[i].tokens[i_dft] == drafts[s].tokens[i_dft]) {
+                                // synchronize active status for sequences with the same drafted token
+                                drafts[i].active = drafts[i].active && accept;
+                                if (!drafts[i].active) {
+                                    active_seqs.erase(s);
+                                }
+                            }
+                        }
                     }
 
-                    if (i_dft < (int) drafts[s].tokens.size() && id == drafts[s].tokens[i_dft]) {
-                        LOG("the sampled target token matches the %dth drafted token of sequence %d (%d, '%s') - accepted\n", i_dft, s, id, token_str.c_str());
+                    if (!accept) {
+                        // all drafted tokens were rejected
+                        // sample from the target model
+                        LOG("all drafted tokens were rejected, sampling from residual distribution\n");
+                        token_id = llama_sample_token(ctx_tgt, &dist_tgt);
+                        llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true);
+                        token_str = llama_token_to_piece(ctx_tgt, token_id);
+                    }
 
-                        s_keep = s;
-                        matches = true;
-                    } else {
-                        drafts[s].active = false;
+                } else {
+                    // greedy verification
+
+                    // sample from the target model
+                    LOG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]);
+                    token_id = llama_sampling_sample(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]);
+
+                    llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true);
+
+                    //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, ctx_sampling->prev).c_str());
+
+                    token_str = llama_token_to_piece(ctx_tgt, token_id);
+
+                    for (int s = 0; s < n_seq_dft; ++s) {
+                        if (!drafts[s].active) {
+                            continue;
+                        }
+
+                        if (i_dft < (int) drafts[s].tokens.size() && token_id == drafts[s].tokens[i_dft]) {
+                            LOG("the sampled target token matches the %dth drafted token of sequence %d (%d, '%s') - accepted\n", i_dft, s, token_id, token_str.c_str());
+
+                            s_keep = s;
+                            accept = true;
+                        } else {
+                            drafts[s].active = false;
+                        }
                     }
                 }
 
-                if (matches) {
+                if (llama_token_is_eog(model_tgt, token_id)) {
+                    has_eos = true;
+                }
+                ++n_predict;
+
+                if (accept) {
                     ++n_accept;
                     ++n_past_tgt;
                     ++n_past_dft;
@@ -245,17 +373,21 @@ int main(int argc, char ** argv) {
                     if (params.use_color) {
                         // Color token according to its origin sequence
                         printf("\u001b[%dm%s\u001b[37m", (36 - s_keep % 6), token_str.c_str());
-                        fflush(stdout);
+                    } else {
+                        printf("%s", token_str.c_str());
                     }
+                    fflush(stdout);
                     continue;
+                } else {
+                    printf("%s", token_str.c_str());
+                    fflush(stdout);
+                    break;
                 }
             }
-            if (params.use_color) {
-                printf("%s", token_str.c_str());
-            }
-            fflush(stdout);
+        }
 
-            LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str());
+        {
+            LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", token_id, token_str.c_str());
 
             // TODO: simplify
             {
@@ -275,21 +407,21 @@ int main(int argc, char ** argv) {
                 drafts[s].active = false;
                 drafts[s].tokens.clear();
                 drafts[s].i_batch_tgt.clear();
+                drafts[s].dists.clear();
             }
             // note: will be erased after the speculation phase
-            drafts[0].tokens.push_back(id);
+            drafts[0].tokens.push_back(token_id);
+            drafts[0].dists.push_back(std::vector<llama_token_data>());
             drafts[0].i_batch_tgt.push_back(0);
 
             llama_batch_clear(batch_dft);
-            llama_batch_add  (batch_dft, id, n_past_dft, { 0 }, true);
+            llama_batch_add  (batch_dft, token_id, n_past_dft, { 0 }, true);
 
             llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1);
             // LOG("dft batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_dft, batch_dft).c_str());
-            llama_decode         (ctx_dft, batch_dft);
+            llama_decode(ctx_dft, batch_dft);
 
             ++n_past_dft;
-
-            break;
         }
 
         if (n_predict > params.n_predict || has_eos) {
@@ -334,12 +466,6 @@ int main(int argc, char ** argv) {
                             k, s, i, cur_p[k].id, cur_p[k].p, llama_token_to_piece(ctx_dft, cur_p[k].id).c_str());
                 }
 
-                if (cur_p[0].p < p_accept) {
-                    LOG("stopping drafting for seq %3d, probability too low: %.3f < %.3f\n", s, cur_p[0].p, p_accept);
-                    drafts[s].drafting = false;
-                    continue;
-                }
-
                 std::vector<int> sa(1, s);
 
                 // attempt to split the branch if the probability is high enough
@@ -367,6 +493,7 @@ int main(int argc, char ** argv) {
                         drafts[n_seq_cur].skip     = true;
 
                         drafts[n_seq_cur].tokens      = drafts[s].tokens;
+                        drafts[n_seq_cur].dists       = drafts[s].dists;
                         drafts[n_seq_cur].i_batch_dft = drafts[s].i_batch_dft;
                         drafts[n_seq_cur].i_batch_tgt = drafts[s].i_batch_tgt;
 
@@ -389,6 +516,8 @@ int main(int argc, char ** argv) {
                     llama_sampling_accept(drafts[s].ctx_sampling, ctx_dft, id, true);
 
                     drafts[s].tokens.push_back(id);
+                    // save cur_p.data into drafts[s].dists
+                    drafts[s].dists.push_back(cur_p);
 
                     // add unique drafted tokens to the target batch
                     drafts[s].i_batch_tgt.push_back(batch_tgt.n_tokens);
@@ -440,6 +569,7 @@ int main(int argc, char ** argv) {
             }
 
             drafts[s].tokens.erase(drafts[s].tokens.begin());
+            drafts[s].dists.erase(drafts[s].dists.begin());
         }
     }
 
diff --git a/examples/sycl/README.md b/examples/sycl/README.md
index b46f17f39..c589c2d3a 100644
--- a/examples/sycl/README.md
+++ b/examples/sycl/README.md
@@ -1,6 +1,6 @@
 # llama.cpp/example/sycl
 
-This example program provide the tools for llama.cpp for SYCL on Intel GPU.
+This example program provides the tools for llama.cpp for SYCL on Intel GPU.
 
 ## Tool
 
diff --git a/examples/sycl/build.sh b/examples/sycl/build.sh
index 26ad2f7da..db46d57ca 100755
--- a/examples/sycl/build.sh
+++ b/examples/sycl/build.sh
@@ -13,8 +13,11 @@ source /opt/intel/oneapi/setvars.sh
 #for FP32
 cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
 
-#build example/main only
+#build example/main
 #cmake --build . --config Release --target main
 
+#build example/llama-bench
+#cmake --build . --config Release --target llama-bench
+
 #build all binary
-cmake --build . --config Release -v
+cmake --build . --config Release -j -v
diff --git a/examples/sycl/ls-sycl-device.cpp b/examples/sycl/ls-sycl-device.cpp
index 52442e4ca..74a8b7fd8 100644
--- a/examples/sycl/ls-sycl-device.cpp
+++ b/examples/sycl/ls-sycl-device.cpp
@@ -7,7 +7,7 @@
 
 #include "ggml-sycl.h"
 
-int main(int argc, char ** argv) {
+int main() {
     ggml_backend_sycl_print_sycl_devices();
     return 0;
 }
diff --git a/examples/sycl/run-llama2.sh b/examples/sycl/run-llama2.sh
index f5f4c1e98..7b39a18c0 100755
--- a/examples/sycl/run-llama2.sh
+++ b/examples/sycl/run-llama2.sh
@@ -8,12 +8,30 @@ INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
 source /opt/intel/oneapi/setvars.sh
 
 if [ $# -gt 0 ]; then
-    export GGML_SYCL_DEVICE=$1
+    GGML_SYCL_DEVICE=$1
+    GGML_SYCL_SINGLE_GPU=1
 else
-    export GGML_SYCL_DEVICE=0
+    GGML_SYCL_DEVICE=0
+    GGML_SYCL_SINGLE_GPU=0
 fi
-echo GGML_SYCL_DEVICE=$GGML_SYCL_DEVICE
-#export GGML_SYCL_DEBUG=1
-./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
-#./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 5 -e -ngl 33 -t 1 -s 0
+
+#export GGML_SYCL_DEBUG=1
+
+
+#ZES_ENABLE_SYSMAN=1, Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory. Recommended to use when --split-mode = layer.
+
+if [ $GGML_SYCL_SINGLE_GPU -eq 1 ]; then
+    echo "use $GGML_SYCL_DEVICE as main GPU"
+    #use signle GPU only
+    ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none
+else
+    #use multiple GPUs with same max compute units
+    ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
+fi
+
+#use main GPU only
+#ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none
+
+#use multiple GPUs with same max compute units
+#ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
 
diff --git a/examples/sycl/win-build-sycl.bat b/examples/sycl/win-build-sycl.bat
index f9d43f8ed..1b0dc41ba 100644
--- a/examples/sycl/win-build-sycl.bat
+++ b/examples/sycl/win-build-sycl.bat
@@ -3,9 +3,13 @@
 ::  Copyright (C) 2024 Intel Corporation
 ::  SPDX-License-Identifier: MIT
 
-mkdir -p build
+
+IF not exist build (mkdir build)
 cd build
+if %errorlevel% neq 0 goto ERROR
+
 @call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
+if %errorlevel% neq 0 goto ERROR
 
 ::  for FP16
 ::  faster for long-prompt inference
@@ -13,11 +17,18 @@ cd build
 
 ::  for FP32
 cmake -G "MinGW Makefiles" ..  -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release
-
-
+if %errorlevel% neq 0 goto ERROR
 ::  build example/main only
 ::  make main
 
 ::  build all binary
 make -j
+if %errorlevel% neq 0 goto ERROR
+
 cd ..
+exit /B 0
+
+:ERROR
+echo comomand error: %errorlevel%
+exit /B %errorlevel%
+
diff --git a/examples/sycl/win-run-llama2.bat b/examples/sycl/win-run-llama2.bat
index cf621c675..1d4d7d2cd 100644
--- a/examples/sycl/win-run-llama2.bat
+++ b/examples/sycl/win-run-llama2.bat
@@ -6,8 +6,6 @@ set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
 @call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
 
 
-set GGML_SYCL_DEVICE=0
-rem set GGML_SYCL_DEBUG=1
 .\build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p %INPUT2% -n 400 -e -ngl 33 -s 0
 
 
diff --git a/examples/tokenize/tokenize.cpp b/examples/tokenize/tokenize.cpp
index d95a92475..8b1baea80 100644
--- a/examples/tokenize/tokenize.cpp
+++ b/examples/tokenize/tokenize.cpp
@@ -26,11 +26,9 @@ int main(int argc, char ** argv) {
     llama_context_params ctx_params = llama_context_default_params();
     llama_context * ctx = llama_new_context_with_model(model, ctx_params);
 
-    const bool add_bos = llama_should_add_bos_token(model);
-
     std::vector<llama_token> tokens;
 
-    tokens = ::llama_tokenize(model, prompt, add_bos, true);
+    tokens = ::llama_tokenize(model, prompt, true, true);
 
     for (int i = 0; i < (int) tokens.size(); i++) {
         if (printing_ids) {
diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp
index e78ab185d..587418cc7 100644
--- a/examples/train-text-from-scratch/train-text-from-scratch.cpp
+++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp
@@ -73,6 +73,7 @@ struct my_llama_model {
 static const char * LLM_KV_TRAINING_TYPE_TRAIN_MODEL     = "train_model";
 static const char * LLM_KV_TRAINING_TYPE                 = "training.type";
 
+static const char * LLM_KV_GENERAL_NAME                = "general.name";
 static const char * LLM_KV_GENERAL_ARCHITECTURE        = "general.architecture";
 static const char * LLM_KV_GENERAL_FILE_TYPE           = "general.file_type";
 
@@ -529,6 +530,7 @@ static void load_llama_model_gguf(struct gguf_context * fctx, struct ggml_contex
 
 static void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vocab_model, struct my_llama_model * model) {
     const char * arch = "llama";
+
     enum llama_ftype ftype = LLAMA_FTYPE_ALL_F32;
 
     std::vector<char> keybuf;
@@ -540,6 +542,7 @@ static void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vo
 
     // set arch
     gguf_set_val_str(fctx, LLM_KV_GENERAL_ARCHITECTURE, arch);
+    gguf_set_val_str(fctx, LLM_KV_GENERAL_NAME, arch);
     gguf_set_val_u32(fctx, LLM_KV_GENERAL_FILE_TYPE, ftype);
 
     // set hparams
@@ -711,6 +714,7 @@ static bool load_checkpoint_file(const char * filename, struct my_llama_model *
 
     load_checkpoint_gguf(fctx, f_ggml_ctx, model, train);
 
+    gguf_free(fctx);
     return true;
 }
 
@@ -960,7 +964,7 @@ int main(int argc, char ** argv) {
     struct ggml_opt_context * opt   = train->opt;
 
     // set opt params from command line
-    opt->params = ggml_opt_default_params(GGML_OPT_ADAM);
+    opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
     opt->params.print_forward_graph     = false;
     opt->params.print_backward_graph    = false;
     opt->params.graph_size              = LLAMA_TRAIN_MAX_NODES;
diff --git a/examples/ts-type-to-grammar.sh b/examples/ts-type-to-grammar.sh
new file mode 100755
index 000000000..9abba2a3d
--- /dev/null
+++ b/examples/ts-type-to-grammar.sh
@@ -0,0 +1,28 @@
+#!/bin/bash
+#
+# ./examples/ts-type-to-grammar.sh "{a:string,b:string,c?:string}"
+# python examples/json_schema_to_grammar.py https://json.schemastore.org/tsconfig.json
+#
+set -euo pipefail
+
+readonly type="$1"
+
+# Create a temporary directory
+TMPDIR=""
+trap 'rm -fR "$TMPDIR"' EXIT
+TMPDIR=$(mktemp -d)
+
+DTS_FILE="$TMPDIR/type.d.ts"
+SCHEMA_FILE="$TMPDIR/schema.json"
+
+echo "export type MyType = $type" > "$DTS_FILE"
+
+# This is a fork of typescript-json-schema, actively maintained as of March 2024:
+# https://github.com/vega/ts-json-schema-generator
+npx ts-json-schema-generator --unstable --no-top-ref --path "$DTS_FILE" --type MyType -e none > "$SCHEMA_FILE"
+
+# Alternative, not actively maintained as of March 2024:
+# https://github.com/YousefED/typescript-json-schema
+# npx typescript-json-schema --defaultProps --required "$DTS_FILE" MyType | tee "$SCHEMA_FILE" >&2
+
+./examples/json_schema_to_grammar.py "$SCHEMA_FILE"
diff --git a/flake.lock b/flake.lock
index 47d6448b5..c9ead0bf7 100644
--- a/flake.lock
+++ b/flake.lock
@@ -5,11 +5,11 @@
         "nixpkgs-lib": "nixpkgs-lib"
       },
       "locked": {
-        "lastModified": 1706830856,
-        "narHash": "sha256-a0NYyp+h9hlb7ddVz4LUn1vT/PLwqfrWYcHMvFB1xYg=",
+        "lastModified": 1714641030,
+        "narHash": "sha256-yzcRNDoyVP7+SCNX0wmuDju1NUCt8Dz9+lyUXEI0dbI=",
         "owner": "hercules-ci",
         "repo": "flake-parts",
-        "rev": "b253292d9c0a5ead9bc98c4e9a26c6312e27d69f",
+        "rev": "e5d10a24b66c3ea8f150e47dfdb0416ab7c3390e",
         "type": "github"
       },
       "original": {
@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1708118438,
-        "narHash": "sha256-kk9/0nuVgA220FcqH/D2xaN6uGyHp/zoxPNUmPCMmEE=",
+        "lastModified": 1714635257,
+        "narHash": "sha256-4cPymbty65RvF1DWQfc+Bc8B233A1BWxJnNULJKQ1EY=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "5863c27340ba4de8f83e7e3c023b9599c3cb3c80",
+        "rev": "63c3a29ca82437c87573e4c6919b09a24ea61b0f",
         "type": "github"
       },
       "original": {
@@ -36,20 +36,14 @@
     },
     "nixpkgs-lib": {
       "locked": {
-        "dir": "lib",
-        "lastModified": 1706550542,
-        "narHash": "sha256-UcsnCG6wx++23yeER4Hg18CXWbgNpqNXcHIo5/1Y+hc=",
-        "owner": "NixOS",
-        "repo": "nixpkgs",
-        "rev": "97b17f32362e475016f942bbdfda4a4a72a8a652",
-        "type": "github"
+        "lastModified": 1714640452,
+        "narHash": "sha256-QBx10+k6JWz6u7VsohfSw8g8hjdBZEf8CFzXH1/1Z94=",
+        "type": "tarball",
+        "url": "https://github.com/NixOS/nixpkgs/archive/50eb7ecf4cd0a5756d7275c8ba36790e5bd53e33.tar.gz"
       },
       "original": {
-        "dir": "lib",
-        "owner": "NixOS",
-        "ref": "nixos-unstable",
-        "repo": "nixpkgs",
-        "type": "github"
+        "type": "tarball",
+        "url": "https://github.com/NixOS/nixpkgs/archive/50eb7ecf4cd0a5756d7275c8ba36790e5bd53e33.tar.gz"
       }
     },
     "root": {
diff --git a/flake.nix b/flake.nix
index ad2f9b295..9cd3756e5 100644
--- a/flake.nix
+++ b/flake.nix
@@ -107,11 +107,12 @@
         # ```
         #
         # Cf. https://nixos.org/manual/nix/unstable/command-ref/new-cli/nix3-flake.html?highlight=flake#flake-format
-        flake.overlays.default =
-          (final: prev: {
+        flake.overlays.default = (
+          final: prev: {
             llamaPackages = final.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
             inherit (final.llamaPackages) llama-cpp;
-          });
+          }
+        );
 
         systems = [
           "aarch64-darwin"
@@ -131,6 +132,9 @@
             ...
           }:
           {
+            # For standardised reproducible formatting with `nix fmt`
+            formatter = pkgs.nixfmt-rfc-style;
+
             # Unlike `.#packages`, legacyPackages may contain values of
             # arbitrary types (including nested attrsets) and may even throw
             # exceptions. This attribute isn't recursed into by `nix flake
@@ -141,6 +145,7 @@
             # the same path you would with an overlay.
             legacyPackages = {
               llamaPackages = pkgs.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
+              llamaPackagesWindows = pkgs.pkgsCross.mingwW64.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
               llamaPackagesCuda = pkgsCuda.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
               llamaPackagesRocm = pkgsRocm.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
             };
@@ -150,6 +155,8 @@
             packages =
               {
                 default = config.legacyPackages.llamaPackages.llama-cpp;
+                vulkan = config.packages.default.override { useVulkan = true; };
+                windows = config.legacyPackages.llamaPackagesWindows.llama-cpp;
               }
               // lib.optionalAttrs pkgs.stdenv.isLinux {
                 opencl = config.packages.default.override { useOpenCL = true; };
@@ -157,16 +164,20 @@
 
                 mpi-cpu = config.packages.default.override { useMpi = true; };
                 mpi-cuda = config.packages.default.override { useMpi = true; };
-                vulkan = config.packages.default.override { useVulkan = true; };
               }
               // lib.optionalAttrs (system == "x86_64-linux") {
                 rocm = config.legacyPackages.llamaPackagesRocm.llama-cpp;
               };
 
             # Packages exposed in `.#checks` will be built by the CI and by
-            # `nix flake check`. Currently we expose all packages, but we could
-            # make more granular choices
-            checks = config.packages;
+            # `nix flake check`.
+            #
+            # We could test all outputs e.g. as `checks = confg.packages`.
+            #
+            # TODO: Build more once https://github.com/ggerganov/llama.cpp/issues/6346 has been addressed
+            checks = {
+              inherit (config.packages) default vulkan;
+            };
           };
       };
 }
diff --git a/ggml-alloc.c b/ggml-alloc.c
index e675306c8..1fbd376ed 100644
--- a/ggml-alloc.c
+++ b/ggml-alloc.c
@@ -61,7 +61,6 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
     }
 }
 
-// TODO: GGML_PAD ?
 static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) {
     assert(alignment && !(alignment & (alignment - 1))); // power of 2
     size_t align = (alignment - (((uintptr_t)buffer + offset) % alignment)) % alignment;
@@ -69,25 +68,14 @@ static size_t aligned_offset(const void * buffer, size_t offset, size_t alignmen
 }
 
 // tallocr
-struct ggml_tallocr {
-    ggml_backend_buffer_t buffer;
-    void * base;
-    size_t alignment;
-    size_t offset;
-};
-
-ggml_tallocr_t ggml_tallocr_new(ggml_backend_buffer_t buffer) {
-    ggml_tallocr_t talloc = malloc(sizeof(struct ggml_tallocr));
-    if (talloc == NULL) {
-        return NULL;
-    }
 
+struct ggml_tallocr ggml_tallocr_new(ggml_backend_buffer_t buffer) {
     void * base = ggml_backend_buffer_get_base(buffer);
     size_t align = ggml_backend_buffer_get_alignment(buffer);
 
     assert(align && !(align & (align - 1))); // power of 2
 
-    *talloc = (struct ggml_tallocr) {
+    struct ggml_tallocr talloc = (struct ggml_tallocr) {
         /*.buffer    = */ buffer,
         /*.base      = */ base,
         /*.alignment = */ align,
@@ -96,11 +84,7 @@ ggml_tallocr_t ggml_tallocr_new(ggml_backend_buffer_t buffer) {
     return talloc;
 }
 
-void ggml_tallocr_free(ggml_tallocr_t talloc) {
-    free(talloc);
-}
-
-void ggml_tallocr_alloc(ggml_tallocr_t talloc, struct ggml_tensor * tensor) {
+void ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor) {
     size_t size = ggml_backend_buffer_get_alloc_size(talloc->buffer, tensor);
     size = GGML_PAD(size, talloc->alignment);
 
@@ -354,12 +338,16 @@ struct hash_node {
     bool allocated;
 };
 
-//
 struct tensor_alloc {
     size_t offset;
     size_t size_max; // 0 = pre-allocated, unused, or view
 };
 
+struct leaf_alloc {
+    int buffer_id;
+    struct tensor_alloc leaf;
+};
+
 struct node_alloc {
     int buffer_id;
     struct tensor_alloc dst;
@@ -378,21 +366,21 @@ struct ggml_gallocr {
     struct node_alloc * node_allocs; // [n_nodes]
     int n_nodes;
 
-    struct tensor_alloc * leaf_allocs; // [n_leafs]
+    struct leaf_alloc * leaf_allocs; // [n_leafs]
     int n_leafs;
 };
 
 ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs) {
-    ggml_gallocr_t galloc = (ggml_gallocr_t)calloc(sizeof(struct ggml_gallocr), 1);
+    ggml_gallocr_t galloc = (ggml_gallocr_t)calloc(1, sizeof(struct ggml_gallocr));
     GGML_ASSERT(galloc != NULL);
 
-    galloc->bufts = calloc(sizeof(ggml_backend_buffer_type_t) * n_bufs, 1);
+    galloc->bufts = calloc(n_bufs, sizeof(ggml_backend_buffer_type_t));
     GGML_ASSERT(galloc->bufts != NULL);
 
-    galloc->buffers = calloc(sizeof(ggml_backend_buffer_t) * n_bufs, 1);
+    galloc->buffers = calloc(n_bufs, sizeof(ggml_backend_buffer_t) * n_bufs);
     GGML_ASSERT(galloc->buffers != NULL);
 
-    galloc->buf_tallocs = calloc(sizeof(struct ggml_dyn_tallocr *) * n_bufs, 1);
+    galloc->buf_tallocs = calloc(n_bufs, sizeof(struct ggml_dyn_tallocr *));
     GGML_ASSERT(galloc->buf_tallocs != NULL);
 
     for (int i = 0; i < n_bufs; i++) {
@@ -543,17 +531,28 @@ static int get_node_buffer_id(const int * node_buffer_ids, int i) {
     return node_buffer_ids ? node_buffer_ids[i] : 0;
 }
 
-static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids) {
+static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
     // clear hash tables
     memset(galloc->hash_set.keys, 0, galloc->hash_set.size * sizeof(struct ggml_tensor *));
     memset(galloc->hash_values,   0, galloc->hash_set.size * sizeof(struct hash_node));
 
+    // allocate leafs
+    // these may be tensors that the application is not using in the graph, but may still want to allocate for other purposes
+    for (int i = 0; i < graph->n_leafs; i++) {
+        struct ggml_tensor * leaf = graph->leafs[i];
+        ggml_gallocr_allocate_node(galloc, leaf, get_node_buffer_id(leaf_buffer_ids, i));
+    }
+
     // count number of children and views
-    // allocate all graph inputs and leafs first to avoid overwriting them
+    // allocate other graph inputs and leafs first to avoid overwriting them
     for (int i = 0; i < graph->n_nodes; i++) {
         struct ggml_tensor * node = graph->nodes[i];
 
-        if (ggml_is_view(node)) {
+        // TODO: better way to add external dependencies
+        // GGML_OP_NONE does not appear normally in the graph nodes, but is used by ggml-backend to add dependencies to
+        // control when some tensors are allocated and freed. in this case, the dependencies are in `src`, but the node
+        // itself is never used and should not be considered a dependency
+        if (ggml_is_view(node) && node->op != GGML_OP_NONE) {
             struct ggml_tensor * view_src = node->view_src;
             ggml_gallocr_hash_get(galloc, view_src)->n_views += 1;
         }
@@ -570,26 +569,13 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr
 
             ggml_gallocr_hash_get(galloc, src)->n_children += 1;
 
-            // allocate explicit inputs and leafs
-            if (src->flags & GGML_TENSOR_FLAG_INPUT || src->op == GGML_OP_NONE) {
+            // allocate explicit inputs
+            if (src->flags & GGML_TENSOR_FLAG_INPUT) {
                 ggml_gallocr_allocate_node(galloc, src, get_node_buffer_id(node_buffer_ids, i));
             }
         }
     }
 
-    // allocate the remaining leafs that are unused on the graph
-    // these are effectively static tensors that the application is not using in the graph, but may still want to allocate for other purposes
-    for (int i = 0; i < graph->n_leafs; i++) {
-        struct ggml_tensor * leaf = graph->leafs[i];
-        struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf);
-
-        if (hn->n_children == 0) {
-            assert(!hn->allocated);
-            // since buffer ids are only given for nodes, these leafs are always allocated in the first buffer
-            ggml_gallocr_allocate_node(galloc, leaf, 0);
-        }
-    }
-
     // allocate tensors
     for (int i = 0; i < graph->n_nodes; i++) {
         struct ggml_tensor * node = graph->nodes[i];
@@ -652,7 +638,7 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr
     }
 }
 
-bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids) {
+bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
     size_t hash_size = graph->visited_hash_table.size;
 
     // initialize hash table
@@ -660,8 +646,8 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
         free(galloc->hash_set.keys);
         free(galloc->hash_values);
         galloc->hash_set.size = hash_size;
-        galloc->hash_set.keys = calloc(sizeof(struct ggml_tensor *), hash_size);
-        galloc->hash_values   = calloc(sizeof(struct hash_node), hash_size);
+        galloc->hash_set.keys = calloc(hash_size, sizeof(struct ggml_tensor *));
+        galloc->hash_values   = calloc(hash_size, sizeof(struct hash_node));
         GGML_ASSERT(galloc->hash_set.keys != NULL);
         GGML_ASSERT(galloc->hash_values != NULL);
     } else {
@@ -676,12 +662,12 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
     }
 
     // allocate in hash table
-    ggml_gallocr_alloc_graph_impl(galloc, graph, node_buffer_ids);
+    ggml_gallocr_alloc_graph_impl(galloc, graph, node_buffer_ids, leaf_buffer_ids);
 
     // set the node_allocs from the hash table
     if (galloc->n_nodes < graph->n_nodes) {
         free(galloc->node_allocs);
-        galloc->node_allocs = calloc(sizeof(struct node_alloc), graph->n_nodes);
+        galloc->node_allocs = calloc(graph->n_nodes, sizeof(struct node_alloc));
         GGML_ASSERT(galloc->node_allocs != NULL);
     }
     galloc->n_nodes = graph->n_nodes;
@@ -711,15 +697,21 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
     }
     if (galloc->n_leafs < graph->n_leafs) {
         free(galloc->leaf_allocs);
-        galloc->leaf_allocs = calloc(sizeof(struct tensor_alloc), graph->n_leafs);
+        galloc->leaf_allocs = calloc(graph->n_leafs, sizeof(galloc->leaf_allocs[0]));
         GGML_ASSERT(galloc->leaf_allocs != NULL);
     }
     galloc->n_leafs = graph->n_leafs;
     for (int i = 0; i < graph->n_leafs; i++) {
         struct ggml_tensor * leaf = graph->leafs[i];
         struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf);
-        galloc->leaf_allocs[i].offset = hn->offset;
-        galloc->leaf_allocs[i].size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf);
+        galloc->leaf_allocs[i].buffer_id = hn->buffer_id;
+        if (leaf->view_src || leaf->data) {
+            galloc->leaf_allocs[i].leaf.offset = SIZE_MAX;
+            galloc->leaf_allocs[i].leaf.size_max = 0;
+        } else {
+            galloc->leaf_allocs[i].leaf.offset = hn->offset;
+            galloc->leaf_allocs[i].leaf.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf);
+        }
     }
 
     // reallocate buffers if needed
@@ -727,7 +719,8 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
         size_t cur_size = galloc->buffers[i] ? ggml_backend_buffer_get_size(galloc->buffers[i]) : 0;
         size_t new_size = ggml_dyn_tallocr_max_size(galloc->buf_tallocs[i]);
 
-        if (new_size > cur_size) {
+        // even if there are no tensors allocated in this buffer, we still need to allocate it to initialize views
+        if (new_size > cur_size || galloc->buffers[i] == NULL) {
 #ifndef NDEBUG
             fprintf(stderr, "%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
 #endif
@@ -744,30 +737,30 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
 }
 
 bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) {
-    return ggml_gallocr_reserve_n(galloc, graph, NULL);
+    return ggml_gallocr_reserve_n(galloc, graph, NULL, NULL);
 }
 
-static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id, struct tensor_alloc * tensor_alloc) {
-    assert(node->data || node->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], node) <= tensor_alloc->size_max);
+static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * tensor, int buffer_id, struct tensor_alloc * tensor_alloc) {
+    assert(tensor->data || tensor->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max);
 
-    if (node->view_src != NULL) {
-        if (node->buffer == NULL) {
+    if (tensor->view_src != NULL) {
+        if (tensor->buffer == NULL) {
             assert(tensor_alloc->offset == SIZE_MAX);
-            if (node->view_src->buffer == NULL) {
+            if (tensor->view_src->buffer == NULL) {
                 // this tensor was allocated without ggml-backend
                 return;
             }
-            ggml_backend_view_init(galloc->buffers[buffer_id], node);
+            ggml_backend_view_init(galloc->buffers[buffer_id], tensor);
         }
     } else {
-        if (node->data == NULL) {
+        if (tensor->data == NULL) {
             assert(tensor_alloc->offset != SIZE_MAX);
-            assert(ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], node) <= tensor_alloc->size_max);
+            assert(ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max);
             void * base = ggml_backend_buffer_get_base(galloc->buffers[buffer_id]);
             void * addr = (char *)base + tensor_alloc->offset;
-            ggml_backend_tensor_alloc(galloc->buffers[buffer_id], node, addr);
+            ggml_backend_tensor_alloc(galloc->buffers[buffer_id], tensor, addr);
         } else {
-            if (node->buffer == NULL) {
+            if (tensor->buffer == NULL) {
                 // this tensor was allocated without ggml-backend
                 return;
             }
@@ -843,13 +836,18 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph)
 
     // reset buffers
     for (int i = 0; i < galloc->n_buffers; i++) {
-        // zero size buffers are not allocated
         if (galloc->buffers[i] != NULL) {
             ggml_backend_buffer_reset(galloc->buffers[i]);
         }
     }
 
     // allocate the graph tensors from the previous assignments
+    // leafs
+    for (int i = 0; i < graph->n_leafs; i++) {
+        struct ggml_tensor * leaf = graph->leafs[i];
+        struct leaf_alloc * leaf_alloc = &galloc->leaf_allocs[i];
+        ggml_gallocr_init_tensor(galloc, leaf, leaf_alloc->buffer_id, &leaf_alloc->leaf);
+    }
     // nodes
     for (int i = 0; i < graph->n_nodes; i++) {
         struct ggml_tensor * node = graph->nodes[i];
@@ -863,12 +861,6 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph)
         }
         ggml_gallocr_init_tensor(galloc, node, node_alloc->buffer_id, &node_alloc->dst);
     }
-    // leafs
-    for (int i = 0; i < graph->n_leafs; i++) {
-        struct ggml_tensor * leaf = graph->leafs[i];
-        struct tensor_alloc * leaf_alloc = &galloc->leaf_allocs[i];
-        ggml_gallocr_init_tensor(galloc, leaf, 0, leaf_alloc);
-    }
 
     return true;
 }
@@ -900,12 +892,12 @@ static bool alloc_tensor_range(struct ggml_context * ctx,
         return false;
     }
 
-    struct ggml_tallocr * tallocr = ggml_tallocr_new(buffer);
+    struct ggml_tallocr tallocr = ggml_tallocr_new(buffer);
 
     for (struct ggml_tensor * t = first; t != last; t = ggml_get_next_tensor(ctx, t)) {
         if (t->data == NULL) {
             if (t->view_src == NULL) {
-                ggml_tallocr_alloc(tallocr, t);
+                ggml_tallocr_alloc(&tallocr, t);
             } else if (t->buffer == NULL) {
                 ggml_backend_view_init(buffer, t);
             }
@@ -917,8 +909,6 @@ static bool alloc_tensor_range(struct ggml_context * ctx,
         }
     }
 
-    ggml_tallocr_free(tallocr);
-
     *buffers = realloc(*buffers, sizeof(ggml_backend_buffer_t) * (*n_buffers + 1));
     (*buffers)[(*n_buffers)++] = buffer;
 
diff --git a/ggml-alloc.h b/ggml-alloc.h
index 1d9085d15..434c13b34 100644
--- a/ggml-alloc.h
+++ b/ggml-alloc.h
@@ -11,11 +11,15 @@ typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
 typedef struct ggml_backend * ggml_backend_t;
 
 // Tensor allocator
-typedef struct ggml_tallocr * ggml_tallocr_t;
+struct ggml_tallocr {
+    ggml_backend_buffer_t buffer;
+    void * base;
+    size_t alignment;
+    size_t offset;
+};
 
-GGML_API ggml_tallocr_t ggml_tallocr_new(ggml_backend_buffer_t buffer);
-GGML_API void           ggml_tallocr_free(ggml_tallocr_t talloc);
-GGML_API void           ggml_tallocr_alloc(ggml_tallocr_t talloc, struct ggml_tensor * tensor);
+GGML_API struct ggml_tallocr ggml_tallocr_new(ggml_backend_buffer_t buffer);
+GGML_API void                ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor);
 
 // Graph allocator
 /*
@@ -50,7 +54,11 @@ GGML_API void           ggml_gallocr_free(ggml_gallocr_t galloc);
 // not strictly required for single buffer usage: ggml_gallocr_alloc_graph will reallocate the buffers automatically if needed
 // returns false if the buffer allocation failed
 GGML_API bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph * graph);
-GGML_API bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids);
+GGML_API bool ggml_gallocr_reserve_n(
+    ggml_gallocr_t galloc,
+    struct ggml_cgraph * graph,
+    const int * node_buffer_ids,
+    const int * leaf_buffer_ids);
 
 // automatic reallocation if the topology changes when using a single buffer
 // returns false if using multiple buffers and a re-allocation is needed (call ggml_gallocr_reserve_n first to set the node buffers)
diff --git a/ggml-backend-impl.h b/ggml-backend-impl.h
index f95df47f7..f121e1de4 100644
--- a/ggml-backend-impl.h
+++ b/ggml-backend-impl.h
@@ -86,29 +86,48 @@ extern "C" {
         // (optional) asynchronous tensor data access
         void (*GGML_CALL set_tensor_async)(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
         void (*GGML_CALL get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
-        bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * src, struct ggml_tensor * dst);
+        bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst);
 
         // (optional) complete all pending operations
         void (*GGML_CALL synchronize)(ggml_backend_t backend);
 
-        // compute graph with a plan
+        // compute graph with a plan (not used currently)
         ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph);
         void                      (*GGML_CALL graph_plan_free)   (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
-        void                      (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
 
+        // compute graph with a plan
+        enum ggml_status (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
         // compute graph without a plan (async)
-        bool (*GGML_CALL graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
+        enum ggml_status (*GGML_CALL graph_compute)     (ggml_backend_t backend, struct ggml_cgraph * cgraph);
 
         // check if the backend supports an operation
         bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
+
+        // check if the backend wants to run an operation, even if the weights are allocated in a CPU buffer
+        // these should be expensive operations with large batch sizes that may benefit from running on this backend
+        // even if the weight has to be copied from the CPU temporarily
+        bool (*GGML_CALL offload_op)(ggml_backend_t backend, const struct ggml_tensor * op);
+
+        // (optional) event synchronization
+        ggml_backend_event_t (*GGML_CALL event_new)         (ggml_backend_t backend);
+        void                 (*GGML_CALL event_free)        (ggml_backend_event_t event);
+        void                 (*GGML_CALL event_record)      (ggml_backend_event_t event);
+        void                 (*GGML_CALL event_wait)        (ggml_backend_t backend, ggml_backend_event_t event);
+        void                 (*GGML_CALL event_synchronize) (ggml_backend_event_t event);
     };
 
     struct ggml_backend {
-        struct ggml_backend_i iface;
+        ggml_guid_t guid;
 
+        struct ggml_backend_i iface;
         ggml_backend_context_t context;
     };
 
+    struct ggml_backend_event {
+        ggml_backend_t backend;
+        void * context;
+    };
+
     //
     // Backend registry
     //
diff --git a/ggml-backend.c b/ggml-backend.c
index 5076d9e5e..dd090a583 100644
--- a/ggml-backend.c
+++ b/ggml-backend.c
@@ -12,7 +12,6 @@
 
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
 
-
 // backend buffer type
 
 const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) {
@@ -159,6 +158,13 @@ bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml
 
 // backend
 
+ggml_guid_t ggml_backend_guid(ggml_backend_t backend) {
+    if (backend == NULL) {
+        return NULL;
+    }
+    return backend->guid;
+}
+
 const char * ggml_backend_name(ggml_backend_t backend) {
     if (backend == NULL) {
         return "NULL";
@@ -215,29 +221,29 @@ void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_ten
 GGML_CALL void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
     ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
 
-    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
     GGML_ASSERT(buf != NULL && "tensor buffer not set");
+    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
     GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
 
     if (!size) {
         return;
     }
 
-    tensor->buffer->iface.set_tensor(buf, tensor, data, offset, size);
+    buf->iface.set_tensor(buf, tensor, data, offset, size);
 }
 
 GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
     ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
 
+    GGML_ASSERT(buf != NULL && "tensor buffer not set");
     GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
-    GGML_ASSERT(tensor->buffer != NULL && "tensor buffer not set");
     GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
 
     if (!size) {
         return;
     }
 
-    tensor->buffer->iface.get_tensor(buf, tensor, data, offset, size);
+    buf->iface.get_tensor(buf, tensor, data, offset, size);
 }
 
 void ggml_backend_synchronize(ggml_backend_t backend) {
@@ -249,18 +255,30 @@ void ggml_backend_synchronize(ggml_backend_t backend) {
 }
 
 ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+    GGML_ASSERT(backend->iface.graph_plan_create != NULL);
+
     return backend->iface.graph_plan_create(backend, cgraph);
 }
 
 void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+    GGML_ASSERT(backend->iface.graph_plan_free != NULL);
+
     backend->iface.graph_plan_free(backend, plan);
 }
 
-void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
-    backend->iface.graph_plan_compute(backend, plan);
+enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+    GGML_ASSERT(backend->iface.graph_plan_compute != NULL);
+
+    return backend->iface.graph_plan_compute(backend, plan);
 }
 
-bool ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+enum ggml_status ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+    enum ggml_status err = ggml_backend_graph_compute_async(backend, cgraph);
+    ggml_backend_synchronize(backend);
+    return err;
+}
+
+enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
     return backend->iface.graph_compute(backend, cgraph);
 }
 
@@ -268,6 +286,13 @@ bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor *
     return backend->iface.supports_op(backend, op);
 }
 
+bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op) {
+    if (backend->iface.offload_op != NULL) {
+        return backend->iface.offload_op(backend, op);
+    }
+    return false;
+}
+
 // backend copy
 
 static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
@@ -308,34 +333,68 @@ void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst
     }
 }
 
-void ggml_backend_tensor_copy_async(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) {
+void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst) {
     GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");
 
     if (src == dst) {
         return;
     }
 
-    if (ggml_backend_buft_supports_backend(src->buffer->buft, backend) && ggml_backend_buft_supports_backend(dst->buffer->buft, backend)) {
-        if (backend->iface.cpy_tensor_async != NULL) {
-            if (backend->iface.cpy_tensor_async(backend, src, dst)) {
-                return;
-            }
+    if (backend_dst->iface.cpy_tensor_async != NULL) {
+        if (backend_dst->iface.cpy_tensor_async(backend_src, backend_dst, src, dst)) {
+            return;
         }
     }
 
-    size_t nbytes = ggml_nbytes(src);
+    // an async copy would normally happen after all the queued operations on both backends are completed
+    // sync src, set_async dst
     if (ggml_backend_buffer_is_host(src->buffer)) {
-        ggml_backend_tensor_set_async(backend, dst, src->data, 0, nbytes);
-    }
-    else {
+        ggml_backend_synchronize(backend_src);
+        ggml_backend_tensor_set_async(backend_dst, dst, src->data, 0, ggml_nbytes(src));
+    } else {
+        ggml_backend_synchronize(backend_src);
         ggml_backend_tensor_copy(src, dst);
+        ggml_backend_synchronize(backend_dst);
     }
 }
 
+// events
+
+ggml_backend_event_t ggml_backend_event_new(ggml_backend_t backend) {
+    if (backend->iface.event_new == NULL) {
+        return NULL;
+    }
+    return backend->iface.event_new(backend);
+}
+
+void ggml_backend_event_free(ggml_backend_event_t event) {
+    if (event == NULL) {
+        return;
+    }
+    event->backend->iface.event_free(event);
+}
+
+void ggml_backend_event_record(ggml_backend_event_t event) {
+    GGML_ASSERT(event->backend->iface.event_record != NULL);
+
+    event->backend->iface.event_record(event);
+}
+
+void ggml_backend_event_synchronize(ggml_backend_event_t event) {
+    GGML_ASSERT(event->backend->iface.event_synchronize != NULL);
+
+    event->backend->iface.event_synchronize(event);
+}
+
+void ggml_backend_event_wait(ggml_backend_t backend, ggml_backend_event_t event) {
+    GGML_ASSERT(backend->iface.event_wait != NULL);
+
+    backend->iface.event_wait(backend, event);
+}
 
 // backend registry
 
-#define GGML_MAX_BACKENDS_REG 16
+#define GGML_REG_MAX_BACKENDS 16
 
 struct ggml_backend_reg {
     char name[128];
@@ -344,7 +403,7 @@ struct ggml_backend_reg {
     void * user_data;
 };
 
-static struct ggml_backend_reg ggml_backend_registry[GGML_MAX_BACKENDS_REG];
+static struct ggml_backend_reg ggml_backend_registry[GGML_REG_MAX_BACKENDS];
 static size_t ggml_backend_registry_count = 0;
 
 GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data);
@@ -361,7 +420,7 @@ GGML_CALL static void ggml_backend_registry_init(void) {
     ggml_backend_register("CPU", ggml_backend_reg_cpu_init, ggml_backend_cpu_buffer_type(), NULL);
 
     // add forward decls here to avoid including the backend headers
-#ifdef GGML_USE_CUBLAS
+#ifdef GGML_USE_CUDA
     extern GGML_CALL void ggml_backend_cuda_reg_devices(void);
     ggml_backend_cuda_reg_devices();
 #endif
@@ -389,7 +448,7 @@ GGML_CALL static void ggml_backend_registry_init(void) {
 }
 
 GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) {
-    GGML_ASSERT(ggml_backend_registry_count < GGML_MAX_BACKENDS_REG);
+    GGML_ASSERT(ggml_backend_registry_count < GGML_REG_MAX_BACKENDS);
 
     size_t id = ggml_backend_registry_count;
 
@@ -709,6 +768,10 @@ GGML_CALL static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(gg
 
     if (cpu_plan->cplan.work_size > 0) {
         cpu_plan->cplan.work_data = malloc(cpu_plan->cplan.work_size);
+        if (cpu_plan->cplan.work_data == NULL) {
+            free(cpu_plan);
+            return NULL;
+        }
     }
 
     cpu_plan->cplan.abort_callback      = cpu_ctx->abort_callback;
@@ -726,22 +789,26 @@ GGML_CALL static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, g
     GGML_UNUSED(backend);
 }
 
-GGML_CALL static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+GGML_CALL static enum ggml_status ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
     struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;
 
-    ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan);
+    return ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan);
 
     GGML_UNUSED(backend);
 }
 
-GGML_CALL static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+GGML_CALL static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
     struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
 
     struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads);
 
     if (cpu_ctx->work_size < cplan.work_size) {
-        // TODO: may be faster to free and use malloc to avoid the copy
-        cpu_ctx->work_data = realloc(cpu_ctx->work_data, cplan.work_size);
+        free(cpu_ctx->work_data);
+        cpu_ctx->work_data = malloc(cplan.work_size);
+        if (cpu_ctx->work_data == NULL) {
+            cpu_ctx->work_size = 0;
+            return GGML_STATUS_ALLOC_FAILED;
+        }
         cpu_ctx->work_size = cplan.work_size;
     }
     cplan.work_data = cpu_ctx->work_data;
@@ -749,14 +816,17 @@ GGML_CALL static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, str
     cplan.abort_callback      = cpu_ctx->abort_callback;
     cplan.abort_callback_data = cpu_ctx->abort_callback_data;
 
-    ggml_graph_compute(cgraph, &cplan);
-    return true;
+    return ggml_graph_compute(cgraph, &cplan);
 }
 
 GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
     switch (op->op) {
         case GGML_OP_CPY:
-            return op->type != GGML_TYPE_IQ2_XXS && op->type != GGML_TYPE_IQ2_XS && op->type != GGML_TYPE_IQ1_S; // missing type_traits.from_float
+            return
+                op->type != GGML_TYPE_IQ2_XXS &&
+                op->type != GGML_TYPE_IQ2_XS  &&
+                op->type != GGML_TYPE_IQ1_S   &&
+                op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float
         case GGML_OP_MUL_MAT:
             return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type;
         default:
@@ -779,8 +849,19 @@ static struct ggml_backend_i cpu_backend_i = {
     /* .graph_plan_compute      = */ ggml_backend_cpu_graph_plan_compute,
     /* .graph_compute           = */ ggml_backend_cpu_graph_compute,
     /* .supports_op             = */ ggml_backend_cpu_supports_op,
+    /* .offload_op              = */ NULL,
+    /* .event_new               = */ NULL,
+    /* .event_free              = */ NULL,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+    /* .event_synchronize       = */ NULL,
 };
 
+static ggml_guid_t ggml_backend_cpu_guid(void) {
+    static ggml_guid guid = { 0xaa, 0x67, 0xc7, 0x43, 0x96, 0xe6, 0xa3, 0x8a, 0xe3, 0xaf, 0xea, 0x92, 0x36, 0xbc, 0xfc, 0x89 };
+    return &guid;
+}
+
 ggml_backend_t ggml_backend_cpu_init(void) {
     struct ggml_backend_cpu_context * ctx = malloc(sizeof(struct ggml_backend_cpu_context));
     if (ctx == NULL) {
@@ -800,6 +881,7 @@ ggml_backend_t ggml_backend_cpu_init(void) {
     }
 
     *cpu_backend = (struct ggml_backend) {
+        /* .guid      = */ ggml_backend_cpu_guid(),
         /* .interface = */ cpu_backend_i,
         /* .context   = */ ctx
     };
@@ -807,7 +889,7 @@ ggml_backend_t ggml_backend_cpu_init(void) {
 }
 
 GGML_CALL bool ggml_backend_is_cpu(ggml_backend_t backend) {
-    return backend && backend->iface.get_name == ggml_backend_cpu_name;
+    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_cpu_guid());
 }
 
 void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) {
@@ -928,15 +1010,27 @@ static bool ggml_is_view_op(enum ggml_op op) {
 
 // scheduler
 
-#define GGML_MAX_BACKENDS 16
-#define GGML_MAX_SPLITS 256
-#define GGML_MAX_SPLIT_INPUTS 16
+#ifndef GGML_SCHED_MAX_BACKENDS
+#define GGML_SCHED_MAX_BACKENDS 16
+#endif
+
+#ifndef GGML_SCHED_MAX_SPLITS
+#define GGML_SCHED_MAX_SPLITS 2048
+#endif
+
+#ifndef GGML_SCHED_MAX_SPLIT_INPUTS
+#define GGML_SCHED_MAX_SPLIT_INPUTS GGML_MAX_SRC
+#endif
+
+#ifndef GGML_SCHED_MAX_COPIES
+#define GGML_SCHED_MAX_COPIES 4
+#endif
 
 struct ggml_backend_sched_split {
     int backend_id;
     int i_start;
     int i_end;
-    struct ggml_tensor * inputs[GGML_MAX_SPLIT_INPUTS];
+    struct ggml_tensor * inputs[GGML_SCHED_MAX_SPLIT_INPUTS];
     int n_inputs;
     // graph view of this split
     struct ggml_cgraph graph;
@@ -944,27 +1038,37 @@ struct ggml_backend_sched_split {
 
 struct ggml_backend_sched {
     bool is_reset; // true if the scheduler has been reset since the last graph split
+    bool is_alloc;
 
     int n_backends;
-    ggml_backend_t backends[GGML_MAX_BACKENDS];
-    ggml_backend_buffer_type_t bufts[GGML_MAX_BACKENDS];
 
+    ggml_backend_t backends[GGML_SCHED_MAX_BACKENDS];
+    ggml_backend_buffer_type_t bufts[GGML_SCHED_MAX_BACKENDS];
     ggml_gallocr_t galloc;
 
     // hash keys of the nodes in the graph
     struct ggml_hash_set    hash_set;
     // hash values
     int * tensor_backend_id;
-    struct ggml_tensor * (* tensor_copies)[GGML_MAX_BACKENDS];
+    struct ggml_tensor * (* tensor_copies)[GGML_SCHED_MAX_BACKENDS][GGML_SCHED_MAX_COPIES];
 
-    int * node_backend_ids; // [n_nodes]
-    int n_nodes;
+    int * node_backend_ids; // [graph_size]
+    int * leaf_backend_ids; // [graph_size]
 
     // copy of the graph with modified inputs
     struct ggml_cgraph * graph;
 
-    struct ggml_backend_sched_split splits[GGML_MAX_SPLITS];
+    // graph splits
+    struct ggml_backend_sched_split * splits;
     int n_splits;
+    int splits_capacity;
+
+    // pipeline parallelism support
+    int n_copies;
+    int cur_copy;
+    ggml_backend_event_t events[GGML_SCHED_MAX_BACKENDS][GGML_SCHED_MAX_COPIES];
+    struct ggml_tensor * graph_inputs[GGML_SCHED_MAX_SPLIT_INPUTS];
+    int n_graph_inputs;
 
     struct ggml_context * ctx;
 
@@ -972,17 +1076,16 @@ struct ggml_backend_sched {
     void * callback_eval_user_data;
 
     // align context_buffer to GGML_MEM_ALIGN
-    #ifdef _MSC_VER
+#ifdef _MSC_VER
     __declspec(align(GGML_MEM_ALIGN))
-    #else
+#else
     __attribute__((aligned(GGML_MEM_ALIGN)))
-    #endif
-    char context_buffer[GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS*2*sizeof(struct ggml_tensor) + sizeof(struct ggml_cgraph)];
+#endif
+    char context_buffer[GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS*2*sizeof(struct ggml_tensor) + sizeof(struct ggml_cgraph)];
 };
 
-#define hash_id(node) ggml_hash_find_or_insert(sched->hash_set, node)
-#define tensor_backend_id(node) sched->tensor_backend_id[hash_id(node)]
-#define tensor_backend(node) (tensor_backend_id(node) == -1 ? NULL : sched->backends[tensor_backend_id(node)])
+#define hash_id(tensor) ggml_hash_find_or_insert(sched->hash_set, tensor)
+#define tensor_backend_id(tensor) sched->tensor_backend_id[hash_id(tensor)]
 
 // returns the priority of the backend, lower id is higher priority
 static int ggml_backend_sched_backend_id(ggml_backend_sched_t sched, ggml_backend_t backend) {
@@ -994,7 +1097,8 @@ static int ggml_backend_sched_backend_id(ggml_backend_sched_t sched, ggml_backen
     return -1;
 }
 
-static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, ggml_backend_buffer_t buffer) {
+static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, const struct ggml_tensor * tensor) {
+    ggml_backend_buffer_t buffer = tensor->buffer;
     if (buffer == NULL) {
         return -1;
     }
@@ -1005,12 +1109,16 @@ static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, gg
             return i;
         }
     }
-    GGML_ASSERT(false && "tensor buffer type not supported by any backend");
-    return -1; // silence warning
+
+    fprintf(stderr, "%s: error: no backend supports buffer type %s used in tensor %s\n",
+        __func__, ggml_backend_buffer_name(buffer), tensor->name);
+    GGML_ASSERT(false);
+
+    return -1;
 }
 
 #if 0
-static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS][128]; // debug only
+static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS][128]; // debug only
 #define SET_CAUSE(node, ...) sprintf(causes[hash_id(node)], __VA_ARGS__)
 #define GET_CAUSE(node) causes[hash_id(node)]
 #else
@@ -1023,31 +1131,48 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st
     // TODO: use supports_op to check if the backend supports the op
 
     // assign pre-allocated nodes to their backend
-    // dst
-    int cur_backend = ggml_backend_sched_backend_from_buffer(sched, tensor->buffer);
-    if (cur_backend != -1) {
-        SET_CAUSE(node, "1.dst");
-        return cur_backend;
+    int cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor);
+    if (cur_backend_id != -1) {
+        SET_CAUSE(tensor, "1.dst");
+        return cur_backend_id;
     }
+
     // view_src
     if (tensor->view_src != NULL) {
-        cur_backend = ggml_backend_sched_backend_from_buffer(sched, tensor->view_src->buffer);
-        if (cur_backend != -1) {
-            SET_CAUSE(node, "1.vsrc");
-            return cur_backend;
+        cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor->view_src);
+        if (cur_backend_id != -1) {
+            SET_CAUSE(tensor, "1.vsrc");
+            return cur_backend_id;
         }
     }
+
+    // graph input
+    if (tensor->flags & GGML_TENSOR_FLAG_INPUT) {
+        cur_backend_id = sched->n_backends - 1; // last backend (assumed CPU)
+        SET_CAUSE(tensor, "1.inp");
+        return cur_backend_id;
+    }
+
     // assign nodes that use weights to the backend of the weights
+    // operations with weights are preferably run on the same backend as the weights
     for (int i = 0; i < GGML_MAX_SRC; i++) {
         const struct ggml_tensor * src = tensor->src[i];
         if (src == NULL) {
             continue;
         }
         if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
-            int src_backend = ggml_backend_sched_backend_from_buffer(sched, src->buffer);
-            // operations with weights are always run on the same backend as the weights
-            SET_CAUSE(node, "1.wgt%d", i);
-            return src_backend;
+            int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src);
+            // check if a backend with higher prio wants to offload the op
+            if (src_backend_id == sched->n_backends - 1) {
+                for (int b = 0; b < src_backend_id; b++) {
+                    if (ggml_backend_offload_op(sched->backends[b], tensor)) {
+                        SET_CAUSE(tensor, "1.off");
+                        return b;
+                    }
+                }
+            }
+            SET_CAUSE(tensor, "1.wgt%d", i);
+            return src_backend_id;
         }
     }
 
@@ -1057,9 +1182,9 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st
 static char * fmt_size(size_t size) {
     static char buffer[128];
     if (size >= 1024*1024) {
-        sprintf(buffer, "%zuM", size/1024/1024);
+        snprintf(buffer, sizeof(buffer), "%zuM", size/1024/1024);
     } else {
-        sprintf(buffer, "%zuK", size/1024);
+        snprintf(buffer, sizeof(buffer), "%zuK", size/1024);
     }
     return buffer;
 }
@@ -1082,7 +1207,7 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str
         if (ggml_is_view_op(node->op)) {
             continue;
         }
-        ggml_backend_t tensor_backend = tensor_backend(node);
+        ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node);
         fprintf(stderr, "node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name,
             fmt_size(ggml_nbytes(node)), tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", GET_CAUSE(node));
         for (int j = 0; j < GGML_MAX_SRC; j++) {
@@ -1090,7 +1215,7 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str
             if (src == NULL) {
                 continue;
             }
-            ggml_backend_t src_backend = tensor_backend(src);
+            ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src);
             fprintf(stderr, " %20.20s (%5.5s) [%5.5s %8.8s]", src->name,
                 fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", GET_CAUSE(src));
         }
@@ -1107,6 +1232,7 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str
 static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
     // reset splits
     sched->n_splits = 0;
+    sched->n_graph_inputs = 0;
     sched->is_reset = false;
 
     struct ggml_init_params params = {
@@ -1126,33 +1252,36 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
     // pass 1: assign backends to ops with pre-allocated inputs
     for (int i = 0; i < graph->n_leafs; i++) {
         struct ggml_tensor * leaf = graph->leafs[i];
-        if (tensor_backend_id(leaf) != -1) {
+        int * leaf_backend_id = &tensor_backend_id(leaf);
+        if (*leaf_backend_id != -1) {
             // do not overwrite user assignments
             continue;
         }
-        tensor_backend_id(leaf) = ggml_backend_sched_backend_id_from_cur(sched, leaf);
+        *leaf_backend_id = ggml_backend_sched_backend_id_from_cur(sched, leaf);
     }
 
     for (int i = 0; i < graph->n_nodes; i++) {
         struct ggml_tensor * node = graph->nodes[i];
-        if (tensor_backend_id(node) != -1) {
+        int * node_backend_id = &tensor_backend_id(node);
+        if (*node_backend_id != -1) {
             // do not overwrite user assignments
             continue;
         }
-        tensor_backend_id(node) = ggml_backend_sched_backend_id_from_cur(sched, node);
+        *node_backend_id = ggml_backend_sched_backend_id_from_cur(sched, node);
         // src
         for (int j = 0; j < GGML_MAX_SRC; j++) {
             struct ggml_tensor * src = node->src[j];
             if (src == NULL) {
                 continue;
             }
-            if (tensor_backend_id(src) == -1) {
-                tensor_backend_id(src) = ggml_backend_sched_backend_id_from_cur(sched, src);
+            int * src_backend_id = &tensor_backend_id(src);
+            if (*src_backend_id == -1) {
+                *src_backend_id = ggml_backend_sched_backend_id_from_cur(sched, src);
             }
         }
     }
 #ifdef DEBUG_PASS1
-    fprintf(stderr, "PASS 1 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
+    fprintf(stderr, "PASS 1 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph);
 #endif
 
     // pass 2: expand current backend assignments
@@ -1160,28 +1289,6 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
     // expand gpu backends (i.e. non last prio) up and down, ignoring cpu (the lowest priority backend)
     // thus, cpu will never be used unless weights are on cpu, or there are no gpu ops between cpu ops
 
-    // pass 2.1 expand gpu up
-    {
-        int cur_backend_id = -1;
-        for (int i = graph->n_nodes - 1; i >= 0; i--) {
-            struct ggml_tensor * node = graph->nodes[i];
-            if (ggml_is_view_op(node->op)) {
-                continue;
-            }
-            int tensor_backend_id = tensor_backend_id(node);
-            if (tensor_backend_id != -1) {
-                if (tensor_backend_id == sched->n_backends - 1) {
-                    // skip cpu (lowest prio backend)
-                    cur_backend_id = -1;
-                } else {
-                    cur_backend_id = tensor_backend_id;
-                }
-            } else {
-                tensor_backend_id(node) = cur_backend_id;
-                SET_CAUSE(node, "2.1");
-            }
-        }
-    }
 
     // pass 2.2 expand gpu down
     {
@@ -1191,22 +1298,21 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
             if (ggml_is_view_op(node->op)) {
                 continue;
             }
-            int tensor_backend_id = tensor_backend_id(node);
-            if (tensor_backend_id != -1) {
-                if (tensor_backend_id == sched->n_backends - 1) {
+            int * node_backend_id = &tensor_backend_id(node);
+            if (*node_backend_id != -1) {
+                if (*node_backend_id == sched->n_backends - 1) {
                     // skip cpu (lowest prio backend)
                     cur_backend_id = -1;
                 } else {
-                    cur_backend_id = tensor_backend_id;
+                    cur_backend_id = *node_backend_id;
                 }
             } else {
-                tensor_backend_id(node) = cur_backend_id;
+                *node_backend_id = cur_backend_id;
                 SET_CAUSE(node, "2.2");
             }
         }
     }
-
-    // pass 2.3 expand rest up
+    // pass 2.1 expand gpu up
     {
         int cur_backend_id = -1;
         for (int i = graph->n_nodes - 1; i >= 0; i--) {
@@ -1214,16 +1320,20 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
             if (ggml_is_view_op(node->op)) {
                 continue;
             }
-            int tensor_backend_id = tensor_backend_id(node);
-            if (tensor_backend_id != -1) {
-                cur_backend_id = tensor_backend_id;
+            int * node_backend_id = &tensor_backend_id(node);
+            if (*node_backend_id != -1) {
+                if (*node_backend_id == sched->n_backends - 1) {
+                    // skip cpu (lowest prio backend)
+                    cur_backend_id = -1;
+                } else {
+                    cur_backend_id = *node_backend_id;
+                }
             } else {
-                tensor_backend_id(node) = cur_backend_id;
-                SET_CAUSE(node, "2.3");
+                *node_backend_id = cur_backend_id;
+                SET_CAUSE(node, "2.1");
             }
         }
     }
-
     // pass 2.4 expand rest down
     {
         int cur_backend_id = -1;
@@ -1232,25 +1342,43 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
             if (ggml_is_view_op(node->op)) {
                 continue;
             }
-            int tensor_backend_id = tensor_backend_id(node);
-            if (tensor_backend_id != -1) {
-                cur_backend_id = tensor_backend_id;
+            int * node_backend_id = &tensor_backend_id(node);
+            if (*node_backend_id != -1) {
+                cur_backend_id = *node_backend_id;
             } else {
-                tensor_backend_id(node) = cur_backend_id;
+                *node_backend_id = cur_backend_id;
                 SET_CAUSE(node, "2.4");
             }
         }
     }
+    // pass 2.3 expand rest up
+    {
+        int cur_backend_id = -1;
+        for (int i = graph->n_nodes - 1; i >= 0; i--) {
+            struct ggml_tensor * node = graph->nodes[i];
+            if (ggml_is_view_op(node->op)) {
+                continue;
+            }
+            int * node_backend_id = &tensor_backend_id(node);
+            if (*node_backend_id != -1) {
+                cur_backend_id = *node_backend_id;
+            } else {
+                *node_backend_id = cur_backend_id;
+                SET_CAUSE(node, "2.3");
+            }
+        }
+    }
+
 #ifdef DEBUG_PASS2
-    fprintf(stderr, "PASS 2 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
+    fprintf(stderr, "PASS 2 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph);
 #endif
 
     // pass 3: assign backends to remaining src from dst and view_src
     for (int i = 0; i < graph->n_nodes; i++) {
         struct ggml_tensor * node = graph->nodes[i];
-        int cur_backend_id = tensor_backend_id(node);
-        if (node->view_src != NULL && cur_backend_id == -1) {
-            cur_backend_id = tensor_backend_id(node) = tensor_backend_id(node->view_src);
+        int * cur_backend_id = &tensor_backend_id(node);
+        if (node->view_src != NULL && *cur_backend_id == -1) {
+            *cur_backend_id = tensor_backend_id(node->view_src);
             SET_CAUSE(node, "3.vsrc");
         }
         for (int j = 0; j < GGML_MAX_SRC; j++) {
@@ -1258,38 +1386,39 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
             if (src == NULL) {
                 continue;
             }
-            int src_backend_id = tensor_backend_id(src);
-            if (src_backend_id == -1) {
+            int * src_backend_id = &tensor_backend_id(src);
+            if (*src_backend_id == -1) {
                 if (src->view_src != NULL) {
                     // views are always on the same backend as the source
-                    tensor_backend_id(src) = tensor_backend_id(src->view_src);
+                    *src_backend_id = tensor_backend_id(src->view_src);
                     SET_CAUSE(src, "3.vsrc");
                 } else {
-                    tensor_backend_id(src) = cur_backend_id;
+                    *src_backend_id = *cur_backend_id;
                     SET_CAUSE(src, "3.cur");
                 }
             }
         }
     }
 #ifdef DEBUG_PASS3
-    fprintf(stderr, "PASS 3 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
+    fprintf(stderr, "PASS 3 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph);
 #endif
 
     // pass 4: split graph, find tensors that need to be copied
     {
-        int cur_split = 0;
+        int i_split = 0;
+        struct ggml_backend_sched_split * split = &sched->splits[0];
         // find the backend of the first split, skipping view ops
         for (int i = 0; i < graph->n_nodes; i++) {
             struct ggml_tensor * node = graph->nodes[i];
             if (!ggml_is_view_op(node->op)) {
-                sched->splits[0].backend_id = tensor_backend_id(node);
+                split->backend_id = tensor_backend_id(node);
                 break;
             }
         }
-        sched->splits[0].i_start = 0;
-        sched->splits[0].n_inputs = 0;
-        memset(sched->splits[0].inputs, 0, sizeof(sched->splits[0].inputs)); //HACK
-        int cur_backend_id = sched->splits[0].backend_id;
+        split->i_start = 0;
+        split->n_inputs = 0;
+        memset(split->inputs, 0, sizeof(split->inputs)); //HACK
+        int cur_backend_id = split->backend_id;
         for (int i = 0; i < graph->n_nodes; i++) {
             struct ggml_tensor * node = graph->nodes[i];
 
@@ -1297,18 +1426,54 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
                 continue;
             }
 
-            int tensor_backend_id = tensor_backend_id(node);
+            const int node_backend_id = tensor_backend_id(node);
 
-            GGML_ASSERT(tensor_backend_id != -1); // all nodes should be assigned by now
+            GGML_ASSERT(node_backend_id != -1); // all nodes should be assigned by now
 
-            if (tensor_backend_id != cur_backend_id) {
-                sched->splits[cur_split].i_end = i;
-                cur_split++;
-                GGML_ASSERT(cur_split < GGML_MAX_SPLITS);
-                sched->splits[cur_split].backend_id = tensor_backend_id;
-                sched->splits[cur_split].i_start = i;
-                sched->splits[cur_split].n_inputs = 0;
-                cur_backend_id = tensor_backend_id;
+            // check if we should start a new split based on the sources of the current node
+            bool need_new_split = false;
+            if (node_backend_id == cur_backend_id && split->n_inputs > 0) {
+                for (int j = 0; j < GGML_MAX_SRC; j++) {
+                    struct ggml_tensor * src = node->src[j];
+                    if (src == NULL) {
+                        continue;
+                    }
+                    // check if a weight is on a different backend
+                    // by starting a new split, the memory of the previously offloaded weights can be reused
+                    if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
+                        int src_backend_id = tensor_backend_id(src);
+                        if (src_backend_id != -1 && src_backend_id != cur_backend_id) {
+                            need_new_split = true;
+                            break;
+                        }
+                    }
+                    // check if the split has too many inputs
+                    if (split->n_inputs == GGML_SCHED_MAX_SPLIT_INPUTS) {
+                        const size_t id = hash_id(src);
+                        int src_backend_id = sched->tensor_backend_id[id];
+                        if (src_backend_id != cur_backend_id && sched->tensor_copies[hash_id(src)][cur_backend_id][0] == NULL) {
+                            //printf("starting new split because of too many inputs: node %s, input %s\n", node->name, src->name);
+                            need_new_split = true;
+                            break;
+                        }
+                    }
+                }
+            }
+
+            if (node_backend_id != cur_backend_id || need_new_split) {
+                split->i_end = i;
+                i_split++;
+                if (i_split >= sched->splits_capacity) {
+                    sched->splits_capacity *= 2;
+                    sched->splits = realloc(sched->splits, sched->splits_capacity * sizeof(struct ggml_backend_sched_split));
+                    GGML_ASSERT(sched->splits != NULL);
+                }
+                GGML_ASSERT(i_split < GGML_SCHED_MAX_SPLITS);
+                split = &sched->splits[i_split];
+                split->backend_id = node_backend_id;
+                split->i_start = i;
+                split->n_inputs = 0;
+                cur_backend_id = node_backend_id;
             }
 
             // find inputs that are not on the same backend
@@ -1317,83 +1482,84 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
                 if (src == NULL) {
                     continue;
                 }
-                int src_backend_id = tensor_backend_id(src);
+
+                const int src_backend_id = tensor_backend_id(src);
                 assert(src_backend_id != -1); // all inputs should be assigned by now
-                if (src_backend_id != tensor_backend_id) {
-                    // create a copy of the input in the split's backend
+
+                if (src->flags & GGML_TENSOR_FLAG_INPUT && sched->n_copies > 1)  {
                     size_t id = hash_id(src);
-                    if (sched->tensor_copies[id][cur_backend_id] == NULL) {
-                        ggml_backend_t backend = sched->backends[cur_backend_id];
-                        struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
-                        ggml_format_name(tensor_copy, "%s#%s", ggml_backend_name(backend), src->name);
-
-                        sched->tensor_copies[id][cur_backend_id] = tensor_copy;
-                        tensor_backend_id(tensor_copy) = cur_backend_id;
-                        SET_CAUSE(tensor_copy, "4.cpy");
-
-                        int n_inputs = sched->splits[cur_split].n_inputs++;
-                        GGML_ASSERT(n_inputs < GGML_MAX_SPLIT_INPUTS);
-                        sched->splits[cur_split].inputs[n_inputs] = src;
+                    if (sched->tensor_copies[id][src_backend_id][0] == NULL) {
+                        ggml_backend_t backend = sched->backends[src_backend_id];
+                        for (int c = 0; c < sched->n_copies; c++) {
+                            struct ggml_tensor * tensor_copy;
+                            if (c == sched->cur_copy) {
+                                tensor_copy = src; // use the original tensor as the current copy
+                            } else {
+                                tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
+                                ggml_format_name(tensor_copy, "%s#%s#%d", ggml_backend_name(backend), src->name, c);
+                            }
+                            if (sched->n_copies > 1) {
+                                ggml_set_input(tensor_copy);
+                                ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor
+                            }
+                            sched->tensor_copies[id][src_backend_id][c] = tensor_copy;
+                            SET_CAUSE(tensor_copy, "4.cpy");
+                        }
+                        int n_graph_inputs = sched->n_graph_inputs++;
+                        GGML_ASSERT(n_graph_inputs < GGML_SCHED_MAX_SPLIT_INPUTS);
+                        sched->graph_inputs[n_graph_inputs] = src;
                     }
-                    node->src[j] = sched->tensor_copies[id][cur_backend_id];
+                }
+
+                if (src_backend_id != node_backend_id) {
+                    // create a copy of the input in the split's backend
+                    const size_t id = hash_id(src);
+                    if (sched->tensor_copies[id][cur_backend_id][0] == NULL) {
+                        ggml_backend_t backend = sched->backends[cur_backend_id];
+                        for (int c = 0; c < sched->n_copies; c++) {
+                            struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
+                            ggml_format_name(tensor_copy, "%s#%s#%d", ggml_backend_name(backend), src->name, c);
+                            if (sched->n_copies > 1) {
+                                ggml_set_input(tensor_copy);
+                                ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor
+                            }
+                            sched->tensor_copies[id][cur_backend_id][c] = tensor_copy;
+                            SET_CAUSE(tensor_copy, "4.cpy");
+                        }
+                        int n_inputs = split->n_inputs++;
+                        GGML_ASSERT(n_inputs < GGML_SCHED_MAX_SPLIT_INPUTS);
+                        split->inputs[n_inputs] = src;
+                    }
+                    node->src[j] = sched->tensor_copies[id][cur_backend_id][sched->cur_copy];
                 }
             }
         }
-        sched->splits[cur_split].i_end = graph->n_nodes;
-        sched->n_splits = cur_split + 1;
+        split->i_end = graph->n_nodes;
+        sched->n_splits = i_split + 1;
     }
 #ifdef DEBUG_PASS4
-    fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
-#endif
-
-#ifndef NDEBUG
-    // sanity check: all sources should have the same backend as the node
-    for (int i = 0; i < graph->n_nodes; i++) {
-        struct ggml_tensor * node = graph->nodes[i];
-        ggml_backend_t tensor_backend = tensor_backend(node);
-        if (tensor_backend == NULL) {
-            fprintf(stderr, "!!!!!!! %s has no backend\n", node->name);
-        }
-        if (node->view_src != NULL && tensor_backend != tensor_backend(node->view_src)) {
-            fprintf(stderr, "!!!!!!! %s has backend %s, view_src %s has backend %s\n",
-                node->name, tensor_backend ? ggml_backend_name(tensor_backend) : "NULL",
-                node->view_src->name, tensor_backend(node->view_src) ? ggml_backend_name(tensor_backend(node->view_src)) : "NULL");
-        }
-        for (int j = 0; j < GGML_MAX_SRC; j++) {
-            struct ggml_tensor * src = node->src[j];
-            if (src == NULL) {
-                continue;
-            }
-            ggml_backend_t src_backend = tensor_backend(src);
-            if (src_backend != tensor_backend /* && src_backend != NULL */) {
-                fprintf(stderr, "!!!! %s has backend %s, src %d (%s) has backend %s\n",
-                    node->name, tensor_backend ? ggml_backend_name(tensor_backend) : "NULL",
-                    j, src->name, src_backend ? ggml_backend_name(src_backend) : "NULL");
-            }
-            if (src->view_src != NULL && src_backend != tensor_backend(src->view_src)) {
-                fprintf(stderr, "!!!!!!! [src] %s has backend %s, view_src %s has backend %s\n",
-                    src->name, src_backend ? ggml_backend_name(src_backend) : "NULL",
-                    src->view_src->name, tensor_backend(src->view_src) ? ggml_backend_name(tensor_backend(src->view_src)) : "NULL");
-            }
-        }
-    }
-    fflush(stderr);
+    fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph);
 #endif
 
     // create copies of the graph for each split
-    // FIXME: avoid this copy, pass split inputs to ggml_gallocr_alloc_graph_n in some other way
-    struct ggml_cgraph * graph_copy = ggml_new_graph_custom(sched->ctx, graph->n_nodes + sched->n_splits*GGML_MAX_SPLIT_INPUTS, false);
+    // TODO: avoid this copy
+    struct ggml_cgraph * graph_copy = ggml_new_graph_custom(sched->ctx, graph->n_nodes + sched->n_splits*GGML_SCHED_MAX_SPLIT_INPUTS*2, false);
     for (int i = 0; i < sched->n_splits; i++) {
         struct ggml_backend_sched_split * split = &sched->splits[i];
         split->graph = ggml_graph_view(graph, split->i_start, split->i_end);
 
+        // add inputs to the graph copy so that they are allocated by ggml-alloc at the start of the split
         for (int j = 0; j < split->n_inputs; j++) {
+            assert(graph_copy->size > (graph_copy->n_nodes + 1));
+
             struct ggml_tensor * input = split->inputs[j];
-            struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split->backend_id];
+            const size_t input_id = hash_id(input);
+            struct ggml_tensor * input_cpy = sched->tensor_copies[input_id][split->backend_id][sched->cur_copy];
 
             // add a dependency to the input source so that it is not freed before the copy is done
             struct ggml_tensor * input_dep = ggml_view_tensor(sched->ctx, input);
-            sched->node_backend_ids[graph_copy->n_nodes] = tensor_backend_id(input);
+            input_dep->src[0] = input;
+            sched->node_backend_ids[graph_copy->n_nodes] = sched->tensor_backend_id[input_id];
             graph_copy->nodes[graph_copy->n_nodes++] = input_dep;
 
             // add a dependency to the input copy so that it is allocated at the start of the split
@@ -1402,22 +1568,61 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
         }
 
         for (int j = split->i_start; j < split->i_end; j++) {
+            assert(graph_copy->size > graph_copy->n_nodes);
             sched->node_backend_ids[graph_copy->n_nodes] = tensor_backend_id(graph->nodes[j]);
             graph_copy->nodes[graph_copy->n_nodes++] = graph->nodes[j];
         }
     }
+
+    if (sched->n_copies > 1) {
+        // add input copies as leafs so that they are allocated first
+        for (int i = 0; i < sched->n_graph_inputs; i++) {
+            struct ggml_tensor * input = sched->graph_inputs[i];
+            size_t id = hash_id(input);
+            int backend_id = tensor_backend_id(input);
+            for (int c = 0; c < sched->n_copies; c++) {
+                struct ggml_tensor * input_cpy = sched->tensor_copies[id][backend_id][c];
+                sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id;
+                graph_copy->leafs[graph_copy->n_leafs++] = input_cpy;
+            }
+        }
+
+        for (int i = 0; i < sched->n_splits; i++) {
+            struct ggml_backend_sched_split * split = &sched->splits[i];
+            int backend_id = split->backend_id;
+            for (int j = 0; j < split->n_inputs; j++) {
+                struct ggml_tensor * input = split->inputs[j];
+                size_t id = hash_id(input);
+                for (int c = 0; c < sched->n_copies; c++) {
+                    struct ggml_tensor * input_cpy = sched->tensor_copies[id][backend_id][c];
+                    sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id;
+                    graph_copy->leafs[graph_copy->n_leafs++] = input_cpy;
+                }
+            }
+        }
+    }
+
+    // add leafs from the original graph
+    for (int i = 0; i < graph->n_leafs; i++) {
+        struct ggml_tensor * leaf = graph->leafs[i];
+        sched->leaf_backend_ids[graph_copy->n_leafs] = tensor_backend_id(leaf);
+        graph_copy->leafs[graph_copy->n_leafs++] = leaf;
+    }
+
     sched->graph = graph_copy;
 }
 
 static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
-    // ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids);
+    // allocate graph
     if (!ggml_gallocr_alloc_graph(sched->galloc, sched->graph)) {
+        // the re-allocation may cause the split inputs to be moved to a different address
+        ggml_backend_sched_synchronize(sched);
 #ifndef NDEBUG
-        fprintf(stderr, "ggml_backend_sched: failed to allocate graph, reserving\n");
+        fprintf(stderr, "%s: failed to allocate graph, reserving\n", __func__);
 #endif
-        ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids);
+        ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids, sched->leaf_backend_ids);
         if (!ggml_gallocr_alloc_graph(sched->galloc, sched->graph)) {
-            fprintf(stderr, "ggml_backend_sched: failed to allocate graph\n");
+            fprintf(stderr, "%s: failed to allocate graph\n", __func__);
             return false;
         }
     }
@@ -1425,10 +1630,7 @@ static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
     return true;
 }
 
-static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) {
-    uint64_t copy_us[GGML_MAX_BACKENDS] = {0};
-    uint64_t compute_us[GGML_MAX_BACKENDS] = {0};
-
+static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) {
     struct ggml_backend_sched_split * splits = sched->splits;
 
     for (int i = 0; i < sched->n_splits; i++) {
@@ -1437,33 +1639,35 @@ static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) {
         ggml_backend_t split_backend = sched->backends[split_backend_id];
 
         // copy the input tensors to the split backend
-        uint64_t copy_start_us = ggml_time_us();
         for (int j = 0; j < split->n_inputs; j++) {
+            ggml_backend_t input_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[j]);
             struct ggml_tensor * input = split->inputs[j];
-            struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split_backend_id];
+            struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split_backend_id][sched->cur_copy];
 
-            GGML_ASSERT(input->buffer != NULL);
-            GGML_ASSERT(input_cpy->buffer != NULL);
-
-            ggml_backend_tensor_copy_async(split_backend, input, input_cpy);
-        }
-        //ggml_backend_synchronize(split_backend); // necessary to measure copy time
-        int64_t copy_end_us = ggml_time_us();
-        copy_us[split_backend_id] += copy_end_us - copy_start_us;
-
-#if 0
-        char split_filename[GGML_MAX_NAME];
-        snprintf(split_filename, GGML_MAX_NAME, "split_%i_%s.dot", i, ggml_backend_name(split_backend));
-        ggml_graph_dump_dot(split->graph, NULL, split_filename);
-#endif
-
-
-        uint64_t compute_start_us = ggml_time_us();
-        if (!sched->callback_eval) {
-            if (!ggml_backend_graph_compute(split_backend, &split->graph)) {
-                return false;
+            if (input->flags & GGML_TENSOR_FLAG_INPUT) {
+                // inputs from the user must be copied immediately to prevent the user overwriting the data before the copy is done
+                if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
+                    ggml_backend_event_synchronize(sched->events[split_backend_id][sched->cur_copy]);
+                } else {
+                    ggml_backend_synchronize(split_backend);
+                }
+                ggml_backend_tensor_copy(input, input_cpy);
+            } else {
+                // wait for the split backend to finish using the input before overwriting it
+                if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
+                    ggml_backend_event_wait(split_backend, sched->events[split_backend_id][sched->cur_copy]);
+                } else {
+                    ggml_backend_synchronize(split_backend);
+                }
+                ggml_backend_tensor_copy_async(input_backend, split_backend, input, input_cpy);
+            }
+        }
+
+        if (!sched->callback_eval) {
+            enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &split->graph);
+            if (ec != GGML_STATUS_SUCCESS) {
+                return ec;
             }
-            //ggml_backend_synchronize(split_backend); // necessary to measure compute time
         } else {
             // similar to ggml_backend_compare_graph_backend
             for (int j0 = 0; j0 < split->graph.n_nodes; j0++) {
@@ -1482,10 +1686,14 @@ static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) {
 
                 struct ggml_cgraph gv = ggml_graph_view(&split->graph, j0, j1 + 1);
 
-                if (!ggml_backend_graph_compute(split_backend, &gv)) {
-                    return false;
+                enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &gv);
+                if (ec != GGML_STATUS_SUCCESS) {
+                    return ec;
                 }
 
+                // TODO: pass backend to the callback, then the user can decide if they want to synchronize
+                ggml_backend_synchronize(split_backend);
+
                 if (need && !sched->callback_eval(t, false, sched->callback_eval_user_data)) {
                     break;
                 }
@@ -1493,39 +1701,58 @@ static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) {
                 j0 = j1;
             }
         }
-        uint64_t compute_end_us = ggml_time_us();
-        compute_us[split_backend_id] += compute_end_us - compute_start_us;
-    }
 
-#if 0
-    // per-backend timings
-    fprintf(stderr, "sched_compute_splits times (%d splits):\n", sched->n_splits);
-    for (int i = 0; i < sched->n_backends; i++) {
-        if (copy_us[i] > 0 || compute_us[i] > 0) {
-            fprintf(stderr, "\t%5.5s: %lu us copy, %lu us compute\n", ggml_backend_name(sched->backends[i]), copy_us[i], compute_us[i]);
+        // record the event of this copy
+        if (split->n_inputs > 0) {
+            if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
+                ggml_backend_event_record(sched->events[split_backend_id][sched->cur_copy]);
+            }
         }
     }
-#endif
 
-    return true;
+    sched->cur_copy = (sched->cur_copy + 1) % sched->n_copies;
+
+    return GGML_STATUS_SUCCESS;
 }
 
-ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size) {
+ggml_backend_sched_t ggml_backend_sched_new(
+        ggml_backend_t * backends,
+        ggml_backend_buffer_type_t * bufts,
+        int n_backends,
+        size_t graph_size,
+        bool parallel) {
     GGML_ASSERT(n_backends > 0);
-    GGML_ASSERT(n_backends <= GGML_MAX_BACKENDS);
+    GGML_ASSERT(n_backends <= GGML_SCHED_MAX_BACKENDS);
+    GGML_ASSERT(ggml_backend_is_cpu(backends[n_backends - 1])); // last backend must be CPU
 
-    struct ggml_backend_sched * sched = calloc(sizeof(struct ggml_backend_sched), 1);
+    struct ggml_backend_sched * sched = calloc(1, sizeof(struct ggml_backend_sched));
 
     // initialize hash table
-    sched->hash_set          = ggml_hash_set_new(graph_size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS);
-    sched->tensor_backend_id = calloc(sizeof(sched->tensor_backend_id[0]), sched->hash_set.size);
-    sched->tensor_copies     = calloc(sizeof(sched->tensor_copies[0]), sched->hash_set.size);
-    sched->node_backend_ids  = calloc(sizeof(sched->node_backend_ids[0]), graph_size);
+    sched->hash_set          = ggml_hash_set_new(graph_size);
+    sched->tensor_backend_id = calloc(sched->hash_set.size, sizeof(sched->tensor_backend_id[0]));
+    sched->tensor_copies     = calloc(sched->hash_set.size, sizeof(sched->tensor_copies[0]));
+
+    const size_t nodes_size = graph_size + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS*2;
+    sched->node_backend_ids  = calloc(nodes_size, sizeof(sched->node_backend_ids[0]));
+    sched->leaf_backend_ids  = calloc(nodes_size, sizeof(sched->leaf_backend_ids[0]));
 
     sched->n_backends = n_backends;
-    for (int i = 0; i < n_backends; i++) {
-        sched->backends[i] = backends[i];
-        sched->bufts[i] = bufts ? bufts[i] : ggml_backend_get_default_buffer_type(backends[i]);
+
+    sched->n_copies = parallel ? GGML_SCHED_MAX_COPIES : 1;
+
+    const int initial_splits_capacity = 16;
+    sched->splits = calloc(initial_splits_capacity, sizeof(sched->splits[0]));
+    sched->splits_capacity = initial_splits_capacity;
+
+    for (int b = 0; b < n_backends; b++) {
+        sched->backends[b] = backends[b];
+        sched->bufts[b] = bufts ? bufts[b] : ggml_backend_get_default_buffer_type(backends[b]);
+        GGML_ASSERT(ggml_backend_buft_supports_backend(sched->bufts[b], backends[b]));
+        if (sched->n_copies > 1) {
+            for (int c = 0; c < sched->n_copies; c++) {
+                sched->events[b][c] = ggml_backend_event_new(backends[b]);
+            }
+        }
     }
 
     sched->galloc = ggml_gallocr_new_n(sched->bufts, n_backends);
@@ -1539,55 +1766,91 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) {
     if (sched == NULL) {
         return;
     }
+    for (int b = 0; b < sched->n_backends; b++) {
+        for (int c = 0; c < sched->n_copies; c++) {
+            ggml_backend_event_free(sched->events[b][c]);
+        }
+    }
     ggml_gallocr_free(sched->galloc);
     ggml_free(sched->ctx);
+    free(sched->splits);
     free(sched->hash_set.keys);
     free(sched->tensor_backend_id);
     free(sched->tensor_copies);
     free(sched->node_backend_ids);
+    free(sched->leaf_backend_ids);
     free(sched);
 }
 
 void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
     // reset state for the next run
-    size_t hash_size = sched->hash_set.size;
-    memset(sched->hash_set.keys,      0, sizeof(sched->hash_set.keys[0])     * hash_size); // NOLINT
-    memset(sched->tensor_backend_id, -1, sizeof(sched->tensor_backend_id[0]) * hash_size);
-    memset(sched->tensor_copies,      0, sizeof(sched->tensor_copies[0])     * hash_size);
+    if (!sched->is_reset) {
+        size_t hash_size = sched->hash_set.size;
+        memset(sched->hash_set.keys,      0, sizeof(sched->hash_set.keys[0])     * hash_size); // NOLINT
+        memset(sched->tensor_backend_id, -1, sizeof(sched->tensor_backend_id[0]) * hash_size);
+        memset(sched->tensor_copies,      0, sizeof(sched->tensor_copies[0])     * hash_size);
 
-    sched->is_reset = true;
+        sched->is_reset = true;
+    }
+    sched->is_alloc = false;
 }
 
 bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) {
+    GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes);
+
     ggml_backend_sched_split_graph(sched, measure_graph);
 
-    if (!ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids)) {
+    // TODO: extract this to a separate function
+    if (!ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids, sched->leaf_backend_ids)) {
         return false;
     }
 
     ggml_backend_sched_reset(sched);
+    ggml_backend_sched_synchronize(sched);
+
     return true;
 }
 
-bool ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
-    GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS);
-
-    if (!sched->is_reset) {
-        ggml_backend_sched_reset(sched);
-    }
+bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
+    GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes);
 
     ggml_backend_sched_split_graph(sched, graph);
+
     if (!ggml_backend_sched_alloc_splits(sched)) {
         return false;
     }
 
-    if (!ggml_backend_sched_compute_splits(sched)) {
-        return false;
-    }
+    sched->is_alloc = true;
 
     return true;
 }
 
+enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
+    enum ggml_status err = ggml_backend_sched_graph_compute_async(sched, graph);
+    ggml_backend_sched_synchronize(sched);
+    return err;
+}
+
+enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
+    if (!sched->is_reset && !sched->is_alloc) {
+        ggml_backend_sched_reset(sched);
+    }
+
+    if (!sched->is_alloc) {
+        if (!ggml_backend_sched_alloc_graph(sched, graph)) {
+            return GGML_STATUS_ALLOC_FAILED;
+        }
+    }
+
+    return ggml_backend_sched_compute_splits(sched);
+}
+
+void ggml_backend_sched_synchronize(ggml_backend_sched_t sched) {
+    for (int i = 0; i < sched->n_backends; i++) {
+        ggml_backend_synchronize(sched->backends[i]);
+    }
+}
+
 void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) {
     sched->callback_eval = callback;
     sched->callback_eval_user_data = user_data;
@@ -1597,19 +1860,24 @@ int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched) {
     return sched->n_splits;
 }
 
+int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched) {
+    return sched->n_copies;
+}
+
 size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend) {
     int backend_index = ggml_backend_sched_backend_id(sched, backend);
     GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
+
     return ggml_gallocr_get_buffer_size(sched->galloc, backend_index);
 }
 
-void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) {
+void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) {
     int backend_index = ggml_backend_sched_backend_id(sched, backend);
     GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
     tensor_backend_id(node) = backend_index;
 }
 
-ggml_backend_t ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) {
+ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) {
     int backend_index = tensor_backend_id(node);
     if (backend_index == -1) {
         return NULL;
@@ -1706,10 +1974,10 @@ static void graph_copy_init_tensor(struct ggml_hash_set hash_set, struct ggml_te
 struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph) {
     struct ggml_hash_set hash_set = {
         /* .size = */ graph->visited_hash_table.size,
-        /* .keys = */ calloc(sizeof(hash_set.keys[0]), graph->visited_hash_table.size) // NOLINT
+        /* .keys = */ calloc(graph->visited_hash_table.size, sizeof(hash_set.keys[0])) // NOLINT
     };
-    struct ggml_tensor ** node_copies = calloc(sizeof(node_copies[0]), hash_set.size); // NOLINT
-    bool * node_init = calloc(sizeof(node_init[0]), hash_set.size);
+    struct ggml_tensor ** node_copies = calloc(hash_set.size, sizeof(node_copies[0])); // NOLINT
+    bool * node_init = calloc(hash_set.size, sizeof(node_init[0]));
 
     struct ggml_init_params params = {
         /* .mem_size   = */ ggml_tensor_overhead()*hash_set.size + ggml_graph_overhead_custom(graph->size, false),
diff --git a/ggml-backend.h b/ggml-backend.h
index f13c69bff..744b6a774 100644
--- a/ggml-backend.h
+++ b/ggml-backend.h
@@ -9,6 +9,7 @@ extern "C" {
 
     typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t;
     typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
+    typedef struct ggml_backend_event * ggml_backend_event_t;
     typedef struct ggml_backend * ggml_backend_t;
     typedef void * ggml_backend_graph_plan_t;
 
@@ -49,7 +50,7 @@ extern "C" {
     // Backend
     //
 
-
+    GGML_API ggml_guid_t  ggml_backend_guid(ggml_backend_t backend);
     GGML_API const char * ggml_backend_name(ggml_backend_t backend);
     GGML_API void         ggml_backend_free(ggml_backend_t backend);
 
@@ -66,16 +67,30 @@ extern "C" {
 
     GGML_API void ggml_backend_synchronize(ggml_backend_t backend);
 
-    GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create (ggml_backend_t backend, struct ggml_cgraph * cgraph);
+    GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph);
+    GGML_API void                      ggml_backend_graph_plan_free  (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
 
-    GGML_API void ggml_backend_graph_plan_free   (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
-    GGML_API void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
-    GGML_API bool ggml_backend_graph_compute     (ggml_backend_t backend, struct ggml_cgraph * cgraph);
-    GGML_API bool ggml_backend_supports_op       (ggml_backend_t backend, const struct ggml_tensor * op);
+    GGML_API enum ggml_status ggml_backend_graph_plan_compute (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
+    GGML_API enum ggml_status ggml_backend_graph_compute      (ggml_backend_t backend, struct ggml_cgraph * cgraph);
+    GGML_API enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph);
+    GGML_API bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op);
+    GGML_API bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op);
 
     // tensor copy between different backends
     GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst);
-    GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); // automatic fallback to sync copy
+
+    // asynchronous copy
+    // the copy is performed after all the currently queued operations in backend_src
+    // backend_dst will wait for the copy to complete before performing other operations
+    // automatic fallback to sync copy if async is not supported
+    GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst);
+
+    // events
+    GGML_API ggml_backend_event_t   ggml_backend_event_new        (ggml_backend_t backend);
+    GGML_API void                   ggml_backend_event_free       (ggml_backend_event_t event);
+    GGML_API void                   ggml_backend_event_record     (ggml_backend_event_t event);
+    GGML_API void                   ggml_backend_event_synchronize(ggml_backend_event_t event);
+    GGML_API void                   ggml_backend_event_wait       (ggml_backend_t backend, ggml_backend_event_t event); // wait async on event
 
     //
     // CPU backend
@@ -122,27 +137,31 @@ extern "C" {
     /*
       Example usage:
 
-        sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, num_backends);
-        // sched is initialized with measure allocators and cannot be used until allocated with a measure graph
+        // operations that use tensors allocated in a buffer with USAGE_WEIGHTS will be assigned
+        // preferrably to run on the same backend as the buffer
+        ggml_backend_buffer_set_usage(buf_weights, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
 
-        // initialize buffers from a measure graph
-        measure_graph = build_graph(sched); // use the allocr to allocate inputs as needed
+        sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, NULL, num_backends, GGML_DEFAULT_GRAPH_SIZE, false);
 
-        // in build_graph:
-        build_graph(...) {
-            // manually assign nodes to a backend (optional, should not be needed in most cases)
-            struct ggml_tensor * node = ggml_mul_mat(ctx, ...);
-            ggml_backend_sched_set_node_backend(sched, node, backend_gpu);
-        }
+        // initialize buffers from a max size graph (optional)
+        reserve_graph = build_graph(sched, max_batch_size);
 
-        // allocate backend buffers from measure graph
-        ggml_backend_sched_init_measure(sched, measure_graph);
+        // manually assign nodes to a backend (optional, should not be needed in most cases)
+        struct ggml_tensor * node = ggml_mul_mat(ctx, ...);
+        ggml_backend_sched_set_tensor_backend(sched, node, backend_gpu);
 
-        // the scheduler is now ready to compute graphs
+        ggml_backend_sched_reserve(sched, reserve_graph);
 
         // compute
         graph = build_graph(sched);
         ggml_backend_sched_graph_compute(sched, graph);
+
+        // if there are graph inputs:
+        ggml_backend_sched_reset(sched);
+        ggml_backend_sched_alloc_graph(sched, graph);
+        ggml_backend_tensor_set(input_tensor, ...);
+        ggml_backend_sched_graph_compute(sched, graph);
+    }
     */
 
     struct ggml_backend_sched;
@@ -157,26 +176,32 @@ extern "C" {
     typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
 
     // Initialize a backend scheduler
-    GGML_API ggml_backend_sched_t  ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size);
-    GGML_API void                  ggml_backend_sched_free(ggml_backend_sched_t sched);
+    GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel);
+    GGML_API void                 ggml_backend_sched_free(ggml_backend_sched_t sched);
+
     // Initialize backend buffers from a measure graph
-    GGML_API bool                  ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph);
+    GGML_API bool                 ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph);
+
     // Get the number of splits of the last graph
-    GGML_API int                   ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched);
+    GGML_API int                  ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched);
+    GGML_API int                  ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched);
 
-    GGML_API size_t                ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
+    GGML_API size_t               ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
 
-    GGML_API void                  ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
-    GGML_API ggml_backend_t        ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
+    GGML_API void                 ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
+    GGML_API ggml_backend_t       ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
 
     // Allocate and compute graph on the backend scheduler
-    GGML_API bool                  ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
+    GGML_API bool                 ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
+    GGML_API enum ggml_status     ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
+    GGML_API enum ggml_status     ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
+    GGML_API void                 ggml_backend_sched_synchronize(ggml_backend_sched_t sched);
 
     // Reset all assignments and allocators - must be called before changing the node backends
-    GGML_API void                  ggml_backend_sched_reset(ggml_backend_sched_t sched);
+    GGML_API void                 ggml_backend_sched_reset(ggml_backend_sched_t sched);
 
     // Set a callback to be called for each resulting node during graph compute
-    GGML_API void                  ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data);
+    GGML_API void                 ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data);
 
     //
     // Utils
diff --git a/ggml-common.h b/ggml-common.h
new file mode 100644
index 000000000..43c7978a0
--- /dev/null
+++ b/ggml-common.h
@@ -0,0 +1,1853 @@
+#ifndef GGML_COMMON_DECL
+
+#if defined(GGML_COMMON_DECL_C)
+#include <stdint.h>
+
+typedef uint16_t ggml_half;
+typedef uint32_t ggml_half2;
+
+#define GGML_COMMON_AGGR
+
+#define GGML_COMMON_DECL
+#elif defined(GGML_COMMON_DECL_METAL)
+#include <metal_stdlib>
+
+typedef half  ggml_half;
+typedef half2 ggml_half2;
+
+#define GGML_COMMON_AGGR
+
+#define GGML_COMMON_DECL
+#elif defined(GGML_COMMON_DECL_CUDA)
+#include <cuda_fp16.h>
+#include <cstdint>
+
+typedef half  ggml_half;
+typedef half2 ggml_half2;
+
+#define GGML_COMMON_AGGR data
+
+#define GGML_COMMON_DECL
+#elif defined(GGML_COMMON_DECL_HIP)
+#include <hip/hip_fp16.h>
+#include <cstdint>
+
+typedef half  ggml_half;
+typedef half2 ggml_half2;
+
+#define GGML_COMMON_AGGR data
+
+#define GGML_COMMON_DECL
+#elif defined(GGML_COMMON_DECL_SYCL)
+#include <sycl/half_type.hpp>
+#include <cstdint>
+
+typedef sycl::half  ggml_half;
+typedef sycl::half2 ggml_half2;
+
+#define GGML_COMMON_AGGR data
+
+#define GGML_COMMON_DECL
+#endif
+
+#if defined(GGML_COMMON_DECL)
+
+#ifndef __cplusplus
+#ifndef static_assert
+#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
+#define static_assert(cond, msg) _Static_assert(cond, msg)
+#else
+#define static_assert(cond, msg) struct global_scope_noop_trick
+#endif
+#endif
+#endif // __cplusplus
+
+// QK = number of values after dequantization
+// QK_K = super-block size
+
+#ifdef GGML_QKK_64
+#define QK_K 64
+#define K_SCALE_SIZE 4
+#else
+#define QK_K 256
+#define K_SCALE_SIZE 12
+#endif // GGML_QKK_64
+
+#if defined(GGML_COMMON_DECL_CUDA) || defined(GGML_COMMON_DECL_HIP) || defined(GGML_COMMON_DECL_SYCL)
+// QR = QK / number of values before dequantization
+// QI = number of 32 bit integers before dequantization
+
+#define QI4_0 (QK4_0 / (4 * QR4_0))
+#define QR4_0 2
+
+#define QI4_1 (QK4_1 / (4 * QR4_1))
+#define QR4_1 2
+
+#define QI5_0 (QK5_0 / (4 * QR5_0))
+#define QR5_0 2
+
+#define QI5_1 (QK5_1 / (4 * QR5_1))
+#define QR5_1 2
+
+#define QI8_0 (QK8_0 / (4 * QR8_0))
+#define QR8_0 1
+
+#define QI8_1 (QK8_1 / (4 * QR8_1))
+#define QR8_1 1
+
+#define QI2_K (QK_K / (4*QR2_K))
+#define QR2_K 4
+
+#define QI3_K (QK_K / (4*QR3_K))
+#define QR3_K 4
+
+#define QI4_K (QK_K / (4*QR4_K))
+#define QR4_K 2
+
+#define QI5_K (QK_K / (4*QR5_K))
+#define QR5_K 2
+
+#define QI6_K (QK_K / (4*QR6_K))
+#define QR6_K 2
+
+#define QI2_XXS (QK_K / (4*QR2_XXS))
+#define QR2_XXS 8
+
+#define QI2_XS (QK_K / (4*QR2_XS))
+#define QR2_XS 8
+
+#define QI2_S (QK_K / (4*QR2_S))
+#define QR2_S 8
+
+#define QI3_XXS (QK_K / (4*QR3_XXS))
+#define QR3_XXS 8
+
+#define QI3_XS (QK_K / (4*QR3_XS))
+#define QR3_XS 8
+
+#define QI1_S (QK_K / (4*QR1_S))
+#define QR1_S 8
+
+#define QI4_NL (QK4_NL / (4*QR4_NL))
+#define QR4_NL 2
+
+#if QK_K == 64
+#define QI4_XS QI4_NL
+#define QR4_XS QR4_NL
+#else
+#define QI4_XS (QK_K / (4*QR4_XS))
+#define QR4_XS 8
+#endif
+
+#endif // GGML_COMMON_DECL_CUDA || GGML_COMMON_DECL_HIP
+
+#define QK4_0 32
+typedef struct {
+    ggml_half d;           // delta
+    uint8_t qs[QK4_0 / 2]; // nibbles / quants
+} block_q4_0;
+static_assert(sizeof(block_q4_0) == sizeof(ggml_half) + QK4_0 / 2, "wrong q4_0 block size/padding");
+
+#define QK4_1 32
+typedef struct {
+    union {
+        struct {
+            ggml_half d; // delta
+            ggml_half m; // min
+        } GGML_COMMON_AGGR;
+        ggml_half2 dm;
+    };
+    uint8_t qs[QK4_1 / 2]; // nibbles / quants
+} block_q4_1;
+static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_half) + QK4_1 / 2, "wrong q4_1 block size/padding");
+
+#define QK5_0 32
+typedef struct {
+    ggml_half d;           // delta
+    uint8_t qh[4];         // 5-th bit of quants
+    uint8_t qs[QK5_0 / 2]; // nibbles / quants
+} block_q5_0;
+static_assert(sizeof(block_q5_0) == sizeof(ggml_half) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
+
+#define QK5_1 32
+typedef struct {
+    union {
+        struct {
+            ggml_half d; // delta
+            ggml_half m; // min
+        } GGML_COMMON_AGGR;
+        ggml_half2 dm;
+    };
+    uint8_t qh[4];         // 5-th bit of quants
+    uint8_t qs[QK5_1 / 2]; // nibbles / quants
+} block_q5_1;
+static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_half) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
+
+#define QK8_0 32
+typedef struct {
+    ggml_half d;       // delta
+    int8_t  qs[QK8_0]; // quants
+} block_q8_0;
+static_assert(sizeof(block_q8_0) == sizeof(ggml_half) + QK8_0, "wrong q8_0 block size/padding");
+
+#define QK8_1 32
+typedef struct {
+    union {
+        struct {
+            ggml_half d; // delta
+            ggml_half s; // d * sum(qs[i])
+        } GGML_COMMON_AGGR;
+        ggml_half2 ds;
+    };
+    int8_t qs[QK8_1]; // quants
+} block_q8_1;
+static_assert(sizeof(block_q8_1) == 2*sizeof(ggml_half) + QK8_1, "wrong q8_1 block size/padding");
+
+//
+// Super-block quantization structures
+//
+
+// 2-bit quantization
+// weight is represented as x = a * q + b
+// 16 blocks of 16 elements each
+// Effectively 2.625 bits per weight
+typedef struct {
+    uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
+    uint8_t qs[QK_K/4];      // quants
+    union {
+        struct {
+            ggml_half d;    // super-block scale for quantized scales
+            ggml_half dmin; // super-block scale for quantized mins
+        } GGML_COMMON_AGGR;
+        ggml_half2 dm;
+    };
+} block_q2_K;
+static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_half) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
+
+// 3-bit quantization
+// weight is represented as x = a * q
+// 16 blocks of 16 elements each
+// Effectively 3.4375 bits per weight
+#ifdef GGML_QKK_64
+typedef struct {
+    uint8_t hmask[QK_K/8]; // quants - high bit
+    uint8_t qs[QK_K/4];    // quants - low 2 bits
+    uint8_t scales[2];
+    ggml_half d;           // super-block scale
+} block_q3_K;
+static_assert(sizeof(block_q3_K) == sizeof(ggml_half) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding");
+#else
+typedef struct {
+    uint8_t hmask[QK_K/8]; // quants - high bit
+    uint8_t qs[QK_K/4];    // quants - low 2 bits
+    uint8_t scales[12];    // scales, quantized with 6 bits
+    ggml_half d;           // super-block scale
+} block_q3_K;
+static_assert(sizeof(block_q3_K) == sizeof(ggml_half) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding");
+#endif
+
+// 4-bit quantization
+// 8 blocks of 32 elements each
+// weight is represented as x = a * q + b
+// Effectively 4.5 bits per weight
+#ifdef GGML_QKK_64
+typedef struct {
+    ggml_half d[2];     // super-block scales/mins
+    uint8_t scales[2];  // 4-bit block scales/mins
+    uint8_t qs[QK_K/2]; // 4--bit quants
+} block_q4_K;
+static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_half) + QK_K/2 + 2, "wrong q4_K block size/padding");
+#else
+typedef struct {
+    union {
+        struct {
+            ggml_half d;    // super-block scale for quantized scales
+            ggml_half dmin; // super-block scale for quantized mins
+        } GGML_COMMON_AGGR;
+        ggml_half2 dm;
+    };
+    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
+    uint8_t qs[QK_K/2];           // 4--bit quants
+} block_q4_K;
+static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_half) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding");
+#endif
+
+// 5-bit quantization
+// 8 blocks of 32 elements each
+// weight is represented as x = a * q + b
+// Effectively 5.5 bits per weight
+#ifdef GGML_QKK_64
+typedef struct {
+    ggml_half d;             // super-block scale
+    int8_t  scales[QK_K/16]; // 8-bit block scales
+    uint8_t qh[QK_K/8];      // quants, high bit
+    uint8_t qs[QK_K/2];      // quants, low 4 bits
+} block_q5_K;
+static_assert(sizeof(block_q5_K) == sizeof(ggml_half) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
+#else
+typedef struct {
+    union {
+        struct {
+            ggml_half d;    // super-block scale for quantized scales
+            ggml_half dmin; // super-block scale for quantized mins
+        } GGML_COMMON_AGGR;
+        ggml_half2 dm;
+    };
+    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
+    uint8_t qh[QK_K/8];           // quants, high bit
+    uint8_t qs[QK_K/2];           // quants, low 4 bits
+} block_q5_K;
+static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_half) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
+#endif
+
+// 6-bit quantization
+// weight is represented as x = a * q
+// 16 blocks of 16 elements each
+// Effectively 6.5625 bits per weight
+typedef struct {
+    uint8_t ql[QK_K/2];      // quants, lower 4 bits
+    uint8_t qh[QK_K/4];      // quants, upper 2 bits
+    int8_t  scales[QK_K/16]; // scales, quantized with 8 bits
+    ggml_half d;             // super-block scale
+} block_q6_K;
+static_assert(sizeof(block_q6_K) == sizeof(ggml_half) + QK_K / 16 + 3*QK_K/4, "wrong q6_K block size/padding");
+
+// This is only used for intermediate quantization and dot products
+typedef struct {
+    float   d;              // delta
+    int8_t  qs[QK_K];       // quants
+    int16_t bsums[QK_K/16]; // sum of quants in groups of 16
+} block_q8_K;
+static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_K block size/padding");
+
+// (Almost) "true" 2-bit quantization.
+// Due to the need to use blocks as per ggml design, it ends up using
+// 2.0625 bpw because of the 16-bit scale for each block of 256.
+typedef struct {
+    ggml_half d;
+    uint16_t qs[QK_K/8];
+} block_iq2_xxs;
+static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_half) + QK_K/8*sizeof(uint16_t), "wrong iq2_xxs block size/padding");
+
+// 2.3125 bpw quants
+typedef struct {
+    ggml_half d;
+    uint16_t qs[QK_K/8];
+    uint8_t  scales[QK_K/32];
+} block_iq2_xs;
+static_assert(sizeof(block_iq2_xs) == sizeof(ggml_half) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding");
+
+// 2.5625 bpw quants
+typedef struct {
+    ggml_half d;
+    uint8_t qs[QK_K/4];
+    uint8_t qh[QK_K/32];
+    uint8_t scales[QK_K/32];
+} block_iq2_s;
+static_assert(sizeof(block_iq2_s) == sizeof(ggml_half) + QK_K/4 + QK_K/16, "wrong iq2_s block size/padding");
+
+// (Almost) "true" 3-bit quantization.
+// Due to the need to use blocks as per ggml design, it ends up using
+// 3.0625 bpw because of the 16-bit scale for each block of 256.
+typedef struct {
+    ggml_half d;
+    uint8_t qs[3*QK_K/8];
+} block_iq3_xxs;
+static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_half) + 3*(QK_K/8), "wrong iq3_xxs block size/padding");
+
+// 3.4375 bpw
+#if QK_K == 64
+#define IQ3S_N_SCALE 2
+#else
+#define IQ3S_N_SCALE QK_K/64
+#endif
+typedef struct {
+    ggml_half d;
+    uint8_t qs[QK_K/4];
+    uint8_t qh[QK_K/32];
+    uint8_t signs[QK_K/8];
+    uint8_t scales[IQ3S_N_SCALE];
+} block_iq3_s;
+static_assert(sizeof(block_iq3_s) == sizeof(ggml_half) + 13*(QK_K/32) + IQ3S_N_SCALE, "wrong iq3_s block size/padding");
+
+typedef struct {
+    ggml_half d;
+    uint8_t  qs[QK_K/8];
+    uint16_t qh[QK_K/32];
+} block_iq1_s;
+static_assert(sizeof(block_iq1_s) == sizeof(ggml_half) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding");
+
+// 1.75 bpw
+typedef struct {
+    uint8_t  qs[QK_K/8];      // grid index, low 8 bits
+    uint8_t  qh[QK_K/16];     // grid index, high 3 bits + grid shift bit (for two groups of 8)
+#if QK_K == 64
+    ggml_half d;
+#endif
+    uint8_t  scales[QK_K/32]; // 3-bit block scales (4-bit if QK_K == 64)
+} block_iq1_m;
+#if QK_K == 64
+static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32 + sizeof(ggml_half), "wrong iq1_m block size/padding");
+#else
+static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32, "wrong iq1_m block size/padding");
+#endif
+
+// Used by IQ1_M quants
+typedef union {
+    ggml_half f16;
+    uint16_t  u16;
+} iq1m_scale_t;
+
+// Non-linear quants
+#define QK4_NL 32
+typedef struct {
+    ggml_half d;
+    uint8_t qs[QK4_NL/2];
+} block_iq4_nl;
+static_assert(sizeof(block_iq4_nl) == sizeof(ggml_half) + QK4_NL/2, "wrong iq4_nl block size/padding");
+
+#if QK_K == 64
+#define block_iq4_xs block_iq4_nl
+#else
+typedef struct {
+    ggml_half d;
+    uint16_t scales_h;
+    uint8_t  scales_l[QK_K/64];
+    uint8_t  qs[QK_K/2];
+} block_iq4_xs;
+static_assert(sizeof(block_iq4_xs) == sizeof(ggml_half) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding");
+#endif
+
+#endif // GGML_COMMON_DECL
+#endif // GGML_COMMON_DECL
+
+////////////////////////////////////////////////////////////////////////////////
+
+#ifndef GGML_COMMON_IMPL
+
+#if defined(GGML_COMMON_IMPL_C)
+#include <stdint.h>
+
+#define GGML_TABLE_BEGIN(type, name, size) static const type name[size] = {
+#define GGML_TABLE_END() };
+
+#define GGML_COMMON_IMPL
+#elif defined(GGML_COMMON_IMPL_METAL)
+#include <metal_stdlib>
+
+#define GGML_TABLE_BEGIN(type, name, size) static const constant type name[size] = {
+#define GGML_TABLE_END() };
+
+#define GGML_COMMON_IMPL
+#elif defined(GGML_COMMON_IMPL_CUDA) || defined(GGML_COMMON_IMPL_HIP)
+#include <cstdint>
+
+#define GGML_TABLE_BEGIN(type, name, size) static const __device__ type name[size] = {
+#define GGML_TABLE_END() };
+
+#define GGML_COMMON_IMPL
+#elif defined(GGML_COMMON_IMPL_SYCL)
+
+#include <cstdint>
+
+#define GGML_TABLE_BEGIN(type, name, size) static const type name[size] = {
+#define GGML_TABLE_END() };
+
+#define GGML_COMMON_IMPL
+#endif
+
+#if defined(GGML_COMMON_IMPL)
+
+GGML_TABLE_BEGIN(uint8_t, kmask_iq2xs, 8)
+    1, 2, 4, 8, 16, 32, 64, 128
+GGML_TABLE_END()
+
+GGML_TABLE_BEGIN(uint8_t, ksigns_iq2xs, 128)
+      0, 129, 130,   3, 132,   5,   6, 135, 136,   9,  10, 139,  12, 141, 142,  15,
+    144,  17,  18, 147,  20, 149, 150,  23,  24, 153, 154,  27, 156,  29,  30, 159,
+    160,  33,  34, 163,  36, 165, 166,  39,  40, 169, 170,  43, 172,  45,  46, 175,
+     48, 177, 178,  51, 180,  53,  54, 183, 184,  57,  58, 187,  60, 189, 190,  63,
+    192,  65,  66, 195,  68, 197, 198,  71,  72, 201, 202,  75, 204,  77,  78, 207,
+     80, 209, 210,  83, 212,  85,  86, 215, 216,  89,  90, 219,  92, 221, 222,  95,
+     96, 225, 226,  99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111,
+    240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255,
+GGML_TABLE_END()
+
+//#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+GGML_TABLE_BEGIN(uint64_t, ksigns64, 128)
+    0x0000000000000000, 0xff000000000000ff, 0xff0000000000ff00, 0x000000000000ffff,
+    0xff00000000ff0000, 0x0000000000ff00ff, 0x0000000000ffff00, 0xff00000000ffffff,
+    0xff000000ff000000, 0x00000000ff0000ff, 0x00000000ff00ff00, 0xff000000ff00ffff,
+    0x00000000ffff0000, 0xff000000ffff00ff, 0xff000000ffffff00, 0x00000000ffffffff,
+    0xff0000ff00000000, 0x000000ff000000ff, 0x000000ff0000ff00, 0xff0000ff0000ffff,
+    0x000000ff00ff0000, 0xff0000ff00ff00ff, 0xff0000ff00ffff00, 0x000000ff00ffffff,
+    0x000000ffff000000, 0xff0000ffff0000ff, 0xff0000ffff00ff00, 0x000000ffff00ffff,
+    0xff0000ffffff0000, 0x000000ffffff00ff, 0x000000ffffffff00, 0xff0000ffffffffff,
+    0xff00ff0000000000, 0x0000ff00000000ff, 0x0000ff000000ff00, 0xff00ff000000ffff,
+    0x0000ff0000ff0000, 0xff00ff0000ff00ff, 0xff00ff0000ffff00, 0x0000ff0000ffffff,
+    0x0000ff00ff000000, 0xff00ff00ff0000ff, 0xff00ff00ff00ff00, 0x0000ff00ff00ffff,
+    0xff00ff00ffff0000, 0x0000ff00ffff00ff, 0x0000ff00ffffff00, 0xff00ff00ffffffff,
+    0x0000ffff00000000, 0xff00ffff000000ff, 0xff00ffff0000ff00, 0x0000ffff0000ffff,
+    0xff00ffff00ff0000, 0x0000ffff00ff00ff, 0x0000ffff00ffff00, 0xff00ffff00ffffff,
+    0xff00ffffff000000, 0x0000ffffff0000ff, 0x0000ffffff00ff00, 0xff00ffffff00ffff,
+    0x0000ffffffff0000, 0xff00ffffffff00ff, 0xff00ffffffffff00, 0x0000ffffffffffff,
+    0xffff000000000000, 0x00ff0000000000ff, 0x00ff00000000ff00, 0xffff00000000ffff,
+    0x00ff000000ff0000, 0xffff000000ff00ff, 0xffff000000ffff00, 0x00ff000000ffffff,
+    0x00ff0000ff000000, 0xffff0000ff0000ff, 0xffff0000ff00ff00, 0x00ff0000ff00ffff,
+    0xffff0000ffff0000, 0x00ff0000ffff00ff, 0x00ff0000ffffff00, 0xffff0000ffffffff,
+    0x00ff00ff00000000, 0xffff00ff000000ff, 0xffff00ff0000ff00, 0x00ff00ff0000ffff,
+    0xffff00ff00ff0000, 0x00ff00ff00ff00ff, 0x00ff00ff00ffff00, 0xffff00ff00ffffff,
+    0xffff00ffff000000, 0x00ff00ffff0000ff, 0x00ff00ffff00ff00, 0xffff00ffff00ffff,
+    0x00ff00ffffff0000, 0xffff00ffffff00ff, 0xffff00ffffffff00, 0x00ff00ffffffffff,
+    0x00ffff0000000000, 0xffffff00000000ff, 0xffffff000000ff00, 0x00ffff000000ffff,
+    0xffffff0000ff0000, 0x00ffff0000ff00ff, 0x00ffff0000ffff00, 0xffffff0000ffffff,
+    0xffffff00ff000000, 0x00ffff00ff0000ff, 0x00ffff00ff00ff00, 0xffffff00ff00ffff,
+    0x00ffff00ffff0000, 0xffffff00ffff00ff, 0xffffff00ffffff00, 0x00ffff00ffffffff,
+    0xffffffff00000000, 0x00ffffff000000ff, 0x00ffffff0000ff00, 0xffffffff0000ffff,
+    0x00ffffff00ff0000, 0xffffffff00ff00ff, 0xffffffff00ffff00, 0x00ffffff00ffffff,
+    0x00ffffffff000000, 0xffffffffff0000ff, 0xffffffffff00ff00, 0x00ffffffff00ffff,
+    0xffffffffffff0000, 0x00ffffffffff00ff, 0x00ffffffffffff00, 0xffffffffffffffff,
+GGML_TABLE_END()
+//#endif
+
+
+GGML_TABLE_BEGIN(uint64_t, iq2xxs_grid, 256)
+    0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08,
+    0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808,
+    0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819,
+    0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, 0x08080808192b0819,
+    0x08080808192b1908, 0x080808082b080808, 0x080808082b08082b, 0x080808082b082b2b,
+    0x080808082b2b082b, 0x0808081908080819, 0x0808081908081908, 0x0808081908190808,
+    0x0808081908191919, 0x0808081919080808, 0x080808192b081908, 0x080808192b192b08,
+    0x0808082b08080808, 0x0808082b0808082b, 0x0808082b082b082b, 0x0808082b2b08082b,
+    0x0808190808080819, 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819,
+    0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, 0x0808190819082b08,
+    0x08081908192b0808, 0x080819082b080819, 0x080819082b081908, 0x080819082b190808,
+    0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08,
+    0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, 0x080819192b080808,
+    0x080819192b190819, 0x0808192b08082b19, 0x0808192b08190808, 0x0808192b19080808,
+    0x0808192b2b081908, 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919,
+    0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, 0x08082b0819080819,
+    0x08082b0819081908, 0x08082b0819190808, 0x08082b081919082b, 0x08082b082b082b08,
+    0x08082b1908081908, 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908,
+    0x0819080808080819, 0x0819080808081908, 0x0819080808190808, 0x08190808082b0819,
+    0x0819080819080808, 0x08190808192b0808, 0x081908082b081908, 0x081908082b190808,
+    0x081908082b191919, 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808,
+    0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, 0x0819082b082b1908,
+    0x0819082b19081919, 0x0819190808080808, 0x0819190808082b08, 0x08191908082b0808,
+    0x08191908082b1919, 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08,
+    0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, 0x08192b0808080819,
+    0x08192b0808081908, 0x08192b0808190808, 0x08192b0819080808, 0x08192b082b080819,
+    0x08192b1908080808, 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819,
+    0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, 0x082b080819081908,
+    0x082b0808192b0819, 0x082b08082b080808, 0x082b08082b08082b, 0x082b0819082b2b19,
+    0x082b081919082b08, 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819,
+    0x082b190808081908, 0x082b190808190808, 0x082b190819080808, 0x082b19081919192b,
+    0x082b191908080808, 0x082b191919080819, 0x082b1919192b1908, 0x082b192b2b190808,
+    0x082b2b0808082b08, 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908,
+    0x1908080808080819, 0x1908080808081908, 0x1908080808190808, 0x1908080808192b08,
+    0x19080808082b0819, 0x19080808082b1908, 0x1908080819080808, 0x1908080819082b08,
+    0x190808081919192b, 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908,
+    0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, 0x19080819192b0819,
+    0x190808192b080808, 0x190808192b081919, 0x1908082b08080819, 0x1908082b08190808,
+    0x1908082b19082b08, 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808,
+    0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, 0x190819082b192b19,
+    0x190819190819082b, 0x19081919082b1908, 0x1908192b08080808, 0x19082b0808080819,
+    0x19082b0808081908, 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919,
+    0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, 0x19082b192b08082b,
+    0x19082b2b19081919, 0x19082b2b2b190808, 0x1919080808080808, 0x1919080808082b08,
+    0x1919080808190819, 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808,
+    0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, 0x191908192b2b1908,
+    0x1919082b2b190819, 0x191919082b190808, 0x191919082b19082b, 0x1919191908082b2b,
+    0x1919192b08080819, 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819,
+    0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, 0x19192b2b08082b08,
+    0x192b080808081908, 0x192b080808190808, 0x192b080819080808, 0x192b0808192b2b08,
+    0x192b081908080808, 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808,
+    0x192b190808080808, 0x192b190808081919, 0x192b191908190808, 0x192b19190819082b,
+    0x192b19192b081908, 0x192b2b081908082b, 0x2b08080808080808, 0x2b0808080808082b,
+    0x2b08080808082b2b, 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908,
+    0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819,
+    0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808,
+    0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908,
+    0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b,
+    0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808,
+    0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b,
+    0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b,
+    0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808,
+    0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19,
+    0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908,
+GGML_TABLE_END()
+
+GGML_TABLE_BEGIN(uint64_t, iq2xs_grid, 512)
+    0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08,
+    0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b,
+    0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919,
+    0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b,
+    0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919,
+    0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808,
+    0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, 0x080808082b190819,
+    0x080808082b191908, 0x080808082b192b19, 0x080808082b2b0808, 0x0808081908080819,
+    0x0808081908081908, 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808,
+    0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, 0x0808081908192b2b,
+    0x08080819082b0819, 0x08080819082b1908, 0x0808081919080808, 0x080808191908082b,
+    0x0808081919081919, 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908,
+    0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, 0x080808192b081908,
+    0x080808192b190808, 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b08081919,
+    0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808,
+    0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, 0x0808082b19191919,
+    0x0808082b2b080808, 0x0808082b2b082b2b, 0x0808190808080819, 0x0808190808081908,
+    0x080819080808192b, 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b,
+    0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908,
+    0x0808190819080808, 0x080819081908082b, 0x0808190819081919, 0x0808190819082b08,
+    0x0808190819190819, 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808,
+    0x080819082b080819, 0x080819082b081908, 0x080819082b190808, 0x0808191908080808,
+    0x080819190808082b, 0x0808191908081919, 0x0808191908082b08, 0x0808191908190819,
+    0x0808191908191908, 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908,
+    0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, 0x0808192b08080819,
+    0x0808192b08081908, 0x0808192b08190808, 0x0808192b082b192b, 0x0808192b19080808,
+    0x0808192b1908082b, 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b,
+    0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, 0x08082b0808190819,
+    0x08082b0808191908, 0x08082b08082b0808, 0x08082b08082b1919, 0x08082b0819080819,
+    0x08082b0819081908, 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808,
+    0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, 0x08082b1908081908,
+    0x08082b1908190808, 0x08082b1919080808, 0x08082b192b080819, 0x08082b192b082b19,
+    0x08082b2b08080808, 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b,
+    0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, 0x081908080808192b,
+    0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, 0x0819080808191919,
+    0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808,
+    0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, 0x0819080819190819,
+    0x0819080819191908, 0x08190808192b0808, 0x08190808192b2b2b, 0x081908082b080819,
+    0x081908082b081908, 0x081908082b190808, 0x0819081908080808, 0x081908190808082b,
+    0x0819081908081919, 0x0819081908082b08, 0x0819081908190819, 0x0819081908191908,
+    0x08190819082b0808, 0x0819081919080819, 0x0819081919081908, 0x0819081919190808,
+    0x081908192b080808, 0x081908192b191908, 0x081908192b19192b, 0x0819082b08080819,
+    0x0819082b08081908, 0x0819082b0808192b, 0x0819082b08190808, 0x0819082b19080808,
+    0x0819082b192b0808, 0x0819190808080808, 0x081919080808082b, 0x0819190808081919,
+    0x0819190808082b08, 0x0819190808190819, 0x0819190808191908, 0x08191908082b0808,
+    0x0819190819080819, 0x0819190819081908, 0x0819190819082b19, 0x0819190819190808,
+    0x08191908192b1908, 0x081919082b080808, 0x0819191908080819, 0x0819191908081908,
+    0x0819191908190808, 0x0819191919080808, 0x0819192b08080808, 0x0819192b08191908,
+    0x0819192b19082b19, 0x08192b0808080819, 0x08192b0808081908, 0x08192b0808190808,
+    0x08192b080819082b, 0x08192b0819080808, 0x08192b0819191908, 0x08192b082b08192b,
+    0x08192b1908080808, 0x08192b1908081919, 0x08192b19192b192b, 0x08192b2b19190819,
+    0x08192b2b2b2b2b19, 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919,
+    0x082b080808082b08, 0x082b080808082b2b, 0x082b080808190819, 0x082b080808191908,
+    0x082b0808082b0808, 0x082b080819080819, 0x082b080819081908, 0x082b080819190808,
+    0x082b08082b080808, 0x082b08082b2b0808, 0x082b081908080819, 0x082b081908081908,
+    0x082b081908190808, 0x082b081919080808, 0x082b081919082b08, 0x082b0819192b1919,
+    0x082b082b08080808, 0x082b082b082b082b, 0x082b082b2b080808, 0x082b082b2b2b2b08,
+    0x082b190808080819, 0x082b190808081908, 0x082b190808190808, 0x082b1908082b2b19,
+    0x082b190819080808, 0x082b191908080808, 0x082b191919080819, 0x082b19191919082b,
+    0x082b19192b192b19, 0x082b192b08080819, 0x082b192b08192b2b, 0x082b192b2b2b192b,
+    0x082b2b0808080808, 0x082b2b0808082b08, 0x082b2b0808082b2b, 0x082b2b08082b0808,
+    0x082b2b0819191919, 0x082b2b082b082b08, 0x082b2b082b2b082b, 0x082b2b19192b2b08,
+    0x082b2b192b190808, 0x082b2b2b08082b08, 0x082b2b2b082b0808, 0x082b2b2b2b08082b,
+    0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, 0x1908080808081908,
+    0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, 0x190808080819082b,
+    0x1908080808191919, 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908,
+    0x1908080819080808, 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08,
+    0x1908080819082b2b, 0x1908080819190819, 0x1908080819191908, 0x19080808192b0808,
+    0x19080808192b1919, 0x190808082b080819, 0x190808082b081908, 0x190808082b190808,
+    0x1908081908080808, 0x190808190808082b, 0x1908081908081919, 0x1908081908082b08,
+    0x1908081908190819, 0x1908081908191908, 0x19080819082b0808, 0x1908081919080819,
+    0x1908081919081908, 0x1908081919190808, 0x190808192b080808, 0x190808192b081919,
+    0x190808192b2b082b, 0x1908082b08080819, 0x1908082b08081908, 0x1908082b08190808,
+    0x1908082b0819082b, 0x1908082b082b2b19, 0x1908082b19080808, 0x1908190808080808,
+    0x190819080808082b, 0x1908190808081919, 0x1908190808082b08, 0x1908190808190819,
+    0x1908190808191908, 0x1908190808192b19, 0x19081908082b0808, 0x1908190819080819,
+    0x1908190819081908, 0x1908190819190808, 0x190819082b080808, 0x190819082b191908,
+    0x1908191908080819, 0x1908191908081908, 0x1908191908190808, 0x19081919082b1908,
+    0x1908191919080808, 0x190819192b192b2b, 0x1908192b08080808, 0x1908192b08082b2b,
+    0x1908192b19081908, 0x1908192b19190808, 0x19082b0808080819, 0x19082b0808081908,
+    0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, 0x19082b0819191908,
+    0x19082b08192b082b, 0x19082b1908080808, 0x19082b1908190819, 0x19082b1919081908,
+    0x19082b1919190808, 0x19082b19192b2b19, 0x19082b2b08081908, 0x1919080808080808,
+    0x191908080808082b, 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819,
+    0x1919080808191908, 0x19190808082b0808, 0x19190808082b2b08, 0x1919080819080819,
+    0x1919080819081908, 0x1919080819190808, 0x191908082b080808, 0x1919081908080819,
+    0x1919081908081908, 0x1919081908190808, 0x1919081908191919, 0x1919081919080808,
+    0x191908191908082b, 0x1919082b08080808, 0x1919082b19081908, 0x1919082b2b2b2b2b,
+    0x1919190808080819, 0x1919190808081908, 0x1919190808190808, 0x19191908082b0819,
+    0x1919190819080808, 0x19191908192b0808, 0x191919082b080819, 0x191919082b2b0819,
+    0x1919191908080808, 0x1919191908082b08, 0x191919192b080808, 0x191919192b082b08,
+    0x1919192b082b0819, 0x1919192b192b2b08, 0x1919192b2b2b0819, 0x19192b0808080808,
+    0x19192b0808191908, 0x19192b0819080819, 0x19192b0819190808, 0x19192b082b192b19,
+    0x19192b1908192b2b, 0x19192b1919080808, 0x19192b191908082b, 0x19192b2b2b081919,
+    0x192b080808080819, 0x192b080808081908, 0x192b080808190808, 0x192b080819080808,
+    0x192b080819191908, 0x192b0808192b082b, 0x192b08082b08192b, 0x192b08082b2b2b19,
+    0x192b081908080808, 0x192b082b082b1908, 0x192b082b19082b2b, 0x192b082b2b19082b,
+    0x192b190808080808, 0x192b19080819192b, 0x192b191908190808, 0x192b191919080808,
+    0x192b191919081919, 0x192b19192b2b1908, 0x192b2b0808080819, 0x192b2b08192b2b2b,
+    0x192b2b19082b1919, 0x192b2b2b0808192b, 0x192b2b2b19191908, 0x192b2b2b192b082b,
+    0x2b08080808080808, 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08,
+    0x2b08080808190819, 0x2b08080808191908, 0x2b080808082b0808, 0x2b080808082b2b2b,
+    0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808082b080808,
+    0x2b0808082b08082b, 0x2b0808082b2b2b08, 0x2b0808082b2b2b2b, 0x2b08081908080819,
+    0x2b08081908081908, 0x2b0808190808192b, 0x2b08081908190808, 0x2b08081919080808,
+    0x2b08081919190819, 0x2b08081919192b19, 0x2b08082b08080808, 0x2b08082b082b0808,
+    0x2b08082b2b080808, 0x2b08082b2b08082b, 0x2b08082b2b2b0808, 0x2b08082b2b2b2b08,
+    0x2b08190808080819, 0x2b08190808081908, 0x2b08190808190808, 0x2b0819080819082b,
+    0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, 0x2b0819082b082b19,
+    0x2b08191908080808, 0x2b08191919081908, 0x2b0819192b2b1919, 0x2b08192b08192b08,
+    0x2b08192b192b2b2b, 0x2b082b0808080808, 0x2b082b0808082b08, 0x2b082b08082b1919,
+    0x2b082b0819192b2b, 0x2b082b082b080808, 0x2b082b082b08082b, 0x2b082b082b2b2b08,
+    0x2b082b190808192b, 0x2b082b2b082b082b, 0x2b082b2b2b080808, 0x2b082b2b2b082b08,
+    0x2b082b2b2b19192b, 0x2b082b2b2b2b2b08, 0x2b19080808080819, 0x2b19080808081908,
+    0x2b19080808190808, 0x2b19080819080808, 0x2b1908081919192b, 0x2b1908082b081908,
+    0x2b19081908080808, 0x2b190819082b082b, 0x2b190819192b1908, 0x2b19082b1919192b,
+    0x2b19082b2b082b19, 0x2b19190808080808, 0x2b19190808081919, 0x2b19190819081908,
+    0x2b19190819190808, 0x2b19190819192b08, 0x2b191919082b2b19, 0x2b1919192b190808,
+    0x2b1919192b19082b, 0x2b19192b19080819, 0x2b192b0819190819, 0x2b192b082b2b192b,
+    0x2b192b1919082b19, 0x2b192b2b08191919, 0x2b192b2b192b0808, 0x2b2b080808080808,
+    0x2b2b08080808082b, 0x2b2b080808082b08, 0x2b2b080808082b2b, 0x2b2b0808082b0808,
+    0x2b2b0808082b2b2b, 0x2b2b08082b2b0808, 0x2b2b081919190819, 0x2b2b081919192b19,
+    0x2b2b08192b2b192b, 0x2b2b082b08080808, 0x2b2b082b0808082b, 0x2b2b082b08082b08,
+    0x2b2b082b082b2b2b, 0x2b2b082b2b080808, 0x2b2b082b2b2b0808, 0x2b2b190819080808,
+    0x2b2b19082b191919, 0x2b2b192b192b1919, 0x2b2b192b2b192b08, 0x2b2b2b0808082b2b,
+    0x2b2b2b08082b0808, 0x2b2b2b08082b082b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b0808,
+    0x2b2b2b082b2b2b08, 0x2b2b2b1908081908, 0x2b2b2b192b081908, 0x2b2b2b192b08192b,
+    0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b,
+GGML_TABLE_END()
+
+GGML_TABLE_BEGIN(uint64_t, iq2s_grid, 1024)
+    0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08,
+    0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b,
+    0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919,
+    0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b,
+    0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919,
+    0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x08080808192b192b,
+    0x08080808192b2b19, 0x080808082b080808, 0x080808082b08082b, 0x080808082b081919,
+    0x080808082b082b08, 0x080808082b190819, 0x080808082b191908, 0x080808082b2b0808,
+    0x080808082b2b1919, 0x080808082b2b2b2b, 0x0808081908080819, 0x0808081908081908,
+    0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, 0x080808190819082b,
+    0x0808081908191919, 0x0808081908192b08, 0x08080819082b0819, 0x08080819082b1908,
+    0x0808081919080808, 0x080808191908082b, 0x0808081919081919, 0x0808081919082b08,
+    0x0808081919190819, 0x0808081919191908, 0x080808191919192b, 0x0808081919192b19,
+    0x08080819192b0808, 0x08080819192b1919, 0x08080819192b2b08, 0x080808192b080819,
+    0x080808192b081908, 0x080808192b190808, 0x080808192b19082b, 0x080808192b191919,
+    0x080808192b2b0819, 0x080808192b2b1908, 0x0808082b08080808, 0x0808082b0808082b,
+    0x0808082b08081919, 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908,
+    0x0808082b082b0808, 0x0808082b082b2b2b, 0x0808082b19080819, 0x0808082b19081908,
+    0x0808082b1908192b, 0x0808082b19082b19, 0x0808082b19190808, 0x0808082b19191919,
+    0x0808082b2b080808, 0x0808082b2b081919, 0x0808082b2b082b2b, 0x0808082b2b191908,
+    0x0808082b2b2b082b, 0x0808190808080819, 0x0808190808081908, 0x080819080808192b,
+    0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, 0x0808190808191919,
+    0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, 0x08081908082b192b,
+    0x08081908082b2b19, 0x0808190819080808, 0x080819081908082b, 0x0808190819081919,
+    0x0808190819082b08, 0x0808190819082b2b, 0x0808190819190819, 0x0808190819191908,
+    0x080819081919192b, 0x0808190819192b19, 0x08081908192b0808, 0x08081908192b082b,
+    0x08081908192b1919, 0x080819082b080819, 0x080819082b081908, 0x080819082b08192b,
+    0x080819082b082b19, 0x080819082b190808, 0x080819082b191919, 0x080819082b192b08,
+    0x080819082b2b0819, 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b,
+    0x0808191908081919, 0x0808191908082b08, 0x0808191908082b2b, 0x0808191908190819,
+    0x0808191908191908, 0x080819190819192b, 0x0808191908192b19, 0x08081919082b0808,
+    0x08081919082b1919, 0x08081919082b2b08, 0x0808191919080819, 0x0808191919081908,
+    0x080819191908192b, 0x0808191919082b19, 0x0808191919190808, 0x080819191919082b,
+    0x0808191919191919, 0x0808191919192b08, 0x08081919192b0819, 0x08081919192b1908,
+    0x080819192b080808, 0x080819192b08082b, 0x080819192b081919, 0x080819192b082b08,
+    0x080819192b190819, 0x080819192b191908, 0x080819192b2b0808, 0x0808192b08080819,
+    0x0808192b08081908, 0x0808192b0808192b, 0x0808192b08082b19, 0x0808192b08190808,
+    0x0808192b08191919, 0x0808192b19080808, 0x0808192b19081919, 0x0808192b19082b08,
+    0x0808192b19190819, 0x0808192b19191908, 0x0808192b192b0808, 0x0808192b2b080819,
+    0x0808192b2b081908, 0x0808192b2b190808, 0x08082b0808080808, 0x08082b080808082b,
+    0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808190819, 0x08082b0808191908,
+    0x08082b080819192b, 0x08082b0808192b19, 0x08082b08082b0808, 0x08082b08082b1919,
+    0x08082b08082b2b2b, 0x08082b0819080819, 0x08082b0819081908, 0x08082b081908192b,
+    0x08082b0819082b19, 0x08082b0819190808, 0x08082b081919082b, 0x08082b0819191919,
+    0x08082b0819192b08, 0x08082b08192b0819, 0x08082b08192b1908, 0x08082b082b080808,
+    0x08082b082b081919, 0x08082b082b191908, 0x08082b082b2b2b2b, 0x08082b1908080819,
+    0x08082b1908081908, 0x08082b1908190808, 0x08082b190819082b, 0x08082b1908191919,
+    0x08082b1908192b08, 0x08082b19082b0819, 0x08082b1919080808, 0x08082b1919081919,
+    0x08082b1919082b08, 0x08082b1919190819, 0x08082b1919191908, 0x08082b19192b0808,
+    0x08082b192b080819, 0x08082b192b190808, 0x08082b2b08080808, 0x08082b2b08190819,
+    0x08082b2b08191908, 0x08082b2b082b082b, 0x08082b2b082b2b08, 0x08082b2b082b2b2b,
+    0x08082b2b19190808, 0x08082b2b2b192b19, 0x0819080808080819, 0x0819080808081908,
+    0x081908080808192b, 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b,
+    0x0819080808191919, 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908,
+    0x08190808082b192b, 0x0819080819080808, 0x081908081908082b, 0x0819080819081919,
+    0x0819080819082b08, 0x0819080819190819, 0x0819080819191908, 0x081908081919192b,
+    0x0819080819192b19, 0x08190808192b0808, 0x08190808192b082b, 0x08190808192b1919,
+    0x08190808192b2b08, 0x081908082b080819, 0x081908082b081908, 0x081908082b08192b,
+    0x081908082b190808, 0x081908082b191919, 0x081908082b192b08, 0x081908082b2b0819,
+    0x081908082b2b1908, 0x0819081908080808, 0x081908190808082b, 0x0819081908081919,
+    0x0819081908082b08, 0x0819081908082b2b, 0x0819081908190819, 0x0819081908191908,
+    0x081908190819192b, 0x0819081908192b19, 0x08190819082b0808, 0x08190819082b082b,
+    0x08190819082b1919, 0x08190819082b2b08, 0x0819081919080819, 0x0819081919081908,
+    0x081908191908192b, 0x0819081919082b19, 0x0819081919190808, 0x081908191919082b,
+    0x0819081919191919, 0x0819081919192b08, 0x08190819192b0819, 0x08190819192b1908,
+    0x081908192b080808, 0x081908192b08082b, 0x081908192b081919, 0x081908192b082b08,
+    0x081908192b190819, 0x081908192b191908, 0x0819082b08080819, 0x0819082b08081908,
+    0x0819082b08082b19, 0x0819082b08190808, 0x0819082b08191919, 0x0819082b082b0819,
+    0x0819082b082b1908, 0x0819082b19080808, 0x0819082b19081919, 0x0819082b19190819,
+    0x0819082b19191908, 0x0819082b2b080819, 0x0819082b2b081908, 0x0819082b2b190808,
+    0x0819190808080808, 0x081919080808082b, 0x0819190808081919, 0x0819190808082b08,
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+    0x2b1919082b080808, 0x2b1919082b19192b, 0x2b19191908080819, 0x2b19191908081908,
+    0x2b19191908190808, 0x2b19191919080808, 0x2b1919192b192b08, 0x2b1919192b2b0819,
+    0x2b19192b08080808, 0x2b19192b1908192b, 0x2b19192b192b1908, 0x2b192b0808080819,
+    0x2b192b0808081908, 0x2b192b0808190808, 0x2b192b08082b192b, 0x2b192b0819080808,
+    0x2b192b082b2b2b19, 0x2b192b1908080808, 0x2b192b1919082b19, 0x2b192b191919082b,
+    0x2b192b2b2b190808, 0x2b2b080808080808, 0x2b2b080808081919, 0x2b2b080808082b2b,
+    0x2b2b080808191908, 0x2b2b0808082b082b, 0x2b2b0808082b2b2b, 0x2b2b080819080819,
+    0x2b2b080819081908, 0x2b2b080819190808, 0x2b2b08082b2b082b, 0x2b2b08082b2b2b2b,
+    0x2b2b081919080808, 0x2b2b0819192b1919, 0x2b2b082b0808082b, 0x2b2b082b08082b2b,
+    0x2b2b082b082b082b, 0x2b2b082b082b2b08, 0x2b2b082b082b2b2b, 0x2b2b082b2b08082b,
+    0x2b2b082b2b082b08, 0x2b2b082b2b082b2b, 0x2b2b082b2b2b2b08, 0x2b2b190808080819,
+    0x2b2b190808081908, 0x2b2b190808190808, 0x2b2b190819080808, 0x2b2b19082b082b19,
+    0x2b2b19082b2b1908, 0x2b2b191908080808, 0x2b2b191908192b19, 0x2b2b192b19190819,
+    0x2b2b2b0808082b2b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b082b, 0x2b2b2b1919191908,
+    0x2b2b2b192b08192b, 0x2b2b2b2b08082b08, 0x2b2b2b2b08082b2b, 0x2b2b2b2b082b0808,
+    0x2b2b2b2b082b082b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b2b082b08, 0x2b2b2b2b2b2b2b2b,
+GGML_TABLE_END()
+
+GGML_TABLE_BEGIN(uint32_t, iq3xxs_grid, 256)
+    0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414,
+    0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14,
+    0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404,
+    0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e,
+    0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c,
+    0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c,
+    0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34,
+    0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c,
+    0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c,
+    0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04,
+    0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c,
+    0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414,
+    0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434,
+    0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c,
+    0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e,
+    0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24,
+    0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24,
+    0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c,
+    0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c,
+    0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14,
+    0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414,
+    0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e,
+    0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404,
+    0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c,
+    0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c,
+    0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14,
+    0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c,
+    0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c,
+    0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14,
+    0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14,
+    0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c,
+    0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04,
+GGML_TABLE_END()
+
+GGML_TABLE_BEGIN(uint32_t, iq3s_grid, 512)
+    0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305,
+    0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905,
+    0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09,
+    0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b,
+    0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b,
+    0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d,
+    0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03,
+    0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505,
+    0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03,
+    0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901,
+    0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d,
+    0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303,
+    0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501,
+    0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105,
+    0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505,
+    0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101,
+    0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707,
+    0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b,
+    0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01,
+    0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f,
+    0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305,
+    0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103,
+    0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509,
+    0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503,
+    0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b,
+    0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f,
+    0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f,
+    0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f,
+    0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109,
+    0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f,
+    0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509,
+    0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501,
+    0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303,
+    0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f,
+    0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907,
+    0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703,
+    0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03,
+    0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01,
+    0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01,
+    0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903,
+    0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505,
+    0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b,
+    0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107,
+    0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509,
+    0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303,
+    0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103,
+    0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05,
+    0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b,
+    0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f,
+    0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701,
+    0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909,
+    0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305,
+    0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d,
+    0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b,
+    0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d,
+    0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307,
+    0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09,
+    0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309,
+    0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709,
+    0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f,
+    0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303,
+    0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503,
+    0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b,
+    0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101,
+GGML_TABLE_END()
+
+#define NGRID_IQ1S 2048
+#define IQ1S_DELTA 0.125f
+#define IQ1M_DELTA 0.125f
+#if defined(GGML_COMMON_IMPL_C)
+GGML_TABLE_BEGIN(uint64_t, iq1s_grid, NGRID_IQ1S)
+    0xffffffffffffffff, 0xffffffffffffff01, 0xffffffffffff0000, 0xffffffffffff01ff,
+    0xffffffffffff0101, 0xffffffffff00ff00, 0xffffffffff000000, 0xffffffffff01ffff,
+    0xffffffffff01ff01, 0xffffffffff0101ff, 0xffffffffff010101, 0xffffffff00ff0000,
+    0xffffffff0000ff00, 0xffffffff000000ff, 0xffffffff00000001, 0xffffffff00010000,
+    0xffffffff01ffffff, 0xffffffff01ffff01, 0xffffffff01ff01ff, 0xffffffff01ff0101,
+    0xffffffff01000000, 0xffffffff0101ffff, 0xffffffff0101ff01, 0xffffffff010101ff,
+    0xffffffff01010101, 0xffffff00ffff00ff, 0xffffff00ffff0000, 0xffffff00ff00ff00,
+    0xffffff00ff0000ff, 0xffffff00ff000001, 0xffffff00ff000100, 0xffffff00ff000101,
+    0xffffff00ff010000, 0xffffff0000ffff00, 0xffffff0000ff0001, 0xffffff0000ff0100,
+    0xffffff000000ff01, 0xffffff0000000000, 0xffffff0000000101, 0xffffff000001ff00,
+    0xffffff00000100ff, 0xffffff0000010001, 0xffffff00000101ff, 0xffffff0001ff0000,
+    0xffffff000100ff00, 0xffffff00010000ff, 0xffffff0001000001, 0xffffff0001010000,
+    0xffffff01ffffffff, 0xffffff01ffffff01, 0xffffff01ffff01ff, 0xffffff01ffff0101,
+    0xffffff01ff000000, 0xffffff01ff01ffff, 0xffffff01ff01ff01, 0xffffff01ff0101ff,
+    0xffffff01ff010101, 0xffffff0100ff0000, 0xffffff010000ff00, 0xffffff0100000100,
+    0xffffff01000100ff, 0xffffff0100010100, 0xffffff0101ffffff, 0xffffff0101ffff01,
+    0xffffff0101ff01ff, 0xffffff0101ff0101, 0xffffff010100ff00, 0xffffff0101000000,
+    0xffffff0101000100, 0xffffff010101ffff, 0xffffff010101ff01, 0xffffff01010101ff,
+    0xffffff0101010101, 0xffff00ffff00ff00, 0xffff00ffff0000ff, 0xffff00ffff000001,
+    0xffff00ffff010000, 0xffff00ff00ffff00, 0xffff00ff00ff0100, 0xffff00ff00000000,
+    0xffff00ff00000101, 0xffff00ff000100ff, 0xffff00ff00010000, 0xffff00ff0100ff00,
+    0xffff00ff01000100, 0xffff00ff01010000, 0xffff0000ffffff00, 0xffff0000ffff00ff,
+    0xffff0000ffff0000, 0xffff0000ffff0001, 0xffff0000ff000000, 0xffff0000ff0001ff,
+    0xffff0000ff000101, 0xffff0000ff010100, 0xffff000000ffffff, 0xffff000000ff0000,
+    0xffff000000ff0101, 0xffff00000000ffff, 0xffff00000000ff00, 0xffff0000000000ff,
+    0xffff000000000000, 0xffff000000000001, 0xffff000000000100, 0xffff00000001ffff,
+    0xffff00000001ff01, 0xffff000000010000, 0xffff0000000101ff, 0xffff000000010101,
+    0xffff000001ffff00, 0xffff00000100ff00, 0xffff000001000000, 0xffff0000010001ff,
+    0xffff000001000101, 0xffff00000101ff00, 0xffff0000010100ff, 0xffff000001010000,
+    0xffff000001010001, 0xffff000001010100, 0xffff0001ff0000ff, 0xffff0001ff000100,
+    0xffff000100ffff00, 0xffff000100ff00ff, 0xffff00010000ffff, 0xffff00010000ff01,
+    0xffff000100000000, 0xffff0001000001ff, 0xffff00010001ffff, 0xffff00010001ff00,
+    0xffff000100010001, 0xffff000100010100, 0xffff000101ff0000, 0xffff00010100ff00,
+    0xffff0001010000ff, 0xffff000101000100, 0xffff01ffffffffff, 0xffff01ffffffff01,
+    0xffff01ffffff01ff, 0xffff01ffffff0101, 0xffff01ffff000000, 0xffff01ffff01ffff,
+    0xffff01ffff01ff01, 0xffff01ffff0101ff, 0xffff01ffff010101, 0xffff01ff00ff0000,
+    0xffff01ff0000ff00, 0xffff01ff00000001, 0xffff01ff00010000, 0xffff01ff01ffffff,
+    0xffff01ff01ffff01, 0xffff01ff01ff01ff, 0xffff01ff01ff0101, 0xffff01ff01000000,
+    0xffff01ff0101ffff, 0xffff01ff0101ff01, 0xffff01ff010101ff, 0xffff01ff01010101,
+    0xffff0100ffff0000, 0xffff0100ff00ff00, 0xffff0100ff0000ff, 0xffff0100ff000100,
+    0xffff0100ff0100ff, 0xffff0100ff010000, 0xffff010000ffff00, 0xffff01000000ffff,
+    0xffff01000000ff00, 0xffff010000000000, 0xffff01000001ff00, 0xffff0100000100ff,
+    0xffff010000010100, 0xffff01000100ff00, 0xffff0100010000ff, 0xffff010001000001,
+    0xffff010001000100, 0xffff010001010000, 0xffff0101ffffffff, 0xffff0101ffffff01,
+    0xffff0101ffff01ff, 0xffff0101ffff0101, 0xffff0101ff000000, 0xffff0101ff01ffff,
+    0xffff0101ff01ff01, 0xffff0101ff0101ff, 0xffff0101ff010101, 0xffff010100ff0000,
+    0xffff01010000ff00, 0xffff010100000100, 0xffff01010001ff00, 0xffff010100010000,
+    0xffff010101ffffff, 0xffff010101ffff01, 0xffff010101ff0000, 0xffff010101ff01ff,
+    0xffff010101ff0101, 0xffff010101000000, 0xffff01010101ffff, 0xffff01010101ff01,
+    0xffff0101010101ff, 0xffff010101010101, 0xff00ffffff00ffff, 0xff00ffffff00ff00,
+    0xff00ffffff0000ff, 0xff00ffffff000100, 0xff00ffffff0100ff, 0xff00ffffff010000,
+    0xff00ffff00ffff00, 0xff00ffff00ff00ff, 0xff00ffff0000ffff, 0xff00ffff00000000,
+    0xff00ffff000001ff, 0xff00ffff0001ff00, 0xff00ffff000100ff, 0xff00ffff00010000,
+    0xff00ffff00010100, 0xff00ffff0100ff00, 0xff00ffff010000ff, 0xff00ffff01000001,
+    0xff00ffff0101ff00, 0xff00ffff01010000, 0xff00ff00ffffff00, 0xff00ff00ffff00ff,
+    0xff00ff00ffff0001, 0xff00ff00ffff0100, 0xff00ff00ff00ffff, 0xff00ff00ff00ff01,
+    0xff00ff00ff000000, 0xff00ff00ff0001ff, 0xff00ff00ff01ff00, 0xff00ff00ff0100ff,
+    0xff00ff00ff010100, 0xff00ff0000ff0000, 0xff00ff0000ff0101, 0xff00ff000000ffff,
+    0xff00ff000000ff00, 0xff00ff000000ff01, 0xff00ff00000000ff, 0xff00ff0000000000,
+    0xff00ff0000000001, 0xff00ff0000000100, 0xff00ff000001ffff, 0xff00ff0000010000,
+    0xff00ff0001ff00ff, 0xff00ff000100ff01, 0xff00ff0001000000, 0xff00ff000101ff00,
+    0xff00ff00010100ff, 0xff00ff01ff00ff00, 0xff00ff01ff0000ff, 0xff00ff01ff000001,
+    0xff00ff01ff010000, 0xff00ff0100ffffff, 0xff00ff0100ff0001, 0xff00ff0100ff0100,
+    0xff00ff010000ff01, 0xff00ff0100000000, 0xff00ff01000001ff, 0xff00ff0100000101,
+    0xff00ff01000100ff, 0xff00ff0100010001, 0xff00ff0101ff0000, 0xff00ff010100ff00,
+    0xff00ff01010000ff, 0xff00ff0101000001, 0xff00ff0101010000, 0xff0000ffffffff00,
+    0xff0000ffffff0001, 0xff0000ffffff0100, 0xff0000ffff0000ff, 0xff0000ffff000000,
+    0xff0000ffff0001ff, 0xff0000ffff000100, 0xff0000ffff01ff00, 0xff0000ffff010001,
+    0xff0000ff00ffff00, 0xff0000ff00ff0000, 0xff0000ff00ff0001, 0xff0000ff00ff01ff,
+    0xff0000ff00ff0101, 0xff0000ff0000ff00, 0xff0000ff000000ff, 0xff0000ff00000000,
+    0xff0000ff00000001, 0xff0000ff00000100, 0xff0000ff0001ff01, 0xff0000ff00010000,
+    0xff0000ff000101ff, 0xff0000ff01ff00ff, 0xff0000ff01ff0100, 0xff0000ff0100ffff,
+    0xff0000ff010000ff, 0xff0000ff01000000, 0xff0000ff010001ff, 0xff0000ff01000100,
+    0xff0000ff01000101, 0xff0000ff0101ff00, 0xff0000ff010100ff, 0xff0000ff01010000,
+    0xff0000ff01010100, 0xff000000ffffff01, 0xff000000ffff0000, 0xff000000ffff0101,
+    0xff000000ff00ff00, 0xff000000ff0000ff, 0xff000000ff000000, 0xff000000ff000001,
+    0xff000000ff000100, 0xff000000ff01ffff, 0xff000000ff01ff01, 0xff000000ff010000,
+    0xff000000ff0101ff, 0xff000000ff010101, 0xff00000000ffff00, 0xff00000000ff00ff,
+    0xff00000000ff0000, 0xff00000000ff0001, 0xff0000000000ff00, 0xff0000000000ff01,
+    0xff000000000000ff, 0xff00000000000000, 0xff00000000000001, 0xff00000000000100,
+    0xff00000000000101, 0xff0000000001ff00, 0xff000000000100ff, 0xff00000000010000,
+    0xff00000000010001, 0xff00000000010100, 0xff00000001ffffff, 0xff00000001ffff01,
+    0xff00000001ff00ff, 0xff00000001ff0000, 0xff00000001ff01ff, 0xff00000001ff0101,
+    0xff0000000100ffff, 0xff0000000100ff00, 0xff000000010000ff, 0xff00000001000000,
+    0xff00000001000001, 0xff00000001000100, 0xff00000001000101, 0xff0000000101ffff,
+    0xff0000000101ff01, 0xff00000001010000, 0xff000001ffffff00, 0xff000001ffff00ff,
+    0xff000001ffff0000, 0xff000001ffff0001, 0xff000001ff000000, 0xff000001ff000001,
+    0xff000001ff0001ff, 0xff000001ff000101, 0xff000001ff01ff00, 0xff000001ff010001,
+    0xff00000100ffffff, 0xff00000100ffff01, 0xff00000100ff00ff, 0xff00000100ff0000,
+    0xff00000100ff01ff, 0xff00000100ff0101, 0xff0000010000ff00, 0xff00000100000000,
+    0xff00000100000001, 0xff000001000001ff, 0xff00000100000100, 0xff0000010001ff00,
+    0xff000001000100ff, 0xff00000100010000, 0xff000001000101ff, 0xff00000100010100,
+    0xff00000100010101, 0xff00000101ff0001, 0xff00000101ff0101, 0xff0000010100ff01,
+    0xff00000101000000, 0xff000001010100ff, 0xff00000101010100, 0xff0001ffff00ff00,
+    0xff0001ffff000001, 0xff0001ffff010000, 0xff0001ff00ffff00, 0xff0001ff00ff00ff,
+    0xff0001ff00ff0001, 0xff0001ff00ff0100, 0xff0001ff0000ffff, 0xff0001ff00000000,
+    0xff0001ff000001ff, 0xff0001ff00000101, 0xff0001ff0001ffff, 0xff0001ff0001ff00,
+    0xff0001ff000100ff, 0xff0001ff00010001, 0xff0001ff00010100, 0xff0001ff01ff0000,
+    0xff0001ff0100ff00, 0xff0001ff010000ff, 0xff0001ff01010000, 0xff000100ff00ffff,
+    0xff000100ff00ff01, 0xff000100ff000000, 0xff000100ff000101, 0xff000100ff01ff00,
+    0xff000100ff010000, 0xff00010000ffff01, 0xff00010000ff00ff, 0xff00010000ff0000,
+    0xff00010000ff01ff, 0xff0001000000ff00, 0xff000100000000ff, 0xff00010000000000,
+    0xff00010000000001, 0xff00010000000100, 0xff00010000000101, 0xff0001000001ffff,
+    0xff00010000010000, 0xff00010000010101, 0xff00010001ff0100, 0xff0001000100ff00,
+    0xff0001000100ff01, 0xff00010001000000, 0xff000100010001ff, 0xff0001000101ff00,
+    0xff00010001010001, 0xff00010001010100, 0xff000101ffff0100, 0xff000101ff000001,
+    0xff000101ff0100ff, 0xff000101ff010001, 0xff00010100ff00ff, 0xff00010100ff0001,
+    0xff00010100ff0100, 0xff0001010000ffff, 0xff0001010000ff01, 0xff00010100000000,
+    0xff000101000001ff, 0xff0001010001ff00, 0xff00010100010001, 0xff00010100010100,
+    0xff00010101ff0000, 0xff0001010100ff00, 0xff00010101000001, 0xff00010101000101,
+    0xff01ffffffffffff, 0xff01ffffffffff01, 0xff01ffffffff01ff, 0xff01ffffffff0101,
+    0xff01ffffff000000, 0xff01ffffff01ffff, 0xff01ffffff01ff01, 0xff01ffffff010000,
+    0xff01ffffff0101ff, 0xff01ffffff010101, 0xff01ffff00ff0000, 0xff01ffff0000ff00,
+    0xff01ffff00000100, 0xff01ffff0001ff00, 0xff01ffff00010000, 0xff01ffff01ffffff,
+    0xff01ffff01ffff01, 0xff01ffff01ff01ff, 0xff01ffff01ff0101, 0xff01ffff01000000,
+    0xff01ffff0101ffff, 0xff01ffff0101ff01, 0xff01ffff01010000, 0xff01ffff010101ff,
+    0xff01ffff01010101, 0xff01ff00ffff0000, 0xff01ff00ff00ff00, 0xff01ff00ff0000ff,
+    0xff01ff00ff000100, 0xff01ff00ff010000, 0xff01ff0000ffff01, 0xff01ff0000ff00ff,
+    0xff01ff0000ff0100, 0xff01ff0000000000, 0xff01ff00000001ff, 0xff01ff0000000101,
+    0xff01ff000001ff00, 0xff01ff00000100ff, 0xff01ff0000010000, 0xff01ff0000010001,
+    0xff01ff0001ff0000, 0xff01ff000100ffff, 0xff01ff0001000001, 0xff01ff0001000100,
+    0xff01ff0001010000, 0xff01ff01ffffff00, 0xff01ff01ffff01ff, 0xff01ff01ffff0101,
+    0xff01ff01ff00ff00, 0xff01ff01ff000000, 0xff01ff01ff01ffff, 0xff01ff01ff01ff01,
+    0xff01ff01ff0101ff, 0xff01ff01ff010101, 0xff01ff0100ff0000, 0xff01ff010000ff00,
+    0xff01ff0100000001, 0xff01ff0100000100, 0xff01ff0100010000, 0xff01ff0101ffff00,
+    0xff01ff0101ff01ff, 0xff01ff0101ff0101, 0xff01ff010100ff00, 0xff01ff0101000000,
+    0xff01ff010101ffff, 0xff01ff010101ff01, 0xff01ff01010101ff, 0xff01ff0101010101,
+    0xff0100ffffff0000, 0xff0100ffff0000ff, 0xff0100ffff000001, 0xff0100ffff000100,
+    0xff0100ffff010000, 0xff0100ff00ff00ff, 0xff0100ff00ff0000, 0xff0100ff00ff0001,
+    0xff0100ff00ff0100, 0xff0100ff0000ff01, 0xff0100ff00000000, 0xff0100ff000001ff,
+    0xff0100ff00000101, 0xff0100ff00010001, 0xff0100ff01ff0000, 0xff0100ff0100ff00,
+    0xff0100ff010000ff, 0xff0100ff01000100, 0xff0100ff0101ff00, 0xff0100ff01010000,
+    0xff010000ffff0100, 0xff010000ff000000, 0xff010000ff01ff00, 0xff010000ff010100,
+    0xff01000000ffffff, 0xff01000000ff0000, 0xff01000000ff01ff, 0xff0100000000ff00,
+    0xff010000000000ff, 0xff01000000000000, 0xff01000000000100, 0xff0100000001ff01,
+    0xff01000000010000, 0xff010000000101ff, 0xff01000001ff0100, 0xff0100000100ffff,
+    0xff010000010000ff, 0xff01000001000000, 0xff010000010001ff, 0xff01000001000101,
+    0xff0100000101ff00, 0xff010000010100ff, 0xff01000001010001, 0xff01000001010100,
+    0xff010001ffff0000, 0xff010001ff00ffff, 0xff010001ff00ff01, 0xff010001ff000100,
+    0xff010001ff010000, 0xff01000100ffff00, 0xff01000100ff0100, 0xff01000100000000,
+    0xff0100010001ffff, 0xff0100010001ff00, 0xff01000100010100, 0xff01000101ff00ff,
+    0xff01000101ff0001, 0xff0100010100ffff, 0xff01000101000101, 0xff0101ffffffffff,
+    0xff0101ffffffff01, 0xff0101ffffff01ff, 0xff0101ffffff0101, 0xff0101ffff000000,
+    0xff0101ffff01ffff, 0xff0101ffff01ff01, 0xff0101ffff0101ff, 0xff0101ffff010101,
+    0xff0101ff00ff0000, 0xff0101ff0000ff00, 0xff0101ff000000ff, 0xff0101ff00010000,
+    0xff0101ff01ffffff, 0xff0101ff01ffff01, 0xff0101ff01ff01ff, 0xff0101ff01ff0101,
+    0xff0101ff0101ffff, 0xff0101ff0101ff01, 0xff0101ff010101ff, 0xff0101ff01010101,
+    0xff010100ffff0100, 0xff010100ff00ff00, 0xff010100ff0000ff, 0xff010100ff000100,
+    0xff010100ff010000, 0xff01010000ff0001, 0xff01010000ff0100, 0xff0101000000ff01,
+    0xff01010000000000, 0xff0101000001ff00, 0xff010100000100ff, 0xff01010000010001,
+    0xff01010000010100, 0xff01010001ff0000, 0xff0101000100ffff, 0xff01010001000001,
+    0xff01010001000100, 0xff010100010100ff, 0xff01010001010000, 0xff010101ffffffff,
+    0xff010101ffffff01, 0xff010101ffff01ff, 0xff010101ffff0101, 0xff010101ff01ffff,
+    0xff010101ff01ff01, 0xff010101ff0101ff, 0xff010101ff010101, 0xff01010100ff0000,
+    0xff0101010000ff00, 0xff01010100000001, 0xff01010100000100, 0xff01010100010000,
+    0xff01010101ffffff, 0xff01010101ffff01, 0xff01010101ff01ff, 0xff01010101ff0101,
+    0xff01010101000000, 0xff0101010101ffff, 0xff0101010101ff01, 0xff010101010101ff,
+    0xff01010101010101, 0x00ffffffffff0000, 0x00ffffffff00ff00, 0x00ffffffff000001,
+    0x00ffffffff010000, 0x00ffffff00ff0100, 0x00ffffff0000ff01, 0x00ffffff00000000,
+    0x00ffffff000001ff, 0x00ffffff00000101, 0x00ffffff0001ff00, 0x00ffffff000100ff,
+    0x00ffffff00010001, 0x00ffffff010000ff, 0x00ffffff01000100, 0x00ffffff0101ff00,
+    0x00ffffff01010001, 0x00ffff00ffffffff, 0x00ffff00ffffff00, 0x00ffff00ffff00ff,
+    0x00ffff00ffff0001, 0x00ffff00ffff0100, 0x00ffff00ff00ff01, 0x00ffff00ff000000,
+    0x00ffff00ff000001, 0x00ffff00ff0001ff, 0x00ffff00ff000101, 0x00ffff00ff01ff00,
+    0x00ffff00ff010001, 0x00ffff00ff010100, 0x00ffff0000ff0000, 0x00ffff0000ff01ff,
+    0x00ffff0000ff0101, 0x00ffff000000ff00, 0x00ffff00000000ff, 0x00ffff0000000000,
+    0x00ffff0000000001, 0x00ffff0000000100, 0x00ffff0000000101, 0x00ffff0000010000,
+    0x00ffff00000101ff, 0x00ffff0000010101, 0x00ffff0001ffff00, 0x00ffff0001ff00ff,
+    0x00ffff0001ff0001, 0x00ffff000100ffff, 0x00ffff000100ff01, 0x00ffff0001000000,
+    0x00ffff000101ffff, 0x00ffff000101ff00, 0x00ffff000101ff01, 0x00ffff01ffff0000,
+    0x00ffff01ff00ff00, 0x00ffff01ff0000ff, 0x00ffff01ff000001, 0x00ffff01ff010000,
+    0x00ffff0100ffff00, 0x00ffff010000ff01, 0x00ffff0100000000, 0x00ffff0100000101,
+    0x00ffff01000100ff, 0x00ffff0100010100, 0x00ffff0101ff0100, 0x00ffff01010000ff,
+    0x00ffff0101010000, 0x00ff00ffffffff00, 0x00ff00ffff000000, 0x00ff00ffff000100,
+    0x00ff00ffff010100, 0x00ff00ff00ff0000, 0x00ff00ff00ff01ff, 0x00ff00ff00ff0101,
+    0x00ff00ff0000ff00, 0x00ff00ff000000ff, 0x00ff00ff00000000, 0x00ff00ff00000001,
+    0x00ff00ff0001ff00, 0x00ff00ff0001ff01, 0x00ff00ff00010000, 0x00ff00ff000101ff,
+    0x00ff00ff00010101, 0x00ff00ff01ffff00, 0x00ff00ff01ff0001, 0x00ff00ff01ff0100,
+    0x00ff00ff0100ffff, 0x00ff00ff0100ff01, 0x00ff00ff01000000, 0x00ff00ff0101ffff,
+    0x00ff00ff0101ff00, 0x00ff00ff01010100, 0x00ff0000ffffff00, 0x00ff0000ffffff01,
+    0x00ff0000ffff0000, 0x00ff0000ffff0101, 0x00ff0000ff00ff00, 0x00ff0000ff0000ff,
+    0x00ff0000ff000000, 0x00ff0000ff000001, 0x00ff0000ff000100, 0x00ff0000ff01ffff,
+    0x00ff0000ff010000, 0x00ff0000ff010101, 0x00ff000000ffff00, 0x00ff000000ff00ff,
+    0x00ff000000ff0000, 0x00ff000000ff0001, 0x00ff000000ff0100, 0x00ff00000000ffff,
+    0x00ff00000000ff00, 0x00ff0000000000ff, 0x00ff000000000000, 0x00ff000000000001,
+    0x00ff0000000001ff, 0x00ff000000000100, 0x00ff00000001ff00, 0x00ff0000000100ff,
+    0x00ff000000010000, 0x00ff000000010001, 0x00ff000000010100, 0x00ff000001ffff01,
+    0x00ff000001ff00ff, 0x00ff000001ff0000, 0x00ff000001ff01ff, 0x00ff00000100ff00,
+    0x00ff0000010000ff, 0x00ff000001000000, 0x00ff000001000001, 0x00ff000001000100,
+    0x00ff000001000101, 0x00ff000001010000, 0x00ff0000010101ff, 0x00ff000001010101,
+    0x00ff0001ffffff00, 0x00ff0001ffff0000, 0x00ff0001ffff0100, 0x00ff0001ff0000ff,
+    0x00ff0001ff000000, 0x00ff0001ff0001ff, 0x00ff0001ff000101, 0x00ff0001ff01ff00,
+    0x00ff0001ff0100ff, 0x00ff0001ff010100, 0x00ff000100ffffff, 0x00ff000100ffff01,
+    0x00ff000100ff0000, 0x00ff000100ff01ff, 0x00ff00010000ffff, 0x00ff00010000ff00,
+    0x00ff00010000ff01, 0x00ff000100000000, 0x00ff000100000001, 0x00ff000100000100,
+    0x00ff00010001ff01, 0x00ff000100010000, 0x00ff0001000101ff, 0x00ff000101ffff00,
+    0x00ff000101ff0000, 0x00ff000101ff0101, 0x00ff0001010000ff, 0x00ff000101000000,
+    0x00ff00010101ff00, 0x00ff0001010100ff, 0x00ff000101010001, 0x00ff01ffffff0000,
+    0x00ff01ffff00ff00, 0x00ff01ffff000000, 0x00ff01ffff000101, 0x00ff01ffff010000,
+    0x00ff01ff00ffff01, 0x00ff01ff00ff0100, 0x00ff01ff0000ffff, 0x00ff01ff00000000,
+    0x00ff01ff000001ff, 0x00ff01ff0001ff00, 0x00ff01ff000100ff, 0x00ff01ff00010001,
+    0x00ff01ff00010100, 0x00ff01ff01ff0000, 0x00ff01ff0100ff00, 0x00ff01ff010000ff,
+    0x00ff01ff01000001, 0x00ff01ff01000100, 0x00ff01ff01010000, 0x00ff0100ffffff00,
+    0x00ff0100ffff0000, 0x00ff0100ffff0001, 0x00ff0100ffff0101, 0x00ff0100ff00ffff,
+    0x00ff0100ff0000ff, 0x00ff0100ff000000, 0x00ff0100ff0001ff, 0x00ff0100ff01ff00,
+    0x00ff0100ff0100ff, 0x00ff0100ff010001, 0x00ff010000ffffff, 0x00ff010000ff0000,
+    0x00ff010000ff0101, 0x00ff01000000ff00, 0x00ff01000000ff01, 0x00ff0100000000ff,
+    0x00ff010000000000, 0x00ff010000000001, 0x00ff010000000100, 0x00ff01000001ffff,
+    0x00ff01000001ff01, 0x00ff010000010000, 0x00ff010000010001, 0x00ff010000010101,
+    0x00ff010001ff0001, 0x00ff010001ff0100, 0x00ff01000100ff01, 0x00ff010001000000,
+    0x00ff010001000001, 0x00ff0100010001ff, 0x00ff01000101ff00, 0x00ff0100010100ff,
+    0x00ff010001010001, 0x00ff010001010100, 0x00ff0101ff000001, 0x00ff010100ff00ff,
+    0x00ff010100ff0001, 0x00ff010100ff0100, 0x00ff010100000000, 0x00ff0101000001ff,
+    0x00ff010100000101, 0x00ff0101000100ff, 0x00ff010100010100, 0x00ff0101010000ff,
+    0x00ff010101010000, 0x0000ffffffffff00, 0x0000ffffffff00ff, 0x0000ffffffff0000,
+    0x0000ffffffff0001, 0x0000ffffffff0100, 0x0000ffffff00ff01, 0x0000ffffff000000,
+    0x0000ffffff000101, 0x0000ffffff01ff00, 0x0000ffffff0100ff, 0x0000ffffff010100,
+    0x0000ffff00ffffff, 0x0000ffff00ff0000, 0x0000ffff00ff01ff, 0x0000ffff0000ff00,
+    0x0000ffff000000ff, 0x0000ffff00000000, 0x0000ffff00000001, 0x0000ffff00000100,
+    0x0000ffff00010000, 0x0000ffff000101ff, 0x0000ffff01ff0001, 0x0000ffff01ff0100,
+    0x0000ffff01000000, 0x0000ffff010001ff, 0x0000ffff0101ffff, 0x0000ffff0101ff00,
+    0x0000ffff01010001, 0x0000ffff01010100, 0x0000ff00ffff0000, 0x0000ff00ffff01ff,
+    0x0000ff00ffff0100, 0x0000ff00ffff0101, 0x0000ff00ff00ff00, 0x0000ff00ff0000ff,
+    0x0000ff00ff000000, 0x0000ff00ff000001, 0x0000ff00ff0001ff, 0x0000ff00ff000100,
+    0x0000ff00ff01ffff, 0x0000ff00ff010000, 0x0000ff00ff010001, 0x0000ff00ff0101ff,
+    0x0000ff00ff010101, 0x0000ff0000ffff00, 0x0000ff0000ff00ff, 0x0000ff0000ff0000,
+    0x0000ff0000ff0001, 0x0000ff0000ff0100, 0x0000ff000000ffff, 0x0000ff000000ff00,
+    0x0000ff000000ff01, 0x0000ff00000000ff, 0x0000ff0000000000, 0x0000ff0000000001,
+    0x0000ff00000001ff, 0x0000ff0000000100, 0x0000ff0000000101, 0x0000ff000001ff00,
+    0x0000ff00000100ff, 0x0000ff0000010000, 0x0000ff0000010001, 0x0000ff0000010100,
+    0x0000ff0001ffff01, 0x0000ff0001ff0000, 0x0000ff000100ff00, 0x0000ff00010000ff,
+    0x0000ff0001000000, 0x0000ff0001000001, 0x0000ff0001000100, 0x0000ff000101ffff,
+    0x0000ff0001010000, 0x0000ff0001010101, 0x0000ff01ffffff00, 0x0000ff01ffff0001,
+    0x0000ff01ff00ff01, 0x0000ff01ff000000, 0x0000ff01ff000101, 0x0000ff01ff01ff00,
+    0x0000ff01ff0100ff, 0x0000ff0100ffff01, 0x0000ff0100ff0000, 0x0000ff0100ff0101,
+    0x0000ff010000ff00, 0x0000ff01000000ff, 0x0000ff0100000000, 0x0000ff0100000001,
+    0x0000ff0100000100, 0x0000ff010001ff01, 0x0000ff0100010000, 0x0000ff0101ff0000,
+    0x0000ff010100ffff, 0x0000ff010100ff01, 0x0000ff0101000000, 0x0000ff0101000100,
+    0x0000ff0101000101, 0x0000ff01010100ff, 0x000000ffffff00ff, 0x000000ffffff0000,
+    0x000000ffff00ff00, 0x000000ffff0000ff, 0x000000ffff000000, 0x000000ffff000001,
+    0x000000ffff0001ff, 0x000000ffff000100, 0x000000ffff01ff00, 0x000000ffff010000,
+    0x000000ffff0101ff, 0x000000ffff010101, 0x000000ff00ffff00, 0x000000ff00ff00ff,
+    0x000000ff00ff0000, 0x000000ff00ff0001, 0x000000ff00ff0100, 0x000000ff00ff0101,
+    0x000000ff0000ffff, 0x000000ff0000ff00, 0x000000ff000000ff, 0x000000ff00000000,
+    0x000000ff00000001, 0x000000ff000001ff, 0x000000ff00000100, 0x000000ff00000101,
+    0x000000ff0001ff00, 0x000000ff0001ff01, 0x000000ff000100ff, 0x000000ff00010000,
+    0x000000ff00010001, 0x000000ff00010100, 0x000000ff01ffffff, 0x000000ff01ff01ff,
+    0x000000ff01ff0101, 0x000000ff0100ff00, 0x000000ff010000ff, 0x000000ff01000000,
+    0x000000ff01000001, 0x000000ff01000100, 0x000000ff0101ff00, 0x000000ff010100ff,
+    0x000000ff01010000, 0x000000ff01010101, 0x00000000ffffff00, 0x00000000ffffff01,
+    0x00000000ffff00ff, 0x00000000ffff0000, 0x00000000ffff0001, 0x00000000ffff0100,
+    0x00000000ff00ffff, 0x00000000ff00ff00, 0x00000000ff00ff01, 0x00000000ff0000ff,
+    0x00000000ff000000, 0x00000000ff000001, 0x00000000ff000100, 0x00000000ff000101,
+    0x00000000ff01ff00, 0x00000000ff0100ff, 0x00000000ff010000, 0x00000000ff010001,
+    0x00000000ff010100, 0x0000000000ffffff, 0x0000000000ffff00, 0x0000000000ffff01,
+    0x0000000000ff00ff, 0x0000000000ff0000, 0x0000000000ff0001, 0x0000000000ff01ff,
+    0x0000000000ff0100, 0x000000000000ffff, 0x000000000000ff00, 0x000000000000ff01,
+    0x00000000000000ff, 0x0000000000000000, 0x0000000000000001, 0x00000000000001ff,
+    0x0000000000000100, 0x0000000000000101, 0x000000000001ffff, 0x000000000001ff00,
+    0x00000000000100ff, 0x0000000000010000, 0x0000000000010001, 0x00000000000101ff,
+    0x0000000000010100, 0x0000000000010101, 0x0000000001ffff00, 0x0000000001ff00ff,
+    0x0000000001ff0000, 0x0000000001ff0100, 0x0000000001ff0101, 0x000000000100ffff,
+    0x000000000100ff00, 0x00000000010000ff, 0x0000000001000000, 0x0000000001000001,
+    0x00000000010001ff, 0x0000000001000100, 0x000000000101ff00, 0x00000000010100ff,
+    0x0000000001010000, 0x0000000001010001, 0x0000000001010100, 0x00000001ffffffff,
+    0x00000001ffffff00, 0x00000001ffffff01, 0x00000001ffff00ff, 0x00000001ffff0001,
+    0x00000001ffff01ff, 0x00000001ffff0100, 0x00000001ff00ff00, 0x00000001ff0000ff,
+    0x00000001ff000000, 0x00000001ff0001ff, 0x00000001ff000100, 0x00000001ff01ffff,
+    0x00000001ff01ff00, 0x00000001ff01ff01, 0x00000001ff0100ff, 0x00000001ff010000,
+    0x00000001ff010001, 0x00000001ff0101ff, 0x00000001ff010100, 0x0000000100ffff00,
+    0x0000000100ff0000, 0x0000000100ff0001, 0x0000000100ff01ff, 0x0000000100ff0100,
+    0x0000000100ff0101, 0x000000010000ffff, 0x000000010000ff00, 0x000000010000ff01,
+    0x00000001000000ff, 0x0000000100000000, 0x0000000100000001, 0x00000001000001ff,
+    0x0000000100000100, 0x0000000100000101, 0x000000010001ff00, 0x00000001000100ff,
+    0x0000000100010000, 0x0000000100010100, 0x0000000101ffff01, 0x0000000101ff0000,
+    0x0000000101ff0001, 0x0000000101ff01ff, 0x0000000101ff0100, 0x0000000101ff0101,
+    0x000000010100ff00, 0x0000000101000000, 0x0000000101000101, 0x000000010101ff01,
+    0x0000000101010000, 0x0000000101010001, 0x00000001010101ff, 0x0000000101010100,
+    0x000001ffffff00ff, 0x000001ffffff0000, 0x000001ffffff0001, 0x000001ffffff0100,
+    0x000001ffff00ffff, 0x000001ffff000000, 0x000001ffff0001ff, 0x000001ffff01ff00,
+    0x000001ffff010101, 0x000001ff00ff0000, 0x000001ff00ff01ff, 0x000001ff00ff0101,
+    0x000001ff0000ff00, 0x000001ff000000ff, 0x000001ff00000000, 0x000001ff00000001,
+    0x000001ff000001ff, 0x000001ff00000100, 0x000001ff0001ffff, 0x000001ff0001ff01,
+    0x000001ff000100ff, 0x000001ff00010000, 0x000001ff01ffff01, 0x000001ff01ff0100,
+    0x000001ff0100ffff, 0x000001ff0100ff01, 0x000001ff01000000, 0x000001ff010001ff,
+    0x000001ff0101ff00, 0x000001ff01010100, 0x00000100ffffff00, 0x00000100ffffff01,
+    0x00000100ffff0000, 0x00000100ffff0101, 0x00000100ff00ff00, 0x00000100ff0000ff,
+    0x00000100ff000000, 0x00000100ff000001, 0x00000100ff000100, 0x00000100ff010000,
+    0x0000010000ffff00, 0x0000010000ff00ff, 0x0000010000ff0000, 0x0000010000ff0001,
+    0x0000010000ff0100, 0x000001000000ffff, 0x000001000000ff00, 0x000001000000ff01,
+    0x00000100000000ff, 0x0000010000000000, 0x0000010000000001, 0x00000100000001ff,
+    0x0000010000000100, 0x0000010000000101, 0x000001000001ff00, 0x00000100000100ff,
+    0x0000010000010000, 0x0000010000010001, 0x0000010000010100, 0x0000010001ffff00,
+    0x0000010001ff0000, 0x0000010001ff0100, 0x000001000100ff00, 0x00000100010000ff,
+    0x0000010001000000, 0x0000010001000001, 0x00000100010001ff, 0x0000010001000100,
+    0x0000010001010000, 0x00000101ffff00ff, 0x00000101ffff01ff, 0x00000101ff000000,
+    0x00000101ff000101, 0x00000101ff01ffff, 0x00000101ff010000, 0x00000101ff010001,
+    0x00000101ff010100, 0x0000010100ff0000, 0x0000010100ff01ff, 0x0000010100ff0100,
+    0x000001010000ff00, 0x0000010100000000, 0x0000010100000001, 0x00000101000001ff,
+    0x0000010100000100, 0x000001010001ff01, 0x0000010100010000, 0x00000101000101ff,
+    0x0000010100010101, 0x0000010101ffff00, 0x0000010101ff0101, 0x000001010100ff01,
+    0x0000010101000000, 0x0000010101000001, 0x00000101010001ff, 0x0000010101000101,
+    0x000001010101ff00, 0x0001ffffffff0000, 0x0001ffffff0000ff, 0x0001ffffff000001,
+    0x0001ffffff000100, 0x0001ffffff010000, 0x0001ffff00ff00ff, 0x0001ffff0000ffff,
+    0x0001ffff00000000, 0x0001ffff00000001, 0x0001ffff000001ff, 0x0001ffff00000101,
+    0x0001ffff0001ff00, 0x0001ffff000100ff, 0x0001ffff00010001, 0x0001ffff00010100,
+    0x0001ffff01ffff00, 0x0001ffff01000001, 0x0001ffff01010000, 0x0001ff00ffffff00,
+    0x0001ff00ffff00ff, 0x0001ff00ffff0001, 0x0001ff00ffff0100, 0x0001ff00ff00ff01,
+    0x0001ff00ff000000, 0x0001ff00ff01ff00, 0x0001ff00ff01ff01, 0x0001ff00ff010001,
+    0x0001ff00ff010100, 0x0001ff0000ff0000, 0x0001ff0000ff0100, 0x0001ff000000ff00,
+    0x0001ff0000000000, 0x0001ff0000000001, 0x0001ff0000000100, 0x0001ff0000010000,
+    0x0001ff0000010001, 0x0001ff0000010101, 0x0001ff0001ff00ff, 0x0001ff0001ff0101,
+    0x0001ff000100ff01, 0x0001ff0001000000, 0x0001ff000101ff00, 0x0001ff0001010001,
+    0x0001ff0001010100, 0x0001ff01ff00ff00, 0x0001ff01ff000001, 0x0001ff01ff000100,
+    0x0001ff0100ffffff, 0x0001ff0100ffff00, 0x0001ff0100ff0001, 0x0001ff0100000000,
+    0x0001ff0100000001, 0x0001ff01000001ff, 0x0001ff010001ffff, 0x0001ff0101ff0000,
+    0x0001ff010100ff00, 0x0001ff0101000001, 0x0001ff0101010000, 0x000100ffff00ff00,
+    0x000100ffff00ff01, 0x000100ffff000000, 0x000100ffff000001, 0x000100ffff000101,
+    0x000100ffff01ff00, 0x000100ffff010001, 0x000100ffff010100, 0x000100ff00ffffff,
+    0x000100ff00ffff01, 0x000100ff00ff0000, 0x000100ff00ff01ff, 0x000100ff00ff0101,
+    0x000100ff0000ff00, 0x000100ff000000ff, 0x000100ff00000000, 0x000100ff00000001,
+    0x000100ff00000100, 0x000100ff00000101, 0x000100ff0001ffff, 0x000100ff0001ff01,
+    0x000100ff00010000, 0x000100ff01ff00ff, 0x000100ff01ff0000, 0x000100ff01ff0100,
+    0x000100ff0100ffff, 0x000100ff0100ff01, 0x000100ff010000ff, 0x000100ff01000000,
+    0x000100ff01000001, 0x000100ff010001ff, 0x000100ff01000101, 0x000100ff0101ff00,
+    0x000100ff010100ff, 0x000100ff01010100, 0x00010000ffff0000, 0x00010000ffff01ff,
+    0x00010000ffff0101, 0x00010000ff00ff00, 0x00010000ff000000, 0x00010000ff000001,
+    0x00010000ff000100, 0x0001000000ff00ff, 0x0001000000ff0000, 0x0001000000ff0001,
+    0x0001000000ff0100, 0x000100000000ffff, 0x000100000000ff00, 0x00010000000000ff,
+    0x0001000000000000, 0x0001000000000001, 0x0001000000000100, 0x000100000001ff00,
+    0x00010000000100ff, 0x0001000000010000, 0x0001000000010001, 0x0001000000010100,
+    0x0001000001ff0001, 0x0001000001ff0100, 0x0001000001ff0101, 0x000100000100ff00,
+    0x0001000001000000, 0x0001000001000001, 0x0001000001000100, 0x0001000001000101,
+    0x000100000101ff01, 0x0001000001010000, 0x0001000001010001, 0x00010000010101ff,
+    0x00010001ffffff01, 0x00010001ffff0100, 0x00010001ff000000, 0x00010001ff01ffff,
+    0x00010001ff010001, 0x00010001ff0101ff, 0x00010001ff010100, 0x0001000100ffffff,
+    0x0001000100ff0000, 0x0001000100ff01ff, 0x0001000100ff0101, 0x000100010000ff00,
+    0x00010001000000ff, 0x0001000100000000, 0x0001000100000001, 0x00010001000001ff,
+    0x0001000100000101, 0x000100010001ffff, 0x0001000100010000, 0x00010001000101ff,
+    0x0001000101ffffff, 0x0001000101ffff01, 0x0001000101ff0000, 0x0001000101ff0101,
+    0x00010001010000ff, 0x0001000101000001, 0x00010001010001ff, 0x0001000101000100,
+    0x000100010101ffff, 0x00010001010100ff, 0x0001000101010001, 0x0001000101010101,
+    0x000101ffff000001, 0x000101ffff000100, 0x000101ffff010000, 0x000101ff00ffff00,
+    0x000101ff0000ff01, 0x000101ff00000000, 0x000101ff00000101, 0x000101ff0001ff00,
+    0x000101ff00010100, 0x000101ff01ff0000, 0x000101ff0100ff00, 0x000101ff010001ff,
+    0x000101ff01010001, 0x00010100ffffff00, 0x00010100ffff00ff, 0x00010100ff00ffff,
+    0x00010100ff000000, 0x00010100ff01ff00, 0x00010100ff0100ff, 0x00010100ff010001,
+    0x00010100ff010100, 0x0001010000ffffff, 0x0001010000ffff00, 0x0001010000ff0000,
+    0x0001010000ff0001, 0x0001010000ff01ff, 0x000101000000ff00, 0x00010100000000ff,
+    0x0001010000000000, 0x0001010000000001, 0x0001010000000100, 0x000101000001ffff,
+    0x0001010000010000, 0x0001010000010101, 0x0001010001ffff01, 0x0001010001ff00ff,
+    0x0001010001ff0101, 0x0001010001000000, 0x000101000101ff00, 0x00010100010100ff,
+    0x0001010001010000, 0x0001010001010100, 0x00010101ff00ff00, 0x00010101ff000001,
+    0x00010101ff0001ff, 0x0001010100ffff00, 0x0001010100ff00ff, 0x0001010100ff0100,
+    0x000101010000ffff, 0x0001010100000000, 0x00010101000001ff, 0x0001010100000101,
+    0x00010101000100ff, 0x0001010100010000, 0x0001010100010100, 0x0001010101ff0001,
+    0x00010101010000ff, 0x00010101010001ff, 0x0001010101000101, 0x0001010101010001,
+    0x01ffffffffffffff, 0x01ffffffffffff01, 0x01ffffffffff01ff, 0x01ffffffffff0101,
+    0x01ffffffff01ffff, 0x01ffffffff01ff01, 0x01ffffffff0101ff, 0x01ffffffff010101,
+    0x01ffffff00ff0000, 0x01ffffff0000ffff, 0x01ffffff0000ff00, 0x01ffffff000000ff,
+    0x01ffffff00000001, 0x01ffffff00000100, 0x01ffffff00010000, 0x01ffffff01ffffff,
+    0x01ffffff01ffff01, 0x01ffffff01ff01ff, 0x01ffffff01ff0101, 0x01ffffff01000000,
+    0x01ffffff0101ffff, 0x01ffffff0101ff01, 0x01ffffff010101ff, 0x01ffffff01010101,
+    0x01ffff00ffff0000, 0x01ffff00ff00ff00, 0x01ffff00ff0000ff, 0x01ffff00ff000001,
+    0x01ffff00ff000100, 0x01ffff00ff010000, 0x01ffff0000ffff00, 0x01ffff0000ff00ff,
+    0x01ffff0000ff0100, 0x01ffff000000ffff, 0x01ffff000000ff01, 0x01ffff0000000000,
+    0x01ffff0000000001, 0x01ffff00000001ff, 0x01ffff0000000100, 0x01ffff00000100ff,
+    0x01ffff0000010001, 0x01ffff0000010100, 0x01ffff0001ff0000, 0x01ffff0001ff0100,
+    0x01ffff00010000ff, 0x01ffff0001000001, 0x01ffff0001000100, 0x01ffff0001010000,
+    0x01ffff01ffffffff, 0x01ffff01ffffff01, 0x01ffff01ffff01ff, 0x01ffff01ffff0101,
+    0x01ffff01ff000000, 0x01ffff01ff01ffff, 0x01ffff01ff01ff01, 0x01ffff01ff0101ff,
+    0x01ffff01ff010101, 0x01ffff010000ff00, 0x01ffff01000000ff, 0x01ffff0100000100,
+    0x01ffff0100010000, 0x01ffff0101ffffff, 0x01ffff0101ffff01, 0x01ffff0101ff01ff,
+    0x01ffff0101ff0101, 0x01ffff0101000000, 0x01ffff010101ffff, 0x01ffff010101ff01,
+    0x01ffff01010101ff, 0x01ffff0101010101, 0x01ff00ffff0000ff, 0x01ff00ffff000100,
+    0x01ff00ff00ffff00, 0x01ff00ff00ff00ff, 0x01ff00ff0000ff00, 0x01ff00ff00000000,
+    0x01ff00ff00000101, 0x01ff00ff0001ff00, 0x01ff00ff000100ff, 0x01ff00ff00010100,
+    0x01ff00ff010000ff, 0x01ff00ff01000100, 0x01ff0000ffffff00, 0x01ff0000ffff0100,
+    0x01ff0000ff00ff01, 0x01ff0000ff000000, 0x01ff0000ff000101, 0x01ff0000ff010001,
+    0x01ff0000ff010100, 0x01ff000000ffffff, 0x01ff000000ffff00, 0x01ff000000ff0000,
+    0x01ff000000ff01ff, 0x01ff00000000ff00, 0x01ff0000000000ff, 0x01ff000000000000,
+    0x01ff000000000001, 0x01ff000000000100, 0x01ff000000000101, 0x01ff000000010000,
+    0x01ff000000010001, 0x01ff0000000101ff, 0x01ff000000010101, 0x01ff000001ffff00,
+    0x01ff000001ff00ff, 0x01ff000001ff0001, 0x01ff000001ff0100, 0x01ff00000100ffff,
+    0x01ff00000100ff01, 0x01ff000001000000, 0x01ff0000010001ff, 0x01ff000001010001,
+    0x01ff0001ff00ff00, 0x01ff0001ff000001, 0x01ff0001ff000100, 0x01ff0001ff010000,
+    0x01ff000100ffff00, 0x01ff000100ff00ff, 0x01ff000100ff0100, 0x01ff000100ff0101,
+    0x01ff00010000ffff, 0x01ff000100000000, 0x01ff000100000100, 0x01ff000100000101,
+    0x01ff00010001ff00, 0x01ff000100010001, 0x01ff000100010101, 0x01ff000101ff0000,
+    0x01ff00010100ff00, 0x01ff000101000101, 0x01ff0001010100ff, 0x01ff01ffffffffff,
+    0x01ff01ffffffff01, 0x01ff01ffffff01ff, 0x01ff01ffffff0101, 0x01ff01ffff000000,
+    0x01ff01ffff01ffff, 0x01ff01ffff01ff01, 0x01ff01ffff0101ff, 0x01ff01ffff010101,
+    0x01ff01ff00ffff00, 0x01ff01ff00ff0000, 0x01ff01ff0000ff00, 0x01ff01ff000000ff,
+    0x01ff01ff00000100, 0x01ff01ff00010000, 0x01ff01ff00010100, 0x01ff01ff01ffffff,
+    0x01ff01ff01ffff01, 0x01ff01ff01ff01ff, 0x01ff01ff01ff0101, 0x01ff01ff01000000,
+    0x01ff01ff0101ffff, 0x01ff01ff0101ff01, 0x01ff01ff010101ff, 0x01ff01ff01010101,
+    0x01ff0100ffff0000, 0x01ff0100ffff0001, 0x01ff0100ff00ff00, 0x01ff0100ff0000ff,
+    0x01ff0100ff000001, 0x01ff0100ff010000, 0x01ff010000ffff00, 0x01ff010000ff00ff,
+    0x01ff010000ff0001, 0x01ff010000ff0100, 0x01ff01000000ffff, 0x01ff01000000ff01,
+    0x01ff010000000000, 0x01ff010000000101, 0x01ff01000001ff00, 0x01ff0100000100ff,
+    0x01ff010001ff0000, 0x01ff010001000001, 0x01ff010001000100, 0x01ff010001010000,
+    0x01ff0101ffffffff, 0x01ff0101ffffff01, 0x01ff0101ffff01ff, 0x01ff0101ffff0101,
+    0x01ff0101ff000000, 0x01ff0101ff01ffff, 0x01ff0101ff01ff01, 0x01ff0101ff0101ff,
+    0x01ff0101ff010101, 0x01ff010100ff0000, 0x01ff01010000ff00, 0x01ff0101000000ff,
+    0x01ff010100000001, 0x01ff010101ffffff, 0x01ff010101ffff01, 0x01ff010101ff01ff,
+    0x01ff010101ff0101, 0x01ff010101000000, 0x01ff01010101ffff, 0x01ff01010101ff01,
+    0x01ff0101010101ff, 0x01ff010101010101, 0x0100ffffffff0000, 0x0100ffffff00ff00,
+    0x0100ffffff000001, 0x0100ffffff0001ff, 0x0100ffffff000100, 0x0100ffffff010000,
+    0x0100ffff00ffff00, 0x0100ffff00ff0001, 0x0100ffff00ff0100, 0x0100ffff00000000,
+    0x0100ffff000001ff, 0x0100ffff00000101, 0x0100ffff00010100, 0x0100ffff00010101,
+    0x0100ffff01ff0000, 0x0100ffff0100ff00, 0x0100ffff010000ff, 0x0100ffff01000001,
+    0x0100ffff01000100, 0x0100ffff01010000, 0x0100ff00ffffff00, 0x0100ff00ffff00ff,
+    0x0100ff00ffff0001, 0x0100ff00ffff0100, 0x0100ff00ff00ffff, 0x0100ff00ff000000,
+    0x0100ff00ff0001ff, 0x0100ff00ff000101, 0x0100ff00ff01ff00, 0x0100ff00ff0100ff,
+    0x0100ff00ff010001, 0x0100ff00ff010100, 0x0100ff0000ffffff, 0x0100ff0000ff0000,
+    0x0100ff000000ffff, 0x0100ff000000ff00, 0x0100ff00000000ff, 0x0100ff0000000000,
+    0x0100ff0000000001, 0x0100ff0000000100, 0x0100ff000001ff01, 0x0100ff0000010000,
+    0x0100ff0001ff00ff, 0x0100ff0001ff0001, 0x0100ff000100ff01, 0x0100ff0001000000,
+    0x0100ff00010001ff, 0x0100ff000101ff00, 0x0100ff00010100ff, 0x0100ff0001010001,
+    0x0100ff0001010100, 0x0100ff01ffff0000, 0x0100ff01ff00ff00, 0x0100ff01ff0000ff,
+    0x0100ff01ff000100, 0x0100ff01ff010000, 0x0100ff0100ff00ff, 0x0100ff0100ff0001,
+    0x0100ff0100ff0100, 0x0100ff010000ffff, 0x0100ff010000ff01, 0x0100ff0100000000,
+    0x0100ff01000001ff, 0x0100ff0100010001, 0x0100ff0100010100, 0x0100ff0101ff0000,
+    0x0100ff01010000ff, 0x0100ff0101000001, 0x0100ff0101010100, 0x010000ffffffff00,
+    0x010000ffffff00ff, 0x010000ffffff0001, 0x010000ffff00ffff, 0x010000ffff000000,
+    0x010000ffff0001ff, 0x010000ffff010001, 0x010000ff00ffffff, 0x010000ff00ff0101,
+    0x010000ff0000ff00, 0x010000ff000000ff, 0x010000ff00000000, 0x010000ff00000001,
+    0x010000ff000001ff, 0x010000ff00000100, 0x010000ff0001ffff, 0x010000ff0001ff00,
+    0x010000ff0001ff01, 0x010000ff00010000, 0x010000ff01ff00ff, 0x010000ff01ff0001,
+    0x010000ff0100ff01, 0x010000ff010000ff, 0x010000ff01000000, 0x010000ff010001ff,
+    0x010000ff0101ff00, 0x010000ff01010100, 0x01000000ffffffff, 0x01000000ffff0000,
+    0x01000000ffff01ff, 0x01000000ffff0101, 0x01000000ff00ffff, 0x01000000ff00ff00,
+    0x01000000ff0000ff, 0x01000000ff000000, 0x01000000ff000001, 0x01000000ff000100,
+    0x01000000ff01ff00, 0x01000000ff010000, 0x01000000ff010100, 0x01000000ff010101,
+    0x0100000000ffff00, 0x0100000000ff00ff, 0x0100000000ff0000, 0x0100000000ff0001,
+    0x0100000000ff0100, 0x010000000000ffff, 0x010000000000ff00, 0x010000000000ff01,
+    0x01000000000000ff, 0x0100000000000000, 0x0100000000000001, 0x01000000000001ff,
+    0x0100000000000100, 0x0100000000000101, 0x010000000001ff00, 0x01000000000100ff,
+    0x0100000000010000, 0x0100000000010001, 0x0100000000010100, 0x0100000001ffff00,
+    0x0100000001ff0000, 0x0100000001ff01ff, 0x010000000100ff00, 0x010000000100ff01,
+    0x01000000010000ff, 0x0100000001000000, 0x0100000001000001, 0x0100000001000100,
+    0x0100000001000101, 0x010000000101ffff, 0x010000000101ff01, 0x0100000001010000,
+    0x01000000010101ff, 0x0100000001010101, 0x01000001ffffff00, 0x01000001ffff00ff,
+    0x01000001ff00ffff, 0x01000001ff000000, 0x01000001ff000100, 0x01000001ff01ffff,
+    0x01000001ff010001, 0x01000001ff010100, 0x0100000100ff0000, 0x0100000100ff01ff,
+    0x0100000100ff0100, 0x010000010000ff00, 0x010000010000ff01, 0x0100000100000000,
+    0x0100000100000001, 0x0100000100000100, 0x0100000100010000, 0x01000001000101ff,
+    0x0100000101ffff01, 0x0100000101ff00ff, 0x0100000101ff0100, 0x0100000101ff0101,
+    0x010000010100ff01, 0x01000001010000ff, 0x0100000101000000, 0x01000001010100ff,
+    0x0100000101010001, 0x0100000101010100, 0x010001ffffff0000, 0x010001ffff000001,
+    0x010001ffff000100, 0x010001ffff010000, 0x010001ff00ffff00, 0x010001ff00ff0001,
+    0x010001ff0000ffff, 0x010001ff0000ff01, 0x010001ff00000000, 0x010001ff00000001,
+    0x010001ff00000101, 0x010001ff000100ff, 0x010001ff00010000, 0x010001ff01ff0000,
+    0x010001ff0100ff00, 0x010001ff01000001, 0x010001ff01000100, 0x010001ff01010000,
+    0x01000100ffff00ff, 0x01000100ffff0001, 0x01000100ffff0100, 0x01000100ff00ffff,
+    0x01000100ff00ff01, 0x01000100ff000000, 0x01000100ff0001ff, 0x01000100ff000101,
+    0x01000100ff01ffff, 0x01000100ff01ff00, 0x01000100ff0100ff, 0x01000100ff010001,
+    0x0100010000ffffff, 0x0100010000ffff01, 0x0100010000ff0000, 0x0100010000ff01ff,
+    0x0100010000ff0101, 0x010001000000ff00, 0x01000100000000ff, 0x0100010000000000,
+    0x0100010000000001, 0x0100010000000100, 0x010001000001ff01, 0x0100010000010000,
+    0x0100010000010001, 0x0100010000010101, 0x0100010001ffff00, 0x0100010001ff00ff,
+    0x010001000100ffff, 0x010001000100ff01, 0x0100010001000000, 0x0100010001000101,
+    0x010001000101ff00, 0x0100010001010001, 0x01000101ffff0000, 0x01000101ff000000,
+    0x01000101ff010000, 0x0100010100ff00ff, 0x0100010100ff0001, 0x0100010100ff0100,
+    0x010001010000ffff, 0x0100010100000000, 0x01000101000001ff, 0x010001010001ff00,
+    0x0100010101ff0000, 0x010001010100ff00, 0x01000101010000ff, 0x0100010101000000,
+    0x0100010101000001, 0x0101ffffffffffff, 0x0101ffffffffff01, 0x0101ffffffff01ff,
+    0x0101ffffffff0101, 0x0101ffffff000000, 0x0101ffffff01ffff, 0x0101ffffff01ff01,
+    0x0101ffffff0101ff, 0x0101ffffff010101, 0x0101ffff00ff0000, 0x0101ffff0000ff00,
+    0x0101ffff000000ff, 0x0101ffff00000001, 0x0101ffff00000100, 0x0101ffff01ffffff,
+    0x0101ffff01ffff01, 0x0101ffff01ff01ff, 0x0101ffff01ff0101, 0x0101ffff01000000,
+    0x0101ffff0101ffff, 0x0101ffff0101ff01, 0x0101ffff010101ff, 0x0101ffff01010101,
+    0x0101ff00ffff0000, 0x0101ff00ffff0100, 0x0101ff00ff00ff00, 0x0101ff00ff0000ff,
+    0x0101ff00ff000001, 0x0101ff00ff000100, 0x0101ff00ff000101, 0x0101ff0000ff0001,
+    0x0101ff0000ff0100, 0x0101ff000000ff00, 0x0101ff0000000000, 0x0101ff00000001ff,
+    0x0101ff0000000101, 0x0101ff000001ff00, 0x0101ff00000100ff, 0x0101ff0001ff0000,
+    0x0101ff000100ffff, 0x0101ff000100ff01, 0x0101ff0001000001, 0x0101ff0001000100,
+    0x0101ff01ffffff01, 0x0101ff01ffff01ff, 0x0101ff01ffff0101, 0x0101ff01ff00ffff,
+    0x0101ff01ff000100, 0x0101ff01ff01ff01, 0x0101ff01ff0101ff, 0x0101ff01ff010101,
+    0x0101ff0100ff0000, 0x0101ff010000ff00, 0x0101ff0100000001, 0x0101ff0100000100,
+    0x0101ff0100010000, 0x0101ff0101ffffff, 0x0101ff0101ffff01, 0x0101ff0101ff01ff,
+    0x0101ff0101ff0101, 0x0101ff0101000000, 0x0101ff010101ffff, 0x0101ff010101ff01,
+    0x0101ff01010101ff, 0x0101ff0101010101, 0x010100ffff000100, 0x010100ffff010000,
+    0x010100ff00ffff00, 0x010100ff00ff00ff, 0x010100ff0000ffff, 0x010100ff000000ff,
+    0x010100ff00000000, 0x010100ff000001ff, 0x010100ff00000101, 0x010100ff0001ff00,
+    0x010100ff00010000, 0x010100ff00010001, 0x010100ff000101ff, 0x010100ff00010100,
+    0x010100ff01ff0000, 0x01010000ffff0001, 0x01010000ffff0100, 0x01010000ff00ffff,
+    0x01010000ff00ff01, 0x01010000ff000000, 0x01010000ff0001ff, 0x01010000ff010001,
+    0x01010000ff010100, 0x0101000000ffff01, 0x0101000000ff0000, 0x010100000000ff00,
+    0x01010000000000ff, 0x0101000000000000, 0x0101000000000001, 0x0101000000000100,
+    0x0101000000010000, 0x0101000000010101, 0x0101000001ffff00, 0x0101000001ff00ff,
+    0x0101000001ff0000, 0x0101000001ff0001, 0x0101000001ff0100, 0x010100000100ff01,
+    0x0101000001000000, 0x01010000010001ff, 0x01010001ffff0000, 0x01010001ff00ff00,
+    0x01010001ff000001, 0x01010001ff000101, 0x01010001ff01ff00, 0x01010001ff010000,
+    0x0101000100ff00ff, 0x0101000100ff0001, 0x0101000100ff0101, 0x010100010000ff01,
+    0x0101000100000000, 0x0101000100000001, 0x01010001000001ff, 0x010100010001ffff,
+    0x010100010001ff01, 0x0101000101ff0001, 0x010100010100ffff, 0x0101000101000000,
+    0x0101000101000001, 0x0101000101000100, 0x010100010101ff00, 0x01010001010100ff,
+    0x0101000101010001, 0x010101ffffffffff, 0x010101ffffffff01, 0x010101ffffff01ff,
+    0x010101ffffff0101, 0x010101ffff01ffff, 0x010101ffff01ff01, 0x010101ffff0101ff,
+    0x010101ffff010101, 0x010101ff0000ff00, 0x010101ff000000ff, 0x010101ff00000001,
+    0x010101ff00000100, 0x010101ff01ffffff, 0x010101ff01ffff01, 0x010101ff01ff01ff,
+    0x010101ff01ff0101, 0x010101ff01000000, 0x010101ff0101ffff, 0x010101ff0101ff01,
+    0x010101ff010101ff, 0x010101ff01010101, 0x01010100ffff0000, 0x01010100ff0000ff,
+    0x01010100ff000100, 0x01010100ff01ff00, 0x01010100ff010000, 0x0101010000ffff00,
+    0x010101000000ffff, 0x0101010000000000, 0x0101010000000101, 0x010101000001ff00,
+    0x0101010000010001, 0x0101010000010100, 0x010101000100ffff, 0x0101010001000001,
+    0x01010101ffffffff, 0x01010101ffffff01, 0x01010101ffff01ff, 0x01010101ffff0101,
+    0x01010101ff01ffff, 0x01010101ff01ff01, 0x01010101ff0101ff, 0x01010101ff010101,
+    0x010101010000ff00, 0x01010101000000ff, 0x0101010100000001, 0x0101010101ffffff,
+    0x0101010101ffff01, 0x0101010101ff01ff, 0x0101010101ff0101, 0x0101010101000000,
+    0x010101010101ffff, 0x010101010101ff01, 0x01010101010101ff, 0x0101010101010101,
+GGML_TABLE_END()
+#else
+GGML_TABLE_BEGIN(uint32_t, iq1s_grid_gpu, NGRID_IQ1S)
+    0x00000000, 0x00000002, 0x00000101, 0x00000200, 0x00000202, 0x00010001, 0x00010101, 0x00020000,
+    0x00020002, 0x00020200, 0x00020202, 0x01000101, 0x01010001, 0x01010100, 0x01010102, 0x01020101,
+    0x02000000, 0x02000002, 0x02000200, 0x02000202, 0x02010101, 0x02020000, 0x02020002, 0x02020200,
+    0x02020202, 0x00000110, 0x00000111, 0x00010011, 0x00010110, 0x00010112, 0x00010211, 0x00010212,
+    0x00020111, 0x01000011, 0x01000112, 0x01000211, 0x01010012, 0x01010111, 0x01010212, 0x01020011,
+    0x01020110, 0x01020112, 0x01020210, 0x02000111, 0x02010011, 0x02010110, 0x02010112, 0x02020111,
+    0x00000020, 0x00000022, 0x00000220, 0x00000222, 0x00010121, 0x00020020, 0x00020022, 0x00020220,
+    0x00020222, 0x01000121, 0x01010021, 0x01010221, 0x01020120, 0x01020221, 0x02000020, 0x02000022,
+    0x02000220, 0x02000222, 0x02010021, 0x02010121, 0x02010221, 0x02020020, 0x02020022, 0x02020220,
+    0x02020222, 0x00011001, 0x00011100, 0x00011102, 0x00021101, 0x01001001, 0x01001201, 0x01011101,
+    0x01011202, 0x01021100, 0x01021101, 0x02011001, 0x02011201, 0x02021101, 0x00001011, 0x00001110,
+    0x00001111, 0x00001112, 0x00011111, 0x00011210, 0x00011212, 0x00021211, 0x01001010, 0x01001111,
+    0x01001212, 0x01011010, 0x01011011, 0x01011110, 0x01011111, 0x01011112, 0x01011211, 0x01021010,
+    0x01021012, 0x01021111, 0x01021210, 0x01021212, 0x02001011, 0x02011011, 0x02011111, 0x02011210,
+    0x02011212, 0x02021011, 0x02021110, 0x02021111, 0x02021112, 0x02021211, 0x00011120, 0x00011221,
+    0x01001021, 0x01001120, 0x01011020, 0x01011022, 0x01011121, 0x01011220, 0x01021020, 0x01021021,
+    0x01021122, 0x01021221, 0x02001121, 0x02011021, 0x02011120, 0x02011221, 0x00002000, 0x00002002,
+    0x00002200, 0x00002202, 0x00012101, 0x00022000, 0x00022002, 0x00022200, 0x00022202, 0x01002101,
+    0x01012001, 0x01012102, 0x01022101, 0x02002000, 0x02002002, 0x02002200, 0x02002202, 0x02012101,
+    0x02022000, 0x02022002, 0x02022200, 0x02022202, 0x00002111, 0x00012011, 0x00012110, 0x00012211,
+    0x00022110, 0x00022111, 0x01002011, 0x01012010, 0x01012011, 0x01012111, 0x01022011, 0x01022110,
+    0x01022211, 0x02012011, 0x02012110, 0x02012112, 0x02012211, 0x02022111, 0x00002020, 0x00002022,
+    0x00002220, 0x00002222, 0x00012121, 0x00022020, 0x00022022, 0x00022220, 0x00022222, 0x01002121,
+    0x01012021, 0x01012221, 0x01022021, 0x01022121, 0x02002020, 0x02002022, 0x02002121, 0x02002220,
+    0x02002222, 0x02012121, 0x02022020, 0x02022022, 0x02022220, 0x02022222, 0x00110000, 0x00110001,
+    0x00110100, 0x00110201, 0x00120100, 0x00120101, 0x01100001, 0x01100100, 0x01110000, 0x01110101,
+    0x01110200, 0x01120001, 0x01120100, 0x01120101, 0x01120201, 0x02110001, 0x02110100, 0x02110102,
+    0x02120001, 0x02120101, 0x00100011, 0x00100110, 0x00100112, 0x00100211, 0x00110010, 0x00110012,
+    0x00110111, 0x00110210, 0x00120011, 0x00120110, 0x00120211, 0x01100111, 0x01100212, 0x01110010,
+    0x01110011, 0x01110012, 0x01110110, 0x01110111, 0x01110112, 0x01110211, 0x01120010, 0x01120111,
+    0x02100110, 0x02110012, 0x02110111, 0x02120011, 0x02120110, 0x00110021, 0x00110120, 0x00110122,
+    0x00120121, 0x01100020, 0x01100122, 0x01100221, 0x01110022, 0x01110121, 0x01110220, 0x01110222,
+    0x01120120, 0x01120122, 0x02100121, 0x02110021, 0x02110120, 0x02110122, 0x02120121, 0x00101001,
+    0x00101102, 0x00101201, 0x00111100, 0x00111101, 0x00111200, 0x00111201, 0x00121001, 0x00121102,
+    0x01101001, 0x01101101, 0x01101102, 0x01101200, 0x01101202, 0x01111001, 0x01111100, 0x01111101,
+    0x01111102, 0x01111201, 0x01121002, 0x01121101, 0x01121200, 0x02101100, 0x02101201, 0x02111000,
+    0x02111100, 0x02111101, 0x02111200, 0x02111201, 0x02111202, 0x02121001, 0x02121100, 0x02121101,
+    0x02121201, 0x00101012, 0x00101111, 0x00101212, 0x00111011, 0x00111110, 0x00111111, 0x00111112,
+    0x00111211, 0x00121010, 0x00121012, 0x00121111, 0x00121210, 0x00121212, 0x01101011, 0x01101110,
+    0x01101111, 0x01101112, 0x01111011, 0x01111012, 0x01111110, 0x01111111, 0x01111112, 0x01111211,
+    0x01111212, 0x01121011, 0x01121110, 0x01121111, 0x01121112, 0x01121211, 0x02101010, 0x02101012,
+    0x02101110, 0x02101111, 0x02101210, 0x02101212, 0x02111010, 0x02111011, 0x02111110, 0x02111111,
+    0x02111112, 0x02111211, 0x02111212, 0x02121010, 0x02121012, 0x02121111, 0x00101021, 0x00101120,
+    0x00101121, 0x00101122, 0x00111121, 0x00111122, 0x00111220, 0x00111222, 0x00121021, 0x00121122,
+    0x01101020, 0x01101022, 0x01101120, 0x01101121, 0x01101220, 0x01101222, 0x01111021, 0x01111121,
+    0x01111122, 0x01111220, 0x01111221, 0x01121021, 0x01121120, 0x01121121, 0x01121220, 0x01121221,
+    0x01121222, 0x02101122, 0x02101222, 0x02111022, 0x02111121, 0x02121120, 0x02121221, 0x00112001,
+    0x00112102, 0x00122101, 0x01102001, 0x01102100, 0x01102102, 0x01102201, 0x01112000, 0x01112101,
+    0x01112200, 0x01112202, 0x01122000, 0x01122001, 0x01122100, 0x01122102, 0x01122201, 0x02102101,
+    0x02112001, 0x02112100, 0x02122101, 0x00112010, 0x00112012, 0x00112111, 0x00112212, 0x00122011,
+    0x00122111, 0x01102012, 0x01102110, 0x01102111, 0x01102210, 0x01112011, 0x01112110, 0x01112111,
+    0x01112112, 0x01112211, 0x01112212, 0x01122010, 0x01122111, 0x01122212, 0x02102211, 0x02112011,
+    0x02112012, 0x02112111, 0x02112210, 0x02122011, 0x02122112, 0x02122211, 0x00102221, 0x00112122,
+    0x00122120, 0x00122122, 0x01102120, 0x01102122, 0x01102221, 0x01112020, 0x01112022, 0x01112121,
+    0x01112220, 0x01122021, 0x01122122, 0x01122221, 0x02102121, 0x02112021, 0x02112122, 0x02112222,
+    0x00200000, 0x00200002, 0x00200200, 0x00200202, 0x00210101, 0x00220000, 0x00220002, 0x00220101,
+    0x00220200, 0x00220202, 0x01200101, 0x01210001, 0x01210201, 0x01220001, 0x01220101, 0x02200000,
+    0x02200002, 0x02200200, 0x02200202, 0x02210101, 0x02220000, 0x02220002, 0x02220101, 0x02220200,
+    0x02220202, 0x00200111, 0x00210011, 0x00210110, 0x00210211, 0x00220111, 0x01200012, 0x01200110,
+    0x01200211, 0x01210111, 0x01210210, 0x01210212, 0x01220011, 0x01220110, 0x01220111, 0x01220112,
+    0x02200111, 0x02210010, 0x02210112, 0x02210211, 0x02220111, 0x00200021, 0x00200220, 0x00200222,
+    0x00210021, 0x00210121, 0x00220020, 0x00220022, 0x00220220, 0x00220222, 0x01200121, 0x01210021,
+    0x01210122, 0x01210221, 0x01220121, 0x02200021, 0x02200220, 0x02200222, 0x02210021, 0x02210121,
+    0x02220020, 0x02220022, 0x02220220, 0x02220222, 0x00201101, 0x00211100, 0x00211102, 0x00211201,
+    0x00221101, 0x01201100, 0x01201101, 0x01201102, 0x01201201, 0x01211002, 0x01211101, 0x01211200,
+    0x01211202, 0x01221102, 0x02201101, 0x02211001, 0x02211100, 0x02211201, 0x02221001, 0x02221101,
+    0x00201211, 0x00211111, 0x00221011, 0x00221211, 0x01201010, 0x01201111, 0x01201210, 0x01211011,
+    0x01211110, 0x01211111, 0x01211211, 0x01221012, 0x01221111, 0x01221210, 0x02201211, 0x02211010,
+    0x02211110, 0x02211111, 0x02211210, 0x02211212, 0x02221011, 0x02221110, 0x02221112, 0x02221211,
+    0x00201121, 0x00211020, 0x00211022, 0x00211221, 0x00221121, 0x01201021, 0x01201221, 0x01211121,
+    0x01221020, 0x01221021, 0x01221221, 0x02201120, 0x02201122, 0x02211020, 0x02211222, 0x00202000,
+    0x00202002, 0x00202200, 0x00202202, 0x00212101, 0x00222000, 0x00222002, 0x00222200, 0x00222202,
+    0x01202101, 0x01212001, 0x01212100, 0x01222101, 0x02202000, 0x02202002, 0x02202200, 0x02202202,
+    0x02222000, 0x02222002, 0x02222200, 0x02222202, 0x00202211, 0x00212011, 0x00212110, 0x00212211,
+    0x00222111, 0x01202112, 0x01202211, 0x01212012, 0x01212111, 0x01222011, 0x01222110, 0x01222112,
+    0x01222211, 0x02202111, 0x02212010, 0x02212112, 0x02212211, 0x02222110, 0x02222111, 0x00202020,
+    0x00202022, 0x00202220, 0x00202222, 0x00222020, 0x00222022, 0x00222220, 0x00222222, 0x01202121,
+    0x01212021, 0x01212122, 0x01212221, 0x01222121, 0x02202020, 0x02202022, 0x02202220, 0x02202222,
+    0x02212121, 0x02222020, 0x02222022, 0x02222220, 0x02222222, 0x10000101, 0x10010001, 0x10010102,
+    0x10020101, 0x11000201, 0x11010002, 0x11010101, 0x11010200, 0x11010202, 0x11020001, 0x11020100,
+    0x11020102, 0x12010100, 0x12010201, 0x12020001, 0x12020102, 0x10000010, 0x10000011, 0x10000110,
+    0x10000112, 0x10000211, 0x10010012, 0x10010111, 0x10010112, 0x10010210, 0x10010212, 0x10020011,
+    0x10020112, 0x10020211, 0x11000111, 0x11000210, 0x11000212, 0x11010011, 0x11010110, 0x11010111,
+    0x11010112, 0x11010211, 0x11010212, 0x11020111, 0x11020210, 0x11020212, 0x12000011, 0x12000110,
+    0x12000112, 0x12010010, 0x12010012, 0x12010111, 0x12020010, 0x12020011, 0x12020012, 0x10000121,
+    0x10010021, 0x10010120, 0x10010122, 0x10020121, 0x11000021, 0x11010022, 0x11010121, 0x11010222,
+    0x11020120, 0x11020221, 0x12000221, 0x12010120, 0x12020121, 0x10001001, 0x10011101, 0x10011201,
+    0x10021201, 0x11001101, 0x11001200, 0x11001202, 0x11011001, 0x11011100, 0x11011101, 0x11011102,
+    0x11021001, 0x11021002, 0x11021101, 0x11021200, 0x11021202, 0x12001001, 0x12001102, 0x12001201,
+    0x12011000, 0x12011002, 0x12011101, 0x12021000, 0x12021001, 0x12021201, 0x10001011, 0x10001012,
+    0x10001111, 0x10001212, 0x10011011, 0x10011110, 0x10011111, 0x10011112, 0x10011211, 0x10021010,
+    0x10021111, 0x10021212, 0x11001011, 0x11001110, 0x11001111, 0x11001112, 0x11001211, 0x11011010,
+    0x11011011, 0x11011110, 0x11011111, 0x11011112, 0x11011210, 0x11011211, 0x11021011, 0x11021110,
+    0x11021111, 0x11021112, 0x11021211, 0x12001012, 0x12001110, 0x12001111, 0x12001210, 0x12011011,
+    0x12011110, 0x12011111, 0x12011112, 0x12011211, 0x12011212, 0x12021111, 0x12021210, 0x12021212,
+    0x10001021, 0x10001121, 0x10001221, 0x10011120, 0x10011121, 0x10011220, 0x10011222, 0x10021021,
+    0x10021120, 0x10021221, 0x11001020, 0x11001022, 0x11001121, 0x11001220, 0x11011020, 0x11011021,
+    0x11011022, 0x11011121, 0x11011122, 0x11011221, 0x11021022, 0x11021121, 0x11021220, 0x12001021,
+    0x12001121, 0x12001222, 0x12011120, 0x12011121, 0x12021021, 0x12021120, 0x12021122, 0x10002101,
+    0x10012001, 0x10012101, 0x10012202, 0x10022101, 0x11002002, 0x11002201, 0x11012000, 0x11012101,
+    0x11012200, 0x11022001, 0x11022100, 0x11022102, 0x11022201, 0x12002101, 0x12012001, 0x12012100,
+    0x12012102, 0x12012201, 0x12022101, 0x10002011, 0x10002111, 0x10002112, 0x10002212, 0x10012010,
+    0x10012110, 0x10012111, 0x10012210, 0x10022011, 0x10022110, 0x10022112, 0x11002010, 0x11002111,
+    0x11002212, 0x11012011, 0x11012012, 0x11012110, 0x11012111, 0x11012112, 0x11012211, 0x11022010,
+    0x11022012, 0x11022111, 0x11022112, 0x11022212, 0x12002112, 0x12002211, 0x12012012, 0x12012111,
+    0x12012112, 0x12012210, 0x12022011, 0x12022110, 0x12022112, 0x12022211, 0x10012122, 0x11002120,
+    0x11002122, 0x11002221, 0x11012121, 0x11012220, 0x11012222, 0x11022120, 0x11022221, 0x12012120,
+    0x12022121, 0x10100001, 0x10100100, 0x10100101, 0x10100102, 0x10100201, 0x10110002, 0x10110101,
+    0x10110202, 0x10120001, 0x10120100, 0x10120201, 0x11100000, 0x11100101, 0x11100200, 0x11110001,
+    0x11110100, 0x11110101, 0x11110102, 0x11110201, 0x11120101, 0x11120200, 0x12100102, 0x12100201,
+    0x12110101, 0x12110200, 0x12120000, 0x12120001, 0x12120102, 0x12120201, 0x10100111, 0x10100210,
+    0x10100211, 0x10100212, 0x10110011, 0x10110110, 0x10110111, 0x10110112, 0x10110210, 0x10110211,
+    0x10120010, 0x10120111, 0x10120112, 0x10120210, 0x10120212, 0x11100011, 0x11100110, 0x11100111,
+    0x11100112, 0x11100211, 0x11110010, 0x11110011, 0x11110012, 0x11110110, 0x11110111, 0x11110112,
+    0x11110210, 0x11110211, 0x11110212, 0x11120011, 0x11120110, 0x11120111, 0x11120112, 0x11120211,
+    0x12100012, 0x12100111, 0x12110011, 0x12110110, 0x12110111, 0x12110112, 0x12110211, 0x12120010,
+    0x12120111, 0x12120212, 0x10100021, 0x10100122, 0x10110022, 0x10110121, 0x10110222, 0x10120021,
+    0x10120120, 0x11100022, 0x11100121, 0x11100222, 0x11110021, 0x11110120, 0x11110121, 0x11110122,
+    0x11110221, 0x11120022, 0x11120121, 0x12100121, 0x12110020, 0x12110022, 0x12110121, 0x12110221,
+    0x12110222, 0x12120120, 0x10101100, 0x10101101, 0x10111001, 0x10111100, 0x10111101, 0x10111102,
+    0x10111200, 0x10111201, 0x10121001, 0x10121101, 0x10121200, 0x10121202, 0x11101001, 0x11101100,
+    0x11101101, 0x11101102, 0x11101201, 0x11101202, 0x11111000, 0x11111001, 0x11111100, 0x11111101,
+    0x11111102, 0x11111200, 0x11111201, 0x11111202, 0x11121001, 0x11121002, 0x11121100, 0x11121101,
+    0x11121102, 0x11121201, 0x12101000, 0x12101200, 0x12101202, 0x12111001, 0x12111100, 0x12111101,
+    0x12111102, 0x12111201, 0x12121001, 0x12121100, 0x12121101, 0x12121202, 0x10101011, 0x10101012,
+    0x10101110, 0x10101111, 0x10101112, 0x10101211, 0x10111010, 0x10111011, 0x10111012, 0x10111110,
+    0x10111111, 0x10111112, 0x10111211, 0x10111212, 0x10121011, 0x10121110, 0x10121111, 0x10121112,
+    0x10121211, 0x11101010, 0x11101011, 0x11101012, 0x11101110, 0x11101111, 0x11101112, 0x11101210,
+    0x11101211, 0x11111010, 0x11111011, 0x11111012, 0x11111110, 0x11111111, 0x11111112, 0x11111210,
+    0x11111211, 0x11111212, 0x11121010, 0x11121011, 0x11121110, 0x11121111, 0x11121112, 0x11121210,
+    0x11121211, 0x11121212, 0x12101011, 0x12101110, 0x12101111, 0x12101211, 0x12101212, 0x12111010,
+    0x12111011, 0x12111110, 0x12111111, 0x12111112, 0x12111210, 0x12111211, 0x12121011, 0x12121110,
+    0x12121111, 0x12121112, 0x12121211, 0x10101020, 0x10101021, 0x10101022, 0x10101120, 0x10101122,
+    0x10101220, 0x10101221, 0x10111021, 0x10111120, 0x10111121, 0x10111220, 0x10111221, 0x10121020,
+    0x10121021, 0x10121022, 0x10121120, 0x10121121, 0x10121122, 0x10121220, 0x10121221, 0x11101021,
+    0x11101121, 0x11101122, 0x11101220, 0x11101221, 0x11101222, 0x11111020, 0x11111021, 0x11111022,
+    0x11111120, 0x11111121, 0x11111122, 0x11111220, 0x11111221, 0x11111222, 0x11121021, 0x11121120,
+    0x11121121, 0x11121221, 0x12101022, 0x12101121, 0x12101122, 0x12101220, 0x12101221, 0x12101222,
+    0x12111021, 0x12111121, 0x12111222, 0x12121022, 0x12121121, 0x12121122, 0x12121220, 0x12121221,
+    0x10102100, 0x10102101, 0x10102102, 0x10102201, 0x10112000, 0x10112101, 0x10112200, 0x10122001,
+    0x10122202, 0x11102101, 0x11102200, 0x11102202, 0x11112001, 0x11112100, 0x11112101, 0x11112102,
+    0x11112200, 0x11112201, 0x11122000, 0x11122002, 0x11122100, 0x11122101, 0x12102002, 0x12102201,
+    0x12112000, 0x12112002, 0x12112101, 0x12112200, 0x12122001, 0x12122201, 0x10102011, 0x10102012,
+    0x10102111, 0x10102212, 0x10112011, 0x10112110, 0x10112111, 0x10112112, 0x10112211, 0x10122111,
+    0x11102011, 0x11102110, 0x11102111, 0x11102112, 0x11102211, 0x11112010, 0x11112011, 0x11112012,
+    0x11112110, 0x11112111, 0x11112112, 0x11112210, 0x11112211, 0x11112212, 0x11122011, 0x11122110,
+    0x11122111, 0x11122112, 0x11122211, 0x12102011, 0x12102111, 0x12102211, 0x12112011, 0x12112110,
+    0x12112111, 0x12112112, 0x12112210, 0x12112211, 0x12122111, 0x10102120, 0x10102220, 0x10112121,
+    0x10112222, 0x10122020, 0x10122121, 0x10122122, 0x10122221, 0x11102121, 0x11102220, 0x11102221,
+    0x11112021, 0x11112121, 0x11112122, 0x11112220, 0x11112221, 0x11122022, 0x11122121, 0x11122220,
+    0x11122222, 0x12102021, 0x12102222, 0x12112022, 0x12112121, 0x12112122, 0x12112220, 0x12112222,
+    0x12122021, 0x10200101, 0x10210100, 0x10210102, 0x10210201, 0x10220101, 0x11200100, 0x11210000,
+    0x11210101, 0x11210102, 0x11210200, 0x11210202, 0x11220001, 0x11220100, 0x11220102, 0x11220201,
+    0x12200001, 0x12210102, 0x12220101, 0x10200011, 0x10200110, 0x10200112, 0x10200211, 0x10210012,
+    0x10210111, 0x10220011, 0x10220012, 0x10220112, 0x10220211, 0x11200111, 0x11200211, 0x11210011,
+    0x11210111, 0x11210112, 0x11210211, 0x11220111, 0x11220112, 0x11220212, 0x12200110, 0x12200212,
+    0x12210012, 0x12210111, 0x12220011, 0x12220112, 0x12220211, 0x10210021, 0x10210122, 0x10210221,
+    0x11200020, 0x11200021, 0x11200122, 0x11210121, 0x11210122, 0x11210220, 0x11220020, 0x12200121,
+    0x12210021, 0x12210122, 0x12220121, 0x10211001, 0x10211002, 0x10211101, 0x10211102, 0x10211202,
+    0x10221001, 0x10221102, 0x10221201, 0x11201000, 0x11201002, 0x11201101, 0x11201200, 0x11201202,
+    0x11211001, 0x11211100, 0x11211101, 0x11211102, 0x11211201, 0x11211202, 0x11221000, 0x11221002,
+    0x11221101, 0x12201100, 0x12201101, 0x12201201, 0x12211000, 0x12211002, 0x12211100, 0x12211101,
+    0x12211102, 0x12211200, 0x12211202, 0x12221001, 0x12221100, 0x12221201, 0x10201111, 0x10201210,
+    0x10201212, 0x10211011, 0x10211111, 0x10211112, 0x10211211, 0x11201110, 0x11201111, 0x11201112,
+    0x11201211, 0x11211010, 0x11211011, 0x11211110, 0x11211111, 0x11211112, 0x11211211, 0x11221011,
+    0x11221110, 0x11221111, 0x11221112, 0x11221211, 0x12201112, 0x12201211, 0x12201212, 0x12211011,
+    0x12211111, 0x12211112, 0x12211211, 0x12211212, 0x12221012, 0x12221111, 0x12221112, 0x12221210,
+    0x10201022, 0x10201221, 0x10211121, 0x10221020, 0x10221122, 0x10221220, 0x10221221, 0x11201020,
+    0x11201121, 0x11201220, 0x11201222, 0x11211021, 0x11211120, 0x11211121, 0x11211122, 0x11211220,
+    0x11211222, 0x11221020, 0x11221121, 0x11221220, 0x12201020, 0x12201022, 0x12201121, 0x12201222,
+    0x12211120, 0x12211122, 0x12211220, 0x12211221, 0x12221020, 0x12221120, 0x12221122, 0x12221222,
+    0x10212102, 0x10212201, 0x10222101, 0x11202001, 0x11212002, 0x11212101, 0x11212202, 0x11222001,
+    0x11222201, 0x12202101, 0x12212001, 0x12212200, 0x12222102, 0x10202011, 0x10202110, 0x10212010,
+    0x10212111, 0x10222011, 0x10222110, 0x10222112, 0x10222211, 0x11202010, 0x11202011, 0x11202111,
+    0x11202112, 0x11202210, 0x11212011, 0x11212110, 0x11212111, 0x11212112, 0x11212211, 0x11222010,
+    0x11222111, 0x11222212, 0x12202012, 0x12202110, 0x12202212, 0x12212111, 0x12222011, 0x12222110,
+    0x12222111, 0x12222211, 0x10212021, 0x10212122, 0x10212220, 0x11202021, 0x11202120, 0x11202221,
+    0x11212020, 0x11212121, 0x11212220, 0x11212222, 0x11222120, 0x11222121, 0x11222221, 0x12202122,
+    0x12212120, 0x12212220, 0x12212222, 0x12222122, 0x20000000, 0x20000002, 0x20000200, 0x20000202,
+    0x20020000, 0x20020002, 0x20020200, 0x20020202, 0x21000101, 0x21010000, 0x21010001, 0x21010100,
+    0x21010102, 0x21010201, 0x21020101, 0x22000000, 0x22000002, 0x22000200, 0x22000202, 0x22010101,
+    0x22020000, 0x22020002, 0x22020200, 0x22020202, 0x20000111, 0x20010011, 0x20010110, 0x20010112,
+    0x20010211, 0x20020111, 0x21000011, 0x21000110, 0x21000211, 0x21010010, 0x21010012, 0x21010111,
+    0x21010112, 0x21010210, 0x21010211, 0x21020110, 0x21020112, 0x21020211, 0x22000111, 0x22000211,
+    0x22010110, 0x22010112, 0x22010211, 0x22020111, 0x20000020, 0x20000022, 0x20000220, 0x20000222,
+    0x20010121, 0x20020020, 0x20020022, 0x20020220, 0x20020222, 0x21010021, 0x21010120, 0x21010221,
+    0x21020121, 0x22000020, 0x22000022, 0x22000220, 0x22000222, 0x22010121, 0x22020020, 0x22020022,
+    0x22020220, 0x22020222, 0x20011100, 0x20011201, 0x21001001, 0x21001100, 0x21011001, 0x21011101,
+    0x21011202, 0x21021001, 0x21021100, 0x21021201, 0x22011100, 0x22011201, 0x20001011, 0x20001211,
+    0x20011012, 0x20011111, 0x20011212, 0x20021112, 0x20021211, 0x21001010, 0x21001011, 0x21001111,
+    0x21001210, 0x21011011, 0x21011110, 0x21011111, 0x21011112, 0x21011211, 0x21011212, 0x21021111,
+    0x21021112, 0x21021210, 0x21021212, 0x22001011, 0x22001110, 0x22001112, 0x22001211, 0x22011010,
+    0x22011012, 0x22011111, 0x22011210, 0x22021112, 0x20011021, 0x20011122, 0x20011221, 0x20021121,
+    0x21001021, 0x21001120, 0x21001221, 0x21001222, 0x21011020, 0x21011121, 0x21011221, 0x21011222,
+    0x21021021, 0x21021122, 0x21021222, 0x22001121, 0x22011021, 0x22011222, 0x22021120, 0x20002000,
+    0x20002002, 0x20002200, 0x20002202, 0x20012101, 0x20022000, 0x20022002, 0x20022200, 0x20022202,
+    0x21002001, 0x21002101, 0x21012001, 0x21012100, 0x21012201, 0x21022101, 0x21022201, 0x22002000,
+    0x22002002, 0x22002200, 0x22002202, 0x22012101, 0x22022000, 0x22022002, 0x22022200, 0x22022202,
+    0x20002111, 0x20002112, 0x20012011, 0x20012110, 0x20012112, 0x20022111, 0x21002011, 0x21002110,
+    0x21002112, 0x21002211, 0x21012010, 0x21012012, 0x21012111, 0x21012212, 0x21022011, 0x21022110,
+    0x22002111, 0x22012112, 0x22012211, 0x22022111, 0x20002020, 0x20002022, 0x20002220, 0x20002222,
+    0x20012121, 0x20022020, 0x20022022, 0x20022220, 0x20022222, 0x21002121, 0x21012021, 0x21012120,
+    0x21012122, 0x22002020, 0x22002022, 0x22002220, 0x22002222, 0x22012121, 0x22022020, 0x22022022,
+    0x22022220, 0x22022222, 0x20100101, 0x20110001, 0x20110102, 0x20110200, 0x20110201, 0x20120101,
+    0x21100001, 0x21100102, 0x21100201, 0x21110101, 0x21110200, 0x21110202, 0x21120201, 0x21120202,
+    0x22100101, 0x22110001, 0x22110100, 0x22110102, 0x22110201, 0x22120101, 0x20100011, 0x20100110,
+    0x20100112, 0x20100211, 0x20110010, 0x20110111, 0x20110210, 0x20110212, 0x20120011, 0x20120110,
+    0x20120112, 0x20120211, 0x21100010, 0x21100111, 0x21110010, 0x21110011, 0x21110110, 0x21110111,
+    0x21110112, 0x21110211, 0x21120012, 0x21120111, 0x22100110, 0x22100112, 0x22110012, 0x22110111,
+    0x22110210, 0x22120011, 0x22120110, 0x22120112, 0x22120211, 0x20100121, 0x20110021, 0x20110120,
+    0x20110221, 0x20120121, 0x21100120, 0x21100122, 0x21100221, 0x21110020, 0x21110022, 0x21110121,
+    0x21110220, 0x21120122, 0x21120221, 0x22100121, 0x22110120, 0x22110122, 0x22120221, 0x20101001,
+    0x20101100, 0x20101102, 0x20111000, 0x20111101, 0x20111200, 0x20121102, 0x21101000, 0x21101202,
+    0x21111001, 0x21111100, 0x21111101, 0x21111102, 0x21111200, 0x21111201, 0x21121000, 0x21121001,
+    0x21121002, 0x21121101, 0x22101100, 0x22101102, 0x22111002, 0x22111100, 0x22111101, 0x22111200,
+    0x22121001, 0x22121201, 0x20101010, 0x20101111, 0x20101210, 0x20101212, 0x20111010, 0x20111011,
+    0x20111110, 0x20111111, 0x20111112, 0x20111211, 0x20121011, 0x20121111, 0x20121211, 0x20121212,
+    0x21101011, 0x21101110, 0x21101111, 0x21101112, 0x21101211, 0x21111010, 0x21111011, 0x21111012,
+    0x21111110, 0x21111111, 0x21111112, 0x21111210, 0x21111211, 0x21111212, 0x21121011, 0x21121110,
+    0x21121111, 0x21121112, 0x21121211, 0x22101011, 0x22101111, 0x22101210, 0x22111011, 0x22111012,
+    0x22111110, 0x22111111, 0x22111112, 0x22111211, 0x22111212, 0x22121010, 0x22121012, 0x22121111,
+    0x22121210, 0x22121212, 0x20101021, 0x20101120, 0x20111020, 0x20111121, 0x20111221, 0x20121020,
+    0x20121122, 0x20121221, 0x21101121, 0x21101220, 0x21101221, 0x21111021, 0x21111022, 0x21111121,
+    0x21111122, 0x21111221, 0x21121121, 0x21121220, 0x22101022, 0x22101120, 0x22101221, 0x22101222,
+    0x22111022, 0x22111120, 0x22111121, 0x22121120, 0x22121122, 0x22121221, 0x20102101, 0x20112102,
+    0x20112201, 0x20122101, 0x21102001, 0x21102102, 0x21112000, 0x21112002, 0x21112101, 0x21112102,
+    0x21112202, 0x21122100, 0x21122101, 0x22102101, 0x22112001, 0x22112102, 0x22112201, 0x22122101,
+    0x20102110, 0x20102112, 0x20102211, 0x20112010, 0x20112012, 0x20112111, 0x20112210, 0x20112212,
+    0x20122010, 0x20122011, 0x20122110, 0x20122112, 0x21102010, 0x21102012, 0x21102111, 0x21102210,
+    0x21102212, 0x21112011, 0x21112110, 0x21112111, 0x21112112, 0x21112211, 0x21122012, 0x21122111,
+    0x21122112, 0x21122212, 0x22102011, 0x22102110, 0x22112010, 0x22112012, 0x22112111, 0x22112212,
+    0x22122011, 0x22122112, 0x20102121, 0x20112121, 0x20122121, 0x21102120, 0x21102122, 0x21102221,
+    0x21112020, 0x21112121, 0x21112220, 0x21122021, 0x22102121, 0x22112021, 0x22112120, 0x22112121,
+    0x22112122, 0x20200000, 0x20200002, 0x20200200, 0x20200202, 0x20210101, 0x20220000, 0x20220002,
+    0x20220200, 0x20220202, 0x21200101, 0x21210001, 0x21210100, 0x21210102, 0x21210201, 0x22200000,
+    0x22200002, 0x22200200, 0x22200202, 0x22210101, 0x22220000, 0x22220002, 0x22220200, 0x22220202,
+    0x20200111, 0x20200211, 0x20210011, 0x20210110, 0x20210112, 0x20210211, 0x20210212, 0x21200112,
+    0x21200211, 0x21210011, 0x21210111, 0x21210210, 0x21210212, 0x21220011, 0x21220110, 0x22200111,
+    0x22210010, 0x22210012, 0x22210112, 0x22210211, 0x20200022, 0x20200220, 0x20200222, 0x20210020,
+    0x20210221, 0x20220022, 0x20220220, 0x20220222, 0x21200121, 0x21210021, 0x21210122, 0x21210221,
+    0x21220121, 0x22200020, 0x22200022, 0x22200220, 0x22200222, 0x22210121, 0x22220020, 0x22220022,
+    0x22220220, 0x22220222, 0x20211201, 0x20221101, 0x21201001, 0x21201100, 0x21211000, 0x21211100,
+    0x21211101, 0x21211200, 0x21211202, 0x21221001, 0x21221101, 0x21221102, 0x21221200, 0x21221201,
+    0x22201101, 0x20201112, 0x20201211, 0x20211010, 0x20211012, 0x20211111, 0x20211210, 0x20221112,
+    0x20221211, 0x21201012, 0x21201111, 0x21211011, 0x21211110, 0x21211111, 0x21211112, 0x21211211,
+    0x21221111, 0x21221212, 0x22201011, 0x22201110, 0x22201111, 0x22201112, 0x22201211, 0x22211012,
+    0x22211111, 0x22211210, 0x20201121, 0x20211021, 0x20211122, 0x20211222, 0x20221021, 0x20221121,
+    0x21201120, 0x21201122, 0x21201222, 0x21211022, 0x21211121, 0x21211122, 0x21211220, 0x21221020,
+    0x21221022, 0x22201122, 0x22211020, 0x22211121, 0x22211122, 0x22211221, 0x22221021, 0x22221120,
+    0x22221122, 0x20202000, 0x20202002, 0x20202200, 0x20202202, 0x20222000, 0x20222002, 0x20222200,
+    0x20222202, 0x21212001, 0x21212100, 0x21212102, 0x21212201, 0x22202000, 0x22202002, 0x22202200,
+    0x22202202, 0x22212101, 0x22222000, 0x22222002, 0x22222200, 0x22222202, 0x20202111, 0x20212110,
+    0x20212211, 0x20222011, 0x20222111, 0x21202011, 0x21212010, 0x21212111, 0x21212212, 0x21222011,
+    0x21222112, 0x21222211, 0x22212010, 0x22212112, 0x20202020, 0x20202022, 0x20202220, 0x20202222,
+    0x20222020, 0x20222022, 0x20222220, 0x20222222, 0x21212021, 0x21212120, 0x21212122, 0x22202020,
+    0x22202022, 0x22202220, 0x22202222, 0x22212121, 0x22222020, 0x22222022, 0x22222220, 0x22222222,
+GGML_TABLE_END()
+#endif
+
+#endif // GGML_COMMON_IMPL
+#endif // GGML_COMMON_IMPL
diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index e091dbdc1..75a2ad480 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -1,236 +1,55 @@
+#include "ggml-cuda.h"
+#include "ggml.h"
+#include "ggml-backend-impl.h"
+
+#include "ggml-cuda/common.cuh"
+#include "ggml-cuda/acc.cuh"
+#include "ggml-cuda/arange.cuh"
+#include "ggml-cuda/argsort.cuh"
+#include "ggml-cuda/binbcast.cuh"
+#include "ggml-cuda/clamp.cuh"
+#include "ggml-cuda/concat.cuh"
+#include "ggml-cuda/convert.cuh"
+#include "ggml-cuda/cpy.cuh"
+#include "ggml-cuda/diagmask.cuh"
+#include "ggml-cuda/dmmv.cuh"
+#include "ggml-cuda/fattn.cuh"
+#include "ggml-cuda/getrows.cuh"
+#include "ggml-cuda/im2col.cuh"
+#include "ggml-cuda/mmq.cuh"
+#include "ggml-cuda/mmvq.cuh"
+#include "ggml-cuda/norm.cuh"
+#include "ggml-cuda/pad.cuh"
+#include "ggml-cuda/pool2d.cuh"
+#include "ggml-cuda/quantize.cuh"
+#include "ggml-cuda/rope.cuh"
+#include "ggml-cuda/scale.cuh"
+#include "ggml-cuda/softmax.cuh"
+#include "ggml-cuda/sumrows.cuh"
+#include "ggml-cuda/tsembd.cuh"
+#include "ggml-cuda/unary.cuh"
+#include "ggml-cuda/upscale.cuh"
+
 #include <algorithm>
-#include <assert.h>
+#include <array>
 #include <atomic>
 #include <cinttypes>
 #include <cstddef>
 #include <cstdint>
 #include <float.h>
 #include <limits>
+#include <map>
+#include <memory>
+#include <mutex>
 #include <stdint.h>
 #include <stdio.h>
 #include <string>
 #include <vector>
-#include <map>
-#include <array>
-
-// stringize macro for converting __CUDA_ARCH_LIST__ (list of integers) to string
-#define STRINGIZE_IMPL(...) #__VA_ARGS__
-#define STRINGIZE(...) STRINGIZE_IMPL(__VA_ARGS__)
-
-#if defined(GGML_USE_HIPBLAS)
-#include <hip/hip_runtime.h>
-#include <hipblas/hipblas.h>
-#include <hip/hip_fp16.h>
-#ifdef __HIP_PLATFORM_AMD__
-// for rocblas_initialize()
-#include "rocblas/rocblas.h"
-#endif // __HIP_PLATFORM_AMD__
-#define CUBLAS_COMPUTE_16F HIPBLAS_R_16F
-#define CUBLAS_COMPUTE_32F HIPBLAS_R_32F
-#define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_R_32F
-#define CUBLAS_GEMM_DEFAULT HIPBLAS_GEMM_DEFAULT
-#define CUBLAS_GEMM_DEFAULT_TENSOR_OP HIPBLAS_GEMM_DEFAULT
-#define CUBLAS_OP_N HIPBLAS_OP_N
-#define CUBLAS_OP_T HIPBLAS_OP_T
-#define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS
-#define CUBLAS_TF32_TENSOR_OP_MATH 0
-#define CUDA_R_16F  HIPBLAS_R_16F
-#define CUDA_R_32F  HIPBLAS_R_32F
-#define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
-#define cublasComputeType_t hipblasDatatype_t //deprecated, new hipblasComputeType_t not in 5.6
-#define cublasCreate hipblasCreate
-#define cublasGemmEx hipblasGemmEx
-#define cublasGemmBatchedEx hipblasGemmBatchedEx
-#define cublasGemmStridedBatchedEx hipblasGemmStridedBatchedEx
-#define cublasHandle_t hipblasHandle_t
-#define cublasSetMathMode(handle, mode) CUBLAS_STATUS_SUCCESS
-#define cublasSetStream hipblasSetStream
-#define cublasSgemm hipblasSgemm
-#define cublasStatus_t hipblasStatus_t
-#define cudaDataType_t hipblasDatatype_t //deprecated, new hipblasDatatype not in 5.6
-#define cudaDeviceCanAccessPeer hipDeviceCanAccessPeer
-#define cudaDeviceDisablePeerAccess hipDeviceDisablePeerAccess
-#define cudaDeviceEnablePeerAccess hipDeviceEnablePeerAccess
-#define cudaDeviceProp hipDeviceProp_t
-#define cudaDeviceSynchronize hipDeviceSynchronize
-#define cudaError_t hipError_t
-#define cudaEventCreateWithFlags hipEventCreateWithFlags
-#define cudaEventDisableTiming hipEventDisableTiming
-#define cudaEventRecord hipEventRecord
-#define cudaEvent_t hipEvent_t
-#define cudaEventDestroy hipEventDestroy
-#define cudaFree hipFree
-#define cudaFreeHost hipHostFree
-#define cudaGetDevice hipGetDevice
-#define cudaGetDeviceCount hipGetDeviceCount
-#define cudaGetDeviceProperties hipGetDeviceProperties
-#define cudaGetErrorString hipGetErrorString
-#define cudaGetLastError hipGetLastError
-#ifdef GGML_HIP_UMA
-#define cudaMalloc hipMallocManaged
-#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size)
-#else
-#define cudaMalloc hipMalloc
-#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault)
-#endif
-#define cudaMemcpy hipMemcpy
-#define cudaMemcpyAsync hipMemcpyAsync
-#define cudaMemcpyPeerAsync hipMemcpyPeerAsync
-#define cudaMemcpy2DAsync hipMemcpy2DAsync
-#define cudaMemcpyDeviceToDevice hipMemcpyDeviceToDevice
-#define cudaMemcpyDeviceToHost hipMemcpyDeviceToHost
-#define cudaMemcpyHostToDevice hipMemcpyHostToDevice
-#define cudaMemcpyKind hipMemcpyKind
-#define cudaMemset hipMemset
-#define cudaMemsetAsync hipMemsetAsync
-#define cudaMemGetInfo hipMemGetInfo
-#define cudaOccupancyMaxPotentialBlockSize hipOccupancyMaxPotentialBlockSize
-#define cudaSetDevice hipSetDevice
-#define cudaStreamCreateWithFlags hipStreamCreateWithFlags
-#define cudaStreamFireAndForget hipStreamFireAndForget
-#define cudaStreamNonBlocking hipStreamNonBlocking
-#define cudaStreamSynchronize hipStreamSynchronize
-#define cudaStreamWaitEvent(stream, event, flags) hipStreamWaitEvent(stream, event, flags)
-#define cudaStream_t hipStream_t
-#define cudaSuccess hipSuccess
-#define __trap abort
-#define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS
-#define CUBLAS_STATUS_NOT_INITIALIZED HIPBLAS_STATUS_NOT_INITIALIZED
-#define CUBLAS_STATUS_ALLOC_FAILED HIPBLAS_STATUS_ALLOC_FAILED
-#define CUBLAS_STATUS_INVALID_VALUE HIPBLAS_STATUS_INVALID_VALUE
-#define CUBLAS_STATUS_ARCH_MISMATCH HIPBLAS_STATUS_ARCH_MISMATCH
-#define CUBLAS_STATUS_MAPPING_ERROR HIPBLAS_STATUS_MAPPING_ERROR
-#define CUBLAS_STATUS_EXECUTION_FAILED HIPBLAS_STATUS_EXECUTION_FAILED
-#define CUBLAS_STATUS_INTERNAL_ERROR HIPBLAS_STATUS_INTERNAL_ERROR
-#define CUBLAS_STATUS_NOT_SUPPORTED HIPBLAS_STATUS_NOT_SUPPORTED
-#else
-#include <cuda_runtime.h>
-#include <cuda.h>
-#include <cublas_v2.h>
-#include <cuda_fp16.h>
-
-#if CUDART_VERSION < 11020
-#define CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED CU_DEVICE_ATTRIBUTE_VIRTUAL_ADDRESS_MANAGEMENT_SUPPORTED
-#define CUBLAS_TF32_TENSOR_OP_MATH CUBLAS_TENSOR_OP_MATH
-#define CUBLAS_COMPUTE_16F CUDA_R_16F
-#define CUBLAS_COMPUTE_32F CUDA_R_32F
-#define cublasComputeType_t cudaDataType_t
-#endif // CUDART_VERSION < 11020
-
-#endif // defined(GGML_USE_HIPBLAS)
-
-// ggml-cuda need half type so keep ggml headers include at last
-#include "ggml-cuda.h"
-#include "ggml.h"
-#include "ggml-backend-impl.h"
-
-#define CUDART_HMAX     11070 // CUDA 11.7, min. ver. for which __hmax and __hmax2 are known to work (may be higher than needed)
-
-#define CC_PASCAL     600
-#define MIN_CC_DP4A   610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
-#define CC_VOLTA      700
-#define CC_OFFSET_AMD 1000000
-#define CC_RDNA1      (CC_OFFSET_AMD + 1010)
-#define CC_RDNA2      (CC_OFFSET_AMD + 1030)
-#define CC_RDNA3      (CC_OFFSET_AMD + 1100)
-
-#define GGML_CUDA_MAX_NODES 8192
-
-// define this if you want to always fallback to MMQ kernels and not use cuBLAS for matrix multiplication
-// on modern hardware, using cuBLAS is recommended as it utilizes F16 tensor cores which are very performant
-// for large computational tasks. the drawback is that this requires some extra amount of VRAM:
-// -  7B quantum model: +100-200 MB
-// - 13B quantum model: +200-400 MB
-//
-//#define GGML_CUDA_FORCE_MMQ
-
-// TODO: improve this to be correct for more hardware
-//       for example, currently fails for GeForce GTX 1660 which is TURING arch (> VOLTA) but does not have tensor cores
-#if !defined(GGML_CUDA_FORCE_MMQ)
-#define CUDA_USE_TENSOR_CORES
-#endif
-
-#define MMVQ_MAX_BATCH_SIZE  8 // max batch size to use MMVQ kernels
-#define  MMQ_MAX_BATCH_SIZE 32 // max batch size to use MMQ kernels when tensor cores are available
-
-#if defined(GGML_USE_HIPBLAS)
-#define __CUDA_ARCH__ 1300
-
-#if defined(__gfx1100__) || defined(__gfx1101__) || defined(__gfx1102__) || defined(__gfx1103__) || \
-    defined(__gfx1150__) || defined(__gfx1151__)
-#define RDNA3
-#endif
-
-#if defined(__gfx1030__) || defined(__gfx1031__) || defined(__gfx1032__) || defined(__gfx1033__) || \
-    defined(__gfx1034__) || defined(__gfx1035__) || defined(__gfx1036__) || defined(__gfx1037__)
-#define RDNA2
-#endif
-
-#ifndef __has_builtin
-    #define __has_builtin(x) 0
-#endif
-
-typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
-static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
-    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
-    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
-#if __has_builtin(__builtin_elementwise_sub_sat)
-    const int8x4_t c = __builtin_elementwise_sub_sat(va, vb);
-    return reinterpret_cast<const int &>(c);
-#else
-    int8x4_t c;
-    int16_t tmp;
-#pragma unroll
-    for (int i = 0; i < 4; i++) {
-        tmp = va[i] - vb[i];
-        if(tmp > std::numeric_limits<int8_t>::max()) tmp = std::numeric_limits<int8_t>::max();
-        if(tmp < std::numeric_limits<int8_t>::min()) tmp = std::numeric_limits<int8_t>::min();
-        c[i] = tmp;
-    }
-    return reinterpret_cast<int &>(c);
-#endif // __has_builtin(__builtin_elementwise_sub_sat)
-}
-
-static __device__ __forceinline__ int __vsub4(const int a, const int b) {
-    return __vsubss4(a, b);
-}
-
-static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
-#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
-    c = __builtin_amdgcn_sdot4(a, b, c, false);
-#elif defined(RDNA3)
-    c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
-#elif defined(__gfx1010__) || defined(__gfx900__)
-    int tmp1;
-    int tmp2;
-    asm("\n \
-        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_0 src1_sel:BYTE_0 \n \
-        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_1 src1_sel:BYTE_1 \n \
-        v_add3_u32 %0, %1, %2, %0 \n \
-        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_2 src1_sel:BYTE_2 \n \
-        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_3 src1_sel:BYTE_3 \n \
-        v_add3_u32 %0, %1, %2, %0 \n \
-        "
-        : "+v"(c), "=&v"(tmp1), "=&v"(tmp2)
-        : "v"(a), "v"(b)
-    );
-#else
-    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
-    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
-    c += va[0] * vb[0] + va[1] * vb[1] + va[2] * vb[2] + va[3] * vb[3];
-#endif
-    return c;
-}
-#endif // defined(GGML_USE_HIPBLAS)
-
-#if defined(_MSC_VER)
-#pragma warning(disable: 4244 4267) // possible loss of data
-#endif
 
 static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
 
 [[noreturn]]
-static void ggml_cuda_error(const char * stmt, const char * func, const char * file, const int line, const char * msg) {
+void ggml_cuda_error(const char * stmt, const char * func, const char * file, int line, const char * msg) {
     int id = -1; // in case cudaGetDevice fails
     cudaGetDevice(&id);
 
@@ -241,351 +60,9 @@ static void ggml_cuda_error(const char * stmt, const char * func, const char * f
     GGML_ASSERT(!"CUDA error");
 }
 
-#define CUDA_CHECK_GEN(err, success, error_fn)                                      \
-     do {                                                                           \
-        auto err_ = (err);                                                          \
-        if (err_ != (success)) {                                                    \
-            ggml_cuda_error(#err, __func__, __FILE__, __LINE__, error_fn(err_));    \
-        }                                                                           \
-    } while (0)
-
-#define CUDA_CHECK(err) CUDA_CHECK_GEN(err, cudaSuccess, cudaGetErrorString)
-
-#if CUDART_VERSION >= 12000
-    static const char * cublas_get_error_str(const cublasStatus_t err) {
-        return cublasGetStatusString(err);
-    }
-#else
-    static const char * cublas_get_error_str(const cublasStatus_t err) {
-        switch (err) {
-            case CUBLAS_STATUS_SUCCESS: return "CUBLAS_STATUS_SUCCESS";
-            case CUBLAS_STATUS_NOT_INITIALIZED: return "CUBLAS_STATUS_NOT_INITIALIZED";
-            case CUBLAS_STATUS_ALLOC_FAILED: return "CUBLAS_STATUS_ALLOC_FAILED";
-            case CUBLAS_STATUS_INVALID_VALUE: return "CUBLAS_STATUS_INVALID_VALUE";
-            case CUBLAS_STATUS_ARCH_MISMATCH: return "CUBLAS_STATUS_ARCH_MISMATCH";
-            case CUBLAS_STATUS_MAPPING_ERROR: return "CUBLAS_STATUS_MAPPING_ERROR";
-            case CUBLAS_STATUS_EXECUTION_FAILED: return "CUBLAS_STATUS_EXECUTION_FAILED";
-            case CUBLAS_STATUS_INTERNAL_ERROR: return "CUBLAS_STATUS_INTERNAL_ERROR";
-            case CUBLAS_STATUS_NOT_SUPPORTED: return "CUBLAS_STATUS_NOT_SUPPORTED";
-            default: return "unknown error";
-        }
-    }
-#endif // CUDART_VERSION >= 12000
-
-#define CUBLAS_CHECK(err) CUDA_CHECK_GEN(err, CUBLAS_STATUS_SUCCESS, cublas_get_error_str)
-
-#if !defined(GGML_USE_HIPBLAS)
-static const char * cu_get_error_str(CUresult err) {
-    const char * err_str;
-    cuGetErrorString(err, &err_str);
-    return err_str;
-}
-#define CU_CHECK(err) CUDA_CHECK_GEN(err, CUDA_SUCCESS, cu_get_error_str)
-#endif
-
-#if CUDART_VERSION >= 11100
-#define GGML_CUDA_ASSUME(x) __builtin_assume(x)
-#else
-#define GGML_CUDA_ASSUME(x)
-#endif // CUDART_VERSION >= 11100
-
-#ifdef GGML_CUDA_F16
-typedef half dfloat; // dequantize float
-typedef half2 dfloat2;
-#else
-typedef float dfloat; // dequantize float
-typedef float2 dfloat2;
-#endif //GGML_CUDA_F16
-
-static __device__ __forceinline__ int get_int_from_int8(const int8_t * x8, const int & i32) {
-    const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
-
-    int x32 = 0;
-    x32 |= x16[0] <<  0;
-    x32 |= x16[1] << 16;
-
-    return x32;
-}
-
-static __device__ __forceinline__ int get_int_from_uint8(const uint8_t * x8, const int & i32) {
-    const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
-
-    int x32 = 0;
-    x32 |= x16[0] <<  0;
-    x32 |= x16[1] << 16;
-
-    return x32;
-}
-
-static __device__ __forceinline__ int get_int_from_int8_aligned(const int8_t * x8, const int & i32) {
-    return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
-}
-
-static __device__ __forceinline__ int get_int_from_uint8_aligned(const uint8_t * x8, const int & i32) {
-    return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
-}
-
-template<typename T>
-using to_t_cuda_t = void (*)(const void * __restrict__ x, T * __restrict__ y, int k, cudaStream_t stream);
-typedef to_t_cuda_t<float> to_fp32_cuda_t;
-typedef to_t_cuda_t<half> to_fp16_cuda_t;
-
-typedef void (*dequantize_kernel_t)(const void * vx, const int ib, const int iqs, dfloat2 & v);
-typedef void (*dot_kernel_k_t)(const void * __restrict__ vx, const int ib, const int iqs, const float * __restrict__ y, float & v);
-typedef void (*cpy_kernel_t)(const char * cx, char * cdst);
-typedef void (*ggml_cuda_func_t)(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
-typedef void (*ggml_cuda_op_mul_mat_t)(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
-    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
-    const int64_t src1_padded_row_size, cudaStream_t stream);
-typedef void (*ggml_cuda_op_flatten_t)(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream);
-
-// QK = number of values after dequantization
-// QR = QK / number of values before dequantization
-// QI = number of 32 bit integers before dequantization
-
-#define QK4_0 32
-#define QR4_0 2
-#define QI4_0 (QK4_0 / (4 * QR4_0))
-typedef struct {
-    half    d;              // delta
-    uint8_t qs[QK4_0 / 2];  // nibbles / quants
-} block_q4_0;
-static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
-
-#define QK4_1 32
-#define QR4_1 2
-#define QI4_1 (QK4_1 / (4 * QR4_1))
-typedef struct {
-    half2   dm;             // dm.x = delta, dm.y = min
-    uint8_t qs[QK4_1 / 2];  // nibbles / quants
-} block_q4_1;
-static_assert(sizeof(block_q4_1) == sizeof(ggml_fp16_t) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding");
-
-#define QK5_0 32
-#define QR5_0 2
-#define QI5_0 (QK5_0 / (4 * QR5_0))
-typedef struct {
-    half d;                 // delta
-    uint8_t qh[4];          // 5-th bit of quants
-    uint8_t qs[QK5_0 / 2];  // nibbles / quants
-} block_q5_0;
-static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
-
-#define QK5_1 32
-#define QR5_1 2
-#define QI5_1 (QK5_1 / (4 * QR5_1))
-typedef struct {
-    half2 dm;               // dm.x = delta, dm.y = min
-    uint8_t qh[4];          // 5-th bit of quants
-    uint8_t qs[QK5_1 / 2];  // nibbles / quants
-} block_q5_1;
-static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
-
-#define QK8_0 32
-#define QR8_0 1
-#define QI8_0 (QK8_0 / (4 * QR8_0))
-typedef struct {
-    half    d;              // delta
-    int8_t  qs[QK8_0];      // quants
-} block_q8_0;
-static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
-
-#define QK8_1 32
-#define QR8_1 1
-#define QI8_1 (QK8_1 / (4 * QR8_1))
-typedef struct {
-    half2   ds;             // ds.x = delta, ds.y = sum
-    int8_t  qs[QK8_0];      // quants
-} block_q8_1;
-static_assert(sizeof(block_q8_1) == 2*sizeof(ggml_fp16_t) + QK8_0, "wrong q8_1 block size/padding");
-
-typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs);
-typedef void (*allocate_tiles_cuda_t)(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc);
-typedef void (*load_tiles_cuda_t)(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row);
-typedef float (*vec_dot_q_mul_mat_cuda_t)(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ms, const int & i, const int & j, const int & k);
-
-//================================= k-quants
-
-#ifdef GGML_QKK_64
-#define QK_K 64
-#define K_SCALE_SIZE 4
-#else
-#define QK_K 256
-#define K_SCALE_SIZE 12
-#endif
-
-#define QR2_K 4
-#define QI2_K (QK_K / (4*QR2_K))
-typedef struct {
-    uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
-    uint8_t qs[QK_K/4];      // quants
-    half2 dm;                // super-block scale for quantized scales/mins
-} block_q2_K;
-static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
-
-#define QR3_K 4
-#define QI3_K (QK_K / (4*QR3_K))
-typedef struct {
-    uint8_t hmask[QK_K/8];     // quants - high bit
-    uint8_t qs[QK_K/4];        // quants - low 2 bits
-#ifdef GGML_QKK_64
-    uint8_t scales[2]; // scales, quantized with 8 bits
-#else
-    uint8_t scales[K_SCALE_SIZE]; // scales, quantized with 6 bits
-#endif
-    half d;             // super-block scale
-} block_q3_K;
-//static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + K_SCALE_SIZE, "wrong q3_K block size/padding");
-
-#define QR4_K 2
-#define QI4_K (QK_K / (4*QR4_K))
-#ifdef GGML_QKK_64
-typedef struct {
-    half    dm[2];             // super-block scales/mins
-    uint8_t scales[2];         // 4-bit block scales/mins
-    uint8_t qs[QK_K/2];        // 4--bit quants
-} block_q4_K;
-static_assert(sizeof(block_q4_K) == sizeof(half2) + QK_K/2 + 2, "wrong q4_K block size/padding");
-#else
-typedef struct {
-    half2 dm;                  // super-block scale for quantized scales/mins
-    uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits
-    uint8_t qs[QK_K/2];        // 4--bit quants
-} block_q4_K;
-static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2, "wrong q4_K block size/padding");
-#endif
-
-#define QR5_K 2
-#define QI5_K (QK_K / (4*QR5_K))
-#ifdef GGML_QKK_64
-typedef struct {
-    half d;                  // super-block scale
-    int8_t scales[QK_K/16];  // block scales
-    uint8_t qh[QK_K/8];      // quants, high bit
-    uint8_t qs[QK_K/2];      // quants, low 4 bits
-} block_q5_K;
-static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
-#else
-typedef struct {
-    half2 dm;                     // super-block scale for quantized scales/mins
-    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
-    uint8_t qh[QK_K/8];           // quants, high bit
-    uint8_t qs[QK_K/2];           // quants, low 4 bits
-} block_q5_K;
-static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
-#endif
-
-#define QR6_K 2
-#define QI6_K (QK_K / (4*QR6_K))
-typedef struct {
-    uint8_t ql[QK_K/2];   // quants, lower 4 bits
-    uint8_t qh[QK_K/4];   // quants, upper 2 bits
-    int8_t  scales[QK_K/16]; // scales
-    half    d;         // delta
-} block_q6_K;
-static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_K block size/padding");
-
-#define QR2_XXS 8
-#define QI2_XXS (QK_K / (4*QR2_XXS))
-typedef struct {
-    half d;
-    uint16_t qs[QK_K/8];
-} block_iq2_xxs;
-static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t), "wrong iq2_xxs block size/padding");
-
-#define QR2_XS 8
-#define QI2_XS (QK_K / (4*QR2_XS))
-typedef struct {
-    half d;
-    uint16_t qs[QK_K/8];
-    uint8_t  scales[QK_K/32];
-} block_iq2_xs;
-static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding");
-
-#define QR3_XXS 8
-#define QI3_XXS (QK_K / (4*QR3_XXS))
-typedef struct {
-    half d;
-    uint8_t qs[3*(QK_K/8)];
-} block_iq3_xxs;
-static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding");
-
-#define QR1_S 8
-#define QI1_S (QK_K / (4*QR1_S))
-typedef struct {
-    half d;
-    uint8_t qs[QK_K/8];
-    uint8_t scales[QK_K/16];
-} block_iq1_s;
-static_assert(sizeof(block_iq1_s) == sizeof(ggml_fp16_t) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding");
-
-#define WARP_SIZE 32
-#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
-
-#define CUDA_GELU_BLOCK_SIZE 256
-#define CUDA_SILU_BLOCK_SIZE 256
-#define CUDA_TANH_BLOCK_SIZE 256
-#define CUDA_RELU_BLOCK_SIZE 256
-#define CUDA_HARDSIGMOID_BLOCK_SIZE 256
-#define CUDA_HARDSWISH_BLOCK_SIZE 256
-#define CUDA_SQR_BLOCK_SIZE 256
-#define CUDA_CPY_BLOCK_SIZE 32
-#define CUDA_SCALE_BLOCK_SIZE 256
-#define CUDA_CLAMP_BLOCK_SIZE 256
-#define CUDA_ROPE_BLOCK_SIZE 256
-#define CUDA_SOFT_MAX_BLOCK_SIZE 1024
-#define CUDA_ALIBI_BLOCK_SIZE 32
-#define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32
-#define CUDA_QUANTIZE_BLOCK_SIZE 256
-#define CUDA_DEQUANTIZE_BLOCK_SIZE 256
-#define CUDA_GET_ROWS_BLOCK_SIZE 256
-#define CUDA_UPSCALE_BLOCK_SIZE 256
-#define CUDA_CONCAT_BLOCK_SIZE 256
-#define CUDA_PAD_BLOCK_SIZE 256
-#define CUDA_ACC_BLOCK_SIZE 256
-#define CUDA_IM2COL_BLOCK_SIZE 256
-#define CUDA_POOL2D_BLOCK_SIZE 256
-
-#define CUDA_Q8_0_NE_ALIGN 2048
-
-// dmmv = dequantize_mul_mat_vec
-#ifndef GGML_CUDA_DMMV_X
-#define GGML_CUDA_DMMV_X 32
-#endif
-#ifndef GGML_CUDA_MMV_Y
-#define GGML_CUDA_MMV_Y 1
-#endif
-
-#ifndef K_QUANTS_PER_ITERATION
-#define K_QUANTS_PER_ITERATION 2
-#else
-static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
-#endif
-
-#ifndef GGML_CUDA_PEER_MAX_BATCH_SIZE
-#define GGML_CUDA_PEER_MAX_BATCH_SIZE 128
-#endif // GGML_CUDA_PEER_MAX_BATCH_SIZE
-
-#define MUL_MAT_SRC1_COL_STRIDE 128
-
-#define MAX_STREAMS 8
-static cudaStream_t g_cudaStreams[GGML_CUDA_MAX_DEVICES][MAX_STREAMS] = { { nullptr } };
-
-struct ggml_tensor_extra_gpu {
-    void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors
-    cudaEvent_t events[GGML_CUDA_MAX_DEVICES][MAX_STREAMS]; // events for synchronizing multiple GPUs
-};
-
 // this is faster on Windows
 // probably because the Windows CUDA libraries forget to make this check before invoking the drivers
-static void ggml_cuda_set_device(const int device) {
+void ggml_cuda_set_device(int device) {
     int current_device;
     CUDA_CHECK(cudaGetDevice(&current_device));
 
@@ -596,5074 +73,994 @@ static void ggml_cuda_set_device(const int device) {
     CUDA_CHECK(cudaSetDevice(device));
 }
 
-static int g_device_count = -1;
-static int g_main_device = 0;
-static std::array<float, GGML_CUDA_MAX_DEVICES> g_default_tensor_split = {};
+int ggml_cuda_get_device() {
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    return id;
+}
 
-struct cuda_device_capabilities {
-    int     cc;                 // compute capability
-    size_t  smpb;               // max. shared memory per block
-    bool    vmm;                // virtual memory support
-    size_t  vmm_granularity;    // granularity of virtual memory
-};
+static ggml_cuda_device_info ggml_cuda_init() {
+#ifdef __HIP_PLATFORM_AMD__
+    // Workaround for a rocBLAS bug when using multiple graphics cards:
+    // https://github.com/ROCmSoftwarePlatform/rocBLAS/issues/1346
+    rocblas_initialize();
+    CUDA_CHECK(cudaDeviceSynchronize());
+#endif
 
-static cuda_device_capabilities g_device_caps[GGML_CUDA_MAX_DEVICES] = { {0, 0, false, 0} };
+    ggml_cuda_device_info info = {};
 
-static cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr};
+    cudaError_t err = cudaGetDeviceCount(&info.device_count);
+    if (err != cudaSuccess) {
+        fprintf(stderr, "%s: failed to initialize " GGML_CUDA_NAME ": %s\n", __func__, cudaGetErrorString(err));
+        return info;
+    }
 
-[[noreturn]]
-static __device__ void no_device_code(
-    const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) {
+    GGML_ASSERT(info.device_count <= GGML_CUDA_MAX_DEVICES);
+
+    int64_t total_vram = 0;
+#if defined(GGML_CUDA_FORCE_MMQ)
+    fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ:   yes\n", __func__);
+#else
+    fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ:   no\n", __func__);
+#endif
+#if defined(CUDA_USE_TENSOR_CORES)
+    fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: yes\n", __func__);
+#else
+    fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: no\n", __func__);
+#endif
+    fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count);
+    for (int id = 0; id < info.device_count; ++id) {
+        int device_vmm = 0;
+
+#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM)
+        CUdevice device;
+        CU_CHECK(cuDeviceGet(&device, id));
+        CU_CHECK(cuDeviceGetAttribute(&device_vmm, CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED, device));
+
+        if (device_vmm) {
+            CUmemAllocationProp alloc_prop = {};
+            alloc_prop.type = CU_MEM_ALLOCATION_TYPE_PINNED;
+            alloc_prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
+            alloc_prop.location.id = id;
+            CU_CHECK(cuMemGetAllocationGranularity(&info.devices[id].vmm_granularity, &alloc_prop, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED));
+        }
+#endif // !defined(GGML_USE_HIPBLAS)
+        info.devices[id].vmm = !!device_vmm;
+
+        cudaDeviceProp prop;
+        CUDA_CHECK(cudaGetDeviceProperties(&prop, id));
+        fprintf(stderr, "  Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no");
+
+        info.default_tensor_split[id] = total_vram;
+        total_vram += prop.totalGlobalMem;
 
 #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-    printf("%s:%d: ERROR: HIP kernel %s has no device code compatible with HIP arch %d.\n",
-           file_name, line, function_name, arch);
-    (void) arch_list;
+        info.devices[id].cc = 100*prop.major + 10*prop.minor + CC_OFFSET_AMD;
 #else
-    printf("%s:%d: ERROR: CUDA kernel %s has no device code compatible with CUDA arch %d. ggml-cuda.cu was compiled for: %s\n",
-           file_name, line, function_name, arch, arch_list);
+        info.devices[id].cc = 100*prop.major + 10*prop.minor;
 #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-    __trap();
-
-    (void) no_device_code; // suppress unused function warning
-}
-
-#ifdef __CUDA_ARCH__
-#define NO_DEVICE_CODE no_device_code(__FILE__, __LINE__, __FUNCTION__, __CUDA_ARCH__, STRINGIZE(__CUDA_ARCH_LIST__))
-#else
-#define NO_DEVICE_CODE GGML_ASSERT(false && "NO_DEVICE_CODE not valid in host code.")
-#endif // __CUDA_ARCH__
-
-static __device__ __forceinline__ float warp_reduce_sum(float x) {
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        x += __shfl_xor_sync(0xffffffff, x, mask, 32);
-    }
-    return x;
-}
-
-static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32);
-        a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32);
-    }
-    return a;
-}
-
-//static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
-//#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
-//#pragma unroll
-//    for (int mask = 16; mask > 0; mask >>= 1) {
-//        a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32));
-//    }
-//    return a;
-//#else
-//    (void) a;
-//    NO_DEVICE_CODE;
-//#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
-//}
-
-static __device__ __forceinline__ float warp_reduce_max(float x) {
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
-    }
-    return x;
-}
-
-//static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
-//#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX
-//#pragma unroll
-//    for (int mask = 16; mask > 0; mask >>= 1) {
-//        x = __hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
-//    }
-//    return x;
-//#else
-//    (void) x;
-//    NO_DEVICE_CODE;
-//#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX
-//}
-
-static __device__ __forceinline__ float op_repeat(const float a, const float b) {
-    return b;
-    GGML_UNUSED(a);
-}
-
-static __device__ __forceinline__ float op_add(const float a, const float b) {
-    return a + b;
-}
-
-static __device__ __forceinline__ float op_mul(const float a, const float b) {
-    return a * b;
-}
-
-static __device__ __forceinline__ float op_div(const float a, const float b) {
-    return a / b;
-}
-
-template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
-static __global__ void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst,
-        int ne0, int ne1, int ne2, int ne3,
-        int ne10, int ne11, int ne12, int ne13,
-        /*int s0, */ int s1,  int s2,  int s3,
-        /*int s10,*/ int s11, int s12, int s13) {
-    const int i0s = blockDim.x*blockIdx.x + threadIdx.x;
-    const int i1 = (blockDim.y*blockIdx.y + threadIdx.y);
-    const int i2 = (blockDim.z*blockIdx.z + threadIdx.z) / ne3;
-    const int i3 = (blockDim.z*blockIdx.z + threadIdx.z) % ne3;
-
-    if (i0s >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) {
-        return;
+        info.devices[id].smpb = prop.sharedMemPerBlock;
+        info.devices[id].nsm  = prop.multiProcessorCount;
     }
 
-    const int i11 = i1 % ne11;
-    const int i12 = i2 % ne12;
-    const int i13 = i3 % ne13;
-
-    const size_t i_src0 = i3*s3 + i2*s2 + i1*s1;
-    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
-    const size_t i_dst  = i_src0;
-
-    const src0_t * src0_row = src0 + i_src0;
-    const src1_t * src1_row = src1 + i_src1;
-    dst_t * dst_row = dst + i_dst;
-
-    for (int i0 = i0s; i0 < ne0; i0 += blockDim.x*gridDim.x) {
-        const int i10 = i0 % ne10;
-        dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
+    for (int id = 0; id < info.device_count; ++id) {
+        info.default_tensor_split[id] /= total_vram;
     }
+
+    // configure logging to stdout
+    // CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr));
+
+    return info;
 }
 
-template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
-static __global__ void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t * dst,
-        int ne0, int ne1, int ne2, int ne3,
-        int ne10, int ne11, int ne12, int ne13,
-        /*int s0, */ int s1,  int s2,  int s3,
-        /*int s10,*/ int s11, int s12, int s13) {
-
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
-
-    const int i3 = i/(ne2*ne1*ne0);
-    const int i2 = (i/(ne1*ne0)) % ne2;
-    const int i1 = (i/ne0) % ne1;
-    const int i0 = i % ne0;
-
-    if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) {
-        return;
-    }
-
-    const int i11 = i1 % ne11;
-    const int i12 = i2 % ne12;
-    const int i13 = i3 % ne13;
-
-    const size_t i_src0 = i3*s3 + i2*s2 + i1*s1;
-    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
-    const size_t i_dst  = i_src0;
-
-    const src0_t * src0_row = src0 + i_src0;
-    const src1_t * src1_row = src1 + i_src1;
-    dst_t * dst_row = dst + i_dst;
-
-    const int i10 = i0 % ne10;
-    dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
+const ggml_cuda_device_info & ggml_cuda_info() {
+    static ggml_cuda_device_info info = ggml_cuda_init();
+    return info;
 }
 
-static __global__ void acc_f32(const float * x, const float * y, float * dst, const int ne,
-    const int ne10, const int ne11, const int ne12,
-    const int nb1, const int nb2, int offset) {
-    const int i = blockDim.x * blockIdx.x + threadIdx.x;
-    if (i >= ne) {
-        return;
-    }
-    int src1_idx = i - offset;
-    int oz = src1_idx / nb2;
-    int oy = (src1_idx - (oz * nb2)) / nb1;
-    int ox = src1_idx % nb1;
-    if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) {
-        dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11];
-    } else {
-        dst[i] = x[i];
-    }
-}
+// #define DEBUG_CUDA_MALLOC
 
-static __global__ void gelu_f32(const float * x, float * dst, const int k) {
-    const float GELU_COEF_A    = 0.044715f;
-    const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+// buffer pool for cuda (legacy)
+struct ggml_cuda_pool_leg : public ggml_cuda_pool {
+    static const int MAX_BUFFERS = 256;
 
-    if (i >= k) {
-        return;
+    int device;
+    struct ggml_cuda_buffer {
+        void * ptr = nullptr;
+        size_t size = 0;
+    };
+
+    ggml_cuda_buffer buffer_pool[MAX_BUFFERS] = {};
+    size_t pool_size = 0;
+
+    explicit ggml_cuda_pool_leg(int device) :
+        device(device) {
     }
 
-    float xi = x[i];
-    dst[i] = 0.5f*xi*(1.0f + tanhf(SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi)));
-}
-
-static __global__ void silu_f32(const float * x, float * dst, const int k) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (i >= k) {
-        return;
-    }
-    dst[i] = x[i] / (1.0f + expf(-x[i]));
-}
-
-static __global__ void gelu_quick_f32(const float * x, float * dst, int k) {
-    const float GELU_QUICK_COEF = -1.702f;
-    const int i  = blockDim.x*blockIdx.x + threadIdx.x;
-    if (i >= k) {
-        return;
-    }
-    dst[i] = x[i] * (1.0f / (1.0f + expf(GELU_QUICK_COEF * x[i])));
-}
-
-static __global__ void tanh_f32(const float * x, float * dst, int k) {
-    const int i  = blockDim.x*blockIdx.x + threadIdx.x;
-    if (i >= k) {
-        return;
-    }
-    dst[i] = tanhf(x[i]);
-}
-
-static __global__ void relu_f32(const float * x, float * dst, const int k) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (i >= k) {
-        return;
-    }
-    dst[i] = fmaxf(x[i], 0);
-}
-
-static __global__ void hardsigmoid_f32(const float * x, float * dst, const int k) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (i >= k) {
-        return;
-    }
-    dst[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f));
-}
-
-static __global__ void hardswish_f32(const float * x, float * dst, const int k) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (i >= k) {
-        return;
-    }
-    dst[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f));
-}
-
-static __global__ void leaky_relu_f32(const float * x, float * dst, const int k, const float negative_slope) {
-    const int i  = blockDim.x*blockIdx.x + threadIdx.x;
-    if (i >= k) {
-        return;
-    }
-    dst[i] = fmaxf(x[i], 0) + fminf(x[i], 0.0f) * negative_slope;
-}
-
-static __global__ void sqr_f32(const float * x, float * dst, const int k) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (i >= k) {
-        return;
-    }
-    dst[i] = x[i] * x[i];
-}
-
-template <int block_size>
-static __global__ void norm_f32(const float * x, float * dst, const int ncols, const float eps) {
-    const int row = blockIdx.x*blockDim.y + threadIdx.y;
-    const int tid = threadIdx.x;
-
-    float2 mean_var = make_float2(0.f, 0.f);
-
-    for (int col = tid; col < ncols; col += block_size) {
-        const float xi = x[row*ncols + col];
-        mean_var.x += xi;
-        mean_var.y += xi * xi;
-    }
-
-    // sum up partial sums
-    mean_var = warp_reduce_sum(mean_var);
-    if (block_size > WARP_SIZE) {
-        __shared__ float2 s_sum[32];
-        int warp_id = threadIdx.x / WARP_SIZE;
-        int lane_id = threadIdx.x % WARP_SIZE;
-        if (lane_id == 0) {
-            s_sum[warp_id] = mean_var;
+    ~ggml_cuda_pool_leg() {
+        ggml_cuda_set_device(device);
+        for (int i = 0; i < MAX_BUFFERS; ++i) {
+            ggml_cuda_buffer & b = buffer_pool[i];
+            if (b.ptr != nullptr) {
+                CUDA_CHECK(cudaFree(b.ptr));
+                pool_size -= b.size;
+            }
         }
-        __syncthreads();
-        mean_var = s_sum[lane_id];
-        mean_var = warp_reduce_sum(mean_var);
+        GGML_ASSERT(pool_size == 0);
     }
 
-    const float mean = mean_var.x / ncols;
-    const float var = mean_var.y / ncols - mean * mean;
-    const float inv_std = rsqrtf(var + eps);
-
-    for (int col = tid; col < ncols; col += block_size) {
-        dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_std;
-    }
-}
-
-static __global__ void concat_f32(const float * x,const float * y, float * dst, const int ne0, const int ne02) {
-    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
-    if (nidx >= ne0) {
-        return;
-    }
-    // operation
-    int offset_dst =
-        nidx +
-        blockIdx.y * ne0 +
-        blockIdx.z * ne0 * gridDim.y;
-    if (blockIdx.z < ne02) { // src0
-        int offset_src =
-            nidx +
-            blockIdx.y * ne0 +
-            blockIdx.z * ne0 * gridDim.y;
-            dst[offset_dst] = x[offset_src];
-    } else {
-        int offset_src =
-            nidx +
-            blockIdx.y * ne0 +
-            (blockIdx.z - ne02) * ne0 *  gridDim.y;
-            dst[offset_dst] = y[offset_src];
-    }
-}
-
-static __global__ void upscale_f32(const float * x, float * dst, const int ne00, const int nb02, const int scale_factor) {
-    int ne0 = ne00 * scale_factor;
-    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
-    if (nidx >= ne0) {
-        return;
-    }
-    // operation
-    int i00 = nidx / scale_factor;
-    int i01 = blockIdx.y / scale_factor;
-    int offset_src =
-        i00 +
-        i01 * ne00 +
-        blockIdx.z * nb02;
-    int offset_dst =
-        nidx +
-        blockIdx.y * ne0 +
-        blockIdx.z * ne0 * gridDim.y;
-    dst[offset_dst] = x[offset_src];
-}
-
-static __global__ void pad_f32(const float * x, float * dst, const int ne0, const int ne00, const int ne01, const int ne02) {
-    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
-    if (nidx >= ne0) {
-        return;
-    }
-
-    // operation
-    int offset_dst =
-        nidx +
-        blockIdx.y * ne0 +
-        blockIdx.z * ne0 * gridDim.y;
-    if (nidx < ne00 && blockIdx.y < ne01 && blockIdx.z < ne02) {
-        int offset_src =
-            nidx +
-            blockIdx.y * ne00 +
-            blockIdx.z * ne00 * ne01;
-            dst[offset_dst] = x[offset_src];
-    } else {
-        dst[offset_dst] = 0.0f;
-    }
-}
-
-template <int block_size>
-static __global__ void group_norm_f32(const float * x, float * dst, const int group_size, const int ne_elements, const float eps) {
-    int start = blockIdx.x * group_size;
-    int end = start + group_size;
-
-    start += threadIdx.x;
-
-    if (end >= ne_elements) {
-        end = ne_elements;
-    }
-
-    float tmp = 0.0f; // partial sum for thread in warp
-
-    for (int j = start; j < end; j += block_size) {
-        tmp += x[j];
-    }
-
-    tmp = warp_reduce_sum(tmp);
-    if (block_size > WARP_SIZE) {
-        __shared__ float s_sum[32];
-        int warp_id = threadIdx.x / WARP_SIZE;
-        int lane_id = threadIdx.x % WARP_SIZE;
-        if (lane_id == 0) {
-            s_sum[warp_id] = tmp;
+    void * alloc(size_t size, size_t * actual_size) override {
+#ifdef DEBUG_CUDA_MALLOC
+        int nnz = 0;
+        size_t max_size = 0;
+#endif
+        size_t best_diff = 1ull << 36;
+        int ibest = -1;
+        for (int i = 0; i < MAX_BUFFERS; ++i) {
+            ggml_cuda_buffer& b = buffer_pool[i];
+            if (b.ptr != nullptr) {
+#ifdef DEBUG_CUDA_MALLOC
+                ++nnz;
+                if (b.size > max_size) max_size = b.size;
+#endif
+                if (b.size >= size) {
+                    size_t diff = b.size - size;
+                    if (diff < best_diff) {
+                        best_diff = diff;
+                        ibest = i;
+                        if (!best_diff) {
+                            void * ptr = b.ptr;
+                            *actual_size = b.size;
+                            b.ptr = nullptr;
+                            b.size = 0;
+                            return ptr;
+                        }
+                    }
+                }
+            }
         }
-        __syncthreads();
-        tmp = s_sum[lane_id];
-        tmp = warp_reduce_sum(tmp);
-    }
-
-    float mean = tmp / group_size;
-    tmp = 0.0f;
-
-    for (int j = start; j < end; j += block_size) {
-        float xi = x[j] - mean;
-        dst[j] = xi;
-        tmp += xi * xi;
-    }
-
-    tmp = warp_reduce_sum(tmp);
-    if (block_size > WARP_SIZE) {
-        __shared__ float s_sum[32];
-        int warp_id = threadIdx.x / WARP_SIZE;
-        int lane_id = threadIdx.x % WARP_SIZE;
-        if (lane_id == 0) {
-            s_sum[warp_id] = tmp;
+        if (ibest >= 0) {
+            ggml_cuda_buffer& b = buffer_pool[ibest];
+            void * ptr = b.ptr;
+            *actual_size = b.size;
+            b.ptr = nullptr;
+            b.size = 0;
+            return ptr;
         }
-        __syncthreads();
-        tmp = s_sum[lane_id];
-        tmp = warp_reduce_sum(tmp);
+        void * ptr;
+        size_t look_ahead_size = (size_t) (1.05 * size);
+        look_ahead_size = 256 * ((look_ahead_size + 255)/256);
+        ggml_cuda_set_device(device);
+        CUDA_CHECK(cudaMalloc((void **) &ptr, look_ahead_size));
+        *actual_size = look_ahead_size;
+        pool_size += look_ahead_size;
+#ifdef DEBUG_CUDA_MALLOC
+        fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, device, nnz,
+                (uint32_t)(max_size/1024/1024), (uint32_t)(pool_size/1024/1024), (uint32_t)(size/1024/1024));
+#endif
+        return ptr;
     }
 
-    float variance = tmp / group_size;
-    float scale = rsqrtf(variance + eps);
-    for (int j = start; j < end; j += block_size) {
-        dst[j] *= scale;
-    }
-}
-
-template <int block_size>
-static __global__ void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps) {
-    const int row = blockIdx.x*blockDim.y + threadIdx.y;
-    const int tid = threadIdx.x;
-
-    float tmp = 0.0f; // partial sum for thread in warp
-
-    for (int col = tid; col < ncols; col += block_size) {
-        const float xi = x[row*ncols + col];
-        tmp += xi * xi;
-    }
-
-    // sum up partial sums
-    tmp = warp_reduce_sum(tmp);
-    if (block_size > WARP_SIZE) {
-        __shared__ float s_sum[32];
-        int warp_id = threadIdx.x / WARP_SIZE;
-        int lane_id = threadIdx.x % WARP_SIZE;
-        if (lane_id == 0) {
-            s_sum[warp_id] = tmp;
+    void free(void * ptr, size_t size) override {
+        for (int i = 0; i < MAX_BUFFERS; ++i) {
+            ggml_cuda_buffer& b = buffer_pool[i];
+            if (b.ptr == nullptr) {
+                b.ptr = ptr;
+                b.size = size;
+                return;
+            }
         }
-        __syncthreads();
-        tmp = s_sum[lane_id];
-        tmp = warp_reduce_sum(tmp);
+        fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n");
+        ggml_cuda_set_device(device);
+        CUDA_CHECK(cudaFree(ptr));
+        pool_size -= size;
+    }
+};
+
+// pool with virtual memory
+#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM)
+struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
+    static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB
+
+    int device;
+    CUdeviceptr pool_addr = 0;
+    size_t pool_used = 0;
+    size_t pool_size = 0;
+    size_t granularity;
+
+    explicit ggml_cuda_pool_vmm(int device) :
+        device(device),
+        granularity(ggml_cuda_info().devices[device].vmm_granularity) {
     }
 
-    const float mean = tmp / ncols;
-    const float scale = rsqrtf(mean + eps);
-
-    for (int col = tid; col < ncols; col += block_size) {
-        dst[row*ncols + col] = scale * x[row*ncols + col];
+    ~ggml_cuda_pool_vmm() {
+        if (pool_addr != 0) {
+            CU_CHECK(cuMemUnmap(pool_addr, pool_size));
+            CU_CHECK(cuMemAddressFree(pool_addr, CUDA_POOL_VMM_MAX_SIZE));
+        }
     }
+
+    void * alloc(size_t size, size_t * actual_size) override {
+        // round up the allocation size to the alignment to ensure that all allocations are aligned for all data types
+        const size_t alignment = 128;
+        size = alignment * ((size + alignment - 1) / alignment);
+
+        size_t avail = pool_size - pool_used;
+
+        if (size > avail) {
+            // round up to the next multiple of the granularity
+            size_t reserve_size = size - avail;
+            reserve_size = granularity * ((reserve_size + granularity - 1) / granularity);
+
+            GGML_ASSERT(pool_size + reserve_size <= CUDA_POOL_VMM_MAX_SIZE);
+
+            // allocate more physical memory
+            CUmemAllocationProp prop = {};
+            prop.type = CU_MEM_ALLOCATION_TYPE_PINNED;
+            prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
+            prop.location.id = device;
+            CUmemGenericAllocationHandle handle;
+            CU_CHECK(cuMemCreate(&handle, reserve_size, &prop, 0));
+
+            // reserve virtual address space (if not already reserved)
+            if (pool_addr == 0) {
+                CU_CHECK(cuMemAddressReserve(&pool_addr, CUDA_POOL_VMM_MAX_SIZE, 0, 0, 0));
+            }
+
+            // map at the end of the pool
+            CU_CHECK(cuMemMap(pool_addr + pool_size, reserve_size, 0, handle, 0));
+
+            // the memory allocation handle is no longer needed after mapping
+            CU_CHECK(cuMemRelease(handle));
+
+            // set access
+            CUmemAccessDesc access = {};
+            access.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
+            access.location.id = device;
+            access.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE;
+            CU_CHECK(cuMemSetAccess(pool_addr + pool_size, reserve_size, &access, 1));
+
+            // add to the pool
+            pool_size += reserve_size;
+
+            //printf("cuda pool[%d]: size increased to %llu MB (reserved %llu MB)\n",
+            //       device, (unsigned long long) (pool_size/1024/1024),
+            //       (unsigned long long) (reserve_size/1024/1024));
+        }
+
+        GGML_ASSERT(pool_addr != 0);
+
+        void * ptr = (void *) (pool_addr + pool_used);
+        *actual_size = size;
+        pool_used += size;
+
+#ifdef DEBUG_CUDA_MALLOC
+        printf("cuda pool[%d]: allocated %llu bytes at %llx\n", device, (unsigned long long) size, ptr);
+#endif
+
+        return ptr;
+    }
+
+    void free(void * ptr, size_t size) override {
+#ifdef DEBUG_CUDA_MALLOC
+        printf("cuda pool[%d]: freed %llu bytes at %llx\n", device, (unsigned long long) size, ptr);
+#endif
+
+        pool_used -= size;
+
+        // all deallocations must be in reverse order of the allocations
+        GGML_ASSERT(ptr == (void *) (pool_addr + pool_used));
+    }
+};
+#endif // !defined(GGML_USE_HIPBLAS)
+
+std::unique_ptr<ggml_cuda_pool> ggml_backend_cuda_context::new_pool_for_device(int device) {
+#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM)
+    if (ggml_cuda_info().devices[device].vmm) {
+        return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_vmm(device));
+    }
+#endif
+    return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_leg(device));
 }
 
-static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
-    const block_q4_0 * x = (const block_q4_0 *) vx;
+// cuda buffer
 
-    const dfloat d = x[ib].d;
+struct ggml_backend_cuda_buffer_context {
+    int device;
+    void * dev_ptr = nullptr;
+    std::string name;
 
-    const int vui = x[ib].qs[iqs];
+    ggml_backend_cuda_buffer_context(int device, void * dev_ptr) :
+        device(device), dev_ptr(dev_ptr),
+        name(GGML_CUDA_NAME + std::to_string(device)) {
+    }
 
-    v.x = vui & 0xF;
-    v.y = vui >> 4;
+    ~ggml_backend_cuda_buffer_context() {
+        CUDA_CHECK(cudaFree(dev_ptr));
+    }
+};
 
-#ifdef GGML_CUDA_F16
-    v = __hsub2(v, {8.0f, 8.0f});
-    v = __hmul2(v, {d, d});
-#else
-    v.x = (v.x - 8.0f) * d;
-    v.y = (v.y - 8.0f) * d;
-#endif // GGML_CUDA_F16
+GGML_CALL static const char * ggml_backend_cuda_buffer_get_name(ggml_backend_buffer_t buffer) {
+    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
+    return ctx->name.c_str();
 }
 
-static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
-    const block_q4_1 * x = (const block_q4_1 *) vx;
-
-    const dfloat d = __low2half(x[ib].dm);
-    const dfloat m = __high2half(x[ib].dm);
-
-    const int vui = x[ib].qs[iqs];
-
-    v.x = vui & 0xF;
-    v.y = vui >> 4;
-
-#ifdef GGML_CUDA_F16
-    v = __hmul2(v, {d, d});
-    v = __hadd2(v, {m, m});
-#else
-    v.x = (v.x * d) + m;
-    v.y = (v.y * d) + m;
-#endif // GGML_CUDA_F16
+GGML_CALL static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) {
+    return buffer->iface.get_name == ggml_backend_cuda_buffer_get_name;
 }
 
-static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
-    const block_q5_0 * x = (const block_q5_0 *) vx;
-
-    const dfloat d = x[ib].d;
-
-    uint32_t qh;
-    memcpy(&qh, x[ib].qh, sizeof(qh));
-
-    const int xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
-    const int xh_1 = ((qh >> (iqs + 12))     ) & 0x10;
-
-    v.x = ((x[ib].qs[iqs] & 0xf) | xh_0);
-    v.y = ((x[ib].qs[iqs] >>  4) | xh_1);
-
-#ifdef GGML_CUDA_F16
-    v = __hsub2(v, {16.0f, 16.0f});
-    v = __hmul2(v, {d, d});
-#else
-    v.x = (v.x - 16.0f) * d;
-    v.y = (v.y - 16.0f) * d;
-#endif // GGML_CUDA_F16
+GGML_CALL static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
+    delete ctx;
 }
 
-static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
-    const block_q5_1 * x = (const block_q5_1 *) vx;
-
-    const dfloat d = __low2half(x[ib].dm);
-    const dfloat m = __high2half(x[ib].dm);
-
-    uint32_t qh;
-    memcpy(&qh, x[ib].qh, sizeof(qh));
-
-    const int xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
-    const int xh_1 = ((qh >> (iqs + 12))     ) & 0x10;
-
-    v.x = ((x[ib].qs[iqs] & 0xf) | xh_0);
-    v.y = ((x[ib].qs[iqs] >>  4) | xh_1);
-
-#ifdef GGML_CUDA_F16
-    v = __hmul2(v, {d, d});
-    v = __hadd2(v, {m, m});
-#else
-    v.x = (v.x * d) + m;
-    v.y = (v.y * d) + m;
-#endif // GGML_CUDA_F16
+GGML_CALL static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) {
+    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
+    return ctx->dev_ptr;
 }
 
-static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
-    const block_q8_0 * x = (const block_q8_0 *) vx;
+GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
+    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
 
-    const dfloat d = x[ib].d;
-
-    v.x = x[ib].qs[iqs + 0];
-    v.y = x[ib].qs[iqs + 1];
-
-#ifdef GGML_CUDA_F16
-    v = __hmul2(v, {d, d});
-#else
-    v.x *= d;
-    v.y *= d;
-#endif // GGML_CUDA_F16
-}
-
-template<typename dst_t>
-static __global__ void dequantize_block_q4_0(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32) {
-
-    const int i = blockIdx.x;
-
-    // assume 32 threads
-    const int tid = threadIdx.x;
-    const int il  = tid/8;
-    const int ir  = tid%8;
-    const int ib = 8*i + ir;
-    if (ib >= nb32) {
+    if (tensor->view_src != NULL) {
+        assert(tensor->view_src->buffer->buft == buffer->buft);
         return;
     }
 
-    dst_t * y = yy + 256*i + 32*ir + 4*il;
+    if (ggml_is_quantized(tensor->type)) {
+        // initialize padding to 0 to avoid possible NaN values
+        size_t original_size = ggml_nbytes(tensor);
+        size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor);
 
-    const block_q4_0 * x = (const block_q4_0 *)vx + ib;
-    const float d = __half2float(x->d);
-    const float dm = -8*d;
-
-    const uint8_t * q = x->qs + 4*il;
-
-    for (int l = 0; l < 4; ++l) {
-        y[l+ 0] = d * (q[l] & 0xF) + dm;
-        y[l+16] = d * (q[l] >>  4) + dm;
+        if (padded_size > original_size && tensor->view_src == nullptr) {
+            ggml_cuda_set_device(ctx->device);
+            CUDA_CHECK(cudaMemset((char *)tensor->data + original_size, 0, padded_size - original_size));
+        }
     }
 }
 
-template<typename dst_t>
-static __global__ void dequantize_block_q4_1(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32) {
+GGML_CALL static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
 
-    const int i = blockIdx.x;
-
-    // assume 32 threads
-    const int tid = threadIdx.x;
-    const int il  = tid/8;
-    const int ir  = tid%8;
-    const int ib = 8*i + ir;
-    if (ib >= nb32) {
-        return;
-    }
-
-    dst_t * y = yy + 256*i + 32*ir + 4*il;
-
-    const block_q4_1 * x = (const block_q4_1 *)vx + ib;
-    const float2 d = __half22float2(x->dm);
-
-    const uint8_t * q = x->qs + 4*il;
-
-    for (int l = 0; l < 4; ++l) {
-        y[l+ 0] = d.x * (q[l] & 0xF) + d.y;
-        y[l+16] = d.x * (q[l] >>  4) + d.y;
-    }
+    ggml_cuda_set_device(ctx->device);
+    CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, cudaStreamPerThread));
+    CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
 }
 
-//================================== k-quants
+GGML_CALL static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
 
-template<typename dst_t>
-static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    ggml_cuda_set_device(ctx->device);
+    CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, cudaStreamPerThread));
+    CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
+}
 
-    const int i   = blockIdx.x;
-    const block_q2_K * x = (const block_q2_K *) vx;
-
-    const int tid = threadIdx.x;
-#if QK_K == 256
-    const int n   = tid/32;
-    const int l   = tid - 32*n;
-    const int is  = 8*n + l/16;
-
-    const uint8_t q = x[i].qs[32*n + l];
-    dst_t * y = yy + i*QK_K + 128*n;
-
-    float dall = __low2half(x[i].dm);
-    float dmin = __high2half(x[i].dm);
-    y[l+ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
-    y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4);
-    y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4);
-    y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4);
+GGML_CALL static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
+    if (ggml_backend_buffer_is_cuda(src->buffer)) {
+        ggml_backend_cuda_buffer_context * src_ctx = (ggml_backend_cuda_buffer_context *)src->buffer->context;
+        ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *)dst->buffer->context;
+        if (src_ctx->device == dst_ctx->device) {
+            CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(src), cudaMemcpyDeviceToDevice, cudaStreamPerThread));
+        } else {
+#ifdef GGML_CUDA_NO_PEER_COPY
+            return false;
 #else
-    const int is = tid/16;  // 0 or 1
-    const int il = tid%16;  // 0...15
-    const uint8_t q = x[i].qs[il] >> (2*is);
-    dst_t * y = yy + i*QK_K + 16*is + il;
-    float dall = __low2half(x[i].dm);
-    float dmin = __high2half(x[i].dm);
-    y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
-    y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4);
+            CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, dst_ctx->device, src->data, src_ctx->device, ggml_nbytes(src), cudaStreamPerThread));
 #endif
-
-}
-
-template<typename dst_t>
-static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
-
-    const int i = blockIdx.x;
-    const block_q3_K * x = (const block_q3_K *) vx;
-
-#if QK_K == 256
-    const int r = threadIdx.x/4;
-    const int tid = r/2;
-    const int is0 = r%2;
-    const int l0 = 16*is0 + 4*(threadIdx.x%4);
-    const int n = tid / 4;
-    const int j = tid - 4*n;
-
-    uint8_t m = 1 << (4*n + j);
-    int is = 8*n + 2*j + is0;
-    int shift = 2*j;
-
-    int8_t us = is <  4 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+8] >> 0) & 3) << 4) :
-                is <  8 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+4] >> 2) & 3) << 4) :
-                is < 12 ? (x[i].scales[is-8] >>  4) | (((x[i].scales[is+0] >> 4) & 3) << 4) :
-                          (x[i].scales[is-8] >>  4) | (((x[i].scales[is-4] >> 6) & 3) << 4);
-    float d_all = x[i].d;
-    float dl = d_all * (us - 32);
-
-    dst_t * y = yy + i*QK_K + 128*n + 32*j;
-    const uint8_t * q = x[i].qs + 32*n;
-    const uint8_t * hm = x[i].hmask;
-
-    for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));
-#else
-    const int tid = threadIdx.x;
-    const int is  = tid/16;  // 0 or 1
-    const int il  = tid%16;  // 0...15
-    const int im  = il/8;    // 0...1
-    const int in  = il%8;    // 0...7
-
-    dst_t * y = yy + i*QK_K + 16*is + il;
-
-    const uint8_t q = x[i].qs[il] >> (2*is);
-    const uint8_t h = x[i].hmask[in] >> (2*is + im);
-    const float   d = (float)x[i].d;
-
-    if (is == 0) {
-        y[ 0] = d * ((x[i].scales[0] & 0xF) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
-        y[32] = d * ((x[i].scales[1] & 0xF) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
-    } else {
-        y[ 0] = d * ((x[i].scales[0] >>  4) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
-        y[32] = d * ((x[i].scales[1] >>  4) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
+        }
+        CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
+        return true;
     }
-#endif
+    return false;
 
+    GGML_UNUSED(buffer);
 }
 
-#if QK_K == 256
-static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
-    if (j < 4) {
-        d = q[j] & 63; m = q[j + 4] & 63;
-    } else {
-        d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
-        m = (q[j+4] >>  4) | ((q[j-0] >> 6) << 4);
+GGML_CALL static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
+
+    ggml_cuda_set_device(ctx->device);
+    CUDA_CHECK(cudaDeviceSynchronize());
+    CUDA_CHECK(cudaMemset(ctx->dev_ptr, value, buffer->size));
+    CUDA_CHECK(cudaDeviceSynchronize());
+}
+
+static ggml_backend_buffer_i ggml_backend_cuda_buffer_interface = {
+    /* .get_name        = */ ggml_backend_cuda_buffer_get_name,
+    /* .free_buffer     = */ ggml_backend_cuda_buffer_free_buffer,
+    /* .get_base        = */ ggml_backend_cuda_buffer_get_base,
+    /* .init_tensor     = */ ggml_backend_cuda_buffer_init_tensor,
+    /* .set_tensor      = */ ggml_backend_cuda_buffer_set_tensor,
+    /* .get_tensor      = */ ggml_backend_cuda_buffer_get_tensor,
+    /* .cpy_tensor      = */ ggml_backend_cuda_buffer_cpy_tensor,
+    /* .clear           = */ ggml_backend_cuda_buffer_clear,
+    /* .reset           = */ NULL,
+};
+
+// cuda buffer type
+struct ggml_backend_cuda_buffer_type_context {
+    int device;
+    std::string name;
+};
+
+GGML_CALL static const char * ggml_backend_cuda_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    ggml_backend_cuda_buffer_type_context * ctx = (ggml_backend_cuda_buffer_type_context *)buft->context;
+
+    return ctx->name.c_str();
+}
+
+GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context;
+
+    ggml_cuda_set_device(buft_ctx->device);
+
+    size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0
+
+    void * dev_ptr;
+    cudaError_t err = cudaMalloc(&dev_ptr, size);
+    if (err != cudaSuccess) {
+        fprintf(stderr, "%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size/1024.0/1024.0, buft_ctx->device, cudaGetErrorString(err));
+        return nullptr;
     }
+
+    ggml_backend_cuda_buffer_context * ctx = new ggml_backend_cuda_buffer_context(buft_ctx->device, dev_ptr);
+
+    return ggml_backend_buffer_init(buft, ggml_backend_cuda_buffer_interface, ctx, size);
 }
-#endif
 
-template<typename dst_t>
-static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
-    const block_q4_K * x = (const block_q4_K *) vx;
+GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+    return 128;
 
-    const int i = blockIdx.x;
+    GGML_UNUSED(buft);
+}
 
-#if QK_K == 256
-    // assume 32 threads
-    const int tid = threadIdx.x;
-    const int il  = tid/8;
-    const int ir  = tid%8;
-    const int is  = 2*il;
-    const int n   = 4;
+GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
+    size_t size = ggml_nbytes(tensor);
+    int64_t ne0 = tensor->ne[0];
 
-    dst_t * y = yy + i*QK_K + 64*il + n*ir;
-
-    const float dall = __low2half(x[i].dm);
-    const float dmin = __high2half(x[i].dm);
-
-    const uint8_t * q = x[i].qs + 32*il + n*ir;
-
-    uint8_t sc, m;
-    get_scale_min_k4(is + 0, x[i].scales, sc, m);
-    const float d1 = dall * sc; const float m1 = dmin * m;
-    get_scale_min_k4(is + 1, x[i].scales, sc, m);
-    const float d2 = dall * sc; const float m2 = dmin * m;
-    for (int l = 0; l < n; ++l) {
-        y[l + 0] = d1 * (q[l] & 0xF) - m1;
-        y[l +32] = d2 * (q[l] >>  4) - m2;
+    if (ggml_is_quantized(tensor->type)) {
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
     }
-#else
-    const int tid = threadIdx.x;
-    const uint8_t * q = x[i].qs;
-    dst_t * y = yy + i*QK_K;
-    const float d = (float)x[i].dm[0];
-    const float m = (float)x[i].dm[1];
-    y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4);
-    y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >>  4) - m * (x[i].scales[1] >> 4);
-#endif
+
+    return size;
+
+    GGML_UNUSED(buft);
 }
 
-template<typename dst_t>
-static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
-    const block_q5_K * x = (const block_q5_K *) vx;
+GGML_CALL static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
+    if (!ggml_backend_is_cuda(backend)) {
+        return false;
+    }
 
-    const int i = blockIdx.x;
+    ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context;
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
 
-#if QK_K == 256
-    // assume 64 threads - this is very slightly better than the one below
-    const int tid = threadIdx.x;
-    const int il  = tid/16;   // il is in 0...3
-    const int ir  = tid%16;   // ir is in 0...15
-    const int is  = 2*il;     // is is in 0...6
-
-    dst_t * y = yy + i*QK_K + 64*il + 2*ir;
-
-    const float dall = __low2half(x[i].dm);
-    const float dmin = __high2half(x[i].dm);
-
-    const uint8_t * ql = x[i].qs + 32*il + 2*ir;
-    const uint8_t * qh = x[i].qh + 2*ir;
-
-    uint8_t sc, m;
-    get_scale_min_k4(is + 0, x[i].scales, sc, m);
-    const float d1 = dall * sc; const float m1 = dmin * m;
-    get_scale_min_k4(is + 1, x[i].scales, sc, m);
-    const float d2 = dall * sc; const float m2 = dmin * m;
-
-    uint8_t   hm  = 1 << (2*il);
-    y[ 0] = d1 * ((ql[ 0] & 0xF) + (qh[ 0] & hm ? 16 : 0)) - m1;
-    y[ 1] = d1 * ((ql[ 1] & 0xF) + (qh[ 1] & hm ? 16 : 0)) - m1;
-    hm <<= 1;
-    y[32] = d2 * ((ql[ 0] >>  4) + (qh[ 0] & hm ? 16 : 0)) - m2;
-    y[33] = d2 * ((ql[ 1] >>  4) + (qh[ 1] & hm ? 16 : 0)) - m2;
-#else
-    const int tid = threadIdx.x;
-    const uint8_t q = x[i].qs[tid];
-    const int im = tid/8;  // 0...3
-    const int in = tid%8;  // 0...7
-    const int is = tid/16; // 0 or 1
-    const uint8_t h = x[i].qh[in] >> im;
-    const float d = x[i].d;
-    dst_t * y = yy + i*QK_K + tid;
-    y[ 0] = d * x[i].scales[is+0] * ((q & 0xF) - ((h >> 0) & 1 ? 0 : 16));
-    y[32] = d * x[i].scales[is+2] * ((q >>  4) - ((h >> 4) & 1 ? 0 : 16));
-#endif
+    return buft_ctx->device == cuda_ctx->device;
 }
 
-template<typename dst_t>
-static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
-    const block_q6_K * x = (const block_q6_K *) vx;
+static ggml_backend_buffer_type_i ggml_backend_cuda_buffer_type_interface = {
+    /* .get_name         = */ ggml_backend_cuda_buffer_type_name,
+    /* .alloc_buffer     = */ ggml_backend_cuda_buffer_type_alloc_buffer,
+    /* .get_alignment    = */ ggml_backend_cuda_buffer_type_get_alignment,
+    /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
+    /* .get_alloc_size   = */ ggml_backend_cuda_buffer_type_get_alloc_size,
+    /* .supports_backend = */ ggml_backend_cuda_buffer_type_supports_backend,
+    /* .is_host          = */ NULL,
+};
 
-    const int i = blockIdx.x;
-#if QK_K == 256
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) {
+    static std::mutex mutex;
+    std::lock_guard<std::mutex> lock(mutex);
 
-    // assume 64 threads - this is very slightly better than the one below
-    const int tid = threadIdx.x;
-    const int ip  = tid/32;   // ip is 0 or 1
-    const int il  = tid - 32*ip; // 0...32
-    const int is  = 8*ip + il/16;
+    if (device >= ggml_backend_cuda_get_device_count()) {
+        return nullptr;
+    }
 
-    dst_t * y = yy + i*QK_K + 128*ip + il;
+    static ggml_backend_buffer_type ggml_backend_cuda_buffer_types[GGML_CUDA_MAX_DEVICES];
 
-    const float d = x[i].d;
+    static bool ggml_backend_cuda_buffer_type_initialized = false;
 
-    const uint8_t * ql = x[i].ql + 64*ip + il;
-    const uint8_t   qh = x[i].qh[32*ip + il];
-    const int8_t  * sc = x[i].scales + is;
+    if (!ggml_backend_cuda_buffer_type_initialized) {
+        for (int i = 0; i < GGML_CUDA_MAX_DEVICES; i++) {
+            ggml_backend_cuda_buffer_types[i] = {
+                /* .iface    = */ ggml_backend_cuda_buffer_type_interface,
+                /* .context  = */ new ggml_backend_cuda_buffer_type_context{i, GGML_CUDA_NAME + std::to_string(i)},
+            };
+        }
+        ggml_backend_cuda_buffer_type_initialized = true;
+    }
 
-    y[ 0] = d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
-    y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32);
-    y[64] = d * sc[4] * ((int8_t)((ql[ 0]  >> 4) | (((qh >> 4) & 3) << 4)) - 32);
-    y[96] = d * sc[6] * ((int8_t)((ql[32]  >> 4) | (((qh >> 6) & 3) << 4)) - 32);
-#else
-
-    // assume 32 threads
-    const int tid = threadIdx.x;
-    const int ip  = tid/16;         // 0 or 1
-    const int il  = tid - 16*ip;    // 0...15
-
-    dst_t * y = yy + i*QK_K + 16*ip + il;
-
-    const float d = x[i].d;
-
-    const uint8_t   ql = x[i].ql[16*ip + il];
-    const uint8_t   qh = x[i].qh[il] >> (2*ip);
-    const int8_t  * sc = x[i].scales;
-
-    y[ 0] = d * sc[ip+0] * ((int8_t)((ql & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
-    y[32] = d * sc[ip+2] * ((int8_t)((ql  >> 4) | (((qh >> 4) & 3) << 4)) - 32);
-#endif
+    return &ggml_backend_cuda_buffer_types[device];
 }
 
-static const __device__ uint64_t iq2xxs_grid[256] = {
-    0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08,
-    0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808,
-    0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819,
-    0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, 0x08080808192b0819,
-    0x08080808192b1908, 0x080808082b080808, 0x080808082b08082b, 0x080808082b082b2b,
-    0x080808082b2b082b, 0x0808081908080819, 0x0808081908081908, 0x0808081908190808,
-    0x0808081908191919, 0x0808081919080808, 0x080808192b081908, 0x080808192b192b08,
-    0x0808082b08080808, 0x0808082b0808082b, 0x0808082b082b082b, 0x0808082b2b08082b,
-    0x0808190808080819, 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819,
-    0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, 0x0808190819082b08,
-    0x08081908192b0808, 0x080819082b080819, 0x080819082b081908, 0x080819082b190808,
-    0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08,
-    0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, 0x080819192b080808,
-    0x080819192b190819, 0x0808192b08082b19, 0x0808192b08190808, 0x0808192b19080808,
-    0x0808192b2b081908, 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919,
-    0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, 0x08082b0819080819,
-    0x08082b0819081908, 0x08082b0819190808, 0x08082b081919082b, 0x08082b082b082b08,
-    0x08082b1908081908, 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908,
-    0x0819080808080819, 0x0819080808081908, 0x0819080808190808, 0x08190808082b0819,
-    0x0819080819080808, 0x08190808192b0808, 0x081908082b081908, 0x081908082b190808,
-    0x081908082b191919, 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808,
-    0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, 0x0819082b082b1908,
-    0x0819082b19081919, 0x0819190808080808, 0x0819190808082b08, 0x08191908082b0808,
-    0x08191908082b1919, 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08,
-    0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, 0x08192b0808080819,
-    0x08192b0808081908, 0x08192b0808190808, 0x08192b0819080808, 0x08192b082b080819,
-    0x08192b1908080808, 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819,
-    0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, 0x082b080819081908,
-    0x082b0808192b0819, 0x082b08082b080808, 0x082b08082b08082b, 0x082b0819082b2b19,
-    0x082b081919082b08, 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819,
-    0x082b190808081908, 0x082b190808190808, 0x082b190819080808, 0x082b19081919192b,
-    0x082b191908080808, 0x082b191919080819, 0x082b1919192b1908, 0x082b192b2b190808,
-    0x082b2b0808082b08, 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908,
-    0x1908080808080819, 0x1908080808081908, 0x1908080808190808, 0x1908080808192b08,
-    0x19080808082b0819, 0x19080808082b1908, 0x1908080819080808, 0x1908080819082b08,
-    0x190808081919192b, 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908,
-    0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, 0x19080819192b0819,
-    0x190808192b080808, 0x190808192b081919, 0x1908082b08080819, 0x1908082b08190808,
-    0x1908082b19082b08, 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808,
-    0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, 0x190819082b192b19,
-    0x190819190819082b, 0x19081919082b1908, 0x1908192b08080808, 0x19082b0808080819,
-    0x19082b0808081908, 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919,
-    0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, 0x19082b192b08082b,
-    0x19082b2b19081919, 0x19082b2b2b190808, 0x1919080808080808, 0x1919080808082b08,
-    0x1919080808190819, 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808,
-    0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, 0x191908192b2b1908,
-    0x1919082b2b190819, 0x191919082b190808, 0x191919082b19082b, 0x1919191908082b2b,
-    0x1919192b08080819, 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819,
-    0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, 0x19192b2b08082b08,
-    0x192b080808081908, 0x192b080808190808, 0x192b080819080808, 0x192b0808192b2b08,
-    0x192b081908080808, 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808,
-    0x192b190808080808, 0x192b190808081919, 0x192b191908190808, 0x192b19190819082b,
-    0x192b19192b081908, 0x192b2b081908082b, 0x2b08080808080808, 0x2b0808080808082b,
-    0x2b08080808082b2b, 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908,
-    0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819,
-    0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808,
-    0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908,
-    0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b,
-    0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808,
-    0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b,
-    0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b,
-    0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808,
-    0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19,
-    0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908,
-};
+// cuda split buffer
 
-static const __device__ uint64_t iq2xs_grid[512] = {
-    0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08,
-    0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b,
-    0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919,
-    0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b,
-    0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919,
-    0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808,
-    0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, 0x080808082b190819,
-    0x080808082b191908, 0x080808082b192b19, 0x080808082b2b0808, 0x0808081908080819,
-    0x0808081908081908, 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808,
-    0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, 0x0808081908192b2b,
-    0x08080819082b0819, 0x08080819082b1908, 0x0808081919080808, 0x080808191908082b,
-    0x0808081919081919, 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908,
-    0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, 0x080808192b081908,
-    0x080808192b190808, 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b08081919,
-    0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808,
-    0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, 0x0808082b19191919,
-    0x0808082b2b080808, 0x0808082b2b082b2b, 0x0808190808080819, 0x0808190808081908,
-    0x080819080808192b, 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b,
-    0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908,
-    0x0808190819080808, 0x080819081908082b, 0x0808190819081919, 0x0808190819082b08,
-    0x0808190819190819, 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808,
-    0x080819082b080819, 0x080819082b081908, 0x080819082b190808, 0x0808191908080808,
-    0x080819190808082b, 0x0808191908081919, 0x0808191908082b08, 0x0808191908190819,
-    0x0808191908191908, 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908,
-    0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, 0x0808192b08080819,
-    0x0808192b08081908, 0x0808192b08190808, 0x0808192b082b192b, 0x0808192b19080808,
-    0x0808192b1908082b, 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b,
-    0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, 0x08082b0808190819,
-    0x08082b0808191908, 0x08082b08082b0808, 0x08082b08082b1919, 0x08082b0819080819,
-    0x08082b0819081908, 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808,
-    0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, 0x08082b1908081908,
-    0x08082b1908190808, 0x08082b1919080808, 0x08082b192b080819, 0x08082b192b082b19,
-    0x08082b2b08080808, 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b,
-    0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, 0x081908080808192b,
-    0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, 0x0819080808191919,
-    0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808,
-    0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, 0x0819080819190819,
-    0x0819080819191908, 0x08190808192b0808, 0x08190808192b2b2b, 0x081908082b080819,
-    0x081908082b081908, 0x081908082b190808, 0x0819081908080808, 0x081908190808082b,
-    0x0819081908081919, 0x0819081908082b08, 0x0819081908190819, 0x0819081908191908,
-    0x08190819082b0808, 0x0819081919080819, 0x0819081919081908, 0x0819081919190808,
-    0x081908192b080808, 0x081908192b191908, 0x081908192b19192b, 0x0819082b08080819,
-    0x0819082b08081908, 0x0819082b0808192b, 0x0819082b08190808, 0x0819082b19080808,
-    0x0819082b192b0808, 0x0819190808080808, 0x081919080808082b, 0x0819190808081919,
-    0x0819190808082b08, 0x0819190808190819, 0x0819190808191908, 0x08191908082b0808,
-    0x0819190819080819, 0x0819190819081908, 0x0819190819082b19, 0x0819190819190808,
-    0x08191908192b1908, 0x081919082b080808, 0x0819191908080819, 0x0819191908081908,
-    0x0819191908190808, 0x0819191919080808, 0x0819192b08080808, 0x0819192b08191908,
-    0x0819192b19082b19, 0x08192b0808080819, 0x08192b0808081908, 0x08192b0808190808,
-    0x08192b080819082b, 0x08192b0819080808, 0x08192b0819191908, 0x08192b082b08192b,
-    0x08192b1908080808, 0x08192b1908081919, 0x08192b19192b192b, 0x08192b2b19190819,
-    0x08192b2b2b2b2b19, 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919,
-    0x082b080808082b08, 0x082b080808082b2b, 0x082b080808190819, 0x082b080808191908,
-    0x082b0808082b0808, 0x082b080819080819, 0x082b080819081908, 0x082b080819190808,
-    0x082b08082b080808, 0x082b08082b2b0808, 0x082b081908080819, 0x082b081908081908,
-    0x082b081908190808, 0x082b081919080808, 0x082b081919082b08, 0x082b0819192b1919,
-    0x082b082b08080808, 0x082b082b082b082b, 0x082b082b2b080808, 0x082b082b2b2b2b08,
-    0x082b190808080819, 0x082b190808081908, 0x082b190808190808, 0x082b1908082b2b19,
-    0x082b190819080808, 0x082b191908080808, 0x082b191919080819, 0x082b19191919082b,
-    0x082b19192b192b19, 0x082b192b08080819, 0x082b192b08192b2b, 0x082b192b2b2b192b,
-    0x082b2b0808080808, 0x082b2b0808082b08, 0x082b2b0808082b2b, 0x082b2b08082b0808,
-    0x082b2b0819191919, 0x082b2b082b082b08, 0x082b2b082b2b082b, 0x082b2b19192b2b08,
-    0x082b2b192b190808, 0x082b2b2b08082b08, 0x082b2b2b082b0808, 0x082b2b2b2b08082b,
-    0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, 0x1908080808081908,
-    0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, 0x190808080819082b,
-    0x1908080808191919, 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908,
-    0x1908080819080808, 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08,
-    0x1908080819082b2b, 0x1908080819190819, 0x1908080819191908, 0x19080808192b0808,
-    0x19080808192b1919, 0x190808082b080819, 0x190808082b081908, 0x190808082b190808,
-    0x1908081908080808, 0x190808190808082b, 0x1908081908081919, 0x1908081908082b08,
-    0x1908081908190819, 0x1908081908191908, 0x19080819082b0808, 0x1908081919080819,
-    0x1908081919081908, 0x1908081919190808, 0x190808192b080808, 0x190808192b081919,
-    0x190808192b2b082b, 0x1908082b08080819, 0x1908082b08081908, 0x1908082b08190808,
-    0x1908082b0819082b, 0x1908082b082b2b19, 0x1908082b19080808, 0x1908190808080808,
-    0x190819080808082b, 0x1908190808081919, 0x1908190808082b08, 0x1908190808190819,
-    0x1908190808191908, 0x1908190808192b19, 0x19081908082b0808, 0x1908190819080819,
-    0x1908190819081908, 0x1908190819190808, 0x190819082b080808, 0x190819082b191908,
-    0x1908191908080819, 0x1908191908081908, 0x1908191908190808, 0x19081919082b1908,
-    0x1908191919080808, 0x190819192b192b2b, 0x1908192b08080808, 0x1908192b08082b2b,
-    0x1908192b19081908, 0x1908192b19190808, 0x19082b0808080819, 0x19082b0808081908,
-    0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, 0x19082b0819191908,
-    0x19082b08192b082b, 0x19082b1908080808, 0x19082b1908190819, 0x19082b1919081908,
-    0x19082b1919190808, 0x19082b19192b2b19, 0x19082b2b08081908, 0x1919080808080808,
-    0x191908080808082b, 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819,
-    0x1919080808191908, 0x19190808082b0808, 0x19190808082b2b08, 0x1919080819080819,
-    0x1919080819081908, 0x1919080819190808, 0x191908082b080808, 0x1919081908080819,
-    0x1919081908081908, 0x1919081908190808, 0x1919081908191919, 0x1919081919080808,
-    0x191908191908082b, 0x1919082b08080808, 0x1919082b19081908, 0x1919082b2b2b2b2b,
-    0x1919190808080819, 0x1919190808081908, 0x1919190808190808, 0x19191908082b0819,
-    0x1919190819080808, 0x19191908192b0808, 0x191919082b080819, 0x191919082b2b0819,
-    0x1919191908080808, 0x1919191908082b08, 0x191919192b080808, 0x191919192b082b08,
-    0x1919192b082b0819, 0x1919192b192b2b08, 0x1919192b2b2b0819, 0x19192b0808080808,
-    0x19192b0808191908, 0x19192b0819080819, 0x19192b0819190808, 0x19192b082b192b19,
-    0x19192b1908192b2b, 0x19192b1919080808, 0x19192b191908082b, 0x19192b2b2b081919,
-    0x192b080808080819, 0x192b080808081908, 0x192b080808190808, 0x192b080819080808,
-    0x192b080819191908, 0x192b0808192b082b, 0x192b08082b08192b, 0x192b08082b2b2b19,
-    0x192b081908080808, 0x192b082b082b1908, 0x192b082b19082b2b, 0x192b082b2b19082b,
-    0x192b190808080808, 0x192b19080819192b, 0x192b191908190808, 0x192b191919080808,
-    0x192b191919081919, 0x192b19192b2b1908, 0x192b2b0808080819, 0x192b2b08192b2b2b,
-    0x192b2b19082b1919, 0x192b2b2b0808192b, 0x192b2b2b19191908, 0x192b2b2b192b082b,
-    0x2b08080808080808, 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08,
-    0x2b08080808190819, 0x2b08080808191908, 0x2b080808082b0808, 0x2b080808082b2b2b,
-    0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808082b080808,
-    0x2b0808082b08082b, 0x2b0808082b2b2b08, 0x2b0808082b2b2b2b, 0x2b08081908080819,
-    0x2b08081908081908, 0x2b0808190808192b, 0x2b08081908190808, 0x2b08081919080808,
-    0x2b08081919190819, 0x2b08081919192b19, 0x2b08082b08080808, 0x2b08082b082b0808,
-    0x2b08082b2b080808, 0x2b08082b2b08082b, 0x2b08082b2b2b0808, 0x2b08082b2b2b2b08,
-    0x2b08190808080819, 0x2b08190808081908, 0x2b08190808190808, 0x2b0819080819082b,
-    0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, 0x2b0819082b082b19,
-    0x2b08191908080808, 0x2b08191919081908, 0x2b0819192b2b1919, 0x2b08192b08192b08,
-    0x2b08192b192b2b2b, 0x2b082b0808080808, 0x2b082b0808082b08, 0x2b082b08082b1919,
-    0x2b082b0819192b2b, 0x2b082b082b080808, 0x2b082b082b08082b, 0x2b082b082b2b2b08,
-    0x2b082b190808192b, 0x2b082b2b082b082b, 0x2b082b2b2b080808, 0x2b082b2b2b082b08,
-    0x2b082b2b2b19192b, 0x2b082b2b2b2b2b08, 0x2b19080808080819, 0x2b19080808081908,
-    0x2b19080808190808, 0x2b19080819080808, 0x2b1908081919192b, 0x2b1908082b081908,
-    0x2b19081908080808, 0x2b190819082b082b, 0x2b190819192b1908, 0x2b19082b1919192b,
-    0x2b19082b2b082b19, 0x2b19190808080808, 0x2b19190808081919, 0x2b19190819081908,
-    0x2b19190819190808, 0x2b19190819192b08, 0x2b191919082b2b19, 0x2b1919192b190808,
-    0x2b1919192b19082b, 0x2b19192b19080819, 0x2b192b0819190819, 0x2b192b082b2b192b,
-    0x2b192b1919082b19, 0x2b192b2b08191919, 0x2b192b2b192b0808, 0x2b2b080808080808,
-    0x2b2b08080808082b, 0x2b2b080808082b08, 0x2b2b080808082b2b, 0x2b2b0808082b0808,
-    0x2b2b0808082b2b2b, 0x2b2b08082b2b0808, 0x2b2b081919190819, 0x2b2b081919192b19,
-    0x2b2b08192b2b192b, 0x2b2b082b08080808, 0x2b2b082b0808082b, 0x2b2b082b08082b08,
-    0x2b2b082b082b2b2b, 0x2b2b082b2b080808, 0x2b2b082b2b2b0808, 0x2b2b190819080808,
-    0x2b2b19082b191919, 0x2b2b192b192b1919, 0x2b2b192b2b192b08, 0x2b2b2b0808082b2b,
-    0x2b2b2b08082b0808, 0x2b2b2b08082b082b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b0808,
-    0x2b2b2b082b2b2b08, 0x2b2b2b1908081908, 0x2b2b2b192b081908, 0x2b2b2b192b08192b,
-    0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b,
-};
+static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_CUDA_MAX_DEVICES> & tensor_split) {
+    int64_t min_compute_capability = INT_MAX;
+    int64_t max_compute_capability = INT_MIN;
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
+        if (tensor_split[id] < (id + 1 < ggml_backend_cuda_get_device_count() ? tensor_split[id + 1] : 1.0f)) {
+            if (min_compute_capability > ggml_cuda_info().devices[id].cc) {
+                min_compute_capability = ggml_cuda_info().devices[id].cc;
+            }
+            if (max_compute_capability < ggml_cuda_info().devices[id].cc) {
+                max_compute_capability = ggml_cuda_info().devices[id].cc;
+            }
+        }
+    }
 
-static const __device__ uint32_t iq3xxs_grid[256] = {
-    0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414,
-    0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14,
-    0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404,
-    0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e,
-    0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c,
-    0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c,
-    0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34,
-    0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c,
-    0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c,
-    0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04,
-    0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c,
-    0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414,
-    0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434,
-    0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c,
-    0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e,
-    0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24,
-    0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24,
-    0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c,
-    0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c,
-    0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14,
-    0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414,
-    0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e,
-    0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404,
-    0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c,
-    0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c,
-    0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14,
-    0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c,
-    0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c,
-    0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14,
-    0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14,
-    0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c,
-    0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04,
-};
-
-static const __device__ uint64_t iq1s_grid[512] = {
-    0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000,
-    0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01,
-    0xffffff00ff000000, 0xffffff000000ff00, 0xffffff00000000ff, 0xffffff0000000100,
-    0xffffff0000010000, 0xffffff0001000000, 0xffffff01ffff00ff, 0xffffff01ff01ff00,
-    0xffffff01ff010100, 0xffffff0100000001, 0xffffff0101ffff00, 0xffffff0101ff0101,
-    0xffffff0101010100, 0xffff00ffff00ff01, 0xffff00ffff0000ff, 0xffff00ff00ff0100,
-    0xffff00ff0100ff00, 0xffff00ff010001ff, 0xffff0000ff0101ff, 0xffff000000ffff00,
-    0xffff000000000000, 0xffff00000001ff01, 0xffff000001000101, 0xffff0000010100ff,
-    0xffff0001ffff0100, 0xffff00010000ff00, 0xffff000100010101, 0xffff000101000000,
-    0xffff01ffffff0000, 0xffff01ffff01ffff, 0xffff01ffff010100, 0xffff01ff00000000,
-    0xffff01ff01ffffff, 0xffff01ff01ff0001, 0xffff01ff0101ffff, 0xffff01ff01010001,
-    0xffff0100ffffff01, 0xffff01000000ffff, 0xffff010000000100, 0xffff010001ff01ff,
-    0xffff010001000000, 0xffff0101ff000000, 0xffff0101000101ff, 0xffff010101ffff01,
-    0xffff01010101ff00, 0xff00ffffff000000, 0xff00ffff00ffff00, 0xff00ffff00000001,
-    0xff00ffff000001ff, 0xff00ffff01010000, 0xff00ff00ffff0000, 0xff00ff00ff00ff00,
-    0xff00ff00ff0000ff, 0xff00ff00ff000100, 0xff00ff00ff010001, 0xff00ff0000ff0001,
-    0xff00ff000000ffff, 0xff00ff0000000000, 0xff00ff000001ff00, 0xff00ff0000010100,
-    0xff00ff0001ff0000, 0xff00ff000100ff00, 0xff00ff0001000100, 0xff00ff01ff000000,
-    0xff00ff0100ff0000, 0xff00ff01000001ff, 0xff00ff0101010001, 0xff0000ff00000000,
-    0xff0000ff0001ff00, 0xff0000ff00010100, 0xff000000ffff0101, 0xff000000ff000000,
-    0xff000000ff01ff00, 0xff00000000ff0000, 0xff0000000000ff00, 0xff000000000000ff,
-    0xff00000000000000, 0xff00000000000001, 0xff00000000000100, 0xff0000000001ffff,
-    0xff00000000010000, 0xff00000001000000, 0xff00000001010100, 0xff000001ff00ff01,
-    0xff000001ff0100ff, 0xff00000100000000, 0xff0000010001ff00, 0xff00000101ff0100,
-    0xff0000010100ff00, 0xff0001ff00ff00ff, 0xff0001ff00000101, 0xff0001ff000100ff,
-    0xff0001ff01000000, 0xff000100ff0001ff, 0xff0001000000ff01, 0xff00010000000000,
-    0xff00010000010001, 0xff00010000010100, 0xff00010001ffff00, 0xff00010001ff0101,
-    0xff00010001010000, 0xff000101ffffffff, 0xff000101ff000101, 0xff00010101ff00ff,
-    0xff00010101000001, 0xff000101010100ff, 0xff01ffffff000101, 0xff01ffffff01ffff,
-    0xff01ffffff01ff01, 0xff01ffffff0101ff, 0xff01ffff00000000, 0xff01ffff01ff0001,
-    0xff01ffff0101ff01, 0xff01ff00ff000000, 0xff01ff0000ff0100, 0xff01ff000000ff01,
-    0xff01ff0000010000, 0xff01ff00010000ff, 0xff01ff01ff01ff00, 0xff01ff0100000101,
-    0xff0100ffffff0000, 0xff0100ffff010000, 0xff0100ff01ff00ff, 0xff0100ff01000100,
-    0xff0100ff010100ff, 0xff010000ffffff01, 0xff01000000000000, 0xff0100000101ff00,
-    0xff010001ffff00ff, 0xff010001ff000100, 0xff01000100ffff00, 0xff01000100010001,
-    0xff01000101ff0001, 0xff010001010001ff, 0xff0101ffffffffff, 0xff0101ffff01ffff,
-    0xff0101ffff010101, 0xff0101ff0000ff00, 0xff0101ff01010001, 0xff010100ff000000,
-    0xff010100ff01ff01, 0xff01010000ff0001, 0xff01010000000100, 0xff01010001000000,
-    0xff0101010100ffff, 0x00ffffff0000ff01, 0x00ffffff000000ff, 0x00ffffff00000100,
-    0x00ffffff00010000, 0x00ffff00ffff0001, 0x00ffff00ff0000ff, 0x00ffff00ff000100,
-    0x00ffff0000000000, 0x00ffff0001000100, 0x00ffff0001010001, 0x00ffff01ff00ff01,
-    0x00ffff0100ff0100, 0x00ffff010000ff00, 0x00ffff01000100ff, 0x00ffff0101ff00ff,
-    0x00ffff010101ff00, 0x00ff00ffffffffff, 0x00ff00ffffff01ff, 0x00ff00ffff000101,
-    0x00ff00ff00000000, 0x00ff00ff000101ff, 0x00ff00ff01010101, 0x00ff0000ff000000,
-    0x00ff0000ff01ffff, 0x00ff000000ff0000, 0x00ff00000000ff00, 0x00ff0000000000ff,
-    0x00ff000000000000, 0x00ff000000000001, 0x00ff000000000100, 0x00ff000000010000,
-    0x00ff000001ffff01, 0x00ff000001000000, 0x00ff0001ff000101, 0x00ff000100ffffff,
-    0x00ff000100000000, 0x00ff0001010001ff, 0x00ff01ffff000000, 0x00ff01ff0001ff00,
-    0x00ff01ff01ff0100, 0x00ff0100ff01ff01, 0x00ff010000ff00ff, 0x00ff010000ff0101,
-    0x00ff010000000000, 0x00ff010000010101, 0x00ff01000100ff00, 0x00ff010001010000,
-    0x00ff0101ffffff00, 0x00ff01010000ff01, 0x00ff010100000100, 0x00ff010101ff0000,
-    0x0000ffffffff0100, 0x0000ffffff00ff00, 0x0000ffffff0000ff, 0x0000ffffff010000,
-    0x0000ffff00000000, 0x0000ffff00010101, 0x0000ffff01ffff01, 0x0000ffff01000100,
-    0x0000ff00ff000000, 0x0000ff00ff01ff00, 0x0000ff00ff0101ff, 0x0000ff0000ff0000,
-    0x0000ff000000ff00, 0x0000ff00000000ff, 0x0000ff0000000000, 0x0000ff0000000001,
-    0x0000ff0000000100, 0x0000ff0000010000, 0x0000ff0001ffffff, 0x0000ff0001ff01ff,
-    0x0000ff0001000000, 0x0000ff000101ffff, 0x0000ff01ffff0101, 0x0000ff01ff010000,
-    0x0000ff0100000000, 0x0000ff0101000101, 0x000000ffffff0001, 0x000000ffff000000,
-    0x000000ff00ff0000, 0x000000ff0000ff00, 0x000000ff000000ff, 0x000000ff00000000,
-    0x000000ff00000001, 0x000000ff00000100, 0x000000ff00010000, 0x000000ff01000000,
-    0x000000ff0101ff00, 0x00000000ffff0000, 0x00000000ff00ff00, 0x00000000ff0000ff,
-    0x00000000ff000000, 0x00000000ff000001, 0x00000000ff000100, 0x00000000ff010000,
-    0x0000000000ffff00, 0x0000000000ff00ff, 0x0000000000ff0000, 0x0000000000ff0001,
-    0x0000000000ff0100, 0x000000000000ffff, 0x000000000000ff00, 0x000000000000ff01,
-    0x00000000000000ff, 0x0000000000000001, 0x00000000000001ff, 0x0000000000000100,
-    0x0000000000000101, 0x000000000001ff00, 0x00000000000100ff, 0x0000000000010000,
-    0x0000000000010001, 0x0000000000010100, 0x0000000001ff0000, 0x000000000100ff00,
-    0x00000000010000ff, 0x0000000001000000, 0x0000000001000001, 0x0000000001000100,
-    0x0000000001010000, 0x00000001ffff01ff, 0x00000001ff000000, 0x0000000100ff0000,
-    0x000000010000ff00, 0x00000001000000ff, 0x0000000100000000, 0x0000000100000001,
-    0x0000000100000100, 0x0000000100010000, 0x0000000101000000, 0x000001ffff00ff00,
-    0x000001ffff010001, 0x000001ffff0101ff, 0x000001ff00ffff01, 0x000001ff0000ffff,
-    0x000001ff00000000, 0x000001ff010000ff, 0x000001ff01010100, 0x00000100ffff0100,
-    0x00000100ff000000, 0x0000010000ff0000, 0x000001000000ff00, 0x00000100000000ff,
-    0x0000010000000000, 0x0000010000000001, 0x0000010000000100, 0x0000010000010000,
-    0x0000010001000000, 0x000001000101ff01, 0x00000101ffff0001, 0x00000101ff01ffff,
-    0x0000010100000000, 0x0000010101010100, 0x0001ffffff000000, 0x0001ffff00ffffff,
-    0x0001ffff00000100, 0x0001ffff0001ff00, 0x0001ffff01000000, 0x0001ff00ffffff00,
-    0x0001ff00ffff01ff, 0x0001ff00ff010000, 0x0001ff0000000000, 0x0001ff0000010001,
-    0x0001ff0001ff0000, 0x0001ff0001010100, 0x0001ff01ff0000ff, 0x0001ff01ff000001,
-    0x0001ff0100ffffff, 0x0001ff010001ffff, 0x0001ff01000101ff, 0x0001ff010100ff01,
-    0x000100ffff00ffff, 0x000100ffff00ff01, 0x000100ffff000100, 0x000100ff00000000,
-    0x000100ff000101ff, 0x000100ff01ff0101, 0x000100ff0100ffff, 0x000100ff01010101,
-    0x00010000ff000000, 0x00010000ff010100, 0x0001000000ff0000, 0x000100000000ff00,
-    0x00010000000000ff, 0x0001000000000000, 0x0001000000000001, 0x0001000000000100,
-    0x0001000000010000, 0x0001000001ffff01, 0x0001000001000000, 0x0001000100ff0101,
-    0x0001000100000000, 0x00010001010100ff, 0x000101ffffff01ff, 0x000101ffffff0101,
-    0x000101ff00010000, 0x000101ff01ff0000, 0x000101ff0100ff01, 0x00010100ffff0000,
-    0x0001010000000000, 0x000101000001ffff, 0x0001010000010101, 0x00010100010001ff,
-    0x00010101ff00ff00, 0x00010101ff010001, 0x0001010100ffffff, 0x0001010100ff01ff,
-    0x00010101000101ff, 0x0001010101ff0000, 0x000101010100ff01, 0x0001010101000101,
-    0x01ffffffffff0101, 0x01ffffffff01ffff, 0x01ffffffff01ff01, 0x01ffffffff0101ff,
-    0x01ffffffff010101, 0x01ffffff00000000, 0x01ffffff01ff01ff, 0x01ffffff01000101,
-    0x01ffffff0101ff01, 0x01ffffff010100ff, 0x01ffff000000ff00, 0x01ffff0000000001,
-    0x01ffff00000001ff, 0x01ffff0000010000, 0x01ffff0001ff0000, 0x01ffff01ffffffff,
-    0x01ffff01ffff01ff, 0x01ffff01ff000000, 0x01ffff01ff01ffff, 0x01ffff01ff0101ff,
-    0x01ffff010100ffff, 0x01ff00ffffff0000, 0x01ff00ffff010000, 0x01ff00ff00ffff01,
-    0x01ff0000ff0000ff, 0x01ff000000000000, 0x01ff00000001ff01, 0x01ff000001ffffff,
-    0x01ff000001010100, 0x01ff0001ffffff01, 0x01ff0001ff010001, 0x01ff000101ff0100,
-    0x01ff000101000001, 0x01ff0001010100ff, 0x01ff01ffff00ffff, 0x01ff01ff00010001,
-    0x01ff01ff01000000, 0x01ff01ff010101ff, 0x01ff0100ff000001, 0x01ff010000ffff00,
-    0x01ff010000000100, 0x01ff010001ff01ff, 0x01ff01000101ffff, 0x01ff0101ffff00ff,
-    0x01ff0101ffff0101, 0x01ff0101ff0101ff, 0x01ff010100010000, 0x0100ffff00ff00ff,
-    0x0100ffff00ff0001, 0x0100ffff00000100, 0x0100ffff0100ff00, 0x0100ff00ffff0000,
-    0x0100ff00ff00ffff, 0x0100ff00ff00ff01, 0x0100ff00ff000100, 0x0100ff00ff010000,
-    0x0100ff0000000000, 0x0100ff00000100ff, 0x0100ff0001ff0101, 0x0100ff0001010101,
-    0x0100ff0100ff00ff, 0x0100ff0100ff0001, 0x0100ff0100000100, 0x0100ff0100010001,
-    0x0100ff0101000000, 0x010000ffff00ff00, 0x010000ff0000ffff, 0x010000ff00000000,
-    0x010000ff010001ff, 0x010000ff01010001, 0x01000000ffffff00, 0x01000000ffff0101,
-    0x01000000ff000000, 0x01000000ff0100ff, 0x01000000ff010101, 0x0100000000ff0000,
-    0x010000000000ff00, 0x01000000000000ff, 0x0100000000000000, 0x0100000000000001,
-    0x0100000000000100, 0x0100000000010000, 0x0100000001000000, 0x0100000100000000,
-    0x01000001000101ff, 0x0100000101ffff01, 0x010001ffff000101, 0x010001ff00ff0100,
-    0x010001ff0000ff00, 0x010001ff000100ff, 0x010001ff01ffffff, 0x01000100ffff0000,
-    0x01000100ff0001ff, 0x0100010000000000, 0x010001000001ff00, 0x0100010001ff0000,
-    0x01000100010000ff, 0x0100010001000101, 0x01000101ff00ff01, 0x0100010100ff0100,
-    0x010001010000ffff, 0x0100010101010001, 0x0101ffffffff0101, 0x0101ffffff0001ff,
-    0x0101ffffff01ffff, 0x0101ffffff010101, 0x0101ffff00000000, 0x0101ffff0101ffff,
-    0x0101ffff010101ff, 0x0101ff00ff000000, 0x0101ff0000ff0100, 0x0101ff000000ff00,
-    0x0101ff0000010000, 0x0101ff00010000ff, 0x0101ff0001000001, 0x0101ff01ff010101,
-    0x0101ff0100000000, 0x0101ff010101ff00, 0x010100ffffff0000, 0x010100ffff010000,
-    0x010100ff00ff01ff, 0x010100ff000000ff, 0x010100ff00000101, 0x010100ff01ffff00,
-    0x01010000ffffff01, 0x01010000ff000100, 0x01010000ff01ff01, 0x0101000000000000,
-    0x01010000000100ff, 0x010100000101ff01, 0x01010001ffff0000, 0x01010001ff00ffff,
-    0x01010001ff010000, 0x0101000101ffffff, 0x0101000101ff01ff, 0x0101000101010101,
-    0x010101ffff01ffff, 0x010101ff00000000, 0x010101ff0001ff01, 0x010101ff0101ffff,
-    0x010101ff010101ff, 0x01010100ffffffff, 0x01010100ff000001, 0x010101000000ff00,
-    0x0101010001010000, 0x0101010100ff0001, 0x010101010001ff01, 0x010101010101ffff,
-};
-
-static const __device__ uint8_t ksigns_iq2xs[128] = {
-      0, 129, 130,   3, 132,   5,   6, 135, 136,   9,  10, 139,  12, 141, 142,  15,
-    144,  17,  18, 147,  20, 149, 150,  23,  24, 153, 154,  27, 156,  29,  30, 159,
-    160,  33,  34, 163,  36, 165, 166,  39,  40, 169, 170,  43, 172,  45,  46, 175,
-     48, 177, 178,  51, 180,  53,  54, 183, 184,  57,  58, 187,  60, 189, 190,  63,
-    192,  65,  66, 195,  68, 197, 198,  71,  72, 201, 202,  75, 204,  77,  78, 207,
-     80, 209, 210,  83, 212,  85,  86, 215, 216,  89,  90, 219,  92, 221, 222,  95,
-     96, 225, 226,  99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111,
-    240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255,
-};
-
-//#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-static const __device__ uint64_t ksigns64[128] = {
-    0x0000000000000000, 0xff000000000000ff, 0xff0000000000ff00, 0x000000000000ffff,
-    0xff00000000ff0000, 0x0000000000ff00ff, 0x0000000000ffff00, 0xff00000000ffffff,
-    0xff000000ff000000, 0x00000000ff0000ff, 0x00000000ff00ff00, 0xff000000ff00ffff,
-    0x00000000ffff0000, 0xff000000ffff00ff, 0xff000000ffffff00, 0x00000000ffffffff,
-    0xff0000ff00000000, 0x000000ff000000ff, 0x000000ff0000ff00, 0xff0000ff0000ffff,
-    0x000000ff00ff0000, 0xff0000ff00ff00ff, 0xff0000ff00ffff00, 0x000000ff00ffffff,
-    0x000000ffff000000, 0xff0000ffff0000ff, 0xff0000ffff00ff00, 0x000000ffff00ffff,
-    0xff0000ffffff0000, 0x000000ffffff00ff, 0x000000ffffffff00, 0xff0000ffffffffff,
-    0xff00ff0000000000, 0x0000ff00000000ff, 0x0000ff000000ff00, 0xff00ff000000ffff,
-    0x0000ff0000ff0000, 0xff00ff0000ff00ff, 0xff00ff0000ffff00, 0x0000ff0000ffffff,
-    0x0000ff00ff000000, 0xff00ff00ff0000ff, 0xff00ff00ff00ff00, 0x0000ff00ff00ffff,
-    0xff00ff00ffff0000, 0x0000ff00ffff00ff, 0x0000ff00ffffff00, 0xff00ff00ffffffff,
-    0x0000ffff00000000, 0xff00ffff000000ff, 0xff00ffff0000ff00, 0x0000ffff0000ffff,
-    0xff00ffff00ff0000, 0x0000ffff00ff00ff, 0x0000ffff00ffff00, 0xff00ffff00ffffff,
-    0xff00ffffff000000, 0x0000ffffff0000ff, 0x0000ffffff00ff00, 0xff00ffffff00ffff,
-    0x0000ffffffff0000, 0xff00ffffffff00ff, 0xff00ffffffffff00, 0x0000ffffffffffff,
-    0xffff000000000000, 0x00ff0000000000ff, 0x00ff00000000ff00, 0xffff00000000ffff,
-    0x00ff000000ff0000, 0xffff000000ff00ff, 0xffff000000ffff00, 0x00ff000000ffffff,
-    0x00ff0000ff000000, 0xffff0000ff0000ff, 0xffff0000ff00ff00, 0x00ff0000ff00ffff,
-    0xffff0000ffff0000, 0x00ff0000ffff00ff, 0x00ff0000ffffff00, 0xffff0000ffffffff,
-    0x00ff00ff00000000, 0xffff00ff000000ff, 0xffff00ff0000ff00, 0x00ff00ff0000ffff,
-    0xffff00ff00ff0000, 0x00ff00ff00ff00ff, 0x00ff00ff00ffff00, 0xffff00ff00ffffff,
-    0xffff00ffff000000, 0x00ff00ffff0000ff, 0x00ff00ffff00ff00, 0xffff00ffff00ffff,
-    0x00ff00ffffff0000, 0xffff00ffffff00ff, 0xffff00ffffffff00, 0x00ff00ffffffffff,
-    0x00ffff0000000000, 0xffffff00000000ff, 0xffffff000000ff00, 0x00ffff000000ffff,
-    0xffffff0000ff0000, 0x00ffff0000ff00ff, 0x00ffff0000ffff00, 0xffffff0000ffffff,
-    0xffffff00ff000000, 0x00ffff00ff0000ff, 0x00ffff00ff00ff00, 0xffffff00ff00ffff,
-    0x00ffff00ffff0000, 0xffffff00ffff00ff, 0xffffff00ffffff00, 0x00ffff00ffffffff,
-    0xffffffff00000000, 0x00ffffff000000ff, 0x00ffffff0000ff00, 0xffffffff0000ffff,
-    0x00ffffff00ff0000, 0xffffffff00ff00ff, 0xffffffff00ffff00, 0x00ffffff00ffffff,
-    0x00ffffffff000000, 0xffffffffff0000ff, 0xffffffffff00ff00, 0x00ffffffff00ffff,
-    0xffffffffffff0000, 0x00ffffffffff00ff, 0x00ffffffffffff00, 0xffffffffffffffff,
-};
-//#endif
-
-static const __device__ uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128};
-
-inline bool ggml_cuda_supports_mmq(enum ggml_type type) {
-    switch (type) {
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+    switch(type) {
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
+            return max_compute_capability >= CC_RDNA2 ? 128 : 64;
+        case GGML_TYPE_F16:
+        case GGML_TYPE_F32:
+            return 1;
+        case GGML_TYPE_Q2_K:
+            return max_compute_capability >= CC_RDNA2 ? 128 : 32;
+        case GGML_TYPE_Q3_K:
+            return min_compute_capability < CC_RDNA2 ? 128 : 64;
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ3_S:
+            return max_compute_capability >= CC_RDNA2 ? 128 : 64;
+        default:
+            GGML_ASSERT(false);
+    }
+#else
+    switch(type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+            return max_compute_capability >= CC_VOLTA ? 128 : 64;
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+            return 64;
+        case GGML_TYPE_F16:
+        case GGML_TYPE_F32:
+            return 1;
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q5_K:
+        case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ3_S:
+            return max_compute_capability >= CC_VOLTA ? 128 : 64;
         case GGML_TYPE_Q6_K:
-            return true;
+            return 64;
         default:
-            return false;
+            GGML_ASSERT(false);
     }
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
 }
 
-template<typename dst_t>
-static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array<float, GGML_CUDA_MAX_DEVICES> & tensor_split, int id) {
+    const int64_t nrows = ggml_nrows(tensor);
+    const int64_t rounding = get_row_rounding(tensor->type, tensor_split);
 
-    const int i   = blockIdx.x;
-    const block_iq2_xxs * x = (const block_iq2_xxs  *) vx;
+    *row_low = id == 0 ? 0 : nrows*tensor_split[id];
+    *row_low -= *row_low % rounding;
 
-    const int tid = threadIdx.x;
-#if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
-    const uint16_t * q2 = x[i].qs + 4*ib;
-    const uint8_t  * aux8 = (const uint8_t *)q2;
-    const uint8_t  * grid = (const uint8_t *)(iq2xxs_grid + aux8[il]);
-    const uint32_t aux32 = q2[2] | (q2[3] << 16);
-    const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f;
-    const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
-    for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
-#else
-    assert(false);
-#endif
-
-}
-
-template<typename dst_t>
-static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
-
-    const int i   = blockIdx.x;
-    const block_iq2_xs * x = (const block_iq2_xs *) vx;
-
-    const int tid = threadIdx.x;
-#if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
-    const uint16_t * q2 = x[i].qs + 4*ib;
-    const uint8_t  * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511));
-    const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
-    const uint8_t signs = ksigns_iq2xs[q2[il] >> 9];
-    for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
-#else
-    assert(false);
-#endif
-
-}
-
-template<typename dst_t>
-static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
-
-    const int i   = blockIdx.x;
-    const block_iq3_xxs * x = (const block_iq3_xxs  *) vx;
-
-    const int tid = threadIdx.x;
-#if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
-    const uint8_t  * q3 = x[i].qs + 8*ib;
-    const uint16_t * gas = (const uint16_t *)(x[i].qs + QK_K/4) + 2*ib;
-    const uint8_t  * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*il+0]);
-    const uint8_t  * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*il+1]);
-    const uint32_t aux32 = gas[0] | (gas[1] << 16);
-    const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.5f;
-    const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
-    for (int j = 0; j < 4; ++j) {
-        y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
-        y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
-    }
-#else
-    assert(false);
-#endif
-
-}
-
-template<typename dst_t>
-static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {
-
-    const int i   = blockIdx.x;
-    const block_iq1_s * x = (const block_iq1_s  *) vx;
-
-    const int tid = threadIdx.x;
-#if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
-    const int i8 = 4*ib+il;
-    uint8_t h = x[i].scales[i8/2] >> 4*(i8%2);
-    const int8_t * grid = (const int8_t *)(iq1s_grid + (x[i].qs[i8] | ((h & 8) << 5)));
-    const float d = (float)x[i].d * (2*(h & 7) + 1);
-    for (int j = 0; j < 8; ++j) y[j] = d * grid[j];
-#else
-    assert(false);
-#endif
-
-}
-
-
-static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
-
-    static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
-
-    const int row = blockIdx.x*blockDim.y + threadIdx.y;
-    if (row > nrows) return;
-
-    const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row;
-
-    const block_q2_K * x = (const block_q2_K *)vx + ib0;
-
-    float tmp = 0; // partial sum for thread in warp
-
-#if QK_K == 256
-    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...15
-    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0,1
-
-    const int step = 16/K_QUANTS_PER_ITERATION;
-
-    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
-    const int in = tid - step*im;                        // 0...15 or 0...7
-
-    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15 or 0...14 in steps of 2
-    const int q_offset = 32*im + l0;
-    const int s_offset = 8*im;
-    const int y_offset = 128*im + l0;
-
-    uint32_t aux[4];
-    const uint8_t * d = (const uint8_t *)aux;
-    const uint8_t * m = (const uint8_t *)(aux + 2);
-
-    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-
-        const float   * y = yy + i * QK_K + y_offset;
-        const uint8_t * q = x[i].qs + q_offset;
-
-        const float dall = __low2half(x[i].dm);
-        const float dmin = __high2half(x[i].dm);
-
-        const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset);
-        aux[0] = a[0] & 0x0f0f0f0f;
-        aux[1] = a[1] & 0x0f0f0f0f;
-        aux[2] = (a[0] >> 4) & 0x0f0f0f0f;
-        aux[3] = (a[1] >> 4) & 0x0f0f0f0f;
-
-        float sum1 = 0, sum2 = 0;
-        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
-            sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3)
-                  + y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3)
-                  + y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3)
-                  + y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3)
-                  + y[l+16] * d[1] * ((q[l+16] >> 0) & 3)
-                  + y[l+48] * d[3] * ((q[l+16] >> 2) & 3)
-                  + y[l+80] * d[5] * ((q[l+16] >> 4) & 3)
-                  +y[l+112] * d[7] * ((q[l+16] >> 6) & 3);
-            sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6]
-                  + y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7];
-
-        }
-        tmp += dall * sum1 - dmin * sum2;
-
-    }
-#else
-    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15 or 0...7
-    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0....1 or 0...3
-    const int offset = tid * K_QUANTS_PER_ITERATION;
-
-    uint32_t uaux[2];
-    const uint8_t * d = (const uint8_t *)uaux;
-
-    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
-
-        const float   * y = yy + i * QK_K + offset;
-        const uint8_t * q = x[i].qs + offset;
-        const uint32_t * s = (const uint32_t *)x[i].scales;
-
-        uaux[0] = s[0] & 0x0f0f0f0f;
-        uaux[1] = (s[0] >> 4) & 0x0f0f0f0f;
-
-        const float2 dall = __half22float2(x[i].dm);
-
-        float sum1 = 0, sum2 = 0;
-        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
-            const uint8_t ql = q[l];
-            sum1 += y[l+ 0] * d[0] * ((ql >> 0) & 3)
-                  + y[l+16] * d[1] * ((ql >> 2) & 3)
-                  + y[l+32] * d[2] * ((ql >> 4) & 3)
-                  + y[l+48] * d[3] * ((ql >> 6) & 3);
-            sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7];
-        }
-        tmp += dall.x * sum1 - dall.y * sum2;
-    }
-#endif
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
-    }
-
-    if (threadIdx.x == 0) {
-        dst[row] = tmp;
-    }
-}
-
-static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
-
-    const int row = blockIdx.x*blockDim.y + threadIdx.y;
-    if (row > nrows) return;
-
-    const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row;
-
-    const block_q3_K * x = (const block_q3_K *)vx + ib0;
-
-    float tmp = 0; // partial sum for thread in warp
-
-#if QK_K == 256
-
-    const uint16_t kmask1 = 0x0303;
-    const uint16_t kmask2 = 0x0f0f;
-
-    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
-    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0,1
-
-    const int n  = K_QUANTS_PER_ITERATION;               // iterations in the inner loop
-    const int step = 16/K_QUANTS_PER_ITERATION;
-    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
-    const int in = tid - step*im;                        // 0....15 or 0...7
-
-    const uint8_t m = 1 << (4*im);
-
-    const int l0 = n*in;                                 // 0...15 or 0...14 in steps of 2
-    const int q_offset =  32*im + l0;
-    const int y_offset = 128*im + l0;
-
-    uint16_t utmp[4];
-    const int8_t * s = (const int8_t *)utmp;
-
-    const uint16_t s_shift = 4*im;
-
-    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-
-        const float   * y  = yy + i * QK_K + y_offset;
-        const uint8_t * q = x[i].qs + q_offset;
-        const uint8_t * h = x[i].hmask + l0;
-
-        const uint16_t * a = (const uint16_t *)x[i].scales;
-        utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4);
-        utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4);
-        utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4);
-        utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4);
-
-        const float d = x[i].d;
-
-        float sum = 0;
-        for (int l = 0; l < n; ++l) {
-            sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4))
-                 + y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4))
-                 + y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4))
-                 + y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4));
-            sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4))
-                 + y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4))
-                 + y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4))
-                + y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4));
-        }
-        tmp += d * sum;
-
-    }
-#else
-
-    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15 or 0...7
-    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0....1 or 0...3
-    const int offset = tid * K_QUANTS_PER_ITERATION;         // 0...15 or 0...14
-    const int in = offset/8;                                 // 0 or 1
-    const int im = offset%8;                                 // 0...7
-
-    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
-
-        const float   * y = yy + i * QK_K + offset;
-        const uint8_t * q = x[i].qs + offset;
-        const uint8_t * s = x[i].scales;
-
-        const float dall = (float)x[i].d;
-
-        float sum = 0;
-        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
-            const uint8_t hl = x[i].hmask[im+l] >> in;
-            const uint8_t ql = q[l];
-            sum += y[l+ 0] * dall * ((s[0] & 0xF) - 8) * ((int8_t)((ql >> 0) & 3) - ((hl >> 0) & 1 ? 0 : 4))
-                 + y[l+16] * dall * ((s[0] >>  4) - 8) * ((int8_t)((ql >> 2) & 3) - ((hl >> 2) & 1 ? 0 : 4))
-                 + y[l+32] * dall * ((s[1] & 0xF) - 8) * ((int8_t)((ql >> 4) & 3) - ((hl >> 4) & 1 ? 0 : 4))
-                 + y[l+48] * dall * ((s[1] >>  4) - 8) * ((int8_t)((ql >> 6) & 3) - ((hl >> 6) & 1 ? 0 : 4));
-        }
-        tmp += sum;
-    }
-#endif
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
-    }
-
-    if (threadIdx.x == 0) {
-        dst[row] = tmp;
-    }
-}
-
-static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
-
-    const int row = blockIdx.x*blockDim.y + threadIdx.y;
-    if (row > nrows) return;
-    const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row;
-
-    const block_q4_K * x = (const block_q4_K *)vx + ib0;
-
-#if QK_K == 256
-    const uint16_t kmask1 = 0x3f3f;
-    const uint16_t kmask2 = 0x0f0f;
-    const uint16_t kmask3 = 0xc0c0;
-
-    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
-    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0,1
-
-    const int step = 8/K_QUANTS_PER_ITERATION;           // 8 or 4
-
-    const int il  = tid/step;                            // 0...3
-    const int ir  = tid - step*il;                       // 0...7 or 0...3
-    const int n   = 2 * K_QUANTS_PER_ITERATION;          // 2 or 4
-
-    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
-    const int in = il%2;
-
-    const int l0 = n*(2*ir + in);
-    const int q_offset = 32*im + l0;
-    const int y_offset = 64*im + l0;
-
-    uint16_t aux[4];
-    const uint8_t * sc = (const uint8_t *)aux;
-
-#if K_QUANTS_PER_ITERATION == 2
-    uint32_t q32[4];
-    const uint8_t * q4 = (const uint8_t *)q32;
-#else
-    uint16_t q16[4];
-    const uint8_t * q4 = (const uint8_t *)q16;
-#endif
-
-    float tmp = 0; // partial sum for thread in warp
-
-    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-
-        const float   * y1 = yy + i*QK_K + y_offset;
-        const float   * y2 = y1 + 128;
-
-        const float dall = __low2half(x[i].dm);
-        const float dmin = __high2half(x[i].dm);
-
-        const uint16_t * a = (const uint16_t *)x[i].scales;
-        aux[0] = a[im+0] & kmask1;
-        aux[1] = a[im+2] & kmask1;
-        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
-        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
-
-#if K_QUANTS_PER_ITERATION == 2
-        const uint32_t * q1 = (const uint32_t *)(x[i].qs + q_offset);
-        const uint32_t * q2 = q1 + 16;
-
-        q32[0] = q1[0] & 0x0f0f0f0f;
-        q32[1] = q1[0] & 0xf0f0f0f0;
-        q32[2] = q2[0] & 0x0f0f0f0f;
-        q32[3] = q2[0] & 0xf0f0f0f0;
-
-        float4 s = {0.f, 0.f, 0.f, 0.f};
-        float smin = 0;
-        for (int l = 0; l < 4; ++l) {
-            s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+ 4];
-            s.z += y2[l] * q4[l+8]; s.w += y2[l+32] * q4[l+12];
-            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
-        }
-        tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin;
-#else
-        const uint16_t * q1 = (const uint16_t *)(x[i].qs + q_offset);
-        const uint16_t * q2 = q1 + 32;
-
-        q16[0] = q1[0] & 0x0f0f;
-        q16[1] = q1[0] & 0xf0f0;
-        q16[2] = q2[0] & 0x0f0f;
-        q16[3] = q2[0] & 0xf0f0;
-
-        float4 s = {0.f, 0.f, 0.f, 0.f};
-        float smin = 0;
-        for (int l = 0; l < 2; ++l) {
-            s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+2];
-            s.z += y2[l] * q4[l+4]; s.w += y2[l+32] * q4[l+6];
-            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
-        }
-        tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin;
-#endif
-
-    }
-#else
-    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15
-    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);
-
-    const int step = tid * K_QUANTS_PER_ITERATION;
-
-    uint16_t aux16[2];
-    const uint8_t * s = (const uint8_t *)aux16;
-
-    float tmp = 0;
-
-    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
-        const uint8_t * q = x[i].qs + step;
-        const float   * y = yy + i*QK_K + step;
-        const uint16_t * a = (const uint16_t *)x[i].scales;
-        aux16[0] = a[0] & 0x0f0f;
-        aux16[1] = (a[0] >> 4) & 0x0f0f;
-        const float d = (float)x[i].dm[0];
-        const float m = (float)x[i].dm[1];
-        float sum = 0.f;
-        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
-            sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2])
-                 + y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2])
-                 + y[j+32] * (d * s[1] * (q[j+ 0] >>  4) - m * s[3])
-                 + y[j+48] * (d * s[1] * (q[j+16] >>  4) - m * s[3]);
-        }
-        tmp += sum;
-    }
-
-#endif
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
-    }
-
-    if (tid == 0) {
-        dst[row] = tmp;
-    }
-}
-
-static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols) {
-
-    const int row = blockIdx.x;
-    const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row;
-
-    const block_q5_K * x = (const block_q5_K *)vx + ib0;
-
-    float tmp = 0; // partial sum for thread in warp
-
-#if QK_K == 256
-    const uint16_t kmask1 = 0x3f3f;
-    const uint16_t kmask2 = 0x0f0f;
-    const uint16_t kmask3 = 0xc0c0;
-
-    const int tid = threadIdx.x/2;  // 0...15
-    const int ix  = threadIdx.x%2;
-
-    const int il  = tid/4;     // 0...3
-    const int ir  = tid - 4*il;// 0...3
-    const int n   = 2;
-
-    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
-    const int in = il%2;
-
-    const int l0 = n*(2*ir + in);
-    const int q_offset = 32*im + l0;
-    const int y_offset = 64*im + l0;
-
-    const uint8_t hm1  = 1 << (2*im);
-    const uint8_t hm2  = hm1 << 4;
-
-    uint16_t aux[4];
-    const uint8_t * sc = (const uint8_t *)aux;
-
-    uint16_t q16[8];
-    const uint8_t * q4 = (const uint8_t *)q16;
-
-    for (int i = ix; i < num_blocks_per_row; i += 2) {
-
-        const uint8_t * ql1 = x[i].qs + q_offset;
-        const uint8_t * qh  = x[i].qh + l0;
-        const float   * y1  = yy + i*QK_K + y_offset;
-        const float   * y2  = y1 + 128;
-
-        const float dall = __low2half(x[i].dm);
-        const float dmin = __high2half(x[i].dm);
-
-        const uint16_t * a = (const uint16_t *)x[i].scales;
-        aux[0] = a[im+0] & kmask1;
-        aux[1] = a[im+2] & kmask1;
-        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
-        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
-
-        float4 sum = {0.f, 0.f, 0.f, 0.f};
-        float smin = 0;
-        const uint16_t * q1 = (const uint16_t *)ql1;
-        const uint16_t * q2 = q1 + 32;
-        q16[0] = q1[0] & 0x0f0f;
-        q16[1] = q1[8] & 0x0f0f;
-        q16[2] = (q1[0] >> 4) & 0x0f0f;
-        q16[3] = (q1[8] >> 4) & 0x0f0f;
-        q16[4] = q2[0] & 0x0f0f;
-        q16[5] = q2[8] & 0x0f0f;
-        q16[6] = (q2[0] >> 4) & 0x0f0f;
-        q16[7] = (q2[8] >> 4) & 0x0f0f;
-        for (int l = 0; l < n; ++l) {
-            sum.x += y1[l+ 0] * (q4[l +0] + (qh[l+ 0] & (hm1 << 0) ? 16 : 0))
-                   + y1[l+16] * (q4[l +2] + (qh[l+16] & (hm1 << 0) ? 16 : 0));
-            sum.y += y1[l+32] * (q4[l +4] + (qh[l+ 0] & (hm1 << 1) ? 16 : 0))
-                   + y1[l+48] * (q4[l +6] + (qh[l+16] & (hm1 << 1) ? 16 : 0));
-            sum.z += y2[l+ 0] * (q4[l +8] + (qh[l+ 0] & (hm2 << 0) ? 16 : 0))
-                   + y2[l+16] * (q4[l+10] + (qh[l+16] & (hm2 << 0) ? 16 : 0));
-            sum.w += y2[l+32] * (q4[l+12] + (qh[l+ 0] & (hm2 << 1) ? 16 : 0))
-                   + y2[l+48] * (q4[l+14] + (qh[l+16] & (hm2 << 1) ? 16 : 0));
-            smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3]
-                  + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7];
-        }
-        tmp += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin;
-    }
-
-#else
-    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15
-    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);
-    const int step = tid * K_QUANTS_PER_ITERATION;
-    const int im = step/8;
-    const int in = step%8;
-
-    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
-        const uint8_t * q = x[i].qs + step;
-        const int8_t  * s = x[i].scales;
-        const float   * y = yy + i*QK_K + step;
-        const float     d = x[i].d;
-        float sum = 0.f;
-        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
-            const uint8_t h = x[i].qh[in+j] >> im;
-            sum += y[j+ 0] * d * s[0] * ((q[j+ 0] & 0xF) - ((h >> 0) & 1 ? 0 : 16))
-                 + y[j+16] * d * s[1] * ((q[j+16] & 0xF) - ((h >> 2) & 1 ? 0 : 16))
-                 + y[j+32] * d * s[2] * ((q[j+ 0] >>  4) - ((h >> 4) & 1 ? 0 : 16))
-                 + y[j+48] * d * s[3] * ((q[j+16] >>  4) - ((h >> 6) & 1 ? 0 : 16));
-        }
-        tmp += sum;
-    }
-#endif
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
-    }
-
-    if (threadIdx.x == 0) {
-        dst[row] = tmp;
-    }
-}
-
-static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
-
-    static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
-
-    const int row = blockIdx.x*blockDim.y + threadIdx.y;
-    if (row > nrows) return;
-
-    const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row;
-
-    const block_q6_K * x = (const block_q6_K *)vx + ib0;
-
-#if QK_K == 256
-
-    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
-    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
-
-    const int step = 16/K_QUANTS_PER_ITERATION;          // 16 or 8
-
-    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
-    const int in = tid - step*im;                        // 0...15 or 0...7
-
-#if K_QUANTS_PER_ITERATION == 1
-    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15
-    const int is = 0;
-#else
-    const int l0 = 4 * in;                               // 0, 4, 8, ..., 28
-    const int is = in / 4;
-#endif
-    const int ql_offset = 64*im + l0;
-    const int qh_offset = 32*im + l0;
-    const int s_offset  =  8*im + is;
-    const int y_offset = 128*im + l0;
-
-    float tmp = 0; // partial sum for thread in warp
-
-    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-
-        const float   * y  = yy + i * QK_K + y_offset;
-        const uint8_t * ql = x[i].ql + ql_offset;
-        const uint8_t * qh = x[i].qh + qh_offset;
-        const int8_t  * s  = x[i].scales + s_offset;
-
-        const float d = x[i].d;
-
-#if K_QUANTS_PER_ITERATION == 1
-        float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
-                  + y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
-                  + y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
-                  + y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
-                  + y[64] * s[4] * d * ((int8_t)((ql[ 0]  >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
-                  + y[80] * s[5] * d * ((int8_t)((ql[16]  >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
-                  + y[96] * s[6] * d * ((int8_t)((ql[32]  >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
-                  +y[112] * s[7] * d * ((int8_t)((ql[48]  >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
-        tmp += sum;
-#else
-        float sum = 0;
-        for (int l = 0; l < 4; ++l) {
-            sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
-                 + y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
-                 + y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0]  >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
-                 + y[l+96] * s[6] * d * ((int8_t)((ql[l+32]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
-        }
-        tmp += sum;
-#endif
-
-    }
-
-#else
-
-    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...7
-    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0...3
-
-    const int step = tid * K_QUANTS_PER_ITERATION;
-
-    float tmp = 0; // partial sum for thread in warp
-
-    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
-
-        const float   * y  = yy + i * QK_K + step;
-        const uint8_t * ql = x[i].ql + step;
-        const uint8_t * qh = x[i].qh + step;
-        const int8_t  * s  = x[i].scales;
-
-        const float d = x[i+0].d;
-
-        float sum = 0;
-        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
-            sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32)
-                 + y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32)
-                 + y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >>  4) | ((qh[j] & 0x30) >> 0)) - 32)
-                 + y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >>  4) | ((qh[j] & 0xc0) >> 2)) - 32);
-        }
-        tmp += sum;
-
-    }
-
-#endif
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
-    }
-
-    if (tid == 0) {
-        dst[row] = tmp;
-    }
-}
-
-static __device__ void convert_f16(const void * vx, const int ib, const int iqs, dfloat2 & v){
-    const half * x = (const half *) vx;
-
-    // automatic half -> float type cast if dfloat == float
-    v.x = x[ib + iqs + 0];
-    v.y = x[ib + iqs + 1];
-}
-
-static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded) {
-    const int ix = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (ix >= kx_padded) {
-        return;
-    }
-
-    const int iy = blockDim.y*blockIdx.y + threadIdx.y;
-
-    const int i_padded = iy*kx_padded + ix;
-
-    block_q8_1 * y = (block_q8_1 *) vy;
-
-    const int ib = i_padded / QK8_1; // block index
-    const int iqs = i_padded % QK8_1; // quant index
-
-    const float xi = ix < kx ? x[iy*kx + ix] : 0.0f;
-    float amax = fabsf(xi);
-    float sum = xi;
-
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        amax = fmaxf(amax, __shfl_xor_sync(0xffffffff, amax, mask, 32));
-        sum += __shfl_xor_sync(0xffffffff, sum, mask, 32);
-    }
-
-    const float d = amax / 127;
-    const int8_t q = amax == 0.0f ? 0 : roundf(xi / d);
-
-    y[ib].qs[iqs] = q;
-
-    if (iqs > 0) {
-        return;
-    }
-
-    reinterpret_cast<half&>(y[ib].ds.x) = d;
-    reinterpret_cast<half&>(y[ib].ds.y) = sum;
-}
-
-template<int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
-static __global__ void k_get_rows(
-            const void * src0, const int32_t * src1, dst_t * dst,
-            int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
-            /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
-            /*size_t s0,*/ size_t s1, size_t s2, size_t s3,
-            /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
-            size_t s10, size_t s11, size_t s12/*, size_t s13*/) {
-
-    const int i00 = (blockIdx.x*blockDim.x + threadIdx.x)*2;
-    const int i10 = blockDim.y*blockIdx.y + threadIdx.y;
-    const int i11 = (blockIdx.z*blockDim.z + threadIdx.z)/ne12;
-    const int i12 = (blockIdx.z*blockDim.z + threadIdx.z)%ne12;
-
-    if (i00 >= ne00) {
-        return;
-    }
-
-    const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
-
-    dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
-    const void * src0_row = (const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03;
-
-    const int ib = i00/qk; // block index
-    const int iqs = (i00%qk)/qr; // quant index
-    const int iybs = i00 - i00%qk; // dst block start index
-    const int y_offset = qr == 1 ? 1 : qk/2;
-
-    // dequantize
-    dfloat2 v;
-    dequantize_kernel(src0_row, ib, iqs, v);
-
-    dst_row[iybs + iqs + 0]        = v.x;
-    dst_row[iybs + iqs + y_offset] = v.y;
-}
-
-template<typename src0_t, typename dst_t>
-static __global__ void k_get_rows_float(
-            const src0_t * src0, const int32_t * src1, dst_t * dst,
-            int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
-            /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
-            /*size_t s0,*/ size_t s1, size_t s2, size_t s3,
-            /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
-            size_t s10, size_t s11, size_t s12/*, size_t s13*/) {
-
-    const int i00 = blockIdx.x*blockDim.x + threadIdx.x;
-    const int i10 = blockDim.y*blockIdx.y + threadIdx.y;
-    const int i11 = (blockIdx.z*blockDim.z + threadIdx.z)/ne12;
-    const int i12 = (blockIdx.z*blockDim.z + threadIdx.z)%ne12;
-
-    if (i00 >= ne00) {
-        return;
-    }
-
-    const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
-
-    dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
-    const src0_t * src0_row = (const src0_t *)((const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03);
-
-    dst_row[i00] = src0_row[i00];
-}
-
-template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
-static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) {
-    const int i = 2*(blockDim.x*blockIdx.x + threadIdx.x);
-
-    if (i >= k) {
-        return;
-    }
-
-    const int ib = i/qk; // block index
-    const int iqs = (i%qk)/qr; // quant index
-    const int iybs = i - i%qk; // y block start index
-    const int y_offset = qr == 1 ? 1 : qk/2;
-
-    // dequantize
-    dfloat2 v;
-    dequantize_kernel(vx, ib, iqs, v);
-
-    y[iybs + iqs + 0]        = v.x;
-    y[iybs + iqs + y_offset] = v.y;
-}
-
-template <typename src_t, typename dst_t>
-static __global__ void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (i >= k) {
-        return;
-    }
-
-    const src_t * x = (src_t *) vx;
-
-    y[i] = x[i];
-}
-
-template <bool need_check>
-static __global__ void dequantize_block_q8_0_f16(const void * __restrict__ vx, half * __restrict__ y, const int k) {
-#if __CUDA_ARCH__ >= CC_PASCAL
-    constexpr int nint = CUDA_Q8_0_NE_ALIGN/sizeof(int) + WARP_SIZE;
-
-    const int   i0 = CUDA_Q8_0_NE_ALIGN*blockIdx.x;
-    const int * x0 = ((int *) vx) + blockIdx.x * nint;
-    half2 * y2 = (half2 *) (y + i0);
-
-    __shared__ int vals[nint];
-
-#pragma unroll
-    for (int ix0 = 0; ix0 < nint; ix0 += WARP_SIZE) {
-        if (need_check && i0*sizeof(block_q8_0)/QK8_0 + sizeof(int)*(ix0 + threadIdx.x) >= k*sizeof(block_q8_0)/QK8_0) {
-            break;
-        }
-
-        const int ix = ix0 + threadIdx.x;
-        vals[ix] = x0[ix];
-    }
-
-#pragma unroll
-    for (int iy = 0; iy < CUDA_Q8_0_NE_ALIGN; iy += 2*WARP_SIZE) {
-        if (need_check && i0 + iy + 2*threadIdx.x >= k) {
-            return;
-        }
-
-        const half * b0 = ((const half  *) vals) + (sizeof(block_q8_0)/sizeof(half)) * ((iy + 2*threadIdx.x)/QK8_0);
-        const half    d = *b0;
-        const char2  qs = ((const char2 *) (b0 + 1))[threadIdx.x % (QK8_0/2)];
-
-        y2[iy/2 + threadIdx.x] = __hmul2(make_half2(qs.x, qs.y), __half2half2(d));
-    }
-#else
-    (void) vx; (void) y; (void) k;
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_PASCAL
-}
-
-// VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
-// MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
-
-#define VDR_Q4_0_Q8_1_MMVQ 2
-#define VDR_Q4_0_Q8_1_MMQ  4
-
-template <int vdr> static __device__ __forceinline__ float vec_dot_q4_0_q8_1_impl(
-    const int * v, const int * u, const float & d4, const half2 & ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
-        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
-
-        // SIMD dot product of quantized values
-        sumi = __dp4a(vi0, u[2*i+0], sumi);
-        sumi = __dp4a(vi1, u[2*i+1], sumi);
-    }
-
-    const float2 ds8f = __half22float2(ds8);
-
-    // second part effectively subtracts 8 from each quant value
-    return d4 * (sumi * ds8f.x - (8*vdr/QI4_0) * ds8f.y);
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q4_1_Q8_1_MMVQ 2
-#define VDR_Q4_1_Q8_1_MMQ  4
-
-template <int vdr> static __device__ __forceinline__ float vec_dot_q4_1_q8_1_impl(
-    const int * v, const int * u, const half2 & dm4, const half2 & ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
-        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
-
-        // SIMD dot product of quantized values
-        sumi = __dp4a(vi0, u[2*i+0], sumi);
-        sumi = __dp4a(vi1, u[2*i+1], sumi);
-    }
-
-#ifdef GGML_CUDA_F16
-    const float2 tmp = __half22float2(__hmul2(dm4, ds8));
-    const float d4d8 = tmp.x;
-    const float m4s8 = tmp.y;
-#else
-    const float2 dm4f = __half22float2(dm4);
-    const float2 ds8f = __half22float2(ds8);
-    const float d4d8 = dm4f.x * ds8f.x;
-    const float m4s8 = dm4f.y * ds8f.y;
-#endif // GGML_CUDA_F16
-
-    // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it
-    return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1));
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q5_0_Q8_1_MMVQ 2
-#define VDR_Q5_0_Q8_1_MMQ  4
-
-template <int vdr> static __device__ __forceinline__ float vec_dot_q5_0_q8_1_impl(
-    const int * vl, const int * vh, const int * u, const float & d5, const half2 & ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
-        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
-        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
-        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
-        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
-        sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
-
-        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
-        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
-        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
-        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
-        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
-        sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
-    }
-
-    const float2 ds8f = __half22float2(ds8);
-
-    // second part effectively subtracts 16 from each quant value
-    return d5 * (sumi * ds8f.x - (16*vdr/QI5_0) * ds8f.y);
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q5_1_Q8_1_MMVQ 2
-#define VDR_Q5_1_Q8_1_MMQ  4
-
-template <int vdr> static __device__ __forceinline__ float vec_dot_q5_1_q8_1_impl(
-    const int * vl, const int * vh, const int * u, const half2 & dm5, const half2 & ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
-        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
-        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
-        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
-        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
-        sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
-
-        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
-        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
-        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
-        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
-        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
-        sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
-    }
-
-#ifdef GGML_CUDA_F16
-    const float2 tmp = __half22float2(__hmul2(dm5, ds8));
-    const float d5d8 = tmp.x;
-    const float m5s8 = tmp.y;
-#else
-    const float2 dm5f = __half22float2(dm5);
-    const float2 ds8f = __half22float2(ds8);
-    const float d5d8 = dm5f.x * ds8f.x;
-    const float m5s8 = dm5f.y * ds8f.y;
-#endif // GGML_CUDA_F16
-
-    // scale second part of sum by QI5_1 / vdr to compensate for multiple threads adding it
-    return sumi*d5d8 + m5s8 / (QI5_1 / vdr);
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q8_0_Q8_1_MMVQ 2
-#define VDR_Q8_0_Q8_1_MMQ 8
-
-template <int vdr> static __device__ __forceinline__ float vec_dot_q8_0_q8_1_impl(
-    const int * v, const int * u, const float & d8_0, const float & d8_1) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        // SIMD dot product of quantized values
-        sumi = __dp4a(v[i], u[i], sumi);
-    }
-
-    return d8_0*d8_1 * sumi;
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-template <int vdr> static __device__ __forceinline__ float vec_dot_q8_1_q8_1_impl(
-    const int * v, const int * u, const half2 & dm8, const half2 & ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        // SIMD dot product of quantized values
-        sumi = __dp4a(v[i], u[i], sumi);
-    }
-
-#ifdef GGML_CUDA_F16
-    const float2 tmp = __half22float2(__hmul2(dm8, ds8));
-    const float d8d8 = tmp.x;
-    const float m8s8 = tmp.y;
-#else
-    const float2 dm8f = __half22float2(dm8);
-    const float2 ds8f = __half22float2(ds8);
-    const float d8d8 = dm8f.x * ds8f.x;
-    const float m8s8 = dm8f.y * ds8f.y;
-#endif // GGML_CUDA_F16
-
-    // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it
-    return sumi*d8d8 + m8s8 / (QI8_1 / vdr);
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q2_K_Q8_1_MMVQ 1
-#define VDR_Q2_K_Q8_1_MMQ  2
-
-// contiguous v/x values
-static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq(
-    const int & v, const int * __restrict__ u, const uint8_t * __restrict__ scales,
-    const half2 & dm2, const float * __restrict__ d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR2_K; ++i) {
-        const int sc = scales[2*i];
-
-        const int vi = (v >> (2*i)) & 0x03030303;
-
-        sumf_d += d8[i] * (__dp4a(vi, u[i], 0) * (sc & 0xF)); // SIMD dot product
-
-        // fill int with 4x m
-        int m = sc >> 4;
-        m |= m <<  8;
-        m |= m << 16;
-        sumf_m += d8[i] * __dp4a(m, u[i], 0); // multiply constant q2_K part with sum of q8_1 values
-    }
-
-    const float2 dm2f = __half22float2(dm2);
-
-    return dm2f.x*sumf_d - dm2f.y*sumf_m;
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-// contiguous u/y values
-static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq(
-    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ scales,
-    const half2 & dm2, const float & d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi_d = 0;
-    int sumi_m = 0;
-
-#pragma unroll
-    for (int i0 = 0; i0 < QI8_1; i0 += QI8_1/2) {
-        int sumi_d_sc = 0;
-
-        const int sc = scales[i0 / (QI8_1/2)];
-
-        // fill int with 4x m
-        int m = sc >> 4;
-        m |= m <<  8;
-        m |= m << 16;
-
-#pragma unroll
-        for (int i = i0; i < i0 + QI8_1/2; ++i) {
-            sumi_d_sc = __dp4a(v[i], u[i], sumi_d_sc); // SIMD dot product
-            sumi_m    = __dp4a(m,    u[i], sumi_m); // multiply sum of q8_1 values with m
-        }
-
-        sumi_d += sumi_d_sc * (sc & 0xF);
-    }
-
-    const float2 dm2f = __half22float2(dm2);
-
-    return d8 * (dm2f.x*sumi_d - dm2f.y*sumi_m);
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q3_K_Q8_1_MMVQ 1
-#define VDR_Q3_K_Q8_1_MMQ  2
-
-// contiguous v/x values
-static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq(
-    const int & vl, const int & vh, const int * __restrict__ u, const uint8_t * __restrict__ scales,
-    const int & scale_offset, const float & d3, const float * __restrict__ d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR3_K; ++i) {
-        const int isc = scale_offset + 2*i;
-
-        const int isc_low = isc % (QK_K/32);
-        const int sc_shift_low = 4 * (isc / (QK_K/32));
-        const int sc_low  = (scales[isc_low] >> sc_shift_low) & 0xF;
-
-        const int isc_high = isc % (QK_K/64);
-        const int sc_shift_high = 2 * (isc / (QK_K/64));
-        const int sc_high = ((scales[(QK_K/32) + isc_high] >> sc_shift_high) & 3) << 4;
-
-        const int sc = (sc_low | sc_high) - 32;
-
-        const int vil = (vl >> (2*i)) & 0x03030303;
-
-        const int vih = ((vh >> i) << 2) & 0x04040404;
-
-        const int vi = __vsubss4(vil, vih);
-
-        sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product
-    }
-
-    return d3 * sumf;
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-// contiguous u/y values
-static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq(
-    const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ scales,
-    const float & d3, const float & d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i0 = 0; i0 < QR3_K*VDR_Q3_K_Q8_1_MMQ; i0 += QI8_1/2) {
-        int sumi_sc = 0;
-
-        for (int i = i0; i < i0 + QI8_1/2; ++i) {
-            sumi_sc = __dp4a(v[i], u[i], sumi_sc); // SIMD dot product
-        }
-
-        sumi += sumi_sc * scales[i0 / (QI8_1/2)];
-    }
-
-    return d3*d8 * sumi;
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q4_K_Q8_1_MMVQ 2
-#define VDR_Q4_K_Q8_1_MMQ  8
-
-// contiguous v/x values
-static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq(
-    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
-    const uint8_t * __restrict__ m, const half2 & dm4, const float * __restrict__ d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR4_K; ++i) {
-        const int v0i = (v[0] >> (4*i)) & 0x0F0F0F0F;
-        const int v1i = (v[1] >> (4*i)) & 0x0F0F0F0F;
-
-        const int dot1 = __dp4a(v1i, u[2*i+1], __dp4a(v0i, u[2*i+0], 0)); // SIMD dot product
-        const int dot2 = __dp4a(0x01010101, u[2*i+1], __dp4a(0x01010101, u[2*i+0], 0)); // sum of u
-
-        sumf_d += d8[i] * (dot1 * sc[i]);
-        sumf_m += d8[i] * (dot2 * m[i]);  // multiply constant part of q4_K with sum of q8_1 values
-    }
-
-    const float2 dm4f = __half22float2(dm4);
-
-    return dm4f.x*sumf_d - dm4f.y*sumf_m;
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-// contiguous u/y values
-static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq(
-    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
-    const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR4_K*VDR_Q4_K_Q8_1_MMQ/QI8_1; ++i) {
-        int sumi_d = 0;
-
-#pragma unroll
-        for (int j = 0; j < QI8_1; ++j) {
-            sumi_d = __dp4a((v[j] >> (4*i)) & 0x0F0F0F0F, u[i*QI8_1 + j], sumi_d); // SIMD dot product
-        }
-
-        const float2 ds8f = __half22float2(ds8[i]);
-
-        sumf_d += ds8f.x * (sc[i] * sumi_d);
-        sumf_m += ds8f.y *   m[i]; // sum of q8_1 block * q4_K min val
-    }
-
-    const float2 dm4f = __half22float2(dm4);
-
-    return dm4f.x*sumf_d - dm4f.y*sumf_m;
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q5_K_Q8_1_MMVQ 2
-#define VDR_Q5_K_Q8_1_MMQ  8
-
-// contiguous v/x values
-static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq(
-    const int * __restrict__ vl, const int * __restrict__ vh, const int * __restrict__ u, const uint8_t * __restrict__ sc,
-    const uint8_t * __restrict__ m, const half2 & dm5, const float * __restrict__ d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR5_K; ++i) {
-        const int vl0i = (vl[0] >> (4*i)) & 0x0F0F0F0F;
-        const int vl1i = (vl[1] >> (4*i)) & 0x0F0F0F0F;
-
-        const int vh0i = ((vh[0] >> i) << 4) & 0x10101010;
-        const int vh1i = ((vh[1] >> i) << 4) & 0x10101010;
-
-        const int v0i = vl0i | vh0i;
-        const int v1i = vl1i | vh1i;
-
-        const int dot1 = __dp4a(v0i, u[2*i+0], __dp4a(v1i, u[2*i+1], 0)); // SIMD dot product
-        const int dot2 = __dp4a(0x01010101, u[2*i+0], __dp4a(0x01010101, u[2*i+1], 0)); // sum of u
-
-        sumf_d += d8[i] * (dot1 * sc[i]);
-        sumf_m += d8[i] * (dot2 * m[i]);
-
-    }
-
-    const float2 dm5f = __half22float2(dm5);
-
-    return dm5f.x*sumf_d - dm5f.y*sumf_m;
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-// contiguous u/y values
-static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq(
-    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
-    const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR5_K*VDR_Q5_K_Q8_1_MMQ/QI8_1; ++i) {
-        int sumi_d = 0;
-
-#pragma unroll
-        for (int j = 0; j < QI8_1; ++j) {
-            sumi_d = __dp4a(v[i*QI8_1 + j], u[i*QI8_1 + j], sumi_d); // SIMD dot product
-        }
-
-        const float2 ds8f = __half22float2(ds8[i]);
-
-        sumf_d += ds8f.x * (sc[i] * sumi_d);
-        sumf_m += ds8f.y *   m[i]; // sum of q8_1 block * q4_K min val
-    }
-
-    const float2 dm4f = __half22float2(dm4);
-
-    return dm4f.x*sumf_d - dm4f.y*sumf_m;
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q6_K_Q8_1_MMVQ 1
-#define VDR_Q6_K_Q8_1_MMQ  8
-
-// contiguous v/x values
-static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq(
-    const int & vl, const int & vh, const int * __restrict__ u, const int8_t * __restrict__ scales,
-    const float & d, const float * __restrict__ d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR6_K; ++i) {
-        const int sc = scales[4*i];
-
-        const int vil = (vl >> (4*i)) & 0x0F0F0F0F;
-
-        const int vih = ((vh >> (4*i)) << 4) & 0x30303030;
-
-        const int vi = __vsubss4((vil | vih), 0x20202020); // vi = (vil | vih) - 32
-
-        sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product
-    }
-
-    return d*sumf;
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-// contiguous u/y values
-static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq(
-    const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ sc,
-    const float & d6, const float * __restrict__ d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf_d = 0.0f;
-
-#pragma unroll
-    for (int i0 = 0; i0 < VDR_Q6_K_Q8_1_MMQ; i0 += 4) {
-        int2 sumi_d = {0, 0}; // 2 q6_K scales per q8_1 scale
-
-#pragma unroll
-        for (int i = i0; i < i0 + 2; ++i) {
-            sumi_d.x = __dp4a(v[2*i+0], u[2*i+0], sumi_d.x); // SIMD dot product
-            sumi_d.x = __dp4a(v[2*i+1], u[2*i+1], sumi_d.x); // SIMD dot product
-
-            sumi_d.y = __dp4a(v[2*i+4], u[2*i+4], sumi_d.y); // SIMD dot product
-            sumi_d.y = __dp4a(v[2*i+5], u[2*i+5], sumi_d.y); // SIMD dot product
-        }
-
-        sumf_d += d8[i0/4] * (sc[i0/2+0]*sumi_d.x + sc[i0/2+1]*sumi_d.y);
-    }
-
-    return d6 * sumf_d;
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-static __device__ __forceinline__ float vec_dot_q4_0_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq;
-
-    int v[VDR_Q4_0_Q8_1_MMVQ];
-    int u[2*VDR_Q4_0_Q8_1_MMVQ];
-
-#pragma unroll
-    for (int i = 0; i < VDR_Q4_0_Q8_1_MMVQ; ++i) {
-        v[i]     = get_int_from_uint8(bq4_0->qs, iqs + i);
-        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_0);
-    }
-
-    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMVQ>(v, u, bq4_0->d, bq8_1->ds);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    (void)x_qh; (void)x_sc;
-
-    __shared__ int  tile_x_qs[mmq_y * (WARP_SIZE)       + mmq_y];
-    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI4_0) + mmq_y/QI4_0];
-
-    *x_ql = tile_x_qs;
-    *x_dm = (half2 *) tile_x_d;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_0(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    (void)x_qh; (void)x_sc;
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI4_0;
-    const int kqsx = k % QI4_0;
-
-    const block_q4_0 * bx0 = (const block_q4_0 *) vx;
-
-    float * x_dmf = (float *) x_dm;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
-        // x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx] = bxi->d;
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI4_0;
-    const int kbxd = k % blocks_per_tile_x_row;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_0) {
-        int i = i0 + i_offset * QI4_0 + k / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd] = bxi->d;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q4_0_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    (void)x_qh; (void)x_sc;
-
-    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
-    const float * x_dmf = (const float *) x_dm;
-
-    int u[2*VDR_Q4_0_Q8_1_MMQ];
-
-#pragma unroll
-    for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) {
-        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
-        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_0) % WARP_SIZE];
-    }
-
-    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMQ>
-        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dmf[i * (WARP_SIZE/QI4_0) + i/QI4_0 + k/QI4_0],
-         y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
-}
-
-static __device__ __forceinline__ float vec_dot_q4_1_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq;
-
-    int v[VDR_Q4_1_Q8_1_MMVQ];
-    int u[2*VDR_Q4_1_Q8_1_MMVQ];
-
-#pragma unroll
-    for (int i = 0; i < VDR_Q4_1_Q8_1_MMVQ; ++i) {
-        v[i]    = get_int_from_uint8_aligned(bq4_1->qs, iqs + i);
-        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_1);
-    }
-
-    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMVQ>(v, u, bq4_1->dm, bq8_1->ds);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    (void)x_qh; (void)x_sc;
-
-    __shared__ int   tile_x_qs[mmq_y * (WARP_SIZE) +     + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_1) + mmq_y/QI4_1];
-
-    *x_ql = tile_x_qs;
-    *x_dm = tile_x_dm;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_1(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    (void)x_qh; (void)x_sc;
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI4_1;
-    const int kqsx = k % QI4_1;
-
-    const block_q4_1 * bx0 = (const block_q4_1 *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI4_1;
-    const int kbxd = k % blocks_per_tile_x_row;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_1) {
-        int i = i0 + i_offset * QI4_1 + k / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd] = bxi->dm;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q4_1_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    (void)x_qh; (void)x_sc;
-
-    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
-
-    int u[2*VDR_Q4_1_Q8_1_MMQ];
-
-#pragma unroll
-    for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) {
-        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
-        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_1) % WARP_SIZE];
-    }
-
-    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMQ>
-        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dm[i * (WARP_SIZE/QI4_1) + i/QI4_1 + k/QI4_1],
-         y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
-}
-
-static __device__ __forceinline__ float vec_dot_q5_0_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq;
-
-    int vl[VDR_Q5_0_Q8_1_MMVQ];
-    int vh[VDR_Q5_0_Q8_1_MMVQ];
-    int  u[2*VDR_Q5_0_Q8_1_MMVQ];
-
-#pragma unroll
-    for (int i = 0; i < VDR_Q5_0_Q8_1_MMVQ; ++i) {
-        vl[i]    = get_int_from_uint8(bq5_0->qs, iqs + i);
-        vh[i]    = get_int_from_uint8(bq5_0->qh, 0) >> (4 * (iqs + i));
-        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_0);
-    }
-
-    return vec_dot_q5_0_q8_1_impl<VDR_Q5_0_Q8_1_MMVQ>(vl, vh, u, bq5_0->d, bq8_1->ds);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    (void)x_qh; (void)x_sc;
-
-    __shared__ int  tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
-    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI5_0) + mmq_y/QI5_0];
-
-    *x_ql = tile_x_ql;
-    *x_dm = (half2 *) tile_x_d;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_0(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    (void)x_qh; (void)x_sc;
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI5_0;
-    const int kqsx = k % QI5_0;
-
-    const block_q5_0 * bx0 = (const block_q5_0 *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbx;
-
-        const int ql = get_int_from_uint8(bxi->qs, kqsx);
-        const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (k % QI5_0));
-
-        int qs0 = (ql >>  0)   & 0x0F0F0F0F;
-        qs0    |= (qh <<  4)   & 0x00000010;  // 0 ->  4
-        qs0    |= (qh << 11)   & 0x00001000;  // 1 -> 12
-        qs0    |= (qh << 18)   & 0x00100000;  // 2 -> 20
-        qs0    |= (qh << 25)   & 0x10000000;  // 3 -> 28
-        qs0     = __vsubss4(qs0, 0x10101010); // subtract 16
-
-        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
-
-        int qs1 = (ql >>  4)   & 0x0F0F0F0F;
-        qs1    |= (qh >> 12)   & 0x00000010;  // 16 ->  4
-        qs1    |= (qh >>  5)   & 0x00001000;  // 17 -> 12
-        qs1    |= (qh <<  2)   & 0x00100000;  // 18 -> 20
-        qs1    |= (qh <<  9)   & 0x10000000;  // 19 -> 28
-        qs1     = __vsubss4(qs1, 0x10101010); // subtract 16
-
-        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI5_0;
-    const int kbxd = k % blocks_per_tile_x_row;
-    float * x_dmf = (float *) x_dm;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_0) {
-        int i = i0 + i_offset * QI5_0 + k / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dmf[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = bxi->d;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q5_0_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    (void)x_qh; (void)x_sc;
-
-    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
-    const int index_bx = i * (WARP_SIZE/QI5_0) + i/QI5_0 + k/QI5_0;
-    const float * x_dmf = (const float *) x_dm;
-    const float * y_df  = (const float *) y_ds;
-
-    int u[2*VDR_Q5_0_Q8_1_MMQ];
-
-#pragma unroll
-    for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) {
-        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
-        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_0) % WARP_SIZE];
-    }
-
-    return vec_dot_q8_0_q8_1_impl<QR5_0*VDR_Q5_0_Q8_1_MMQ>
-        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dmf[index_bx], y_df[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
-}
-
-static __device__ __forceinline__ float vec_dot_q5_1_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq;
-
-    int vl[VDR_Q5_1_Q8_1_MMVQ];
-    int vh[VDR_Q5_1_Q8_1_MMVQ];
-    int  u[2*VDR_Q5_1_Q8_1_MMVQ];
-
-#pragma unroll
-    for (int i = 0; i < VDR_Q5_1_Q8_1_MMVQ; ++i) {
-        vl[i]   = get_int_from_uint8_aligned(bq5_1->qs, iqs + i);
-        vh[i]   = get_int_from_uint8_aligned(bq5_1->qh, 0) >> (4 * (iqs + i));
-        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_1);
-    }
-
-    return vec_dot_q5_1_q8_1_impl<VDR_Q5_1_Q8_1_MMVQ>(vl, vh, u, bq5_1->dm, bq8_1->ds);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    (void)x_qh; (void)x_sc;
-
-    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_1) + mmq_y/QI5_1];
-
-    *x_ql = tile_x_ql;
-    *x_dm = tile_x_dm;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_1(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    (void)x_qh; (void)x_sc;
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset < nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI5_1;
-    const int kqsx = k % QI5_1;
-
-    const block_q5_1 * bx0 = (const block_q5_1 *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbx;
-
-        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
-        const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (k % QI5_1));
-
-        int qs0 = (ql >>  0) & 0x0F0F0F0F;
-        qs0    |= (qh <<  4) & 0x00000010; // 0 ->  4
-        qs0    |= (qh << 11) & 0x00001000; // 1 -> 12
-        qs0    |= (qh << 18) & 0x00100000; // 2 -> 20
-        qs0    |= (qh << 25) & 0x10000000; // 3 -> 28
-
-        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
-
-        int qs1 = (ql >>  4) & 0x0F0F0F0F;
-        qs1    |= (qh >> 12) & 0x00000010; // 16 ->  4
-        qs1    |= (qh >>  5) & 0x00001000; // 17 -> 12
-        qs1    |= (qh <<  2) & 0x00100000; // 18 -> 20
-        qs1    |= (qh <<  9) & 0x10000000; // 19 -> 28
-
-        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI5_1;
-    const int kbxd = k % blocks_per_tile_x_row;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_1) {
-        int i = i0 + i_offset * QI5_1 + k / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q5_1_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    (void)x_qh; (void)x_sc;
-
-    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
-    const int index_bx = i * (WARP_SIZE/QI5_1) + + i/QI5_1 + k/QI5_1;
-
-    int u[2*VDR_Q5_1_Q8_1_MMQ];
-
-#pragma unroll
-    for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) {
-        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
-        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_1) % WARP_SIZE];
-    }
-
-    return vec_dot_q8_1_q8_1_impl<QR5_1*VDR_Q5_1_Q8_1_MMQ>
-        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dm[index_bx], y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
-}
-
-static __device__ __forceinline__ float vec_dot_q8_0_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq;
-
-    int v[VDR_Q8_0_Q8_1_MMVQ];
-    int u[VDR_Q8_0_Q8_1_MMVQ];
-
-#pragma unroll
-    for (int i = 0; i < VDR_Q8_0_Q8_1_MMVQ; ++i) {
-        v[i] = get_int_from_int8(bq8_0->qs, iqs + i);
-        u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-    }
-
-    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMVQ>(v, u, bq8_0->d, __low2half(bq8_1->ds));
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q8_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    (void)x_qh; (void)x_sc;
-
-    __shared__ int  tile_x_qs[mmq_y * (WARP_SIZE)       + mmq_y];
-    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI8_0) + mmq_y/QI8_0];
-
-    *x_ql = tile_x_qs;
-    *x_dm = (half2 *) tile_x_d;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q8_0(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    (void)x_qh; (void)x_sc;
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI8_0;
-    const int kqsx = k % QI8_0;
-    float * x_dmf = (float *) x_dm;
-
-    const block_q8_0 * bx0 = (const block_q8_0 *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_int8(bxi->qs, kqsx);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI8_0;
-    const int kbxd = k % blocks_per_tile_x_row;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0) {
-        int i = i0 + i_offset * QI8_0 + k / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dmf[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbxd] = bxi->d;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q8_0_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    (void)x_qh; (void)x_sc;
-
-    const float * x_dmf = (const float *) x_dm;
-    const float * y_df  = (const float *) y_ds;
-
-    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMQ>
-        (&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[j * WARP_SIZE + k], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0],
-         y_df[j * (WARP_SIZE/QI8_1) + k/QI8_1]);
-}
-
-static __device__ __forceinline__ float vec_dot_q2_K_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q2_K * bq2_K = (const block_q2_K *) vbq;
-
-    const int bq8_offset = QR2_K * (iqs / QI8_1);
-    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
-
-    const uint8_t * scales = bq2_K->scales + scale_offset;
-
-    const int v = get_int_from_uint8_aligned(bq2_K->qs, iqs);
-    int    u[QR2_K];
-    float d8[QR2_K];
-
-#pragma unroll
-    for (int i = 0; i < QR2_K; ++ i) {
-        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
-        d8[i] = __low2half(bq8_1[bq8_offset + i].ds);
-    }
-
-    return vec_dot_q2_K_q8_1_impl_mmvq(v, u, scales, bq2_K->dm, d8);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q2_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    (void)x_qh;
-
-    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI2_K) + mmq_y/QI2_K];
-    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/4)     + mmq_y/4];
-
-    *x_ql = tile_x_ql;
-    *x_dm = tile_x_dm;
-    *x_sc = tile_x_sc;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q2_K(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    (void)x_qh;
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI2_K;
-    const int kqsx = k % QI2_K;
-
-    const block_q2_K * bx0 = (const block_q2_K *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI2_K;
-    const int kbxd = k % blocks_per_tile_x_row;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI2_K) {
-        int i = (i0 + i_offset * QI2_K + k / blocks_per_tile_x_row) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dm[i * (WARP_SIZE/QI2_K) + i / QI2_K + kbxd] = bxi->dm;
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
-        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q2_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI2_K/4);
-
-        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = get_int_from_uint8_aligned(bxi->scales, k % (QI2_K/4));
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q2_K_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    (void)x_qh;
-
-    const int kbx = k / QI2_K;
-    const int ky  = (k % QI2_K) * QR2_K;
-    const float * y_df = (const float *) y_ds;
-
-    int v[QR2_K*VDR_Q2_K_Q8_1_MMQ];
-
-    const int kqsx = i * (WARP_SIZE + 1) + kbx*QI2_K + (QI2_K/2) * (ky/(2*QI2_K)) + ky % (QI2_K/2);
-    const int shift = 2 * ((ky % (2*QI2_K)) / (QI2_K/2));
-
-#pragma unroll
-    for (int l = 0; l < QR2_K*VDR_Q2_K_Q8_1_MMQ; ++l) {
-        v[l] = (x_ql[kqsx + l] >> shift) & 0x03030303;
-    }
-
-    const uint8_t * scales = ((const uint8_t *) &x_sc[i * (WARP_SIZE/4) + i/4 + kbx*4]) + ky/4;
-
-    const int index_y = j * WARP_SIZE + (QR2_K*k) % WARP_SIZE;
-    return vec_dot_q2_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dm[i * (WARP_SIZE/QI2_K) + i/QI2_K + kbx], y_df[index_y/QI8_1]);
-}
-
-static __device__ __forceinline__ float vec_dot_q3_K_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q3_K * bq3_K = (const block_q3_K *) vbq;
-
-    const int bq8_offset = QR3_K * (iqs / (QI3_K/2));
-    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
-
-    const float d = bq3_K->d;
-
-    const int vl = get_int_from_uint8(bq3_K->qs, iqs);
-
-    // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
-    const int vh = ~get_int_from_uint8(bq3_K->hmask, iqs % (QI3_K/2)) >> bq8_offset;
-
-    int    u[QR3_K];
-    float d8[QR3_K];
-
-#pragma unroll
-    for (int i = 0; i < QR3_K; ++i) {
-        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
-        d8[i] = __low2half(bq8_1[bq8_offset + i].ds);
-    }
-
-    return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q3_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-
-    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI3_K) + mmq_y/QI3_K];
-    __shared__ int   tile_x_qh[mmq_y * (WARP_SIZE/2)     + mmq_y/2];
-    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/4)     + mmq_y/4];
-
-    *x_ql = tile_x_ql;
-    *x_dm = tile_x_dm;
-    *x_qh = tile_x_qh;
-    *x_sc = tile_x_sc;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q3_K(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI3_K;
-    const int kqsx = k % QI3_K;
-
-    const block_q3_K * bx0 = (const block_q3_K *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI3_K;
-    const int kbxd = k % blocks_per_tile_x_row;
-    float * x_dmf = (float *) x_dm;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI3_K) {
-        int i = (i0 + i_offset * QI3_K + k / blocks_per_tile_x_row) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dmf[i * (WARP_SIZE/QI3_K) + i / QI3_K + kbxd] = bxi->d;
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 2) {
-        int i = i0 + i_offset * 2 + k / (WARP_SIZE/2);
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/2)) / (QI3_K/2);
-
-        // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
-        x_qh[i * (WARP_SIZE/2) + i / 2 + k % (WARP_SIZE/2)] = ~get_int_from_uint8(bxi->hmask, k % (QI3_K/2));
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
-        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI3_K/4);
-
-        const int ksc = k % (QI3_K/4);
-
-        const int ksc_low = ksc % (QI3_K/8);
-        const int shift_low = 4 * (ksc / (QI3_K/8));
-        const int sc_low = (get_int_from_uint8(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F;
-
-        const int ksc_high = QI3_K/8;
-        const int shift_high = 2 * ksc;
-        const int sc_high = ((get_int_from_uint8(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030;
-
-        const int sc = __vsubss4(sc_low | sc_high, 0x20202020);
-
-        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = sc;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q3_K_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-
-    const int kbx  = k / QI3_K;
-    const int ky  = (k % QI3_K) * QR3_K;
-    const float * x_dmf = (const float *) x_dm;
-    const float * y_df  = (const float *) y_ds;
-
-    const int8_t * scales = ((const int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4;
-
-    int v[QR3_K*VDR_Q3_K_Q8_1_MMQ];
-
-#pragma unroll
-    for (int l = 0; l < QR3_K*VDR_Q3_K_Q8_1_MMQ; ++l) {
-        const int kqsx = i * (WARP_SIZE + 1) + kbx*QI3_K + (QI3_K/2) * (ky/(2*QI3_K)) + ky % (QI3_K/2);
-        const int shift = 2 * ((ky % 32) / 8);
-        const int vll = (x_ql[kqsx + l] >> shift) & 0x03030303;
-
-        const int vh = x_qh[i * (WARP_SIZE/2) + i/2 + kbx * (QI3_K/2) + (ky+l)%8] >> ((ky+l) / 8);
-        const int vlh = (vh << 2) & 0x04040404;
-
-        v[l] = __vsubss4(vll, vlh);
-    }
-
-    const int index_y = j * WARP_SIZE + (k*QR3_K) % WARP_SIZE;
-    return vec_dot_q3_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dmf[i * (WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[index_y/QI8_1]);
-}
-
-static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-#ifndef GGML_QKK_64
-    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
-
-    int    v[2];
-    int    u[2*QR4_K];
-    float d8[QR4_K];
-
-    // iqs is in 0,2..30. bq8_offset = iqs/4 -> bq8_offset = 0, 2, 4, 6
-    const int bq8_offset = QR4_K * ((iqs/2) / (QI8_1/2));
-
-    // iqs = 0....3 -> bq8_offset = 0, want q4_offset = 0, 4, 8, 12
-    // iqs = 4....7 -> bq8_offset = 2, want q4_offset = 32, 36, 40, 44
-    // iqs = 8...11 -> bq8_offset = 4, want q4_offset = 64, 68, 72, 76
-    // iqs = 12..15 -> bq8_offset = 6, want q4_offset = 96, 100, 104, 108
-
-    const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
-    v[0] = q4[0];
-    v[1] = q4[4];
-
-    const uint16_t * scales = (const uint16_t *)bq4_K->scales;
-    uint16_t aux[2];
-    const int j = bq8_offset/2;
-    if (j < 2) {
-        aux[0] = scales[j+0] & 0x3f3f;
-        aux[1] = scales[j+2] & 0x3f3f;
+    if (id == ggml_backend_cuda_get_device_count() - 1) {
+        *row_high = nrows;
     } else {
-        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
-        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
-    }
-    const uint8_t * sc = (const uint8_t *)aux;
-    const uint8_t * m  = sc + 2;
-
-    for (int i = 0; i < QR4_K; ++i) {
-        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
-        d8[i] = __low2half(bq8i->ds);
-
-        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
-        u[2*i+0] = q8[0];
-        u[2*i+1] = q8[4];
-    }
-
-    return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8);
-
-#else
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
-
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-    uint16_t aux16[2];
-    const uint8_t * s = (const uint8_t *)aux16;
-
-    const uint16_t * a = (const uint16_t *)bq4_K->scales;
-    aux16[0] = a[0] & 0x0f0f;
-    aux16[1] = (a[0] >> 4) & 0x0f0f;
-
-    const float dall = bq4_K->dm[0];
-    const float dmin = bq4_K->dm[1];
-
-    const float d8_1 = __low2float(bq8_1[0].ds);
-    const float d8_2 = __low2float(bq8_1[1].ds);
-
-    const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
-    const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
-    const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
-    const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
-
-    const int * q4 = (const int *)bq4_K->qs + (iqs/2);
-    const int v1 = q4[0];
-    const int v2 = q4[4];
-
-    const int dot1 = __dp4a(ui2, v2 & 0x0f0f0f0f, __dp4a(ui1, v1 & 0x0f0f0f0f, 0));
-    const int dot2 = __dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, __dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0));
-    const int dot3 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0));
-    const int dot4 = __dp4a(0x01010101, ui4, __dp4a(0x01010101, ui3, 0));
-
-    sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]);
-    sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]);
-
-    return dall * sumf_d - dmin * sumf_m;
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-
-#endif
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    (void)x_qh;
-
-    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_K) + mmq_y/QI4_K];
-    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];
-
-    *x_ql = tile_x_ql;
-    *x_dm = tile_x_dm;
-    *x_sc = tile_x_sc;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_K(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    (void)x_qh;
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI4_K; // == 0 if QK_K == 256
-    const int kqsx = k % QI4_K; // == k if QK_K == 256
-
-    const block_q4_K * bx0 = (const block_q4_K *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256
-    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_K) {
-        int i = (i0 + i_offset * QI4_K + k / blocks_per_tile_x_row) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd;
-
-#if QK_K == 256
-        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm;
-#else
-        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]};
-#endif
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
-        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI4_K/8);
-
-        const int * scales = (const int *) bxi->scales;
-
-        const int ksc = k % (WARP_SIZE/8);
-
-        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
-        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
-        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
-
-        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
+        *row_high = nrows*tensor_split[id + 1];
+        *row_high -= *row_high % rounding;
     }
 }
 
-static __device__ __forceinline__ float vec_dot_q4_K_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    (void)x_qh;
+static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
-    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2*((k % 16) / 8);
-
-    const int index_y = j * WARP_SIZE + (QR4_K*k) % WARP_SIZE;
-    return vec_dot_q4_K_q8_1_impl_mmq(&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[index_y], sc, sc+8,
-                                      x_dm[i * (WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[index_y/QI8_1]);
+    return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]);
 }
 
-static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-#ifndef GGML_QKK_64
-    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
-
-    int   vl[2];
-    int   vh[2];
-    int    u[2*QR5_K];
-    float d8[QR5_K];
-
-    const int bq8_offset = QR5_K * ((iqs/2) / (QI8_1/2));
-    const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
-    const int * qh = (const int *)(bq5_K->qh + 4 * ((iqs/2)%4));
-
-    vl[0] = ql[0];
-    vl[1] = ql[4];
-
-    vh[0] = qh[0] >> bq8_offset;
-    vh[1] = qh[4] >> bq8_offset;
-
-    const uint16_t * scales = (const uint16_t *)bq5_K->scales;
-    uint16_t aux[2];
-    const int j = bq8_offset/2;
-    if (j < 2) {
-        aux[0] = scales[j+0] & 0x3f3f;
-        aux[1] = scales[j+2] & 0x3f3f;
-    } else {
-        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
-        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
-    }
-    const uint8_t * sc = (const uint8_t *)aux;
-    const uint8_t * m  = sc + 2;
-
-#pragma unroll
-    for (int i = 0; i < QR5_K; ++i) {
-        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
-        d8[i] = __low2float(bq8i->ds);
-
-        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
-        u[2*i+0] = q8[0];
-        u[2*i+1] = q8[4];
-    }
-
-    return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, bq5_K->dm, d8);
-
-#else
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
-
-    const int8_t * s = bq5_K->scales;
-
-    const float d = bq5_K->d;
-
-    const float d8_1 = __low2half(bq8_1[0].ds);
-    const float d8_2 = __low2half(bq8_1[1].ds);
-
-    const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
-    const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
-    const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
-    const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
-
-    const int * ql = (const int *)bq5_K->qs + (iqs/2);
-    const int vl1 = ql[0];
-    const int vl2 = ql[4];
-
-    const int step = 4 * (iqs/2); // 0, 4, 8, 12
-    const int im = step/8; // = 0 for iqs = 0, 2, = 1 for iqs = 4, 6
-    const int in = step%8; // 0, 4, 0, 4
-    const int vh = (*((const int *)(bq5_K->qh + in))) >> im;
-
-    const int v1 = (((vh << 4) & 0x10101010) ^ 0x10101010) | ((vl1 >> 0) & 0x0f0f0f0f);
-    const int v2 = (((vh << 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 0) & 0x0f0f0f0f);
-    const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f);
-    const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f);
-
-    const float sumf_d = d8_1 * (__dp4a(ui1, v1, 0) * s[0] + __dp4a(ui2, v2, 0) * s[1])
-                       + d8_2 * (__dp4a(ui3, v3, 0) * s[2] + __dp4a(ui4, v4, 0) * s[3]);
-
-    return d * sumf_d;
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-
-#endif
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    (void)x_qh;
-
-    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_K) + mmq_y/QI5_K];
-    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];
-
-    *x_ql = tile_x_ql;
-    *x_dm = tile_x_dm;
-    *x_sc = tile_x_sc;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_K(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    (void)x_qh;
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI5_K; // == 0 if QK_K == 256
-    const int kqsx = k % QI5_K; // == k if QK_K == 256
-
-    const block_q5_K * bx0 = (const block_q5_K *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbx;
-        const int ky = QR5_K*kqsx;
-
-        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
-        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
-        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
-
-        const int qh = get_int_from_uint8_aligned(bxi->qh, kqsx % (QI5_K/4));
-        const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010;
-        const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010;
-
-        const int kq0 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + 0;
-        const int kq1 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + (QI5_K/4);
-
-        x_ql[i * (2*WARP_SIZE + 1) + kq0] = ql0 | qh0;
-        x_ql[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1;
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256
-    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_K) {
-        int i = (i0 + i_offset * QI5_K + k / blocks_per_tile_x_row) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd;
-
-#if QK_K == 256
-        x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm;
-#endif
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
-        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI5_K/8);
-
-        const int * scales = (const int *) bxi->scales;
-
-        const int ksc = k % (WARP_SIZE/8);
-
-        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
-        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
-        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
-
-        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q5_K_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    (void)x_qh;
-
-    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2 * ((k % 16) / 8);
-
-    const int index_x = i * (QR5_K*WARP_SIZE + 1) +  QR5_K*k;
-    const int index_y = j * WARP_SIZE             + (QR5_K*k) % WARP_SIZE;
-    return vec_dot_q5_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, sc+8,
-                                      x_dm[i * (WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[index_y/QI8_1]);
-}
-
-static __device__ __forceinline__ float vec_dot_q6_K_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q6_K * bq6_K = (const block_q6_K *) vbq;
-
-    const int bq8_offset = 2 * QR6_K * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/4);
-    const int scale_offset = (QI6_K/4) * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/8);
-    const int vh_shift = 2 * ((iqs % (QI6_K/2)) / (QI6_K/4));
-
-    const int vl = get_int_from_uint8(bq6_K->ql, iqs);
-    const int vh = get_int_from_uint8(bq6_K->qh, (QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4)) >> vh_shift;
-
-    const int8_t * scales = bq6_K->scales + scale_offset;
-
-    int    u[QR6_K];
-    float d8[QR6_K];
-
-#pragma unroll
-    for (int i = 0; i < QR6_K; ++i) {
-        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1);
-        d8[i] = __low2half(bq8_1[bq8_offset + 2*i].ds);
-    }
-
-    return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q6_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    (void)x_qh;
-
-    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI6_K) + mmq_y/QI6_K];
-    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];
-
-    *x_ql = tile_x_ql;
-    *x_dm = tile_x_dm;
-    *x_sc = tile_x_sc;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q6_K(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    (void)x_qh;
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI6_K; // == 0 if QK_K == 256
-    const int kqsx = k % QI6_K; // == k if QK_K == 256
-
-    const block_q6_K * bx0 = (const block_q6_K *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbx;
-        const int ky = QR6_K*kqsx;
-
-        const int ql = get_int_from_uint8(bxi->ql, kqsx);
-        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
-        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
-
-        const int qh = get_int_from_uint8(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4));
-        const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030;
-        const int qh1 =  (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4))))       & 0x30303030;
-
-        const int kq0 = ky - ky % QI6_K + k % (QI6_K/2) + 0;
-        const int kq1 = ky - ky % QI6_K + k % (QI6_K/2) + (QI6_K/2);
-
-        x_ql[i * (2*WARP_SIZE + 1) + kq0] = __vsubss4(ql0 | qh0, 0x20202020);
-        x_ql[i * (2*WARP_SIZE + 1) + kq1] = __vsubss4(ql1 | qh1, 0x20202020);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256
-    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
-    float * x_dmf = (float *) x_dm;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI6_K) {
-        int i = (i0 + i_offset * QI6_K + k / blocks_per_tile_x_row) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dmf[i * (WARP_SIZE/QI6_K) + i / QI6_K + kbxd] = bxi->d;
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
-        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q6_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / 4;
-
-        x_sc[i * (WARP_SIZE/8) + i / 8 + k % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, k % (QI6_K/8));
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q6_K_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    (void)x_qh;
-
-    const float * x_dmf = (const float *) x_dm;
-    const float * y_df  = (const float *) y_ds;
-
-    const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/8]);
-
-    const int index_x = i * (QR6_K*WARP_SIZE + 1) +  QR6_K*k;
-    const int index_y = j * WARP_SIZE             + (QR6_K*k) % WARP_SIZE;
-    return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]);
-}
-
-static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-#if QK_K == 256
-    const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq;
-
-#if QR2_XXS == 8
-    const int ib32 = iqs;
-    const uint16_t * q2 = bq2->qs + 4*ib32;
-    const uint8_t  * aux8 = (const uint8_t *)q2;
-    const int8_t   * q8 = bq8_1[ib32].qs;
-    uint32_t aux32 = q2[2] | (q2[3] << 16);
-    int sumi = 0;
-    for (int l = 0; l < 4; ++l) {
-        const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]);
-        const uint8_t  signs = ksigns_iq2xs[aux32 & 127];
-        for (int j = 0; j < 8; ++j) {
-            sumi += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
-        }
-        q8 += 8;
-        aux32 >>= 7;
-    }
-    const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.25f;
-    return d * sumi;
-#else
-    // iqs is 0...15
-    const int ib32 = iqs/2;
-    const int il = iqs%2;
-    const uint16_t * q2 = bq2->qs + 4*ib32;
-    const uint8_t  * aux8 = (const uint8_t *)q2;
-    const uint8_t  * grid1 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+0]);
-    const uint8_t  * grid2 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+1]);
-    const uint32_t aux32 = q2[2] | (q2[3] << 16);
-    const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * __low2float(bq8_1[ib32].ds) * 0.25f;
-    const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127];
-    const uint8_t signs2 = ksigns_iq2xs[(aux32 >> (14*il + 7)) & 127];
-    const int8_t * q8 = bq8_1[ib32].qs + 16*il;
-    int sumi1 = 0, sumi2 = 0;
-    for (int j = 0; j < 8; ++j) {
-        sumi1 += q8[j+0] * grid1[j] * (signs1 & kmask_iq2xs[j] ? -1 : 1);
-        sumi2 += q8[j+8] * grid2[j] * (signs2 & kmask_iq2xs[j] ? -1 : 1);
-    }
-    return d * (sumi1 + sumi2);
-#endif
-#else
-    assert(false);
-    return 0.f;
-#endif
-}
-
-static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-#if QK_K == 256
-    const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq;
-
-    const int ib32 = iqs;
-    const uint16_t * q2 = bq2->qs + 4*ib32;
-    const int8_t   * q8 = bq8_1[ib32].qs;
-    const uint8_t ls1 = bq2->scales[ib32] & 0xf;
-    const uint8_t ls2 = bq2->scales[ib32] >>  4;
-    int sumi1 = 0;
-    for (int l = 0; l < 2; ++l) {
-        const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511));
-        const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9));
-        const int grid_l = __vsub4(grid[0] ^ signs[0], signs[0]);
-        const int grid_h = __vsub4(grid[1] ^ signs[1], signs[1]);
-        sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1);
-        sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1);
-        q8 += 8;
-    }
-    int sumi2 = 0;
-    for (int l = 2; l < 4; ++l) {
-        const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511));
-        const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9));
-        const int grid_l = __vsub4(grid[0] ^ signs[0], signs[0]);
-        const int grid_h = __vsub4(grid[1] ^ signs[1], signs[1]);
-        sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2);
-        sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2);
-        q8 += 8;
-    }
-    const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f;
-    return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
-#else
-    (void) ksigns64;
-    assert(false);
-    return 0.f;
-#endif
-#else
-    (void) ksigns64;
-    assert(false);
-    return 0.f;
-#endif
-}
-
-static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-#if QK_K == 256
-    const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq;
-
-    const int ib32 = iqs;
-    const uint8_t  * q3 = bq2->qs + 8*ib32;
-    const uint16_t * gas = (const uint16_t *)(bq2->qs + QK_K/4) + 2*ib32;
-    const int8_t   * q8 = bq8_1[ib32].qs;
-    uint32_t aux32 = gas[0] | (gas[1] << 16);
-    int sumi = 0;
-    for (int l = 0; l < 4; ++l) {
-        const uint32_t * grid1 = iq3xxs_grid + q3[2*l+0];
-        const uint32_t * grid2 = iq3xxs_grid + q3[2*l+1];
-        const uint32_t * signs = (const uint32_t *)(ksigns64 + (aux32 & 127));
-        const int grid_l = __vsub4(grid1[0] ^ signs[0], signs[0]);
-        const int grid_h = __vsub4(grid2[0] ^ signs[1], signs[1]);
-        sumi = __dp4a(grid_l, *((int *)q8+0), sumi);
-        sumi = __dp4a(grid_h, *((int *)q8+1), sumi);
-        q8 += 8;
-        aux32 >>= 7;
-    }
-    const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.5f;
-    return d * sumi;
-#else
-    assert(false);
-    return 0.f;
-#endif
-#else
-    assert(false);
-    return 0.f;
-#endif
-}
-
-static __device__ __forceinline__ float vec_dot_iq1_s_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-#if QK_K == 256
-    const block_iq1_s * bq1 = (const block_iq1_s *) vbq;
-
-    const int ib32 = iqs;
-    int sumi1 = 0, sumi2 = 0, sumi3 = 0, sumi4 = 0;
-    const uint8_t h1 = bq1->scales[2*ib32+0];
-    const uint8_t h2 = bq1->scales[2*ib32+1];
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const int * q8 = (const int *)bq8_1[ib32].qs;
-    const int * grid1 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+0] | ((h1 & 0x08) << 5)));
-    const int * grid2 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+1] | ((h1 & 0x80) << 1)));
-    const int * grid3 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+2] | ((h2 & 0x08) << 5)));
-    const int * grid4 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+3] | ((h2 & 0x80) << 1)));
-    for (int j = 0; j < 2; ++j) {
-        sumi1 = __dp4a(q8[j+0], grid1[j], sumi1);
-        sumi2 = __dp4a(q8[j+2], grid2[j], sumi2);
-        sumi3 = __dp4a(q8[j+4], grid3[j], sumi3);
-        sumi4 = __dp4a(q8[j+6], grid4[j], sumi4);
-    }
-#else
-    const int8_t   * q8 = bq8_1[ib32].qs;
-    const int8_t * grid1 = (const int8_t *)(iq1s_grid + (bq1->qs[4*ib32+0] | ((h1 & 0x08) << 5)));
-    const int8_t * grid2 = (const int8_t *)(iq1s_grid + (bq1->qs[4*ib32+1] | ((h1 & 0x80) << 1)));
-    const int8_t * grid3 = (const int8_t *)(iq1s_grid + (bq1->qs[4*ib32+2] | ((h2 & 0x08) << 5)));
-    const int8_t * grid4 = (const int8_t *)(iq1s_grid + (bq1->qs[4*ib32+3] | ((h2 & 0x80) << 1)));
-    for (int j = 0; j < 8; ++j) {
-        sumi1 += q8[j+ 0] * grid1[j];
-        sumi2 += q8[j+ 8] * grid2[j];
-        sumi3 += q8[j+16] * grid3[j];
-        sumi4 += q8[j+24] * grid4[j];
-    }
-#endif
-    const float d = (float)bq1->d * __low2float(bq8_1[ib32].ds);
-    return d * (sumi1 * (2*(h1 & 7) + 1) + sumi2 * (2*((h1 >> 4) & 7) + 1) +
-                sumi3 * (2*(h2 & 7) + 1) + sumi4 * (2*((h2 >> 4) & 7) + 1));
-#else
-    assert(false);
-    return 0.f;
-#endif
-}
-
-template <int qk, int qr, int qi, bool need_sum, typename block_q_t, int mmq_x, int mmq_y, int nwarps,
-              allocate_tiles_cuda_t allocate_tiles, load_tiles_cuda_t load_tiles, int vdr, vec_dot_q_mul_mat_cuda_t vec_dot>
-static __device__ __forceinline__ void mul_mat_q(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-    const block_q_t  * x = (const block_q_t  *) vx;
-    const block_q8_1 * y = (const block_q8_1 *) vy;
-
-    const int blocks_per_row_x = ncols_x / qk;
-    const int blocks_per_col_y = nrows_y / QK8_1;
-    const int blocks_per_warp = WARP_SIZE / qi;
-
-    const int & ncols_dst = ncols_y;
-
-    const int row_dst_0 = blockIdx.x*mmq_y;
-    const int & row_x_0 = row_dst_0;
-
-    const int col_dst_0 = blockIdx.y*mmq_x;
-    const int & col_y_0 = col_dst_0;
-
-    int   * tile_x_ql = nullptr;
-    half2 * tile_x_dm = nullptr;
-    int   * tile_x_qh = nullptr;
-    int   * tile_x_sc = nullptr;
-
-    allocate_tiles(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc);
-
-    __shared__ int    tile_y_qs[mmq_x * WARP_SIZE];
-    __shared__ half2  tile_y_ds[mmq_x * WARP_SIZE/QI8_1];
-
-    float sum[mmq_y/WARP_SIZE][mmq_x/nwarps] = {{0.0f}};
-
-    for (int ib0 = 0; ib0 < blocks_per_row_x; ib0 += blocks_per_warp) {
-
-        load_tiles(x + row_x_0*blocks_per_row_x + ib0, tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc,
-                   threadIdx.y, nrows_x-row_x_0-1, threadIdx.x, blocks_per_row_x);
-
-#pragma unroll
-        for (int ir = 0; ir < qr; ++ir) {
-            const int kqs = ir*WARP_SIZE + threadIdx.x;
-            const int kbxd = kqs / QI8_1;
-
-#pragma unroll
-            for (int i = 0; i < mmq_x; i += nwarps) {
-                const int col_y_eff = min(col_y_0 + threadIdx.y + i, ncols_y-1); // to prevent out-of-bounds memory accesses
-
-                const block_q8_1 * by0 = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + kbxd];
-
-                const int index_y = (threadIdx.y + i) * WARP_SIZE + kqs % WARP_SIZE;
-                tile_y_qs[index_y] = get_int_from_int8_aligned(by0->qs, threadIdx.x % QI8_1);
-            }
-
-#pragma unroll
-            for (int ids0 = 0; ids0 < mmq_x; ids0 += nwarps * QI8_1) {
-                const int ids = (ids0 + threadIdx.y * QI8_1 + threadIdx.x / (WARP_SIZE/QI8_1)) % mmq_x;
-                const int kby = threadIdx.x % (WARP_SIZE/QI8_1);
-                const int col_y_eff = min(col_y_0 + ids, ncols_y-1);
-
-                // if the sum is not needed it's faster to transform the scale to f32 ahead of time
-                const half2 * dsi_src = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + ir*(WARP_SIZE/QI8_1) + kby].ds;
-                half2       * dsi_dst = &tile_y_ds[ids * (WARP_SIZE/QI8_1) + kby];
-                if (need_sum) {
-                    *dsi_dst = *dsi_src;
-                } else {
-                    float * dfi_dst = (float *) dsi_dst;
-                    *dfi_dst = __low2half(*dsi_src);
-                }
-            }
-
-            __syncthreads();
-
-// #pragma unroll // unrolling this loop causes too much register pressure
-            for (int k = ir*WARP_SIZE/qr; k < (ir+1)*WARP_SIZE/qr; k += vdr) {
-#pragma unroll
-                for (int j = 0; j < mmq_x; j += nwarps) {
-#pragma unroll
-                    for (int i = 0; i < mmq_y; i += WARP_SIZE) {
-                        sum[i/WARP_SIZE][j/nwarps] += vec_dot(
-                            tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, tile_y_qs, tile_y_ds,
-                            threadIdx.x + i, threadIdx.y + j, k);
+struct ggml_backend_cuda_split_buffer_type_context {
+    std::array<float, GGML_CUDA_MAX_DEVICES> tensor_split;
+};
+
+struct ggml_backend_cuda_split_buffer_context {
+    ~ggml_backend_cuda_split_buffer_context() {
+        for (ggml_tensor_extra_gpu * extra : tensor_extras) {
+            for (int id = 0; id < GGML_CUDA_MAX_DEVICES; ++id) {
+                for (int64_t is = 0; is < GGML_CUDA_MAX_STREAMS; ++is) {
+                    if (extra->events[id][is] != nullptr) {
+                        CUDA_CHECK(cudaEventDestroy(extra->events[id][is]));
                     }
                 }
+                if (extra->data_device[id] != nullptr) {
+                    CUDA_CHECK(cudaFree(extra->data_device[id]));
+                }
             }
-
-            __syncthreads();
+            delete extra;
         }
     }
 
-#pragma unroll
-    for (int j = 0; j < mmq_x; j += nwarps) {
-        const int col_dst = col_dst_0 + j + threadIdx.y;
+    std::vector<ggml_tensor_extra_gpu *> tensor_extras;
+};
 
-        if (col_dst >= ncols_dst) {
-            return;
+GGML_CALL static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_t buffer) {
+    return GGML_CUDA_NAME "_Split";
+
+    GGML_UNUSED(buffer);
+}
+
+static bool ggml_backend_buffer_is_cuda_split(ggml_backend_buffer_t buffer) {
+    return buffer->iface.get_name == ggml_backend_cuda_split_buffer_get_name;
+    GGML_UNUSED(ggml_backend_buffer_is_cuda_split); // only used in debug builds currently, avoid unused function warning in release builds
+}
+
+GGML_CALL static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context;
+    delete ctx;
+}
+
+GGML_CALL static void * ggml_backend_cuda_split_buffer_get_base(ggml_backend_buffer_t buffer) {
+    // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced
+    return (void *)0x1000;
+
+    GGML_UNUSED(buffer);
+}
+
+GGML_CALL static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
+    GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported
+
+    ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context;
+    ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context;
+
+    const int64_t ne0 = tensor->ne[0];
+
+    ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{};
+    ctx->tensor_extras.push_back(extra);
+
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
+        int64_t row_low, row_high;
+        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id);
+
+        int64_t nrows_split = row_high - row_low;
+        if (nrows_split == 0) {
+            continue;
         }
 
-#pragma unroll
-        for (int i = 0; i < mmq_y; i += WARP_SIZE) {
-            const int row_dst = row_dst_0 + threadIdx.x + i;
+        size_t size = ggml_nbytes_split(tensor, nrows_split);
+        const size_t original_size = size;
 
-            if (row_dst >= nrows_dst) {
-                continue;
-            }
-
-            dst[col_dst*nrows_dst + row_dst] = sum[i/WARP_SIZE][j/nwarps];
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
         }
+
+        // FIXME: do not crash if cudaMalloc fails
+        // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first
+        ggml_cuda_set_device(id);
+        char * buf;
+        CUDA_CHECK(cudaMalloc(&buf, size));
+
+        // set padding to 0 to avoid possible NaN values
+        if (size > original_size) {
+            CUDA_CHECK(cudaMemset(buf + original_size, 0, size - original_size));
+        }
+
+        extra->data_device[id] = buf;
+
+        for (int64_t is = 0; is < GGML_CUDA_MAX_STREAMS; ++is) {
+            CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id][is], cudaEventDisableTiming));
+        }
+    }
+    tensor->extra = extra;
+}
+
+GGML_CALL static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    // split tensors must always be set in their entirety at once
+    GGML_ASSERT(offset == 0);
+    GGML_ASSERT(size == ggml_nbytes(tensor));
+
+    ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context;
+
+    const int64_t ne0 = tensor->ne[0];
+    const size_t nb1 = tensor->nb[1];
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra;
+
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
+        int64_t row_low, row_high;
+        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id);
+
+        int64_t nrows_split = row_high - row_low;
+        if (nrows_split == 0) {
+            continue;
+        }
+
+        const size_t offset_split = row_low*nb1;
+        size_t size = ggml_nbytes_split(tensor, nrows_split);
+        const size_t original_size = size;
+
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
+
+        const char * buf_host = (const char *)data + offset_split;
+        CUDA_CHECK(cudaMemcpyAsync(extra->data_device[id], buf_host, original_size, cudaMemcpyHostToDevice, cudaStreamPerThread));
+    }
+
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
+        CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
     }
 }
 
-#define  MMQ_X_Q4_0_RDNA2  64
-#define  MMQ_Y_Q4_0_RDNA2  128
-#define NWARPS_Q4_0_RDNA2  8
-#define  MMQ_X_Q4_0_RDNA1  64
-#define  MMQ_Y_Q4_0_RDNA1  64
-#define NWARPS_Q4_0_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q4_0_AMPERE 4
-#define  MMQ_Y_Q4_0_AMPERE 32
-#define NWARPS_Q4_0_AMPERE 4
-#else
-#define  MMQ_X_Q4_0_AMPERE 64
-#define  MMQ_Y_Q4_0_AMPERE 128
-#define NWARPS_Q4_0_AMPERE 4
-#endif
-#define  MMQ_X_Q4_0_PASCAL 64
-#define  MMQ_Y_Q4_0_PASCAL 64
-#define NWARPS_Q4_0_PASCAL 8
+GGML_CALL static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    // split tensors must always be set in their entirety at once
+    GGML_ASSERT(offset == 0);
+    GGML_ASSERT(size == ggml_nbytes(tensor));
 
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q4_0_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-    mul_mat_q4_0(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+    ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context;
 
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q4_0_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q4_0_RDNA2;
-    const int nwarps = NWARPS_Q4_0_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q4_0_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q4_0_RDNA1;
-    const int nwarps = NWARPS_Q4_0_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
+    const int64_t ne0 = tensor->ne[0];
+    const size_t nb1 = tensor->nb[1];
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra;
 
-    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
-        load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
+        int64_t row_low, row_high;
+        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id);
 
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q4_0_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q4_0_AMPERE;
-    const int nwarps = NWARPS_Q4_0_AMPERE;
+        int64_t nrows_split = row_high - row_low;
+        if (nrows_split == 0) {
+            continue;
+        }
 
-    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
-        load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+        const size_t offset_split = row_low*nb1;
+        size_t size = ggml_nbytes_split(tensor, nrows_split);
+        const size_t original_size = size;
 
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q4_0_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q4_0_PASCAL;
-    const int nwarps = NWARPS_Q4_0_PASCAL;
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
 
-    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
-        load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    (void) vec_dot_q4_0_q8_1_mul_mat;
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_VOLTA
+        char * buf_host = (char *)data + offset_split;
+        CUDA_CHECK(cudaMemcpyAsync(buf_host, extra->data_device[id], original_size, cudaMemcpyDeviceToHost, cudaStreamPerThread));
+    }
+
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
+        CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
+    }
 }
 
-#define  MMQ_X_Q4_1_RDNA2  64
-#define  MMQ_Y_Q4_1_RDNA2  128
-#define NWARPS_Q4_1_RDNA2  8
-#define  MMQ_X_Q4_1_RDNA1  64
-#define  MMQ_Y_Q4_1_RDNA1  64
-#define NWARPS_Q4_1_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q4_1_AMPERE 4
-#define  MMQ_Y_Q4_1_AMPERE 32
-#define NWARPS_Q4_1_AMPERE 4
-#else
-#define  MMQ_X_Q4_1_AMPERE 64
-#define  MMQ_Y_Q4_1_AMPERE 128
-#define NWARPS_Q4_1_AMPERE 4
-#endif
-#define  MMQ_X_Q4_1_PASCAL 64
-#define  MMQ_Y_Q4_1_PASCAL 64
-#define NWARPS_Q4_1_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q4_1_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#elif __CUDA_ARCH__ < CC_VOLTA
-    __launch_bounds__(WARP_SIZE*NWARPS_Q4_1_PASCAL, 2)
-#endif // __CUDA_ARCH__ < CC_VOLTA
-    mul_mat_q4_1(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q4_1_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q4_1_RDNA2;
-    const int nwarps = NWARPS_Q4_1_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q4_1_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q4_1_RDNA1;
-    const int nwarps = NWARPS_Q4_1_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
-        load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q4_1_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q4_1_AMPERE;
-    const int nwarps = NWARPS_Q4_1_AMPERE;
-
-    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
-        load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q4_1_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q4_1_PASCAL;
-    const int nwarps = NWARPS_Q4_1_PASCAL;
-
-    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
-        load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    (void) vec_dot_q4_1_q8_1_mul_mat;
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_VOLTA
+GGML_CALL static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    GGML_UNUSED(buffer);
+    GGML_UNUSED(value);
 }
 
-#define  MMQ_X_Q5_0_RDNA2  64
-#define  MMQ_Y_Q5_0_RDNA2  128
-#define NWARPS_Q5_0_RDNA2  8
-#define  MMQ_X_Q5_0_RDNA1  64
-#define  MMQ_Y_Q5_0_RDNA1  64
-#define NWARPS_Q5_0_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q5_0_AMPERE 4
-#define  MMQ_Y_Q5_0_AMPERE 32
-#define NWARPS_Q5_0_AMPERE 4
-#else
-#define  MMQ_X_Q5_0_AMPERE 128
-#define  MMQ_Y_Q5_0_AMPERE 64
-#define NWARPS_Q5_0_AMPERE 4
-#endif
-#define  MMQ_X_Q5_0_PASCAL 64
-#define  MMQ_Y_Q5_0_PASCAL 64
-#define NWARPS_Q5_0_PASCAL 8
+static struct ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = {
+    /* .get_name        = */ ggml_backend_cuda_split_buffer_get_name,
+    /* .free_buffer     = */ ggml_backend_cuda_split_buffer_free_buffer,
+    /* .get_base        = */ ggml_backend_cuda_split_buffer_get_base,
+    /* .init_tensor     = */ ggml_backend_cuda_split_buffer_init_tensor,
+    /* .set_tensor      = */ ggml_backend_cuda_split_buffer_set_tensor,
+    /* .get_tensor      = */ ggml_backend_cuda_split_buffer_get_tensor,
+    /* .cpy_tensor      = */ NULL,
+    /* .clear           = */ ggml_backend_cuda_split_buffer_clear,
+    /* .reset           = */ NULL,
+};
 
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q5_0_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-    mul_mat_q5_0(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+// cuda split buffer type
 
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q5_0_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q5_0_RDNA2;
-    const int nwarps = NWARPS_Q5_0_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q5_0_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q5_0_RDNA1;
-    const int nwarps = NWARPS_Q5_0_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
+GGML_CALL static const char * ggml_backend_cuda_split_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    return GGML_CUDA_NAME "_Split";
 
-    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
-        load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q5_0_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q5_0_AMPERE;
-    const int nwarps = NWARPS_Q5_0_AMPERE;
-
-    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
-        load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q5_0_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q5_0_PASCAL;
-    const int nwarps = NWARPS_Q5_0_PASCAL;
-
-    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
-        load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    (void) vec_dot_q5_0_q8_1_mul_mat;
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_VOLTA
+    GGML_UNUSED(buft);
 }
 
-#define  MMQ_X_Q5_1_RDNA2  64
-#define  MMQ_Y_Q5_1_RDNA2  128
-#define NWARPS_Q5_1_RDNA2  8
-#define  MMQ_X_Q5_1_RDNA1  64
-#define  MMQ_Y_Q5_1_RDNA1  64
-#define NWARPS_Q5_1_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q5_1_AMPERE 4
-#define  MMQ_Y_Q5_1_AMPERE 32
-#define NWARPS_Q5_1_AMPERE 4
-#else
-#define  MMQ_X_Q5_1_AMPERE 128
-#define  MMQ_Y_Q5_1_AMPERE 64
-#define NWARPS_Q5_1_AMPERE 4
-#endif
-#define  MMQ_X_Q5_1_PASCAL 64
-#define  MMQ_Y_Q5_1_PASCAL 64
-#define NWARPS_Q5_1_PASCAL 8
+GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point
+    // instead, we allocate them for each tensor separately in init_tensor
+    // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated,
+    // as returned by get_alloc_size. this limit is enforced during tensor allocation by ggml-alloc, so it must be correct.
+    ggml_backend_cuda_split_buffer_context * ctx = new ggml_backend_cuda_split_buffer_context();
 
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q5_1_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-mul_mat_q5_1(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q5_1_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q5_1_RDNA2;
-    const int nwarps = NWARPS_Q5_1_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q5_1_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q5_1_RDNA1;
-    const int nwarps = NWARPS_Q5_1_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
-        load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q5_1_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q5_1_AMPERE;
-    const int nwarps = NWARPS_Q5_1_AMPERE;
-
-    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
-        load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q5_1_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q5_1_PASCAL;
-    const int nwarps = NWARPS_Q5_1_PASCAL;
-
-    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
-        load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    (void) vec_dot_q5_1_q8_1_mul_mat;
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_VOLTA
+    return ggml_backend_buffer_init(buft, ggml_backend_cuda_split_buffer_interface, ctx, size);
 }
 
-#define  MMQ_X_Q8_0_RDNA2  64
-#define  MMQ_Y_Q8_0_RDNA2  128
-#define NWARPS_Q8_0_RDNA2  8
-#define  MMQ_X_Q8_0_RDNA1  64
-#define  MMQ_Y_Q8_0_RDNA1  64
-#define NWARPS_Q8_0_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q8_0_AMPERE 4
-#define  MMQ_Y_Q8_0_AMPERE 32
-#define NWARPS_Q8_0_AMPERE 4
-#else
-#define  MMQ_X_Q8_0_AMPERE 128
-#define  MMQ_Y_Q8_0_AMPERE 64
-#define NWARPS_Q8_0_AMPERE 4
-#endif
-#define  MMQ_X_Q8_0_PASCAL 64
-#define  MMQ_Y_Q8_0_PASCAL 64
-#define NWARPS_Q8_0_PASCAL 8
+GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+    return 128;
 
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q8_0_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-    mul_mat_q8_0(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q8_0_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q8_0_RDNA2;
-    const int nwarps = NWARPS_Q8_0_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q8_0_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q8_0_RDNA1;
-    const int nwarps = NWARPS_Q8_0_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
-        load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q8_0_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q8_0_AMPERE;
-    const int nwarps = NWARPS_Q8_0_AMPERE;
-
-    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
-        load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q8_0_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q8_0_PASCAL;
-    const int nwarps = NWARPS_Q8_0_PASCAL;
-
-    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
-        load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    (void) vec_dot_q8_0_q8_1_mul_mat;
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_VOLTA
+    GGML_UNUSED(buft);
 }
 
-#define  MMQ_X_Q2_K_RDNA2  64
-#define  MMQ_Y_Q2_K_RDNA2  128
-#define NWARPS_Q2_K_RDNA2  8
-#define  MMQ_X_Q2_K_RDNA1  128
-#define  MMQ_Y_Q2_K_RDNA1  32
-#define NWARPS_Q2_K_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q2_K_AMPERE 4
-#define  MMQ_Y_Q2_K_AMPERE 32
-#define NWARPS_Q2_K_AMPERE 4
-#else
-#define  MMQ_X_Q2_K_AMPERE 64
-#define  MMQ_Y_Q2_K_AMPERE 128
-#define NWARPS_Q2_K_AMPERE 4
-#endif
-#define  MMQ_X_Q2_K_PASCAL 64
-#define  MMQ_Y_Q2_K_PASCAL 64
-#define NWARPS_Q2_K_PASCAL 8
+GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
+    ggml_backend_cuda_split_buffer_type_context * ctx = (ggml_backend_cuda_split_buffer_type_context *)buft->context;
 
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q2_K_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-mul_mat_q2_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+    size_t total_size = 0;
 
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q2_K_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q2_K_RDNA2;
-    const int nwarps = NWARPS_Q2_K_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q2_K_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q2_K_RDNA1;
-    const int nwarps = NWARPS_Q2_K_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
+    const int64_t ne0 = tensor->ne[0];
 
-    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
-        load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
+        int64_t row_low, row_high;
+        get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, id);
 
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q2_K_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q2_K_AMPERE;
-    const int nwarps = NWARPS_Q2_K_AMPERE;
+        int64_t nrows_split = row_high - row_low;
+        if (nrows_split == 0) {
+            continue;
+        }
 
-    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
-        load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+        total_size += ggml_nbytes_split(tensor, nrows_split);
 
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q2_K_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q2_K_PASCAL;
-    const int nwarps = NWARPS_Q2_K_PASCAL;
-
-    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
-        load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    (void) vec_dot_q2_K_q8_1_mul_mat;
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q3_K_RDNA2  128
-#define  MMQ_Y_Q3_K_RDNA2  64
-#define NWARPS_Q3_K_RDNA2  8
-#define  MMQ_X_Q3_K_RDNA1  32
-#define  MMQ_Y_Q3_K_RDNA1  128
-#define NWARPS_Q3_K_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q3_K_AMPERE 4
-#define  MMQ_Y_Q3_K_AMPERE 32
-#define NWARPS_Q3_K_AMPERE 4
-#else
-#define  MMQ_X_Q3_K_AMPERE 128
-#define  MMQ_Y_Q3_K_AMPERE 128
-#define NWARPS_Q3_K_AMPERE 4
-#endif
-#define  MMQ_X_Q3_K_PASCAL 64
-#define  MMQ_Y_Q3_K_PASCAL 64
-#define NWARPS_Q3_K_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q3_K_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#elif __CUDA_ARCH__ < CC_VOLTA
-    __launch_bounds__(WARP_SIZE*NWARPS_Q3_K_PASCAL, 2)
-#endif // __CUDA_ARCH__ < CC_VOLTA
-    mul_mat_q3_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q3_K_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q3_K_RDNA2;
-    const int nwarps = NWARPS_Q3_K_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q3_K_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q3_K_RDNA1;
-    const int nwarps = NWARPS_Q3_K_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
-        load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q3_K_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q3_K_AMPERE;
-    const int nwarps = NWARPS_Q3_K_AMPERE;
-
-    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
-        load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q3_K_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q3_K_PASCAL;
-    const int nwarps = NWARPS_Q3_K_PASCAL;
-
-    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
-        load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    (void) vec_dot_q3_K_q8_1_mul_mat;
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q4_K_RDNA2  64
-#define  MMQ_Y_Q4_K_RDNA2  128
-#define NWARPS_Q4_K_RDNA2  8
-#define  MMQ_X_Q4_K_RDNA1  32
-#define  MMQ_Y_Q4_K_RDNA1  64
-#define NWARPS_Q4_K_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q4_K_AMPERE 4
-#define  MMQ_Y_Q4_K_AMPERE 32
-#define NWARPS_Q4_K_AMPERE 4
-#else
-#define  MMQ_X_Q4_K_AMPERE 64
-#define  MMQ_Y_Q4_K_AMPERE 128
-#define NWARPS_Q4_K_AMPERE 4
-#endif
-#define  MMQ_X_Q4_K_PASCAL 64
-#define  MMQ_Y_Q4_K_PASCAL 64
-#define NWARPS_Q4_K_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q4_K_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#elif __CUDA_ARCH__ < CC_VOLTA
-    __launch_bounds__(WARP_SIZE*NWARPS_Q4_K_PASCAL, 2)
-#endif // __CUDA_ARCH__ < CC_VOLTA
-    mul_mat_q4_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q4_K_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q4_K_RDNA2;
-    const int nwarps = NWARPS_Q4_K_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q4_K_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q4_K_RDNA1;
-    const int nwarps = NWARPS_Q4_K_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
-        load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q4_K_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q4_K_AMPERE;
-    const int nwarps = NWARPS_Q4_K_AMPERE;
-
-    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
-        load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q4_K_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q4_K_PASCAL;
-    const int nwarps = NWARPS_Q4_K_PASCAL;
-
-    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
-        load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    (void) vec_dot_q4_K_q8_1_mul_mat;
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q5_K_RDNA2  64
-#define  MMQ_Y_Q5_K_RDNA2  128
-#define NWARPS_Q5_K_RDNA2  8
-#define  MMQ_X_Q5_K_RDNA1  32
-#define  MMQ_Y_Q5_K_RDNA1  64
-#define NWARPS_Q5_K_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q5_K_AMPERE 4
-#define  MMQ_Y_Q5_K_AMPERE 32
-#define NWARPS_Q5_K_AMPERE 4
-#else
-#define  MMQ_X_Q5_K_AMPERE 64
-#define  MMQ_Y_Q5_K_AMPERE 128
-#define NWARPS_Q5_K_AMPERE 4
-#endif
-#define  MMQ_X_Q5_K_PASCAL 64
-#define  MMQ_Y_Q5_K_PASCAL 64
-#define NWARPS_Q5_K_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q5_K_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-mul_mat_q5_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q5_K_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q5_K_RDNA2;
-    const int nwarps = NWARPS_Q5_K_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q5_K_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q5_K_RDNA1;
-    const int nwarps = NWARPS_Q5_K_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
-        load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q5_K_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q5_K_AMPERE;
-    const int nwarps = NWARPS_Q5_K_AMPERE;
-
-    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
-        load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q5_K_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q5_K_PASCAL;
-    const int nwarps = NWARPS_Q5_K_PASCAL;
-
-    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
-        load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    (void) vec_dot_q5_K_q8_1_mul_mat;
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q6_K_RDNA2  64
-#define  MMQ_Y_Q6_K_RDNA2  128
-#define NWARPS_Q6_K_RDNA2  8
-#define  MMQ_X_Q6_K_RDNA1  32
-#define  MMQ_Y_Q6_K_RDNA1  64
-#define NWARPS_Q6_K_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q6_K_AMPERE 4
-#define  MMQ_Y_Q6_K_AMPERE 32
-#define NWARPS_Q6_K_AMPERE 4
-#else
-#define  MMQ_X_Q6_K_AMPERE 64
-#define  MMQ_Y_Q6_K_AMPERE 64
-#define NWARPS_Q6_K_AMPERE 4
-#endif
-#define  MMQ_X_Q6_K_PASCAL 64
-#define  MMQ_Y_Q6_K_PASCAL 64
-#define NWARPS_Q6_K_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q6_K_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#elif __CUDA_ARCH__ < CC_VOLTA
-    __launch_bounds__(WARP_SIZE*NWARPS_Q6_K_PASCAL, 2)
-#endif // __CUDA_ARCH__ < CC_VOLTA
-    mul_mat_q6_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q6_K_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q6_K_RDNA2;
-    const int nwarps = NWARPS_Q6_K_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q6_K_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q6_K_RDNA1;
-    const int nwarps = NWARPS_Q6_K_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
-        load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q6_K_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q6_K_AMPERE;
-    const int nwarps = NWARPS_Q6_K_AMPERE;
-
-    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
-        load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q6_K_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q6_K_PASCAL;
-    const int nwarps = NWARPS_Q6_K_PASCAL;
-
-    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
-        load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    (void) vec_dot_q6_K_q8_1_mul_mat;
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-template <int ncols_y, int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
-#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
-// tell the compiler to use as many registers as it wants, see nwarps definition below
-__launch_bounds__((ncols_y <= 4 ? 4 : 2)*WARP_SIZE, 1)
-#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
-static __global__ void mul_mat_vec_q(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && (defined(RDNA2) || defined(RDNA3))
-    constexpr int nwarps              = 1;
-    constexpr int rows_per_cuda_block = 1;
-#else
-    constexpr int nwarps              = ncols_y <= 4 ? 4 : 2;
-    constexpr int rows_per_cuda_block = ncols_y == 1 ? 1 : 2;
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && !defined(RDNA2) && !defined(RDNA3)
-
-    const     int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
-    const     int row0 = rows_per_cuda_block*blockIdx.x;
-    const     int blocks_per_row_x = ncols_x / qk;
-    const     int blocks_per_col_y = nrows_y / QK8_1;
-    constexpr int blocks_per_iter = vdr * nwarps*WARP_SIZE / qi;
-
-// partial sum for each thread
-    float tmp[ncols_y][rows_per_cuda_block] = {0.0f};
-
-    const block_q_t  * x = (const block_q_t  *) vx;
-    const block_q8_1 * y = (const block_q8_1 *) vy;
-
-    for (int kbx = tid / (qi/vdr); kbx < blocks_per_row_x; kbx += blocks_per_iter) {
-        const int kby = kbx * (qk/QK8_1); // y block index that aligns with kbx
-
-        // x block quant index when casting the quants to int
-        const int kqs = vdr * (tid % (qi/vdr));
-
-#pragma unroll
-        for (int j = 0; j < ncols_y; ++j) {
-#pragma unroll
-            for (int i = 0; i < rows_per_cuda_block; ++i) {
-                tmp[j][i] += vec_dot_q_cuda(
-                    &x[kbx + (row0 + i)*blocks_per_row_x], &y[j*blocks_per_col_y + kby], kqs);
-            }
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            total_size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
         }
     }
 
-    __shared__ float tmp_shared[nwarps-1 > 0 ? nwarps-1 : 1][ncols_y][rows_per_cuda_block][WARP_SIZE];
-    if (threadIdx.y > 0) {
-#pragma unroll
-        for (int j = 0; j < ncols_y; ++j) {
-#pragma unroll
-            for (int i = 0; i < rows_per_cuda_block; ++i) {
-                tmp_shared[threadIdx.y-1][j][i][threadIdx.x] = tmp[j][i];
-            }
-        }
-    }
-    __syncthreads();
-    if (threadIdx.y > 0) {
-        return;
-    }
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int j = 0; j < ncols_y; ++j) {
-#pragma unroll
-        for (int i = 0; i < rows_per_cuda_block; ++i) {
-#pragma unroll
-            for (int l = 0; l < nwarps-1; ++l) {
-                tmp[j][i] += tmp_shared[l][j][i][threadIdx.x];
-            }
-            tmp[j][i] = warp_reduce_sum(tmp[j][i]);
-        }
-
-        if (threadIdx.x < rows_per_cuda_block) {
-            dst[j*nrows_dst + row0 + threadIdx.x] = tmp[j][threadIdx.x];
-        }
-    }
+    return total_size;
 }
 
-template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
-static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows) {
-    // qk = quantized weights per x block
-    // qr = number of quantized weights per data value in x block
-    const int row = blockIdx.x*blockDim.y + threadIdx.y;
+GGML_CALL static bool ggml_backend_cuda_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
+    return ggml_backend_is_cuda(backend);
 
-    if (row >= nrows) {
-        return;
-    }
+    GGML_UNUSED(buft);
+}
 
-    const int tid = threadIdx.x;
+GGML_CALL static bool ggml_backend_cuda_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
+    return false;
 
-    const int iter_stride = 2*GGML_CUDA_DMMV_X;
-    const int vals_per_iter = iter_stride / WARP_SIZE; // num quantized vals per thread and i iter
-    const int y_offset = qr == 1 ? 1 : qk/2;
+    GGML_UNUSED(buft);
+}
 
-// partial sum for each thread
-#ifdef GGML_CUDA_F16
-    half2 tmp = {0.0f, 0.0f}; // two sums for f16 to take advantage of half2 intrinsics
-#else
-    float tmp = 0.0f;
-#endif // GGML_CUDA_F16
+static ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_interface = {
+    /* .get_name         = */ ggml_backend_cuda_split_buffer_type_name,
+    /* .alloc_buffer     = */ ggml_backend_cuda_split_buffer_type_alloc_buffer,
+    /* .get_alignment    = */ ggml_backend_cuda_split_buffer_type_get_alignment,
+    /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
+    /* .get_alloc_size   = */ ggml_backend_cuda_split_buffer_type_get_alloc_size,
+    /* .supports_backend = */ ggml_backend_cuda_split_buffer_type_supports_backend,
+    /* .is_host          = */ ggml_backend_cuda_split_buffer_type_is_host,
+};
 
-    for (int i = 0; i < ncols; i += iter_stride) {
-        const int col = i + vals_per_iter*tid;
-        const int ib = (row*ncols + col)/qk; // x block index
-        const int iqs = (col%qk)/qr; // x quant index
-        const int iybs = col - col%qk; // y block start index
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) {
+    static std::mutex mutex;
+    std::lock_guard<std::mutex> lock(mutex);
 
-// processing >2 values per i iter is faster for fast GPUs
-#pragma unroll
-        for (int j = 0; j < vals_per_iter; j += 2) {
-            // process 2 vals per j iter
+    static std::map<std::array<float, GGML_CUDA_MAX_DEVICES>, struct ggml_backend_buffer_type> buft_map;
 
-            // dequantize
-            // for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val
-            dfloat2 v;
-            dequantize_kernel(vx, ib, iqs + j/qr, v);
+    std::array<float, GGML_CUDA_MAX_DEVICES> tensor_split_arr = {};
 
-            // matrix multiplication
-            // for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2
-#ifdef GGML_CUDA_F16
-            tmp += __hmul2(v, {
-                y[iybs + iqs + j/qr + 0],
-                y[iybs + iqs + j/qr + y_offset]
-            });
-#else
-            tmp += v.x * y[iybs + iqs + j/qr + 0];
-            tmp += v.y * y[iybs + iqs + j/qr + y_offset];
-#endif // GGML_CUDA_F16
+    bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_CUDA_MAX_DEVICES, [](float x) { return x == 0.0f; });
+    if (all_zero) {
+        tensor_split_arr = ggml_cuda_info().default_tensor_split;
+    } else {
+        float split_sum = 0.0f;
+        for (int i = 0; i < ggml_backend_cuda_get_device_count(); ++i) {
+            tensor_split_arr[i] = split_sum;
+            split_sum += tensor_split[i];
+        }
+        for (int i = 0; i < ggml_backend_cuda_get_device_count(); ++i) {
+            tensor_split_arr[i] /= split_sum;
         }
     }
 
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
+    auto it = buft_map.find(tensor_split_arr);
+    if (it != buft_map.end()) {
+        return &it->second;
     }
 
-    if (tid == 0) {
-#ifdef GGML_CUDA_F16
-        dst[row] = tmp.x + tmp.y;
-#else
-        dst[row] = tmp;
-#endif // GGML_CUDA_F16
-    }
+    struct ggml_backend_buffer_type buft {
+        /* .iface   = */ ggml_backend_cuda_split_buffer_type_interface,
+        /* .context = */ new ggml_backend_cuda_split_buffer_type_context{tensor_split_arr},
+    };
+
+    auto result = buft_map.emplace(tensor_split_arr, buft);
+    return &result.first->second;
 }
 
+// host buffer type
+
+GGML_CALL static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    return GGML_CUDA_NAME "_Host";
+
+    GGML_UNUSED(buft);
+}
+
+GGML_CALL static const char * ggml_backend_cuda_host_buffer_name(ggml_backend_buffer_t buffer) {
+    return GGML_CUDA_NAME "_Host";
+
+    GGML_UNUSED(buffer);
+}
+
+GGML_CALL static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    CUDA_CHECK(cudaFreeHost(buffer->context));
+}
+
+static void * ggml_cuda_host_malloc(size_t size) {
+    if (getenv("GGML_CUDA_NO_PINNED") != nullptr) {
+        return nullptr;
+    }
+
+    void * ptr = nullptr;
+    cudaError_t err = cudaMallocHost((void **) &ptr, size);
+    if (err != cudaSuccess) {
+        // clear the error
+        cudaGetLastError();
+        fprintf(stderr, "%s: warning: failed to allocate %.2f MiB of pinned memory: %s\n", __func__,
+            size/1024.0/1024.0, cudaGetErrorString(err));
+        return nullptr;
+    }
+
+    return ptr;
+}
+
+GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    void * ptr = ggml_cuda_host_malloc(size);
+
+    if (ptr == nullptr) {
+        // fallback to cpu buffer
+        return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size);
+    }
+
+    ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
+    buffer->buft = buft;
+    buffer->iface.get_name = ggml_backend_cuda_host_buffer_name;
+    buffer->iface.free_buffer = ggml_backend_cuda_host_buffer_free_buffer;
+
+    return buffer;
+}
+
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() {
+    static struct ggml_backend_buffer_type ggml_backend_cuda_buffer_type_host = {
+        /* .iface    = */ {
+            /* .get_name         = */ ggml_backend_cuda_host_buffer_type_name,
+            /* .alloc_buffer     = */ ggml_backend_cuda_host_buffer_type_alloc_buffer,
+            /* .get_alignment    = */ ggml_backend_cpu_buffer_type()->iface.get_alignment,
+            /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
+            /* .get_alloc_size   = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
+            /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend,
+            /* .is_host          = */ ggml_backend_cpu_buffer_type()->iface.is_host,
+        },
+        /* .context  = */ nullptr,
+    };
+
+    return &ggml_backend_cuda_buffer_type_host;
+}
+
+//static bool ggml_backend_buffer_is_cuda_host(ggml_backend_buffer_t buffer) {
+//    return buffer->buft->iface.get_name == ggml_backend_cuda_host_buffer_type_name;
+//}
+
+/// kernels
+
+typedef void (*ggml_cuda_op_mul_mat_t)(
+    ggml_backend_cuda_context & ctx,
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, cudaStream_t stream);
+
+#ifndef GGML_CUDA_PEER_MAX_BATCH_SIZE
+#define GGML_CUDA_PEER_MAX_BATCH_SIZE 128
+#endif // GGML_CUDA_PEER_MAX_BATCH_SIZE
+
+#define MUL_MAT_SRC1_COL_STRIDE 128
+
 static __global__ void mul_mat_p021_f16_f32(
     const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst,
     const int ncols_x, const int nrows_x, const int nchannels_x, const int nchannels_y) {
@@ -5703,10 +1100,7 @@ static __global__ void mul_mat_p021_f16_f32(
     const int idst = channel*nrows_dst + row_dst;
 
     // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
-    }
+    tmp = warp_reduce_sum(tmp);
 
     if (threadIdx.x == 0) {
         dst[idst] = tmp;
@@ -5749,1748 +1143,13 @@ static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous
     }
 
     // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
-    }
+    tmp = warp_reduce_sum(tmp);
 
     if (threadIdx.x == 0) {
         dst[idst] = tmp;
     }
 }
 
-static __device__ void cpy_1_f32_f32(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    float * dsti = (float *) cdsti;
-
-    *dsti = *xi;
-}
-
-static __device__ void cpy_1_f32_f16(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    half * dsti = (half *) cdsti;
-
-    *dsti = __float2half(*xi);
-}
-
-static __device__ void cpy_1_f16_f16(const char * cxi, char * cdsti) {
-    const half * xi = (const half *) cxi;
-    half * dsti = (half *) cdsti;
-
-    *dsti = *xi;
-}
-
-static __device__ void cpy_1_f16_f32(const char * cxi, char * cdsti) {
-    const half * xi = (const half *) cxi;
-    float * dsti = (float *) cdsti;
-
-    *dsti = *xi;
-}
-
-template <cpy_kernel_t cpy_1>
-static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne,
-                                   const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
-                                   const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
-                                   const int nb12, const int nb13) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (i >= ne) {
-        return;
-    }
-
-    // determine indices i03/i13, i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor
-    // then combine those indices with the corresponding byte offsets to get the total offsets
-    const int i03 = i/(ne00 * ne01 * ne02);
-    const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
-    const int i01 = (i - i03*ne00*ne01*ne02  -  i02*ne01*ne00) / ne00;
-    const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
-    const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
-
-    const int i13 = i/(ne10 * ne11 * ne12);
-    const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
-    const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
-    const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
-    const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13;
-
-    cpy_1(cx + x_offset, cdst + dst_offset);
-}
-
-static __device__ void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    block_q8_0 * dsti = (block_q8_0 *) cdsti;
-
-    float amax = 0.0f; // absolute max
-
-    for (int j = 0; j < QK8_0; j++) {
-        const float v = xi[j];
-        amax = fmaxf(amax, fabsf(v));
-    }
-
-    const float d = amax / ((1 << 7) - 1);
-    const float id = d ? 1.0f/d : 0.0f;
-
-    dsti->d = d;
-
-    for (int j = 0; j < QK8_0; ++j) {
-        const float x0 = xi[j]*id;
-
-        dsti->qs[j] = roundf(x0);
-    }
-}
-
-static __device__ void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    block_q4_0 * dsti = (block_q4_0 *) cdsti;
-
-    float amax = 0.0f;
-    float vmax = 0.0f;
-
-    for (int j = 0; j < QK4_0; ++j) {
-        const float v = xi[j];
-        if (amax < fabsf(v)) {
-            amax = fabsf(v);
-            vmax = v;
-        }
-    }
-
-    const float d  = vmax / -8;
-    const float id = d ? 1.0f/d : 0.0f;
-
-    dsti->d = d;
-
-    for (int j = 0; j < QK4_0/2; ++j) {
-        const float x0 = xi[0       + j]*id;
-        const float x1 = xi[QK4_0/2 + j]*id;
-
-        const uint8_t xi0 = min(15, (int8_t)(x0 + 8.5f));
-        const uint8_t xi1 = min(15, (int8_t)(x1 + 8.5f));
-
-        dsti->qs[j]  = xi0;
-        dsti->qs[j] |= xi1 << 4;
-    }
-}
-
-static __device__ void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    block_q4_1 * dsti = (block_q4_1 *) cdsti;
-
-    float vmin = FLT_MAX;
-    float vmax = -FLT_MAX;
-
-    for (int j = 0; j < QK4_1; ++j) {
-        const float v = xi[j];
-
-        if (v < vmin) vmin = v;
-        if (v > vmax) vmax = v;
-    }
-
-    const float d  = (vmax - vmin) / ((1 << 4) - 1);
-    const float id = d ? 1.0f/d : 0.0f;
-
-    dsti->dm.x = d;
-    dsti->dm.y = vmin;
-
-    for (int j = 0; j < QK4_1/2; ++j) {
-        const float x0 = (xi[0       + j] - vmin)*id;
-        const float x1 = (xi[QK4_1/2 + j] - vmin)*id;
-
-        const uint8_t xi0 = min(15, (int8_t)(x0 + 0.5f));
-        const uint8_t xi1 = min(15, (int8_t)(x1 + 0.5f));
-
-        dsti->qs[j]  = xi0;
-        dsti->qs[j] |= xi1 << 4;
-    }
-}
-
-template <cpy_kernel_t cpy_blck, int qk>
-static __global__ void cpy_f32_q(const char * cx, char * cdst, const int ne,
-                                 const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
-                                 const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
-                                 const int nb12, const int nb13) {
-    const int i = (blockDim.x*blockIdx.x + threadIdx.x)*qk;
-
-    if (i >= ne) {
-        return;
-    }
-
-    const int i03 = i/(ne00 * ne01 * ne02);
-    const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
-    const int i01 = (i - i03*ne00*ne01*ne02  -  i02*ne01*ne00) / ne00;
-    const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
-    const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
-
-    const int i13 = i/(ne10 * ne11 * ne12);
-    const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
-    const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
-    const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
-    const int dst_offset = (i10/qk)*nb10 + i11*nb11 + i12*nb12 + i13*nb13;
-
-    cpy_blck(cx + x_offset, cdst + dst_offset);
-}
-
-static __device__ float rope_yarn_ramp(const float low, const float high, const int i0) {
-    const float y = (i0 / 2 - low) / max(0.001f, high - low);
-    return 1.0f - min(1.0f, max(0.0f, y));
-}
-
-struct rope_corr_dims {
-    float v[4];
-};
-
-// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
-// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
-static __device__ void rope_yarn(
-    float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale,
-    float * cos_theta, float * sin_theta
-) {
-    // Get n-d rotational scaling corrected for extrapolation
-    float theta_interp = freq_scale * theta_extrap;
-    float theta = theta_interp;
-    if (ext_factor != 0.0f) {
-        float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor;
-        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
-
-        // Get n-d magnitude scaling corrected for interpolation
-        mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
-    }
-    *cos_theta = cosf(theta) * mscale;
-    *sin_theta = sinf(theta) * mscale;
-}
-
-// rope == RoPE == rotary positional embedding
-template<typename T, bool has_pos>
-static __global__ void rope(
-    const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
-    float ext_factor, float attn_factor, rope_corr_dims corr_dims
-) {
-    const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
-
-    if (col >= ncols) {
-        return;
-    }
-
-    const int row = blockDim.x*blockIdx.x + threadIdx.x;
-    const int i = row*ncols + col;
-    const int i2 = row/p_delta_rows;
-
-    const int p = has_pos ? pos[i2] : 0;
-    const float theta_base = p*powf(freq_base, -float(col)/ncols);
-
-    float cos_theta, sin_theta;
-    rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta);
-
-    const float x0 = x[i + 0];
-    const float x1 = x[i + 1];
-
-    dst[i + 0] = x0*cos_theta - x1*sin_theta;
-    dst[i + 1] = x0*sin_theta + x1*cos_theta;
-}
-
-template<typename T, bool has_pos>
-static __global__ void rope_neox(
-    const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims
-) {
-    const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
-
-    if (col >= ncols) {
-        return;
-    }
-
-    const int row = blockDim.x*blockIdx.x + threadIdx.x;
-    const int ib = col / n_dims;
-    const int ic = col % n_dims;
-
-    if (ib > 0) {
-        const int i = row*ncols + ib*n_dims + ic;
-
-        dst[i + 0] = x[i + 0];
-        dst[i + 1] = x[i + 1];
-
-        return;
-    }
-
-    const int i  = row*ncols + ib*n_dims + ic/2;
-    const int i2 = row/p_delta_rows;
-
-    float cur_rot = inv_ndims * ic - ib;
-
-    const int p = has_pos ? pos[i2] : 0;
-    const float theta_base = p*freq_scale*powf(theta_scale, col/2.0f);
-
-    float cos_theta, sin_theta;
-    rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta);
-
-    const float x0 = x[i + 0];
-    const float x1 = x[i + n_dims/2];
-
-    dst[i + 0]        = x0*cos_theta - x1*sin_theta;
-    dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
-}
-
-static __global__ void rope_glm_f32(
-    const float * x, float * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
-    int n_ctx
-) {
-    const int col = blockDim.x*blockIdx.x + threadIdx.x;
-    const int half_n_dims = ncols/4;
-
-    if (col >= half_n_dims) {
-        return;
-    }
-
-    const int row = blockDim.y*blockIdx.y + threadIdx.y;
-    const int i = row*ncols + col;
-    const int i2 = row/p_delta_rows;
-
-    const float col_theta_scale = powf(freq_base, -2.0f*col/ncols);
-     // FIXME: this is likely wrong
-    const int p = pos != nullptr ? pos[i2] : 0;
-
-    const float theta = min(p, n_ctx - 2)*freq_scale*col_theta_scale;
-    const float sin_theta = sinf(theta);
-    const float cos_theta = cosf(theta);
-
-    const float x0 = x[i + 0];
-    const float x1 = x[i + half_n_dims];
-
-    dst[i + 0]           = x0*cos_theta - x1*sin_theta;
-    dst[i + half_n_dims] = x0*sin_theta + x1*cos_theta;
-
-    const float block_theta = ((float)max(p - n_ctx - 2, 0))*col_theta_scale;
-    const float sin_block_theta = sinf(block_theta);
-    const float cos_block_theta = cosf(block_theta);
-
-    const float x2 = x[i + half_n_dims * 2];
-    const float x3 = x[i + half_n_dims * 3];
-
-    dst[i + half_n_dims * 2] = x2*cos_block_theta - x3*sin_block_theta;
-    dst[i + half_n_dims * 3] = x2*sin_block_theta + x3*cos_block_theta;
-}
-
-static __global__ void alibi_f32(const float * x, float * dst, const int ncols, const int k_rows,
-                                 const int n_heads_log2_floor, const float m0, const float m1) {
-    const int col = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (col >= ncols) {
-        return;
-    }
-
-    const int row = blockDim.y*blockIdx.y + threadIdx.y;
-    const int i = row*ncols + col;
-
-    const int k = row/k_rows;
-
-    float m_k;
-    if (k < n_heads_log2_floor) {
-        m_k = powf(m0, k + 1);
-    } else {
-        m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
-    }
-
-    dst[i] = col * m_k + x[i];
-}
-
-static __global__ void k_sum_rows_f32(const float * x, float * dst, const int ncols) {
-    const int row = blockIdx.x;
-    const int col = threadIdx.x;
-
-    float sum = 0.0f;
-    for (int i = col; i < ncols; i += blockDim.x) {
-        sum += x[row * ncols + i];
-    }
-
-    sum = warp_reduce_sum(sum);
-
-    if (col == 0) {
-        dst[row] = sum;
-    }
-}
-
-template<typename T>
-static inline __device__ void swap(T & a, T & b) {
-    T tmp = a;
-    a = b;
-    b = tmp;
-}
-
-template<ggml_sort_order order>
-static __global__ void k_argsort_f32_i32(const float * x, int * dst, const int ncols) {
-    // bitonic sort
-    int col = threadIdx.x;
-    int row = blockIdx.y;
-
-    if (col >= ncols) return;
-
-    const float * x_row = x + row * ncols;
-    int * dst_row = dst + row * ncols;
-
-    // initialize indices
-    if (col < ncols) {
-        dst_row[col] = col;
-    }
-    __syncthreads();
-
-    for (int k = 2; k <= ncols; k *= 2) {
-        for (int j = k / 2; j > 0; j /= 2) {
-            int ixj = col ^ j;
-            if (ixj > col) {
-                if ((col & k) == 0) {
-                    if (order == GGML_SORT_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) {
-                        swap(dst_row[col], dst_row[ixj]);
-                    }
-                } else {
-                    if (order == GGML_SORT_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) {
-                        swap(dst_row[col], dst_row[ixj]);
-                    }
-                }
-            }
-            __syncthreads();
-        }
-    }
-}
-
-static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past) {
-    const int col = blockDim.y*blockIdx.y + threadIdx.y;
-    const int row = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (col >= ncols) {
-        return;
-    }
-
-    const int i = row*ncols + col;
-    //dst[i] = col > (n_past + row % rows_per_channel) ? -INFINITY : x[i];
-    //dst[i] = x[i] - (col > n_past + row % rows_per_channel) * INT_MAX; // equivalent within rounding error but slightly faster on GPU
-    dst[i] = x[i] - (col > n_past + row % rows_per_channel) * FLT_MAX;
-}
-
-template <bool vals_smem, int ncols_template, int block_size_template>
-static __global__ void soft_max_f32(const float * x, const float * mask, const float * pos, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) {
-    const int ncols = ncols_template == 0 ? ncols_par : ncols_template;
-
-    const int tid  = threadIdx.x;
-    const int rowx = blockIdx.x;
-    const int rowy = rowx % nrows_y; // broadcast the mask in the row dimension
-
-    const int block_size = block_size_template == 0 ? blockDim.x : block_size_template;
-
-    const int warp_id = threadIdx.x / WARP_SIZE;
-    const int lane_id = threadIdx.x % WARP_SIZE;
-
-    float slope = 0.0f;
-
-    // ALiBi
-    if (max_bias > 0.0f) {
-        const int h = rowx/nrows_y; // head index
-
-        const float base = h < n_head_log2 ? m0 : m1;
-        const int   exp  = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
-
-        slope = powf(base, exp);
-    }
-
-    extern __shared__ float data_soft_max_f32[];
-    float * buf_iw = data_soft_max_f32; // shared memory buffer for inter-warp communication
-    // shared memory buffer to cache values between iterations:
-    float * vals = vals_smem ? buf_iw + WARP_SIZE : dst + rowx*ncols;
-
-    float max_val = -INFINITY;
-
-#pragma unroll
-    for (int col0 = 0; col0 < ncols; col0 += block_size) {
-        const int col = col0 + tid;
-
-        if (ncols_template == 0 && col >= ncols) {
-            break;
-        }
-
-        const int ix = rowx*ncols + col;
-        const int iy = rowy*ncols + col;
-
-        const float val = x[ix]*scale + (mask ? mask[iy] : 0.0f) + (pos ? slope*pos[col] : 0.0f);
-
-        vals[col] = val;
-        max_val = max(max_val, val);
-    }
-
-    // find the max value in the block
-    max_val = warp_reduce_max(max_val);
-    if (block_size > WARP_SIZE) {
-        if (warp_id == 0) {
-            buf_iw[lane_id] = -INFINITY;
-        }
-        __syncthreads();
-
-        if (lane_id == 0) {
-            buf_iw[warp_id] = max_val;
-        }
-        __syncthreads();
-
-        max_val = buf_iw[lane_id];
-        max_val = warp_reduce_max(max_val);
-    }
-
-    float tmp = 0.0f; // partial sum
-
-#pragma unroll
-    for (int col0 = 0; col0 < ncols; col0 += block_size) {
-        const int col = col0 + tid;
-
-        if (ncols_template == 0 && col >= ncols) {
-            break;
-        }
-
-        const float val = expf(vals[col] - max_val);
-        tmp += val;
-        vals[col] = val;
-    }
-
-    // find the sum of exps in the block
-    tmp = warp_reduce_sum(tmp);
-    if (block_size > WARP_SIZE) {
-        if (warp_id == 0) {
-            buf_iw[lane_id] = 0.0f;
-        }
-        __syncthreads();
-
-        if (lane_id == 0) {
-            buf_iw[warp_id] = tmp;
-        }
-        __syncthreads();
-
-        tmp = buf_iw[lane_id];
-        tmp = warp_reduce_sum(tmp);
-    }
-
-    const float inv_sum = 1.0f / tmp;
-
-#pragma unroll
-    for (int col0 = 0; col0 < ncols; col0 += block_size) {
-        const int col = col0 + tid;
-
-        if (ncols_template == 0 && col >= ncols) {
-            return;
-        }
-
-        const int idst = rowx*ncols + col;
-        dst[idst] = vals[col] * inv_sum;
-    }
-}
-
-static __global__ void scale_f32(const float * x, float * dst, const float scale, const int k) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (i >= k) {
-        return;
-    }
-
-    dst[i] = scale * x[i];
-}
-
-static __global__ void clamp_f32(const float * x, float * dst, const float min, const float max, const int k) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (i >= k) {
-        return;
-    }
-
-    dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]);
-}
-
-template <typename T>
-static  __global__ void im2col_kernel(
-        const float * x, T * dst, int batch_offset,
-        int offset_delta, int IC, int IW, int IH, int OH, int OW, int KW, int KH, int pelements, int CHW,
-        int s0, int s1, int p0, int p1, int d0, int d1) {
-    const int i = threadIdx.x + blockIdx.x * blockDim.x;
-    if (i >= pelements) {
-        return;
-    }
-
-    const int ksize = OW * (KH > 1 ? KW : 1);
-    const int kx = i / ksize;
-    const int kd = kx * ksize;
-    const int ky = (i - kd) / OW;
-    const int ix = i % OW;
-
-    const int oh = blockIdx.y;
-    const int batch = blockIdx.z / IC;
-    const int ic = blockIdx.z % IC;
-
-    const int64_t iiw = ix * s0 + kx * d0 - p0;
-    const int64_t iih = oh * s1 + ky * d1 - p1;
-
-    const int64_t offset_dst =
-        ((batch * OH + oh) * OW + ix) * CHW +
-        (ic * (KW * KH) + ky * KW + kx);
-
-    if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
-        dst[offset_dst] = 0.0f;
-    } else {
-        const int64_t offset_src = ic * offset_delta + batch * batch_offset;
-        dst[offset_dst] = x[offset_src + iih * IW + iiw];
-    }
-}
-
-template <typename Ti, typename To>
-static  __global__ void pool2d_nchw_kernel(
-        const int ih, const int iw, const int oh, const int ow,
-        const int kh, const int kw, const int sh, const int sw,
-        const int ph, const int pw, const int parallel_elements,
-        const Ti* src, To* dst, const enum ggml_op_pool op) {
-        int idx = threadIdx.x + blockIdx.x * blockDim.x;
-        if (idx >= parallel_elements) {
-            return;
-        }
-
-        const int I_HW = ih * iw;
-        const int O_HW = oh * ow;
-        const int nc = idx / O_HW;
-        const int cur_oh = idx % O_HW / ow;
-        const int cur_ow = idx % O_HW % ow;
-        const Ti* i_ptr = src + nc * I_HW;
-        To* o_ptr = dst + nc * O_HW;
-        const int start_h = cur_oh * sh - ph;
-        const int bh = max(0, start_h);
-        const int eh = min(ih, start_h + kh);
-        const int start_w = cur_ow * sw - pw;
-        const int bw = max(0, start_w);
-        const int ew = min(iw, start_w + kw);
-        const To scale = 1. / (kh * kw);
-        To res = 0;
-
-        switch (op) {
-            case GGML_OP_POOL_AVG: res = 0; break;
-            case GGML_OP_POOL_MAX: res = -FLT_MAX; break;
-        }
-
-        for (int i = bh; i < eh; i += 1) {
-            for (int j = bw; j < ew; j += 1) {
-    #if __CUDA_ARCH__ >= 350
-                Ti cur = __ldg(i_ptr + i * iw + j);
-    #else
-                Ti cur = i_ptr[i * iw + j];
-    #endif
-                switch (op) {
-                    case GGML_OP_POOL_AVG: res += cur * scale; break;
-                    case GGML_OP_POOL_MAX: res = max(res, (To)cur); break;
-                }
-            }
-        }
-        o_ptr[cur_oh * ow + cur_ow] = res;
-}
-
-template<int qk, int qr, dequantize_kernel_t dq>
-static void get_rows_cuda(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-                            const void * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) {
-
-    GGML_TENSOR_BINARY_OP_LOCALS
-
-    const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
-    const int block_num_x = (ne00 + 2*CUDA_GET_ROWS_BLOCK_SIZE - 1) / (2*CUDA_GET_ROWS_BLOCK_SIZE);
-    const dim3 block_nums(block_num_x, ne10, ne11*ne12);
-
-    // strides in elements
-    //const size_t s0 = nb0 / ggml_element_size(dst);
-    const size_t s1 = nb1 / ggml_element_size(dst);
-    const size_t s2 = nb2 / ggml_element_size(dst);
-    const size_t s3 = nb3 / ggml_element_size(dst);
-
-    const size_t s10 = nb10 / ggml_element_size(src1);
-    const size_t s11 = nb11 / ggml_element_size(src1);
-    const size_t s12 = nb12 / ggml_element_size(src1);
-    //const size_t s13 = nb13 / ggml_element_size(src1);
-
-    GGML_ASSERT(ne00 % 2 == 0);
-
-    k_get_rows<qk, qr, dq><<<block_nums, block_dims, 0, stream>>>(
-            src0_dd, src1_dd, dst_dd,
-            ne00, /*ne01, ne02, ne03,*/
-            /*ne10, ne11,*/ ne12, /*ne13,*/
-            /* s0,*/ s1, s2, s3,
-            /* nb00,*/ nb01, nb02, nb03,
-            s10, s11, s12/*, s13*/);
-
-    (void) dst;
-}
-
-template<typename src0_t>
-static void get_rows_cuda_float(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-                                const src0_t * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) {
-
-    GGML_TENSOR_BINARY_OP_LOCALS
-
-    const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
-    const int block_num_x = (ne00 + CUDA_GET_ROWS_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BLOCK_SIZE;
-    const dim3 block_nums(block_num_x, ne10, ne11*ne12);
-
-    // strides in elements
-    //const size_t s0 = nb0 / ggml_element_size(dst);
-    const size_t s1 = nb1 / ggml_element_size(dst);
-    const size_t s2 = nb2 / ggml_element_size(dst);
-    const size_t s3 = nb3 / ggml_element_size(dst);
-
-    const size_t s10 = nb10 / ggml_element_size(src1);
-    const size_t s11 = nb11 / ggml_element_size(src1);
-    const size_t s12 = nb12 / ggml_element_size(src1);
-    //const size_t s13 = nb13 / ggml_element_size(src1);
-
-    k_get_rows_float<<<block_nums, block_dims, 0, stream>>>(
-            src0_dd, src1_dd, dst_dd,
-            ne00, /*ne01, ne02, ne03,*/
-            /*ne10, ne11,*/ ne12, /*ne13,*/
-            /* s0,*/ s1, s2, s3,
-            /* nb00,*/ nb01, nb02, nb03,
-            s10, s11, s12/*, s13*/);
-
-    (void) dst;
-}
-
-template<float (*bin_op)(const float, const float)>
-struct bin_bcast_cuda {
-    template<typename src0_t, typename src1_t, typename dst_t>
-    void operator()(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst,
-            const src0_t * src0_dd, const src1_t * src1_dd, dst_t * dst_dd,
-            cudaStream_t stream) {
-
-        GGML_TENSOR_BINARY_OP_LOCALS
-
-        int nr0 = ne10/ne0;
-        int nr1 = ne11/ne1;
-        int nr2 = ne12/ne2;
-        int nr3 = ne13/ne3;
-
-        int nr[4] = { nr0, nr1, nr2, nr3 };
-
-        // collapse dimensions until first broadcast dimension
-        int64_t cne0[] = {ne0, ne1, ne2, ne3};
-        int64_t cne1[] = {ne10, ne11, ne12, ne13};
-        size_t cnb0[] = {nb0, nb1, nb2, nb3};
-        size_t cnb1[] = {nb10, nb11, nb12, nb13};
-        auto collapse = [](int64_t cne[]) {
-            cne[0] *= cne[1];
-            cne[1] = cne[2];
-            cne[2] = cne[3];
-            cne[3] = 1;
-        };
-
-        auto collapse_nb = [](size_t cnb[], const int64_t cne[]) {
-            cnb[1] *= cne[1];
-            cnb[2] *= cne[2];
-            cnb[3] *= cne[3];
-        };
-
-        for (int i = 0; i < 4; i++) {
-            if (nr[i] != 1) {
-                break;
-            }
-            if (i > 0) {
-                collapse_nb(cnb0, cne0);
-                collapse_nb(cnb1, cne1);
-                collapse(cne0);
-                collapse(cne1);
-            }
-        }
-        {
-            int64_t ne0 = cne0[0];
-            int64_t ne1 = cne0[1];
-            int64_t ne2 = cne0[2];
-            int64_t ne3 = cne0[3];
-
-            int64_t ne10 = cne1[0];
-            int64_t ne11 = cne1[1];
-            int64_t ne12 = cne1[2];
-            int64_t ne13 = cne1[3];
-
-            size_t nb0 = cnb0[0];
-            size_t nb1 = cnb0[1];
-            size_t nb2 = cnb0[2];
-            size_t nb3 = cnb0[3];
-
-            size_t nb10 = cnb1[0];
-            size_t nb11 = cnb1[1];
-            size_t nb12 = cnb1[2];
-            size_t nb13 = cnb1[3];
-
-            size_t s0 = nb0 / sizeof(dst_t);
-            size_t s1 = nb1 / sizeof(dst_t);
-            size_t s2 = nb2 / sizeof(dst_t);
-            size_t s3 = nb3 / sizeof(dst_t);
-
-            size_t s10 = nb10 / sizeof(src1_t);
-            size_t s11 = nb11 / sizeof(src1_t);
-            size_t s12 = nb12 / sizeof(src1_t);
-            size_t s13 = nb13 / sizeof(src1_t);
-
-            GGML_ASSERT(s0 == 1);
-            GGML_ASSERT(s10 == 1);
-
-            const int block_size = 128;
-
-            int64_t hne0 = std::max(ne0/2LL, 1LL);
-
-            dim3 block_dims;
-            block_dims.x = std::min<unsigned int>(hne0, block_size);
-            block_dims.y = std::min<unsigned int>(ne1, block_size / block_dims.x);
-            block_dims.z = std::min(std::min<unsigned int>(ne2*ne3, block_size / block_dims.x / block_dims.y), 64U);
-
-            dim3 block_nums(
-                (hne0 + block_dims.x - 1) / block_dims.x,
-                (ne1 + block_dims.y - 1) / block_dims.y,
-                (ne2*ne3 + block_dims.z - 1) / block_dims.z
-            );
-
-            if (block_nums.z > 65535) {
-                // this is the maximum number of blocks in z direction, fallback to 1D grid kernel
-                int block_num = (ne0*ne1*ne2*ne3 + block_size - 1) / block_size;
-                k_bin_bcast_unravel<bin_op><<<block_num, block_size, 0, stream>>>(
-                    src0_dd, src1_dd, dst_dd,
-                    ne0, ne1, ne2, ne3,
-                    ne10, ne11, ne12, ne13,
-                    /* s0, */ s1, s2, s3,
-                    /* s10, */ s11, s12, s13);
-            } else {
-                k_bin_bcast<bin_op><<<block_nums, block_dims, 0, stream>>>(
-                    src0_dd, src1_dd, dst_dd,
-                    ne0, ne1, ne2, ne3,
-                    ne10, ne11, ne12, ne13,
-                    /* s0, */ s1, s2, s3,
-                    /* s10, */ s11, s12, s13);
-            }
-        }
-    }
-};
-
-static void acc_f32_cuda(const float * x, const float * y, float * dst, const int n_elements,
-    const int ne10, const int ne11, const int ne12,
-    const int nb1, const int nb2, const int offset, cudaStream_t stream) {
-    int num_blocks = (n_elements + CUDA_ACC_BLOCK_SIZE - 1) / CUDA_ACC_BLOCK_SIZE;
-    acc_f32<<<num_blocks, CUDA_ACC_BLOCK_SIZE, 0, stream>>>(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset);
-}
-
-static void gelu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE;
-    gelu_f32<<<num_blocks, CUDA_GELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
-}
-
-static void silu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_SILU_BLOCK_SIZE - 1) / CUDA_SILU_BLOCK_SIZE;
-    silu_f32<<<num_blocks, CUDA_SILU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
-}
-
-static void gelu_quick_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE;
-    gelu_quick_f32<<<num_blocks, CUDA_GELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
-}
-
-static void tanh_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_TANH_BLOCK_SIZE - 1) / CUDA_TANH_BLOCK_SIZE;
-    tanh_f32<<<num_blocks, CUDA_TANH_BLOCK_SIZE, 0, stream>>>(x, dst, k);
-}
-
-static void relu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE;
-    relu_f32<<<num_blocks, CUDA_RELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
-}
-
-static void hardsigmoid_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_HARDSIGMOID_BLOCK_SIZE - 1) / CUDA_HARDSIGMOID_BLOCK_SIZE;
-    hardsigmoid_f32<<<num_blocks, CUDA_HARDSIGMOID_BLOCK_SIZE, 0, stream>>>(x, dst, k);
-}
-
-static void hardswish_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_HARDSWISH_BLOCK_SIZE - 1) / CUDA_HARDSWISH_BLOCK_SIZE;
-    hardswish_f32<<<num_blocks, CUDA_HARDSWISH_BLOCK_SIZE, 0, stream>>>(x, dst, k);
-}
-
-static void leaky_relu_f32_cuda(const float * x, float * dst, const int k, const float negative_slope, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE;
-    leaky_relu_f32<<<num_blocks, CUDA_RELU_BLOCK_SIZE, 0, stream>>>(x, dst, k, negative_slope);
-}
-
-static void sqr_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_SQR_BLOCK_SIZE - 1) / CUDA_SQR_BLOCK_SIZE;
-    sqr_f32<<<num_blocks, CUDA_SQR_BLOCK_SIZE, 0, stream>>>(x, dst, k);
-}
-
-static void norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) {
-    GGML_ASSERT(ncols % WARP_SIZE == 0);
-    if (ncols < 1024) {
-        const dim3 block_dims(WARP_SIZE, 1, 1);
-        norm_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
-    } else {
-        const dim3 block_dims(1024, 1, 1);
-        norm_f32<1024><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
-    }
-}
-
-static void group_norm_f32_cuda(const float * x, float * dst, const int num_groups, const int group_size, const int ne_elements, cudaStream_t stream) {
-    static const float eps = 1e-6f;
-    if (group_size < 1024) {
-        const dim3 block_dims(WARP_SIZE, 1, 1);
-        group_norm_f32<WARP_SIZE><<<num_groups, block_dims, 0, stream>>>(x, dst, group_size, ne_elements, eps);
-    } else {
-        const dim3 block_dims(1024, 1, 1);
-        group_norm_f32<1024><<<num_groups, block_dims, 0, stream>>>(x, dst, group_size, ne_elements, eps);
-    }
-}
-
-static void concat_f32_cuda(const float * x, const float * y, float * dst, const int ne0, int ne1, int ne2, int ne02, cudaStream_t stream) {
-    int num_blocks = (ne0 + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE;
-    dim3 gridDim(num_blocks, ne1, ne2);
-    concat_f32<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne02);
-}
-
-static void upscale_f32_cuda(const float * x, float * dst, const int ne00, const int ne01, const int ne02, const int scale_factor, cudaStream_t stream) {
-    int ne0 = (ne00 * scale_factor);
-    int num_blocks = (ne0 + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE;
-    dim3 gridDim(num_blocks, (ne01 * scale_factor), ne02);
-    upscale_f32<<<gridDim, CUDA_UPSCALE_BLOCK_SIZE, 0, stream>>>(x, dst, ne00, ne00 * ne01, scale_factor);
-}
-
-static void pad_f32_cuda(const float * x, float * dst,
-    const int ne00, const int ne01, const int ne02,
-    const int ne0, const int ne1, const int ne2, cudaStream_t stream) {
-    int num_blocks = (ne0 + CUDA_PAD_BLOCK_SIZE - 1) / CUDA_PAD_BLOCK_SIZE;
-    dim3 gridDim(num_blocks, ne1, ne2);
-    pad_f32<<<gridDim, CUDA_PAD_BLOCK_SIZE, 0, stream>>>(x, dst, ne0, ne00, ne01, ne02);
-}
-
-static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) {
-    GGML_ASSERT(ncols % WARP_SIZE == 0);
-    if (ncols < 1024) {
-        const dim3 block_dims(WARP_SIZE, 1, 1);
-        rms_norm_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
-    } else {
-        const dim3 block_dims(1024, 1, 1);
-        rms_norm_f32<1024><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
-    }
-}
-
-static void quantize_row_q8_1_cuda(const float * x, void * vy, const int kx, const int ky, const int kx_padded, cudaStream_t stream) {
-    const int block_num_x = (kx_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
-    const dim3 num_blocks(block_num_x, ky, 1);
-    const dim3 block_size(CUDA_DEQUANTIZE_BLOCK_SIZE, 1, 1);
-    quantize_q8_1<<<num_blocks, block_size, 0, stream>>>(x, vy, kx, kx_padded);
-}
-
-template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
-static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + 2*CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / (2*CUDA_DEQUANTIZE_BLOCK_SIZE);
-    dequantize_block<qk, qr, dequantize_kernel><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
-}
-
-static void dequantize_block_q8_0_f16_cuda(const void * __restrict__ vx, half * __restrict__ y, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_Q8_0_NE_ALIGN - 1) / CUDA_Q8_0_NE_ALIGN;
-    if (k % CUDA_Q8_0_NE_ALIGN == 0) {
-        const bool need_check = false;
-        dequantize_block_q8_0_f16<need_check><<<num_blocks, WARP_SIZE, 0, stream>>>(vx, y, k);
-    } else {
-        const bool need_check = true;
-        dequantize_block_q8_0_f16<need_check><<<num_blocks, WARP_SIZE, 0, stream>>>(vx, y, k);
-    }
-}
-
-template<typename dst_t>
-static void dequantize_row_q2_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
-    const int nb = k / QK_K;
-#if QK_K == 256
-    dequantize_block_q2_K<<<nb, 64, 0, stream>>>(vx, y);
-#else
-    dequantize_block_q2_K<<<nb, 32, 0, stream>>>(vx, y);
-#endif
-}
-
-template<typename dst_t>
-static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
-    const int nb = k / QK_K;
-#if QK_K == 256
-    dequantize_block_q3_K<<<nb, 64, 0, stream>>>(vx, y);
-#else
-    dequantize_block_q3_K<<<nb, 32, 0, stream>>>(vx, y);
-#endif
-}
-
-template<typename dst_t>
-static void dequantize_row_q4_0_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
-    const int nb32 = k / 32;
-    const int nb = (k + 255) / 256;
-    dequantize_block_q4_0<<<nb, 32, 0, stream>>>(vx, y, nb32);
-}
-
-template<typename dst_t>
-static void dequantize_row_q4_1_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
-    const int nb32 = k / 32;
-    const int nb = (k + 255) / 256;
-    dequantize_block_q4_1<<<nb, 32, 0, stream>>>(vx, y, nb32);
-}
-
-template<typename dst_t>
-static void dequantize_row_q4_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
-    const int nb = k / QK_K;
-    dequantize_block_q4_K<<<nb, 32, 0, stream>>>(vx, y);
-}
-
-template<typename dst_t>
-static void dequantize_row_q5_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
-    const int nb = k / QK_K;
-#if QK_K == 256
-    dequantize_block_q5_K<<<nb, 64, 0, stream>>>(vx, y);
-#else
-    dequantize_block_q5_K<<<nb, 32, 0, stream>>>(vx, y);
-#endif
-}
-
-template<typename dst_t>
-static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
-    const int nb = k / QK_K;
-#if QK_K == 256
-    dequantize_block_q6_K<<<nb, 64, 0, stream>>>(vx, y);
-#else
-    dequantize_block_q6_K<<<nb, 32, 0, stream>>>(vx, y);
-#endif
-}
-
-template<typename dst_t>
-static void dequantize_row_iq2_xxs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
-    const int nb = k / QK_K;
-    dequantize_block_iq2_xxs<<<nb, 32, 0, stream>>>(vx, y);
-}
-
-template<typename dst_t>
-static void dequantize_row_iq2_xs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
-    const int nb = k / QK_K;
-    dequantize_block_iq2_xs<<<nb, 32, 0, stream>>>(vx, y);
-}
-
-template<typename dst_t>
-static void dequantize_row_iq3_xxs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
-    const int nb = k / QK_K;
-    dequantize_block_iq3_xxs<<<nb, 32, 0, stream>>>(vx, y);
-}
-
-template<typename dst_t>
-static void dequantize_row_iq1_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
-    const int nb = k / QK_K;
-    dequantize_block_iq1_s<<<nb, 32, 0, stream>>>(vx, y);
-}
-
-template <typename src_t, typename dst_t>
-static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
-    convert_unary<src_t><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
-}
-
-static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
-    int id;
-    switch (type) {
-        case GGML_TYPE_Q4_0:
-            return dequantize_row_q4_0_cuda;
-        case GGML_TYPE_Q4_1:
-            return dequantize_row_q4_1_cuda;
-        case GGML_TYPE_Q5_0:
-            return dequantize_block_cuda<QK5_0, QR5_0, dequantize_q5_0>;
-        case GGML_TYPE_Q5_1:
-            return dequantize_block_cuda<QK5_1, QR5_1, dequantize_q5_1>;
-        case GGML_TYPE_Q8_0:
-            CUDA_CHECK(cudaGetDevice(&id));
-            if (g_device_caps[id].cc >= CC_PASCAL) {
-                return dequantize_block_q8_0_f16_cuda;
-            }
-            return dequantize_block_cuda<QK8_0, QR8_0, dequantize_q8_0>;
-        case GGML_TYPE_Q2_K:
-            return dequantize_row_q2_K_cuda;
-        case GGML_TYPE_Q3_K:
-            return dequantize_row_q3_K_cuda;
-        case GGML_TYPE_Q4_K:
-            return dequantize_row_q4_K_cuda;
-        case GGML_TYPE_Q5_K:
-            return dequantize_row_q5_K_cuda;
-        case GGML_TYPE_Q6_K:
-            return dequantize_row_q6_K_cuda;
-        case GGML_TYPE_IQ2_XXS:
-            return dequantize_row_iq2_xxs_cuda;
-        case GGML_TYPE_IQ2_XS:
-            return dequantize_row_iq2_xs_cuda;
-        case GGML_TYPE_IQ3_XXS:
-            return dequantize_row_iq3_xxs_cuda;
-        case GGML_TYPE_IQ1_S:
-            return dequantize_row_iq1_s_cuda;
-        case GGML_TYPE_F32:
-            return convert_unary_cuda<float>;
-        default:
-            return nullptr;
-    }
-}
-
-static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
-    switch (type) {
-        case GGML_TYPE_Q4_0:
-            return dequantize_row_q4_0_cuda;
-        case GGML_TYPE_Q4_1:
-            return dequantize_row_q4_1_cuda;
-        case GGML_TYPE_Q5_0:
-            return dequantize_block_cuda<QK5_0, QR5_0, dequantize_q5_0>;
-        case GGML_TYPE_Q5_1:
-            return dequantize_block_cuda<QK5_1, QR5_1, dequantize_q5_1>;
-        case GGML_TYPE_Q8_0:
-            return dequantize_block_cuda<QK8_0, QR8_0, dequantize_q8_0>;
-        case GGML_TYPE_Q2_K:
-            return dequantize_row_q2_K_cuda;
-        case GGML_TYPE_Q3_K:
-            return dequantize_row_q3_K_cuda;
-        case GGML_TYPE_Q4_K:
-            return dequantize_row_q4_K_cuda;
-        case GGML_TYPE_Q5_K:
-            return dequantize_row_q5_K_cuda;
-        case GGML_TYPE_Q6_K:
-            return dequantize_row_q6_K_cuda;
-        case GGML_TYPE_IQ2_XXS:
-            return dequantize_row_iq2_xxs_cuda;
-        case GGML_TYPE_IQ2_XS:
-            return dequantize_row_iq2_xs_cuda;
-        case GGML_TYPE_IQ3_XXS:
-            return dequantize_row_iq3_xxs_cuda;
-        case GGML_TYPE_IQ1_S:
-            return dequantize_row_iq1_s_cuda;
-        case GGML_TYPE_F16:
-            return convert_unary_cuda<half>;
-        default:
-            return nullptr;
-    }
-}
-
-static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>
-        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
-}
-
-static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
-}
-
-static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>
-        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
-}
-
-static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
-}
-
-static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>
-        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
-}
-
-static void dequantize_mul_mat_vec_q2_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2
-    const int block_num_y = (nrows + ny - 1) / ny;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(32, ny, 1);
-    dequantize_mul_mat_vec_q2_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
-}
-
-static void dequantize_mul_mat_vec_q3_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int ny = 2 / K_QUANTS_PER_ITERATION;
-    const int block_num_y = (nrows + ny - 1) / ny;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(32, ny, 1);
-    dequantize_mul_mat_vec_q3_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
-}
-
-static void dequantize_mul_mat_vec_q4_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int ny = 2 / K_QUANTS_PER_ITERATION;
-    const int block_num_y = (nrows + ny - 1) / ny;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(32, ny, 1);
-    dequantize_mul_mat_vec_q4_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
-}
-
-static void dequantize_mul_mat_vec_q5_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const dim3 block_dims(32, 1, 1);
-    dequantize_mul_mat_vec_q5_k<<<nrows, block_dims, 0, stream>>>(vx, y, dst, ncols);
-}
-
-static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int ny = 2 / K_QUANTS_PER_ITERATION;
-    const int block_num_y = (nrows + ny - 1) / ny;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(32, ny, 1);
-    dequantize_mul_mat_vec_q6_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
-}
-
-static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    dequantize_mul_mat_vec<1, 1, convert_f16>
-        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
-}
-
-template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot>
-static void mul_mat_vec_q_cuda(
-    const void * vx, const void * vy, float * dst,
-    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
-
-    GGML_ASSERT(ncols_x % qk == 0);
-    GGML_ASSERT(ncols_y <= MMVQ_MAX_BATCH_SIZE);
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-
-    int64_t nwarps = 1;
-    int64_t rows_per_cuda_block = 1;
-
-    if (g_device_caps[id].cc < CC_RDNA2) { // NVIDIA and AMD older than RDNA2
-        switch(ncols_y) {
-            case 1:
-                nwarps = 4;
-                rows_per_cuda_block = 1;
-                break;
-            case 2:
-            case 3:
-            case 4:
-                nwarps = 4;
-                rows_per_cuda_block = 2;
-                break;
-            case 5:
-            case 6:
-            case 7:
-            case 8:
-                nwarps = 2;
-                rows_per_cuda_block = 2;
-                break;
-            default:
-                GGML_ASSERT(false);
-                break;
-        }
-    }
-    const int64_t nblocks = (nrows_x + rows_per_cuda_block - 1) / rows_per_cuda_block;
-    const dim3 block_nums(nblocks, 1, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    switch (ncols_y) {
-        case 1:
-            mul_mat_vec_q<1, qk, qi, block_q_t, vdr, vec_dot>
-                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
-            break;
-        case 2:
-            mul_mat_vec_q<2, qk, qi, block_q_t, vdr, vec_dot>
-                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
-            break;
-        case 3:
-            mul_mat_vec_q<3, qk, qi, block_q_t, vdr, vec_dot>
-                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
-            break;
-        case 4:
-            mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot>
-                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
-            break;
-        case 5:
-            mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot>
-                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
-            break;
-        case 6:
-            mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot>
-                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
-            break;
-        case 7:
-            mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot>
-                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
-            break;
-        case 8:
-            mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot>
-                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
-            break;
-        default:
-            GGML_ASSERT(false);
-            break;
-    }
-}
-
-static void ggml_mul_mat_q4_0_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q4_0_RDNA2;
-        mmq_y  =  MMQ_Y_Q4_0_RDNA2;
-        nwarps = NWARPS_Q4_0_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q4_0_RDNA1;
-        mmq_y  =  MMQ_Y_Q4_0_RDNA1;
-        nwarps = NWARPS_Q4_0_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q4_0_AMPERE;
-        mmq_y  =  MMQ_Y_Q4_0_AMPERE;
-        nwarps = NWARPS_Q4_0_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q4_0_PASCAL;
-        mmq_y  =  MMQ_Y_Q4_0_PASCAL;
-        nwarps = NWARPS_Q4_0_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q4_0<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q4_0<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q4_1_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q4_1_RDNA2;
-        mmq_y  =  MMQ_Y_Q4_1_RDNA2;
-        nwarps = NWARPS_Q4_1_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q4_1_RDNA1;
-        mmq_y  =  MMQ_Y_Q4_1_RDNA1;
-        nwarps = NWARPS_Q4_1_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q4_1_AMPERE;
-        mmq_y  =  MMQ_Y_Q4_1_AMPERE;
-        nwarps = NWARPS_Q4_1_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q4_1_PASCAL;
-        mmq_y  =  MMQ_Y_Q4_1_PASCAL;
-        nwarps = NWARPS_Q4_1_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q4_1<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q4_1<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q5_0_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q5_0_RDNA2;
-        mmq_y  =  MMQ_Y_Q5_0_RDNA2;
-        nwarps = NWARPS_Q5_0_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q5_0_RDNA1;
-        mmq_y  =  MMQ_Y_Q5_0_RDNA1;
-        nwarps = NWARPS_Q5_0_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q5_0_AMPERE;
-        mmq_y  =  MMQ_Y_Q5_0_AMPERE;
-        nwarps = NWARPS_Q5_0_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q5_0_PASCAL;
-        mmq_y  =  MMQ_Y_Q5_0_PASCAL;
-        nwarps = NWARPS_Q5_0_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q5_0<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q5_0<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q5_1_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q5_1_RDNA2;
-        mmq_y  =  MMQ_Y_Q5_1_RDNA2;
-        nwarps = NWARPS_Q5_1_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q5_1_RDNA1;
-        mmq_y  =  MMQ_Y_Q5_1_RDNA1;
-        nwarps = NWARPS_Q5_1_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q5_1_AMPERE;
-        mmq_y  =  MMQ_Y_Q5_1_AMPERE;
-        nwarps = NWARPS_Q5_1_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q5_1_PASCAL;
-        mmq_y  =  MMQ_Y_Q5_1_PASCAL;
-        nwarps = NWARPS_Q5_1_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q5_1<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q5_1<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q8_0_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q8_0_RDNA2;
-        mmq_y  =  MMQ_Y_Q8_0_RDNA2;
-        nwarps = NWARPS_Q8_0_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q8_0_RDNA1;
-        mmq_y  =  MMQ_Y_Q8_0_RDNA1;
-        nwarps = NWARPS_Q8_0_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q8_0_AMPERE;
-        mmq_y  =  MMQ_Y_Q8_0_AMPERE;
-        nwarps = NWARPS_Q8_0_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q8_0_PASCAL;
-        mmq_y  =  MMQ_Y_Q8_0_PASCAL;
-        nwarps = NWARPS_Q8_0_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q8_0<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q8_0<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q2_K_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q2_K_RDNA2;
-        mmq_y  =  MMQ_Y_Q2_K_RDNA2;
-        nwarps = NWARPS_Q2_K_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q2_K_RDNA1;
-        mmq_y  =  MMQ_Y_Q2_K_RDNA1;
-        nwarps = NWARPS_Q2_K_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q2_K_AMPERE;
-        mmq_y  =  MMQ_Y_Q2_K_AMPERE;
-        nwarps = NWARPS_Q2_K_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q2_K_PASCAL;
-        mmq_y  =  MMQ_Y_Q2_K_PASCAL;
-        nwarps = NWARPS_Q2_K_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q2_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q2_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q3_K_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-#if QK_K == 256
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q3_K_RDNA2;
-        mmq_y  =  MMQ_Y_Q3_K_RDNA2;
-        nwarps = NWARPS_Q3_K_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q3_K_RDNA1;
-        mmq_y  =  MMQ_Y_Q3_K_RDNA1;
-        nwarps = NWARPS_Q3_K_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q3_K_AMPERE;
-        mmq_y  =  MMQ_Y_Q3_K_AMPERE;
-        nwarps = NWARPS_Q3_K_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q3_K_PASCAL;
-        mmq_y  =  MMQ_Y_Q3_K_PASCAL;
-        nwarps = NWARPS_Q3_K_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-#endif
-}
-
-static void ggml_mul_mat_q4_K_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q4_K_RDNA2;
-        mmq_y  =  MMQ_Y_Q4_K_RDNA2;
-        nwarps = NWARPS_Q4_K_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q4_K_RDNA1;
-        mmq_y  =  MMQ_Y_Q4_K_RDNA1;
-        nwarps = NWARPS_Q4_K_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q4_K_AMPERE;
-        mmq_y  =  MMQ_Y_Q4_K_AMPERE;
-        nwarps = NWARPS_Q4_K_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q4_K_PASCAL;
-        mmq_y  =  MMQ_Y_Q4_K_PASCAL;
-        nwarps = NWARPS_Q4_K_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q4_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q4_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q5_K_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q5_K_RDNA2;
-        mmq_y  =  MMQ_Y_Q5_K_RDNA2;
-        nwarps = NWARPS_Q5_K_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q5_K_RDNA1;
-        mmq_y  =  MMQ_Y_Q5_K_RDNA1;
-        nwarps = NWARPS_Q5_K_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q5_K_AMPERE;
-        mmq_y  =  MMQ_Y_Q5_K_AMPERE;
-        nwarps = NWARPS_Q5_K_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q5_K_PASCAL;
-        mmq_y  =  MMQ_Y_Q5_K_PASCAL;
-        nwarps = NWARPS_Q5_K_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q5_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q5_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q6_K_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q6_K_RDNA2;
-        mmq_y  =  MMQ_Y_Q6_K_RDNA2;
-        nwarps = NWARPS_Q6_K_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q6_K_RDNA1;
-        mmq_y  =  MMQ_Y_Q6_K_RDNA1;
-        nwarps = NWARPS_Q6_K_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q6_K_AMPERE;
-        mmq_y  =  MMQ_Y_Q6_K_AMPERE;
-        nwarps = NWARPS_Q6_K_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q6_K_PASCAL;
-        mmq_y  =  MMQ_Y_Q6_K_PASCAL;
-        nwarps = NWARPS_Q6_K_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q6_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q6_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
 static void ggml_mul_mat_p021_f16_f32_cuda(
     const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x,
     const int nchannels_x, const int nchannels_y, cudaStream_t stream) {
@@ -7510,618 +1169,11 @@ static void ggml_mul_mat_vec_nc_f16_f32_cuda(
         (vx, y, dst, ncols_x, nrows_x, row_stride_x, channel_stride_x, nchannels_y/nchannels_x);
 }
 
-
-static void ggml_cpy_f16_f32_cuda(
-    const char * cx, char * cdst, const int ne,
-    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
-    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
-
-    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
-    cpy_f32_f16<cpy_1_f16_f32><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
-        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
-}
-
-static void ggml_cpy_f32_f32_cuda(
-    const char * cx, char * cdst, const int ne,
-    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
-    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
-
-    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
-    cpy_f32_f16<cpy_1_f32_f32><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
-        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
-}
-
-static void ggml_cpy_f32_f16_cuda(
-    const char * cx, char * cdst, const int ne,
-    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
-    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
-
-    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
-    cpy_f32_f16<cpy_1_f32_f16><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
-        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
-}
-
-static void ggml_cpy_f32_q8_0_cuda(
-    const char * cx, char * cdst, const int ne,
-    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
-    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
-
-    GGML_ASSERT(ne % QK8_0 == 0);
-    const int num_blocks = ne / QK8_0;
-    cpy_f32_q<cpy_blck_f32_q8_0, QK8_0><<<num_blocks, 1, 0, stream>>>
-        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
-}
-
-static void ggml_cpy_f32_q4_0_cuda(
-    const char * cx, char * cdst, const int ne,
-    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
-    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
-
-    GGML_ASSERT(ne % QK4_0 == 0);
-    const int num_blocks = ne / QK4_0;
-    cpy_f32_q<cpy_blck_f32_q4_0, QK4_0><<<num_blocks, 1, 0, stream>>>
-        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
-}
-
-static void ggml_cpy_f32_q4_1_cuda(
-    const char * cx, char * cdst, const int ne,
-    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
-    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
-
-    GGML_ASSERT(ne % QK4_1 == 0);
-    const int num_blocks = ne / QK4_1;
-    cpy_f32_q<cpy_blck_f32_q4_1, QK4_1><<<num_blocks, 1, 0, stream>>>
-        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
-}
-
-static void ggml_cpy_f16_f16_cuda(
-    const char * cx, char * cdst, const int ne,
-    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
-    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
-
-    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
-    cpy_f32_f16<cpy_1_f16_f16><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
-        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
-}
-
-
-
-static void scale_f32_cuda(const float * x, float * dst, const float scale, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE;
-    scale_f32<<<num_blocks, CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, k);
-}
-
-static void clamp_f32_cuda(const float * x, float * dst, const float min, const float max, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_CLAMP_BLOCK_SIZE - 1) / CUDA_CLAMP_BLOCK_SIZE;
-    clamp_f32<<<num_blocks, CUDA_CLAMP_BLOCK_SIZE, 0, stream>>>(x, dst, min, max, k);
-}
-
-template<typename T>
-static void rope_cuda(
-    const T * x, T * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream
-) {
-    GGML_ASSERT(ncols % 2 == 0);
-    const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
-    const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
-    const dim3 block_nums(nrows, num_blocks_x, 1);
-    if (pos == nullptr) {
-        rope<T, false><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims
-        );
-    } else {
-        rope<T, true><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims
-        );
-    }
-}
-
-template<typename T>
-static void rope_neox_cuda(
-    const T * x, T * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream
-) {
-    GGML_ASSERT(ncols % 2 == 0);
-    const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
-    const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
-    const dim3 block_nums(nrows, num_blocks_x, 1);
-
-    const float theta_scale = powf(freq_base, -2.0f/n_dims);
-    const float inv_ndims = -1.0f / n_dims;
-
-    if (pos == nullptr) {
-        rope_neox<T, false><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
-            theta_scale, inv_ndims
-        );
-    } else {
-        rope_neox<T, true><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
-            theta_scale, inv_ndims
-        );
-    }
-}
-
-static void rope_glm_f32_cuda(
-    const float * x, float * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, int n_ctx, cudaStream_t stream
-) {
-    GGML_ASSERT(ncols % 4 == 0);
-    const dim3 block_dims(CUDA_ROPE_BLOCK_SIZE/4, 1, 1);
-    const int num_blocks_x = (ncols + CUDA_ROPE_BLOCK_SIZE - 1) / CUDA_ROPE_BLOCK_SIZE;
-    const dim3 block_nums(num_blocks_x, nrows, 1);
-    rope_glm_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, n_ctx);
-}
-
-static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const int nrows,
-                           const int k_rows, const int n_heads_log2_floor, const float m0,
-                           const float m1, cudaStream_t stream) {
-    const dim3 block_dims(CUDA_ALIBI_BLOCK_SIZE, 1, 1);
-    const int num_blocks_x = (ncols + CUDA_ALIBI_BLOCK_SIZE - 1) / (CUDA_ALIBI_BLOCK_SIZE);
-    const dim3 block_nums(num_blocks_x, nrows, 1);
-    alibi_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, k_rows, n_heads_log2_floor, m0, m1);
-}
-
-static void sum_rows_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    const dim3 block_dims(WARP_SIZE, 1, 1);
-    const dim3 block_nums(nrows, 1, 1);
-    k_sum_rows_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
-}
-
-static void argsort_f32_i32_cuda(const float * x, int * dst, const int ncols, const int nrows, ggml_sort_order order, cudaStream_t stream) {
-    // bitonic sort requires ncols to be power of 2
-    GGML_ASSERT((ncols & (ncols - 1)) == 0);
-
-    const dim3 block_dims(ncols, 1, 1);
-    const dim3 block_nums(1, nrows, 1);
-    if (order == GGML_SORT_ASC) {
-        k_argsort_f32_i32<GGML_SORT_ASC><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
-    } else if (order == GGML_SORT_DESC) {
-        k_argsort_f32_i32<GGML_SORT_DESC><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
-    } else {
-        GGML_ASSERT(false);
-    }
-}
-
-static void diag_mask_inf_f32_cuda(const float * x, float * dst, const int ncols_x, const int nrows_x, const int rows_per_channel, const int n_past, cudaStream_t stream) {
-    const dim3 block_dims(1, CUDA_DIAG_MASK_INF_BLOCK_SIZE, 1);
-    const int block_num_x = (ncols_x + CUDA_DIAG_MASK_INF_BLOCK_SIZE - 1) / CUDA_DIAG_MASK_INF_BLOCK_SIZE;
-    const dim3 block_nums(nrows_x, block_num_x, 1);
-    diag_mask_inf_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols_x, rows_per_channel, n_past);
-}
-
-static void soft_max_f32_cuda(const float * x, const float * mask, const float * pos, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, cudaStream_t stream) {
-    int nth = WARP_SIZE;
-    while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2;
-    const dim3 block_dims(nth,     1, 1);
-    const dim3 block_nums(nrows_x, 1, 1);
-    const size_t shmem = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE)*sizeof(float);
-    static_assert(CUDA_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted.");
-
-    const uint32_t n_head_kv   = nrows_x/nrows_y;
-    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
-
-    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
-    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
-
-    if (shmem < g_device_caps[g_main_device].smpb) {
-        switch (ncols_x) {
-            case 32:
-                soft_max_f32<true, 32, 32><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
-                break;
-            case 64:
-                soft_max_f32<true, 64, 64><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
-                break;
-            case 128:
-                soft_max_f32<true, 128, 128><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
-                break;
-            case 256:
-                soft_max_f32<true, 256, 256><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
-                break;
-            case 512:
-                soft_max_f32<true, 512, 512><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
-                break;
-            case 1024:
-                soft_max_f32<true, 1024, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
-                break;
-            case 2048:
-                soft_max_f32<true, 2048, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
-                break;
-            case 4096:
-                soft_max_f32<true, 4096, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
-                break;
-            default:
-                soft_max_f32<true, 0, 0><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
-                break;
-        }
-    } else {
-        const size_t shmem_low = WARP_SIZE*sizeof(float);
-        soft_max_f32<false, 0, 0><<<block_nums, block_dims, shmem_low, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
-    }
-}
-
-template <typename T>
-static void im2col_cuda(const float* x, T* dst,
-    int IW, int IH, int OW, int OH, int KW, int KH, int IC,
-    int batch, int batch_offset, int offset_delta,
-    int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) {
-    const int parallel_elements = OW * KW * KH;
-    const int num_blocks = (parallel_elements + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE;
-    dim3 block_nums(num_blocks, OH, batch * IC);
-    im2col_kernel<<<block_nums, CUDA_IM2COL_BLOCK_SIZE, 0, stream>>>(x, dst, batch_offset, offset_delta, IC, IW, IH, OH, OW, KW, KH, parallel_elements, (IC * KH * KW), s0, s1, p0, p1, d0, d1);
-}
-
-// buffer pool for cuda
-#define MAX_CUDA_BUFFERS 256
-
-struct scoped_spin_lock {
-    std::atomic_flag& lock;
-    scoped_spin_lock(std::atomic_flag& lock) : lock(lock) {
-        while (lock.test_and_set(std::memory_order_acquire)) {
-            ; // spin
-        }
-    }
-    ~scoped_spin_lock() {
-        lock.clear(std::memory_order_release);
-    }
-    scoped_spin_lock(const scoped_spin_lock&) = delete;
-    scoped_spin_lock& operator=(const scoped_spin_lock&) = delete;
-};
-
-static std::atomic_flag g_cuda_pool_lock = ATOMIC_FLAG_INIT;
-
-// #define DEBUG_CUDA_MALLOC
-struct ggml_cuda_buffer {
-    void * ptr = nullptr;
-    size_t size = 0;
-};
-
-static ggml_cuda_buffer g_cuda_buffer_pool[GGML_CUDA_MAX_DEVICES][MAX_CUDA_BUFFERS];
-static size_t g_cuda_pool_size[GGML_CUDA_MAX_DEVICES] = {0};
-
-static void * ggml_cuda_pool_malloc_leg(int device, size_t size, size_t * actual_size) {
-    scoped_spin_lock lock(g_cuda_pool_lock);
-#ifdef DEBUG_CUDA_MALLOC
-    int nnz = 0;
-    size_t max_size = 0;
-#endif
-    size_t best_diff = 1ull << 36;
-    int ibest = -1;
-    for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
-        ggml_cuda_buffer& b = g_cuda_buffer_pool[device][i];
-        if (b.ptr != nullptr) {
-#ifdef DEBUG_CUDA_MALLOC
-            ++nnz;
-            if (b.size > max_size) max_size = b.size;
-#endif
-            if (b.size >= size) {
-                size_t diff = b.size - size;
-                if (diff < best_diff) {
-                    best_diff = diff;
-                    ibest = i;
-                    if (!best_diff) {
-                        void * ptr = b.ptr;
-                        *actual_size = b.size;
-                        b.ptr = nullptr;
-                        b.size = 0;
-                        return ptr;
-                    }
-                }
-            }
-        }
-    }
-    if (ibest >= 0) {
-        ggml_cuda_buffer& b = g_cuda_buffer_pool[device][ibest];
-        void * ptr = b.ptr;
-        *actual_size = b.size;
-        b.ptr = nullptr;
-        b.size = 0;
-        return ptr;
-    }
-    void * ptr;
-    size_t look_ahead_size = (size_t) (1.05 * size);
-    look_ahead_size = 256 * ((look_ahead_size + 255)/256);
-    ggml_cuda_set_device(device);
-    CUDA_CHECK(cudaMalloc((void **) &ptr, look_ahead_size));
-    *actual_size = look_ahead_size;
-    g_cuda_pool_size[device] += look_ahead_size;
-#ifdef DEBUG_CUDA_MALLOC
-    fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz,
-            (uint32_t)(max_size/1024/1024), (uint32_t)(g_cuda_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024));
-#endif
-    return ptr;
-}
-
-static void ggml_cuda_pool_free_leg(int device, void * ptr, size_t size) {
-    scoped_spin_lock lock(g_cuda_pool_lock);
-
-    for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
-        ggml_cuda_buffer& b = g_cuda_buffer_pool[device][i];
-        if (b.ptr == nullptr) {
-            b.ptr = ptr;
-            b.size = size;
-            return;
-        }
-    }
-    fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n");
-    ggml_cuda_set_device(device);
-    CUDA_CHECK(cudaFree(ptr));
-    g_cuda_pool_size[device] -= size;
-}
-
-#if !defined(GGML_USE_HIPBLAS)
-// pool with virtual memory
-static CUdeviceptr g_cuda_pool_addr[GGML_CUDA_MAX_DEVICES] = {0};
-static size_t g_cuda_pool_used[GGML_CUDA_MAX_DEVICES] = {0};
-static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB
-
-static void * ggml_cuda_pool_malloc_vmm(int device, size_t size, size_t * actual_size) {
-    scoped_spin_lock lock(g_cuda_pool_lock);
-
-    // round up the allocation size to the alignment to ensure that all allocations are aligned for all data types
-    const size_t alignment = 128;
-    size = alignment * ((size + alignment - 1) / alignment);
-
-    size_t avail = g_cuda_pool_size[device] - g_cuda_pool_used[device];
-
-    if (size > avail) {
-        // round up to the next multiple of the granularity
-        size_t reserve_size = size - avail;
-        const size_t granularity = g_device_caps[device].vmm_granularity;
-        reserve_size = granularity * ((reserve_size + granularity - 1) / granularity);
-
-        GGML_ASSERT(g_cuda_pool_size[device] + reserve_size <= CUDA_POOL_VMM_MAX_SIZE);
-
-        // allocate more physical memory
-        CUmemAllocationProp prop = {};
-        prop.type = CU_MEM_ALLOCATION_TYPE_PINNED;
-        prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
-        prop.location.id = device;
-        CUmemGenericAllocationHandle handle;
-        CU_CHECK(cuMemCreate(&handle, reserve_size, &prop, 0));
-
-        // reserve virtual address space (if not already reserved)
-        if (g_cuda_pool_addr[device] == 0) {
-            CU_CHECK(cuMemAddressReserve(&g_cuda_pool_addr[device], CUDA_POOL_VMM_MAX_SIZE, 0, 0, 0));
-        }
-
-        // map at the end of the pool
-        CU_CHECK(cuMemMap(g_cuda_pool_addr[device] + g_cuda_pool_size[device], reserve_size, 0, handle, 0));
-
-        // the memory allocation handle is no longer needed after mapping
-        CU_CHECK(cuMemRelease(handle));
-
-        // set access
-        CUmemAccessDesc access = {};
-        access.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
-        access.location.id = device;
-        access.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE;
-        CU_CHECK(cuMemSetAccess(g_cuda_pool_addr[device] + g_cuda_pool_size[device], reserve_size, &access, 1));
-
-        // add to the pool
-        g_cuda_pool_size[device] += reserve_size;
-
-        //printf("cuda pool[%d]: size increased to %llu MB (reserved %llu MB)\n",
-        //       id, (unsigned long long) (g_cuda_pool_size[id]/1024/1024),
-        //       (unsigned long long) (reserve_size/1024/1024));
-    }
-
-    GGML_ASSERT(g_cuda_pool_addr[device] != 0);
-
-    void * ptr = (void *) (g_cuda_pool_addr[device] + g_cuda_pool_used[device]);
-    *actual_size = size;
-    g_cuda_pool_used[device] += size;
-
-#ifdef DEBUG_CUDA_MALLOC
-    printf("cuda pool[%d]: allocated %llu bytes at %llx [%s]\n", id, (unsigned long long) size, ptr);
-#endif
-
-    return ptr;
-}
-
-static void ggml_cuda_pool_free_vmm(int device, void * ptr, size_t size) {
-    scoped_spin_lock lock(g_cuda_pool_lock);
-
-#ifdef DEBUG_CUDA_MALLOC
-    printf("cuda pool[%d]: freed %llu bytes at %llx\n", id, (unsigned long long) size, ptr);
-#endif
-
-    g_cuda_pool_used[device] -= size;
-
-    // all deallocations must be in reverse order of the allocations
-    GGML_ASSERT(ptr == (void *) (g_cuda_pool_addr[device] + g_cuda_pool_used[device]));
-}
-
-static void * ggml_cuda_pool_malloc(int device, size_t size, size_t * actual_size) {
-    if (g_device_caps[device].vmm) {
-        return ggml_cuda_pool_malloc_vmm(device, size, actual_size);
-    } else {
-        return ggml_cuda_pool_malloc_leg(device, size, actual_size);
-    }
-}
-
-static void ggml_cuda_pool_free(int device, void * ptr, size_t size) {
-    if (g_device_caps[device].vmm) {
-        ggml_cuda_pool_free_vmm(device, ptr, size);
-    } else {
-        ggml_cuda_pool_free_leg(device, ptr, size);
-    }
-}
-#else
-#define ggml_cuda_pool_malloc ggml_cuda_pool_malloc_leg
-#define ggml_cuda_pool_free ggml_cuda_pool_free_leg
-#endif // !defined(GGML_USE_HIPBLAS)
-
-template<typename T>
-struct cuda_pool_alloc {
-    int device = -1;
-    T * ptr = nullptr;
-    size_t actual_size = 0;
-
-    // size is in number of elements
-    T * alloc(size_t size) {
-        GGML_ASSERT(ptr == nullptr);
-        CUDA_CHECK(cudaGetDevice(&device));
-        ptr = (T *) ggml_cuda_pool_malloc(device, size * sizeof(T), &this->actual_size);
-        return ptr;
-    }
-
-    cuda_pool_alloc(size_t size) {
-        alloc(size);
-    }
-
-    ~cuda_pool_alloc() {
-        if (ptr != nullptr) {
-            ggml_cuda_pool_free(device, ptr, actual_size);
-        }
-    }
-
-    T * get() {
-        return ptr;
-    }
-
-    cuda_pool_alloc() = default;
-    cuda_pool_alloc(const cuda_pool_alloc &) = delete;
-    cuda_pool_alloc(cuda_pool_alloc &&) = delete;
-    cuda_pool_alloc& operator=(const cuda_pool_alloc &) = delete;
-    cuda_pool_alloc& operator=(cuda_pool_alloc &&) = delete;
-};
-
-static bool g_cublas_loaded = false;
-
-GGML_CALL bool ggml_cublas_loaded(void) {
-    return g_cublas_loaded;
-}
-
-GGML_CALL void ggml_init_cublas() {
-    static bool initialized = false;
-
-    if (!initialized) {
-
-#ifdef __HIP_PLATFORM_AMD__
-        // Workaround for a rocBLAS bug when using multiple graphics cards:
-        // https://github.com/ROCmSoftwarePlatform/rocBLAS/issues/1346
-        rocblas_initialize();
-        CUDA_CHECK(cudaDeviceSynchronize());
-#endif
-
-        if (cudaGetDeviceCount(&g_device_count) != cudaSuccess) {
-            initialized = true;
-            g_cublas_loaded = false;
-            fprintf(stderr, "%s: no " GGML_CUDA_NAME " devices found, " GGML_CUDA_NAME " will be disabled\n", __func__);
-            return;
-        }
-
-        GGML_ASSERT(g_device_count <= GGML_CUDA_MAX_DEVICES);
-        int64_t total_vram = 0;
-#if defined(GGML_CUDA_FORCE_MMQ)
-        fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ:   yes\n", __func__);
-#else
-        fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ:   no\n", __func__);
-#endif
-#if defined(CUDA_USE_TENSOR_CORES)
-        fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: yes\n", __func__);
-#else
-        fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: no\n", __func__);
-#endif
-        fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, g_device_count);
-        for (int id = 0; id < g_device_count; ++id) {
-            int device_vmm = 0;
-
-#if !defined(GGML_USE_HIPBLAS)
-            CUdevice device;
-            CU_CHECK(cuDeviceGet(&device, id));
-            CU_CHECK(cuDeviceGetAttribute(&device_vmm, CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED, device));
-
-            if (device_vmm) {
-                CUmemAllocationProp alloc_prop = {};
-                alloc_prop.type = CU_MEM_ALLOCATION_TYPE_PINNED;
-                alloc_prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
-                alloc_prop.location.id = id;
-                CU_CHECK(cuMemGetAllocationGranularity(&g_device_caps[id].vmm_granularity, &alloc_prop, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED));
-            }
-#endif // !defined(GGML_USE_HIPBLAS)
-            g_device_caps[id].vmm = !!device_vmm;
-
-            cudaDeviceProp prop;
-            CUDA_CHECK(cudaGetDeviceProperties(&prop, id));
-            fprintf(stderr, "  Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no");
-
-            g_default_tensor_split[id] = total_vram;
-            total_vram += prop.totalGlobalMem;
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-            g_device_caps[id].cc = 100*prop.major + 10*prop.minor + CC_OFFSET_AMD;
-#else
-            g_device_caps[id].cc = 100*prop.major + 10*prop.minor;
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-            g_device_caps[id].smpb = prop.sharedMemPerBlock;
-        }
-        for (int id = 0; id < g_device_count; ++id) {
-            g_default_tensor_split[id] /= total_vram;
-        }
-
-        for (int id = 0; id < g_device_count; ++id) {
-            ggml_cuda_set_device(id);
-
-            // create cuda streams
-            for (int is = 0; is < MAX_STREAMS; ++is) {
-                CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStreams[id][is], cudaStreamNonBlocking));
-            }
-
-            // create cublas handle
-            CUBLAS_CHECK(cublasCreate(&g_cublas_handles[id]));
-            CUBLAS_CHECK(cublasSetMathMode(g_cublas_handles[id], CUBLAS_TF32_TENSOR_OP_MATH));
-        }
-
-        // configure logging to stdout
-        // CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr));
-
-        initialized = true;
-        g_cublas_loaded = true;
-    }
-}
-
-GGML_CALL void * ggml_cuda_host_malloc(size_t size) {
-    if (getenv("GGML_CUDA_NO_PINNED") != nullptr) {
-        return nullptr;
-    }
-
-    void * ptr = nullptr;
-    cudaError_t err = cudaMallocHost((void **) &ptr, size);
-    if (err != cudaSuccess) {
-        // clear the error
-        cudaGetLastError();
-        fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory: %s\n",
-            size/1024.0/1024.0, cudaGetErrorString(err));
-        return nullptr;
-    }
-
-    return ptr;
-}
-
-GGML_CALL void ggml_cuda_host_free(void * ptr) {
-    CUDA_CHECK(cudaFreeHost(ptr));
-}
-
 static cudaError_t ggml_cuda_cpy_tensor_2d(
     void * dst, const struct ggml_tensor * src, int64_t i3, int64_t i2, int64_t i1_low, int64_t i1_high, cudaStream_t stream) {
 
-    cudaMemcpyKind kind;
-    char * src_ptr;
-    if (src->backend == GGML_BACKEND_CPU) {
-        kind = cudaMemcpyHostToDevice;
-        src_ptr = (char *) src->data;
-    } else if (src->backend == GGML_BACKEND_GPU || src->backend == GGML_BACKEND_GPU_SPLIT) {
-        GGML_ASSERT(src->backend != GGML_BACKEND_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1]));
-        kind = cudaMemcpyDeviceToDevice;
-        ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra;
-        int id;
-        CUDA_CHECK(cudaGetDevice(&id));
-        src_ptr = (char *) extra->data_device[id];
-    } else {
-        GGML_ASSERT(false);
-    }
+    GGML_ASSERT(ggml_backend_buffer_is_cuda(src->buffer));
+    char * src_ptr = (char *) src->data;
     char * dst_ptr = (char *) dst;
 
     const int64_t ne0 = src->ne[0];
@@ -8136,681 +1188,25 @@ static cudaError_t ggml_cuda_cpy_tensor_2d(
 
     const char * x = src_ptr + i1_low*nb1 + i2*nb2 + i3*nb3;
     if (nb0 == ts && nb1 == ts*ne0/bs) {
-        return cudaMemcpyAsync(dst_ptr, x, i1_diff*nb1, kind, stream);
+        return cudaMemcpyAsync(dst_ptr, x, i1_diff*nb1, cudaMemcpyDeviceToDevice, stream);
     } else if (nb0 == ts) {
-        return cudaMemcpy2DAsync(dst_ptr, ts*ne0/bs, x, nb1, ts*ne0/bs, i1_diff, kind, stream);
+        return cudaMemcpy2DAsync(dst_ptr, ts*ne0/bs, x, nb1, ts*ne0/bs, i1_diff, cudaMemcpyDeviceToDevice, stream);
     } else {
         for (int64_t i1 = 0; i1 < i1_diff; i1++) {
             const void * rx = (const void *) ((const char *) x + i1*nb1);
             void * rd = (void *) (dst_ptr + i1*ts*ne0/bs);
             // pretend the row is a matrix with cols=1
-            cudaError_t r = cudaMemcpy2DAsync(rd, ts/bs, rx, nb0, ts/bs, ne0, kind, stream);
-            if (r != cudaSuccess) return r;
+            cudaError_t r = cudaMemcpy2DAsync(rd, ts/bs, rx, nb0, ts/bs, ne0, cudaMemcpyDeviceToDevice, stream);
+            if (r != cudaSuccess) {
+                return r;
+            }
         }
         return cudaSuccess;
     }
 }
 
-static void ggml_cuda_op_get_rows(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_d, const float * src1_d, float * dst_d, cudaStream_t stream) {
-
-    GGML_ASSERT(src1->type == GGML_TYPE_I32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
-
-    GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));
-    GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type));
-    GGML_ASSERT(dst->nb[0] == ggml_type_size(dst->type));
-
-    const int32_t * src1_i32 = (const int32_t *) src1_d;
-
-    switch (src0->type) {
-        case GGML_TYPE_F16:
-            get_rows_cuda_float(src0, src1, dst, (const half *)src0_d, src1_i32, dst_d, stream);
-            break;
-        case GGML_TYPE_F32:
-            get_rows_cuda_float(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
-            break;
-        case GGML_TYPE_Q4_0:
-            get_rows_cuda<QK4_0, QR4_0, dequantize_q4_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
-            break;
-        case GGML_TYPE_Q4_1:
-            get_rows_cuda<QK4_1, QR4_1, dequantize_q4_1>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
-            break;
-        case GGML_TYPE_Q5_0:
-            get_rows_cuda<QK5_0, QR5_0, dequantize_q5_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
-            break;
-        case GGML_TYPE_Q5_1:
-            get_rows_cuda<QK5_1, QR5_1, dequantize_q5_1>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
-            break;
-        case GGML_TYPE_Q8_0:
-            get_rows_cuda<QK8_0, QR8_0, dequantize_q8_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
-            break;
-        default:
-            // TODO: k-quants
-            fprintf(stderr, "%s: unsupported type: %s\n", __func__, ggml_type_name(src0->type));
-            GGML_ASSERT(false);
-            break;
-    }
-}
-
-template<class op>
-static void ggml_cuda_op_bin_bcast(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-
-    if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-        op()(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
-    } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
-        op()(src0, src1, dst, (const half *) src0_dd, src1_dd, (half *) dst_dd, main_stream);
-    } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
-        op()(src0, src1, dst, (const half *) src0_dd, src1_dd, dst_dd, main_stream);
-    } else {
-        fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__,
-            ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type));
-        GGML_ASSERT(false);
-    }
-}
-
-static void ggml_cuda_op_repeat(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_d, const float * src1_d, float * dst_d, cudaStream_t main_stream) {
-
-    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_repeat>>(dst, src0, dst, nullptr, src0_d, dst_d, main_stream);
-
-    (void) src1;
-    (void) src1_d;
-}
-
-static void ggml_cuda_op_add(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_add>>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
-}
-
-static void ggml_cuda_op_acc(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported
-
-    int nb1 = dst->op_params[0] / 4; // 4 bytes of float32
-    int nb2 = dst->op_params[1] / 4; // 4 bytes of float32
-    // int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused
-    int offset = dst->op_params[3] / 4; // offset in bytes
-
-    acc_f32_cuda(src0_dd, src1_dd, dst_dd, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, main_stream);
-
-    (void) dst;
-}
-
-static void ggml_cuda_op_mul(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_mul>>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
-}
-
-static void ggml_cuda_op_div(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_div>>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
-}
-
-static void ggml_cuda_op_gelu(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    gelu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_silu(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    silu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_gelu_quick(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    gelu_quick_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_tanh(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    tanh_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_relu(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    relu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_hardsigmoid(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    hardsigmoid_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_hardswish(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    hardswish_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_leaky_relu(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    float negative_slope;
-    memcpy(&negative_slope, dst->op_params, sizeof(float));
-
-    leaky_relu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), negative_slope, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_sqr(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    sqr_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_norm(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t nrows = ggml_nrows(src0);
-
-    float eps;
-    memcpy(&eps, dst->op_params, sizeof(float));
-
-    norm_f32_cuda(src0_dd, dst_dd, ne00, nrows, eps, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_group_norm(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    int num_groups = dst->op_params[0];
-    int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups);
-    group_norm_f32_cuda(src0_dd, dst_dd, num_groups, group_size, src0->ne[0] * src0->ne[1] * src0->ne[2], main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_concat(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
-
-    for (int i3 = 0; i3 < dst->ne[3]; i3++) {
-        concat_f32_cuda(src0_dd + i3 * (src0->nb[3] / 4), src1_dd + i3 * (src1->nb[3] / 4), dst_dd + i3 * (dst->nb[3] / 4), dst->ne[0], dst->ne[1], dst->ne[2], src0->ne[2], main_stream);
-    }
-
-    (void) src1;
-    (void) dst;
-}
-
-static void ggml_cuda_op_upscale(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
-    GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
-
-    const int scale_factor = dst->op_params[0];
-
-    upscale_f32_cuda(src0_dd, dst_dd, src0->ne[0], src0->ne[1], src0->ne[2], scale_factor, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_pad(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
-    GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
-
-    pad_f32_cuda(src0_dd, dst_dd,
-        src0->ne[0], src0->ne[1], src0->ne[2],
-        dst->ne[0], dst->ne[1], dst->ne[2], main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_rms_norm(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t nrows = ggml_nrows(src0);
-
-    float eps;
-    memcpy(&eps, dst->op_params, sizeof(float));
-
-    rms_norm_f32_cuda(src0_dd, dst_dd, ne00, nrows, eps, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_mul_mat_q(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
-    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
-    const int64_t src1_padded_row_size, cudaStream_t stream) {
-
-    const int64_t ne00 = src0->ne[0];
-
-    const int64_t ne10 = src1->ne[0];
-    GGML_ASSERT(ne10 % QK8_1 == 0);
-
-    const int64_t ne0 = dst->ne[0];
-
-    const int64_t row_diff = row_high - row_low;
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-
-    // the main device has a larger memory buffer to hold the results from all GPUs
-    // nrows_dst == nrows of the matrix that the kernel writes into
-    const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff;
-
-    switch (src0->type) {
-        case GGML_TYPE_Q4_0:
-            ggml_mul_mat_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q4_1:
-            ggml_mul_mat_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q5_0:
-            ggml_mul_mat_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q5_1:
-            ggml_mul_mat_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q8_0:
-            ggml_mul_mat_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q2_K:
-            ggml_mul_mat_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q3_K:
-            ggml_mul_mat_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q4_K:
-            ggml_mul_mat_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q5_K:
-            ggml_mul_mat_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q6_K:
-            ggml_mul_mat_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        default:
-            GGML_ASSERT(false);
-            break;
-    }
-
-    (void) src1;
-    (void) dst;
-    (void) src1_ddf_i;
-}
-
-static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_CUDA_MAX_DEVICES> & tensor_split) {
-    int64_t min_compute_capability = INT_MAX;
-    int64_t max_compute_capability = INT_MIN;
-    for (int id = 0; id < g_device_count; ++id) {
-        if (tensor_split[id] < (id + 1 < g_device_count ? tensor_split[id + 1] : 1.0f)) {
-            if (min_compute_capability > g_device_caps[id].cc) {
-                min_compute_capability = g_device_caps[id].cc;
-            }
-            if (max_compute_capability < g_device_caps[id].cc) {
-                max_compute_capability = g_device_caps[id].cc;
-            }
-        }
-    }
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-    switch(type) {
-        case GGML_TYPE_Q4_0:
-        case GGML_TYPE_Q4_1:
-        case GGML_TYPE_Q5_0:
-        case GGML_TYPE_Q5_1:
-        case GGML_TYPE_Q8_0:
-            return max_compute_capability >= CC_RDNA2 ? 128 : 64;
-        case GGML_TYPE_F16:
-        case GGML_TYPE_F32:
-            return 1;
-        case GGML_TYPE_Q2_K:
-            return max_compute_capability >= CC_RDNA2 ? 128 : 32;
-        case GGML_TYPE_Q3_K:
-            return min_compute_capability < CC_RDNA2 ? 128 : 64;
-        case GGML_TYPE_Q4_K:
-        case GGML_TYPE_Q5_K:
-        case GGML_TYPE_Q6_K:
-        case GGML_TYPE_IQ2_XXS:
-        case GGML_TYPE_IQ2_XS:
-        case GGML_TYPE_IQ3_XXS:
-        case GGML_TYPE_IQ1_S:
-            return max_compute_capability >= CC_RDNA2 ? 128 : 64;
-        default:
-            GGML_ASSERT(false);
-    }
-#else
-    switch(type) {
-        case GGML_TYPE_Q4_0:
-        case GGML_TYPE_Q4_1:
-            return max_compute_capability >= CC_VOLTA ? 128 : 64;
-        case GGML_TYPE_Q5_0:
-        case GGML_TYPE_Q5_1:
-        case GGML_TYPE_Q8_0:
-            return 64;
-        case GGML_TYPE_F16:
-        case GGML_TYPE_F32:
-            return 1;
-        case GGML_TYPE_Q2_K:
-        case GGML_TYPE_Q3_K:
-        case GGML_TYPE_Q4_K:
-        case GGML_TYPE_Q5_K:
-        case GGML_TYPE_IQ2_XXS:
-        case GGML_TYPE_IQ2_XS:
-        case GGML_TYPE_IQ3_XXS:
-        case GGML_TYPE_IQ1_S:
-            return max_compute_capability >= CC_VOLTA ? 128 : 64;
-        case GGML_TYPE_Q6_K:
-            return 64;
-        default:
-            GGML_ASSERT(false);
-    }
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-}
-
-static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array<float, GGML_CUDA_MAX_DEVICES> & tensor_split, int id) {
-    const int64_t nrows = ggml_nrows(tensor);
-    const int64_t rounding = get_row_rounding(tensor->type, tensor_split);
-
-    *row_low = id == 0 ? 0 : nrows*tensor_split[id];
-    *row_low -= *row_low % rounding;
-
-    if (id == g_device_count - 1) {
-        *row_high = nrows;
-    } else {
-        *row_high = nrows*tensor_split[id + 1];
-        *row_high -= *row_high % rounding;
-    }
-}
-
-static void ggml_cuda_op_mul_mat_vec_q(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
-    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
-    const int64_t src1_padded_row_size, cudaStream_t stream) {
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t row_diff = row_high - row_low;
-
-    const int64_t ne10 = src1->ne[0];
-    GGML_ASSERT(ne10 % QK8_1 == 0);
-
-    const int64_t ne0 = dst->ne[0];
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-
-    // the main device has a larger memory buffer to hold the results from all GPUs
-    // nrows_dst == nrows of the matrix that the kernel writes into
-    const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff;
-
-    switch (src0->type) {
-        case GGML_TYPE_Q4_0:
-            mul_mat_vec_q_cuda<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
-                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q4_1:
-            mul_mat_vec_q_cuda<QK4_1, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
-                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q5_0:
-            mul_mat_vec_q_cuda<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
-                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q5_1:
-            mul_mat_vec_q_cuda<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
-                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q8_0:
-            mul_mat_vec_q_cuda<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
-                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q2_K:
-            mul_mat_vec_q_cuda<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
-                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q3_K:
-            mul_mat_vec_q_cuda<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
-                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q4_K:
-            mul_mat_vec_q_cuda<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
-                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q5_K:
-            mul_mat_vec_q_cuda<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
-                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q6_K:
-            mul_mat_vec_q_cuda<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
-                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
-            break;
-        case GGML_TYPE_IQ2_XXS:
-            mul_mat_vec_q_cuda<QK_K, QI2_XXS, block_iq2_xxs, 1, vec_dot_iq2_xxs_q8_1>
-                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
-            break;
-        case GGML_TYPE_IQ2_XS:
-            mul_mat_vec_q_cuda<QK_K, QI2_XS, block_iq2_xs, 1, vec_dot_iq2_xs_q8_1>
-                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
-            break;
-        case GGML_TYPE_IQ3_XXS:
-            mul_mat_vec_q_cuda<QK_K, QI3_XXS, block_iq3_xxs, 1, vec_dot_iq3_xxs_q8_1>
-                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
-            break;
-        case GGML_TYPE_IQ1_S:
-            mul_mat_vec_q_cuda<QK_K, QI1_S, block_iq1_s, 1, vec_dot_iq1_s_q8_1>
-                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
-            break;
-        default:
-            GGML_ASSERT(false);
-            break;
-    }
-
-    (void) src1;
-    (void) dst;
-    (void) src1_ddf_i;
-    (void) src1_ncols;
-    (void) src1_padded_row_size;
-}
-
-static void ggml_cuda_op_dequantize_mul_mat_vec(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
-    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
-    const int64_t src1_padded_row_size, cudaStream_t stream) {
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t row_diff = row_high - row_low;
-
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-
-    // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
-#ifdef GGML_CUDA_F16
-    cuda_pool_alloc<half> src1_dfloat_a;
-    half * src1_dfloat = nullptr; // dfloat == half
-
-    bool src1_convert_f16 =
-        src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 ||
-        src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 ||
-        src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16;
-
-    if (src1_convert_f16) {
-        src1_dfloat = src1_dfloat_a.alloc(ne00);
-        const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
-        GGML_ASSERT(to_fp16_cuda != nullptr);
-        to_fp16_cuda(src1_ddf_i, src1_dfloat, ne00, stream);
-    }
-#else
-    const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion
-#endif // GGML_CUDA_F16
-
-    switch (src0->type) {
-        case GGML_TYPE_Q4_0:
-            dequantize_mul_mat_vec_q4_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q4_1:
-            dequantize_mul_mat_vec_q4_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q5_0:
-            dequantize_mul_mat_vec_q5_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q5_1:
-            dequantize_mul_mat_vec_q5_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q8_0:
-            dequantize_mul_mat_vec_q8_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q2_K:
-            dequantize_mul_mat_vec_q2_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q3_K:
-            dequantize_mul_mat_vec_q3_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q4_K:
-            dequantize_mul_mat_vec_q4_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q5_K:
-            dequantize_mul_mat_vec_q5_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q6_K:
-            dequantize_mul_mat_vec_q6_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_F16:
-            convert_mul_mat_vec_f16_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
-            break;
-        default:
-            GGML_ASSERT(false);
-            break;
-    }
-
-    (void) src1;
-    (void) dst;
-    (void) src1_ddq_i;
-    (void) src1_ncols;
-    (void) src1_padded_row_size;
-}
-
 static void ggml_cuda_op_mul_mat_cublas(
+    ggml_backend_cuda_context & ctx,
     const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
     const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
     const int64_t src1_padded_row_size, cudaStream_t stream) {
@@ -8826,19 +1222,17 @@ static void ggml_cuda_op_mul_mat_cublas(
 
     const int64_t row_diff = row_high - row_low;
 
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
+    int id = ggml_cuda_get_device();
 
     // the main device has a larger memory buffer to hold the results from all GPUs
     // ldc == nrows of the matrix that cuBLAS writes into
-    int ldc = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff;
+    int64_t ldc = id == ctx.device ? ne0 : row_diff;
 
-    const int compute_capability = g_device_caps[id].cc;
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
 
     if (compute_capability >= CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) {
-        //printf("this branch\n");
         // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32
-        cuda_pool_alloc<half> src0_as_f16;
+        ggml_cuda_pool_alloc<half> src0_as_f16(ctx.pool(id));
         if (src0->type != GGML_TYPE_F16) {
             const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src0->type);
             GGML_ASSERT(to_fp16_cuda != nullptr);
@@ -8848,7 +1242,7 @@ static void ggml_cuda_op_mul_mat_cublas(
         }
         const half * src0_ptr = src0->type == GGML_TYPE_F16 ? (const half *) src0_dd_i : src0_as_f16.get();
 
-        cuda_pool_alloc<half> src1_as_f16;
+        ggml_cuda_pool_alloc<half> src1_as_f16(ctx.pool(id));
         if (src1->type != GGML_TYPE_F16) {
             const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
             GGML_ASSERT(to_fp16_cuda != nullptr);
@@ -8857,14 +1251,14 @@ static void ggml_cuda_op_mul_mat_cublas(
             to_fp16_cuda(src1_ddf_i, src1_as_f16.get(), ne, stream);
         }
         const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddf_i : src1_as_f16.get();
-        cuda_pool_alloc<half> dst_f16(row_diff*src1_ncols);
+        ggml_cuda_pool_alloc<half> dst_f16(ctx.pool(id), row_diff*src1_ncols);
 
         const half alpha_f16 = 1.0f;
         const half beta_f16 = 0.0f;
 
-        CUBLAS_CHECK(cublasSetStream(g_cublas_handles[id], stream));
+        CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream));
         CUBLAS_CHECK(
-            cublasGemmEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
+            cublasGemmEx(ctx.cublas_handle(id), CUBLAS_OP_T, CUBLAS_OP_N,
                     row_diff, src1_ncols, ne10,
                     &alpha_f16, src0_ptr,       CUDA_R_16F, ne00,
                                 src1_ptr,       CUDA_R_16F, ne10,
@@ -8875,8 +1269,8 @@ static void ggml_cuda_op_mul_mat_cublas(
         const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
         to_fp32_cuda(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream);
     } else {
-        cuda_pool_alloc<float> src0_ddq_as_f32;
-        cuda_pool_alloc<float> src1_ddq_as_f32;
+        ggml_cuda_pool_alloc<float> src0_ddq_as_f32(ctx.pool(id));
+        ggml_cuda_pool_alloc<float> src1_ddq_as_f32(ctx.pool(id));
 
         if (src0->type != GGML_TYPE_F32) {
             const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src0->type);
@@ -8897,405 +1291,21 @@ static void ggml_cuda_op_mul_mat_cublas(
         const float alpha = 1.0f;
         const float beta = 0.0f;
 
-        CUBLAS_CHECK(cublasSetStream(g_cublas_handles[id], stream));
+        CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream));
         CUBLAS_CHECK(
-            cublasSgemm(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
+            cublasSgemm(ctx.cublas_handle(id), CUBLAS_OP_T, CUBLAS_OP_N,
                     row_diff, src1_ncols, ne10,
                     &alpha, src0_ddf_i,  ne00,
                             src1_ddf1_i, ne10,
                     &beta,  dst_dd_i,    ldc));
     }
 
-    (void) dst;
-    (void) src1_ddq_i;
-    (void) src1_padded_row_size;
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_ddq_i);
+    GGML_UNUSED(src1_padded_row_size);
 }
 
-static void ggml_cuda_op_rope(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32 ||  dst->type == GGML_TYPE_F16);
-    GGML_ASSERT(src0->type == dst->type);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne2 = dst->ne[2];
-    const int64_t nrows = ggml_nrows(src0);
-
-    //const int n_past      = ((int32_t *) dst->op_params)[0];
-    const int n_dims      = ((int32_t *) dst->op_params)[1];
-    const int mode        = ((int32_t *) dst->op_params)[2];
-    const int n_ctx       = ((int32_t *) dst->op_params)[3];
-    const int n_orig_ctx  = ((int32_t *) dst->op_params)[4];
-
-    // RoPE alteration for extended context
-    float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
-    memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
-    memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
-    memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
-    memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
-    memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
-    memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
-
-    const int32_t * pos = nullptr;
-    if ((mode & 1) == 0) {
-        GGML_ASSERT(src1->type == GGML_TYPE_I32);
-        GGML_ASSERT(src1->ne[0] == ne2);
-        pos = (const int32_t *) src1_dd;
-    }
-
-    const bool is_neox = mode & 2;
-    const bool is_glm  = mode & 4;
-
-    rope_corr_dims corr_dims;
-    ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v);
-
-    // compute
-    if (is_glm) {
-        GGML_ASSERT(false);
-        rope_glm_f32_cuda(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, n_ctx, main_stream);
-    } else if (is_neox) {
-        if (src0->type == GGML_TYPE_F32) {
-            rope_neox_cuda(
-                (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, main_stream
-            );
-        } else if (src0->type == GGML_TYPE_F16) {
-            rope_neox_cuda(
-                (const half *)src0_dd, (half *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, main_stream
-            );
-        } else {
-            GGML_ASSERT(false);
-        }
-    } else {
-        if (src0->type == GGML_TYPE_F32) {
-            rope_cuda(
-                (const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, main_stream
-            );
-        } else if (src0->type == GGML_TYPE_F16) {
-            rope_cuda(
-                (const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, main_stream
-            );
-        } else {
-            GGML_ASSERT(false);
-        }
-    }
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_alibi(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t nrows = ggml_nrows(src0);
-
-    //const int n_past = ((int32_t *) dst->op_params)[0];
-    const int n_head = ((int32_t *) dst->op_params)[1];
-    float max_bias;
-    memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
-
-    //GGML_ASSERT(ne01 + n_past == ne00);
-    GGML_ASSERT(n_head == ne02);
-
-    const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
-
-    const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
-    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
-
-    alibi_f32_cuda(src0_dd, dst_dd, ne00, nrows, ne01, n_heads_log2_floor, m0, m1, main_stream);
-
-    (void) src1;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_pool2d(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    const int32_t * opts = (const int32_t *)dst->op_params;
-    enum ggml_op_pool op = static_cast<ggml_op_pool>(opts[0]);
-    const int k0 = opts[1];
-    const int k1 = opts[2];
-    const int s0 = opts[3];
-    const int s1 = opts[4];
-    const int p0 = opts[5];
-    const int p1 = opts[6];
-
-    const int64_t IH = src0->ne[1];
-    const int64_t IW = src0->ne[0];
-
-    const int64_t N = dst->ne[3];
-    const int64_t OC = dst->ne[2];
-    const int64_t OH = dst->ne[1];
-    const int64_t OW = dst->ne[0];
-
-    const int parallel_elements = N * OC * OH * OW;
-    const int num_blocks = (parallel_elements + CUDA_POOL2D_BLOCK_SIZE - 1) / CUDA_POOL2D_BLOCK_SIZE;
-    dim3 block_nums(num_blocks);
-    pool2d_nchw_kernel<<<block_nums, CUDA_IM2COL_BLOCK_SIZE, 0, main_stream>>>(IH, IW, OH, OW, k1, k0, s1, s0, p1, p0, parallel_elements, src0_dd, dst_dd, op);
-
-    (void) src1;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_im2col(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F16);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);
-
-    const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
-    const int32_t s1 = ((const int32_t*)(dst->op_params))[1];
-    const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
-    const int32_t p1 = ((const int32_t*)(dst->op_params))[3];
-    const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
-    const int32_t d1 = ((const int32_t*)(dst->op_params))[5];
-
-    const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1;
-
-    const int64_t IC = src1->ne[is_2D ? 2 : 1];
-    const int64_t IH = is_2D ? src1->ne[1] : 1;
-    const int64_t IW =         src1->ne[0];
-
-    const int64_t KH = is_2D ? src0->ne[1] : 1;
-    const int64_t KW =         src0->ne[0];
-
-    const int64_t OH = is_2D ? dst->ne[2] : 1;
-    const int64_t OW =         dst->ne[1];
-
-    const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
-    const int64_t batch = src1->ne[3];
-    const size_t batch_offset = src1->nb[3] / 4; // nb is byte offset, src is type float32
-
-    if(dst->type == GGML_TYPE_F16) {
-        im2col_cuda(src1_dd, (half*) dst_dd, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, main_stream);
-    } else {
-        im2col_cuda(src1_dd, (float*) dst_dd, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, main_stream);
-    }
-
-    (void) src0;
-    (void) src0_dd;
-}
-
-static void ggml_cuda_op_sum_rows(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    const int64_t ncols = src0->ne[0];
-    const int64_t nrows = ggml_nrows(src0);
-
-    sum_rows_f32_cuda(src0_dd, dst_dd, ncols, nrows, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_argsort(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_I32);
-
-    const int64_t ncols = src0->ne[0];
-    const int64_t nrows = ggml_nrows(src0);
-
-    enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0];
-
-    argsort_f32_i32_cuda(src0_dd, (int *)dst_dd, ncols, nrows, order, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_diag_mask_inf(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int nrows0 = ggml_nrows(src0);
-
-    const int n_past = ((int32_t *) dst->op_params)[0];
-
-    diag_mask_inf_f32_cuda(src0_dd, dst_dd, ne00, nrows0, ne01, n_past, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_soft_max(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
-
-    const int64_t ne00    = src0->ne[0];
-    const int64_t nrows_x = ggml_nrows(src0);
-    const int64_t nrows_y = src0->ne[1];
-
-    float scale    = 1.0f;
-    float max_bias = 0.0f;
-
-    memcpy(&scale,    (float *) dst->op_params + 0, sizeof(float));
-    memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float));
-
-    // positions tensor
-    float * src2_dd = nullptr;
-    cuda_pool_alloc<float> src2_f;
-
-    ggml_tensor * src2 = dst->src[2];
-    const bool use_src2 = src2 != nullptr;
-
-    if (use_src2) {
-        const bool src2_on_device = src2->backend == GGML_BACKEND_GPU;
-
-        if (src2_on_device) {
-            ggml_tensor_extra_gpu * src2_extra = (ggml_tensor_extra_gpu *) src2->extra;
-            src2_dd = (float *) src2_extra->data_device[g_main_device];
-        } else {
-            src2_dd = src2_f.alloc(ggml_nelements(src2));
-            CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src2_dd, src2, 0, 0, 0, 1, main_stream));
-        }
-    }
-
-    soft_max_f32_cuda(src0_dd, src1 ? src1_dd : nullptr, src2_dd, dst_dd, ne00, nrows_x, nrows_y, scale, max_bias, main_stream);
-}
-
-static void ggml_cuda_op_scale(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    float scale;
-    memcpy(&scale, dst->op_params, sizeof(float));
-
-    scale_f32_cuda(src0_dd, dst_dd, scale, ggml_nelements(src0), main_stream);
-    CUDA_CHECK(cudaGetLastError());
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_clamp(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    float min;
-    float max;
-    memcpy(&min, dst->op_params, sizeof(float));
-    memcpy(&max, (float *) dst->op_params + 1, sizeof(float));
-
-    clamp_f32_cuda(src0_dd, dst_dd, min, max, ggml_nelements(src0), main_stream);
-    CUDA_CHECK(cudaGetLastError());
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const ggml_cuda_op_flatten_t op) {
-    const int64_t nrows0 = ggml_nrows(src0);
-
-    const bool use_src1 = src1 != nullptr;
-    const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1;
-
-    GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_GPU_SPLIT);
-    GGML_ASSERT(              dst->backend != GGML_BACKEND_GPU_SPLIT);
-
-    ggml_tensor_extra_gpu * src0_extra =            (ggml_tensor_extra_gpu *) src0->extra;
-    ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
-    ggml_tensor_extra_gpu * dst_extra  =            (ggml_tensor_extra_gpu *)  dst->extra;
-
-    const bool src0_on_device =             src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT;
-    const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_GPU;
-    const bool  dst_on_device =              dst->backend == GGML_BACKEND_GPU;
-
-    // dd = data device
-    float * src0_ddf = nullptr;
-    float * src1_ddf = nullptr;
-    float *  dst_ddf = nullptr;
-
-    cuda_pool_alloc<float> src0_f;
-    cuda_pool_alloc<float> src1_f;
-    cuda_pool_alloc<float>  dst_f;
-
-    ggml_cuda_set_device(g_main_device);
-    cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
-
-    if (src0_on_device) {
-        src0_ddf = (float *) src0_extra->data_device[g_main_device];
-    } else {
-        src0_ddf = src0_f.alloc(ggml_nelements(src0));
-        CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src0_ddf, src0, 0, 0, 0, nrows0, main_stream));
-    }
-
-    if (use_src1) {
-        if (src1_on_device) {
-            src1_ddf = (float *) src1_extra->data_device[g_main_device];
-        } else {
-            src1_ddf = src1_f.alloc(ggml_nelements(src1));
-            CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src1_ddf, src1, 0, 0, 0, nrows1, main_stream));
-        }
-    }
-    if (dst_on_device) {
-        dst_ddf = (float *) dst_extra->data_device[g_main_device];
-    } else {
-        dst_ddf = dst_f.alloc(ggml_nelements(dst));
-    }
-
-    // do the computation
-    op(src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream);
-    CUDA_CHECK(cudaGetLastError());
-
-    // copy dst to host if necessary
-    if (!dst_on_device) {
-        CUDA_CHECK(cudaMemcpyAsync(dst->data, dst_ddf, ggml_nbytes(dst), cudaMemcpyDeviceToHost, main_stream));
-    }
-
-    if (dst->backend == GGML_BACKEND_CPU) {
-        CUDA_CHECK(cudaDeviceSynchronize());
-    }
-}
-
-static void ggml_cuda_set_peer_access(const int n_tokens) {
+static void ggml_cuda_set_peer_access(const int n_tokens, int main_device) {
     static bool peer_access_enabled = false;
 
     const bool enable_peer_access = n_tokens <= GGML_CUDA_PEER_MAX_BATCH_SIZE;
@@ -9305,19 +1315,19 @@ static void ggml_cuda_set_peer_access(const int n_tokens) {
     }
 
 #ifdef NDEBUG
-    for (int id = 0; id < g_device_count; ++id) {
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
         ggml_cuda_set_device(id);
         CUDA_CHECK(cudaDeviceSynchronize());
     }
 
-    for (int id = 0; id < g_device_count; ++id) {
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
         ggml_cuda_set_device(id);
 
-        for (int id_other = 0; id_other < g_device_count; ++id_other) {
+        for (int id_other = 0; id_other < ggml_backend_cuda_get_device_count(); ++id_other) {
             if (id == id_other) {
                 continue;
             }
-            if (id != g_main_device && id_other != g_main_device) {
+            if (id != main_device && id_other != main_device) {
                 continue;
             }
 
@@ -9325,24 +1335,30 @@ static void ggml_cuda_set_peer_access(const int n_tokens) {
             CUDA_CHECK(cudaDeviceCanAccessPeer(&can_access_peer, id, id_other));
             if (can_access_peer) {
                 if (enable_peer_access) {
-                    CUDA_CHECK(cudaDeviceEnablePeerAccess(id_other, 0));
+                    cudaError_t err = cudaDeviceEnablePeerAccess(id_other, 0);
+                    if (err != cudaErrorPeerAccessAlreadyEnabled) {
+                        CUDA_CHECK(err);
+                    }
                 } else {
-                    CUDA_CHECK(cudaDeviceDisablePeerAccess(id_other));
+                    cudaError_t err = cudaDeviceDisablePeerAccess(id_other);
+                    if (err != cudaErrorPeerAccessNotEnabled) {
+                        CUDA_CHECK(err);
+                    }
                 }
             }
         }
     }
+
+    ggml_cuda_set_device(main_device);
 #endif // NDEBUG
 
     peer_access_enabled = enable_peer_access;
+
+    GGML_UNUSED(main_device);
 }
 
-// FIXME: move this somewhere else
-struct ggml_backend_cuda_split_buffer_type_context {
-    std::array<float, GGML_CUDA_MAX_DEVICES> tensor_split;
-};
-
 static void ggml_cuda_op_mul_mat(
+    ggml_backend_cuda_context & ctx,
     const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_cuda_op_mul_mat_t op,
     const bool convert_src1_to_q8_1) {
 
@@ -9362,11 +1378,14 @@ static void ggml_cuda_op_mul_mat(
     const int64_t ne0 = dst->ne[0];
     const int64_t ne1 = dst->ne[1];
 
-    const int nb2 = dst->nb[2];
-    const int nb3 = dst->nb[3];
+    const int64_t nb2 = dst->nb[2];
+    const int64_t nb3 = dst->nb[3];
+
+    GGML_ASSERT(ggml_backend_buffer_is_cuda(dst->buffer));
+    GGML_ASSERT(ggml_backend_buffer_is_cuda(src1->buffer));
+    ggml_backend_cuda_buffer_context * src1_ctx = (ggml_backend_cuda_buffer_context *) src1->buffer->context;
+    ggml_backend_cuda_buffer_context * dst_ctx  = (ggml_backend_cuda_buffer_context *) dst->buffer->context;
 
-    GGML_ASSERT(dst->backend != GGML_BACKEND_GPU_SPLIT);
-    GGML_ASSERT(src1->backend != GGML_BACKEND_GPU_SPLIT);
     GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1));
 
     GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0);
@@ -9378,34 +1397,30 @@ static void ggml_cuda_op_mul_mat(
     const size_t q8_1_ts = sizeof(block_q8_1);
     const size_t q8_1_bs = QK8_1;
 
-    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    ggml_tensor_extra_gpu *  dst_extra = (ggml_tensor_extra_gpu *)  dst->extra;
-
-    const bool src0_on_device = src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT;
     const bool src0_is_contiguous = ggml_is_contiguous(src0);
     const bool src1_is_contiguous = ggml_is_contiguous(src1);
 
     const int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING);
 
-    const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
+    const bool split = ggml_backend_buffer_is_cuda_split(src0->buffer);
     GGML_ASSERT(!(split && ne02 > 1));
     GGML_ASSERT(!(split && ne03 > 1));
     GGML_ASSERT(!(split && ne02 < ne12));
 
+    ggml_tensor_extra_gpu * src0_extra = split ? (ggml_tensor_extra_gpu *) src0->extra : nullptr;
+
+
     std::array<float, GGML_CUDA_MAX_DEVICES> tensor_split;
     if (split) {
-        // TODO: check that src0->buffer->buft is a split buffer type, replace GGML_BACKEND_GPU_SPLIT check
-        // GGML_ASSERT(src0->buffer != nullptr && src0->buffer->buft == ...);
         ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context;
         tensor_split = buft_ctx->tensor_split;
     }
 
     struct dev_data {
-        cuda_pool_alloc<char>  src0_dd_alloc;
-        cuda_pool_alloc<float> src1_ddf_alloc;
-        cuda_pool_alloc<char>  src1_ddq_alloc;
-        cuda_pool_alloc<float>   dst_dd_alloc;
+        ggml_cuda_pool_alloc<char>  src0_dd_alloc;
+        ggml_cuda_pool_alloc<float> src1_ddf_alloc;
+        ggml_cuda_pool_alloc<char>  src1_ddq_alloc;
+        ggml_cuda_pool_alloc<float>   dst_dd_alloc;
 
         char  *  src0_dd = nullptr;
         float * src1_ddf = nullptr; // float
@@ -9420,7 +1435,7 @@ static void ggml_cuda_op_mul_mat(
 
     int used_devices = 0;
 
-    for (int id = 0; id < g_device_count; ++id) {
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
         // by default, use all rows
         dev[id].row_low  = 0;
         dev[id].row_high = ne01;
@@ -9437,7 +1452,7 @@ static void ggml_cuda_op_mul_mat(
                 }
             }
 
-            if (id != g_device_count - 1) {
+            if (id != ggml_backend_cuda_get_device_count() - 1) {
                 dev[id].row_high  = ne01*tensor_split[id + 1];
                 if (dev[id].row_high < ne01) {
                     dev[id].row_high -= dev[id].row_high % rounding;
@@ -9446,33 +1461,33 @@ static void ggml_cuda_op_mul_mat(
         }
     }
 
-    for (int id = 0; id < g_device_count; ++id) {
-        if ((!split && id != g_main_device) || dev[id].row_low == dev[id].row_high) {
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
+        if ((!split && id != ctx.device) || dev[id].row_low == dev[id].row_high) {
             continue;
         }
 
         used_devices++;
 
-        const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device;
-        const bool  dst_on_device =  dst->backend == GGML_BACKEND_GPU && id == g_main_device;
+        const bool src1_on_device = id == src1_ctx->device;
+        const bool  dst_on_device = id == dst_ctx->device;
 
         ggml_cuda_set_device(id);
-        cudaStream_t stream = g_cudaStreams[id][0];
+        cudaStream_t stream = ctx.stream(id, 0);
 
-        if (src0_on_device && src0_is_contiguous) {
-            dev[id].src0_dd = (char *) src0_extra->data_device[id];
+        if (src0_is_contiguous) {
+            dev[id].src0_dd = split ? (char *) src0_extra->data_device[id] : (char *) src0->data;
         } else {
-            dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ggml_nbytes(src0));
+            dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ctx.pool(id), ggml_nbytes(src0));
         }
 
         if (src1_on_device && src1_is_contiguous) {
-            dev[id].src1_ddf = (float *) src1_extra->data_device[id];
+            dev[id].src1_ddf = (float *) src1->data;
         } else {
-            dev[id].src1_ddf = dev[id].src1_ddf_alloc.alloc(ggml_nelements(src1));
+            dev[id].src1_ddf = dev[id].src1_ddf_alloc.alloc(ctx.pool(id), ggml_nelements(src1));
         }
 
         if (convert_src1_to_q8_1) {
-            dev[id].src1_ddq = dev[id].src1_ddq_alloc.alloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs);
+            dev[id].src1_ddq = dev[id].src1_ddq_alloc.alloc(ctx.pool(id), nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs);
 
             if (src1_on_device && src1_is_contiguous) {
                 quantize_row_q8_1_cuda(dev[id].src1_ddf, dev[id].src1_ddq, ne10, nrows1, src1_padded_col_size, stream);
@@ -9481,40 +1496,40 @@ static void ggml_cuda_op_mul_mat(
         }
 
         if (dst_on_device) {
-            dev[id].dst_dd = (float *) dst_extra->data_device[id];
+            dev[id].dst_dd = (float *) dst->data;
         } else {
             const size_t size_dst_ddf = split ? (dev[id].row_high - dev[id].row_low)*ne1 : ggml_nelements(dst);
-            dev[id].dst_dd = dev[id].dst_dd_alloc.alloc(size_dst_ddf);
+            dev[id].dst_dd = dev[id].dst_dd_alloc.alloc(ctx.pool(id), size_dst_ddf);
         }
     }
 
     // if multiple devices are used they need to wait for the main device
     // here an event is recorded that signals that the main device has finished calculating the input data
     if (split && used_devices > 1) {
-        ggml_cuda_set_device(g_main_device);
-        CUDA_CHECK(cudaEventRecord(src0_extra->events[g_main_device][0], g_cudaStreams[g_main_device][0]));
+        ggml_cuda_set_device(ctx.device);
+        CUDA_CHECK(cudaEventRecord(src0_extra->events[ctx.device][0], ctx.stream()));
     }
 
     const int64_t src1_col_stride = split && used_devices > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11;
     for (int64_t src1_col_0 = 0; src1_col_0 < ne11; src1_col_0 += src1_col_stride) {
-        const int64_t is = split ? (src1_col_0/src1_col_stride) % MAX_STREAMS : 0;
+        const int64_t is = split ? (src1_col_0/src1_col_stride) % GGML_CUDA_MAX_STREAMS : 0;
         const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride;
 
-        for (int id = 0; id < g_device_count; ++id) {
-            if ((!split && id != g_main_device) || dev[id].row_low == dev[id].row_high) {
+        for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
+            if ((!split && id != ctx.device) || dev[id].row_low == dev[id].row_high) {
                 continue;
             }
 
-            const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device;
-            const bool  dst_on_device =  dst->backend == GGML_BACKEND_GPU && id == g_main_device;
+            const bool src1_on_device = id == src1_ctx->device;
+            const bool  dst_on_device = id == dst_ctx->device;
             const int64_t row_diff = dev[id].row_high - dev[id].row_low;
 
             ggml_cuda_set_device(id);
-            cudaStream_t stream = g_cudaStreams[id][is];
+            cudaStream_t stream = ctx.stream(id, is);
 
             // wait for main GPU data if necessary
-            if (split && (id != g_main_device || is != 0)) {
-                CUDA_CHECK(cudaStreamWaitEvent(stream, src0_extra->events[g_main_device][0], 0));
+            if (split && (id != ctx.device || is != 0)) {
+                CUDA_CHECK(cudaStreamWaitEvent(stream, src0_extra->events[ctx.device][0], 0));
             }
 
             for (int64_t i0 = 0; i0 < ne13*ne12; ++i0) {
@@ -9531,58 +1546,48 @@ static void ggml_cuda_op_mul_mat(
 
                 // the main device memory buffer can be on VRAM scratch, with space for all partial results
                 // in that case an offset on dst_ddf_i is needed
-                if (dst->backend == GGML_BACKEND_GPU && id == g_main_device) {
+                if (id == ctx.device) {
                     dst_dd_i += dev[id].row_low; // offset is 0 if no tensor split
                 }
 
                 // copy src0, src1 to device if necessary
-                if (src1->backend == GGML_BACKEND_GPU && src1_is_contiguous) {
-                    if (id != g_main_device) {
+                if (src1_is_contiguous) {
+                    if (id != ctx.device) {
                         if (convert_src1_to_q8_1) {
-                            char * src1_ddq_i_source = dev[g_main_device].src1_ddq + src1_ddq_i_offset;
-                            CUDA_CHECK(cudaMemcpyPeerAsync(src1_ddq_i, id, src1_ddq_i_source, g_main_device,
+                            char * src1_ddq_i_source = dev[ctx.device].src1_ddq + src1_ddq_i_offset;
+                            CUDA_CHECK(cudaMemcpyPeerAsync(src1_ddq_i, id, src1_ddq_i_source, ctx.device,
                                                             src1_ncols*src1_padded_col_size*q8_1_ts/q8_1_bs, stream));
                         } else {
-                            float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device];
+                            float * src1_ddf_i_source = (float *) src1->data;
                             src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10;
-                            CUDA_CHECK(cudaMemcpyPeerAsync(src1_ddf_i, id, src1_ddf_i_source, g_main_device,
+                            CUDA_CHECK(cudaMemcpyPeerAsync(src1_ddf_i, id, src1_ddf_i_source, ctx.device,
                                                             src1_ncols*ne10*sizeof(float), stream));
                         }
                     }
-                } else if (src1->backend == GGML_BACKEND_CPU || (src1_on_device && !src1_is_contiguous)) {
+                } else if (src1_on_device && !src1_is_contiguous) {
                     CUDA_CHECK(ggml_cuda_cpy_tensor_2d(
                                 src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream));
                 } else {
                     GGML_ASSERT(false);
                 }
 
-                if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_CPU || !src1_is_contiguous)) {
+                if (convert_src1_to_q8_1 && !src1_is_contiguous) {
                     quantize_row_q8_1_cuda(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream);
                     CUDA_CHECK(cudaGetLastError());
                 }
 
-                if (src1_col_0 == 0 && (!src0_on_device || !src0_is_contiguous) && i02 % i02_divisor == 0) {
+                if (src1_col_0 == 0 && !src0_is_contiguous && i02 % i02_divisor == 0) {
                     CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, dev[id].row_low, dev[id].row_high, stream));
                 }
 
                 // do the computation
-                op(src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i,
+                op(ctx, src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i,
                     dev[id].row_low, dev[id].row_high, src1_ncols, src1_padded_col_size, stream);
                 CUDA_CHECK(cudaGetLastError());
 
                 // copy dst to host or other device if necessary
                 if (!dst_on_device) {
-                    void * dst_off_device;
-                    cudaMemcpyKind kind;
-                    if (dst->backend == GGML_BACKEND_CPU) {
-                        dst_off_device = dst->data;
-                        kind = cudaMemcpyDeviceToHost;
-                    } else if (dst->backend == GGML_BACKEND_GPU) {
-                        dst_off_device = dst_extra->data_device[g_main_device];
-                        kind = cudaMemcpyDeviceToDevice;
-                    } else {
-                        GGML_ASSERT(false);
-                    }
+                    void * dst_off_device = dst->data;
                     if (split) {
                         // src0 = weight matrix is saved as a transposed matrix for better memory layout.
                         // dst is NOT transposed.
@@ -9593,33 +1598,31 @@ static void ggml_cuda_op_mul_mat(
                         GGML_ASSERT(dst->nb[1] == ne0*sizeof(float));
                         dhf_dst_i += src1_col_0*ne0 + dev[id].row_low;
 #if !defined(GGML_USE_HIPBLAS)
-                        if (kind == cudaMemcpyDeviceToDevice) {
-                            // cudaMemcpy2DAsync may fail with copies between vmm pools of different devices
-                            cudaMemcpy3DPeerParms p = {};
-                            p.dstDevice = g_main_device;
-                            p.dstPtr = make_cudaPitchedPtr(dhf_dst_i, ne0*sizeof(float), row_diff, src1_ncols);
-                            p.srcDevice = id;
-                            p.srcPtr = make_cudaPitchedPtr(dst_dd_i, row_diff*sizeof(float), row_diff, src1_ncols);
-                            p.extent = make_cudaExtent(row_diff*sizeof(float), src1_ncols, 1);
-                            CUDA_CHECK(cudaMemcpy3DPeerAsync(&p, stream));
-                        } else
+                        // cudaMemcpy2DAsync may fail with copies between vmm pools of different devices
+                        cudaMemcpy3DPeerParms p = {};
+                        p.dstDevice = ctx.device;
+                        p.dstPtr = make_cudaPitchedPtr(dhf_dst_i, ne0*sizeof(float), row_diff, src1_ncols);
+                        p.srcDevice = id;
+                        p.srcPtr = make_cudaPitchedPtr(dst_dd_i, row_diff*sizeof(float), row_diff, src1_ncols);
+                        p.extent = make_cudaExtent(row_diff*sizeof(float), src1_ncols, 1);
+                        CUDA_CHECK(cudaMemcpy3DPeerAsync(&p, stream));
+#else
+                        // HIP does not support cudaMemcpy3DPeerAsync or vmm pools
+                        CUDA_CHECK(cudaMemcpy2DAsync(dhf_dst_i, ne0*sizeof(float),
+                                                        dst_dd_i, row_diff*sizeof(float),
+                                                        row_diff*sizeof(float), src1_ncols,
+                                                        cudaMemcpyDeviceToDevice, stream));
 #endif
-                        {
-                            CUDA_CHECK(cudaMemcpy2DAsync(dhf_dst_i, ne0*sizeof(float),
-                                                            dst_dd_i, row_diff*sizeof(float),
-                                                            row_diff*sizeof(float), src1_ncols,
-                                                            kind, stream));
-                        }
                     } else {
                         float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3);
                         GGML_ASSERT(dst->nb[1] == ne0*sizeof(float));
                         dhf_dst_i += src1_col_0*ne0;
-                        CUDA_CHECK(cudaMemcpyAsync(dhf_dst_i, dst_dd_i, src1_ncols*ne0*sizeof(float), kind, stream));
+                        CUDA_CHECK(cudaMemcpyAsync(dhf_dst_i, dst_dd_i, src1_ncols*ne0*sizeof(float), cudaMemcpyDeviceToDevice, stream));
                     }
                 }
 
                 // add event for the main device to wait on until other device is done
-                if (split && (id != g_main_device || is != 0)) {
+                if (split && (id != ctx.device || is != 0)) {
                     CUDA_CHECK(cudaEventRecord(src0_extra->events[id][is], stream));
                 }
             }
@@ -9627,128 +1630,25 @@ static void ggml_cuda_op_mul_mat(
     }
 
     // main device waits for all other devices to be finished
-    if (split && g_device_count > 1) {
+    if (split && ggml_backend_cuda_get_device_count() > 1) {
         int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE;
-        is_max = is_max <= MAX_STREAMS ? is_max : MAX_STREAMS;
+        is_max = is_max <= GGML_CUDA_MAX_STREAMS ? is_max : GGML_CUDA_MAX_STREAMS;
 
-        ggml_cuda_set_device(g_main_device);
-        for (int id = 0; id < g_device_count; ++id) {
+        ggml_cuda_set_device(ctx.device);
+        for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
             if (dev[id].row_low == dev[id].row_high) {
                 continue;
             }
             for (int64_t is = 0; is < is_max; ++is) {
-                CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams[g_main_device][0], src0_extra->events[id][is], 0));
+                CUDA_CHECK(cudaStreamWaitEvent(ctx.stream(), src0_extra->events[id][is], 0));
             }
         }
     }
-
-    if (dst->backend == GGML_BACKEND_CPU) {
-        ggml_cuda_set_device(g_main_device);
-        CUDA_CHECK(cudaDeviceSynchronize());
-    }
 }
 
-static void ggml_cuda_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_repeat);
-}
-
-static void ggml_cuda_get_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_get_rows);
-}
-
-static void ggml_cuda_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_add);
-}
-
-static void ggml_cuda_acc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_acc);
-}
-
-static void ggml_cuda_mul(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_mul);
-}
-
-static void ggml_cuda_div(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_div);
-}
-
-static void ggml_cuda_gelu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_gelu);
-}
-
-static void ggml_cuda_silu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_silu);
-}
-
-static void ggml_cuda_gelu_quick(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_gelu_quick);
-}
-
-static void ggml_cuda_tanh(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_tanh);
-}
-
-static void ggml_cuda_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_relu);
-}
-
-static void ggml_cuda_hardsigmoid(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_hardsigmoid);
-}
-
-static void ggml_cuda_hardswish(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_hardswish);
-}
-static void ggml_cuda_leaky_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_leaky_relu);
-}
-
-static void ggml_cuda_sqr(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_sqr);
-}
-
-static void ggml_cuda_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_norm);
-}
-
-static void ggml_cuda_group_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_group_norm);
-}
-
-static void ggml_cuda_concat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_concat);
-}
-
-static void ggml_cuda_upscale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_upscale);
-}
-
-static void ggml_cuda_pad(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_pad);
-}
-
-static void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rms_norm);
-}
-
-GGML_CALL bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
-    if (!g_cublas_loaded) return false;
-
-    const int64_t ne10 = src1->ne[0];
-
-    const int64_t ne0 = dst->ne[0];
-    const int64_t ne1 = dst->ne[1];
-
-    // TODO: find the optimal values for these
-    return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
-            src1->type == GGML_TYPE_F32 &&
-             dst->type == GGML_TYPE_F32 &&
-            (ne0 >= 32 && ne1 >= 32 && ne10 >= 32);
-}
-
-static void ggml_cuda_mul_mat_vec_p021(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
+static void ggml_cuda_mul_mat_vec_p021(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
-    GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer));
     GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation
     GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
@@ -9760,26 +1660,20 @@ static void ggml_cuda_mul_mat_vec_p021(const ggml_tensor * src0, const ggml_tens
 
     const int64_t ne12 = src1->ne[2];
 
-    ggml_cuda_set_device(g_main_device);
-    cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
+    cudaStream_t main_stream = ctx.stream();
 
-    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    void * src0_ddq = src0_extra->data_device[g_main_device];
-
-    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
-
-    ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
-    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
+    void  * src0_ddq = src0->data;
+    float * src1_ddf = (float *) src1->data;
+    float * dst_ddf  = (float *) dst->data;
 
     ggml_mul_mat_p021_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream);
 }
 
-static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
+static void ggml_cuda_mul_mat_vec_nc(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_ASSERT(!ggml_is_transposed(src0));
     GGML_ASSERT(!ggml_is_transposed(src1));
     GGML_ASSERT(!ggml_is_permuted(src0));
-    GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer));
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
 
@@ -9792,17 +1686,11 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor
 
     const int64_t ne12 = src1->ne[2];
 
-    ggml_cuda_set_device(g_main_device);
-    cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
+    cudaStream_t main_stream = ctx.stream();
 
-    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    void * src0_ddq = src0_extra->data_device[g_main_device];
-
-    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
-
-    ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
-    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
+    void  * src0_ddq = src0->data;
+    float * src1_ddf = (float *) src1->data;
+    float * dst_ddf  = (float *) dst->data;
 
     const int64_t row_stride_x = nb01 / sizeof(half);
     const int64_t channel_stride_x = nb02 / sizeof(half);
@@ -9834,34 +1722,28 @@ static __global__ void k_compute_batched_ptrs(
     ptrs_dst[0*ne23 + i12 + i13*ne12] = (      char *)         dst + i12*nbd2 + i13*nbd3;
 }
 
-static void ggml_cuda_mul_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_ASSERT(!ggml_is_transposed(src0));
     GGML_ASSERT(!ggml_is_transposed(src1));
 
-    GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer));
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
 
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int64_t ne_dst = ggml_nelements(dst);
 
-    ggml_cuda_set_device(g_main_device);
-    cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
+    cudaStream_t main_stream = ctx.stream();
 
-    CUBLAS_CHECK(cublasSetStream(g_cublas_handles[g_main_device], main_stream));
+    CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(), main_stream));
 
-    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    void * src0_ddq = src0_extra->data_device[g_main_device];
+    void * src0_ddq = src0->data;
     half * src0_f16 = (half *) src0_ddq;
-
-    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
-
-    ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
-    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
+    float * src1_ddf = (float *) src1->data;
+    float * dst_ddf  = (float *) dst->data;
 
     // convert src1 to fp16
-    cuda_pool_alloc<half> src1_f16_alloc;
+    ggml_cuda_pool_alloc<half> src1_f16_alloc(ctx.pool());
     if (src1->type != GGML_TYPE_F16) {
         const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
         const int64_t ne_src1 = ggml_nelements(src1);
@@ -9871,7 +1753,7 @@ static void ggml_cuda_mul_mat_batched_cublas(const ggml_tensor * src0, const ggm
     }
     half * src1_f16 = src1->type == GGML_TYPE_F16 ? (half *) src1_ddf : src1_f16_alloc.get();
 
-    cuda_pool_alloc<half> dst_f16;
+    ggml_cuda_pool_alloc<half> dst_f16(ctx.pool());
     char * dst_t;
 
     cublasComputeType_t cu_compute_type = CUBLAS_COMPUTE_16F;
@@ -9936,7 +1818,7 @@ static void ggml_cuda_mul_mat_batched_cublas(const ggml_tensor * src0, const ggm
         // there is no broadcast and src0, src1 are contiguous across dims 2, 3
         // use cublasGemmStridedBatchedEx
         CUBLAS_CHECK(
-        cublasGemmStridedBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
+        cublasGemmStridedBatchedEx(ctx.cublas_handle(), CUBLAS_OP_T, CUBLAS_OP_N,
                 ne01, ne11, ne10,
                 alpha, (const char *) src0_f16, CUDA_R_16F,   nb01/nb00, nb02/nb00,  // strideA
                        (const char *) src1_f16, CUDA_R_16F,   nb11/nb10, nb12/nb10,  // strideB
@@ -9948,8 +1830,8 @@ static void ggml_cuda_mul_mat_batched_cublas(const ggml_tensor * src0, const ggm
         // use cublasGemmBatchedEx
         const int ne23 = ne12*ne13;
 
-        cuda_pool_alloc<const void *> ptrs_src(2*ne23);
-        cuda_pool_alloc<      void *> ptrs_dst(1*ne23);
+        ggml_cuda_pool_alloc<const void *> ptrs_src(ctx.pool(), 2*ne23);
+        ggml_cuda_pool_alloc<      void *> ptrs_dst(ctx.pool(), 1*ne23);
 
         dim3 block_dims(ne13, ne12);
         k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>(
@@ -9965,7 +1847,7 @@ static void ggml_cuda_mul_mat_batched_cublas(const ggml_tensor * src0, const ggm
         CUDA_CHECK(cudaGetLastError());
 
         CUBLAS_CHECK(
-        cublasGemmBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
+        cublasGemmBatchedEx(ctx.cublas_handle(), CUBLAS_OP_T, CUBLAS_OP_N,
                 ne01, ne11, ne10,
                 alpha, (const void **) (ptrs_src.get() + 0*ne23), CUDA_R_16F,   nb01/nb00,
                        (const void **) (ptrs_src.get() + 1*ne23), CUDA_R_16F,   nb11/nb10,
@@ -9982,13 +1864,8 @@ static void ggml_cuda_mul_mat_batched_cublas(const ggml_tensor * src0, const ggm
     }
 }
 
-static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    const bool all_on_device =
-        (src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) &&
-        (src1->backend == GGML_BACKEND_GPU) &&
-        ( dst->backend == GGML_BACKEND_GPU);
-
-    const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
+static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    const bool split = ggml_backend_buffer_is_cuda_split(src0->buffer);
 
     int64_t min_compute_capability = INT_MAX;
 
@@ -9996,22 +1873,22 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
     if (split) {
         ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context;
         auto & tensor_split = buft_ctx->tensor_split;
-        for (int id = 0; id < g_device_count; ++id) {
+        for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
             // skip devices that are not going to do any work:
-            if (tensor_split[id] >= (id + 1 < g_device_count ? tensor_split[id + 1] : 1.0f)) {
+            if (tensor_split[id] >= (id + 1 < ggml_backend_cuda_get_device_count() ? tensor_split[id + 1] : 1.0f)) {
                 continue;
             }
 
-            if (min_compute_capability > g_device_caps[id].cc) {
-                min_compute_capability = g_device_caps[id].cc;
+            if (min_compute_capability > ggml_cuda_info().devices[id].cc) {
+                min_compute_capability = ggml_cuda_info().devices[id].cc;
             }
-            if (g_device_caps[id].cc == 610) {
+            if (ggml_cuda_info().devices[id].cc == 610) {
                 any_pascal_with_slow_fp16 = true;
             }
         }
     } else {
-        min_compute_capability    = g_device_caps[g_main_device].cc;
-        any_pascal_with_slow_fp16 = g_device_caps[g_main_device].cc == 610;
+        min_compute_capability    = ggml_cuda_info().devices[ctx.device].cc;
+        any_pascal_with_slow_fp16 = ggml_cuda_info().devices[ctx.device].cc == 610;
     }
 
     // check data types and tensor shapes for custom matrix multiplication kernels:
@@ -10064,1251 +1941,374 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
     //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
     //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
 
-    if (!split && all_on_device && !fp16_performance_good && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
+    if (!split && !fp16_performance_good && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
         // KQ single-batch
-        ggml_cuda_mul_mat_vec_p021(src0, src1, dst);
-    } else if (!split && all_on_device && !fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
+        ggml_cuda_mul_mat_vec_p021(ctx, src0, src1, dst);
+    } else if (!split && !fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
         // KQV single-batch
-        ggml_cuda_mul_mat_vec_nc(src0, src1, dst);
-    } else if (!split && all_on_device && fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
+        ggml_cuda_mul_mat_vec_nc(ctx, src0, src1, dst);
+    } else if (!split && src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16 || fp16_performance_good) && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
         // KQ + KQV multi-batch
-        ggml_cuda_mul_mat_batched_cublas(src0, src1, dst);
+        ggml_cuda_mul_mat_batched_cublas(ctx, src0, src1, dst);
     } else if (use_dequantize_mul_mat_vec) {
-        ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false);
+        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false);
     } else if (use_mul_mat_vec_q) {
-        ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true);
+        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true);
     } else if (use_mul_mat_q) {
-        ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true);
+        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_q, true);
     } else {
-        ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);
+        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);
     }
 }
 
-#if 0
-template<typename ... Srcs>
-static __global__ void k_compute_batched_ptrs_id(
-        const void ** ptrs_src, void ** ptrs_dst,
-        int ne12, int ne13,
-        int ne23,
-        int nb02, int nb03,
-        int nb12, int nb13,
-        int nb2, int nb3,
-        int r2, int r3,
-        ggml_type src0_type, half * src0_as_f16, int64_t src0_ne,
-        const half * src1_f16, half * dst_f16,
-        const int32_t * ids, const int id,
-        Srcs... src0s) {
+struct mmid_row_mapping {
+    int32_t i1;
+    int32_t i2;
+};
 
-    int i = ids[id];
+static __global__ void k_copy_src1_to_contiguous(const char * __restrict__ src1_original, char * __restrict__ src1_contiguous,
+                                                 int * __restrict__ cur_src1_row, mmid_row_mapping * __restrict__ row_mapping,
+                                                 const char * __restrict ids, int64_t i02, size_t ids_nb1, size_t ids_nb0,
+                                                 int64_t ne11, int64_t ne10,
+                                                 size_t nb11, size_t nb12) {
+    int32_t iid1 = blockIdx.x;
+    int32_t id = blockIdx.y;
 
-    half * src0_f16;
-    const void * srcs_ar[] = { (const half *) src0s... };
-    if (src0_type == GGML_TYPE_F16) {
-        src0_f16 = (half *) srcs_ar[i];
-    } else {
-        src0_f16 = src0_as_f16;
-        if (threadIdx.x == 0 && threadIdx.y == 0) {
-            const to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(src0_type);
-            to_fp16(srcs_ar[i], src0_f16, src0_ne, cudaStreamFireAndForget);
-        }
-    }
+    const int32_t row_id_i = *(const int32_t *) (ids + iid1*ids_nb1 + id*ids_nb0);
 
-    int i13 = blockIdx.x * blockDim.x + threadIdx.x;
-    int i12 = blockIdx.y * blockDim.y + threadIdx.y;
-
-    if (i13 >= ne13 || i12 >= ne12) {
+    if (row_id_i != i02) {
         return;
     }
 
-    int i03 = i13 / r3;
-    int i02 = i12 / r2;
+    const int64_t i11 = id % ne11;
+    const int64_t i12 = iid1;
 
-    ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_f16 + i02*nb02   + i03*nb03;
-    ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_f16 + i12*nb12/2 + i13*nb13/2;
-    ptrs_dst[0*ne23 + i12 + i13*ne12] = (      char *)  dst_f16 + i12* nb2/2 + i13* nb3/2;
+    __shared__ int src1_row;
+    if (threadIdx.x == 0) {
+        src1_row = atomicAdd(cur_src1_row, 1);
+        row_mapping[src1_row] = {id, iid1};
+    }
+    __syncthreads();
+
+    const float * src1_row_original = (const float *)(src1_original + i11*nb11 + i12*nb12);
+    float * src1_row_contiguous = (float *)(src1_contiguous + src1_row*nb11);
+
+    for (int i = threadIdx.x; i < ne10; i += blockDim.x) {
+        src1_row_contiguous[i] = src1_row_original[i];
+    }
 }
 
-static void ggml_cuda_mul_mat_id_cublas(ggml_tensor * dst) {
-    const struct ggml_tensor * ids = dst->src[0];
-    const struct ggml_tensor * src1 = dst->src[1];
-    const struct ggml_tensor * src00 = dst->src[2];
+static __global__ void k_copy_dst_from_contiguous(char * __restrict__ dst_original, const char * __restrict__ dst_contiguous,
+                                                  const mmid_row_mapping * __restrict__ row_mapping,
+                                                  int64_t ne0,
+                                                  size_t nb1, size_t nb2) {
+    int32_t i = blockIdx.x;
 
-    const int id = dst->op_params[0];
+    const int32_t i1 = row_mapping[i].i1;
+    const int32_t i2 = row_mapping[i].i2;
 
-    GGML_ASSERT(!ggml_is_transposed(src00));
-    GGML_ASSERT(!ggml_is_transposed(src1));
+    const float * dst_row_contiguous = (const float *)(dst_contiguous + i*nb1);
+    float * dst_row_original = (float *)(dst_original + i1*nb1 + i2*nb2);
 
-    GGML_ASSERT(src00->backend != GGML_BACKEND_GPU_SPLIT);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-
-    const int64_t ne00 = src00->ne[0]; GGML_UNUSED(ne00);
-    const int64_t ne01 = src00->ne[1];
-    const int64_t ne02 = src00->ne[2];
-    const int64_t ne03 = src00->ne[3];
-
-    //const int64_t nb01 = src00->nb[1];
-    const int64_t nb02 = src00->nb[2]; GGML_UNUSED(nb02);
-    const int64_t nb03 = src00->nb[3]; GGML_UNUSED(nb03);
-
-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    const int64_t ne12 = src1->ne[2];
-    const int64_t ne13 = src1->ne[3];
-
-    //const int64_t nb11 = src1->nb[1];
-    const int64_t nb12 = src1->nb[2]; GGML_UNUSED(nb12);
-    const int64_t nb13 = src1->nb[3]; GGML_UNUSED(nb13);
-
-    const int64_t ne1 = ggml_nelements(src1);
-    const int64_t ne  = ggml_nelements(dst);
-
-    ggml_cuda_set_device(g_main_device);
-    cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
-
-    CUBLAS_CHECK(cublasSetStream(g_cublas_handles[g_main_device], main_stream));
-
-    //ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    //void * src0_ddq = src0_extra->data_device[g_main_device];
-    //half * src0_as_f16 = (half *) src0_ddq;
-
-    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
-
-    ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
-    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
-
-    // convert src1 to fp16
-    const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
-    GGML_ASSERT(to_fp16_cuda != nullptr);
-
-    size_t src1_as = 0;
-    half * src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne1 * sizeof(half), &src1_as);
-    to_fp16_cuda(src1_ddf, src1_as_f16, ne1, main_stream);
-
-    size_t dst_as = 0;
-    half * dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as);
-
-    GGML_ASSERT(ne12 % ne02 == 0);
-    GGML_ASSERT(ne13 % ne03 == 0);
-
-    // broadcast factors
-    const int64_t r2 = ne12/ne02;
-    const int64_t r3 = ne13/ne03;
-
-    const half alpha_f16 = 1.0f;
-    const half beta_f16  = 0.0f;
-
-    // use cublasGemmBatchedEx
-    const int ne23 = ne12*ne13;
-
-    const void ** ptrs_src = nullptr;
-          void ** ptrs_dst = nullptr;
-
-    size_t ptrs_src_s = 0;
-    size_t ptrs_dst_s = 0;
-
-    ptrs_src = (const void **) ggml_cuda_pool_malloc(2*ne23*sizeof(void *), &ptrs_src_s);
-    ptrs_dst = (      void **) ggml_cuda_pool_malloc(1*ne23*sizeof(void *), &ptrs_dst_s);
-
-    int64_t src0_ne = ggml_nelements(src00);
-    half * src0_as_f16 = nullptr;
-    size_t src0_as = 0;
-    if (src00->type != GGML_TYPE_F16) {
-        src0_as_f16 = (half *) ggml_cuda_pool_malloc(src0_ne * sizeof(half), &src0_as);
+    for (int j = threadIdx.x; j < ne0; j += blockDim.x) {
+        dst_row_original[j] = dst_row_contiguous[j];
     }
-
-    static_assert(GGML_MAX_SRC == 6, "GGML_MAX_SRC == 6");
-    dim3 block_dims(ne13, ne12);
-    k_compute_batched_ptrs_id<<<1, block_dims, 0, main_stream>>>(
-            ptrs_src, ptrs_dst,
-            ne12, ne13,
-            ne23,
-            ne00*ne01*sizeof(half), ne00*ne01*ne02*sizeof(half),
-            nb12, nb13,
-            dst->nb[2], dst->nb[3],
-            r2, r3,
-            src00->type, src0_as_f16, src0_ne,
-            src1_as_f16, dst_f16,
-            (const int *)((ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device], id,
-            dst->src[2] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[2]->extra)->data_device[g_main_device] : nullptr,
-            dst->src[3] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[3]->extra)->data_device[g_main_device] : nullptr,
-            dst->src[4] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[4]->extra)->data_device[g_main_device] : nullptr,
-            dst->src[5] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[5]->extra)->data_device[g_main_device] : nullptr
-    );
-    CUDA_CHECK(cudaGetLastError());
-
-    CUBLAS_CHECK(
-    cublasGemmBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
-            ne01, ne11, ne10,
-            &alpha_f16, (const void **) (ptrs_src + 0*ne23), CUDA_R_16F, ne00,
-                        (const void **) (ptrs_src + 1*ne23), CUDA_R_16F, ne10,
-            &beta_f16,  (      void **) (ptrs_dst + 0*ne23), CUDA_R_16F, ne01,
-            ne23,
-            CUBLAS_COMPUTE_16F,
-            CUBLAS_GEMM_DEFAULT_TENSOR_OP));
-
-    if (src0_as != 0) {
-        ggml_cuda_pool_free(src0_as_f16, src0_as);
-    }
-    if (ptrs_src_s != 0) {
-        ggml_cuda_pool_free(ptrs_src, ptrs_src_s);
-    }
-    if (ptrs_dst_s != 0) {
-        ggml_cuda_pool_free(ptrs_dst, ptrs_dst_s);
-    }
-
-    const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
-    to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream);
-
-    ggml_cuda_pool_free(src1_as_f16, src1_as);
-    ggml_cuda_pool_free(dst_f16, dst_as);
 }
-#endif
 
-static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-#if 0
-    ggml_cuda_mul_mat_id_cublas(dst);
-    // TODO: mmq/mmv support
-#endif
+static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const ggml_tensor * ids  = dst->src[2];
 
-    const size_t nb11 = src1->nb[1];
-    const size_t nb1  =  dst->nb[1];
+    GGML_TENSOR_BINARY_OP_LOCALS
 
-    const struct ggml_tensor * ids = src0;
-    const int32_t id = ((int32_t *) dst->op_params)[0];
-    const int32_t n_as = ((int32_t *) dst->op_params)[1];
+    GGML_ASSERT(!ggml_backend_buffer_is_cuda_split(src0->buffer) && "mul_mat_id does not support split buffers");
+
+    cudaStream_t stream = ctx.stream();
+
+    const int64_t n_as = ne02;
+    const int64_t n_ids = ids->ne[0];
 
     std::vector<char> ids_host(ggml_nbytes(ids));
+    const char * ids_dev = (const char *) ids->data;
+    CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream));
+    CUDA_CHECK(cudaStreamSynchronize(stream));
 
-    cudaStream_t stream = g_cudaStreams[g_main_device][0];
-
-    if (ids->backend == GGML_BACKEND_GPU) {
-        const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device];
-        CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream));
-        CUDA_CHECK(cudaStreamSynchronize(stream));
-    } else {
-        memcpy(ids_host.data(), ids->data, ggml_nbytes(ids));
-    }
-
-    const ggml_tensor_extra_gpu * src1_extra = (const ggml_tensor_extra_gpu *) src1->extra;
-    const ggml_tensor_extra_gpu * dst_extra = (const ggml_tensor_extra_gpu *) dst->extra;
-
-    ggml_tensor_extra_gpu src1_row_extra;
-    ggml_tensor_extra_gpu dst_row_extra;
-
+    ggml_tensor src0_row = *src0;
     ggml_tensor src1_row = *src1;
-    ggml_tensor dst_row = *dst;
+    ggml_tensor dst_row  = *dst;
 
-    src1_row.backend = GGML_BACKEND_GPU;
-    dst_row.backend  = GGML_BACKEND_GPU;
+    char * src0_original = (char *) src0->data;
+    char * src1_original = (char *) src1->data;
+    char * dst_original  = (char *)  dst->data;
 
-    src1_row.extra = &src1_row_extra;
-    dst_row.extra = &dst_row_extra;
+    src0_row.ne[2] = 1;
+    src0_row.ne[3] = 1;
+    src0_row.nb[3] = nb02;
 
-    char * src1_original = src1->backend == GGML_BACKEND_CPU ?
-        (char *) src1->data : (char *) src1_extra->data_device[g_main_device];
-    char * dst_original  =  dst->backend == GGML_BACKEND_CPU ?
-        (char *)  dst->data : (char *)  dst_extra->data_device[g_main_device];
+    src1_row.ne[1] = 1;
+    src1_row.ne[2] = 1;
+    src1_row.ne[3] = 1;
+    src1_row.nb[2] = nb11;
+    src1_row.nb[3] = nb11;
 
-    if (src1->ne[1] == 1) {
-        GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
-        GGML_ASSERT(dst->backend  == GGML_BACKEND_GPU);
+    dst_row.ne[1] = 1;
+    dst_row.ne[2] = 1;
+    dst_row.ne[3] = 1;
+    dst_row.nb[2] = nb1;
+    dst_row.nb[3] = nb1;
 
-        for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
-            //int32_t row_id;
-            //CUDA_CHECK(cudaMemcpyAsync(&row_id, ids_dev + i01*ids->nb[1] + id*ids->nb[0], sizeof(int32_t), cudaMemcpyDeviceToHost, g_cudaStreams[g_main_device][0]));
-            //CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[g_main_device][0]));
+    if (ne12 == 1) {
+        for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) {
+            for (int64_t id = 0; id < n_ids; id++) {
+                const int32_t i02 = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]);
 
-            const int32_t row_id = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
+                GGML_ASSERT(i02 >= 0 && i02 < n_as);
 
-            GGML_ASSERT(row_id >= 0 && row_id < n_as);
+                const int64_t i11 = id % ne11;
+                const int64_t i12 = iid1;
 
-            const struct ggml_tensor * src0_row = dst->src[row_id + 2];
+                const int64_t i1 = id;
+                const int64_t i2 = i12;
 
-            src1_row_extra.data_device[g_main_device] = src1_original + i01*src1->nb[1];
-            src1_row.data = (char *) src1->data + i01*src1->nb[1]; // TODO why is this set?
+                src0_row.data = src0_original + i02*nb02;
+                src1_row.data = src1_original + i11*nb11 + i12*nb12;
+                dst_row.data  =  dst_original + i1*nb1   + i2*nb2;
 
-            dst_row_extra.data_device[g_main_device] = dst_original + i01*dst->nb[1];
-            dst_row.data = (char *) dst->data + i01*dst->nb[1]; // TODO why is this set?
-
-            ggml_cuda_mul_mat(src0_row, &src1_row, &dst_row);
+                ggml_cuda_mul_mat(ctx, &src0_row, &src1_row, &dst_row);
+            }
         }
     } else {
-        cuda_pool_alloc<char> src1_contiguous(sizeof(float)*ggml_nelements(src1));
-        cuda_pool_alloc<char>  dst_contiguous(sizeof(float)*ggml_nelements(dst));
+        ggml_cuda_pool_alloc<char> src1_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(src1));
+        ggml_cuda_pool_alloc<char>  dst_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(dst));
 
-        src1_row_extra.data_device[g_main_device] = src1_contiguous.get();
-        dst_row_extra.data_device[g_main_device]  =  dst_contiguous.get();
-
-        const cudaMemcpyKind src1_kind = src1->backend == GGML_BACKEND_CPU ?
-            cudaMemcpyHostToDevice : cudaMemcpyDeviceToDevice;
-        const cudaMemcpyKind dst_kind  =  dst->backend == GGML_BACKEND_CPU ?
-            cudaMemcpyDeviceToHost : cudaMemcpyDeviceToDevice;
-
-        for (int32_t row_id = 0; row_id < n_as; ++row_id) {
-            const struct ggml_tensor * src0_row = dst->src[row_id + 2];
+        src1_row.data = src1_contiguous.get();
+        dst_row.data  =  dst_contiguous.get();
 
+        for (int64_t i02 = 0; i02 < n_as; i02++) {
             int64_t num_src1_rows = 0;
-            for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
-                const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
 
-                if (row_id_i != row_id) {
-                    continue;
+            for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) {
+                for (int64_t id = 0; id < n_ids; id++) {
+                    const int32_t row_id_i = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]);
+
+                    GGML_ASSERT(row_id_i >= 0 && row_id_i < n_as);
+
+                    if (row_id_i != i02) {
+                        continue;
+                    }
+
+                    num_src1_rows++;
                 }
-
-                GGML_ASSERT(row_id >= 0 && row_id < n_as);
-
-                CUDA_CHECK(cudaMemcpyAsync(src1_contiguous.get() + num_src1_rows*nb11, src1_original + i01*nb11,
-                                        nb11, src1_kind, stream));
-                num_src1_rows++;
             }
 
             if (num_src1_rows == 0) {
                 continue;
             }
 
-            src1_row.ne[1] = num_src1_rows;
-            dst_row.ne[1] = num_src1_rows;
+            ggml_cuda_pool_alloc<int> dev_cur_src1_row(ctx.pool(), 1);
+            ggml_cuda_pool_alloc<mmid_row_mapping> dev_row_mapping(ctx.pool(), num_src1_rows);
+            CUDA_CHECK(cudaMemsetAsync(dev_cur_src1_row.get(), 0, sizeof(int), stream));
 
+            {
+                dim3 block_dims(std::min((unsigned int)ne10, 768u));
+                dim3 grid_dims(ids->ne[1], n_ids);
+                k_copy_src1_to_contiguous<<<grid_dims, block_dims, 0, stream>>>(
+                        src1_original, src1_contiguous.get(),
+                        dev_cur_src1_row.get(), dev_row_mapping.get(),
+                        ids_dev, i02, ids->nb[1], ids->nb[0],
+                        ne11, ne10,
+                        nb11, nb12);
+                CUDA_CHECK(cudaGetLastError());
+            }
+
+            src0_row.data = src0_original + i02*nb02;
+
+            GGML_ASSERT(nb11 == sizeof(float)*ne10);
+            GGML_ASSERT(nb1 == sizeof(float)*ne0);
+
+            src1_row.ne[1] = num_src1_rows;
             src1_row.nb[1] = nb11;
             src1_row.nb[2] = num_src1_rows*nb11;
             src1_row.nb[3] = num_src1_rows*nb11;
 
+            dst_row.ne[1] = num_src1_rows;
             dst_row.nb[1] = nb1;
             dst_row.nb[2] = num_src1_rows*nb1;
             dst_row.nb[3] = num_src1_rows*nb1;
 
-            ggml_cuda_mul_mat(src0_row, &src1_row, &dst_row);
+            ggml_cuda_mul_mat(ctx, &src0_row, &src1_row, &dst_row);
 
-            num_src1_rows = 0;
-            for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
-                const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
-
-                if (row_id_i != row_id) {
-                    continue;
-                }
-
-                GGML_ASSERT(row_id >= 0 && row_id < n_as);
-
-                CUDA_CHECK(cudaMemcpyAsync(dst_original + i01*nb1, dst_contiguous.get() + num_src1_rows*nb1,
-                                        nb1, dst_kind, stream));
-                num_src1_rows++;
+            {
+                dim3 block_dims(std::min((unsigned int)ne0, 768u));
+                dim3 grid_dims(num_src1_rows);
+                k_copy_dst_from_contiguous<<<grid_dims, block_dims, 0, stream>>>(
+                        dst_original, dst_contiguous.get(),
+                        dev_row_mapping.get(),
+                        ne0,
+                        nb1, nb2);
+                CUDA_CHECK(cudaGetLastError());
             }
         }
     }
-
-    if (dst->backend == GGML_BACKEND_CPU) {
-        CUDA_CHECK(cudaStreamSynchronize(stream));
-    }
 }
 
-static void ggml_cuda_scale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_scale);
-}
-
-static void ggml_cuda_clamp(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_clamp);
-}
-
-static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    const int64_t ne = ggml_nelements(src0);
-    GGML_ASSERT(ne == ggml_nelements(src1));
-
-    GGML_ASSERT(src0->backend == GGML_BACKEND_GPU);
-    GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
-
-    GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX);
-    GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-
-    //GGML_ASSERT(src0->ne[3] == 1);
-
-    const int64_t nb00 = src0->nb[0];
-    const int64_t nb01 = src0->nb[1];
-    const int64_t nb02 = src0->nb[2];
-    const int64_t nb03 = src0->nb[3];
-
-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    const int64_t ne12 = src1->ne[2];
-
-    //GGML_ASSERT(src1->ne[3] == 1);
-
-    const int64_t nb10 = src1->nb[0];
-    const int64_t nb11 = src1->nb[1];
-    const int64_t nb12 = src1->nb[2];
-    const int64_t nb13 = src1->nb[3];
-
-    ggml_cuda_set_device(g_main_device);
-    cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
-
-    const ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    const ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-
-    char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
-    char * src1_ddc = (char *) src1_extra->data_device[g_main_device];
-
-    if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
-        ggml_cpy_f32_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
-    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
-        ggml_cpy_f32_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
-    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
-        ggml_cpy_f32_q8_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
-    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
-        ggml_cpy_f32_q4_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
-    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
-        ggml_cpy_f32_q4_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
-    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
-        ggml_cpy_f16_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
-    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
-        ggml_cpy_f16_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
-    } else {
-        fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
-                ggml_type_name(src0->type), ggml_type_name(src1->type));
-        GGML_ASSERT(false);
+static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct ggml_tensor * dst) {
+    // why is this here instead of mul_mat?
+    if (dst->src[0] != nullptr && ggml_backend_buffer_is_cuda_split(dst->src[0]->buffer)) {
+        ggml_cuda_set_peer_access(dst->src[1]->ne[1], ctx.device);
     }
 
-    (void) dst;
-}
-
-static void ggml_cuda_dup(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    // TODO: why do we pass dst as src1 here?
-    ggml_cuda_cpy(src0, dst, nullptr);
-    (void) src1;
-}
-
-static void ggml_cuda_diag_mask_inf(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_diag_mask_inf);
-}
-
-static void ggml_cuda_soft_max(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_soft_max);
-}
-
-static void ggml_cuda_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(ggml_is_contiguous(src0)); // TODO: this restriction is temporary until non-cont support is implemented
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rope);
-}
-
-static void ggml_cuda_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_alibi);
-}
-
-static void ggml_cuda_pool2d(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_pool2d);
-}
-
-static void ggml_cuda_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_im2col);
-}
-
-static void ggml_cuda_sum_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(ggml_is_contiguous(src0));
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_sum_rows);
-}
-
-static void ggml_cuda_argsort(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(ggml_is_contiguous(src0));
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_argsort);
-}
-
-static void ggml_cuda_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    (void) src0;
-    (void) src1;
-    (void) dst;
-}
-
-static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) {
-    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
-
-    return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]);
-}
-
-GGML_CALL static void ggml_cuda_set_main_device(const int main_device) {
-    if (main_device >= g_device_count) {
-        fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n",
-                main_device, g_device_count, g_main_device);
-        return;
-    }
-
-    if (g_main_device != main_device && g_device_count > 1) {
-        g_main_device = main_device;
-        //cudaDeviceProp prop;
-        //CUDA_CHECK(cudaGetDeviceProperties(&prop, g_main_device));
-        //fprintf(stderr, "%s: using device %d (%s) as main device\n", __func__, g_main_device, prop.name);
-    }
-}
-
-GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
-    if (!g_cublas_loaded) return false;
-
-    ggml_cuda_func_t func;
-    const bool any_on_device = tensor->backend == GGML_BACKEND_GPU
-        || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT))
-        || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU);
-
-    if (!any_on_device && tensor->op != GGML_OP_MUL_MAT && tensor->op != GGML_OP_MUL_MAT_ID) {
-        return false;
-    }
-
-    if (tensor->op == GGML_OP_MUL_MAT) {
-        if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) {
-#ifndef NDEBUG
-            fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, tensor->name, tensor->src[0]->ne[3], tensor->src[1]->ne[3]);
-#endif
-            return false;
-        }
-    }
-
-    switch (tensor->op) {
+    switch (dst->op) {
         case GGML_OP_REPEAT:
-            func = ggml_cuda_repeat;
+            ggml_cuda_op_repeat(ctx, dst);
             break;
         case GGML_OP_GET_ROWS:
-            func = ggml_cuda_get_rows;
+            ggml_cuda_op_get_rows(ctx, dst);
             break;
         case GGML_OP_DUP:
-            func = ggml_cuda_dup;
+            ggml_cuda_dup(ctx, dst);
+            break;
+        case GGML_OP_CPY:
+            ggml_cuda_cpy(ctx, dst->src[0], dst->src[1]);
+            break;
+        case GGML_OP_CONT:
+            ggml_cuda_dup(ctx, dst);
             break;
         case GGML_OP_ADD:
-            func = ggml_cuda_add;
+            ggml_cuda_op_add(ctx, dst);
             break;
         case GGML_OP_ACC:
-            func = ggml_cuda_acc;
+            ggml_cuda_op_acc(ctx, dst);
             break;
         case GGML_OP_MUL:
-            func = ggml_cuda_mul;
+            ggml_cuda_op_mul(ctx, dst);
             break;
         case GGML_OP_DIV:
-            func = ggml_cuda_div;
+            ggml_cuda_op_div(ctx, dst);
             break;
         case GGML_OP_UNARY:
-            switch (ggml_get_unary_op(tensor)) {
+            switch (ggml_get_unary_op(dst)) {
                 case GGML_UNARY_OP_GELU:
-                    func = ggml_cuda_gelu;
+                    ggml_cuda_op_gelu(ctx, dst);
                     break;
                 case GGML_UNARY_OP_SILU:
-                    func = ggml_cuda_silu;
+                    ggml_cuda_op_silu(ctx, dst);
                     break;
                 case GGML_UNARY_OP_GELU_QUICK:
-                    func = ggml_cuda_gelu_quick;
+                    ggml_cuda_op_gelu_quick(ctx, dst);
                     break;
                 case GGML_UNARY_OP_TANH:
-                    func = ggml_cuda_tanh;
+                    ggml_cuda_op_tanh(ctx, dst);
                     break;
                 case GGML_UNARY_OP_RELU:
-                    func = ggml_cuda_relu;
+                    ggml_cuda_op_relu(ctx, dst);
+                    break;
+                case GGML_UNARY_OP_SIGMOID:
+                    ggml_cuda_op_sigmoid(ctx, dst);
                     break;
                 case GGML_UNARY_OP_HARDSIGMOID:
-                    func = ggml_cuda_hardsigmoid;
+                    ggml_cuda_op_hardsigmoid(ctx, dst);
                     break;
                 case GGML_UNARY_OP_HARDSWISH:
-                    func = ggml_cuda_hardswish;
+                    ggml_cuda_op_hardswish(ctx, dst);
                     break;
                 default:
                     return false;
             }
             break;
         case GGML_OP_NORM:
-            func = ggml_cuda_norm;
+            ggml_cuda_op_norm(ctx, dst);
             break;
         case GGML_OP_GROUP_NORM:
-            func = ggml_cuda_group_norm;
+            ggml_cuda_op_group_norm(ctx, dst);
             break;
         case GGML_OP_CONCAT:
-            func = ggml_cuda_concat;
+            ggml_cuda_op_concat(ctx, dst);
             break;
         case GGML_OP_UPSCALE:
-            func = ggml_cuda_upscale;
+            ggml_cuda_op_upscale(ctx, dst);
             break;
         case GGML_OP_PAD:
-            func = ggml_cuda_pad;
+            ggml_cuda_op_pad(ctx, dst);
+            break;
+        case GGML_OP_ARANGE:
+            ggml_cuda_op_arange(ctx, dst);
+            break;
+        case GGML_OP_TIMESTEP_EMBEDDING:
+            ggml_cuda_op_timestep_embedding(ctx, dst);
             break;
         case GGML_OP_LEAKY_RELU:
-            func = ggml_cuda_leaky_relu;
+            ggml_cuda_op_leaky_relu(ctx, dst);
             break;
         case GGML_OP_RMS_NORM:
-            func = ggml_cuda_rms_norm;
+            ggml_cuda_op_rms_norm(ctx, dst);
             break;
         case GGML_OP_MUL_MAT:
-            if (!any_on_device && !ggml_cuda_can_mul_mat(tensor->src[0], tensor->src[1], tensor)) {
+            if (dst->src[0]->ne[3] != dst->src[1]->ne[3]) {
+                fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, dst->name, dst->src[0]->ne[3], dst->src[1]->ne[3]);
                 return false;
+            } else {
+                ggml_cuda_mul_mat(ctx, dst->src[0], dst->src[1], dst);
             }
-            func = ggml_cuda_mul_mat;
             break;
         case GGML_OP_MUL_MAT_ID:
-            if (!any_on_device && !ggml_cuda_can_mul_mat(tensor->src[2], tensor->src[1], tensor)) {
-                return false;
-            }
-            func = ggml_cuda_mul_mat_id;
+            ggml_cuda_mul_mat_id(ctx, dst);
             break;
         case GGML_OP_SCALE:
-            func = ggml_cuda_scale;
+            ggml_cuda_op_scale(ctx, dst);
             break;
         case GGML_OP_SQR:
-            func = ggml_cuda_sqr;
+            ggml_cuda_op_sqr(ctx, dst);
             break;
         case GGML_OP_CLAMP:
-            func = ggml_cuda_clamp;
-            break;
-        case GGML_OP_CPY:
-            func = ggml_cuda_cpy;
-            break;
-        case GGML_OP_CONT:
-            func = ggml_cuda_dup;
+            ggml_cuda_op_clamp(ctx, dst);
             break;
         case GGML_OP_NONE:
         case GGML_OP_RESHAPE:
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
         case GGML_OP_TRANSPOSE:
-            func = ggml_cuda_nop;
-            break;
+                break;
         case GGML_OP_DIAG_MASK_INF:
-            func = ggml_cuda_diag_mask_inf;
+            ggml_cuda_op_diag_mask_inf(ctx, dst);
             break;
         case GGML_OP_SOFT_MAX:
-            func = ggml_cuda_soft_max;
+            ggml_cuda_op_soft_max(ctx, dst);
             break;
         case GGML_OP_ROPE:
-            func = ggml_cuda_rope;
-            break;
-        case GGML_OP_ALIBI:
-            func = ggml_cuda_alibi;
+            ggml_cuda_op_rope(ctx, dst);
             break;
         case GGML_OP_IM2COL:
-            func = ggml_cuda_im2col;
+            ggml_cuda_op_im2col(ctx, dst);
             break;
         case GGML_OP_POOL_2D:
-            func = ggml_cuda_pool2d;
+            ggml_cuda_op_pool2d(ctx, dst);
             break;
         case GGML_OP_SUM_ROWS:
-            func = ggml_cuda_sum_rows;
+            ggml_cuda_op_sum_rows(ctx, dst);
             break;
         case GGML_OP_ARGSORT:
-            func = ggml_cuda_argsort;
+            ggml_cuda_op_argsort(ctx, dst);
+            break;
+        case GGML_OP_FLASH_ATTN_EXT:
+            ggml_cuda_flash_attn_ext(ctx, dst);
             break;
         default:
             return false;
     }
 
-    if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT) {
-        ggml_cuda_set_peer_access(tensor->src[1]->ne[1]);
+    cudaError_t err = cudaGetLastError();
+    if (err != cudaSuccess) {
+        fprintf(stderr, "%s: %s failed\n", __func__, ggml_op_desc(dst));
+        CUDA_CHECK(err);
     }
 
-    if (params->ith != 0) {
-        return true;
-    }
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
-        return true;
-    }
-    func(tensor->src[0], tensor->src[1], tensor);
     return true;
 }
 
-GGML_CALL int ggml_cuda_get_device_count() {
-    int device_count;
-    if (cudaGetDeviceCount(&device_count) != cudaSuccess) {
-        return 0;
-    }
-    return device_count;
-}
-
-GGML_CALL void ggml_cuda_get_device_description(int device, char * description, size_t description_size) {
-    cudaDeviceProp prop;
-    CUDA_CHECK(cudaGetDeviceProperties(&prop, device));
-    snprintf(description, description_size, "%s", prop.name);
-}
-
 ////////////////////////////////////////////////////////////////////////////////
 
-// backend interface
-
-#define UNUSED GGML_UNUSED
-
-struct ggml_backend_cuda_context {
-    int device;
-    std::string name;
-};
-
-// cuda buffer
-
-struct ggml_backend_cuda_buffer_context {
-    int device;
-    void * dev_ptr = nullptr;
-    ggml_tensor_extra_gpu * temp_tensor_extras = nullptr;
-    size_t temp_tensor_extra_index = 0;
-    std::string name;
-
-    ggml_backend_cuda_buffer_context(int device, void * dev_ptr) :
-        device(device), dev_ptr(dev_ptr),
-        name(GGML_CUDA_NAME + std::to_string(device)) {
-    }
-
-    ~ggml_backend_cuda_buffer_context() {
-        delete[] temp_tensor_extras;
-    }
-
-    ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() {
-        // TODO: remove GGML_CUDA_MAX_NODES, allocate dynamically and reuse in backend_buffer_reset
-        if (temp_tensor_extras == nullptr) {
-            temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_CUDA_MAX_NODES];
-        }
-
-        size_t alloc_index = temp_tensor_extra_index;
-        temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_CUDA_MAX_NODES;
-        ggml_tensor_extra_gpu * extra = &temp_tensor_extras[alloc_index];
-        memset(extra, 0, sizeof(*extra));
-
-        return extra;
-    }
-};
-
-GGML_CALL static const char * ggml_backend_cuda_buffer_get_name(ggml_backend_buffer_t buffer) {
-    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
-    return ctx->name.c_str();
-}
-
-GGML_CALL static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) {
-    return buffer->iface.get_name == ggml_backend_cuda_buffer_get_name;
-}
-
-GGML_CALL static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) {
-    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
-    CUDA_CHECK(cudaFree(ctx->dev_ptr));
-    delete ctx;
-}
-
-GGML_CALL static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) {
-    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
-    return ctx->dev_ptr;
-}
-
-GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
-    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
-
-    if (tensor->view_src != NULL && tensor->view_offs == 0) {
-        assert(tensor->view_src->buffer->buft == buffer->buft);
-        tensor->backend = tensor->view_src->backend;
-        tensor->extra = tensor->view_src->extra;
-        return;
-    }
-
-    ggml_tensor_extra_gpu * extra = ctx->ggml_cuda_alloc_temp_tensor_extra();
-
-    extra->data_device[ctx->device] = tensor->data;
-
-    tensor->backend = GGML_BACKEND_GPU;
-    tensor->extra = extra;
-
-    if (ggml_is_quantized(tensor->type)) {
-        // initialize padding to 0 to avoid possible NaN values
-        size_t original_size = ggml_nbytes(tensor);
-        size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor);
-
-        if (padded_size > original_size && tensor->view_src == nullptr) {
-            CUDA_CHECK(cudaMemset((char *)tensor->data + original_size, 0, padded_size - original_size));
-        }
-    }
-}
-
-GGML_CALL static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
-
-    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
-
-    ggml_cuda_set_device(ctx->device);
-    CUDA_CHECK(cudaDeviceSynchronize());
-    CUDA_CHECK(cudaMemcpy((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice));
-    CUDA_CHECK(cudaDeviceSynchronize());
-}
-
-GGML_CALL static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
-
-    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
-
-    ggml_cuda_set_device(ctx->device);
-    CUDA_CHECK(cudaDeviceSynchronize());
-    CUDA_CHECK(cudaMemcpy(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost));
-    CUDA_CHECK(cudaDeviceSynchronize());
-}
-
-GGML_CALL static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
-    if (ggml_backend_buffer_is_cuda(src->buffer)) {
-        ggml_backend_cuda_buffer_context * src_ctx = (ggml_backend_cuda_buffer_context *)src->buffer->context;
-        ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
-
-        ggml_cuda_set_device(src_ctx->device);
-        CUDA_CHECK(cudaDeviceSynchronize());
-        ggml_cuda_set_device(dst_ctx->device);
-        CUDA_CHECK(cudaDeviceSynchronize());
-        CUDA_CHECK(cudaMemcpy((char *)dst->data, (const char *)src->data, ggml_nbytes(src), cudaMemcpyDeviceToDevice));
-        CUDA_CHECK(cudaDeviceSynchronize());
-
-        return true;
-    }
-    return false;
-}
-
-GGML_CALL static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
-    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
-
-    ggml_cuda_set_device(ctx->device);
-    CUDA_CHECK(cudaDeviceSynchronize());
-    CUDA_CHECK(cudaMemset(ctx->dev_ptr, value, buffer->size));
-    CUDA_CHECK(cudaDeviceSynchronize());
-}
-
-static ggml_backend_buffer_i ggml_backend_cuda_buffer_interface = {
-    /* .get_name        = */ ggml_backend_cuda_buffer_get_name,
-    /* .free_buffer     = */ ggml_backend_cuda_buffer_free_buffer,
-    /* .get_base        = */ ggml_backend_cuda_buffer_get_base,
-    /* .init_tensor     = */ ggml_backend_cuda_buffer_init_tensor,
-    /* .set_tensor      = */ ggml_backend_cuda_buffer_set_tensor,
-    /* .get_tensor      = */ ggml_backend_cuda_buffer_get_tensor,
-    /* .cpy_tensor      = */ ggml_backend_cuda_buffer_cpy_tensor,
-    /* .clear           = */ ggml_backend_cuda_buffer_clear,
-    /* .reset           = */ NULL,
-};
-
-// cuda buffer type
-struct ggml_backend_cuda_buffer_type_context {
-    int device;
-    std::string name;
-};
-
-GGML_CALL static const char * ggml_backend_cuda_buffer_type_name(ggml_backend_buffer_type_t buft) {
-    ggml_backend_cuda_buffer_type_context * ctx = (ggml_backend_cuda_buffer_type_context *)buft->context;
-
-    return ctx->name.c_str();
-}
-
-GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
-    ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context;
-
-    ggml_cuda_set_device(buft_ctx->device);
-
-    size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0
-
-    void * dev_ptr;
-    cudaError_t err = cudaMalloc(&dev_ptr, size);
-    if (err != cudaSuccess) {
-        fprintf(stderr, "%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size/1024.0/1024.0, buft_ctx->device, cudaGetErrorString(err));
-        return nullptr;
-    }
-
-    ggml_backend_cuda_buffer_context * ctx = new ggml_backend_cuda_buffer_context(buft_ctx->device, dev_ptr);
-
-    return ggml_backend_buffer_init(buft, ggml_backend_cuda_buffer_interface, ctx, size);
-}
-
-GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
-    return 128;
-
-    UNUSED(buft);
-}
-
-GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
-    size_t size = ggml_nbytes(tensor);
-    int64_t ne0 = tensor->ne[0];
-
-    if (ggml_is_quantized(tensor->type)) {
-        if (ne0 % MATRIX_ROW_PADDING != 0) {
-            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
-        }
-    }
-
-    return size;
-
-    UNUSED(buft);
-}
-
-GGML_CALL static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
-    if (!ggml_backend_is_cuda(backend)) {
-        return false;
-    }
-
-    ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context;
-    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
-
-    return buft_ctx->device == cuda_ctx->device;
-}
-
-static ggml_backend_buffer_type_i ggml_backend_cuda_buffer_type_interface = {
-    /* .get_name         = */ ggml_backend_cuda_buffer_type_name,
-    /* .alloc_buffer     = */ ggml_backend_cuda_buffer_type_alloc_buffer,
-    /* .get_alignment    = */ ggml_backend_cuda_buffer_type_get_alignment,
-    /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
-    /* .get_alloc_size   = */ ggml_backend_cuda_buffer_type_get_alloc_size,
-    /* .supports_backend = */ ggml_backend_cuda_buffer_type_supports_backend,
-    /* .is_host          = */ NULL,
-};
-
-GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) {
-    // FIXME: this is not thread safe
-    if (device >= ggml_backend_cuda_get_device_count()) {
-        return nullptr;
-    }
-
-    static ggml_backend_buffer_type ggml_backend_cuda_buffer_types[GGML_CUDA_MAX_DEVICES];
-
-    static bool ggml_backend_cuda_buffer_type_initialized = false;
-
-    if (!ggml_backend_cuda_buffer_type_initialized) {
-        for (int i = 0; i < GGML_CUDA_MAX_DEVICES; i++) {
-            ggml_backend_cuda_buffer_types[i] = {
-                /* .iface    = */ ggml_backend_cuda_buffer_type_interface,
-                /* .context  = */ new ggml_backend_cuda_buffer_type_context{i, GGML_CUDA_NAME + std::to_string(i)},
-            };
-        }
-        ggml_backend_cuda_buffer_type_initialized = true;
-    }
-
-    return &ggml_backend_cuda_buffer_types[device];
-}
-
-// cuda split buffer
-
-struct ggml_backend_cuda_split_buffer_context {
-    ~ggml_backend_cuda_split_buffer_context() {
-        for (ggml_tensor_extra_gpu * extra : tensor_extras) {
-            for (int id = 0; id < g_device_count; ++id) {
-                for (int64_t is = 0; is < MAX_STREAMS; ++is) {
-                    if (extra->events[id][is] != nullptr) {
-                        CUDA_CHECK(cudaEventDestroy(extra->events[id][is]));
-                    }
-                }
-                if (extra->data_device[id] != nullptr) {
-                    CUDA_CHECK(cudaFree(extra->data_device[id]));
-                }
-            }
-            delete extra;
-        }
-    }
-
-    std::vector<ggml_tensor_extra_gpu *> tensor_extras;
-};
-
-GGML_CALL static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_t buffer) {
-    return GGML_CUDA_NAME "_Split";
-
-    UNUSED(buffer);
-}
-
-// unused at the moment
-//static bool ggml_backend_buffer_is_cuda_split(ggml_backend_buffer_t buffer) {
-//    return buffer->iface.get_name == ggml_backend_cuda_split_buffer_get_name;
-//}
-
-GGML_CALL static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) {
-    ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context;
-    delete ctx;
-}
-
-GGML_CALL static void * ggml_backend_cuda_split_buffer_get_base(ggml_backend_buffer_t buffer) {
-    // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced
-    return (void *)0x1000;
-
-    UNUSED(buffer);
-}
-
-GGML_CALL static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
-    GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported
-
-    ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context;
-    ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context;
-
-    const int64_t ne0 = tensor->ne[0];
-
-    ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{};
-
-    ctx->tensor_extras.push_back(extra);
-
-    for (int id = 0; id < g_device_count; ++id) {
-        int64_t row_low, row_high;
-        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id);
-
-        int64_t nrows_split = row_high - row_low;
-        if (nrows_split == 0) {
-            continue;
-        }
-
-        size_t size = ggml_nbytes_split(tensor, nrows_split);
-        const size_t original_size = size;
-
-        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
-        if (ne0 % MATRIX_ROW_PADDING != 0) {
-            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
-        }
-
-        // FIXME: do not crash if cudaMalloc fails
-        // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first
-        ggml_cuda_set_device(id);
-        char * buf;
-        CUDA_CHECK(cudaMalloc(&buf, size));
-
-        // set padding to 0 to avoid possible NaN values
-        if (size > original_size) {
-            CUDA_CHECK(cudaMemset(buf + original_size, 0, size - original_size));
-        }
-
-        extra->data_device[id] = buf;
-
-        for (int64_t is = 0; is < MAX_STREAMS; ++is) {
-            CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id][is], cudaEventDisableTiming));
-        }
-    }
-    tensor->backend = GGML_BACKEND_GPU_SPLIT;
-    tensor->extra = extra;
-}
-
-GGML_CALL static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
-    // split tensors must always be set in their entirety at once
-    GGML_ASSERT(offset == 0);
-    GGML_ASSERT(size == ggml_nbytes(tensor));
-
-    ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context;
-
-    const int64_t ne0 = tensor->ne[0];
-    const size_t nb1 = tensor->nb[1];
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra;
-
-    for (int id = 0; id < g_device_count; ++id) {
-        int64_t row_low, row_high;
-        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id);
-
-        int64_t nrows_split = row_high - row_low;
-        if (nrows_split == 0) {
-            continue;
-        }
-
-        const size_t offset_split = row_low*nb1;
-        size_t size = ggml_nbytes_split(tensor, nrows_split);
-        const size_t original_size = size;
-
-        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
-        if (ne0 % MATRIX_ROW_PADDING != 0) {
-            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
-        }
-
-        const char * buf_host = (const char *)data + offset_split;
-        CUDA_CHECK(cudaMemcpy(extra->data_device[id], buf_host, original_size, cudaMemcpyHostToDevice));
-    }
-}
-
-GGML_CALL static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
-    // split tensors must always be set in their entirety at once
-    GGML_ASSERT(offset == 0);
-    GGML_ASSERT(size == ggml_nbytes(tensor));
-
-    ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context;
-
-    const int64_t ne0 = tensor->ne[0];
-    const size_t nb1 = tensor->nb[1];
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra;
-
-    for (int id = 0; id < g_device_count; ++id) {
-        int64_t row_low, row_high;
-        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id);
-
-        int64_t nrows_split = row_high - row_low;
-        if (nrows_split == 0) {
-            continue;
-        }
-
-        const size_t offset_split = row_low*nb1;
-        size_t size = ggml_nbytes_split(tensor, nrows_split);
-        const size_t original_size = size;
-
-        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
-        if (ne0 % MATRIX_ROW_PADDING != 0) {
-            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
-        }
-
-        char * buf_host = (char *)data + offset_split;
-        CUDA_CHECK(cudaMemcpy(buf_host, extra->data_device[id], original_size, cudaMemcpyDeviceToHost));
-    }
-}
-
-GGML_CALL static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
-    UNUSED(buffer);
-    UNUSED(value);
-}
-
-static struct ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = {
-    /* .get_name        = */ ggml_backend_cuda_split_buffer_get_name,
-    /* .free_buffer     = */ ggml_backend_cuda_split_buffer_free_buffer,
-    /* .get_base        = */ ggml_backend_cuda_split_buffer_get_base,
-    /* .init_tensor     = */ ggml_backend_cuda_split_buffer_init_tensor,
-    /* .set_tensor      = */ ggml_backend_cuda_split_buffer_set_tensor,
-    /* .get_tensor      = */ ggml_backend_cuda_split_buffer_get_tensor,
-    /* .cpy_tensor      = */ NULL,
-    /* .clear           = */ ggml_backend_cuda_split_buffer_clear,
-    /* .reset           = */ NULL,
-};
-
-// cuda split buffer type
-
-GGML_CALL static const char * ggml_backend_cuda_split_buffer_type_name(ggml_backend_buffer_type_t buft) {
-    return GGML_CUDA_NAME "_Split";
-
-    UNUSED(buft);
-}
-
-GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
-    // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point
-    // instead, we allocate them for each tensor separately in init_tensor
-    // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated,
-    // as returned by get_alloc_size. this limit is enforced during tensor allocation by ggml-alloc, so it must be correct.
-    ggml_backend_cuda_split_buffer_context * ctx = new ggml_backend_cuda_split_buffer_context();
-
-    return ggml_backend_buffer_init(buft, ggml_backend_cuda_split_buffer_interface, ctx, size);
-}
-
-GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
-    return 128;
-
-    UNUSED(buft);
-}
-
-GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
-    ggml_backend_cuda_split_buffer_type_context * ctx = (ggml_backend_cuda_split_buffer_type_context *)buft->context;
-
-    size_t total_size = 0;
-
-    const int64_t ne0 = tensor->ne[0];
-
-    for (int id = 0; id < g_device_count; ++id) {
-        int64_t row_low, row_high;
-        get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, id);
-
-        int64_t nrows_split = row_high - row_low;
-        if (nrows_split == 0) {
-            continue;
-        }
-
-        total_size += ggml_nbytes_split(tensor, nrows_split);
-
-        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
-        if (ne0 % MATRIX_ROW_PADDING != 0) {
-            total_size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
-        }
-    }
-
-    return total_size;
-}
-
-GGML_CALL static bool ggml_backend_cuda_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
-    return ggml_backend_is_cuda(backend);
-
-    UNUSED(buft);
-}
-
-GGML_CALL static bool ggml_backend_cuda_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
-    return false;
-
-    UNUSED(buft);
-}
-
-static ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_interface = {
-    /* .get_name         = */ ggml_backend_cuda_split_buffer_type_name,
-    /* .alloc_buffer     = */ ggml_backend_cuda_split_buffer_type_alloc_buffer,
-    /* .get_alignment    = */ ggml_backend_cuda_split_buffer_type_get_alignment,
-    /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
-    /* .get_alloc_size   = */ ggml_backend_cuda_split_buffer_type_get_alloc_size,
-    /* .supports_backend = */ ggml_backend_cuda_split_buffer_type_supports_backend,
-    /* .is_host          = */ ggml_backend_cuda_split_buffer_type_is_host,
-};
-
-GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) {
-    // FIXME: this is not thread safe
-    static std::map<std::array<float, GGML_CUDA_MAX_DEVICES>, struct ggml_backend_buffer_type> buft_map;
-
-    std::array<float, GGML_CUDA_MAX_DEVICES> tensor_split_arr = {};
-
-    bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_CUDA_MAX_DEVICES, [](float x) { return x == 0.0f; });
-    if (all_zero) {
-        tensor_split_arr = g_default_tensor_split;
-    } else {
-        float split_sum = 0.0f;
-        for (int i = 0; i < g_device_count; ++i) {
-            tensor_split_arr[i] = split_sum;
-            split_sum += tensor_split[i];
-        }
-        for (int i = 0; i < g_device_count; ++i) {
-            tensor_split_arr[i] /= split_sum;
-        }
-    }
-
-    auto it = buft_map.find(tensor_split_arr);
-    if (it != buft_map.end()) {
-        return &it->second;
-    }
-
-    struct ggml_backend_buffer_type buft {
-        /* .iface   = */ ggml_backend_cuda_split_buffer_type_interface,
-        /* .context = */ new ggml_backend_cuda_split_buffer_type_context{tensor_split_arr},
-    };
-
-    auto result = buft_map.emplace(tensor_split_arr, buft);
-    return &result.first->second;
-}
-
-// host buffer type
-
-GGML_CALL static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
-    return GGML_CUDA_NAME "_Host";
-
-    UNUSED(buft);
-}
-
-GGML_CALL static const char * ggml_backend_cuda_host_buffer_name(ggml_backend_buffer_t buffer) {
-    return GGML_CUDA_NAME "_Host";
-
-    UNUSED(buffer);
-}
-
-GGML_CALL static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
-    ggml_cuda_host_free(buffer->context);
-}
-
-GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
-    void * ptr = ggml_cuda_host_malloc(size);
-
-    if (ptr == nullptr) {
-        // fallback to cpu buffer
-        return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size);
-    }
-
-    ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
-    buffer->buft = buft;
-    buffer->iface.get_name = ggml_backend_cuda_host_buffer_name;
-    buffer->iface.free_buffer = ggml_backend_cuda_host_buffer_free_buffer;
-
-    return buffer;
-}
-
-GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() {
-    static struct ggml_backend_buffer_type ggml_backend_cuda_buffer_type_host = {
-        /* .iface    = */ {
-            /* .get_name         = */ ggml_backend_cuda_host_buffer_type_name,
-            /* .alloc_buffer     = */ ggml_backend_cuda_host_buffer_type_alloc_buffer,
-            /* .get_alignment    = */ ggml_backend_cpu_buffer_type()->iface.get_alignment,
-            /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
-            /* .get_alloc_size   = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
-            /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend,
-            /* .is_host          = */ ggml_backend_cpu_buffer_type()->iface.is_host,
-        },
-        /* .context  = */ nullptr,
-    };
-
-    return &ggml_backend_cuda_buffer_type_host;
-}
-
 // backend
 
 GGML_CALL static const char * ggml_backend_cuda_name(ggml_backend_t backend) {
@@ -11332,87 +2332,395 @@ GGML_CALL static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer
 
 GGML_CALL static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
     ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
 
-    GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(buf->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
 
-    CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, g_cudaStreams[cuda_ctx->device][0]));
+    CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, cuda_ctx->stream()));
 }
 
 GGML_CALL static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
     ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
 
-    GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(buf->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
 
-    CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[cuda_ctx->device][0]));
+    CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, cuda_ctx->stream()));
 }
 
-GGML_CALL static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) {
-    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+GGML_CALL static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const ggml_tensor * src, ggml_tensor * dst) {
+    GGML_ASSERT(ggml_backend_is_cuda(backend_src) || ggml_backend_is_cuda(backend_dst));
 
-    if (dst->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && ggml_backend_buffer_is_cuda(src->buffer)) {
-        CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, g_cudaStreams[cuda_ctx->device][0]));
-        return true;
+    ggml_backend_buffer_t buf_src = src->view_src ? src->view_src->buffer : src->buffer;
+    ggml_backend_buffer_t buf_dst = dst->view_src ? dst->view_src->buffer : dst->buffer;
+
+    if (!ggml_backend_buffer_is_cuda(src->buffer)) {
+        return false;
     }
 
-    return false;
+    if (!ggml_backend_buffer_is_cuda(dst->buffer)) {
+        return false;
+    }
+
+    // device -> device
+    ggml_backend_cuda_context * cuda_ctx_src = (ggml_backend_cuda_context *)backend_src->context;
+    ggml_backend_cuda_context * cuda_ctx_dst = (ggml_backend_cuda_context *)backend_dst->context;
+
+    if (backend_src != backend_dst) {
+        ggml_backend_cuda_buffer_context * buf_ctx_src = (ggml_backend_cuda_buffer_context *)buf_src->context;
+        ggml_backend_cuda_buffer_context * buf_ctx_dst = (ggml_backend_cuda_buffer_context *)buf_dst->context;
+
+        GGML_ASSERT(cuda_ctx_src->device == buf_ctx_src->device);
+        GGML_ASSERT(cuda_ctx_dst->device == buf_ctx_dst->device);
+
+        // copy on src stream
+        if (cuda_ctx_src->device == cuda_ctx_dst->device) {
+            CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, cuda_ctx_dst->stream()));
+        } else {
+#ifdef GGML_CUDA_NO_PEER_COPY
+            return false;
+#else
+            CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, cuda_ctx_dst->device, src->data, cuda_ctx_src->device, ggml_nbytes(dst), cuda_ctx_src->stream()));
+#endif
+        }
+
+        // record event on src stream
+        if (!cuda_ctx_src->copy_event) {
+            ggml_cuda_set_device(cuda_ctx_src->device);
+            CUDA_CHECK(cudaEventCreateWithFlags(&cuda_ctx_src->copy_event, cudaEventDisableTiming));
+        }
+
+        CUDA_CHECK(cudaEventRecord(cuda_ctx_src->copy_event, cuda_ctx_src->stream()));
+
+        // wait on dst stream for the copy to complete
+        CUDA_CHECK(cudaStreamWaitEvent(cuda_ctx_dst->stream(), cuda_ctx_src->copy_event, 0));
+    } else {
+        // src and dst are on the same backend
+        CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, cuda_ctx_dst->stream()));
+    }
+    return true;
 }
 
 GGML_CALL static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
     ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
 
-    CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[cuda_ctx->device][0]));
+    CUDA_CHECK(cudaStreamSynchronize(cuda_ctx->stream()));
 
-    UNUSED(backend);
+    GGML_UNUSED(backend);
 }
 
-GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
-    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+static void set_ggml_graph_node_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
+    graph_node_properties->node_address = node->data;
+    graph_node_properties->node_op = node->op;
+    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+        graph_node_properties->ne[i] = node->ne[i];
+        graph_node_properties->nb[i] = node->nb[i];
+    }
+    for (int i = 0; i < GGML_MAX_SRC; i++) {
+        graph_node_properties->src_address[i] = node->src[i] ? node->src[i]->data : nullptr;
+    }
+}
 
-    ggml_cuda_set_main_device(cuda_ctx->device);
-
-    ggml_compute_params params = {};
-    params.type = GGML_TASK_COMPUTE;
-    params.ith = 0;
-    for (int i = 0; i < cgraph->n_nodes; i++) {
-        ggml_tensor * node = cgraph->nodes[i];
-
-        if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
-            continue;
-        }
-
-#ifndef NDEBUG
-        assert(node->backend == GGML_BACKEND_GPU || node->backend == GGML_BACKEND_GPU_SPLIT);
-        assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
-        assert(node->extra != nullptr);
-
-        for (int j = 0; j < GGML_MAX_SRC; j++) {
-            if (node->src[j] != nullptr) {
-                assert(node->src[j]->backend == GGML_BACKEND_GPU || node->src[j]->backend == GGML_BACKEND_GPU_SPLIT);
-                assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
-                assert(node->src[j]->extra != nullptr);
-            }
-        }
-#endif
-
-        bool ok = ggml_cuda_compute_forward(&params, node);
-        if (!ok) {
-            fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
-        }
-        GGML_ASSERT(ok);
+static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
+    if (node->data != graph_node_properties->node_address &&
+          node->op != GGML_OP_CPY &&
+          node->op != GGML_OP_VIEW) {
+        return false;
     }
 
+    if (node->op != graph_node_properties->node_op) {
+        return false;
+    }
+
+    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+        if (node->ne[i] != graph_node_properties->ne[i]) {
+            return false;
+        }
+        if (node->nb[i] != graph_node_properties->nb[i]) {
+            return false;
+        }
+    }
+
+    for (int i = 0; i < GGML_MAX_SRC; i++) {
+        if (node->src[i] &&
+            node->src[i]->data != graph_node_properties->src_address[i] &&
+            node->op != GGML_OP_CPY &&
+            node->op != GGML_OP_VIEW
+        ) {
+            return false;
+        }
+    }
     return true;
 }
 
+GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+
+    ggml_cuda_set_device(cuda_ctx->device);
+
+#ifdef USE_CUDA_GRAPH
+    static const bool disable_cuda_graphs_due_to_env = (getenv("GGML_CUDA_DISABLE_GRAPHS") != nullptr);
+
+    // Objects required for CUDA Graph
+    if (cuda_ctx->cuda_graph == nullptr) {
+        cuda_ctx->cuda_graph.reset(new ggml_cuda_graph());
+    }
+
+    bool use_cuda_graph = true;
+    bool cuda_graph_update_required = false;
+    // pointer to CUDA cpy kernel, which is required to identify
+    // kernel parameters which need updated in the graph for each token
+    void * ggml_cuda_cpy_fn_ptr = nullptr;
+
+    if (cuda_ctx->cuda_graph->graph == nullptr) {
+        if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) {
+            cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true;
+#ifndef NDEBUG
+            fprintf(stderr, "%s: disabling CUDA graphs due to GPU architecture\n", __func__);
+#endif
+        }
+    }
+
+    // Disable CUDA graphs in presence of env var, old GPU, use-case which is changing too rapidly,
+    // or previous graph capture failure.
+    // Also disable for multi-gpu for now. TO DO investigate
+    if (disable_cuda_graphs_due_to_env
+        || cuda_ctx->cuda_graph->disable_due_to_gpu_arch
+        || cuda_ctx->cuda_graph->disable_due_to_too_many_updates
+        || cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture) {
+        use_cuda_graph = false;
+    }
+
+    if (use_cuda_graph) {
+        if (cuda_ctx->cuda_graph->instance == nullptr) {
+            cuda_graph_update_required = true;
+        }
+
+        // Check if the graph size has changed
+        if (cuda_ctx->cuda_graph->ggml_graph_properties.size() != (size_t)cgraph->n_nodes) {
+            cuda_graph_update_required = true;
+            cuda_ctx->cuda_graph->ggml_graph_properties.resize(cgraph->n_nodes);
+        }
+
+        // Loop over nodes in GGML graph to determine if CUDA graph update is required
+        // and store properties to allow this comparison for the next token
+        for (int i = 0; i < cgraph->n_nodes; i++) {
+            bool has_matching_properties = true;
+            if (!cuda_graph_update_required) {
+                has_matching_properties = ggml_graph_node_has_matching_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]);
+            }
+            if (!has_matching_properties) {
+                cuda_graph_update_required = true;
+            }
+            set_ggml_graph_node_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]);
+        }
+
+        // Loop over nodes in GGML graph to obtain info needed for CUDA graph
+        cuda_ctx->cuda_graph->updated_kernel_arg.clear();
+        for (int i = 0; i < cgraph->n_nodes; i++) {
+            ggml_tensor * node = cgraph->nodes[i];
+
+            if (node->src[0] && ggml_backend_buffer_is_cuda_split(node->src[0]->buffer)) {
+                use_cuda_graph = false; // Split buffers are not supported by CUDA graph capture
+#ifndef NDEBUG
+                fprintf(stderr, "%s: disabling CUDA graphs due to split buffer\n", __func__);
+#endif
+            }
+
+            if (node->op == GGML_OP_MUL_MAT_ID) {
+                use_cuda_graph = false; // This node type is not supported by CUDA graph capture
+#ifndef NDEBUG
+                fprintf(stderr, "%s: disabling CUDA graphs due to mul_mat_id\n", __func__);
+#endif
+            }
+
+            if (node->op == GGML_OP_ADD && node->src[1] && node->src[1]->ne[1] > 1) {
+                // disable CUDA graphs for batch size > 1 for now.
+                // Changes in batch size or context size can cause changes to the grid size of some kernels.
+                use_cuda_graph = false;
+#ifndef NDEBUG
+                fprintf(stderr, "%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]);
+#endif
+            }
+
+            if (node->op == GGML_OP_CPY) {
+                // store the copy op parameter which changes with each token.
+                cuda_ctx->cuda_graph->updated_kernel_arg.push_back((char **) &(node->src[1]->data));
+                if (ggml_cuda_cpy_fn_ptr == nullptr) {
+                    // store a pointer to the copy op CUDA kernel to identify it later
+                    ggml_cuda_cpy_fn_ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]);
+                }
+            }
+
+            if (!use_cuda_graph) {
+                break;
+            }
+        }
+
+        // Disable CUDA graphs (from the next token) if the use-case is demanding too many consecutive graph updates.
+        if (cuda_graph_update_required) {
+            cuda_ctx->cuda_graph->number_consecutive_updates++;
+        } else {
+            cuda_ctx->cuda_graph->number_consecutive_updates = 0;
+        }
+
+        if (cuda_ctx->cuda_graph->number_consecutive_updates >= 4) {
+            cuda_ctx->cuda_graph->disable_due_to_too_many_updates = true;
+#ifndef NDEBUG
+            fprintf(stderr, "%s: disabling CUDA graphs due to too many consecutive updates\n", __func__);
+#endif
+        }
+    }
+
+    if (use_cuda_graph && cuda_graph_update_required) { // Start CUDA graph capture
+        CUDA_CHECK(cudaStreamBeginCapture(cuda_ctx->stream(), cudaStreamCaptureModeRelaxed));
+    }
+
+#else
+    bool use_cuda_graph = false;
+    bool cuda_graph_update_required = false;
+#endif // USE_CUDA_GRAPH
+
+    bool graph_evaluated_or_captured = false;
+
+    while (!graph_evaluated_or_captured) {
+        // Only perform the graph execution if CUDA graphs are not enabled, or we are capturing the graph.
+        // With the use of CUDA graphs, the execution will be performed by the graph launch.
+        if (!use_cuda_graph || cuda_graph_update_required) {
+            for (int i = 0; i < cgraph->n_nodes; i++) {
+                ggml_tensor * node = cgraph->nodes[i];
+
+                if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
+                    continue;
+                }
+
+#ifndef NDEBUG
+                assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
+                for (int j = 0; j < GGML_MAX_SRC; j++) {
+                    if (node->src[j] != nullptr) {
+                        assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) || ggml_backend_buffer_is_cuda_split(node->src[j]->buffer));
+                    }
+                }
+#endif
+
+                bool ok = ggml_cuda_compute_forward(*cuda_ctx, node);
+                if (!ok) {
+                    fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
+                }
+                GGML_ASSERT(ok);
+            }
+        }
+
+#ifdef USE_CUDA_GRAPH
+        if (use_cuda_graph && cuda_graph_update_required) { // End CUDA graph capture
+            if (cuda_ctx->cuda_graph->graph != nullptr) {
+                CUDA_CHECK(cudaGraphDestroy(cuda_ctx->cuda_graph->graph));
+                cuda_ctx->cuda_graph->graph = nullptr;
+            }
+            CUDA_CHECK(cudaStreamEndCapture(cuda_ctx->stream(), &cuda_ctx->cuda_graph->graph));
+
+#if 0
+            if (disable_cuda_graphs_due_to_failed_capture) {
+                use_cuda_graph = false;
+                cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture = true;
+#ifndef NDEBUG
+                fprintf(stderr, "%s: disabling CUDA graphs due to failed graph capture\n", __func__);
+#endif
+            } else {
+                graph_evaluated_or_captured = true; // CUDA graph has been captured
+            }
+#endif
+            graph_evaluated_or_captured = true; // CUDA graph has been captured
+        } else {
+            graph_evaluated_or_captured = true; // ggml graph has been directly evaluated
+        }
+    }
+
+    if (use_cuda_graph) {
+        if (cuda_ctx->cuda_graph->instance == nullptr) { // Create executable graph from captured graph.
+            CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0));
+        }
+
+        // Perform update to graph (if required for this token), and change copy parameter (required for every token)
+
+        if (cuda_graph_update_required) {
+            // Extract nodes from graph
+            if (cuda_ctx->cuda_graph->num_nodes == 0) {
+                // First call with null argument gets number of nodes in graph
+                CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, nullptr, &cuda_ctx->cuda_graph->num_nodes));
+            }
+            // Subsequent call with non-null argument gets nodes
+            cuda_ctx->cuda_graph->nodes.resize(cuda_ctx->cuda_graph->num_nodes);
+            cuda_ctx->cuda_graph->params.resize(cuda_ctx->cuda_graph->num_nodes);
+            if (cuda_ctx->cuda_graph->num_nodes > 0) {
+                CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, cuda_ctx->cuda_graph->nodes.data(), &cuda_ctx->cuda_graph->num_nodes));
+
+                // Loop over nodes, and extract kernel parameters from each node
+                for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
+                    cudaGraphNodeType node_type;
+                    CUDA_CHECK(cudaGraphNodeGetType(cuda_ctx->cuda_graph->nodes[i], &node_type));
+                    if (node_type == cudaGraphNodeTypeKernel) {
+                        cudaError_t stat = cudaGraphKernelNodeGetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]); // Get params using runtime
+                        if (stat == cudaErrorInvalidDeviceFunction) {
+                            // Fails due to incorrect handling by CUDA runtime of CUDA BLAS node.
+                            // We don't need to update blas nodes, so clear error and move on.
+                            cudaGetLastError();
+                        } else {
+                            GGML_ASSERT(stat == cudaSuccess);
+                        }
+                    }
+                }
+            }
+        }
+
+        // One of the arguments to the copy kernel is updated for each token, hence we need to
+        // replace that argument with the updated value in the CUDA graph
+        if (!cuda_graph_update_required) { // on update steps, the live parameters will already be captured
+            int k = 0;
+            for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
+                if (cuda_ctx->cuda_graph->params[i].func == ggml_cuda_cpy_fn_ptr) {
+                    char ** updated_kernel_arg_ptr = cuda_ctx->cuda_graph->updated_kernel_arg.at(k++);
+                    cuda_ctx->cuda_graph->params[i].kernelParams[1] = updated_kernel_arg_ptr;
+                    CUDA_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]));
+                }
+            }
+        }
+
+        // Update graph executable
+        cudaGraphExecUpdateResultInfo result_info;
+        cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info);
+        if (stat == cudaErrorGraphExecUpdateFailure) {
+#ifndef NDEBUG
+            fprintf(stderr, "%s: CUDA graph update failed\n", __func__);
+#endif
+            // The pre-existing graph exec cannot be updated due to violated constraints
+            // so instead clear error and re-instantiate
+            cudaGetLastError();
+            CUDA_CHECK(cudaGraphExecDestroy(cuda_ctx->cuda_graph->instance));
+            cuda_ctx->cuda_graph->instance = nullptr;
+            CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0));
+        } else {
+            GGML_ASSERT(stat == cudaSuccess);
+        }
+        // Launch graph
+        CUDA_CHECK(cudaGraphLaunch(cuda_ctx->cuda_graph->instance, cuda_ctx->stream()));
+#else
+        graph_evaluated_or_captured = true;
+#endif // USE_CUDA_GRAPH
+    }
+
+    return GGML_STATUS_SUCCESS;
+}
+
 GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backend->context;
     switch (op->op) {
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(op)) {
                 case GGML_UNARY_OP_GELU:
                 case GGML_UNARY_OP_SILU:
                 case GGML_UNARY_OP_RELU:
+                case GGML_UNARY_OP_SIGMOID:
                 case GGML_UNARY_OP_HARDSIGMOID:
                 case GGML_UNARY_OP_HARDSWISH:
                 case GGML_UNARY_OP_GELU_QUICK:
@@ -11438,7 +2746,9 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
                     return false;
                 }
                 ggml_type a_type = a->type;
-                if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS || a_type == GGML_TYPE_IQ1_S) {
+                if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS ||
+                    a_type == GGML_TYPE_IQ1_S   || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S   ||
+                    a_type == GGML_TYPE_IQ1_M   || a_type == GGML_TYPE_IQ2_S  || a_type == GGML_TYPE_IQ4_XS) {
                     if (b->ne[1] == 1 && ggml_nrows(b) > 1) {
                         return false;
                     }
@@ -11479,6 +2789,15 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
                 if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_1) {
                     return true;
                 }
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q5_0) {
+                    return true;
+                }
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q5_1) {
+                    return true;
+                }
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_IQ4_NL) {
+                    return true;
+                }
                 if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
                     return true;
                 }
@@ -11511,7 +2830,6 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_SOFT_MAX:
         case GGML_OP_ROPE:
-        case GGML_OP_ALIBI:
         case GGML_OP_IM2COL:
         case GGML_OP_POOL_2D:
         case GGML_OP_SUM_ROWS:
@@ -11520,13 +2838,86 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
         case GGML_OP_GROUP_NORM:
         case GGML_OP_UPSCALE:
         case GGML_OP_PAD:
+        case GGML_OP_ARANGE:
+        case GGML_OP_TIMESTEP_EMBEDDING:
         case GGML_OP_LEAKY_RELU:
             return true;
+        case GGML_OP_FLASH_ATTN_EXT:
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+            return op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128;
+#else
+            if (op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128) {
+                return true;
+            }
+            return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA;
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
         default:
             return false;
     }
 
-    UNUSED(backend);
+    GGML_UNUSED(backend);
+}
+
+GGML_CALL static bool ggml_backend_cuda_offload_op(ggml_backend_t backend, const ggml_tensor * op) {
+    const int min_batch_size = 32;
+
+    return (op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS) ||
+           (op->ne[2] >= min_batch_size && op->op == GGML_OP_MUL_MAT_ID);
+
+    GGML_UNUSED(backend);
+}
+
+static ggml_backend_event_t ggml_backend_cuda_event_new(ggml_backend_t backend) {
+#ifdef GGML_CUDA_NO_PEER_COPY
+    return nullptr;
+#else
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+
+    ggml_cuda_set_device(cuda_ctx->device);
+
+    cudaEvent_t event;
+    CUDA_CHECK(cudaEventCreateWithFlags(&event, cudaEventDisableTiming));
+
+    return new ggml_backend_event {
+        /* .backend = */ backend,
+        /* .context = */ event,
+    };
+#endif
+}
+
+static void ggml_backend_cuda_event_free(ggml_backend_event_t event) {
+    CUDA_CHECK(cudaEventDestroy((cudaEvent_t)event->context));
+
+    delete event;
+}
+
+static void ggml_backend_cuda_event_record(ggml_backend_event_t event) {
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)event->backend->context;
+
+    CUDA_CHECK(cudaEventRecord((cudaEvent_t)event->context, cuda_ctx->stream()));
+}
+
+static void ggml_backend_cuda_event_wait(ggml_backend_t backend, ggml_backend_event_t event) {
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+
+    if (ggml_backend_is_cuda(event->backend)) {
+        CUDA_CHECK(cudaStreamWaitEvent(cuda_ctx->stream(), (cudaEvent_t)event->context, 0));
+    } else {
+#if 0
+        // untested
+        auto wait_fn = [](void * user_data) {
+            ggml_backend_event_t event = (ggml_backend_event_t)user_data;
+            ggml_backend_event_synchronize(event);
+        };
+
+        CUDA_CHECK(cudaLaunchHostFunc(cuda_ctx->stream(), wait_fn, event));
+#endif
+        GGML_ASSERT(false);
+    }
+}
+
+static void ggml_backend_cuda_event_synchronize(ggml_backend_event_t event) {
+    CUDA_CHECK(cudaEventSynchronize((cudaEvent_t)event->context));
 }
 
 static ggml_backend_i ggml_backend_cuda_interface = {
@@ -11542,25 +2933,33 @@ static ggml_backend_i ggml_backend_cuda_interface = {
     /* .graph_plan_compute      = */ NULL,
     /* .graph_compute           = */ ggml_backend_cuda_graph_compute,
     /* .supports_op             = */ ggml_backend_cuda_supports_op,
+    /* .offload_op              = */ ggml_backend_cuda_offload_op,
+    /* .event_new               = */ ggml_backend_cuda_event_new,
+    /* .event_free              = */ ggml_backend_cuda_event_free,
+    /* .event_record            = */ ggml_backend_cuda_event_record,
+    /* .event_wait              = */ ggml_backend_cuda_event_wait,
+    /* .event_synchronize       = */ ggml_backend_cuda_event_synchronize,
 };
 
-GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) {
-    ggml_init_cublas(); // TODO: remove from ggml.c
+static ggml_guid_t ggml_backend_cuda_guid() {
+    static ggml_guid guid = { 0x2c, 0xdd, 0xe8, 0x1c, 0x65, 0xb3, 0x65, 0x73, 0x6a, 0x12, 0x88, 0x61, 0x1c, 0xc9, 0xdc, 0x25 };
+    return &guid;
+}
 
-    if (device < 0 || device >= ggml_cuda_get_device_count()) {
+GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) {
+    if (device < 0 || device >= ggml_backend_cuda_get_device_count()) {
         fprintf(stderr, "%s: error: invalid device %d\n", __func__, device);
         return nullptr;
     }
 
-    // not strictly necessary, but it may reduce the overhead of the first graph_compute
-    ggml_cuda_set_main_device(device);
-
-    ggml_backend_cuda_context * ctx = new ggml_backend_cuda_context {
-        /* .device = */ device,
-        /* .name   = */ GGML_CUDA_NAME + std::to_string(device),
-    };
+    ggml_backend_cuda_context * ctx = new ggml_backend_cuda_context(device);
+    if (ctx == nullptr) {
+        fprintf(stderr, "%s: error: failed to allocate context\n", __func__);
+        return nullptr;
+    }
 
     ggml_backend_t cuda_backend = new ggml_backend {
+        /* .guid      = */ ggml_backend_cuda_guid(),
         /* .interface = */ ggml_backend_cuda_interface,
         /* .context   = */ ctx
     };
@@ -11569,15 +2968,17 @@ GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) {
 }
 
 GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend) {
-    return backend && backend->iface.get_name == ggml_backend_cuda_name;
+    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_cuda_guid());
 }
 
 GGML_CALL int ggml_backend_cuda_get_device_count() {
-    return ggml_cuda_get_device_count();
+    return ggml_cuda_info().device_count;
 }
 
 GGML_CALL void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size) {
-    ggml_cuda_get_device_description(device, description, description_size);
+    cudaDeviceProp prop;
+    CUDA_CHECK(cudaGetDeviceProperties(&prop, device));
+    snprintf(description, description_size, "%s", prop.name);
 }
 
 GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total) {
@@ -11586,18 +2987,51 @@ GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, si
     CUDA_CHECK(cudaMemGetInfo(free, total));
 }
 
+GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size) {
+    if (getenv("GGML_CUDA_REGISTER_HOST") == nullptr) {
+        return false;
+    }
+
+#if CUDART_VERSION >= 11100
+    cudaError_t err = cudaHostRegister(buffer, size, cudaHostRegisterPortable | cudaHostRegisterReadOnly);
+    if (err != cudaSuccess) {
+        // clear the error
+        cudaGetLastError();
+
+        fprintf(stderr, "%s: warning: failed to register %.2f MiB of pinned memory: %s\n", __func__,
+                size/1024.0/1024.0, cudaGetErrorString(err));
+        return false;
+    }
+    return true;
+#else
+    return false;
+#endif
+}
+
+GGML_CALL void ggml_backend_cuda_unregister_host_buffer(void * buffer) {
+    if (getenv("GGML_CUDA_REGISTER_HOST") == nullptr) {
+        return;
+    }
+
+    cudaError_t err = cudaHostUnregister(buffer);
+    if (err != cudaSuccess) {
+        // clear the error
+        cudaGetLastError();
+    }
+}
+
 // backend registry
 GGML_CALL static ggml_backend_t ggml_backend_reg_cuda_init(const char * params, void * user_data) {
     ggml_backend_t cuda_backend = ggml_backend_cuda_init((int) (intptr_t) user_data);
     return cuda_backend;
 
-    UNUSED(params);
+    GGML_UNUSED(params);
 }
 
 extern "C" GGML_CALL int ggml_backend_cuda_reg_devices();
 
 GGML_CALL int ggml_backend_cuda_reg_devices() {
-    int device_count = ggml_cuda_get_device_count();
+    int device_count = ggml_backend_cuda_get_device_count();
     //int device_count = 1; // DEBUG: some tools require delaying CUDA initialization
     for (int i = 0; i < device_count; i++) {
         char name[128];
diff --git a/ggml-cuda.h b/ggml-cuda.h
index b1ebd61d7..5eb4af40f 100644
--- a/ggml-cuda.h
+++ b/ggml-cuda.h
@@ -17,29 +17,17 @@ extern "C" {
 
 #define GGML_CUDA_MAX_DEVICES       16
 
-// Always success. To check if CUDA is actually loaded, use `ggml_cublas_loaded`.
-GGML_API GGML_CALL void   ggml_init_cublas(void);
-
-// Returns `true` if there are available CUDA devices and cublas loads successfully; otherwise, it returns `false`.
-GGML_API GGML_CALL bool   ggml_cublas_loaded(void);
-
-GGML_API GGML_CALL void * ggml_cuda_host_malloc(size_t size);
-GGML_API GGML_CALL void   ggml_cuda_host_free(void * ptr);
-
-GGML_API GGML_CALL bool   ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
-GGML_API GGML_CALL bool   ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
-
-GGML_API GGML_CALL int    ggml_cuda_get_device_count(void);
-GGML_API GGML_CALL void   ggml_cuda_get_device_description(int device, char * description, size_t description_size);
-
 // backend API
 GGML_API GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device);
 
 GGML_API GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend);
 
+// device buffer
 GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
+
 // split tensor buffer that splits matrices by rows across multiple devices
 GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split);
+
 // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
 GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
 
@@ -47,6 +35,9 @@ GGML_API GGML_CALL int  ggml_backend_cuda_get_device_count(void);
 GGML_API GGML_CALL void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size);
 GGML_API GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total);
 
+GGML_API GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size);
+GGML_API GGML_CALL void ggml_backend_cuda_unregister_host_buffer(void * buffer);
+
 #ifdef  __cplusplus
 }
 #endif
diff --git a/ggml-cuda/acc.cu b/ggml-cuda/acc.cu
new file mode 100644
index 000000000..96bfe1c9d
--- /dev/null
+++ b/ggml-cuda/acc.cu
@@ -0,0 +1,47 @@
+#include "acc.cuh"
+
+static __global__ void acc_f32(const float * x, const float * y, float * dst, const int ne,
+    const int ne10, const int ne11, const int ne12,
+    const int nb1, const int nb2, int offset) {
+    const int i = blockDim.x * blockIdx.x + threadIdx.x;
+    if (i >= ne) {
+        return;
+    }
+    int src1_idx = i - offset;
+    int oz = src1_idx / nb2;
+    int oy = (src1_idx - (oz * nb2)) / nb1;
+    int ox = src1_idx % nb1;
+    if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) {
+        dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11];
+    } else {
+        dst[i] = x[i];
+    }
+}
+
+static void acc_f32_cuda(const float * x, const float * y, float * dst, const int n_elements,
+    const int ne10, const int ne11, const int ne12,
+    const int nb1, const int nb2, const int offset, cudaStream_t stream) {
+    int num_blocks = (n_elements + CUDA_ACC_BLOCK_SIZE - 1) / CUDA_ACC_BLOCK_SIZE;
+    acc_f32<<<num_blocks, CUDA_ACC_BLOCK_SIZE, 0, stream>>>(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset);
+}
+
+void ggml_cuda_op_acc(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const float * src0_d = (const float *)src0->data;
+    const float * src1_d = (const float *)src1->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported
+
+    int nb1 = dst->op_params[0] / 4; // 4 bytes of float32
+    int nb2 = dst->op_params[1] / 4; // 4 bytes of float32
+    // int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused
+    int offset = dst->op_params[3] / 4; // offset in bytes
+
+    acc_f32_cuda(src0_d, src1_d, dst_d, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, stream);
+}
diff --git a/ggml-cuda/acc.cuh b/ggml-cuda/acc.cuh
new file mode 100644
index 000000000..1168ea1b2
--- /dev/null
+++ b/ggml-cuda/acc.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_ACC_BLOCK_SIZE 256
+
+void ggml_cuda_op_acc(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/arange.cu b/ggml-cuda/arange.cu
new file mode 100644
index 000000000..b5e495a24
--- /dev/null
+++ b/ggml-cuda/arange.cu
@@ -0,0 +1,34 @@
+#include "arange.cuh"
+
+static __global__ void arange_f32(float * dst, const int ne0, const float start, const float step) {
+    // blockIDx.x: idx of ne0 / BLOCK_SIZE
+    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
+    if (nidx >= ne0) {
+        return;
+    }
+    dst[nidx] = start + step * nidx;
+}
+
+static void arange_f32_cuda(float * dst, const int ne0, const float start, const float step, cudaStream_t stream) {
+    int num_blocks = (ne0 + CUDA_ARANGE_BLOCK_SIZE - 1) / CUDA_ARANGE_BLOCK_SIZE;
+    arange_f32<<<num_blocks, CUDA_ARANGE_BLOCK_SIZE, 0, stream>>>(dst, ne0, start,  step);
+}
+
+void ggml_cuda_op_arange(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    float start;
+    float stop;
+    float step;
+    memcpy(&start, (float *)dst->op_params + 0, sizeof(float));
+    memcpy(&stop,  (float *)dst->op_params + 1, sizeof(float));
+    memcpy(&step,  (float *)dst->op_params + 2, sizeof(float));
+
+    int64_t steps = (int64_t)ceil((stop - start) / step);
+    GGML_ASSERT(ggml_nelements(dst) == steps);
+
+    arange_f32_cuda(dst_d, dst->ne[0], start, step, stream);
+}
diff --git a/ggml-cuda/arange.cuh b/ggml-cuda/arange.cuh
new file mode 100644
index 000000000..41e74fdfc
--- /dev/null
+++ b/ggml-cuda/arange.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_ARANGE_BLOCK_SIZE 256
+
+void ggml_cuda_op_arange(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/argsort.cu b/ggml-cuda/argsort.cu
new file mode 100644
index 000000000..164144061
--- /dev/null
+++ b/ggml-cuda/argsort.cu
@@ -0,0 +1,103 @@
+#include "argsort.cuh"
+
+template<typename T>
+static inline __device__ void ggml_cuda_swap(T & a, T & b) {
+    T tmp = a;
+    a = b;
+    b = tmp;
+}
+
+template<ggml_sort_order order>
+static __global__ void k_argsort_f32_i32(const float * x, int * dst, const int ncols, int ncols_pad) {
+    // bitonic sort
+    int col = threadIdx.x;
+    int row = blockIdx.y;
+
+    if (col >= ncols_pad) {
+        return;
+    }
+
+    const float * x_row = x + row * ncols;
+    extern __shared__ int dst_row[];
+
+    // initialize indices
+    dst_row[col] = col;
+
+    __syncthreads();
+
+    for (int k = 2; k <= ncols_pad; k *= 2) {
+        for (int j = k / 2; j > 0; j /= 2) {
+            int ixj = col ^ j;
+            if (ixj > col) {
+                if ((col & k) == 0) {
+                    if (dst_row[col] >= ncols ||
+                        (dst_row[ixj] < ncols && (order == GGML_SORT_ORDER_ASC ?
+                            x_row[dst_row[col]] > x_row[dst_row[ixj]] :
+                            x_row[dst_row[col]] < x_row[dst_row[ixj]]))
+                    ) {
+                        ggml_cuda_swap(dst_row[col], dst_row[ixj]);
+                    }
+                } else {
+                    if (dst_row[ixj] >= ncols ||
+                        (dst_row[col] < ncols && (order == GGML_SORT_ORDER_ASC ?
+                            x_row[dst_row[col]] < x_row[dst_row[ixj]] :
+                            x_row[dst_row[col]] > x_row[dst_row[ixj]]))
+                    ) {
+                        ggml_cuda_swap(dst_row[col], dst_row[ixj]);
+                    }
+                }
+            }
+            __syncthreads();
+        }
+    }
+
+    // copy the result to dst without the padding
+    if (col < ncols) {
+        dst[row * ncols + col] = dst_row[col];
+    }
+}
+
+static int next_power_of_2(int x) {
+    int n = 1;
+    while (n < x) {
+        n *= 2;
+    }
+    return n;
+}
+
+static void argsort_f32_i32_cuda(const float * x, int * dst, const int ncols, const int nrows, ggml_sort_order order, cudaStream_t stream) {
+    // bitonic sort requires ncols to be power of 2
+    const int ncols_pad = next_power_of_2(ncols);
+
+    const dim3 block_dims(ncols_pad, 1, 1);
+    const dim3 block_nums(1, nrows, 1);
+    const size_t shared_mem = ncols_pad * sizeof(int);
+
+    GGML_ASSERT(shared_mem <= ggml_cuda_info().devices[ggml_cuda_get_device()].smpb);
+
+    if (order == GGML_SORT_ORDER_ASC) {
+        k_argsort_f32_i32<GGML_SORT_ORDER_ASC><<<block_nums, block_dims, shared_mem, stream>>>(x, dst, ncols, ncols_pad);
+    } else if (order == GGML_SORT_ORDER_DESC) {
+        k_argsort_f32_i32<GGML_SORT_ORDER_DESC><<<block_nums, block_dims, shared_mem, stream>>>(x, dst, ncols, ncols_pad);
+    } else {
+        GGML_ASSERT(false);
+    }
+}
+
+void ggml_cuda_op_argsort(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_I32);
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
+    const int64_t ncols = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0];
+
+    argsort_f32_i32_cuda(src0_d, (int *)dst_d, ncols, nrows, order, stream);
+}
diff --git a/ggml-cuda/argsort.cuh b/ggml-cuda/argsort.cuh
new file mode 100644
index 000000000..68a001547
--- /dev/null
+++ b/ggml-cuda/argsort.cuh
@@ -0,0 +1,3 @@
+#include "common.cuh"
+
+void ggml_cuda_op_argsort(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/binbcast.cu b/ggml-cuda/binbcast.cu
new file mode 100644
index 000000000..19b08b74f
--- /dev/null
+++ b/ggml-cuda/binbcast.cu
@@ -0,0 +1,280 @@
+#include "binbcast.cuh"
+
+static __device__ __forceinline__ float op_repeat(const float a, const float b) {
+    return b;
+    GGML_UNUSED(a);
+}
+
+static __device__ __forceinline__ float op_add(const float a, const float b) {
+    return a + b;
+}
+
+static __device__ __forceinline__ float op_mul(const float a, const float b) {
+    return a * b;
+}
+
+static __device__ __forceinline__ float op_div(const float a, const float b) {
+    return a / b;
+}
+
+template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
+static __global__ void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst,
+        int ne0, int ne1, int ne2, int ne3,
+        int ne10, int ne11, int ne12, int ne13,
+        /*int s0, */ int s1,  int s2,  int s3,
+        /*int s00,*/ int s01, int s02, int s03,
+        /*int s10,*/ int s11, int s12, int s13) {
+    const int i0s = blockDim.x*blockIdx.x + threadIdx.x;
+    const int i1 = (blockDim.y*blockIdx.y + threadIdx.y);
+    const int i2 = (blockDim.z*blockIdx.z + threadIdx.z) / ne3;
+    const int i3 = (blockDim.z*blockIdx.z + threadIdx.z) % ne3;
+
+    if (i0s >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) {
+        return;
+    }
+
+    const int i11 = i1 % ne11;
+    const int i12 = i2 % ne12;
+    const int i13 = i3 % ne13;
+
+    const size_t i_src0 =  i3*s03 +  i2*s02 +  i1*s01;
+    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
+    const size_t i_dst  =  i3*s3  +  i2*s2  +  i1*s1;
+
+    const src0_t * src0_row = src0 + i_src0;
+    const src1_t * src1_row = src1 + i_src1;
+    dst_t * dst_row = dst + i_dst;
+
+    for (int i0 = i0s; i0 < ne0; i0 += blockDim.x*gridDim.x) {
+        const int i10 = i0 % ne10;
+        dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
+    }
+}
+
+template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
+static __global__ void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t * dst,
+        int ne0, int ne1, int ne2, int ne3,
+        int ne10, int ne11, int ne12, int ne13,
+        /*int s0, */ int s1,  int s2,  int s3,
+        /*int s00,*/ int s01, int s02, int s03,
+        /*int s10,*/ int s11, int s12, int s13) {
+
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    const int i3 = i/(ne2*ne1*ne0);
+    const int i2 = (i/(ne1*ne0)) % ne2;
+    const int i1 = (i/ne0) % ne1;
+    const int i0 = i % ne0;
+
+    if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) {
+        return;
+    }
+
+    const int i11 = i1 % ne11;
+    const int i12 = i2 % ne12;
+    const int i13 = i3 % ne13;
+
+    const size_t i_src0 =  i3*s03 +  i2*s02 +  i1*s01;
+    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
+    const size_t i_dst  =  i3*s3  +  i2*s2  +  i1*s1;
+
+    const src0_t * src0_row = src0 + i_src0;
+    const src1_t * src1_row = src1 + i_src1;
+    dst_t * dst_row = dst + i_dst;
+
+    const int i10 = i0 % ne10;
+    dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
+}
+
+template<float (*bin_op)(const float, const float)>
+struct bin_bcast_cuda {
+    template<typename src0_t, typename src1_t, typename dst_t>
+    void operator()(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst,
+            const src0_t * src0_dd, const src1_t * src1_dd, dst_t * dst_dd,
+            cudaStream_t stream) {
+
+        GGML_TENSOR_BINARY_OP_LOCALS
+
+        int nr0 = ne10/ne0;
+        int nr1 = ne11/ne1;
+        int nr2 = ne12/ne2;
+        int nr3 = ne13/ne3;
+
+        int nr[4] = { nr0, nr1, nr2, nr3 };
+
+        // collapse dimensions until first broadcast dimension
+        int64_t cne[] = {ne0, ne1, ne2, ne3};
+        int64_t cne0[] = {ne00, ne01, ne02, ne03};
+        int64_t cne1[] = {ne10, ne11, ne12, ne13};
+
+        size_t cnb[] = {nb0, nb1, nb2, nb3};
+        size_t cnb0[] = {nb00, nb01, nb02, nb03};
+        size_t cnb1[] = {nb10, nb11, nb12, nb13};
+
+        auto collapse = [](int64_t cne[]) {
+            cne[0] *= cne[1];
+            cne[1] = cne[2];
+            cne[2] = cne[3];
+            cne[3] = 1;
+        };
+
+        auto collapse_nb = [](size_t cnb[], const int64_t cne[]) {
+            cnb[1] *= cne[1];
+            cnb[2] *= cne[2];
+            cnb[3] *= cne[3];
+        };
+
+        if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ggml_is_contiguous(dst)) {
+            for (int i = 0; i < 4; i++) {
+                if (nr[i] != 1) {
+                    break;
+                }
+                if (i > 0) {
+                    collapse_nb(cnb, cne);
+                    collapse_nb(cnb0, cne0);
+                    collapse_nb(cnb1, cne1);
+                    collapse(cne);
+                    collapse(cne0);
+                    collapse(cne1);
+                }
+            }
+        }
+
+        {
+            int64_t ne0 = cne[0];
+            int64_t ne1 = cne[1];
+            int64_t ne2 = cne[2];
+            int64_t ne3 = cne[3];
+
+            //int64_t ne00 = cne0[0]; GGML_UNUSED(ne00);
+            //int64_t ne01 = cne0[1]; GGML_UNUSED(ne01);
+            //int64_t ne02 = cne0[2]; GGML_UNUSED(ne02);
+            //int64_t ne03 = cne0[3]; GGML_UNUSED(ne03);
+
+            int64_t ne10 = cne1[0];
+            int64_t ne11 = cne1[1];
+            int64_t ne12 = cne1[2];
+            int64_t ne13 = cne1[3];
+
+            size_t nb0 = cnb[0];
+            size_t nb1 = cnb[1];
+            size_t nb2 = cnb[2];
+            size_t nb3 = cnb[3];
+
+            size_t nb00 = cnb0[0];
+            size_t nb01 = cnb0[1];
+            size_t nb02 = cnb0[2];
+            size_t nb03 = cnb0[3];
+
+            size_t nb10 = cnb1[0];
+            size_t nb11 = cnb1[1];
+            size_t nb12 = cnb1[2];
+            size_t nb13 = cnb1[3];
+
+            size_t s0 = nb0 / sizeof(dst_t);
+            size_t s1 = nb1 / sizeof(dst_t);
+            size_t s2 = nb2 / sizeof(dst_t);
+            size_t s3 = nb3 / sizeof(dst_t);
+
+            size_t s10 = nb10 / sizeof(src1_t);
+            size_t s11 = nb11 / sizeof(src1_t);
+            size_t s12 = nb12 / sizeof(src1_t);
+            size_t s13 = nb13 / sizeof(src1_t);
+
+            size_t s00 = nb00 / sizeof(src0_t);
+            size_t s01 = nb01 / sizeof(src0_t);
+            size_t s02 = nb02 / sizeof(src0_t);
+            size_t s03 = nb03 / sizeof(src0_t);
+
+            GGML_ASSERT(nb0 % sizeof(dst_t) == 0);
+            GGML_ASSERT(nb1 % sizeof(dst_t) == 0);
+            GGML_ASSERT(nb2 % sizeof(dst_t) == 0);
+            GGML_ASSERT(nb3 % sizeof(dst_t) == 0);
+
+            GGML_ASSERT(nb00 % sizeof(src0_t) == 0);
+            GGML_ASSERT(nb01 % sizeof(src0_t) == 0);
+            GGML_ASSERT(nb02 % sizeof(src0_t) == 0);
+            GGML_ASSERT(nb03 % sizeof(src0_t) == 0);
+
+            GGML_ASSERT(nb10 % sizeof(src1_t) == 0);
+            GGML_ASSERT(nb11 % sizeof(src1_t) == 0);
+            GGML_ASSERT(nb12 % sizeof(src1_t) == 0);
+            GGML_ASSERT(nb13 % sizeof(src1_t) == 0);
+
+            GGML_ASSERT(s0 == 1);
+            GGML_ASSERT(s00 == 1);
+            GGML_ASSERT(s10 == 1);
+
+            const int block_size = 128;
+
+            int64_t hne0 = std::max(ne0/2LL, 1LL);
+
+            dim3 block_dims;
+            block_dims.x = std::min<unsigned int>(hne0, block_size);
+            block_dims.y = std::min<unsigned int>(ne1, block_size / block_dims.x);
+            block_dims.z = std::min(std::min<unsigned int>(ne2*ne3, block_size / block_dims.x / block_dims.y), 64U);
+
+            dim3 block_nums(
+                (hne0 + block_dims.x - 1) / block_dims.x,
+                (ne1 + block_dims.y - 1) / block_dims.y,
+                (ne2*ne3 + block_dims.z - 1) / block_dims.z
+            );
+
+            if (block_nums.z > 65535) {
+                // this is the maximum number of blocks in z dimension, fallback to 1D grid kernel
+                int block_num = (ne0*ne1*ne2*ne3 + block_size - 1) / block_size;
+                k_bin_bcast_unravel<bin_op><<<block_num, block_size, 0, stream>>>(
+                    src0_dd, src1_dd, dst_dd,
+                    ne0, ne1, ne2, ne3,
+                    ne10, ne11, ne12, ne13,
+                    /* s0, */ s1, s2, s3,
+                    /* s00, */ s01, s02, s03,
+                    /* s10, */ s11, s12, s13);
+            } else {
+                k_bin_bcast<bin_op><<<block_nums, block_dims, 0, stream>>>(
+                    src0_dd, src1_dd, dst_dd,
+                    ne0, ne1, ne2, ne3,
+                    ne10, ne11, ne12, ne13,
+                    /* s0, */ s1, s2, s3,
+                    /* s00, */ s01, s02, s03,
+                    /* s10, */ s11, s12, s13);
+            }
+        }
+    }
+};
+
+template<class op>
+static void ggml_cuda_op_bin_bcast(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+    const void * src0_dd, const void * src1_dd, void * dst_dd, cudaStream_t stream) {
+
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+        op()(src0, src1, dst, (const float *)src0_dd, (const float *)src1_dd, (float *)dst_dd, stream);
+    } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
+        op()(src0, src1, dst, (const half *) src0_dd, (const float *)src1_dd, (half *) dst_dd, stream);
+    } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
+        op()(src0, src1, dst, (const half *) src0_dd, (const float *)src1_dd, (float *)dst_dd, stream);
+    } else {
+        fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__,
+            ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type));
+        GGML_ASSERT(false);
+    }
+}
+
+void ggml_cuda_op_repeat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_repeat>>(dst, dst->src[0], dst, nullptr, dst->src[0]->data, dst->data, ctx.stream());
+}
+
+void ggml_cuda_op_add(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_add>>(dst->src[0], dst->src[1], dst, dst->src[0]->data, dst->src[1]->data, dst->data, ctx.stream());
+}
+
+void ggml_cuda_op_mul(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_mul>>(dst->src[0], dst->src[1], dst, dst->src[0]->data, dst->src[1]->data, dst->data, ctx.stream());
+}
+
+void ggml_cuda_op_div(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_div>>(dst->src[0], dst->src[1], dst, dst->src[0]->data, dst->src[1]->data, dst->data, ctx.stream());
+}
diff --git a/ggml-cuda/binbcast.cuh b/ggml-cuda/binbcast.cuh
new file mode 100644
index 000000000..4f63d6372
--- /dev/null
+++ b/ggml-cuda/binbcast.cuh
@@ -0,0 +1,6 @@
+#include "common.cuh"
+
+void ggml_cuda_op_repeat(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+void ggml_cuda_op_add(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+void ggml_cuda_op_mul(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+void ggml_cuda_op_div(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/clamp.cu b/ggml-cuda/clamp.cu
new file mode 100644
index 000000000..8009a3e3d
--- /dev/null
+++ b/ggml-cuda/clamp.cu
@@ -0,0 +1,34 @@
+#include "clamp.cuh"
+
+static __global__ void clamp_f32(const float * x, float * dst, const float min, const float max, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]);
+}
+
+static void clamp_f32_cuda(const float * x, float * dst, const float min, const float max, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_CLAMP_BLOCK_SIZE - 1) / CUDA_CLAMP_BLOCK_SIZE;
+    clamp_f32<<<num_blocks, CUDA_CLAMP_BLOCK_SIZE, 0, stream>>>(x, dst, min, max, k);
+}
+
+
+void ggml_cuda_op_clamp(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    float min;
+    float max;
+    memcpy(&min, dst->op_params, sizeof(float));
+    memcpy(&max, (float *) dst->op_params + 1, sizeof(float));
+
+    clamp_f32_cuda(src0_d, dst_d, min, max, ggml_nelements(src0), stream);
+}
diff --git a/ggml-cuda/clamp.cuh b/ggml-cuda/clamp.cuh
new file mode 100644
index 000000000..7f9559dd1
--- /dev/null
+++ b/ggml-cuda/clamp.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_CLAMP_BLOCK_SIZE 256
+
+void ggml_cuda_op_clamp(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/common.cuh b/ggml-cuda/common.cuh
new file mode 100644
index 000000000..b6f0bc36a
--- /dev/null
+++ b/ggml-cuda/common.cuh
@@ -0,0 +1,658 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-cuda.h"
+
+#include <memory>
+
+#if defined(GGML_USE_HIPBLAS)
+#define GGML_COMMON_DECL_HIP
+#define GGML_COMMON_IMPL_HIP
+#else
+#define GGML_COMMON_DECL_CUDA
+#define GGML_COMMON_IMPL_CUDA
+#endif
+#include "ggml-common.h"
+
+#include <cstdio>
+#include <array>
+#include <cassert>
+#include <cfloat>
+#include <string>
+#include <vector>
+
+#if defined(GGML_USE_HIPBLAS)
+#include <hip/hip_runtime.h>
+#include <hipblas/hipblas.h>
+#include <hip/hip_fp16.h>
+#ifdef __HIP_PLATFORM_AMD__
+// for rocblas_initialize()
+#include "rocblas/rocblas.h"
+#endif // __HIP_PLATFORM_AMD__
+#define CUBLAS_COMPUTE_16F HIPBLAS_R_16F
+#define CUBLAS_COMPUTE_32F HIPBLAS_R_32F
+#define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_R_32F
+#define CUBLAS_GEMM_DEFAULT HIPBLAS_GEMM_DEFAULT
+#define CUBLAS_GEMM_DEFAULT_TENSOR_OP HIPBLAS_GEMM_DEFAULT
+#define CUBLAS_OP_N HIPBLAS_OP_N
+#define CUBLAS_OP_T HIPBLAS_OP_T
+#define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS
+#define CUBLAS_TF32_TENSOR_OP_MATH 0
+#define CUDA_R_16F  HIPBLAS_R_16F
+#define CUDA_R_32F  HIPBLAS_R_32F
+#define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
+#define cublasComputeType_t hipblasDatatype_t //deprecated, new hipblasComputeType_t not in 5.6
+#define cublasCreate hipblasCreate
+#define cublasDestroy hipblasDestroy
+#define cublasGemmEx hipblasGemmEx
+#define cublasGemmBatchedEx hipblasGemmBatchedEx
+#define cublasGemmStridedBatchedEx hipblasGemmStridedBatchedEx
+#define cublasHandle_t hipblasHandle_t
+#define cublasSetMathMode(handle, mode) CUBLAS_STATUS_SUCCESS
+#define cublasSetStream hipblasSetStream
+#define cublasSgemm hipblasSgemm
+#define cublasStatus_t hipblasStatus_t
+#define cudaDataType_t hipblasDatatype_t //deprecated, new hipblasDatatype not in 5.6
+#define cudaDeviceCanAccessPeer hipDeviceCanAccessPeer
+#define cudaDeviceDisablePeerAccess hipDeviceDisablePeerAccess
+#define cudaDeviceEnablePeerAccess hipDeviceEnablePeerAccess
+#define cudaDeviceProp hipDeviceProp_t
+#define cudaDeviceSynchronize hipDeviceSynchronize
+#define cudaError_t hipError_t
+#define cudaErrorPeerAccessAlreadyEnabled hipErrorPeerAccessAlreadyEnabled
+#define cudaErrorPeerAccessNotEnabled hipErrorPeerAccessNotEnabled
+#define cudaEventCreateWithFlags hipEventCreateWithFlags
+#define cudaEventDisableTiming hipEventDisableTiming
+#define cudaEventRecord hipEventRecord
+#define cudaEventSynchronize hipEventSynchronize
+#define cudaEvent_t hipEvent_t
+#define cudaEventDestroy hipEventDestroy
+#define cudaFree hipFree
+#define cudaFreeHost hipHostFree
+#define cudaGetDevice hipGetDevice
+#define cudaGetDeviceCount hipGetDeviceCount
+#define cudaGetDeviceProperties hipGetDeviceProperties
+#define cudaGetErrorString hipGetErrorString
+#define cudaGetLastError hipGetLastError
+#define cudaHostRegister hipHostRegister
+#define cudaHostRegisterPortable hipHostRegisterPortable
+#define cudaHostRegisterReadOnly hipHostRegisterReadOnly
+#define cudaHostUnregister hipHostUnregister
+#define cudaLaunchHostFunc hipLaunchHostFunc
+#ifdef GGML_HIP_UMA
+#define cudaMalloc hipMallocManaged
+#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size)
+#else
+#define cudaMalloc hipMalloc
+#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault)
+#endif
+#define cudaMemcpy hipMemcpy
+#define cudaMemcpyAsync hipMemcpyAsync
+#define cudaMemcpyPeerAsync hipMemcpyPeerAsync
+#define cudaMemcpy2DAsync hipMemcpy2DAsync
+#define cudaMemcpyDeviceToDevice hipMemcpyDeviceToDevice
+#define cudaMemcpyDeviceToHost hipMemcpyDeviceToHost
+#define cudaMemcpyHostToDevice hipMemcpyHostToDevice
+#define cudaMemcpyKind hipMemcpyKind
+#define cudaMemset hipMemset
+#define cudaMemsetAsync hipMemsetAsync
+#define cudaMemGetInfo hipMemGetInfo
+#define cudaOccupancyMaxPotentialBlockSize hipOccupancyMaxPotentialBlockSize
+#define cudaSetDevice hipSetDevice
+#define cudaStreamCreateWithFlags hipStreamCreateWithFlags
+#define cudaStreamDestroy hipStreamDestroy
+#define cudaStreamFireAndForget hipStreamFireAndForget
+#define cudaStreamNonBlocking hipStreamNonBlocking
+#define cudaStreamPerThread hipStreamPerThread
+#define cudaStreamSynchronize hipStreamSynchronize
+#define cudaStreamWaitEvent(stream, event, flags) hipStreamWaitEvent(stream, event, flags)
+#define cudaStream_t hipStream_t
+#define cudaSuccess hipSuccess
+#define __trap abort
+#define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS
+#define CUBLAS_STATUS_NOT_INITIALIZED HIPBLAS_STATUS_NOT_INITIALIZED
+#define CUBLAS_STATUS_ALLOC_FAILED HIPBLAS_STATUS_ALLOC_FAILED
+#define CUBLAS_STATUS_INVALID_VALUE HIPBLAS_STATUS_INVALID_VALUE
+#define CUBLAS_STATUS_ARCH_MISMATCH HIPBLAS_STATUS_ARCH_MISMATCH
+#define CUBLAS_STATUS_MAPPING_ERROR HIPBLAS_STATUS_MAPPING_ERROR
+#define CUBLAS_STATUS_EXECUTION_FAILED HIPBLAS_STATUS_EXECUTION_FAILED
+#define CUBLAS_STATUS_INTERNAL_ERROR HIPBLAS_STATUS_INTERNAL_ERROR
+#define CUBLAS_STATUS_NOT_SUPPORTED HIPBLAS_STATUS_NOT_SUPPORTED
+#else
+#include <cuda_runtime.h>
+#include <cuda.h>
+#include <cublas_v2.h>
+#include <cuda_fp16.h>
+
+#if CUDART_VERSION < 11020
+#define CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED CU_DEVICE_ATTRIBUTE_VIRTUAL_ADDRESS_MANAGEMENT_SUPPORTED
+#define CUBLAS_TF32_TENSOR_OP_MATH CUBLAS_TENSOR_OP_MATH
+#define CUBLAS_COMPUTE_16F CUDA_R_16F
+#define CUBLAS_COMPUTE_32F CUDA_R_32F
+#define cublasComputeType_t cudaDataType_t
+#endif // CUDART_VERSION < 11020
+
+#endif // defined(GGML_USE_HIPBLAS)
+
+#define STRINGIZE_IMPL(...) #__VA_ARGS__
+#define STRINGIZE(...) STRINGIZE_IMPL(__VA_ARGS__)
+
+#define WARP_SIZE 32
+#define CUDART_HMAX   11070 // CUDA 11.7, min. ver. for which __hmax and __hmax2 are known to work (may be higher than needed)
+#define CUDART_HMASK  12000 // CUDA 12.0, min. ver. for half2 -> uint mask comparisons
+
+#define CC_PASCAL     600
+#define MIN_CC_DP4A   610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
+#define CC_VOLTA      700
+#define CC_AMPERE     800
+#define CC_OFFSET_AMD 1000000
+#define CC_RDNA1      (CC_OFFSET_AMD + 1010)
+#define CC_RDNA2      (CC_OFFSET_AMD + 1030)
+#define CC_RDNA3      (CC_OFFSET_AMD + 1100)
+
+// define this if you want to always fallback to MMQ kernels and not use cuBLAS for matrix multiplication
+// on modern hardware, using cuBLAS is recommended as it utilizes F16 tensor cores which are very performant
+// for large computational tasks. the drawback is that this requires some extra amount of VRAM:
+// -  7B quantum model: +100-200 MB
+// - 13B quantum model: +200-400 MB
+//
+//#define GGML_CUDA_FORCE_MMQ
+
+// TODO: improve this to be correct for more hardware
+//       for example, currently fails for GeForce GTX 1660 which is TURING arch (> VOLTA) but does not have tensor cores
+#if !defined(GGML_CUDA_FORCE_MMQ)
+#define CUDA_USE_TENSOR_CORES
+#endif
+
+#define MMVQ_MAX_BATCH_SIZE  8 // max batch size to use MMVQ kernels
+#define  MMQ_MAX_BATCH_SIZE 32 // max batch size to use MMQ kernels when tensor cores are available
+
+#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+#define GGML_CUDA_MAX_STREAMS 8
+
+[[noreturn]]
+void ggml_cuda_error(const char * stmt, const char * func, const char * file, int line, const char * msg);
+
+#define CUDA_CHECK_GEN(err, success, error_fn)                                      \
+     do {                                                                           \
+        auto err_ = (err);                                                          \
+        if (err_ != (success)) {                                                    \
+            ggml_cuda_error(#err, __func__, __FILE__, __LINE__, error_fn(err_));    \
+        }                                                                           \
+    } while (0)
+
+#define CUDA_CHECK(err) CUDA_CHECK_GEN(err, cudaSuccess, cudaGetErrorString)
+
+#if CUDART_VERSION >= 12000
+    static const char * cublas_get_error_str(const cublasStatus_t err) {
+        return cublasGetStatusString(err);
+    }
+#else
+    static const char * cublas_get_error_str(const cublasStatus_t err) {
+        switch (err) {
+            case CUBLAS_STATUS_SUCCESS: return "CUBLAS_STATUS_SUCCESS";
+            case CUBLAS_STATUS_NOT_INITIALIZED: return "CUBLAS_STATUS_NOT_INITIALIZED";
+            case CUBLAS_STATUS_ALLOC_FAILED: return "CUBLAS_STATUS_ALLOC_FAILED";
+            case CUBLAS_STATUS_INVALID_VALUE: return "CUBLAS_STATUS_INVALID_VALUE";
+            case CUBLAS_STATUS_ARCH_MISMATCH: return "CUBLAS_STATUS_ARCH_MISMATCH";
+            case CUBLAS_STATUS_MAPPING_ERROR: return "CUBLAS_STATUS_MAPPING_ERROR";
+            case CUBLAS_STATUS_EXECUTION_FAILED: return "CUBLAS_STATUS_EXECUTION_FAILED";
+            case CUBLAS_STATUS_INTERNAL_ERROR: return "CUBLAS_STATUS_INTERNAL_ERROR";
+            case CUBLAS_STATUS_NOT_SUPPORTED: return "CUBLAS_STATUS_NOT_SUPPORTED";
+            default: return "unknown error";
+        }
+    }
+#endif // CUDART_VERSION >= 12000
+
+#define CUBLAS_CHECK(err) CUDA_CHECK_GEN(err, CUBLAS_STATUS_SUCCESS, cublas_get_error_str)
+
+#if !defined(GGML_USE_HIPBLAS)
+static const char * cu_get_error_str(CUresult err) {
+    const char * err_str;
+    cuGetErrorString(err, &err_str);
+    return err_str;
+}
+#define CU_CHECK(err) CUDA_CHECK_GEN(err, CUDA_SUCCESS, cu_get_error_str)
+#endif
+
+#if CUDART_VERSION >= 11100
+#define GGML_CUDA_ASSUME(x) __builtin_assume(x)
+#else
+#define GGML_CUDA_ASSUME(x)
+#endif // CUDART_VERSION >= 11100
+
+#ifdef GGML_CUDA_F16
+typedef half dfloat; // dequantize float
+typedef half2 dfloat2;
+#else
+typedef float dfloat; // dequantize float
+typedef float2 dfloat2;
+#endif //GGML_CUDA_F16
+
+#if defined(GGML_USE_HIPBLAS)
+#define __CUDA_ARCH__ 1300
+
+#if defined(__gfx1100__) || defined(__gfx1101__) || defined(__gfx1102__) || defined(__gfx1103__) || \
+    defined(__gfx1150__) || defined(__gfx1151__)
+#define RDNA3
+#endif
+
+#if defined(__gfx1030__) || defined(__gfx1031__) || defined(__gfx1032__) || defined(__gfx1033__) || \
+    defined(__gfx1034__) || defined(__gfx1035__) || defined(__gfx1036__) || defined(__gfx1037__)
+#define RDNA2
+#endif
+
+#ifndef __has_builtin
+    #define __has_builtin(x) 0
+#endif
+
+typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
+typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4)));
+static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
+    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
+    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
+#if __has_builtin(__builtin_elementwise_sub_sat)
+    const int8x4_t c = __builtin_elementwise_sub_sat(va, vb);
+    return reinterpret_cast<const int &>(c);
+#else
+    int8x4_t c;
+    int16_t tmp;
+#pragma unroll
+    for (int i = 0; i < 4; i++) {
+        tmp = va[i] - vb[i];
+        if(tmp > std::numeric_limits<int8_t>::max()) tmp = std::numeric_limits<int8_t>::max();
+        if(tmp < std::numeric_limits<int8_t>::min()) tmp = std::numeric_limits<int8_t>::min();
+        c[i] = tmp;
+    }
+    return reinterpret_cast<int &>(c);
+#endif // __has_builtin(__builtin_elementwise_sub_sat)
+}
+
+static __device__ __forceinline__ int __vsub4(const int a, const int b) {
+    return __vsubss4(a, b);
+}
+
+static __device__ __forceinline__ unsigned int __vcmpeq4(unsigned int a, unsigned int b) {
+    const uint8x4_t& va = reinterpret_cast<const uint8x4_t&>(a);
+    const uint8x4_t& vb = reinterpret_cast<const uint8x4_t&>(b);
+    unsigned int c;
+    uint8x4_t& vc = reinterpret_cast<uint8x4_t&>(c);
+#pragma unroll
+    for (int i = 0; i < 4; ++i) {
+        vc[i] = va[i] == vb[i] ? 0xff : 0x00;
+    }
+    return c;
+}
+
+static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
+#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
+    c = __builtin_amdgcn_sdot4(a, b, c, false);
+#elif defined(RDNA3)
+    c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
+#elif defined(__gfx1010__) || defined(__gfx900__)
+    int tmp1;
+    int tmp2;
+    asm("\n \
+        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_0 src1_sel:BYTE_0 \n \
+        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_1 src1_sel:BYTE_1 \n \
+        v_add3_u32 %0, %1, %2, %0 \n \
+        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_2 src1_sel:BYTE_2 \n \
+        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_3 src1_sel:BYTE_3 \n \
+        v_add3_u32 %0, %1, %2, %0 \n \
+        "
+        : "+v"(c), "=&v"(tmp1), "=&v"(tmp2)
+        : "v"(a), "v"(b)
+    );
+#else
+    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
+    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
+    c += va[0] * vb[0] + va[1] * vb[1] + va[2] * vb[2] + va[3] * vb[3];
+#endif
+    return c;
+}
+#endif // defined(GGML_USE_HIPBLAS)
+
+#define FP16_AVAILABLE (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL
+
+#define FP16_MMA_AVAILABLE !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA
+
+static bool fast_fp16_available(const int cc) {
+    return cc >= CC_PASCAL && cc != 610;
+}
+
+static bool fp16_mma_available(const int cc) {
+    return cc < CC_OFFSET_AMD && cc >= CC_VOLTA;
+}
+
+[[noreturn]]
+static __device__ void no_device_code(
+    const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+    printf("%s:%d: ERROR: HIP kernel %s has no device code compatible with HIP arch %d.\n",
+           file_name, line, function_name, arch);
+    GGML_UNUSED(arch_list);
+#else
+    printf("%s:%d: ERROR: CUDA kernel %s has no device code compatible with CUDA arch %d. ggml-cuda.cu was compiled for: %s\n",
+           file_name, line, function_name, arch, arch_list);
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+    __trap();
+
+    GGML_UNUSED(no_device_code); // suppress unused function warning
+}
+
+#ifdef __CUDA_ARCH__
+#define NO_DEVICE_CODE no_device_code(__FILE__, __LINE__, __FUNCTION__, __CUDA_ARCH__, STRINGIZE(__CUDA_ARCH_LIST__))
+#else
+#define NO_DEVICE_CODE //GGML_ASSERT(false && "NO_DEVICE_CODE not valid in host code.")
+#endif // __CUDA_ARCH__
+
+static __device__ __forceinline__ float warp_reduce_sum(float x) {
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        x += __shfl_xor_sync(0xffffffff, x, mask, 32);
+    }
+    return x;
+}
+
+static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32);
+        a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32);
+    }
+    return a;
+}
+
+static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
+#if FP16_AVAILABLE
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        const half2 a_other = __shfl_xor_sync(0xffffffff, a, mask, 32);
+        reinterpret_cast<half&>(a.x) +=  __low2half(a_other);
+        reinterpret_cast<half&>(a.y) += __high2half(a_other);
+    }
+    return a;
+#else
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32));
+    }
+    return a;
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+
+#else
+    NO_DEVICE_CODE;
+    return a;
+#endif // FP16_AVAILABLE
+}
+
+static __device__ __forceinline__ float warp_reduce_max(float x) {
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
+    }
+    return x;
+}
+
+static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b) {
+#if FP16_AVAILABLE
+
+#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX
+    return __float2half(fmaxf(__half2float(a), __half2float(b)));
+#else
+    return __hmax(a, b);
+#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX
+
+#else
+   NO_DEVICE_CODE;
+   GGML_UNUSED(b);
+   return a;
+#endif // FP16_AVAILABLE
+}
+
+static __device__ __forceinline__ half2 ggml_cuda_hmax2(const half2 a, const half2 b) {
+#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+
+#if CUDART_VERSION >= CUDART_HMAX
+    return __hmax2(a, b);
+#else
+    half2 ret;
+    reinterpret_cast<half&>(ret.x) = __float2half(fmaxf( __low2float(a),  __low2float(b)));
+    reinterpret_cast<half&>(ret.y) = __float2half(fmaxf(__high2float(a), __high2float(b)));
+    return ret;
+#endif // CUDART_VERSION >= CUDART_HMAX
+
+#else
+    GGML_UNUSED(a);
+    GGML_UNUSED(b);
+    NO_DEVICE_CODE;
+#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+}
+
+static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
+#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
+#pragma unroll
+   for (int mask = 16; mask > 0; mask >>= 1) {
+       x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
+   }
+   return x;
+#else
+   GGML_UNUSED(x);
+   NO_DEVICE_CODE;
+#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
+}
+
+#if CUDART_VERSION < CUDART_HMASK
+static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half2 b) {
+    const uint32_t mask_low  = 0x0000FFFF * (float( __low2half(a)) > float( __low2half(b)));
+    const uint32_t mask_high = 0xFFFF0000 * (float(__high2half(a)) > float(__high2half(b)));
+    return mask_low | mask_high;
+}
+#endif // CUDART_VERSION < 12000
+
+// TODO: move to ggml-common.h
+static const __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
+
+typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, dfloat2 & v);
+
+
+//////////////////////
+
+struct ggml_cuda_device_info {
+    int device_count;
+
+    struct cuda_device_info {
+        int     cc;                 // compute capability
+        int     nsm;                // number of streaming multiprocessors
+        size_t  smpb;               // max. shared memory per block
+        bool    vmm;                // virtual memory support
+        size_t  vmm_granularity;    // granularity of virtual memory
+        size_t  total_vram;
+    };
+
+    cuda_device_info devices[GGML_CUDA_MAX_DEVICES] = {};
+
+    std::array<float, GGML_CUDA_MAX_DEVICES> default_tensor_split = {};
+};
+
+const ggml_cuda_device_info & ggml_cuda_info();
+
+void ggml_cuda_set_device(int device);
+int ggml_cuda_get_device();
+
+struct ggml_cuda_pool {
+    virtual ~ggml_cuda_pool() = default;
+
+    virtual void * alloc(size_t size, size_t * actual_size) = 0;
+    virtual void free(void * ptr, size_t size) = 0;
+};
+
+template<typename T>
+struct ggml_cuda_pool_alloc {
+    ggml_cuda_pool * pool = nullptr;
+    T * ptr = nullptr;
+    size_t actual_size = 0;
+
+    ggml_cuda_pool_alloc() = default;
+
+    explicit ggml_cuda_pool_alloc(ggml_cuda_pool & pool) : pool(&pool) {
+    }
+
+    ggml_cuda_pool_alloc(ggml_cuda_pool & pool, size_t size) : pool(&pool) {
+        alloc(size);
+    }
+
+    ~ggml_cuda_pool_alloc() {
+        if (ptr != nullptr) {
+            pool->free(ptr, actual_size);
+        }
+    }
+
+    // size is in number of elements
+    T * alloc(size_t size) {
+        GGML_ASSERT(pool != nullptr);
+        GGML_ASSERT(ptr == nullptr);
+        ptr = (T *) pool->alloc(size * sizeof(T), &this->actual_size);
+        return ptr;
+    }
+
+    T * alloc(ggml_cuda_pool & pool, size_t size) {
+        this->pool = &pool;
+        return alloc(size);
+    }
+
+    T * get() {
+        return ptr;
+    }
+
+    ggml_cuda_pool_alloc(const ggml_cuda_pool_alloc &) = delete;
+    ggml_cuda_pool_alloc(ggml_cuda_pool_alloc &&) = delete;
+    ggml_cuda_pool_alloc& operator=(const ggml_cuda_pool_alloc &) = delete;
+    ggml_cuda_pool_alloc& operator=(ggml_cuda_pool_alloc &&) = delete;
+};
+
+
+// backend interface
+
+struct ggml_tensor_extra_gpu {
+    void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors
+    cudaEvent_t events[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS]; // events for synchronizing multiple GPUs
+};
+
+
+#if (CUDART_VERSION >= 12000) && defined(GGML_CUDA_USE_GRAPHS)
+#define USE_CUDA_GRAPH
+#endif
+
+struct ggml_graph_node_properties {
+    void * node_address;
+    ggml_op node_op;
+    int64_t ne[GGML_MAX_DIMS];
+    size_t nb[GGML_MAX_DIMS];
+    void * src_address[GGML_MAX_SRC];
+};
+
+struct ggml_cuda_graph {
+#ifdef USE_CUDA_GRAPH
+    ~ggml_cuda_graph() {
+        if (instance != nullptr) {
+            CUDA_CHECK(cudaGraphExecDestroy(instance));
+        }
+        if (graph != nullptr) {
+            CUDA_CHECK(cudaGraphDestroy(graph));
+        }
+    }
+    cudaGraph_t graph = nullptr;
+    cudaGraphExec_t instance = nullptr;
+    size_t num_nodes = 0;
+    std::vector<cudaGraphNode_t> nodes;
+    std::vector<cudaKernelNodeParams> params;
+    bool disable_due_to_gpu_arch = false;
+    bool disable_due_to_too_many_updates = false;
+    bool disable_due_to_failed_graph_capture = false;
+    int number_consecutive_updates = 0;
+    std::vector<ggml_graph_node_properties> ggml_graph_properties;
+    std::vector<char **> updated_kernel_arg;
+#endif
+};
+
+struct ggml_backend_cuda_context {
+    int device;
+    std::string name;
+    cudaEvent_t copy_event = nullptr;
+
+    cudaStream_t streams[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS] = { { nullptr } };
+    cublasHandle_t cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr};
+
+    std::unique_ptr<ggml_cuda_graph> cuda_graph;
+
+    explicit ggml_backend_cuda_context(int device) :
+        device(device),
+        name(GGML_CUDA_NAME + std::to_string(device)) {
+    }
+
+    ~ggml_backend_cuda_context() {
+        if (copy_event != nullptr) {
+            CUDA_CHECK(cudaEventDestroy(copy_event));
+        }
+        for (int i = 0; i < GGML_CUDA_MAX_DEVICES; ++i) {
+            for (int j = 0; j < GGML_CUDA_MAX_STREAMS; ++j) {
+                if (streams[i][j] != nullptr) {
+                    CUDA_CHECK(cudaStreamDestroy(streams[i][j]));
+                }
+            }
+            if (cublas_handles[i] != nullptr) {
+                CUBLAS_CHECK(cublasDestroy(cublas_handles[i]));
+            }
+        }
+    }
+
+    cudaStream_t stream(int device, int stream) {
+        if (streams[device][stream] == nullptr) {
+            ggml_cuda_set_device(device);
+            CUDA_CHECK(cudaStreamCreateWithFlags(&streams[device][stream], cudaStreamNonBlocking));
+        }
+        return streams[device][stream];
+    }
+
+    cudaStream_t stream() {
+        return stream(device, 0);
+    }
+
+    cublasHandle_t cublas_handle(int device) {
+        if (cublas_handles[device] == nullptr) {
+            ggml_cuda_set_device(device);
+            CUBLAS_CHECK(cublasCreate(&cublas_handles[device]));
+            CUBLAS_CHECK(cublasSetMathMode(cublas_handles[device], CUBLAS_TF32_TENSOR_OP_MATH));
+        }
+        return cublas_handles[device];
+    }
+
+    cublasHandle_t cublas_handle() {
+        return cublas_handle(device);
+    }
+
+    // pool
+    std::unique_ptr<ggml_cuda_pool> pools[GGML_CUDA_MAX_DEVICES];
+
+    static std::unique_ptr<ggml_cuda_pool> new_pool_for_device(int device);
+
+    ggml_cuda_pool & pool(int device) {
+        if (pools[device] == nullptr) {
+            pools[device] = new_pool_for_device(device);
+        }
+        return *pools[device];
+    }
+
+    ggml_cuda_pool & pool() {
+        return pool(device);
+    }
+};
diff --git a/ggml-cuda/concat.cu b/ggml-cuda/concat.cu
new file mode 100644
index 000000000..2941d2f17
--- /dev/null
+++ b/ggml-cuda/concat.cu
@@ -0,0 +1,49 @@
+#include "concat.cuh"
+
+static __global__ void concat_f32(const float * x,const float * y, float * dst, const int ne0, const int ne02) {
+    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
+    if (nidx >= ne0) {
+        return;
+    }
+    // operation
+    int offset_dst =
+        nidx +
+        blockIdx.y * ne0 +
+        blockIdx.z * ne0 * gridDim.y;
+    if (blockIdx.z < ne02) { // src0
+        int offset_src =
+            nidx +
+            blockIdx.y * ne0 +
+            blockIdx.z * ne0 * gridDim.y;
+        dst[offset_dst] = x[offset_src];
+    } else {
+        int offset_src =
+            nidx +
+            blockIdx.y * ne0 +
+            (blockIdx.z - ne02) * ne0 *  gridDim.y;
+        dst[offset_dst] = y[offset_src];
+    }
+}
+
+static void concat_f32_cuda(const float * x, const float * y, float * dst, const int ne0, int ne1, int ne2, int ne02, cudaStream_t stream) {
+    int num_blocks = (ne0 + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE;
+    dim3 gridDim(num_blocks, ne1, ne2);
+    concat_f32<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne02);
+}
+
+void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const float * src0_d = (const float *)src0->data;
+    const float * src1_d = (const float *)src1->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    for (int i3 = 0; i3 < dst->ne[3]; i3++) {
+        concat_f32_cuda(src0_d + i3 * (src0->nb[3] / 4), src1_d + i3 * (src1->nb[3] / 4), dst_d + i3 * (dst->nb[3] / 4), dst->ne[0], dst->ne[1], dst->ne[2], src0->ne[2], stream);
+    }
+}
diff --git a/ggml-cuda/concat.cuh b/ggml-cuda/concat.cuh
new file mode 100644
index 000000000..aa506a05f
--- /dev/null
+++ b/ggml-cuda/concat.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_CONCAT_BLOCK_SIZE 256
+
+void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/convert.cu b/ggml-cuda/convert.cu
new file mode 100644
index 000000000..830e2d756
--- /dev/null
+++ b/ggml-cuda/convert.cu
@@ -0,0 +1,824 @@
+#include "convert.cuh"
+#include "dequantize.cuh"
+
+#define CUDA_Q8_0_NE_ALIGN 2048
+
+template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
+static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k) {
+    const int64_t i = (int64_t)2*(blockDim.x*blockIdx.x + threadIdx.x);
+
+    if (i >= k) {
+        return;
+    }
+
+    const int64_t ib = i/qk; // block index
+    const int64_t iqs = (i%qk)/qr; // quant index
+    const int64_t iybs = i - i%qk; // y block start index
+    const int64_t y_offset = qr == 1 ? 1 : qk/2;
+
+    // dequantize
+    dfloat2 v;
+    dequantize_kernel(vx, ib, iqs, v);
+
+    y[iybs + iqs + 0]        = v.x;
+    y[iybs + iqs + y_offset] = v.y;
+}
+
+template <bool need_check>
+static __global__ void dequantize_block_q8_0_f16(const void * __restrict__ vx, half * __restrict__ y, const int64_t k) {
+#if __CUDA_ARCH__ >= CC_PASCAL
+    constexpr int nint = CUDA_Q8_0_NE_ALIGN/sizeof(int) + WARP_SIZE;
+
+    const int64_t   i0 = CUDA_Q8_0_NE_ALIGN*blockIdx.x;
+    const int * x0 = ((int *) vx) + blockIdx.x * nint;
+    half2 * y2 = (half2 *) (y + i0);
+
+    __shared__ int vals[nint];
+
+#pragma unroll
+    for (int ix0 = 0; ix0 < nint; ix0 += WARP_SIZE) {
+        if (need_check && i0*sizeof(block_q8_0)/QK8_0 + sizeof(int)*(ix0 + threadIdx.x) >= k*sizeof(block_q8_0)/QK8_0) {
+            break;
+        }
+
+        const int ix = ix0 + threadIdx.x;
+        vals[ix] = x0[ix];
+    }
+
+    __syncthreads();
+
+#pragma unroll
+    for (int iy = 0; iy < CUDA_Q8_0_NE_ALIGN; iy += 2*WARP_SIZE) {
+        if (need_check && i0 + iy + 2*threadIdx.x >= k) {
+            return;
+        }
+
+        const half * b0 = ((const half  *) vals) + (sizeof(block_q8_0)/sizeof(half)) * ((iy + 2*threadIdx.x)/QK8_0);
+        const half    d = *b0;
+        const char2  qs = ((const char2 *) (b0 + 1))[threadIdx.x % (QK8_0/2)];
+
+        y2[iy/2 + threadIdx.x] = __hmul2(make_half2(qs.x, qs.y), __half2half2(d));
+    }
+#else
+    GGML_UNUSED(vx);
+    GGML_UNUSED(y);
+    GGML_UNUSED(k);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_PASCAL
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_q4_0(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32) {
+
+    const int64_t i = blockIdx.x;
+
+    // assume 32 threads
+    const int64_t tid = threadIdx.x;
+    const int64_t il  = tid/8;
+    const int64_t ir  = tid%8;
+    const int64_t ib = 8*i + ir;
+    if (ib >= nb32) {
+        return;
+    }
+
+    dst_t * y = yy + 256*i + 32*ir + 4*il;
+
+    const block_q4_0 * x = (const block_q4_0 *)vx + ib;
+    const float d = __half2float(x->d);
+    const float dm = -8*d;
+
+    const uint8_t * q = x->qs + 4*il;
+
+    for (int l = 0; l < 4; ++l) {
+        y[l+ 0] = d * (q[l] & 0xF) + dm;
+        y[l+16] = d * (q[l] >>  4) + dm;
+    }
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_q4_1(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32) {
+
+    const int64_t i = blockIdx.x;
+
+    // assume 32 threads
+    const int64_t tid = threadIdx.x;
+    const int64_t il  = tid/8;
+    const int64_t ir  = tid%8;
+    const int64_t ib = 8*i + ir;
+    if (ib >= nb32) {
+        return;
+    }
+
+    dst_t * y = yy + 256*i + 32*ir + 4*il;
+
+    const block_q4_1 * x = (const block_q4_1 *)vx + ib;
+    const float2 d = __half22float2(x->dm);
+
+    const uint8_t * q = x->qs + 4*il;
+
+    for (int l = 0; l < 4; ++l) {
+        y[l+ 0] = d.x * (q[l] & 0xF) + d.y;
+        y[l+16] = d.x * (q[l] >>  4) + d.y;
+    }
+}
+
+//================================== k-quants
+
+template<typename dst_t>
+static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int64_t i   = blockIdx.x;
+    const block_q2_K * x = (const block_q2_K *) vx;
+
+    const int64_t tid = threadIdx.x;
+#if QK_K == 256
+    const int64_t n   = tid/32;
+    const int64_t l   = tid - 32*n;
+    const int64_t is  = 8*n + l/16;
+
+    const uint8_t q = x[i].qs[32*n + l];
+    dst_t * y = yy + i*QK_K + 128*n;
+
+    float dall = __low2half(x[i].dm);
+    float dmin = __high2half(x[i].dm);
+    y[l+ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
+    y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4);
+    y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4);
+    y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4);
+#else
+    const int64_t is = tid/16;  // 0 or 1
+    const int64_t il = tid%16;  // 0...15
+    const uint8_t q = x[i].qs[il] >> (2*is);
+    dst_t * y = yy + i*QK_K + 16*is + il;
+    float dall = __low2half(x[i].dm);
+    float dmin = __high2half(x[i].dm);
+    y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
+    y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4);
+#endif
+
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int64_t i = blockIdx.x;
+    const block_q3_K * x = (const block_q3_K *) vx;
+
+#if QK_K == 256
+    const int64_t r = threadIdx.x/4;
+    const int64_t tid = r/2;
+    const int64_t is0 = r%2;
+    const int64_t l0 = 16*is0 + 4*(threadIdx.x%4);
+    const int64_t n = tid / 4;
+    const int64_t j = tid - 4*n;
+
+    uint8_t m = 1 << (4*n + j);
+    int64_t is = 8*n + 2*j + is0;
+    int shift = 2*j;
+
+    int8_t us = is <  4 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+8] >> 0) & 3) << 4) :
+                is <  8 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+4] >> 2) & 3) << 4) :
+                is < 12 ? (x[i].scales[is-8] >>  4) | (((x[i].scales[is+0] >> 4) & 3) << 4) :
+                          (x[i].scales[is-8] >>  4) | (((x[i].scales[is-4] >> 6) & 3) << 4);
+    float d_all = x[i].d;
+    float dl = d_all * (us - 32);
+
+    dst_t * y = yy + i*QK_K + 128*n + 32*j;
+    const uint8_t * q = x[i].qs + 32*n;
+    const uint8_t * hm = x[i].hmask;
+
+    for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));
+#else
+    const int64_t tid = threadIdx.x;
+    const int64_t is  = tid/16;  // 0 or 1
+    const int64_t il  = tid%16;  // 0...15
+    const int64_t im  = il/8;    // 0...1
+    const int64_t in  = il%8;    // 0...7
+
+    dst_t * y = yy + i*QK_K + 16*is + il;
+
+    const uint8_t q = x[i].qs[il] >> (2*is);
+    const uint8_t h = x[i].hmask[in] >> (2*is + im);
+    const float   d = (float)x[i].d;
+
+    if (is == 0) {
+        y[ 0] = d * ((x[i].scales[0] & 0xF) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
+        y[32] = d * ((x[i].scales[1] & 0xF) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
+    } else {
+        y[ 0] = d * ((x[i].scales[0] >>  4) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
+        y[32] = d * ((x[i].scales[1] >>  4) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
+    }
+#endif
+
+}
+
+#if QK_K == 256
+static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
+    if (j < 4) {
+        d = q[j] & 63; m = q[j + 4] & 63;
+    } else {
+        d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
+        m = (q[j+4] >>  4) | ((q[j-0] >> 6) << 4);
+    }
+}
+#endif
+
+template<typename dst_t>
+static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const block_q4_K * x = (const block_q4_K *) vx;
+
+    const int64_t i = blockIdx.x;
+
+#if QK_K == 256
+    // assume 32 threads
+    const int64_t tid = threadIdx.x;
+    const int64_t il  = tid/8;
+    const int64_t ir  = tid%8;
+    const int64_t is  = 2*il;
+    const int64_t n   = 4;
+
+    dst_t * y = yy + i*QK_K + 64*il + n*ir;
+
+    const float dall = __low2half(x[i].dm);
+    const float dmin = __high2half(x[i].dm);
+
+    const uint8_t * q = x[i].qs + 32*il + n*ir;
+
+    uint8_t sc, m;
+    get_scale_min_k4(is + 0, x[i].scales, sc, m);
+    const float d1 = dall * sc; const float m1 = dmin * m;
+    get_scale_min_k4(is + 1, x[i].scales, sc, m);
+    const float d2 = dall * sc; const float m2 = dmin * m;
+    for (int l = 0; l < n; ++l) {
+        y[l + 0] = d1 * (q[l] & 0xF) - m1;
+        y[l +32] = d2 * (q[l] >>  4) - m2;
+    }
+#else
+    const int64_t tid = threadIdx.x;
+    const uint8_t * q = x[i].qs;
+    dst_t * y = yy + i*QK_K;
+    const float d = (float)x[i].dm[0];
+    const float m = (float)x[i].dm[1];
+    y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4);
+    y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >>  4) - m * (x[i].scales[1] >> 4);
+#endif
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const block_q5_K * x = (const block_q5_K *) vx;
+
+    const int64_t i = blockIdx.x;
+
+#if QK_K == 256
+    // assume 64 threads - this is very slightly better than the one below
+    const int64_t tid = threadIdx.x;
+    const int64_t il  = tid/16;   // il is in 0...3
+    const int64_t ir  = tid%16;   // ir is in 0...15
+    const int64_t is  = 2*il;     // is is in 0...6
+
+    dst_t * y = yy + i*QK_K + 64*il + 2*ir;
+
+    const float dall = __low2half(x[i].dm);
+    const float dmin = __high2half(x[i].dm);
+
+    const uint8_t * ql = x[i].qs + 32*il + 2*ir;
+    const uint8_t * qh = x[i].qh + 2*ir;
+
+    uint8_t sc, m;
+    get_scale_min_k4(is + 0, x[i].scales, sc, m);
+    const float d1 = dall * sc; const float m1 = dmin * m;
+    get_scale_min_k4(is + 1, x[i].scales, sc, m);
+    const float d2 = dall * sc; const float m2 = dmin * m;
+
+    uint8_t   hm  = 1 << (2*il);
+    y[ 0] = d1 * ((ql[ 0] & 0xF) + (qh[ 0] & hm ? 16 : 0)) - m1;
+    y[ 1] = d1 * ((ql[ 1] & 0xF) + (qh[ 1] & hm ? 16 : 0)) - m1;
+    hm <<= 1;
+    y[32] = d2 * ((ql[ 0] >>  4) + (qh[ 0] & hm ? 16 : 0)) - m2;
+    y[33] = d2 * ((ql[ 1] >>  4) + (qh[ 1] & hm ? 16 : 0)) - m2;
+#else
+    const int64_t tid = threadIdx.x;
+    const uint8_t q = x[i].qs[tid];
+    const int64_t im = tid/8;  // 0...3
+    const int64_t in = tid%8;  // 0...7
+    const int64_t is = tid/16; // 0 or 1
+    const uint8_t h = x[i].qh[in] >> im;
+    const float d = x[i].d;
+    dst_t * y = yy + i*QK_K + tid;
+    y[ 0] = d * x[i].scales[is+0] * ((q & 0xF) - ((h >> 0) & 1 ? 0 : 16));
+    y[32] = d * x[i].scales[is+2] * ((q >>  4) - ((h >> 4) & 1 ? 0 : 16));
+#endif
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const block_q6_K * x = (const block_q6_K *) vx;
+
+    const int64_t i = blockIdx.x;
+#if QK_K == 256
+
+    // assume 64 threads - this is very slightly better than the one below
+    const int64_t tid = threadIdx.x;
+    const int64_t ip  = tid/32;   // ip is 0 or 1
+    const int64_t il  = tid - 32*ip; // 0...32
+    const int64_t is  = 8*ip + il/16;
+
+    dst_t * y = yy + i*QK_K + 128*ip + il;
+
+    const float d = x[i].d;
+
+    const uint8_t * ql = x[i].ql + 64*ip + il;
+    const uint8_t   qh = x[i].qh[32*ip + il];
+    const int8_t  * sc = x[i].scales + is;
+
+    y[ 0] = d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
+    y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32);
+    y[64] = d * sc[4] * ((int8_t)((ql[ 0]  >> 4) | (((qh >> 4) & 3) << 4)) - 32);
+    y[96] = d * sc[6] * ((int8_t)((ql[32]  >> 4) | (((qh >> 6) & 3) << 4)) - 32);
+#else
+
+    // assume 32 threads
+    const int64_t tid = threadIdx.x;
+    const int64_t ip  = tid/16;         // 0 or 1
+    const int64_t il  = tid - 16*ip;    // 0...15
+
+    dst_t * y = yy + i*QK_K + 16*ip + il;
+
+    const float d = x[i].d;
+
+    const uint8_t   ql = x[i].ql[16*ip + il];
+    const uint8_t   qh = x[i].qh[il] >> (2*ip);
+    const int8_t  * sc = x[i].scales;
+
+    y[ 0] = d * sc[ip+0] * ((int8_t)((ql & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
+    y[32] = d * sc[ip+2] * ((int8_t)((ql  >> 4) | (((qh >> 4) & 3) << 4)) - 32);
+#endif
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int64_t i   = blockIdx.x;
+    const block_iq2_xxs * x = (const block_iq2_xxs  *) vx;
+
+    const int64_t tid = threadIdx.x;
+#if QK_K == 256
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint16_t * q2 = x[i].qs + 4*ib;
+    const uint8_t  * aux8 = (const uint8_t *)q2;
+    const uint8_t  * grid = (const uint8_t *)(iq2xxs_grid + aux8[il]);
+    const uint32_t aux32 = q2[2] | (q2[3] << 16);
+    const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f;
+    const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
+    for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
+#else
+    NO_DEVICE_CODE;
+#endif
+
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int64_t i   = blockIdx.x;
+    const block_iq2_xs * x = (const block_iq2_xs *) vx;
+
+    const int64_t tid = threadIdx.x;
+#if QK_K == 256
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint16_t * q2 = x[i].qs + 4*ib;
+    const uint8_t  * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511));
+    const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
+    const uint8_t signs = ksigns_iq2xs[q2[il] >> 9];
+    for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
+#else
+    NO_DEVICE_CODE;
+#endif
+
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int64_t i   = blockIdx.x;
+    const block_iq2_s * x = (const block_iq2_s *) vx;
+
+    const int64_t tid = threadIdx.x;
+#if QK_K == 256
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint8_t * grid = (const uint8_t *)(iq2s_grid + (x[i].qs[4*ib+il] | ((x[i].qh[ib] << (8-2*il)) & 0x300)));
+    const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
+    const uint8_t signs = x[i].qs[QK_K/8+4*ib+il];
+    for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
+#else
+    NO_DEVICE_CODE;
+#endif
+
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int64_t i   = blockIdx.x;
+    const block_iq3_xxs * x = (const block_iq3_xxs  *) vx;
+
+    const int64_t tid = threadIdx.x;
+#if QK_K == 256
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint8_t  * q3 = x[i].qs + 8*ib;
+    const uint16_t * gas = (const uint16_t *)(x[i].qs + QK_K/4) + 2*ib;
+    const uint8_t  * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*il+0]);
+    const uint8_t  * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*il+1]);
+    const uint32_t aux32 = gas[0] | (gas[1] << 16);
+    const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.5f;
+    const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
+    for (int j = 0; j < 4; ++j) {
+        y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
+        y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
+    }
+#else
+    NO_DEVICE_CODE;
+#endif
+
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int64_t i   = blockIdx.x;
+    const block_iq3_s * x = (const block_iq3_s *) vx;
+
+    const int64_t tid = threadIdx.x;
+#if QK_K == 256
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint8_t * qs = x[i].qs + 8*ib;
+    const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256)));
+    const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256)));
+    const float d = (float)x[i].d * (1 + 2*((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf));
+    const uint8_t signs = x[i].signs[4*ib + il];
+    for (int j = 0; j < 4; ++j) {
+        y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
+        y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
+    }
+#else
+    NO_DEVICE_CODE;
+#endif
+
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int64_t i   = blockIdx.x;
+    const block_iq1_s * x = (const block_iq1_s  *) vx;
+
+    const int64_t tid = threadIdx.x;
+#if QK_K == 256
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const float delta = x[i].qh[ib] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA;
+    const float d = (float)x[i].d * (2*((x[i].qh[ib] >> 12) & 7) + 1);
+    uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32;
+    grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[ib] >> 3*il) & 7) << 8)];
+    grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f;
+    grid32[0] &= 0x0f0f0f0f;
+    for (int j = 0; j < 8; ++j) {
+        y[j] = d * (q[j] + delta);
+    }
+#else
+    NO_DEVICE_CODE;
+#endif
+
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int64_t i   = blockIdx.x;
+    const block_iq1_m * x = (const block_iq1_m  *) vx;
+
+    const int64_t tid = threadIdx.x;
+#if QK_K == 256
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint16_t * sc = (const uint16_t *)x[i].scales;
+    iq1m_scale_t scale;
+    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+    const int64_t ib16 = 2*ib + il/2; // sc[ib16/4] >> 3*(ib16%4) -> sc[ib/2] >> 3*((2*ib+il/2)%4);
+    const float d = (float)scale.f16 * (2*((sc[ib16/4] >> 3*(ib16%4)) & 0x7) + 1);
+    const float delta = x[i].qh[2*ib+il/2] & (0x08 << 4*(il%2)) ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA;
+    uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32;
+    grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[2*ib+il/2] >> 4*(il%2)) & 7) << 8)];
+    grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f;
+    grid32[0] &= 0x0f0f0f0f;
+    for (int j = 0; j < 8; ++j) {
+        y[j] = d * (q[j] + delta);
+    }
+#else
+    NO_DEVICE_CODE;
+#endif
+
+}
+
+
+template<typename dst_t>
+static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int64_t i   = blockIdx.x;
+    const block_iq4_nl * x = (const block_iq4_nl *) vx + i*(QK_K/QK4_NL);
+
+    const int64_t tid = threadIdx.x;
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
+    const uint8_t  * q4 = x[ib].qs + 4*il;
+    const float d = (float)x[ib].d;
+    for (int j = 0; j < 4; ++j) {
+        y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf];
+        y[j+16] = d * kvalues_iq4nl[q4[j] >>  4];
+    }
+
+}
+
+#if QK_K != 64
+template<typename dst_t>
+static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const int64_t i   = blockIdx.x;
+    const block_iq4_xs * x = (const block_iq4_xs *)vx;
+
+    const int64_t tid = threadIdx.x;
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
+    const uint8_t  * q4 = x[i].qs + 16*ib + 4*il;
+    const float d = (float)x[i].d * ((((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4)) - 32);
+    for (int j = 0; j < 4; ++j) {
+        y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf];
+        y[j+16] = d * kvalues_iq4nl[q4[j] >>  4];
+    }
+}
+#endif
+
+template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
+static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k, cudaStream_t stream) {
+    const int num_blocks = (k + 2*CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / (2*CUDA_DEQUANTIZE_BLOCK_SIZE);
+    dequantize_block<qk, qr, dequantize_kernel><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
+}
+
+static void dequantize_block_q8_0_f16_cuda(const void * __restrict__ vx, half * __restrict__ y, const int64_t k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_Q8_0_NE_ALIGN - 1) / CUDA_Q8_0_NE_ALIGN;
+    if (k % CUDA_Q8_0_NE_ALIGN == 0) {
+        const bool need_check = false;
+        dequantize_block_q8_0_f16<need_check><<<num_blocks, WARP_SIZE, 0, stream>>>(vx, y, k);
+    } else {
+        const bool need_check = true;
+        dequantize_block_q8_0_f16<need_check><<<num_blocks, WARP_SIZE, 0, stream>>>(vx, y, k);
+    }
+}
+
+template<typename dst_t>
+static void dequantize_row_q2_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+#if QK_K == 256
+    dequantize_block_q2_K<<<nb, 64, 0, stream>>>(vx, y);
+#else
+    dequantize_block_q2_K<<<nb, 32, 0, stream>>>(vx, y);
+#endif
+}
+
+template<typename dst_t>
+static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+#if QK_K == 256
+    dequantize_block_q3_K<<<nb, 64, 0, stream>>>(vx, y);
+#else
+    dequantize_block_q3_K<<<nb, 32, 0, stream>>>(vx, y);
+#endif
+}
+
+template<typename dst_t>
+static void dequantize_row_q4_0_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+    const int nb32 = k / 32;
+    const int nb = (k + 255) / 256;
+    dequantize_block_q4_0<<<nb, 32, 0, stream>>>(vx, y, nb32);
+}
+
+template<typename dst_t>
+static void dequantize_row_q4_1_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+    const int nb32 = k / 32;
+    const int nb = (k + 255) / 256;
+    dequantize_block_q4_1<<<nb, 32, 0, stream>>>(vx, y, nb32);
+}
+
+template<typename dst_t>
+static void dequantize_row_q4_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+    dequantize_block_q4_K<<<nb, 32, 0, stream>>>(vx, y);
+}
+
+template<typename dst_t>
+static void dequantize_row_q5_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+#if QK_K == 256
+    dequantize_block_q5_K<<<nb, 64, 0, stream>>>(vx, y);
+#else
+    dequantize_block_q5_K<<<nb, 32, 0, stream>>>(vx, y);
+#endif
+}
+
+template<typename dst_t>
+static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+#if QK_K == 256
+    dequantize_block_q6_K<<<nb, 64, 0, stream>>>(vx, y);
+#else
+    dequantize_block_q6_K<<<nb, 32, 0, stream>>>(vx, y);
+#endif
+}
+
+template<typename dst_t>
+static void dequantize_row_iq2_xxs_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+    dequantize_block_iq2_xxs<<<nb, 32, 0, stream>>>(vx, y);
+}
+
+template<typename dst_t>
+static void dequantize_row_iq2_xs_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+    dequantize_block_iq2_xs<<<nb, 32, 0, stream>>>(vx, y);
+}
+
+template<typename dst_t>
+static void dequantize_row_iq2_s_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+    dequantize_block_iq2_s<<<nb, 32, 0, stream>>>(vx, y);
+}
+
+template<typename dst_t>
+static void dequantize_row_iq3_xxs_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+    dequantize_block_iq3_xxs<<<nb, 32, 0, stream>>>(vx, y);
+}
+
+template<typename dst_t>
+static void dequantize_row_iq3_s_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+    dequantize_block_iq3_s<<<nb, 32, 0, stream>>>(vx, y);
+}
+
+template<typename dst_t>
+static void dequantize_row_iq1_s_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+    dequantize_block_iq1_s<<<nb, 32, 0, stream>>>(vx, y);
+}
+
+template<typename dst_t>
+static void dequantize_row_iq4_nl_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+    const int nb = (k + QK_K - 1) / QK_K;
+    dequantize_block_iq4_nl<<<nb, 32, 0, stream>>>(vx, y);
+}
+
+template<typename dst_t>
+static void dequantize_row_iq1_m_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+    dequantize_block_iq1_m<<<nb, 32, 0, stream>>>(vx, y);
+}
+
+template<typename dst_t>
+static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+    const int nb = (k + QK_K - 1) / QK_K;
+#if QK_K == 64
+    dequantize_block_iq4_nl<<<nb, 32, 0, stream>>>(vx, y);
+#else
+    dequantize_block_iq4_xs<<<nb, 32, 0, stream>>>(vx, y);
+#endif
+}
+
+template <typename src_t, typename dst_t>
+static __global__ void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k) {
+    const int64_t i = (int64_t)blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    const src_t * x = (src_t *) vx;
+
+    y[i] = x[i];
+}
+
+template <typename src_t, typename dst_t>
+static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
+    convert_unary<src_t><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
+}
+
+to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+            return dequantize_row_q4_0_cuda;
+        case GGML_TYPE_Q4_1:
+            return dequantize_row_q4_1_cuda;
+        case GGML_TYPE_Q5_0:
+            return dequantize_block_cuda<QK5_0, QR5_0, dequantize_q5_0>;
+        case GGML_TYPE_Q5_1:
+            return dequantize_block_cuda<QK5_1, QR5_1, dequantize_q5_1>;
+        case GGML_TYPE_Q8_0:
+            if (ggml_cuda_info().devices[ggml_cuda_get_device()].cc >= CC_PASCAL) {
+                return dequantize_block_q8_0_f16_cuda;
+            }
+            return dequantize_block_cuda<QK8_0, QR8_0, dequantize_q8_0>;
+        case GGML_TYPE_Q2_K:
+            return dequantize_row_q2_K_cuda;
+        case GGML_TYPE_Q3_K:
+            return dequantize_row_q3_K_cuda;
+        case GGML_TYPE_Q4_K:
+            return dequantize_row_q4_K_cuda;
+        case GGML_TYPE_Q5_K:
+            return dequantize_row_q5_K_cuda;
+        case GGML_TYPE_Q6_K:
+            return dequantize_row_q6_K_cuda;
+        case GGML_TYPE_IQ2_XXS:
+            return dequantize_row_iq2_xxs_cuda;
+        case GGML_TYPE_IQ2_XS:
+            return dequantize_row_iq2_xs_cuda;
+        case GGML_TYPE_IQ2_S:
+            return dequantize_row_iq2_s_cuda;
+        case GGML_TYPE_IQ3_XXS:
+            return dequantize_row_iq3_xxs_cuda;
+        case GGML_TYPE_IQ1_S:
+            return dequantize_row_iq1_s_cuda;
+        case GGML_TYPE_IQ1_M:
+            return dequantize_row_iq1_m_cuda;
+        case GGML_TYPE_IQ4_NL:
+            return dequantize_row_iq4_nl_cuda;
+        case GGML_TYPE_IQ4_XS:
+            return dequantize_row_iq4_xs_cuda;
+        case GGML_TYPE_IQ3_S:
+            return dequantize_row_iq3_s_cuda;
+        case GGML_TYPE_F32:
+            return convert_unary_cuda<float>;
+        default:
+            return nullptr;
+    }
+}
+
+to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+            return dequantize_row_q4_0_cuda;
+        case GGML_TYPE_Q4_1:
+            return dequantize_row_q4_1_cuda;
+        case GGML_TYPE_Q5_0:
+            return dequantize_block_cuda<QK5_0, QR5_0, dequantize_q5_0>;
+        case GGML_TYPE_Q5_1:
+            return dequantize_block_cuda<QK5_1, QR5_1, dequantize_q5_1>;
+        case GGML_TYPE_Q8_0:
+            return dequantize_block_cuda<QK8_0, QR8_0, dequantize_q8_0>;
+        case GGML_TYPE_Q2_K:
+            return dequantize_row_q2_K_cuda;
+        case GGML_TYPE_Q3_K:
+            return dequantize_row_q3_K_cuda;
+        case GGML_TYPE_Q4_K:
+            return dequantize_row_q4_K_cuda;
+        case GGML_TYPE_Q5_K:
+            return dequantize_row_q5_K_cuda;
+        case GGML_TYPE_Q6_K:
+            return dequantize_row_q6_K_cuda;
+        case GGML_TYPE_IQ2_XXS:
+            return dequantize_row_iq2_xxs_cuda;
+        case GGML_TYPE_IQ2_XS:
+            return dequantize_row_iq2_xs_cuda;
+        case GGML_TYPE_IQ2_S:
+            return dequantize_row_iq2_s_cuda;
+        case GGML_TYPE_IQ3_XXS:
+            return dequantize_row_iq3_xxs_cuda;
+        case GGML_TYPE_IQ1_S:
+            return dequantize_row_iq1_s_cuda;
+        case GGML_TYPE_IQ1_M:
+            return dequantize_row_iq1_m_cuda;
+        case GGML_TYPE_IQ4_NL:
+            return dequantize_row_iq4_nl_cuda;
+        case GGML_TYPE_IQ4_XS:
+            return dequantize_row_iq4_xs_cuda;
+        case GGML_TYPE_IQ3_S:
+            return dequantize_row_iq3_s_cuda;
+        case GGML_TYPE_F16:
+            return convert_unary_cuda<half>;
+        default:
+            return nullptr;
+    }
+}
diff --git a/ggml-cuda/convert.cuh b/ggml-cuda/convert.cuh
new file mode 100644
index 000000000..5394be9f1
--- /dev/null
+++ b/ggml-cuda/convert.cuh
@@ -0,0 +1,13 @@
+#include "common.cuh"
+
+#define CUDA_DEQUANTIZE_BLOCK_SIZE 256
+
+template<typename T>
+using to_t_cuda_t = void (*)(const void * __restrict__ x, T * __restrict__ y, int64_t k, cudaStream_t stream);
+
+typedef to_t_cuda_t<float> to_fp32_cuda_t;
+typedef to_t_cuda_t<half> to_fp16_cuda_t;
+
+to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type);
+
+to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type);
diff --git a/ggml-cuda/cpy.cu b/ggml-cuda/cpy.cu
new file mode 100644
index 000000000..12d741f01
--- /dev/null
+++ b/ggml-cuda/cpy.cu
@@ -0,0 +1,490 @@
+#include "cpy.cuh"
+
+typedef void (*cpy_kernel_t)(const char * cx, char * cdst);
+
+static __device__ void cpy_1_f32_f32(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    float * dsti = (float *) cdsti;
+
+    *dsti = *xi;
+}
+
+static __device__ void cpy_1_f32_f16(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    half * dsti = (half *) cdsti;
+
+    *dsti = __float2half(*xi);
+}
+
+static __device__ void cpy_1_f16_f16(const char * cxi, char * cdsti) {
+    const half * xi = (const half *) cxi;
+    half * dsti = (half *) cdsti;
+
+    *dsti = *xi;
+}
+
+static __device__ void cpy_1_f16_f32(const char * cxi, char * cdsti) {
+    const half * xi = (const half *) cxi;
+    float * dsti = (float *) cdsti;
+
+    *dsti = *xi;
+}
+
+template <cpy_kernel_t cpy_1>
+static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne,
+                                   const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+                                   const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13) {
+    const int64_t i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= ne) {
+        return;
+    }
+
+    // determine indices i03/i13, i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor
+    // then combine those indices with the corresponding byte offsets to get the total offsets
+    const int64_t i03 = i/(ne00 * ne01 * ne02);
+    const int64_t i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
+    const int64_t i01 = (i - i03*ne00*ne01*ne02  -  i02*ne01*ne00) / ne00;
+    const int64_t i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
+    const int64_t x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
+
+    const int64_t i13 = i/(ne10 * ne11 * ne12);
+    const int64_t i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
+    const int64_t i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
+    const int64_t i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
+    const int64_t dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13;
+
+    cpy_1(cx + x_offset, cdst + dst_offset);
+}
+
+static __device__ void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    block_q8_0 * dsti = (block_q8_0 *) cdsti;
+
+    float amax = 0.0f; // absolute max
+
+    for (int j = 0; j < QK8_0; j++) {
+        const float v = xi[j];
+        amax = fmaxf(amax, fabsf(v));
+    }
+
+    const float d = amax / ((1 << 7) - 1);
+    const float id = d ? 1.0f/d : 0.0f;
+
+    dsti->d = d;
+
+    for (int j = 0; j < QK8_0; ++j) {
+        const float x0 = xi[j]*id;
+
+        dsti->qs[j] = roundf(x0);
+    }
+}
+
+static __device__ void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    block_q4_0 * dsti = (block_q4_0 *) cdsti;
+
+    float amax = 0.0f;
+    float vmax = 0.0f;
+
+    for (int j = 0; j < QK4_0; ++j) {
+        const float v = xi[j];
+        if (amax < fabsf(v)) {
+            amax = fabsf(v);
+            vmax = v;
+        }
+    }
+
+    const float d  = vmax / -8;
+    const float id = d ? 1.0f/d : 0.0f;
+
+    dsti->d = d;
+
+    for (int j = 0; j < QK4_0/2; ++j) {
+        const float x0 = xi[0       + j]*id;
+        const float x1 = xi[QK4_0/2 + j]*id;
+
+        const uint8_t xi0 = min(15, (int8_t)(x0 + 8.5f));
+        const uint8_t xi1 = min(15, (int8_t)(x1 + 8.5f));
+
+        dsti->qs[j]  = xi0;
+        dsti->qs[j] |= xi1 << 4;
+    }
+}
+
+static __device__ void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    block_q4_1 * dsti = (block_q4_1 *) cdsti;
+
+    float vmin = FLT_MAX;
+    float vmax = -FLT_MAX;
+
+    for (int j = 0; j < QK4_1; ++j) {
+        const float v = xi[j];
+
+        if (v < vmin) vmin = v;
+        if (v > vmax) vmax = v;
+    }
+
+    const float d  = (vmax - vmin) / ((1 << 4) - 1);
+    const float id = d ? 1.0f/d : 0.0f;
+
+    dsti->dm.x = d;
+    dsti->dm.y = vmin;
+
+    for (int j = 0; j < QK4_1/2; ++j) {
+        const float x0 = (xi[0       + j] - vmin)*id;
+        const float x1 = (xi[QK4_1/2 + j] - vmin)*id;
+
+        const uint8_t xi0 = min(15, (int8_t)(x0 + 0.5f));
+        const uint8_t xi1 = min(15, (int8_t)(x1 + 0.5f));
+
+        dsti->qs[j]  = xi0;
+        dsti->qs[j] |= xi1 << 4;
+    }
+}
+
+static __device__ void cpy_blck_f32_q5_0(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    block_q5_0 * dsti = (block_q5_0 *) cdsti;
+
+    float amax = 0.0f;
+    float vmax = 0.0f;
+
+    for (int j = 0; j < QK5_0; ++j) {
+        const float v = xi[j];
+        if (amax < fabsf(v)) {
+            amax = fabsf(v);
+            vmax = v;
+        }
+    }
+
+    const float d  = vmax / -16;
+    const float id = d ? 1.0f/d : 0.0f;
+
+    dsti->d = d;
+
+    uint32_t qh = 0;
+    for (int j = 0; j < QK5_0/2; ++j) {
+        const float x0 = xi[0       + j]*id;
+        const float x1 = xi[QK5_0/2 + j]*id;
+
+        const uint8_t xi0 = min(31, (int8_t)(x0 + 16.5f));
+        const uint8_t xi1 = min(31, (int8_t)(x1 + 16.5f));
+
+        dsti->qs[j]  = (xi0 & 0xf) | ((xi1 & 0xf) << 4);
+        qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
+        qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_0/2);
+    }
+    memcpy(dsti->qh, &qh, sizeof(qh));
+}
+
+static __device__ void cpy_blck_f32_q5_1(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    block_q5_1 * dsti = (block_q5_1 *) cdsti;
+
+    float min = xi[0];
+    float max = xi[0];
+
+    for (int j = 1; j < QK5_1; ++j) {
+        const float v = xi[j];
+        min = v < min ? v : min;
+        max = v > max ? v : max;
+    }
+
+    const float d  = (max - min) / 31;
+    const float id = d ? 1.0f/d : 0.0f;
+
+    dsti->dm.x = d;
+    dsti->dm.y = min;
+
+    uint32_t qh = 0;
+    for (int j = 0; j < QK5_1/2; ++j) {
+        const float x0 = (xi[0       + j] - min)*id;
+        const float x1 = (xi[QK5_1/2 + j] - min)*id;
+
+        const uint8_t xi0 = (uint8_t)(x0 + 0.5f);
+        const uint8_t xi1 = (uint8_t)(x1 + 0.5f);
+
+        dsti->qs[j]  = (xi0 & 0xf) | ((xi1 & 0xf) << 4);
+        qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
+        qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_1/2);
+    }
+    memcpy(dsti->qh, &qh, sizeof(qh));
+}
+
+
+static __device__ __forceinline__ int best_index_int8(int n, const int8_t * val, float x) {
+    if (x <= val[0]) return 0;
+    if (x >= val[n-1]) return n-1;
+    int ml = 0, mu = n-1;
+    while (mu-ml > 1) {
+        int mav = (ml+mu)/2;
+        if (x < val[mav]) mu = mav; else ml = mav;
+    }
+    return x - val[mu-1] < val[mu] - x ? mu-1 : mu;
+}
+
+static __device__ void cpy_blck_f32_iq4_nl(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    block_iq4_nl * dsti = (block_iq4_nl *) cdsti;
+
+    float amax = 0.0f;
+    float vmax = 0.0f;
+
+    for (int j = 0; j < QK4_NL; ++j) {
+        const float v = xi[j];
+        if (amax < fabsf(v)) {
+            amax = fabsf(v);
+            vmax = v;
+        }
+    }
+
+    float d = vmax / kvalues_iq4nl[0];
+    const float id = d ? 1.0f/d : 0.0f;
+
+    float sumqx = 0, sumq2 = 0;
+    for (int j = 0; j < QK4_NL/2; ++j) {
+        const float x0 = xi[0        + j]*id;
+        const float x1 = xi[QK4_NL/2 + j]*id;
+        const uint8_t xi0 = best_index_int8(16, kvalues_iq4nl, x0);
+        const uint8_t xi1 = best_index_int8(16, kvalues_iq4nl, x1);
+        dsti->qs[j] = xi0 | (xi1 << 4);
+        const float v0 = kvalues_iq4nl[xi0];
+        const float v1 = kvalues_iq4nl[xi1];
+        const float w0 = xi[0        + j]*xi[0        + j];
+        const float w1 = xi[QK4_NL/2 + j]*xi[QK4_NL/2 + j];
+        sumqx += w0*v0*xi[j] + w1*v1*xi[QK4_NL/2 + j];
+        sumq2 += w0*v0*v0 + w1*v1*v1;
+    }
+
+    dsti->d = sumq2 > 0 ? sumqx/sumq2 : d;
+}
+
+template <cpy_kernel_t cpy_blck, int qk>
+static __global__ void cpy_f32_q(const char * cx, char * cdst, const int ne,
+                                 const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+                                 const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                 const int nb12, const int nb13) {
+    const int i = (blockDim.x*blockIdx.x + threadIdx.x)*qk;
+
+    if (i >= ne) {
+        return;
+    }
+
+    const int i03 = i/(ne00 * ne01 * ne02);
+    const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
+    const int i01 = (i - i03*ne00*ne01*ne02  -  i02*ne01*ne00) / ne00;
+    const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
+    const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
+
+    const int i13 = i/(ne10 * ne11 * ne12);
+    const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
+    const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
+    const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
+    const int dst_offset = (i10/qk)*nb10 + i11*nb11 + i12*nb12 + i13*nb13;
+
+    cpy_blck(cx + x_offset, cdst + dst_offset);
+}
+
+static void ggml_cpy_f16_f32_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
+
+    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
+    cpy_f32_f16<cpy_1_f16_f32><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+}
+
+static void ggml_cpy_f32_f32_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
+
+    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
+    cpy_f32_f16<cpy_1_f32_f32><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+}
+
+static void ggml_cpy_f32_f16_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
+
+    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
+    cpy_f32_f16<cpy_1_f32_f16><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+}
+
+static void ggml_cpy_f32_q8_0_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
+
+    GGML_ASSERT(ne % QK8_0 == 0);
+    const int num_blocks = ne / QK8_0;
+    cpy_f32_q<cpy_blck_f32_q8_0, QK8_0><<<num_blocks, 1, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+}
+
+static void ggml_cpy_f32_q4_0_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
+
+    GGML_ASSERT(ne % QK4_0 == 0);
+    const int num_blocks = ne / QK4_0;
+    cpy_f32_q<cpy_blck_f32_q4_0, QK4_0><<<num_blocks, 1, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+}
+
+static void ggml_cpy_f32_q4_1_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
+
+    GGML_ASSERT(ne % QK4_1 == 0);
+    const int num_blocks = ne / QK4_1;
+    cpy_f32_q<cpy_blck_f32_q4_1, QK4_1><<<num_blocks, 1, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+}
+
+static void ggml_cpy_f32_q5_0_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
+
+    GGML_ASSERT(ne % QK5_0 == 0);
+    const int num_blocks = ne / QK5_0;
+    cpy_f32_q<cpy_blck_f32_q5_0, QK5_0><<<num_blocks, 1, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+}
+
+static void ggml_cpy_f32_q5_1_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
+
+    GGML_ASSERT(ne % QK5_1 == 0);
+    const int num_blocks = ne / QK5_1;
+    cpy_f32_q<cpy_blck_f32_q5_1, QK5_1><<<num_blocks, 1, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+}
+
+static void ggml_cpy_f32_iq4_nl_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
+
+    GGML_ASSERT(ne % QK4_NL == 0);
+    const int num_blocks = ne / QK4_NL;
+    cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL><<<num_blocks, 1, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+}
+
+static void ggml_cpy_f16_f16_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
+
+    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
+    cpy_f32_f16<cpy_1_f16_f16><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+}
+
+void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1) {
+    const int64_t ne = ggml_nelements(src0);
+    GGML_ASSERT(ne == ggml_nelements(src1));
+
+    GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX);
+    GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+
+    //GGML_ASSERT(src0->ne[3] == 1);
+
+    const int64_t nb00 = src0->nb[0];
+    const int64_t nb01 = src0->nb[1];
+    const int64_t nb02 = src0->nb[2];
+    const int64_t nb03 = src0->nb[3];
+
+    const int64_t ne10 = src1->ne[0];
+    const int64_t ne11 = src1->ne[1];
+    const int64_t ne12 = src1->ne[2];
+
+    //GGML_ASSERT(src1->ne[3] == 1);
+
+    const int64_t nb10 = src1->nb[0];
+    const int64_t nb11 = src1->nb[1];
+    const int64_t nb12 = src1->nb[2];
+    const int64_t nb13 = src1->nb[3];
+
+    cudaStream_t main_stream = ctx.stream();
+
+    char * src0_ddc = (char *) src0->data;
+    char * src1_ddc = (char *) src1->data;
+
+    if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
+        ggml_cpy_f32_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
+        ggml_cpy_f32_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
+        ggml_cpy_f32_q8_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
+        ggml_cpy_f32_q4_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
+        ggml_cpy_f32_q4_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_0) {
+        ggml_cpy_f32_q5_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_IQ4_NL) {
+        ggml_cpy_f32_iq4_nl_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_1) {
+        ggml_cpy_f32_q5_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
+        ggml_cpy_f16_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
+        ggml_cpy_f16_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else {
+        fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
+                ggml_type_name(src0->type), ggml_type_name(src1->type));
+        GGML_ASSERT(false);
+    }
+}
+
+void ggml_cuda_dup(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    ggml_cuda_cpy(ctx, src0, dst);
+}
+
+void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) {
+    if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
+            return (void*) cpy_f32_f16<cpy_1_f32_f32>;
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
+            return (void*) cpy_f32_f16<cpy_1_f32_f16>;
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
+            return (void*) cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>;
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
+            return (void*) cpy_f32_q<cpy_blck_f32_q4_0, QK4_0>;
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
+            return (void*) cpy_f32_q<cpy_blck_f32_q4_1, QK4_1>;
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_0) {
+            return (void*) cpy_f32_q<cpy_blck_f32_q5_0, QK5_0>;
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_IQ4_NL) {
+            return (void*) cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL>;
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_1) {
+            return (void*) cpy_f32_q<cpy_blck_f32_q5_1, QK5_1>;
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
+            return (void*) cpy_f32_f16<cpy_1_f32_f16>;
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
+            return (void*) cpy_f32_f16<cpy_1_f16_f32>;
+    } else {
+        fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
+                ggml_type_name(src0->type), ggml_type_name(src1->type));
+        GGML_ASSERT(false);
+    }
+}
+
diff --git a/ggml-cuda/cpy.cuh b/ggml-cuda/cpy.cuh
new file mode 100644
index 000000000..796167426
--- /dev/null
+++ b/ggml-cuda/cpy.cuh
@@ -0,0 +1,9 @@
+#include "common.cuh"
+
+#define CUDA_CPY_BLOCK_SIZE 32
+
+void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1);
+
+void ggml_cuda_dup(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1);
diff --git a/ggml-cuda/dequantize.cuh b/ggml-cuda/dequantize.cuh
new file mode 100644
index 000000000..bd3c2d9db
--- /dev/null
+++ b/ggml-cuda/dequantize.cuh
@@ -0,0 +1,103 @@
+#include "common.cuh"
+
+static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
+    const block_q4_0 * x = (const block_q4_0 *) vx;
+
+    const dfloat d = x[ib].d;
+
+    const int vui = x[ib].qs[iqs];
+
+    v.x = vui & 0xF;
+    v.y = vui >> 4;
+
+#ifdef GGML_CUDA_F16
+    v = __hsub2(v, {8.0f, 8.0f});
+    v = __hmul2(v, {d, d});
+#else
+    v.x = (v.x - 8.0f) * d;
+    v.y = (v.y - 8.0f) * d;
+#endif // GGML_CUDA_F16
+}
+
+static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
+    const block_q4_1 * x = (const block_q4_1 *) vx;
+
+    const dfloat d = __low2half(x[ib].dm);
+    const dfloat m = __high2half(x[ib].dm);
+
+    const int vui = x[ib].qs[iqs];
+
+    v.x = vui & 0xF;
+    v.y = vui >> 4;
+
+#ifdef GGML_CUDA_F16
+    v = __hmul2(v, {d, d});
+    v = __hadd2(v, {m, m});
+#else
+    v.x = (v.x * d) + m;
+    v.y = (v.y * d) + m;
+#endif // GGML_CUDA_F16
+}
+
+static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
+    const block_q5_0 * x = (const block_q5_0 *) vx;
+
+    const dfloat d = x[ib].d;
+
+    uint32_t qh;
+    memcpy(&qh, x[ib].qh, sizeof(qh));
+
+    const int xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
+    const int xh_1 = ((qh >> (iqs + 12))     ) & 0x10;
+
+    v.x = ((x[ib].qs[iqs] & 0xf) | xh_0);
+    v.y = ((x[ib].qs[iqs] >>  4) | xh_1);
+
+#ifdef GGML_CUDA_F16
+    v = __hsub2(v, {16.0f, 16.0f});
+    v = __hmul2(v, {d, d});
+#else
+    v.x = (v.x - 16.0f) * d;
+    v.y = (v.y - 16.0f) * d;
+#endif // GGML_CUDA_F16
+}
+
+static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
+    const block_q5_1 * x = (const block_q5_1 *) vx;
+
+    const dfloat d = __low2half(x[ib].dm);
+    const dfloat m = __high2half(x[ib].dm);
+
+    uint32_t qh;
+    memcpy(&qh, x[ib].qh, sizeof(qh));
+
+    const int xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
+    const int xh_1 = ((qh >> (iqs + 12))     ) & 0x10;
+
+    v.x = ((x[ib].qs[iqs] & 0xf) | xh_0);
+    v.y = ((x[ib].qs[iqs] >>  4) | xh_1);
+
+#ifdef GGML_CUDA_F16
+    v = __hmul2(v, {d, d});
+    v = __hadd2(v, {m, m});
+#else
+    v.x = (v.x * d) + m;
+    v.y = (v.y * d) + m;
+#endif // GGML_CUDA_F16
+}
+
+static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
+    const block_q8_0 * x = (const block_q8_0 *) vx;
+
+    const dfloat d = x[ib].d;
+
+    v.x = x[ib].qs[iqs + 0];
+    v.y = x[ib].qs[iqs + 1];
+
+#ifdef GGML_CUDA_F16
+    v = __hmul2(v, {d, d});
+#else
+    v.x *= d;
+    v.y *= d;
+#endif // GGML_CUDA_F16
+}
diff --git a/ggml-cuda/diagmask.cu b/ggml-cuda/diagmask.cu
new file mode 100644
index 000000000..4b713ba22
--- /dev/null
+++ b/ggml-cuda/diagmask.cu
@@ -0,0 +1,40 @@
+#include "diagmask.cuh"
+
+static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past) {
+    const int col = blockDim.y*blockIdx.y + threadIdx.y;
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (col >= ncols) {
+        return;
+    }
+
+    const int i = row*ncols + col;
+    //dst[i] = col > (n_past + row % rows_per_channel) ? -INFINITY : x[i];
+    //dst[i] = x[i] - (col > n_past + row % rows_per_channel) * INT_MAX; // equivalent within rounding error but slightly faster on GPU
+    dst[i] = x[i] - (col > n_past + row % rows_per_channel) * FLT_MAX;
+}
+
+static void diag_mask_inf_f32_cuda(const float * x, float * dst, const int ncols_x, const int nrows_x, const int rows_per_channel, const int n_past, cudaStream_t stream) {
+    const dim3 block_dims(1, CUDA_DIAG_MASK_INF_BLOCK_SIZE, 1);
+    const int block_num_x = (ncols_x + CUDA_DIAG_MASK_INF_BLOCK_SIZE - 1) / CUDA_DIAG_MASK_INF_BLOCK_SIZE;
+    const dim3 block_nums(nrows_x, block_num_x, 1);
+    diag_mask_inf_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols_x, rows_per_channel, n_past);
+}
+
+void ggml_cuda_op_diag_mask_inf(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int nrows0 = ggml_nrows(src0);
+
+    const int n_past = ((int32_t *) dst->op_params)[0];
+
+    diag_mask_inf_f32_cuda(src0_d, dst_d, ne00, nrows0, ne01, n_past, stream);
+}
diff --git a/ggml-cuda/diagmask.cuh b/ggml-cuda/diagmask.cuh
new file mode 100644
index 000000000..6cdbef17e
--- /dev/null
+++ b/ggml-cuda/diagmask.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32
+
+void ggml_cuda_op_diag_mask_inf(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/dmmv.cu b/ggml-cuda/dmmv.cu
new file mode 100644
index 000000000..7313e3e17
--- /dev/null
+++ b/ggml-cuda/dmmv.cu
@@ -0,0 +1,813 @@
+#include "dmmv.cuh"
+#include "dequantize.cuh"
+#include "convert.cuh"
+
+#ifndef K_QUANTS_PER_ITERATION
+#define K_QUANTS_PER_ITERATION 2
+#else
+static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
+#endif
+
+static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
+
+    static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
+
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
+    if (row > nrows) return;
+
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q2_K * x = (const block_q2_K *)vx + ib0;
+
+    float tmp = 0; // partial sum for thread in warp
+
+#if QK_K == 256
+    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...15
+    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0,1
+
+    const int step = 16/K_QUANTS_PER_ITERATION;
+
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0...15 or 0...7
+
+    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15 or 0...14 in steps of 2
+    const int q_offset = 32*im + l0;
+    const int s_offset = 8*im;
+    const int y_offset = 128*im + l0;
+
+    uint32_t aux[4];
+    const uint8_t * d = (const uint8_t *)aux;
+    const uint8_t * m = (const uint8_t *)(aux + 2);
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        const float   * y = yy + i * QK_K + y_offset;
+        const uint8_t * q = x[i].qs + q_offset;
+
+        const float dall = __low2half(x[i].dm);
+        const float dmin = __high2half(x[i].dm);
+
+        const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset);
+        aux[0] = a[0] & 0x0f0f0f0f;
+        aux[1] = a[1] & 0x0f0f0f0f;
+        aux[2] = (a[0] >> 4) & 0x0f0f0f0f;
+        aux[3] = (a[1] >> 4) & 0x0f0f0f0f;
+
+        float sum1 = 0, sum2 = 0;
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3)
+                  + y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3)
+                  + y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3)
+                  + y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3)
+                  + y[l+16] * d[1] * ((q[l+16] >> 0) & 3)
+                  + y[l+48] * d[3] * ((q[l+16] >> 2) & 3)
+                  + y[l+80] * d[5] * ((q[l+16] >> 4) & 3)
+                  +y[l+112] * d[7] * ((q[l+16] >> 6) & 3);
+            sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6]
+                  + y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7];
+
+        }
+        tmp += dall * sum1 - dmin * sum2;
+
+    }
+#else
+    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15 or 0...7
+    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0....1 or 0...3
+    const int offset = tid * K_QUANTS_PER_ITERATION;
+
+    uint32_t uaux[2];
+    const uint8_t * d = (const uint8_t *)uaux;
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+
+        const float   * y = yy + i * QK_K + offset;
+        const uint8_t * q = x[i].qs + offset;
+        const uint32_t * s = (const uint32_t *)x[i].scales;
+
+        uaux[0] = s[0] & 0x0f0f0f0f;
+        uaux[1] = (s[0] >> 4) & 0x0f0f0f0f;
+
+        const float2 dall = __half22float2(x[i].dm);
+
+        float sum1 = 0, sum2 = 0;
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            const uint8_t ql = q[l];
+            sum1 += y[l+ 0] * d[0] * ((ql >> 0) & 3)
+                  + y[l+16] * d[1] * ((ql >> 2) & 3)
+                  + y[l+32] * d[2] * ((ql >> 4) & 3)
+                  + y[l+48] * d[3] * ((ql >> 6) & 3);
+            sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7];
+        }
+        tmp += dall.x * sum1 - dall.y * sum2;
+    }
+#endif
+
+    // sum up partial sums and write back result
+    tmp = warp_reduce_sum(tmp);
+
+    if (threadIdx.x == 0) {
+        dst[row] = tmp;
+    }
+}
+
+static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
+
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
+    if (row > nrows) return;
+
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q3_K * x = (const block_q3_K *)vx + ib0;
+
+    float tmp = 0; // partial sum for thread in warp
+
+#if QK_K == 256
+
+    const uint16_t kmask1 = 0x0303;
+    const uint16_t kmask2 = 0x0f0f;
+
+    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0,1
+
+    const int n  = K_QUANTS_PER_ITERATION;               // iterations in the inner loop
+    const int step = 16/K_QUANTS_PER_ITERATION;
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0....15 or 0...7
+
+    const uint8_t m = 1 << (4*im);
+
+    const int l0 = n*in;                                 // 0...15 or 0...14 in steps of 2
+    const int q_offset =  32*im + l0;
+    const int y_offset = 128*im + l0;
+
+    uint16_t utmp[4];
+    const int8_t * s = (const int8_t *)utmp;
+
+    const uint16_t s_shift = 4*im;
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        const float   * y  = yy + i * QK_K + y_offset;
+        const uint8_t * q = x[i].qs + q_offset;
+        const uint8_t * h = x[i].hmask + l0;
+
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4);
+        utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4);
+        utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4);
+        utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4);
+
+        const float d = x[i].d;
+
+        float sum = 0;
+        for (int l = 0; l < n; ++l) {
+            sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4))
+                 + y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4))
+                 + y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4))
+                 + y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4));
+            sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4))
+                 + y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4))
+                 + y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4))
+                + y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4));
+        }
+        tmp += d * sum;
+
+    }
+#else
+
+    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15 or 0...7
+    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0....1 or 0...3
+    const int offset = tid * K_QUANTS_PER_ITERATION;         // 0...15 or 0...14
+    const int in = offset/8;                                 // 0 or 1
+    const int im = offset%8;                                 // 0...7
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+
+        const float   * y = yy + i * QK_K + offset;
+        const uint8_t * q = x[i].qs + offset;
+        const uint8_t * s = x[i].scales;
+
+        const float dall = (float)x[i].d;
+
+        float sum = 0;
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            const uint8_t hl = x[i].hmask[im+l] >> in;
+            const uint8_t ql = q[l];
+            sum += y[l+ 0] * dall * ((s[0] & 0xF) - 8) * ((int8_t)((ql >> 0) & 3) - ((hl >> 0) & 1 ? 0 : 4))
+                 + y[l+16] * dall * ((s[0] >>  4) - 8) * ((int8_t)((ql >> 2) & 3) - ((hl >> 2) & 1 ? 0 : 4))
+                 + y[l+32] * dall * ((s[1] & 0xF) - 8) * ((int8_t)((ql >> 4) & 3) - ((hl >> 4) & 1 ? 0 : 4))
+                 + y[l+48] * dall * ((s[1] >>  4) - 8) * ((int8_t)((ql >> 6) & 3) - ((hl >> 6) & 1 ? 0 : 4));
+        }
+        tmp += sum;
+    }
+#endif
+
+    // sum up partial sums and write back result
+    tmp = warp_reduce_sum(tmp);
+
+    if (threadIdx.x == 0) {
+        dst[row] = tmp;
+    }
+}
+
+static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
+
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
+    if (row > nrows) return;
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q4_K * x = (const block_q4_K *)vx + ib0;
+
+#if QK_K == 256
+    const uint16_t kmask1 = 0x3f3f;
+    const uint16_t kmask2 = 0x0f0f;
+    const uint16_t kmask3 = 0xc0c0;
+
+    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0,1
+
+    const int step = 8/K_QUANTS_PER_ITERATION;           // 8 or 4
+
+    const int il  = tid/step;                            // 0...3
+    const int ir  = tid - step*il;                       // 0...7 or 0...3
+    const int n   = 2 * K_QUANTS_PER_ITERATION;          // 2 or 4
+
+    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const int in = il%2;
+
+    const int l0 = n*(2*ir + in);
+    const int q_offset = 32*im + l0;
+    const int y_offset = 64*im + l0;
+
+    uint16_t aux[4];
+    const uint8_t * sc = (const uint8_t *)aux;
+
+#if K_QUANTS_PER_ITERATION == 2
+    uint32_t q32[4];
+    const uint8_t * q4 = (const uint8_t *)q32;
+#else
+    uint16_t q16[4];
+    const uint8_t * q4 = (const uint8_t *)q16;
+#endif
+
+    float tmp = 0; // partial sum for thread in warp
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        const float   * y1 = yy + i*QK_K + y_offset;
+        const float   * y2 = y1 + 128;
+
+        const float dall = __low2half(x[i].dm);
+        const float dmin = __high2half(x[i].dm);
+
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        aux[0] = a[im+0] & kmask1;
+        aux[1] = a[im+2] & kmask1;
+        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
+        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
+
+#if K_QUANTS_PER_ITERATION == 2
+        const uint32_t * q1 = (const uint32_t *)(x[i].qs + q_offset);
+        const uint32_t * q2 = q1 + 16;
+
+        q32[0] = q1[0] & 0x0f0f0f0f;
+        q32[1] = q1[0] & 0xf0f0f0f0;
+        q32[2] = q2[0] & 0x0f0f0f0f;
+        q32[3] = q2[0] & 0xf0f0f0f0;
+
+        float4 s = {0.f, 0.f, 0.f, 0.f};
+        float smin = 0;
+        for (int l = 0; l < 4; ++l) {
+            s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+ 4];
+            s.z += y2[l] * q4[l+8]; s.w += y2[l+32] * q4[l+12];
+            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
+        }
+        tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin;
+#else
+        const uint16_t * q1 = (const uint16_t *)(x[i].qs + q_offset);
+        const uint16_t * q2 = q1 + 32;
+
+        q16[0] = q1[0] & 0x0f0f;
+        q16[1] = q1[0] & 0xf0f0;
+        q16[2] = q2[0] & 0x0f0f;
+        q16[3] = q2[0] & 0xf0f0;
+
+        float4 s = {0.f, 0.f, 0.f, 0.f};
+        float smin = 0;
+        for (int l = 0; l < 2; ++l) {
+            s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+2];
+            s.z += y2[l] * q4[l+4]; s.w += y2[l+32] * q4[l+6];
+            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
+        }
+        tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin;
+#endif
+
+    }
+#else
+    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15
+    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);
+
+    const int step = tid * K_QUANTS_PER_ITERATION;
+
+    uint16_t aux16[2];
+    const uint8_t * s = (const uint8_t *)aux16;
+
+    float tmp = 0;
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+        const uint8_t * q = x[i].qs + step;
+        const float   * y = yy + i*QK_K + step;
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        aux16[0] = a[0] & 0x0f0f;
+        aux16[1] = (a[0] >> 4) & 0x0f0f;
+        const float d = (float)x[i].dm[0];
+        const float m = (float)x[i].dm[1];
+        float sum = 0.f;
+        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
+            sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2])
+                 + y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2])
+                 + y[j+32] * (d * s[1] * (q[j+ 0] >>  4) - m * s[3])
+                 + y[j+48] * (d * s[1] * (q[j+16] >>  4) - m * s[3]);
+        }
+        tmp += sum;
+    }
+
+#endif
+
+    // sum up partial sums and write back result
+    tmp = warp_reduce_sum(tmp);
+
+    if (tid == 0) {
+        dst[row] = tmp;
+    }
+}
+
+static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols) {
+
+    const int row = blockIdx.x;
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q5_K * x = (const block_q5_K *)vx + ib0;
+
+    float tmp = 0; // partial sum for thread in warp
+
+#if QK_K == 256
+    const uint16_t kmask1 = 0x3f3f;
+    const uint16_t kmask2 = 0x0f0f;
+    const uint16_t kmask3 = 0xc0c0;
+
+    const int tid = threadIdx.x/2;  // 0...15
+    const int ix  = threadIdx.x%2;
+
+    const int il  = tid/4;     // 0...3
+    const int ir  = tid - 4*il;// 0...3
+    const int n   = 2;
+
+    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const int in = il%2;
+
+    const int l0 = n*(2*ir + in);
+    const int q_offset = 32*im + l0;
+    const int y_offset = 64*im + l0;
+
+    const uint8_t hm1  = 1 << (2*im);
+    const uint8_t hm2  = hm1 << 4;
+
+    uint16_t aux[4];
+    const uint8_t * sc = (const uint8_t *)aux;
+
+    uint16_t q16[8];
+    const uint8_t * q4 = (const uint8_t *)q16;
+
+    for (int i = ix; i < num_blocks_per_row; i += 2) {
+
+        const uint8_t * ql1 = x[i].qs + q_offset;
+        const uint8_t * qh  = x[i].qh + l0;
+        const float   * y1  = yy + i*QK_K + y_offset;
+        const float   * y2  = y1 + 128;
+
+        const float dall = __low2half(x[i].dm);
+        const float dmin = __high2half(x[i].dm);
+
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        aux[0] = a[im+0] & kmask1;
+        aux[1] = a[im+2] & kmask1;
+        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
+        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
+
+        float4 sum = {0.f, 0.f, 0.f, 0.f};
+        float smin = 0;
+        const uint16_t * q1 = (const uint16_t *)ql1;
+        const uint16_t * q2 = q1 + 32;
+        q16[0] = q1[0] & 0x0f0f;
+        q16[1] = q1[8] & 0x0f0f;
+        q16[2] = (q1[0] >> 4) & 0x0f0f;
+        q16[3] = (q1[8] >> 4) & 0x0f0f;
+        q16[4] = q2[0] & 0x0f0f;
+        q16[5] = q2[8] & 0x0f0f;
+        q16[6] = (q2[0] >> 4) & 0x0f0f;
+        q16[7] = (q2[8] >> 4) & 0x0f0f;
+        for (int l = 0; l < n; ++l) {
+            sum.x += y1[l+ 0] * (q4[l +0] + (qh[l+ 0] & (hm1 << 0) ? 16 : 0))
+                   + y1[l+16] * (q4[l +2] + (qh[l+16] & (hm1 << 0) ? 16 : 0));
+            sum.y += y1[l+32] * (q4[l +4] + (qh[l+ 0] & (hm1 << 1) ? 16 : 0))
+                   + y1[l+48] * (q4[l +6] + (qh[l+16] & (hm1 << 1) ? 16 : 0));
+            sum.z += y2[l+ 0] * (q4[l +8] + (qh[l+ 0] & (hm2 << 0) ? 16 : 0))
+                   + y2[l+16] * (q4[l+10] + (qh[l+16] & (hm2 << 0) ? 16 : 0));
+            sum.w += y2[l+32] * (q4[l+12] + (qh[l+ 0] & (hm2 << 1) ? 16 : 0))
+                   + y2[l+48] * (q4[l+14] + (qh[l+16] & (hm2 << 1) ? 16 : 0));
+            smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3]
+                  + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7];
+        }
+        tmp += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin;
+    }
+
+#else
+    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15
+    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);
+    const int step = tid * K_QUANTS_PER_ITERATION;
+    const int im = step/8;
+    const int in = step%8;
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+        const uint8_t * q = x[i].qs + step;
+        const int8_t  * s = x[i].scales;
+        const float   * y = yy + i*QK_K + step;
+        const float     d = x[i].d;
+        float sum = 0.f;
+        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
+            const uint8_t h = x[i].qh[in+j] >> im;
+            sum += y[j+ 0] * d * s[0] * ((q[j+ 0] & 0xF) - ((h >> 0) & 1 ? 0 : 16))
+                 + y[j+16] * d * s[1] * ((q[j+16] & 0xF) - ((h >> 2) & 1 ? 0 : 16))
+                 + y[j+32] * d * s[2] * ((q[j+ 0] >>  4) - ((h >> 4) & 1 ? 0 : 16))
+                 + y[j+48] * d * s[3] * ((q[j+16] >>  4) - ((h >> 6) & 1 ? 0 : 16));
+        }
+        tmp += sum;
+    }
+#endif
+
+    // sum up partial sums and write back result
+    tmp = warp_reduce_sum(tmp);
+
+    if (threadIdx.x == 0) {
+        dst[row] = tmp;
+    }
+}
+
+static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
+
+    static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
+
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
+    if (row > nrows) return;
+
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q6_K * x = (const block_q6_K *)vx + ib0;
+
+#if QK_K == 256
+
+    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const int step = 16/K_QUANTS_PER_ITERATION;          // 16 or 8
+
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0...15 or 0...7
+
+#if K_QUANTS_PER_ITERATION == 1
+    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15
+    const int is = 0;
+#else
+    const int l0 = 4 * in;                               // 0, 4, 8, ..., 28
+    const int is = in / 4;
+#endif
+    const int ql_offset = 64*im + l0;
+    const int qh_offset = 32*im + l0;
+    const int s_offset  =  8*im + is;
+    const int y_offset = 128*im + l0;
+
+    float tmp = 0; // partial sum for thread in warp
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        const float   * y  = yy + i * QK_K + y_offset;
+        const uint8_t * ql = x[i].ql + ql_offset;
+        const uint8_t * qh = x[i].qh + qh_offset;
+        const int8_t  * s  = x[i].scales + s_offset;
+
+        const float d = x[i].d;
+
+#if K_QUANTS_PER_ITERATION == 1
+        float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
+                  + y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
+                  + y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
+                  + y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
+                  + y[64] * s[4] * d * ((int8_t)((ql[ 0]  >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
+                  + y[80] * s[5] * d * ((int8_t)((ql[16]  >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
+                  + y[96] * s[6] * d * ((int8_t)((ql[32]  >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
+                  +y[112] * s[7] * d * ((int8_t)((ql[48]  >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
+        tmp += sum;
+#else
+        float sum = 0;
+        for (int l = 0; l < 4; ++l) {
+            sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
+                 + y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
+                 + y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0]  >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
+                 + y[l+96] * s[6] * d * ((int8_t)((ql[l+32]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
+        }
+        tmp += sum;
+#endif
+
+    }
+
+#else
+
+    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...7
+    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0...3
+
+    const int step = tid * K_QUANTS_PER_ITERATION;
+
+    float tmp = 0; // partial sum for thread in warp
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+
+        const float   * y  = yy + i * QK_K + step;
+        const uint8_t * ql = x[i].ql + step;
+        const uint8_t * qh = x[i].qh + step;
+        const int8_t  * s  = x[i].scales;
+
+        const float d = x[i+0].d;
+
+        float sum = 0;
+        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
+            sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32)
+                 + y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32)
+                 + y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >>  4) | ((qh[j] & 0x30) >> 0)) - 32)
+                 + y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >>  4) | ((qh[j] & 0xc0) >> 2)) - 32);
+        }
+        tmp += sum;
+
+    }
+
+#endif
+
+    // sum up partial sums and write back result
+    tmp = warp_reduce_sum(tmp);
+
+    if (tid == 0) {
+        dst[row] = tmp;
+    }
+}
+
+static __device__ void convert_f16(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
+    const half * x = (const half *) vx;
+
+    // automatic half -> float type cast if dfloat == float
+    v.x = x[ib + iqs + 0];
+    v.y = x[ib + iqs + 1];
+}
+
+template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
+static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows) {
+    // qk = quantized weights per x block
+    // qr = number of quantized weights per data value in x block
+    const int64_t row = (int64_t)blockIdx.x*blockDim.y + threadIdx.y;
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int tid = threadIdx.x;
+
+    const int iter_stride = 2*GGML_CUDA_DMMV_X;
+    const int vals_per_iter = iter_stride / WARP_SIZE; // num quantized vals per thread and i iter
+    const int y_offset = qr == 1 ? 1 : qk/2;
+
+// partial sum for each thread
+#ifdef GGML_CUDA_F16
+    half2 tmp = {0.0f, 0.0f}; // two sums for f16 to take advantage of half2 intrinsics
+#else
+    float tmp = 0.0f;
+#endif // GGML_CUDA_F16
+
+    for (int i = 0; i < ncols; i += iter_stride) {
+        const int col = i + vals_per_iter*tid;
+        const int64_t ib = ((int64_t)row*ncols + col)/qk; // x block index
+        const int iqs = (col%qk)/qr; // x quant index
+        const int iybs = col - col%qk; // y block start index
+
+// processing >2 values per i iter is faster for fast GPUs
+#pragma unroll
+        for (int j = 0; j < vals_per_iter; j += 2) {
+            // process 2 vals per j iter
+
+            // dequantize
+            // for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val
+            dfloat2 v;
+            dequantize_kernel(vx, ib, iqs + j/qr, v);
+
+            // matrix multiplication
+            // for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2
+#ifdef GGML_CUDA_F16
+            tmp += __hmul2(v, {
+                y[iybs + iqs + j/qr + 0],
+                y[iybs + iqs + j/qr + y_offset]
+            });
+#else
+            tmp += v.x * y[iybs + iqs + j/qr + 0];
+            tmp += v.y * y[iybs + iqs + j/qr + y_offset];
+#endif // GGML_CUDA_F16
+        }
+    }
+
+    // sum up partial sums and write back result
+    tmp = warp_reduce_sum(tmp);
+
+    if (tid == 0) {
+#ifdef GGML_CUDA_F16
+        dst[row] = tmp.x + tmp.y;
+#else
+        dst[row] = tmp;
+#endif // GGML_CUDA_F16
+    }
+}
+
+static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>
+        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>
+        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>
+        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q2_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(32, ny, 1);
+    dequantize_mul_mat_vec_q2_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q3_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int ny = 2 / K_QUANTS_PER_ITERATION;
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(32, ny, 1);
+    dequantize_mul_mat_vec_q3_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q4_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int ny = 2 / K_QUANTS_PER_ITERATION;
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(32, ny, 1);
+    dequantize_mul_mat_vec_q4_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q5_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const dim3 block_dims(32, 1, 1);
+    dequantize_mul_mat_vec_q5_k<<<nrows, block_dims, 0, stream>>>(vx, y, dst, ncols);
+}
+
+static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int ny = 2 / K_QUANTS_PER_ITERATION;
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(32, ny, 1);
+    dequantize_mul_mat_vec_q6_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    dequantize_mul_mat_vec<1, 1, convert_f16>
+        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+void ggml_cuda_op_dequantize_mul_mat_vec(
+    ggml_backend_cuda_context & ctx,
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, cudaStream_t stream) {
+    GGML_UNUSED(ctx);
+    const int64_t ne00 = src0->ne[0];
+    const int64_t row_diff = row_high - row_low;
+
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
+#ifdef GGML_CUDA_F16
+    ggml_cuda_pool_alloc<half> src1_dfloat_a(ctx.pool());
+    half * src1_dfloat = nullptr; // dfloat == half
+
+    bool src1_convert_f16 =
+        src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 ||
+        src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 ||
+        src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16;
+
+    if (src1_convert_f16) {
+        src1_dfloat = src1_dfloat_a.alloc(ne00);
+        const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
+        GGML_ASSERT(to_fp16_cuda != nullptr);
+        to_fp16_cuda(src1_ddf_i, src1_dfloat, ne00, stream);
+    }
+#else
+    const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion
+#endif // GGML_CUDA_F16
+
+    switch (src0->type) {
+        case GGML_TYPE_Q4_0:
+            dequantize_mul_mat_vec_q4_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            dequantize_mul_mat_vec_q4_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            dequantize_mul_mat_vec_q5_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            dequantize_mul_mat_vec_q5_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            dequantize_mul_mat_vec_q8_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q2_K:
+            dequantize_mul_mat_vec_q2_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q3_K:
+            dequantize_mul_mat_vec_q3_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q4_K:
+            dequantize_mul_mat_vec_q4_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_K:
+            dequantize_mul_mat_vec_q5_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q6_K:
+            dequantize_mul_mat_vec_q6_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_F16:
+            convert_mul_mat_vec_f16_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_ddq_i);
+    GGML_UNUSED(src1_ncols);
+    GGML_UNUSED(src1_padded_row_size);
+}
diff --git a/ggml-cuda/dmmv.cuh b/ggml-cuda/dmmv.cuh
new file mode 100644
index 000000000..4c5ebd475
--- /dev/null
+++ b/ggml-cuda/dmmv.cuh
@@ -0,0 +1,18 @@
+#include "common.cuh"
+
+// dmmv = dequantize_mul_mat_vec
+
+// TODO: remove this?
+#ifndef GGML_CUDA_DMMV_X
+#define GGML_CUDA_DMMV_X 32
+#endif
+
+#ifndef GGML_CUDA_MMV_Y
+#define GGML_CUDA_MMV_Y 1
+#endif
+
+void ggml_cuda_op_dequantize_mul_mat_vec(
+    ggml_backend_cuda_context & ctx,
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, cudaStream_t stream);
diff --git a/ggml-cuda/fattn-common.cuh b/ggml-cuda/fattn-common.cuh
new file mode 100644
index 000000000..33f640691
--- /dev/null
+++ b/ggml-cuda/fattn-common.cuh
@@ -0,0 +1,47 @@
+#define FATTN_KQ_STRIDE       256
+#define HALF_MAX_HALF         __float2half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction.
+#define SOFTMAX_FTZ_THRESHOLD -20.0f                   // Softmax exp. of values smaller than this are flushed to zero to avoid NaNs.
+
+template<int D, int parallel_blocks> // D == head size
+#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+__launch_bounds__(D, 1)
+#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+static __global__ void flash_attn_combine_results(
+        const float  * __restrict__ VKQ_parts,
+        const float2 * __restrict__ VKQ_meta,
+        float * __restrict__ dst) {
+    VKQ_parts += parallel_blocks*D * gridDim.y*blockIdx.x;
+    VKQ_meta  += parallel_blocks   * gridDim.y*blockIdx.x;
+    dst       +=                 D * gridDim.y*blockIdx.x;
+
+    const int tid = threadIdx.x;
+    __builtin_assume(tid < D);
+
+    __shared__ float2 meta[parallel_blocks];
+    if (tid < 2*parallel_blocks) {
+        ((float *) meta)[threadIdx.x] = ((const float *)VKQ_meta) [blockIdx.y*(2*parallel_blocks) + tid];
+    }
+
+    __syncthreads();
+
+    float kqmax = meta[0].x;
+#pragma unroll
+    for (int l = 1; l < parallel_blocks; ++l) {
+        kqmax = max(kqmax, meta[l].x);
+    }
+
+    float VKQ_numerator   = 0.0f;
+    float VKQ_denominator = 0.0f;
+#pragma unroll
+    for (int l = 0; l < parallel_blocks; ++l) {
+        const float diff = meta[l].x - kqmax;
+        const float KQ_max_scale = expf(diff);
+        const uint32_t ftz_mask = 0xFFFFFFFF * (diff > SOFTMAX_FTZ_THRESHOLD);
+        *((uint32_t *) &KQ_max_scale) &= ftz_mask;
+
+        VKQ_numerator   += KQ_max_scale * VKQ_parts[l*gridDim.y*D + blockIdx.y*D + tid];
+        VKQ_denominator += KQ_max_scale * meta[l].y;
+    }
+
+    dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator;
+}
diff --git a/ggml-cuda/fattn-vec-f16.cu b/ggml-cuda/fattn-vec-f16.cu
new file mode 100644
index 000000000..cbf5f7835
--- /dev/null
+++ b/ggml-cuda/fattn-vec-f16.cu
@@ -0,0 +1,430 @@
+#include "common.cuh"
+#include "fattn-common.cuh"
+#include "fattn-vec-f16.cuh"
+
+template<int D, int ncols, int parallel_blocks> // D == head size
+#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+__launch_bounds__(D, 1)
+#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+static __global__ void flash_attn_vec_ext_f16(
+        const char * __restrict__ Q,
+        const char * __restrict__ K,
+        const char * __restrict__ V,
+        const char * __restrict__ mask,
+        float      * __restrict__ dst,
+        float2     * __restrict__ dst_meta,
+        const float scale,
+        const float max_bias,
+        const float m0,
+        const float m1,
+        const uint32_t n_head_log2,
+        const int ne00,
+        const int ne01,
+        const int ne02,
+        const int ne03,
+        const int ne10,
+        const int ne11,
+        const int ne12,
+        const int ne13,
+        const int ne31,
+        const int nb31,
+        const int nb01,
+        const int nb02,
+        const int nb03,
+        const int nb11,
+        const int nb12,
+        const int nb13,
+        const int ne0,
+        const int ne1,
+        const int ne2,
+        const int ne3) {
+#if FP16_AVAILABLE
+    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
+
+    const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
+    const int ip  =  blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
+
+    const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
+    const float2 * Q_f2  = (const float2 *) (Q    + nb02* blockIdx.y              + nb01*ic0);
+    const half2  * K_h2  = (const half2  *) (K    + nb12*(blockIdx.y / gqa_ratio));
+    const half   * V_h   = (const half   *) (V    + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
+    const half   * maskh = (const half   *)  mask + ne11*ic0;
+
+    const int stride_KV  = nb11 / sizeof(half);
+    const int stride_KV2 = nb11 / sizeof(half2);
+
+    half  slopeh = __float2half(1.0f);
+
+    // ALiBi
+    if (max_bias > 0.0f) {
+        const int h = blockIdx.y;
+
+        const float base = h < n_head_log2 ? m0 : m1;
+        const int   exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+        slopeh = __float2half(powf(base, exph));
+    }
+
+    static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64.");
+    constexpr int nwarps = D / WARP_SIZE;
+    const int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
+    __builtin_assume(tid < D);
+
+    __shared__ half KQ[ncols*D];
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        KQ[j*D + tid] = -HALF_MAX_HALF;
+    }
+    half2 * KQ2 = (half2 *) KQ;
+
+    half kqmax[ncols];
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        kqmax[j] = -HALF_MAX_HALF;
+    }
+    half kqsum[ncols] = {0.0f};
+
+    __shared__ half kqmax_shared[ncols][WARP_SIZE];
+    __shared__ half kqsum_shared[ncols][WARP_SIZE];
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        if (threadIdx.y == 0) {
+            kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF;
+            kqsum_shared[j][threadIdx.x] = 0.0f;
+        }
+    }
+    __syncthreads();
+
+    // Convert Q to half2 and store in registers:
+    half2 Q_h2[ncols][D/(2*WARP_SIZE)];
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+#pragma unroll
+        for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
+            const int i = i0 + threadIdx.x;
+
+            const float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i];
+            Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y);
+        }
+    }
+
+    half2 VKQ[ncols] = {{0.0f, 0.0f}};
+
+    const int k_start = parallel_blocks == 1 ? 0 : ip*D;
+    for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) {
+        // Calculate KQ tile and keep track of new maximum KQ values:
+
+        // For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression,
+        // see https://github.com/ggerganov/llama.cpp/pull/7061 .
+        // Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable).
+        half kqmax_new = kqmax[0];
+        half kqmax_new_arr[ncols];
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            kqmax_new_arr[j] = kqmax[j];
+        }
+
+#pragma unroll
+        for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) {
+            const int i_KQ = i_KQ_0 + threadIdx.y;
+
+            if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) {
+                break;
+            }
+
+            half2 sum2[ncols] = {{0.0f, 0.0f}};
+#pragma unroll
+            for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
+                const int k_KQ = k_KQ_0 + threadIdx.x;
+
+                const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ];
+#pragma unroll
+                for (int j = 0; j < ncols; ++j) {
+                    sum2[j] += K_ik * Q_h2[j][k_KQ_0/WARP_SIZE];
+                }
+            }
+
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+                sum2[j] = warp_reduce_sum(sum2[j]);
+                half sum = __low2half(sum2[j]) + __high2half(sum2[j]);
+                sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f);
+
+                if (ncols == 1) {
+                    kqmax_new        = ggml_cuda_hmax(kqmax_new,        sum);
+                } else {
+                    kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum);
+                }
+
+                if (threadIdx.x == 0) {
+                    KQ[j*D + i_KQ] = sum;
+                }
+            }
+        }
+
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j];
+
+            kqmax_new_j = warp_reduce_max(kqmax_new_j);
+            if (threadIdx.x == 0) {
+                kqmax_shared[j][threadIdx.y] = kqmax_new_j;
+            }
+        }
+
+        __syncthreads();
+
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            half kqmax_new_j = kqmax_shared[j][threadIdx.x];
+            kqmax_new_j = warp_reduce_max(kqmax_new_j);
+
+            const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j);
+            kqmax[j] = kqmax_new_j;
+
+            const half val = hexp(KQ[j*D + tid] - kqmax[j]);
+            kqsum[j] = kqsum[j]*KQ_max_scale + val;
+            KQ[j*D + tid] = val;
+
+            VKQ[j] *= __half2half2(KQ_max_scale);
+        }
+
+        __syncthreads();
+
+#pragma unroll
+        for (int k0 = 0; k0 < D; k0 += 2) {
+            if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) {
+                break;
+            }
+
+            half2 V_k;
+            reinterpret_cast<half&>(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid];
+            reinterpret_cast<half&>(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid];
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+                VKQ[j] += V_k*KQ2[j*(D/2) + k0/2];
+            }
+        }
+
+        __syncthreads();
+    }
+
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        kqsum[j] = warp_reduce_sum(kqsum[j]);
+        if (threadIdx.x == 0) {
+            kqsum_shared[j][threadIdx.y] = kqsum[j];
+        }
+    }
+
+    __syncthreads();
+
+#pragma unroll
+    for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) {
+        kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x];
+        kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]);
+
+        half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ]));
+        if (parallel_blocks == 1) {
+            dst_val /= kqsum[j_VKQ];
+        }
+        const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
+        dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val;
+    }
+
+    if (parallel_blocks != 1 && tid != 0) {
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            dst_meta[(ic0 + j)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j], kqsum[j]);
+        }
+    }
+#else
+   NO_DEVICE_CODE;
+#endif // FP16_AVAILABLE
+}
+
+template <int D, int cols_per_block, int parallel_blocks> void launch_fattn_vec_f16(
+        const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask,
+        ggml_cuda_pool & pool, cudaStream_t main_stream
+) {
+    ggml_cuda_pool_alloc<float>  dst_tmp(pool);
+    ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool);
+
+    if (parallel_blocks > 1) {
+        dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));
+        dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV));
+    }
+
+    constexpr int  nwarps = (D + WARP_SIZE - 1) / WARP_SIZE;
+    const     dim3 block_dim(WARP_SIZE, nwarps, 1);
+    const     dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]);
+    const     int  shmem = 0;
+
+    float scale    = 1.0f;
+    float max_bias = 0.0f;
+
+    memcpy(&scale,    (float *) KQV->op_params + 0, sizeof(float));
+    memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float));
+
+    const uint32_t n_head      = Q->ne[2];
+    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
+
+    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+    flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>
+        <<<blocks_num, block_dim, shmem, main_stream>>> (
+                (const char *) Q->data,
+                (const char *) K->data,
+                (const char *) V->data,
+                mask ? ((const char *) mask->data) : nullptr,
+                parallel_blocks == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr,
+                scale, max_bias, m0, m1, n_head_log2,
+                Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3],
+                K->ne[0], K->ne[1], K->ne[2], K->ne[3],
+                mask ? mask->ne[1] : 0, mask ?  mask->nb[1] : 0,
+                Q->nb[1], Q->nb[2], Q->nb[3],
+                K->nb[1], K->nb[2], K->nb[3],
+                KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3]
+                );
+    CUDA_CHECK(cudaGetLastError());
+
+    if (parallel_blocks == 1) {
+        return;
+    }
+
+    const dim3 block_dim_combine(D, 1, 1);
+    const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z);
+    const int  shmem_combine = 0;
+
+    flash_attn_combine_results<D, parallel_blocks>
+        <<<blocks_num_combine, block_dim_combine, shmem_combine, main_stream>>>
+        (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data);
+    CUDA_CHECK(cudaGetLastError());
+}
+
+void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * Q = dst->src[0];
+    const ggml_tensor * K = dst->src[1];
+    const ggml_tensor * V = dst->src[2];
+
+    const ggml_tensor * mask = dst->src[3];
+
+    ggml_tensor * KQV = dst;
+
+    const int32_t precision = KQV->op_params[2];
+    GGML_ASSERT(precision == GGML_PREC_DEFAULT);
+
+    constexpr int cols_per_block = 1;
+    constexpr int parallel_blocks = 4;
+    switch (Q->ne[0]) {
+        case 64:
+            launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+            break;
+        case 128:
+            launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+            break;
+        case 256:
+            launch_fattn_vec_f16<256, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+}
+
+void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * Q = dst->src[0];
+    const ggml_tensor * K = dst->src[1];
+    const ggml_tensor * V = dst->src[2];
+
+    const ggml_tensor * mask = dst->src[3];
+
+    ggml_tensor * KQV = dst;
+
+    const int32_t precision = KQV->op_params[2];
+    GGML_ASSERT(precision == GGML_PREC_DEFAULT);
+    GGML_ASSERT(Q->ne[0] == 64 || Q->ne[0] == 128 && "FlashAttention without tensor cores only supports head sizes 64 and 128.");
+
+    if (Q->ne[1] == 1) {
+        constexpr int cols_per_block = 1;
+        constexpr int parallel_blocks = 4;
+        switch (Q->ne[0]) {
+            case 64:
+                launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+                break;
+            case 128:
+                launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
+        return;
+    }
+
+    if (Q->ne[1] == 2) {
+        constexpr int cols_per_block = 2;
+        constexpr int parallel_blocks = 4;
+        switch (Q->ne[0]) {
+            case 64:
+                launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+                break;
+            case 128:
+                launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
+        return;
+    }
+
+    if (Q->ne[1] <= 4) {
+        constexpr int cols_per_block = 4;
+        constexpr int parallel_blocks = 4;
+        switch (Q->ne[0]) {
+            case 64:
+                launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+                break;
+            case 128:
+                launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
+        return;
+    }
+
+    if (Q->ne[1] <= 8) {
+        constexpr int cols_per_block = 8;
+        constexpr int parallel_blocks = 4;
+        switch (Q->ne[0]) {
+            case 64:
+                launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+                break;
+            case 128:
+                launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
+        return;
+    }
+
+    constexpr int cols_per_block = 8;
+    constexpr int parallel_blocks = 1;
+    switch (Q->ne[0]) {
+        case 64:
+            launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+            break;
+        case 128:
+            launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+}
diff --git a/ggml-cuda/fattn-vec-f16.cuh b/ggml-cuda/fattn-vec-f16.cuh
new file mode 100644
index 000000000..c7023610a
--- /dev/null
+++ b/ggml-cuda/fattn-vec-f16.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/fattn-vec-f32.cu b/ggml-cuda/fattn-vec-f32.cu
new file mode 100644
index 000000000..40c336ce3
--- /dev/null
+++ b/ggml-cuda/fattn-vec-f32.cu
@@ -0,0 +1,384 @@
+#include "common.cuh"
+#include "fattn-common.cuh"
+#include "fattn-vec-f32.cuh"
+
+template<int D, int ncols, int parallel_blocks> // D == head size
+#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+__launch_bounds__(D, 1)
+#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+static __global__ void flash_attn_vec_ext_f32(
+        const char * __restrict__ Q,
+        const char * __restrict__ K,
+        const char * __restrict__ V,
+        const char * __restrict__ mask,
+        float      * __restrict__ dst,
+        float2     * __restrict__ dst_meta,
+        const float scale,
+        const float max_bias,
+        const float m0,
+        const float m1,
+        const uint32_t n_head_log2,
+        const int ne00,
+        const int ne01,
+        const int ne02,
+        const int ne03,
+        const int ne10,
+        const int ne11,
+        const int ne12,
+        const int ne13,
+        const int ne31,
+        const int nb31,
+        const int nb01,
+        const int nb02,
+        const int nb03,
+        const int nb11,
+        const int nb12,
+        const int nb13,
+        const int ne0,
+        const int ne1,
+        const int ne2,
+        const int ne3) {
+    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
+
+    const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
+    const int ip  =  blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
+
+    const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
+    const float2 * Q_f2  = (const float2 *) (Q    + nb02* blockIdx.y              + nb01*ic0);
+    const half2  * K_h2  = (const half2  *) (K    + nb12*(blockIdx.y / gqa_ratio));
+    const half   * V_h   = (const half   *) (V    + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
+    const half   * maskh = (const half   *)  mask + ne11*ic0;
+
+    const int stride_KV  = nb11 / sizeof(half);
+    const int stride_KV2 = nb11 / sizeof(half2);
+
+    float slope = 1.0f;
+
+    // ALiBi
+    if (max_bias > 0.0f) {
+        const int h = blockIdx.y;
+
+        const float base = h < n_head_log2 ? m0 : m1;
+        const int   exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+        slope = powf(base, exph);
+    }
+
+    static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64.");
+    constexpr int nwarps = D / WARP_SIZE;
+    const int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
+    __builtin_assume(tid < D);
+
+    __shared__ float KQ[ncols*D];
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        KQ[j*D + tid] = -FLT_MAX/2.0f;
+    }
+
+    float kqmax[ncols];
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        kqmax[j] = -FLT_MAX/2.0f;
+    }
+    float kqsum[ncols] = {0.0f};
+
+    __shared__ float kqmax_shared[ncols][WARP_SIZE];
+    __shared__ float kqsum_shared[ncols][WARP_SIZE];
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        if (threadIdx.y == 0) {
+            kqmax_shared[j][threadIdx.x] = -FLT_MAX/2.0f;
+            kqsum_shared[j][threadIdx.x] = 0.0f;
+        }
+    }
+    __syncthreads();
+
+    // Convert Q to half2 and store in registers:
+    float2 Q_h2[ncols][D/(2*WARP_SIZE)];
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+#pragma unroll
+        for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
+            const int i = i0 + threadIdx.x;
+
+            Q_h2[j][i0/WARP_SIZE]    = Q_f2[j*(nb01/sizeof(float2)) + i];
+            Q_h2[j][i0/WARP_SIZE].x *= scale;
+            Q_h2[j][i0/WARP_SIZE].y *= scale;
+        }
+    }
+
+    float VKQ[ncols] = {0.0f};
+
+    const int k_start = parallel_blocks == 1 ? 0 : ip*D;
+    for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) {
+        // Calculate KQ tile and keep track of new maximum KQ values:
+
+        float kqmax_new_arr[ncols];
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            kqmax_new_arr[j] = kqmax[j];
+        }
+
+#pragma unroll
+        for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) {
+            const int i_KQ = i_KQ_0 + threadIdx.y;
+
+            if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) {
+                break;
+            }
+
+            float sum[ncols] = {0.0f};
+#pragma unroll
+            for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
+                const int k_KQ = k_KQ_0 + threadIdx.x;
+
+                const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ];
+#pragma unroll
+                for (int j = 0; j < ncols; ++j) {
+                    sum[j] +=  __low2float(K_ik) * Q_h2[j][k_KQ_0/WARP_SIZE].x;
+                    sum[j] += __high2float(K_ik) * Q_h2[j][k_KQ_0/WARP_SIZE].y;
+                }
+            }
+
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+                sum[j] = warp_reduce_sum(sum[j]);
+                sum[j] += mask ? slope*__half2float(maskh[j*ne11 + k_VKQ_0 + i_KQ]) : 0.0f;
+
+                kqmax_new_arr[j] = fmaxf(kqmax_new_arr[j], sum[j]);
+
+                if (threadIdx.x == 0) {
+                    KQ[j*D + i_KQ] = sum[j];
+                }
+            }
+        }
+
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            float kqmax_new_j = kqmax_new_arr[j];
+
+            kqmax_new_j = warp_reduce_max(kqmax_new_j);
+            if (threadIdx.x == 0) {
+                kqmax_shared[j][threadIdx.y] = kqmax_new_j;
+            }
+        }
+
+        __syncthreads();
+
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            float kqmax_new_j = kqmax_shared[j][threadIdx.x];
+            kqmax_new_j = warp_reduce_max(kqmax_new_j);
+
+            const float KQ_max_scale = expf(kqmax[j] - kqmax_new_j);
+            kqmax[j] = kqmax_new_j;
+
+            const float val = expf(KQ[j*D + tid] - kqmax[j]);
+            kqsum[j] = kqsum[j]*KQ_max_scale + val;
+            KQ[j*D + tid] = val;
+
+            VKQ[j] *= KQ_max_scale;
+        }
+
+        __syncthreads();
+
+#pragma unroll
+        for (int k = 0; k < D; ++k) {
+            if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k >= ne11) {
+                break;
+            }
+
+            const float V_ki = __half2float(V_h[(k_VKQ_0 + k)*stride_KV + tid]);
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+                VKQ[j] += V_ki*KQ[j*D + k];
+            }
+        }
+
+        __syncthreads();
+    }
+
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        kqsum[j] = warp_reduce_sum(kqsum[j]);
+        if (threadIdx.x == 0) {
+            kqsum_shared[j][threadIdx.y] = kqsum[j];
+        }
+    }
+
+    __syncthreads();
+
+#pragma unroll
+    for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) {
+        kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x];
+        kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]);
+
+        float dst_val = VKQ[j_VKQ];
+        if (parallel_blocks == 1) {
+            dst_val /= kqsum[j_VKQ];
+        }
+        const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
+        dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val;
+    }
+
+    if (parallel_blocks != 1 && tid != 0) {
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            dst_meta[(ic0 + j)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j], kqsum[j]);
+        }
+    }
+}
+
+template <int D, int cols_per_block, int parallel_blocks> void launch_fattn_vec_f32(
+        const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask,
+        ggml_cuda_pool & pool, cudaStream_t main_stream
+) {
+    ggml_cuda_pool_alloc<float>  dst_tmp(pool);
+    ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool);
+
+    if (parallel_blocks > 1) {
+        dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));
+        dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV));
+    }
+
+    constexpr int  nwarps = (D + WARP_SIZE - 1) / WARP_SIZE;
+    const     dim3 block_dim(WARP_SIZE, nwarps, 1);
+    const     dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]);
+    const     int  shmem = 0;
+
+    float scale    = 1.0f;
+    float max_bias = 0.0f;
+
+    memcpy(&scale,    (float *) KQV->op_params + 0, sizeof(float));
+    memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float));
+
+    const uint32_t n_head      = Q->ne[2];
+    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
+
+    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+    flash_attn_vec_ext_f32<D, cols_per_block, parallel_blocks>
+        <<<blocks_num, block_dim, shmem, main_stream>>> (
+                (const char *) Q->data,
+                (const char *) K->data,
+                (const char *) V->data,
+                mask ? ((const char *) mask->data) : nullptr,
+                parallel_blocks == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr,
+                scale, max_bias, m0, m1, n_head_log2,
+                Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3],
+                K->ne[0], K->ne[1], K->ne[2], K->ne[3],
+                mask ? mask->ne[1] : 0, mask ?  mask->nb[1] : 0,
+                Q->nb[1], Q->nb[2], Q->nb[3],
+                K->nb[1], K->nb[2], K->nb[3],
+                KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3]
+                );
+    CUDA_CHECK(cudaGetLastError());
+
+    if (parallel_blocks == 1) {
+        return;
+    }
+
+    const dim3 block_dim_combine(D, 1, 1);
+    const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z);
+    const int  shmem_combine = 0;
+
+    flash_attn_combine_results<D, parallel_blocks>
+        <<<blocks_num_combine, block_dim_combine, shmem_combine, main_stream>>>
+        (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data);
+    CUDA_CHECK(cudaGetLastError());
+}
+
+void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * Q = dst->src[0];
+    const ggml_tensor * K = dst->src[1];
+    const ggml_tensor * V = dst->src[2];
+
+    const ggml_tensor * mask = dst->src[3];
+
+    ggml_tensor * KQV = dst;
+
+    GGML_ASSERT(Q->ne[0] == 64 || Q->ne[0] == 128 && "FlashAttention without tensor cores only supports head sizes 64 and 128.");
+
+    if (Q->ne[1] == 1) {
+        constexpr int cols_per_block = 1;
+        constexpr int parallel_blocks = 4;
+        switch (Q->ne[0]) {
+            case 64:
+                launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+                break;
+            case 128:
+                launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
+        return;
+    }
+
+    if (Q->ne[1] == 2) {
+        constexpr int cols_per_block = 2;
+        constexpr int parallel_blocks = 4;
+        switch (Q->ne[0]) {
+            case 64:
+                launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+                break;
+            case 128:
+                launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
+        return;
+    }
+
+    if (Q->ne[1] <= 4) {
+        constexpr int cols_per_block = 4;
+        constexpr int parallel_blocks = 4;
+        switch (Q->ne[0]) {
+            case 64:
+                launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+                break;
+            case 128:
+                launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
+        return;
+    }
+
+    if (Q->ne[1] <= 8) {
+        constexpr int cols_per_block = 8;
+        constexpr int parallel_blocks = 4;
+        switch (Q->ne[0]) {
+            case 64:
+                launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+                break;
+            case 128:
+                launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
+        return;
+    }
+
+    constexpr int cols_per_block = 8;
+    constexpr int parallel_blocks = 1;
+    switch (Q->ne[0]) {
+        case 64:
+            launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+            break;
+        case 128:
+            launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+}
diff --git a/ggml-cuda/fattn-vec-f32.cuh b/ggml-cuda/fattn-vec-f32.cuh
new file mode 100644
index 000000000..614d54ae3
--- /dev/null
+++ b/ggml-cuda/fattn-vec-f32.cuh
@@ -0,0 +1,3 @@
+#include "common.cuh"
+
+void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/fattn.cu b/ggml-cuda/fattn.cu
new file mode 100644
index 000000000..419f8e752
--- /dev/null
+++ b/ggml-cuda/fattn.cu
@@ -0,0 +1,699 @@
+#include "common.cuh"
+#include "fattn-common.cuh"
+#include "fattn-vec-f16.cuh"
+#include "fattn-vec-f32.cuh"
+#include "fattn.cuh"
+
+#include <cstdint>
+
+#if FP16_MMA_AVAILABLE
+#include <mma.h>
+#endif
+
+// D == head size, VKQ_stride == num VKQ rows calculated in parallel:
+template<int D, int ncols, int nwarps, int VKQ_stride, int parallel_blocks, typename KQ_acc_t>
+#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+__launch_bounds__(nwarps*WARP_SIZE, 1)
+#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+static __global__ void flash_attn_ext_f16(
+        const char * __restrict__ Q,
+        const char * __restrict__ K,
+        const char * __restrict__ V,
+        const char * __restrict__ mask,
+        float      * __restrict__ dst,
+        float2     * __restrict__ dst_meta,
+        const float scale,
+        const float max_bias,
+        const float m0,
+        const float m1,
+        const uint32_t n_head_log2,
+        const int ne00,
+        const int ne01,
+        const int ne02,
+        const int ne03,
+        const int ne10,
+        const int ne11,
+        const int ne12,
+        const int ne13,
+        const int ne31,
+        const int nb31,
+        const int nb01,
+        const int nb02,
+        const int nb03,
+        const int nb11,
+        const int nb12,
+        const int nb13,
+        const int ne0,
+        const int ne1,
+        const int ne2,
+        const int ne3) {
+#if FP16_MMA_AVAILABLE
+    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
+
+    const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first Q/QKV column to work on.
+    const int ip  =        blockIdx.x % parallel_blocks;  // Index in group of blocks running for the same column in parallel.
+
+    static_assert(D <= FATTN_KQ_STRIDE, "D must be <= FATTN_KQ_STRIDE.");
+    static_assert(ncols == 8 || ncols % 16 == 0, "ncols must be 8 or a multiple of 16.");
+    constexpr int frag_m = ncols == 8 ? 32 : 16;
+    constexpr int frag_n = ncols == 8 ?  8 : 16;
+    static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0.");
+    typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a,    frag_m, frag_n, 16, half, nvcuda::wmma::row_major> frag_a_K;
+    typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a,    frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_a_V;
+    typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_b,    frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_b;
+    typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, KQ_acc_t>                      frag_c_KQ;
+    typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, half>                          frag_c_VKQ;
+
+    constexpr int KQ_stride_tc  = nwarps*frag_m; // Number of KQ rows calculated in parallel.
+    constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy.
+    static_assert(VKQ_ratio <= nwarps, "VKQ_ratio must be <= nwarps.");
+
+    // Pad internal representation of KQ, KQV to reduce shared memory bank conflicts:
+    constexpr int D_padded = D + 8;
+    constexpr int kqs_padded = FATTN_KQ_STRIDE + 8;
+    constexpr int kqar = sizeof(KQ_acc_t)/sizeof(half);
+
+    const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
+    const float * Q_f   = (const float *) (Q + nb02* blockIdx.y              + nb01*ic0);
+    const half  * K_h   = (const half  *) (K + nb12*(blockIdx.y / gqa_ratio));
+    const half  * V_h   = (const half  *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
+    const half  * maskh = (const half  *)  mask + (nb31/sizeof(half))* ic0;
+    const half2 * mask2 = (const half2 *)  mask + (nb31/sizeof(half))*(ic0/2);
+
+    const int stride_Q  = nb01 / sizeof(float);
+    const int stride_KV = nb11 / sizeof(half);
+
+    half  slopeh = __float2half(1.0f);
+    half2 slope2 = make_half2(1.0f, 1.0f);
+
+    // ALiBi
+    if (max_bias > 0.0f) {
+        const int h = blockIdx.y;
+
+        const float base = h < n_head_log2 ? m0 : m1;
+        const int   exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+        slopeh = __float2half(powf(base, exph));
+        slope2 = make_half2(slopeh, slopeh);
+    }
+
+    frag_b Q_b[D/16][ncols/frag_n];
+
+    // A single buffer for temporarily holding tiles of KQ and VKQ parts:
+    constexpr int mem_KQ = ncols*kqs_padded*kqar;
+    constexpr int mem_VKQ_parts = VKQ_ratio*ncols*D_padded;
+    __shared__ half KQ[mem_KQ >= mem_VKQ_parts ? mem_KQ : mem_VKQ_parts];
+    float * KQ_f = (float *) KQ;
+    half2 * KQ2 = (half2 *) KQ;
+
+    float    KQ_rowsum_f[ncols/nwarps] = {0.0f};
+    float       KQ_max_f[ncols/nwarps];
+    float KQ_max_scale_f[ncols/nwarps] = {0.0f};
+
+#pragma unroll
+    for (int j = 0; j < ncols/nwarps; ++j) {
+        KQ_max_f[j] = -FLT_MAX/2.0f;
+    }
+
+    half2    KQ_rowsum_h2[ncols/nwarps] = {{0.0f, 0.0f}};
+    half2       KQ_max_h2[ncols/nwarps];
+    half2 KQ_max_scale_h2[ncols/nwarps] = {{0.0f, 0.0f}};
+
+#pragma unroll
+    for (int j = 0; j < ncols/nwarps; ++j) {
+        KQ_max_h2[j] = make_half2(-HALF_MAX_HALF, -HALF_MAX_HALF);
+    }
+
+    __shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice.
+    half2 * VKQ2 = (half2 *) VKQ;
+#pragma unroll
+    for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+        const int j = j0 + threadIdx.y;
+#pragma unroll
+        for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
+            const int i = i0 + threadIdx.x;
+            if (i0 + WARP_SIZE > D/2 && i >= D/2) {
+                break;
+            }
+            VKQ2[j*(D_padded/2) + i] = make_half2(0.0f, 0.0f);
+        }
+    }
+
+    // Convert Q to half and apply scale, temporarily store in KQ:
+#pragma unroll
+    for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+        const int j = j0 + threadIdx.y;
+#pragma unroll
+        for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
+            const int i = i0 + threadIdx.x;
+            if (i0 + WARP_SIZE > D && i >= D) {
+                break;
+            }
+            KQ[j*D_padded + i] = ic0 + j < ne01 ? Q_f[j*stride_Q + i] * scale : 0.0f;
+        }
+    }
+
+    __syncthreads();
+
+    // Load Q into tensor core fragments/registers since it will be used frequently:
+#pragma unroll
+    for (int i0 = 0; i0 < D; i0 += 16) {
+#pragma unroll
+        for (int j0 = 0; j0 < ncols; j0 += frag_n) {
+            nvcuda::wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ + j0*D_padded + i0, D_padded);
+        }
+    }
+
+    __syncthreads();
+
+    // Iterate over ne11 == previous tokens:
+    for (int k_VKQ_0 = ip*FATTN_KQ_STRIDE; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE) {
+        // Calculate tile of KQ:
+#pragma unroll
+        for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE; i_KQ_0 += KQ_stride_tc) {
+            frag_c_KQ KQ_c[ncols/frag_n];
+#pragma unroll
+            for (int j = 0; j < ncols/frag_n; ++j) {
+                nvcuda::wmma::fill_fragment(KQ_c[j], 0.0f);
+            }
+#pragma unroll
+            for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) {
+                frag_a_K K_a;
+                nvcuda::wmma::load_matrix_sync(K_a, K_h + (k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV);
+#pragma unroll
+                for (int j = 0; j < ncols/frag_n; ++j) {
+                    nvcuda::wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]);
+                }
+            }
+#pragma unroll
+            for (int j0 = 0; j0 < ncols; j0 += frag_n) {
+                nvcuda::wmma::store_matrix_sync((KQ_acc_t *) KQ + j0*kqs_padded + i_KQ_0 + frag_m*threadIdx.y, KQ_c[j0/frag_n], kqs_padded, nvcuda::wmma::mem_col_major);
+            }
+        }
+
+        __syncthreads();
+
+        // Calculate softmax for each KQ column using the current max. value.
+        // The divisor is stored in KQ_rowsum and will be applied at the end.
+#pragma unroll
+        for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+            if (std::is_same<KQ_acc_t, float>::value) {
+                float KQ_f_tmp[FATTN_KQ_STRIDE / WARP_SIZE];
+#pragma unroll
+                for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
+                    const int k = k0 + threadIdx.x;
+
+                    KQ_f_tmp[k0/WARP_SIZE] = KQ_f[j*kqs_padded + k];
+                }
+
+                float KQ_max_new = KQ_max_f[j0/nwarps];
+#pragma unroll
+                for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
+                    const int k = k0 + threadIdx.x;
+
+                    KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(slopeh*maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f;
+                    KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/WARP_SIZE]);
+                }
+                KQ_max_new = warp_reduce_max(KQ_max_new);
+
+                const float diff = KQ_max_f[j0/nwarps] - KQ_max_new;
+                KQ_max_scale_f[j0/nwarps] = expf(diff);
+                if (diff <= SOFTMAX_FTZ_THRESHOLD) {
+                    KQ_max_scale_f[j0/nwarps] = 0.0f;
+                }
+                KQ_max_f[j0/nwarps] = KQ_max_new;
+
+                float KQ_rowsum_add = 0.0f;
+#pragma unroll
+                for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
+                    const int k = k0 + threadIdx.x;
+
+                    const float diff = KQ_f_tmp[k0/WARP_SIZE] - KQ_max_f[j0/nwarps];
+                    KQ_f_tmp[k0/WARP_SIZE] = expf(diff);
+                    if (diff <= SOFTMAX_FTZ_THRESHOLD) {
+                        KQ_f_tmp[k0/WARP_SIZE] = 0.0f;
+                    }
+                    KQ_rowsum_add += KQ_f_tmp[k0/WARP_SIZE];
+                    KQ[j*(kqar*kqs_padded) + k] = KQ_f_tmp[k0/WARP_SIZE];
+                }
+                KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
+
+                // Scale previous KQ_rowsum to account for a potential increase in KQ_max:
+                KQ_rowsum_f[j0/nwarps] = KQ_max_scale_f[j0/nwarps]*KQ_rowsum_f[j0/nwarps] + KQ_rowsum_add;
+            } else {
+                half2 KQ2_tmp[FATTN_KQ_STRIDE/(2*WARP_SIZE)];
+#pragma unroll
+                for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
+                    const int k = k0 + threadIdx.x;
+
+                    KQ2_tmp[k0/WARP_SIZE] = KQ2[j*(kqs_padded/2) + k];
+                }
+
+                half2 KQ_max_new = KQ_max_h2[j0/nwarps];
+#pragma unroll
+                for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
+                    const int k = k0 + threadIdx.x;
+
+                    KQ2_tmp[k0/WARP_SIZE] += mask ? slope2*mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f);
+                    KQ_max_new = ggml_cuda_hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]);
+                }
+                KQ_max_new = __half2half2(warp_reduce_max(ggml_cuda_hmax(__low2half(KQ_max_new), __high2half(KQ_max_new))));
+                const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new;
+                KQ_max_scale_h2[j0/nwarps] = h2exp(diff);
+                const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
+                *((uint32_t *) &KQ_max_scale_h2[j0/nwarps]) &= ftz_mask;
+                KQ_max_h2[j0/nwarps] = KQ_max_new;
+
+                half2 KQ_rowsum_add = make_half2(0.0f, 0.0f);
+#pragma unroll
+                for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
+                    const int k = k0 + threadIdx.x;
+
+                    const half2 diff = KQ2_tmp[k0/WARP_SIZE] - KQ_max_h2[j0/nwarps];
+                    KQ2_tmp[k0/WARP_SIZE] = h2exp(diff);
+                    const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
+                    *((uint32_t *) &KQ2_tmp[k0/WARP_SIZE]) &= ftz_mask;
+                    KQ_rowsum_add += KQ2_tmp[k0/WARP_SIZE];
+                    KQ2[j*(kqs_padded/2) + k] = KQ2_tmp[k0/WARP_SIZE];
+                }
+                KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
+
+                // Scale previous KQ_rowsum to account for a potential increase in KQ_max:
+                KQ_rowsum_h2[j0/nwarps] = KQ_max_scale_h2[j0/nwarps]*KQ_rowsum_h2[j0/nwarps] + KQ_rowsum_add;
+            }
+        }
+
+        __syncthreads();
+
+        frag_b KQ_b[FATTN_KQ_STRIDE/(VKQ_ratio*16)][ncols/frag_n];
+#pragma unroll
+        for (int j0 = 0; j0 < ncols; j0 += frag_n) {
+#pragma unroll
+            for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
+                const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
+                nvcuda::wmma::load_matrix_sync(
+                    KQ_b[k0/(VKQ_ratio*16)][j0/frag_n],
+                    KQ + j0*(kqar*kqs_padded) + k,
+                    kqar*kqs_padded);
+            }
+        }
+
+        frag_c_VKQ VKQ_c[D/VKQ_stride][ncols/frag_n];
+#pragma unroll
+        for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += VKQ_stride) {
+#pragma unroll
+            for (int j = 0; j < ncols/frag_n; ++j) {
+                nvcuda::wmma::fill_fragment(VKQ_c[i_VKQ_0/VKQ_stride][j], 0.0f);
+            }
+
+#pragma unroll
+            for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
+                const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
+
+                frag_a_V v_a;
+                nvcuda::wmma::load_matrix_sync(v_a, V_h + (k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV);
+#pragma unroll
+                for (int j = 0; j < ncols/frag_n; ++j) {
+                    nvcuda::wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]);
+                }
+            }
+        }
+
+        __syncthreads();
+
+        const int offset_k = (threadIdx.y % VKQ_ratio) * (ncols*D_padded);
+#pragma unroll
+        for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += VKQ_stride) {
+#pragma unroll
+            for (int j0 = 0; j0 < ncols; j0 += frag_n) {
+                nvcuda::wmma::store_matrix_sync(
+                    KQ + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio),
+                    VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n],
+                    D_padded, nvcuda::wmma::mem_col_major);
+            }
+        }
+
+        __syncthreads();
+
+#pragma unroll
+        for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+            half2 VKQ_scale;
+            if (std::is_same<KQ_acc_t, float>::value) {
+                VKQ_scale = make_half2(KQ_max_scale_f[j0/nwarps], KQ_max_scale_f[j0/nwarps]);
+            } else {
+                VKQ_scale = KQ_max_scale_h2[j0/nwarps];
+            }
+
+#pragma unroll
+            for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
+                if (i0 + WARP_SIZE > D/2 && i >= D/2) {
+                    break;
+                }
+
+                half2 VKQ_add = make_half2(0.0f, 0.0f);
+#pragma unroll
+                for (int l = 0; l < VKQ_ratio; ++l) {
+                    VKQ_add += KQ2[l*(ncols*D_padded/2) + j*(D_padded/2) + i];
+                }
+                VKQ2[j*(D_padded/2) + i] = VKQ_scale*VKQ2[j*(D_padded/2) + i] + VKQ_add;
+            }
+        }
+
+        __syncthreads();
+    }
+
+#pragma unroll
+    for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+        const int j_VKQ = j0 + threadIdx.y;
+        if (ic0 + j_VKQ >= ne01) {
+            return;
+        }
+        const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
+
+        float KQ_rowsum_j;
+        if (std::is_same<KQ_acc_t, float>::value) {
+            KQ_rowsum_j = KQ_rowsum_f[j0/nwarps];
+        } else {
+            KQ_rowsum_j = __low2float(KQ_rowsum_h2[j0/nwarps]) + __high2float(KQ_rowsum_h2[j0/nwarps]);
+        }
+
+#pragma unroll
+        for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
+            const int i = i0 + threadIdx.x;
+            if (i0 + WARP_SIZE > D && i >= D) {
+                break;
+            }
+            float dst_val = VKQ[j_VKQ*D_padded + i];
+            if (parallel_blocks == 1) {
+                dst_val /= KQ_rowsum_j;
+            }
+            dst[j_dst*gridDim.y*D + blockIdx.y*D + i] = dst_val;
+        }
+
+        if (parallel_blocks == 1 || threadIdx.x != 0) {
+            continue;
+        }
+
+        float2 dst_meta_val;
+        if (std::is_same<KQ_acc_t, float>::value) {
+            dst_meta_val.x = KQ_max_f[j0/nwarps];
+        } else {
+            dst_meta_val.x = __low2float(KQ_max_h2[j0/nwarps]);
+        }
+        dst_meta_val.y = KQ_rowsum_j;
+        dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = dst_meta_val;
+    }
+#else
+   NO_DEVICE_CODE;
+#endif // FP16_MMA_AVAILABLE
+}
+
+constexpr int get_max_power_of_2(int x) {
+    return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1;
+}
+
+static_assert(get_max_power_of_2(1) == 1, "Test failed.");
+static_assert(get_max_power_of_2(2) == 2, "Test failed.");
+static_assert(get_max_power_of_2(4) == 4, "Test failed.");
+static_assert(get_max_power_of_2(6) == 2, "Test failed.");
+
+// Number of VKQ rows calculated in parallel:
+constexpr int get_VKQ_stride(int D, int nwarps, int frag_m) {
+    return (get_max_power_of_2(D/frag_m) < nwarps ? get_max_power_of_2(D/frag_m) : nwarps)*frag_m;
+}
+
+static_assert(get_VKQ_stride(128, 1, 32) ==  32, "Test failed.");
+static_assert(get_VKQ_stride(128, 2, 32) ==  64, "Test failed.");
+static_assert(get_VKQ_stride(128, 4, 32) == 128, "Test failed.");
+static_assert(get_VKQ_stride( 64, 1, 32) ==  32, "Test failed.");
+static_assert(get_VKQ_stride( 64, 2, 32) ==  64, "Test failed.");
+static_assert(get_VKQ_stride( 64, 4, 32) ==  64, "Test failed.");
+static_assert(get_VKQ_stride( 80, 1, 16) ==  16, "Test failed.");
+static_assert(get_VKQ_stride( 80, 2, 16) ==  16, "Test failed.");
+static_assert(get_VKQ_stride( 80, 4, 16) ==  16, "Test failed.");
+
+template <int D, int cols_per_block, int nwarps, int parallel_blocks, typename KQ_acc_t> void launch_fattn_f16_impl(
+        const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask,
+        ggml_cuda_pool & pool, cudaStream_t main_stream
+) {
+    ggml_cuda_pool_alloc<float>  dst_tmp(pool);
+    ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool);
+
+    if (parallel_blocks > 1) {
+        dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));
+        dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV));
+    }
+
+    constexpr int  frag_m = (cols_per_block) == 8 && (D) % 32 == 0 ? 32 : 16;
+    const     dim3 block_dim(WARP_SIZE, nwarps, 1);
+    const     dim3 blocks_num(parallel_blocks*(Q->ne[1] + cols_per_block - 1) / cols_per_block, Q->ne[2], Q->ne[3]);
+    const     int  shmem = 0;
+
+    float scale    = 1.0f;
+    float max_bias = 0.0f;
+
+    memcpy(&scale,    (float *) KQV->op_params + 0, sizeof(float));
+    memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float));
+
+    const uint32_t n_head      = Q->ne[2];
+    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
+
+    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+    flash_attn_ext_f16<D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t>
+        <<<blocks_num, block_dim, shmem, main_stream>>> (
+                (const char *) Q->data,
+                (const char *) K->data,
+                (const char *) V->data,
+                mask ? ((const char *) mask->data) : nullptr,
+                (parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr,
+                scale, max_bias, m0, m1, n_head_log2,
+                Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3],
+                K->ne[0], K->ne[1], K->ne[2], K->ne[3],
+                mask ? mask->ne[1] : 0, mask ?  mask->nb[1] : 0,
+                Q->nb[1], Q->nb[2], Q->nb[3],
+                K->nb[1], K->nb[2], K->nb[3],
+                KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3]
+                );
+    CUDA_CHECK(cudaGetLastError());
+
+    if ((parallel_blocks) == 1) {
+        return;
+    }
+
+    const dim3 block_dim_combine(D, 1, 1);
+    const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z);
+    const int  shmem_combine = 0;
+
+    flash_attn_combine_results<D, parallel_blocks>
+        <<<blocks_num_combine, block_dim_combine, shmem_combine, main_stream>>>
+        (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data);
+    CUDA_CHECK(cudaGetLastError());
+}
+
+template <int D, int cols_per_block, int nwarps, typename KQ_acc_t> void launch_fattn_f16(
+        const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask,
+        const int nsm, ggml_cuda_pool & pool, cudaStream_t main_stream
+) {
+    const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3];
+
+    if (4*blocks_num_pb1 < 2*nsm) {
+        launch_fattn_f16_impl<D, cols_per_block, nwarps, 4, KQ_acc_t>(Q, K, V, KQV, mask, pool, main_stream);
+        return;
+    }
+    if (2*blocks_num_pb1 < 2*nsm) {
+        launch_fattn_f16_impl<D, cols_per_block, nwarps, 2, KQ_acc_t>(Q, K, V, KQV, mask, pool, main_stream);
+        return;
+    }
+    launch_fattn_f16_impl<D, cols_per_block, nwarps, 1, KQ_acc_t>(Q, K, V, KQV, mask, pool, main_stream);
+}
+
+void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * Q = dst->src[0];
+    const ggml_tensor * K = dst->src[1];
+    const ggml_tensor * V = dst->src[2];
+
+    const ggml_tensor * mask = dst->src[3];
+
+    ggml_tensor * KQV = dst;
+
+    GGML_ASSERT(Q->type == GGML_TYPE_F32);
+    GGML_ASSERT(K->type == GGML_TYPE_F16);
+    GGML_ASSERT(V->type == GGML_TYPE_F16);
+    GGML_ASSERT(KQV->type == GGML_TYPE_F32);
+
+    GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16);
+    GGML_ASSERT(!mask || mask->ne[1] >= GGML_PAD(Q->ne[1], 16) &&
+                                "the Flash-Attention CUDA kernel requires the mask to be padded to 16 and at least n_queries big");
+
+    GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding.");
+
+    ggml_cuda_set_device(ctx.device);
+
+    const int cc  = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
+    const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm;
+
+    const int32_t precision = KQV->op_params[2];
+
+    if (!fast_fp16_available(cc)) {
+        ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
+        return;
+    }
+
+    if (!fp16_mma_available(cc)) {
+        ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst);
+        return;
+    }
+
+    if (precision != GGML_PREC_DEFAULT) {
+        if (Q->ne[1] == 1 && (Q->ne[0] == 64 || Q->ne[0] == 128)) {
+            ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
+            return;
+        }
+
+        if (Q->ne[1] <= 32 || Q->ne[0] > 128) {
+            constexpr int cols_per_block = 16;
+            constexpr int nwarps         =  4;
+            switch (Q->ne[0]) {
+                case 64:
+                    launch_fattn_f16< 64, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                case 80:
+                    launch_fattn_f16< 80, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                case 96:
+                    launch_fattn_f16< 96, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                case 112:
+                    launch_fattn_f16<112, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                case 128:
+                    launch_fattn_f16<128, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                case 256:
+                    launch_fattn_f16<256, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                default:
+                    GGML_ASSERT(false);
+                    break;
+            }
+        } else {
+            constexpr int cols_per_block = 32;
+            constexpr int nwarps         =  4;
+            switch (Q->ne[0]) {
+                case 64:
+                    launch_fattn_f16< 64, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                case 80:
+                    launch_fattn_f16< 80, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                case 96:
+                    launch_fattn_f16< 96, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                case 112:
+                    launch_fattn_f16<112, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                case 128:
+                    launch_fattn_f16<128, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                // case 256:
+                //     launch_fattn_f16<256, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                //     break;
+                default:
+                    GGML_ASSERT(false);
+                    break;
+            }
+        }
+        return;
+    }
+
+    if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) {
+        ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
+        return;
+    }
+
+    if (Q->ne[1] <= 8 && Q->ne[0] % WARP_SIZE == 0) {
+        constexpr int cols_per_block = 8;
+        constexpr int nwarps         = 4;
+        switch (Q->ne[0]) {
+            case 64:
+                launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                break;
+            case 96:
+                launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                break;
+            case 128:
+                launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                break;
+            case 256:
+                launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
+        return;
+    }
+
+    if (Q->ne[1] <= 32) {
+        constexpr int cols_per_block = 16;
+        constexpr int nwarps         =  4;
+        switch (Q->ne[0]) {
+            case 64:
+                launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                break;
+            case 80:
+                launch_fattn_f16< 80, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                break;
+            case 96:
+                launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                break;
+            case 112:
+                launch_fattn_f16<112, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                break;
+            case 128:
+                launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                break;
+            case 256:
+                launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
+        return;
+    }
+
+    constexpr int cols_per_block = 32;
+    constexpr int nwarps         =  4;
+    switch (Q->ne[0]) {
+        case 64:
+            launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+            break;
+        case 80:
+            launch_fattn_f16< 80, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+            break;
+        case 96:
+            launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+            break;
+        case 112:
+            launch_fattn_f16<112, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+            break;
+        case 128:
+            launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+            break;
+        case 256:
+            launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+    return;
+}
diff --git a/ggml-cuda/fattn.cuh b/ggml-cuda/fattn.cuh
new file mode 100644
index 000000000..ad3ca7a8d
--- /dev/null
+++ b/ggml-cuda/fattn.cuh
@@ -0,0 +1,3 @@
+#include "common.cuh"
+
+void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/getrows.cu b/ggml-cuda/getrows.cu
new file mode 100644
index 000000000..55af195fd
--- /dev/null
+++ b/ggml-cuda/getrows.cu
@@ -0,0 +1,178 @@
+#include "getrows.cuh"
+#include "dequantize.cuh"
+
+template<int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
+static __global__ void k_get_rows(
+            const void * src0, const int32_t * src1, dst_t * dst,
+            int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
+            /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
+            /*size_t s0,*/ size_t s1, size_t s2, size_t s3,
+            /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
+            size_t s10, size_t s11, size_t s12/*, size_t s13*/) {
+
+    const int i00 = (blockIdx.x*blockDim.x + threadIdx.x)*2;
+    const int i10 = blockDim.y*blockIdx.y + threadIdx.y;
+    const int i11 = (blockIdx.z*blockDim.z + threadIdx.z)/ne12;
+    const int i12 = (blockIdx.z*blockDim.z + threadIdx.z)%ne12;
+
+    if (i00 >= ne00) {
+        return;
+    }
+
+    const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
+
+    dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
+    const void * src0_row = (const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03;
+
+    const int ib = i00/qk; // block index
+    const int iqs = (i00%qk)/qr; // quant index
+    const int iybs = i00 - i00%qk; // dst block start index
+    const int y_offset = qr == 1 ? 1 : qk/2;
+
+    // dequantize
+    dfloat2 v;
+    dequantize_kernel(src0_row, ib, iqs, v);
+
+    dst_row[iybs + iqs + 0]        = v.x;
+    dst_row[iybs + iqs + y_offset] = v.y;
+}
+
+template<typename src0_t, typename dst_t>
+static __global__ void k_get_rows_float(
+            const src0_t * src0, const int32_t * src1, dst_t * dst,
+            int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
+            /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
+            /*size_t s0,*/ size_t s1, size_t s2, size_t s3,
+            /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
+            size_t s10, size_t s11, size_t s12/*, size_t s13*/) {
+
+    const int i00 = blockIdx.x*blockDim.x + threadIdx.x;
+    const int i10 = blockDim.y*blockIdx.y + threadIdx.y;
+    const int i11 = (blockIdx.z*blockDim.z + threadIdx.z)/ne12;
+    const int i12 = (blockIdx.z*blockDim.z + threadIdx.z)%ne12;
+
+    if (i00 >= ne00) {
+        return;
+    }
+
+    const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
+
+    dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
+    const src0_t * src0_row = (const src0_t *)((const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03);
+
+    dst_row[i00] = src0_row[i00];
+}
+
+template<int qk, int qr, dequantize_kernel_t dq>
+static void get_rows_cuda(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+                            const void * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) {
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
+    const int block_num_x = (ne00 + 2*CUDA_GET_ROWS_BLOCK_SIZE - 1) / (2*CUDA_GET_ROWS_BLOCK_SIZE);
+    const dim3 block_nums(block_num_x, ne10, ne11*ne12);
+
+    // strides in elements
+    //const size_t s0 = nb0 / ggml_element_size(dst);
+    const size_t s1 = nb1 / ggml_element_size(dst);
+    const size_t s2 = nb2 / ggml_element_size(dst);
+    const size_t s3 = nb3 / ggml_element_size(dst);
+
+    const size_t s10 = nb10 / ggml_element_size(src1);
+    const size_t s11 = nb11 / ggml_element_size(src1);
+    const size_t s12 = nb12 / ggml_element_size(src1);
+    //const size_t s13 = nb13 / ggml_element_size(src1);
+
+    GGML_ASSERT(ne00 % 2 == 0);
+
+    k_get_rows<qk, qr, dq><<<block_nums, block_dims, 0, stream>>>(
+            src0_dd, src1_dd, dst_dd,
+            ne00, /*ne01, ne02, ne03,*/
+            /*ne10, ne11,*/ ne12, /*ne13,*/
+            /* s0,*/ s1, s2, s3,
+            /* nb00,*/ nb01, nb02, nb03,
+            s10, s11, s12/*, s13*/);
+
+    GGML_UNUSED(dst);
+}
+
+template<typename src0_t>
+static void get_rows_cuda_float(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+                                const src0_t * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) {
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
+    const int block_num_x = (ne00 + CUDA_GET_ROWS_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BLOCK_SIZE;
+    const dim3 block_nums(block_num_x, ne10, ne11*ne12);
+
+    // strides in elements
+    //const size_t s0 = nb0 / ggml_element_size(dst);
+    const size_t s1 = nb1 / ggml_element_size(dst);
+    const size_t s2 = nb2 / ggml_element_size(dst);
+    const size_t s3 = nb3 / ggml_element_size(dst);
+
+    const size_t s10 = nb10 / ggml_element_size(src1);
+    const size_t s11 = nb11 / ggml_element_size(src1);
+    const size_t s12 = nb12 / ggml_element_size(src1);
+    //const size_t s13 = nb13 / ggml_element_size(src1);
+
+    k_get_rows_float<<<block_nums, block_dims, 0, stream>>>(
+            src0_dd, src1_dd, dst_dd,
+            ne00, /*ne01, ne02, ne03,*/
+            /*ne10, ne11,*/ ne12, /*ne13,*/
+            /* s0,*/ s1, s2, s3,
+            /* nb00,*/ nb01, nb02, nb03,
+            s10, s11, s12/*, s13*/);
+
+    GGML_UNUSED(dst);
+}
+
+void ggml_cuda_op_get_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const float * src0_d = (const float *)src0->data;
+    const float * src1_d = (const float *)src1->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+
+    GGML_ASSERT(src1->type == GGML_TYPE_I32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));
+    GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type));
+    GGML_ASSERT(dst->nb[0] == ggml_type_size(dst->type));
+
+    const int32_t * src1_i32 = (const int32_t *) src1_d;
+
+    switch (src0->type) {
+        case GGML_TYPE_F16:
+            get_rows_cuda_float(src0, src1, dst, (const half *)src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_F32:
+            get_rows_cuda_float(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_Q4_0:
+            get_rows_cuda<QK4_0, QR4_0, dequantize_q4_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            get_rows_cuda<QK4_1, QR4_1, dequantize_q4_1>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            get_rows_cuda<QK5_0, QR5_0, dequantize_q5_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            get_rows_cuda<QK5_1, QR5_1, dequantize_q5_1>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            get_rows_cuda<QK8_0, QR8_0, dequantize_q8_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        default:
+            // TODO: k-quants
+            fprintf(stderr, "%s: unsupported type: %s\n", __func__, ggml_type_name(src0->type));
+            GGML_ASSERT(false);
+            break;
+    }
+}
diff --git a/ggml-cuda/getrows.cuh b/ggml-cuda/getrows.cuh
new file mode 100644
index 000000000..bbf130232
--- /dev/null
+++ b/ggml-cuda/getrows.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_GET_ROWS_BLOCK_SIZE 256
+
+void ggml_cuda_op_get_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/im2col.cu b/ggml-cuda/im2col.cu
new file mode 100644
index 000000000..3d0d8d4e6
--- /dev/null
+++ b/ggml-cuda/im2col.cu
@@ -0,0 +1,104 @@
+#include "im2col.cuh"
+
+template <typename T>
+static  __global__ void im2col_kernel(
+        const float * x, T * dst, int64_t batch_offset,
+        int64_t offset_delta, int64_t IC, int64_t IW, int64_t IH, int64_t OH, int64_t OW, int64_t KW, int64_t KH, int64_t pelements, int64_t CHW,
+        int s0, int s1, int p0, int p1, int d0, int d1) {
+    const int64_t i = threadIdx.x + blockIdx.x * blockDim.x;
+    if (i >= pelements) {
+        return;
+    }
+
+    const int64_t  ksize = OW * (KH > 1 ? KW : 1);
+    const int64_t  kx = i / ksize;
+    const int64_t  kd = kx * ksize;
+    const int64_t  ky = (i - kd) / OW;
+    const int64_t  ix = i % OW;
+
+    const int64_t  oh = blockIdx.y;
+    const int64_t  batch = blockIdx.z / IC;
+    const int64_t  ic = blockIdx.z % IC;
+
+    const int64_t iiw = ix * s0 + kx * d0 - p0;
+    const int64_t iih = oh * s1 + ky * d1 - p1;
+
+    const int64_t offset_dst =
+        ((batch * OH + oh) * OW + ix) * CHW +
+        (ic * (KW * KH) + ky * KW + kx);
+
+    if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
+        dst[offset_dst] = 0.0f;
+    } else {
+        const int64_t offset_src = ic * offset_delta + batch * batch_offset;
+        dst[offset_dst] = x[offset_src + iih * IW + iiw];
+    }
+}
+
+template <typename T>
+static void im2col_cuda(const float * x, T* dst,
+    int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC,
+    int64_t batch, int64_t batch_offset, int64_t offset_delta,
+    int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) {
+    const int parallel_elements = OW * KW * KH;
+    const int num_blocks = (parallel_elements + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE;
+    dim3 block_nums(num_blocks, OH, batch * IC);
+    im2col_kernel<<<block_nums, CUDA_IM2COL_BLOCK_SIZE, 0, stream>>>(x, dst, batch_offset, offset_delta, IC, IW, IH, OH, OW, KW, KH, parallel_elements, (IC * KH * KW), s0, s1, p0, p1, d0, d1);
+}
+
+static void im2col_cuda_f16(const float * x, half * dst,
+    int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC,
+    int64_t batch, int64_t batch_offset, int64_t offset_delta,
+    int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) {
+
+    im2col_cuda<half>(x, dst, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, offset_delta, s0, s1, p0, p1, d0, d1, stream);
+}
+
+static void im2col_cuda_f32(const float * x, float * dst,
+    int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC,
+    int64_t batch, int64_t batch_offset, int64_t offset_delta,
+    int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) {
+
+    im2col_cuda<float>(x, dst, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, offset_delta, s0, s1, p0, p1, d0, d1, stream);
+}
+
+void ggml_cuda_op_im2col(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const float * src1_d = (const float *)src1->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);
+
+    const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
+    const int32_t s1 = ((const int32_t*)(dst->op_params))[1];
+    const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
+    const int32_t p1 = ((const int32_t*)(dst->op_params))[3];
+    const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
+    const int32_t d1 = ((const int32_t*)(dst->op_params))[5];
+
+    const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1;
+
+    const int64_t IC = src1->ne[is_2D ? 2 : 1];
+    const int64_t IH = is_2D ? src1->ne[1] : 1;
+    const int64_t IW =         src1->ne[0];
+
+    const int64_t KH = is_2D ? src0->ne[1] : 1;
+    const int64_t KW =         src0->ne[0];
+
+    const int64_t OH = is_2D ? dst->ne[2] : 1;
+    const int64_t OW =         dst->ne[1];
+
+    const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
+    const int64_t batch = src1->ne[3];
+    const size_t batch_offset = src1->nb[3] / 4; // nb is byte offset, src is type float32
+
+    if(dst->type == GGML_TYPE_F16) {
+        im2col_cuda_f16(src1_d, (half *) dst_d, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, stream);
+    } else {
+        im2col_cuda_f32(src1_d, (float *) dst_d, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, stream);
+    }
+}
diff --git a/ggml-cuda/im2col.cuh b/ggml-cuda/im2col.cuh
new file mode 100644
index 000000000..1ce8fae4d
--- /dev/null
+++ b/ggml-cuda/im2col.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_IM2COL_BLOCK_SIZE 256
+
+void ggml_cuda_op_im2col(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/mmq.cu b/ggml-cuda/mmq.cu
new file mode 100644
index 000000000..7948f1b12
--- /dev/null
+++ b/ggml-cuda/mmq.cu
@@ -0,0 +1,2255 @@
+#include "mmq.cuh"
+#include "vecdotq.cuh"
+
+typedef void (*allocate_tiles_cuda_t)(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc);
+typedef void (*load_tiles_cuda_t)(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row);
+typedef float (*vec_dot_q_mul_mat_cuda_t)(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ms, const int & i, const int & j, const int & k);
+typedef void (*dot_kernel_k_t)(const void * __restrict__ vx, const int ib, const int iqs, const float * __restrict__ y, float & v);
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+    GGML_UNUSED(x_qh);
+    GGML_UNUSED(x_sc);
+
+    __shared__ int  tile_x_qs[mmq_y * (WARP_SIZE)       + mmq_y];
+    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI4_0) + mmq_y/QI4_0];
+
+    *x_ql = tile_x_qs;
+    *x_dm = (half2 *) tile_x_d;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_0(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI4_0;
+    const int kqsx = k % QI4_0;
+
+    const block_q4_0 * bx0 = (const block_q4_0 *) vx;
+
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
+        // x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx] = bxi->d;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI4_0;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_0) {
+        int i = i0 + i_offset * QI4_0 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd] = bxi->d;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q4_0_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+    const float * x_dmf = (const float *) x_dm;
+
+    int u[2*VDR_Q4_0_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
+        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_0) % WARP_SIZE];
+    }
+
+    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMQ>
+        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dmf[i * (WARP_SIZE/QI4_0) + i/QI4_0 + k/QI4_0],
+         y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    __shared__ int   tile_x_qs[mmq_y * (WARP_SIZE) +     + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_1) + mmq_y/QI4_1];
+
+    *x_ql = tile_x_qs;
+    *x_dm = tile_x_dm;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_1(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI4_1;
+    const int kqsx = k % QI4_1;
+
+    const block_q4_1 * bx0 = (const block_q4_1 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI4_1;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_1) {
+        int i = i0 + i_offset * QI4_1 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd] = bxi->dm;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q4_1_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+
+    int u[2*VDR_Q4_1_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
+        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_1) % WARP_SIZE];
+    }
+
+    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMQ>
+        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dm[i * (WARP_SIZE/QI4_1) + i/QI4_1 + k/QI4_1],
+         y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    __shared__ int  tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
+    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI5_0) + mmq_y/QI5_0];
+
+    *x_ql = tile_x_ql;
+    *x_dm = (half2 *) tile_x_d;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_0(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI5_0;
+    const int kqsx = k % QI5_0;
+
+    const block_q5_0 * bx0 = (const block_q5_0 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        const int ql = get_int_from_uint8(bxi->qs, kqsx);
+        const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (k % QI5_0));
+
+        int qs0 = (ql >>  0)   & 0x0F0F0F0F;
+        qs0    |= (qh <<  4)   & 0x00000010;  // 0 ->  4
+        qs0    |= (qh << 11)   & 0x00001000;  // 1 -> 12
+        qs0    |= (qh << 18)   & 0x00100000;  // 2 -> 20
+        qs0    |= (qh << 25)   & 0x10000000;  // 3 -> 28
+        qs0     = __vsubss4(qs0, 0x10101010); // subtract 16
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
+
+        int qs1 = (ql >>  4)   & 0x0F0F0F0F;
+        qs1    |= (qh >> 12)   & 0x00000010;  // 16 ->  4
+        qs1    |= (qh >>  5)   & 0x00001000;  // 17 -> 12
+        qs1    |= (qh <<  2)   & 0x00100000;  // 18 -> 20
+        qs1    |= (qh <<  9)   & 0x10000000;  // 19 -> 28
+        qs1     = __vsubss4(qs1, 0x10101010); // subtract 16
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI5_0;
+    const int kbxd = k % blocks_per_tile_x_row;
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_0) {
+        int i = i0 + i_offset * QI5_0 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = bxi->d;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q5_0_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+    const int index_bx = i * (WARP_SIZE/QI5_0) + i/QI5_0 + k/QI5_0;
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    int u[2*VDR_Q5_0_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
+        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_0) % WARP_SIZE];
+    }
+
+    return vec_dot_q8_0_q8_1_impl<QR5_0*VDR_Q5_0_Q8_1_MMQ>
+        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dmf[index_bx], y_df[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
+}
+
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_1) + mmq_y/QI5_1];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_1(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset < nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI5_1;
+    const int kqsx = k % QI5_1;
+
+    const block_q5_1 * bx0 = (const block_q5_1 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
+        const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (k % QI5_1));
+
+        int qs0 = (ql >>  0) & 0x0F0F0F0F;
+        qs0    |= (qh <<  4) & 0x00000010; // 0 ->  4
+        qs0    |= (qh << 11) & 0x00001000; // 1 -> 12
+        qs0    |= (qh << 18) & 0x00100000; // 2 -> 20
+        qs0    |= (qh << 25) & 0x10000000; // 3 -> 28
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
+
+        int qs1 = (ql >>  4) & 0x0F0F0F0F;
+        qs1    |= (qh >> 12) & 0x00000010; // 16 ->  4
+        qs1    |= (qh >>  5) & 0x00001000; // 17 -> 12
+        qs1    |= (qh <<  2) & 0x00100000; // 18 -> 20
+        qs1    |= (qh <<  9) & 0x10000000; // 19 -> 28
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI5_1;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_1) {
+        int i = i0 + i_offset * QI5_1 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q5_1_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+    const int index_bx = i * (WARP_SIZE/QI5_1) + + i/QI5_1 + k/QI5_1;
+
+    int u[2*VDR_Q5_1_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
+        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_1) % WARP_SIZE];
+    }
+
+    return vec_dot_q8_1_q8_1_impl<QR5_1*VDR_Q5_1_Q8_1_MMQ>
+        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dm[index_bx], y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q8_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    __shared__ int  tile_x_qs[mmq_y * (WARP_SIZE)       + mmq_y];
+    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI8_0) + mmq_y/QI8_0];
+
+    *x_ql = tile_x_qs;
+    *x_dm = (half2 *) tile_x_d;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q8_0(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI8_0;
+    const int kqsx = k % QI8_0;
+    float * x_dmf = (float *) x_dm;
+
+    const block_q8_0 * bx0 = (const block_q8_0 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_int8(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI8_0;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0) {
+        int i = i0 + i_offset * QI8_0 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbxd] = bxi->d;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q8_0_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMQ>
+        (&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[j * WARP_SIZE + k], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0],
+         y_df[j * (WARP_SIZE/QI8_1) + k/QI8_1]);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q2_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+    GGML_UNUSED(x_qh);
+
+    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI2_K) + mmq_y/QI2_K];
+    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/4)     + mmq_y/4];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_sc = tile_x_sc;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q2_K(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+    GGML_UNUSED(x_qh);
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI2_K;
+    const int kqsx = k % QI2_K;
+
+    const block_q2_K * bx0 = (const block_q2_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI2_K;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI2_K) {
+        int i = (i0 + i_offset * QI2_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dm[i * (WARP_SIZE/QI2_K) + i / QI2_K + kbxd] = bxi->dm;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
+        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q2_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI2_K/4);
+
+        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = get_int_from_uint8_aligned(bxi->scales, k % (QI2_K/4));
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q2_K_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+    GGML_UNUSED(x_qh);
+
+    const int kbx = k / QI2_K;
+    const int ky  = (k % QI2_K) * QR2_K;
+    const float * y_df = (const float *) y_ds;
+
+    int v[QR2_K*VDR_Q2_K_Q8_1_MMQ];
+
+    const int kqsx = i * (WARP_SIZE + 1) + kbx*QI2_K + (QI2_K/2) * (ky/(2*QI2_K)) + ky % (QI2_K/2);
+    const int shift = 2 * ((ky % (2*QI2_K)) / (QI2_K/2));
+
+#pragma unroll
+    for (int l = 0; l < QR2_K*VDR_Q2_K_Q8_1_MMQ; ++l) {
+        v[l] = (x_ql[kqsx + l] >> shift) & 0x03030303;
+    }
+
+    const uint8_t * scales = ((const uint8_t *) &x_sc[i * (WARP_SIZE/4) + i/4 + kbx*4]) + ky/4;
+
+    const int index_y = j * WARP_SIZE + (QR2_K*k) % WARP_SIZE;
+    return vec_dot_q2_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dm[i * (WARP_SIZE/QI2_K) + i/QI2_K + kbx], y_df[index_y/QI8_1]);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q3_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI3_K) + mmq_y/QI3_K];
+    __shared__ int   tile_x_qh[mmq_y * (WARP_SIZE/2)     + mmq_y/2];
+    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/4)     + mmq_y/4];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_qh = tile_x_qh;
+    *x_sc = tile_x_sc;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q3_K(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI3_K;
+    const int kqsx = k % QI3_K;
+
+    const block_q3_K * bx0 = (const block_q3_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI3_K;
+    const int kbxd = k % blocks_per_tile_x_row;
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI3_K) {
+        int i = (i0 + i_offset * QI3_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI3_K) + i / QI3_K + kbxd] = bxi->d;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 2) {
+        int i = i0 + i_offset * 2 + k / (WARP_SIZE/2);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/2)) / (QI3_K/2);
+
+        // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
+        x_qh[i * (WARP_SIZE/2) + i / 2 + k % (WARP_SIZE/2)] = ~get_int_from_uint8(bxi->hmask, k % (QI3_K/2));
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
+        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI3_K/4);
+
+        const int ksc = k % (QI3_K/4);
+
+        const int ksc_low = ksc % (QI3_K/8);
+        const int shift_low = 4 * (ksc / (QI3_K/8));
+        const int sc_low = (get_int_from_uint8(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F;
+
+        const int ksc_high = QI3_K/8;
+        const int shift_high = 2 * ksc;
+        const int sc_high = ((get_int_from_uint8(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030;
+
+        const int sc = __vsubss4(sc_low | sc_high, 0x20202020);
+
+        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = sc;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q3_K_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    const int kbx  = k / QI3_K;
+    const int ky  = (k % QI3_K) * QR3_K;
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    const int8_t * scales = ((const int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4;
+
+    int v[QR3_K*VDR_Q3_K_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < QR3_K*VDR_Q3_K_Q8_1_MMQ; ++l) {
+        const int kqsx = i * (WARP_SIZE + 1) + kbx*QI3_K + (QI3_K/2) * (ky/(2*QI3_K)) + ky % (QI3_K/2);
+        const int shift = 2 * ((ky % 32) / 8);
+        const int vll = (x_ql[kqsx + l] >> shift) & 0x03030303;
+
+        const int vh = x_qh[i * (WARP_SIZE/2) + i/2 + kbx * (QI3_K/2) + (ky+l)%8] >> ((ky+l) / 8);
+        const int vlh = (vh << 2) & 0x04040404;
+
+        v[l] = __vsubss4(vll, vlh);
+    }
+
+    const int index_y = j * WARP_SIZE + (k*QR3_K) % WARP_SIZE;
+    return vec_dot_q3_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dmf[i * (WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[index_y/QI8_1]);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+    GGML_UNUSED(x_qh);
+
+    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_K) + mmq_y/QI4_K];
+    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_sc = tile_x_sc;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_K(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+    GGML_UNUSED(x_qh);
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI4_K; // == 0 if QK_K == 256
+    const int kqsx = k % QI4_K; // == k if QK_K == 256
+
+    const block_q4_K * bx0 = (const block_q4_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256
+    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_K) {
+        int i = (i0 + i_offset * QI4_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+#if QK_K == 256
+        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm;
+#else
+        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]};
+#endif
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
+        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI4_K/8);
+
+        const int * scales = (const int *) bxi->scales;
+
+        const int ksc = k % (WARP_SIZE/8);
+
+        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
+        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
+        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
+
+        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q4_K_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+    GGML_UNUSED(x_qh);
+
+    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2*((k % 16) / 8);
+
+    const int index_y = j * WARP_SIZE + (QR4_K*k) % WARP_SIZE;
+    return vec_dot_q4_K_q8_1_impl_mmq(&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[index_y], sc, sc+8,
+                                      x_dm[i * (WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[index_y/QI8_1]);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+    GGML_UNUSED(x_qh);
+
+    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_K) + mmq_y/QI5_K];
+    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_sc = tile_x_sc;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_K(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+    GGML_UNUSED(x_qh);
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI5_K; // == 0 if QK_K == 256
+    const int kqsx = k % QI5_K; // == k if QK_K == 256
+
+    const block_q5_K * bx0 = (const block_q5_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbx;
+        const int ky = QR5_K*kqsx;
+
+        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
+        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
+        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
+
+        const int qh = get_int_from_uint8_aligned(bxi->qh, kqsx % (QI5_K/4));
+        const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010;
+        const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010;
+
+        const int kq0 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + 0;
+        const int kq1 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + (QI5_K/4);
+
+        x_ql[i * (2*WARP_SIZE + 1) + kq0] = ql0 | qh0;
+        x_ql[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256
+    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_K) {
+        int i = (i0 + i_offset * QI5_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+#if QK_K == 256
+        x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm;
+#endif
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
+        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI5_K/8);
+
+        const int * scales = (const int *) bxi->scales;
+
+        const int ksc = k % (WARP_SIZE/8);
+
+        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
+        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
+        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
+
+        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q5_K_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+    GGML_UNUSED(x_qh);
+
+    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2 * ((k % 16) / 8);
+
+    const int index_x = i * (QR5_K*WARP_SIZE + 1) +  QR5_K*k;
+    const int index_y = j * WARP_SIZE             + (QR5_K*k) % WARP_SIZE;
+    return vec_dot_q5_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, sc+8,
+                                      x_dm[i * (WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[index_y/QI8_1]);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q6_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+    GGML_UNUSED(x_qh);
+
+    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI6_K) + mmq_y/QI6_K];
+    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_sc = tile_x_sc;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q6_K(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+    GGML_UNUSED(x_qh);
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI6_K; // == 0 if QK_K == 256
+    const int kqsx = k % QI6_K; // == k if QK_K == 256
+
+    const block_q6_K * bx0 = (const block_q6_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbx;
+        const int ky = QR6_K*kqsx;
+
+        const int ql = get_int_from_uint8(bxi->ql, kqsx);
+        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
+        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
+
+        const int qh = get_int_from_uint8(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4));
+        const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030;
+        const int qh1 =  (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4))))       & 0x30303030;
+
+        const int kq0 = ky - ky % QI6_K + k % (QI6_K/2) + 0;
+        const int kq1 = ky - ky % QI6_K + k % (QI6_K/2) + (QI6_K/2);
+
+        x_ql[i * (2*WARP_SIZE + 1) + kq0] = __vsubss4(ql0 | qh0, 0x20202020);
+        x_ql[i * (2*WARP_SIZE + 1) + kq1] = __vsubss4(ql1 | qh1, 0x20202020);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256
+    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI6_K) {
+        int i = (i0 + i_offset * QI6_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI6_K) + i / QI6_K + kbxd] = bxi->d;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
+        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q6_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / 4;
+
+        x_sc[i * (WARP_SIZE/8) + i / 8 + k % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, k % (QI6_K/8));
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q6_K_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+    GGML_UNUSED(x_qh);
+
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/8]);
+
+    const int index_x = i * (QR6_K*WARP_SIZE + 1) +  QR6_K*k;
+    const int index_y = j * WARP_SIZE             + (QR6_K*k) % WARP_SIZE;
+    return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]);
+}
+
+#define  MMQ_X_Q4_0_RDNA2  64
+#define  MMQ_Y_Q4_0_RDNA2  128
+#define NWARPS_Q4_0_RDNA2  8
+#define  MMQ_X_Q4_0_RDNA1  64
+#define  MMQ_Y_Q4_0_RDNA1  64
+#define NWARPS_Q4_0_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q4_0_AMPERE 4
+#define  MMQ_Y_Q4_0_AMPERE 32
+#define NWARPS_Q4_0_AMPERE 4
+#else
+#define  MMQ_X_Q4_0_AMPERE 64
+#define  MMQ_Y_Q4_0_AMPERE 128
+#define NWARPS_Q4_0_AMPERE 4
+#endif
+#define  MMQ_X_Q4_0_PASCAL 64
+#define  MMQ_Y_Q4_0_PASCAL 64
+#define NWARPS_Q4_0_PASCAL 8
+
+template <int qk, int qr, int qi, bool need_sum, typename block_q_t, int mmq_x, int mmq_y, int nwarps,
+              allocate_tiles_cuda_t allocate_tiles, load_tiles_cuda_t load_tiles, int vdr, vec_dot_q_mul_mat_cuda_t vec_dot>
+static __device__ __forceinline__ void mul_mat_q(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    const int blocks_per_row_x = ncols_x / qk;
+    const int blocks_per_col_y = nrows_y / QK8_1;
+    const int blocks_per_warp = WARP_SIZE / qi;
+
+    const int & ncols_dst = ncols_y;
+
+    const int row_dst_0 = blockIdx.x*mmq_y;
+    const int & row_x_0 = row_dst_0;
+
+    const int col_dst_0 = blockIdx.y*mmq_x;
+    const int & col_y_0 = col_dst_0;
+
+    int   * tile_x_ql = nullptr;
+    half2 * tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+    allocate_tiles(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc);
+
+    __shared__ int    tile_y_qs[mmq_x * WARP_SIZE];
+    __shared__ half2  tile_y_ds[mmq_x * WARP_SIZE/QI8_1];
+
+    float sum[mmq_y/WARP_SIZE][mmq_x/nwarps] = {{0.0f}};
+
+    for (int ib0 = 0; ib0 < blocks_per_row_x; ib0 += blocks_per_warp) {
+
+        load_tiles(x + row_x_0*blocks_per_row_x + ib0, tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc,
+                   threadIdx.y, nrows_x-row_x_0-1, threadIdx.x, blocks_per_row_x);
+
+#pragma unroll
+        for (int ir = 0; ir < qr; ++ir) {
+            const int kqs = ir*WARP_SIZE + threadIdx.x;
+            const int kbxd = kqs / QI8_1;
+
+#pragma unroll
+            for (int i = 0; i < mmq_x; i += nwarps) {
+                const int col_y_eff = min(col_y_0 + threadIdx.y + i, ncols_y-1); // to prevent out-of-bounds memory accesses
+
+                const block_q8_1 * by0 = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + kbxd];
+
+                const int index_y = (threadIdx.y + i) * WARP_SIZE + kqs % WARP_SIZE;
+                tile_y_qs[index_y] = get_int_from_int8_aligned(by0->qs, threadIdx.x % QI8_1);
+            }
+
+#pragma unroll
+            for (int ids0 = 0; ids0 < mmq_x; ids0 += nwarps * QI8_1) {
+                const int ids = (ids0 + threadIdx.y * QI8_1 + threadIdx.x / (WARP_SIZE/QI8_1)) % mmq_x;
+                const int kby = threadIdx.x % (WARP_SIZE/QI8_1);
+                const int col_y_eff = min(col_y_0 + ids, ncols_y-1);
+
+                // if the sum is not needed it's faster to transform the scale to f32 ahead of time
+                const half2 * dsi_src = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + ir*(WARP_SIZE/QI8_1) + kby].ds;
+                half2       * dsi_dst = &tile_y_ds[ids * (WARP_SIZE/QI8_1) + kby];
+                if (need_sum) {
+                    *dsi_dst = *dsi_src;
+                } else {
+                    float * dfi_dst = (float *) dsi_dst;
+                    *dfi_dst = __low2float(*dsi_src);
+                }
+            }
+
+            __syncthreads();
+
+// #pragma unroll // unrolling this loop causes too much register pressure
+            for (int k = ir*WARP_SIZE/qr; k < (ir+1)*WARP_SIZE/qr; k += vdr) {
+#pragma unroll
+                for (int j = 0; j < mmq_x; j += nwarps) {
+#pragma unroll
+                    for (int i = 0; i < mmq_y; i += WARP_SIZE) {
+                        sum[i/WARP_SIZE][j/nwarps] += vec_dot(
+                            tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, tile_y_qs, tile_y_ds,
+                            threadIdx.x + i, threadIdx.y + j, k);
+                    }
+                }
+            }
+
+            __syncthreads();
+        }
+    }
+
+#pragma unroll
+    for (int j = 0; j < mmq_x; j += nwarps) {
+        const int col_dst = col_dst_0 + j + threadIdx.y;
+
+        if (col_dst >= ncols_dst) {
+            return;
+        }
+
+#pragma unroll
+        for (int i = 0; i < mmq_y; i += WARP_SIZE) {
+            const int row_dst = row_dst_0 + threadIdx.x + i;
+
+            if (row_dst >= nrows_dst) {
+                continue;
+            }
+
+            dst[col_dst*nrows_dst + row_dst] = sum[i/WARP_SIZE][j/nwarps];
+        }
+    }
+}
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q4_0_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+    mul_mat_q4_0(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q4_0_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q4_0_RDNA2;
+    const int nwarps = NWARPS_Q4_0_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q4_0_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q4_0_RDNA1;
+    const int nwarps = NWARPS_Q4_0_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
+        load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q4_0_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q4_0_AMPERE;
+    const int nwarps = NWARPS_Q4_0_AMPERE;
+
+    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
+        load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q4_0_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q4_0_PASCAL;
+    const int nwarps = NWARPS_Q4_0_PASCAL;
+
+    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
+        load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    GGML_UNUSED(vec_dot_q4_0_q8_1_mul_mat);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q4_1_RDNA2  64
+#define  MMQ_Y_Q4_1_RDNA2  128
+#define NWARPS_Q4_1_RDNA2  8
+#define  MMQ_X_Q4_1_RDNA1  64
+#define  MMQ_Y_Q4_1_RDNA1  64
+#define NWARPS_Q4_1_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q4_1_AMPERE 4
+#define  MMQ_Y_Q4_1_AMPERE 32
+#define NWARPS_Q4_1_AMPERE 4
+#else
+#define  MMQ_X_Q4_1_AMPERE 64
+#define  MMQ_Y_Q4_1_AMPERE 128
+#define NWARPS_Q4_1_AMPERE 4
+#endif
+#define  MMQ_X_Q4_1_PASCAL 64
+#define  MMQ_Y_Q4_1_PASCAL 64
+#define NWARPS_Q4_1_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q4_1_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#elif __CUDA_ARCH__ < CC_VOLTA
+    __launch_bounds__(WARP_SIZE*NWARPS_Q4_1_PASCAL, 2)
+#endif // __CUDA_ARCH__ < CC_VOLTA
+    mul_mat_q4_1(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q4_1_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q4_1_RDNA2;
+    const int nwarps = NWARPS_Q4_1_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q4_1_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q4_1_RDNA1;
+    const int nwarps = NWARPS_Q4_1_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
+        load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q4_1_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q4_1_AMPERE;
+    const int nwarps = NWARPS_Q4_1_AMPERE;
+
+    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
+        load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q4_1_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q4_1_PASCAL;
+    const int nwarps = NWARPS_Q4_1_PASCAL;
+
+    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
+        load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    GGML_UNUSED(vec_dot_q4_1_q8_1_mul_mat);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q5_0_RDNA2  64
+#define  MMQ_Y_Q5_0_RDNA2  128
+#define NWARPS_Q5_0_RDNA2  8
+#define  MMQ_X_Q5_0_RDNA1  64
+#define  MMQ_Y_Q5_0_RDNA1  64
+#define NWARPS_Q5_0_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q5_0_AMPERE 4
+#define  MMQ_Y_Q5_0_AMPERE 32
+#define NWARPS_Q5_0_AMPERE 4
+#else
+#define  MMQ_X_Q5_0_AMPERE 128
+#define  MMQ_Y_Q5_0_AMPERE 64
+#define NWARPS_Q5_0_AMPERE 4
+#endif
+#define  MMQ_X_Q5_0_PASCAL 64
+#define  MMQ_Y_Q5_0_PASCAL 64
+#define NWARPS_Q5_0_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q5_0_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+    mul_mat_q5_0(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q5_0_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q5_0_RDNA2;
+    const int nwarps = NWARPS_Q5_0_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q5_0_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q5_0_RDNA1;
+    const int nwarps = NWARPS_Q5_0_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
+        load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q5_0_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q5_0_AMPERE;
+    const int nwarps = NWARPS_Q5_0_AMPERE;
+
+    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
+        load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q5_0_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q5_0_PASCAL;
+    const int nwarps = NWARPS_Q5_0_PASCAL;
+
+    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
+        load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    GGML_UNUSED(vec_dot_q5_0_q8_1_mul_mat);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q5_1_RDNA2  64
+#define  MMQ_Y_Q5_1_RDNA2  128
+#define NWARPS_Q5_1_RDNA2  8
+#define  MMQ_X_Q5_1_RDNA1  64
+#define  MMQ_Y_Q5_1_RDNA1  64
+#define NWARPS_Q5_1_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q5_1_AMPERE 4
+#define  MMQ_Y_Q5_1_AMPERE 32
+#define NWARPS_Q5_1_AMPERE 4
+#else
+#define  MMQ_X_Q5_1_AMPERE 128
+#define  MMQ_Y_Q5_1_AMPERE 64
+#define NWARPS_Q5_1_AMPERE 4
+#endif
+#define  MMQ_X_Q5_1_PASCAL 64
+#define  MMQ_Y_Q5_1_PASCAL 64
+#define NWARPS_Q5_1_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q5_1_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+mul_mat_q5_1(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q5_1_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q5_1_RDNA2;
+    const int nwarps = NWARPS_Q5_1_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q5_1_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q5_1_RDNA1;
+    const int nwarps = NWARPS_Q5_1_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
+        load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q5_1_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q5_1_AMPERE;
+    const int nwarps = NWARPS_Q5_1_AMPERE;
+
+    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
+        load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q5_1_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q5_1_PASCAL;
+    const int nwarps = NWARPS_Q5_1_PASCAL;
+
+    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
+        load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    GGML_UNUSED(vec_dot_q5_1_q8_1_mul_mat);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q8_0_RDNA2  64
+#define  MMQ_Y_Q8_0_RDNA2  128
+#define NWARPS_Q8_0_RDNA2  8
+#define  MMQ_X_Q8_0_RDNA1  64
+#define  MMQ_Y_Q8_0_RDNA1  64
+#define NWARPS_Q8_0_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q8_0_AMPERE 4
+#define  MMQ_Y_Q8_0_AMPERE 32
+#define NWARPS_Q8_0_AMPERE 4
+#else
+#define  MMQ_X_Q8_0_AMPERE 128
+#define  MMQ_Y_Q8_0_AMPERE 64
+#define NWARPS_Q8_0_AMPERE 4
+#endif
+#define  MMQ_X_Q8_0_PASCAL 64
+#define  MMQ_Y_Q8_0_PASCAL 64
+#define NWARPS_Q8_0_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q8_0_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+    mul_mat_q8_0(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q8_0_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q8_0_RDNA2;
+    const int nwarps = NWARPS_Q8_0_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q8_0_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q8_0_RDNA1;
+    const int nwarps = NWARPS_Q8_0_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
+        load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q8_0_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q8_0_AMPERE;
+    const int nwarps = NWARPS_Q8_0_AMPERE;
+
+    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
+        load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q8_0_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q8_0_PASCAL;
+    const int nwarps = NWARPS_Q8_0_PASCAL;
+
+    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
+        load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    GGML_UNUSED(vec_dot_q8_0_q8_1_mul_mat);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q2_K_RDNA2  64
+#define  MMQ_Y_Q2_K_RDNA2  128
+#define NWARPS_Q2_K_RDNA2  8
+#define  MMQ_X_Q2_K_RDNA1  128
+#define  MMQ_Y_Q2_K_RDNA1  32
+#define NWARPS_Q2_K_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q2_K_AMPERE 4
+#define  MMQ_Y_Q2_K_AMPERE 32
+#define NWARPS_Q2_K_AMPERE 4
+#else
+#define  MMQ_X_Q2_K_AMPERE 64
+#define  MMQ_Y_Q2_K_AMPERE 128
+#define NWARPS_Q2_K_AMPERE 4
+#endif
+#define  MMQ_X_Q2_K_PASCAL 64
+#define  MMQ_Y_Q2_K_PASCAL 64
+#define NWARPS_Q2_K_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q2_K_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+mul_mat_q2_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q2_K_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q2_K_RDNA2;
+    const int nwarps = NWARPS_Q2_K_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q2_K_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q2_K_RDNA1;
+    const int nwarps = NWARPS_Q2_K_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
+        load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q2_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q2_K_AMPERE;
+    const int nwarps = NWARPS_Q2_K_AMPERE;
+
+    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
+        load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q2_K_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q2_K_PASCAL;
+    const int nwarps = NWARPS_Q2_K_PASCAL;
+
+    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
+        load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    GGML_UNUSED(vec_dot_q2_K_q8_1_mul_mat);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q3_K_RDNA2  128
+#define  MMQ_Y_Q3_K_RDNA2  64
+#define NWARPS_Q3_K_RDNA2  8
+#define  MMQ_X_Q3_K_RDNA1  32
+#define  MMQ_Y_Q3_K_RDNA1  128
+#define NWARPS_Q3_K_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q3_K_AMPERE 4
+#define  MMQ_Y_Q3_K_AMPERE 32
+#define NWARPS_Q3_K_AMPERE 4
+#else
+#define  MMQ_X_Q3_K_AMPERE 128
+#define  MMQ_Y_Q3_K_AMPERE 128
+#define NWARPS_Q3_K_AMPERE 4
+#endif
+#define  MMQ_X_Q3_K_PASCAL 64
+#define  MMQ_Y_Q3_K_PASCAL 64
+#define NWARPS_Q3_K_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q3_K_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#elif __CUDA_ARCH__ < CC_VOLTA
+    __launch_bounds__(WARP_SIZE*NWARPS_Q3_K_PASCAL, 2)
+#endif // __CUDA_ARCH__ < CC_VOLTA
+    mul_mat_q3_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q3_K_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q3_K_RDNA2;
+    const int nwarps = NWARPS_Q3_K_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q3_K_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q3_K_RDNA1;
+    const int nwarps = NWARPS_Q3_K_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
+        load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q3_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q3_K_AMPERE;
+    const int nwarps = NWARPS_Q3_K_AMPERE;
+
+    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
+        load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q3_K_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q3_K_PASCAL;
+    const int nwarps = NWARPS_Q3_K_PASCAL;
+
+    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
+        load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    GGML_UNUSED(vec_dot_q3_K_q8_1_mul_mat);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q4_K_RDNA2  64
+#define  MMQ_Y_Q4_K_RDNA2  128
+#define NWARPS_Q4_K_RDNA2  8
+#define  MMQ_X_Q4_K_RDNA1  32
+#define  MMQ_Y_Q4_K_RDNA1  64
+#define NWARPS_Q4_K_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q4_K_AMPERE 4
+#define  MMQ_Y_Q4_K_AMPERE 32
+#define NWARPS_Q4_K_AMPERE 4
+#else
+#define  MMQ_X_Q4_K_AMPERE 64
+#define  MMQ_Y_Q4_K_AMPERE 128
+#define NWARPS_Q4_K_AMPERE 4
+#endif
+#define  MMQ_X_Q4_K_PASCAL 64
+#define  MMQ_Y_Q4_K_PASCAL 64
+#define NWARPS_Q4_K_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q4_K_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#elif __CUDA_ARCH__ < CC_VOLTA
+    __launch_bounds__(WARP_SIZE*NWARPS_Q4_K_PASCAL, 2)
+#endif // __CUDA_ARCH__ < CC_VOLTA
+    mul_mat_q4_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q4_K_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q4_K_RDNA2;
+    const int nwarps = NWARPS_Q4_K_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q4_K_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q4_K_RDNA1;
+    const int nwarps = NWARPS_Q4_K_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
+        load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q4_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q4_K_AMPERE;
+    const int nwarps = NWARPS_Q4_K_AMPERE;
+
+    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
+        load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q4_K_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q4_K_PASCAL;
+    const int nwarps = NWARPS_Q4_K_PASCAL;
+
+    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
+        load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    GGML_UNUSED(vec_dot_q4_K_q8_1_mul_mat);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q5_K_RDNA2  64
+#define  MMQ_Y_Q5_K_RDNA2  128
+#define NWARPS_Q5_K_RDNA2  8
+#define  MMQ_X_Q5_K_RDNA1  32
+#define  MMQ_Y_Q5_K_RDNA1  64
+#define NWARPS_Q5_K_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q5_K_AMPERE 4
+#define  MMQ_Y_Q5_K_AMPERE 32
+#define NWARPS_Q5_K_AMPERE 4
+#else
+#define  MMQ_X_Q5_K_AMPERE 64
+#define  MMQ_Y_Q5_K_AMPERE 128
+#define NWARPS_Q5_K_AMPERE 4
+#endif
+#define  MMQ_X_Q5_K_PASCAL 64
+#define  MMQ_Y_Q5_K_PASCAL 64
+#define NWARPS_Q5_K_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q5_K_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+mul_mat_q5_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q5_K_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q5_K_RDNA2;
+    const int nwarps = NWARPS_Q5_K_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q5_K_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q5_K_RDNA1;
+    const int nwarps = NWARPS_Q5_K_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
+        load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q5_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q5_K_AMPERE;
+    const int nwarps = NWARPS_Q5_K_AMPERE;
+
+    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
+        load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q5_K_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q5_K_PASCAL;
+    const int nwarps = NWARPS_Q5_K_PASCAL;
+
+    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
+        load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    GGML_UNUSED(vec_dot_q5_K_q8_1_mul_mat);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q6_K_RDNA2  64
+#define  MMQ_Y_Q6_K_RDNA2  128
+#define NWARPS_Q6_K_RDNA2  8
+#define  MMQ_X_Q6_K_RDNA1  32
+#define  MMQ_Y_Q6_K_RDNA1  64
+#define NWARPS_Q6_K_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q6_K_AMPERE 4
+#define  MMQ_Y_Q6_K_AMPERE 32
+#define NWARPS_Q6_K_AMPERE 4
+#else
+#define  MMQ_X_Q6_K_AMPERE 64
+#define  MMQ_Y_Q6_K_AMPERE 64
+#define NWARPS_Q6_K_AMPERE 4
+#endif
+#define  MMQ_X_Q6_K_PASCAL 64
+#define  MMQ_Y_Q6_K_PASCAL 64
+#define NWARPS_Q6_K_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q6_K_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#elif __CUDA_ARCH__ < CC_VOLTA
+    __launch_bounds__(WARP_SIZE*NWARPS_Q6_K_PASCAL, 2)
+#endif // __CUDA_ARCH__ < CC_VOLTA
+    mul_mat_q6_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q6_K_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q6_K_RDNA2;
+    const int nwarps = NWARPS_Q6_K_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q6_K_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q6_K_RDNA1;
+    const int nwarps = NWARPS_Q6_K_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
+        load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q6_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q6_K_AMPERE;
+    const int nwarps = NWARPS_Q6_K_AMPERE;
+
+    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
+        load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q6_K_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q6_K_PASCAL;
+    const int nwarps = NWARPS_Q6_K_PASCAL;
+
+    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
+        load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    GGML_UNUSED(vec_dot_q6_K_q8_1_mul_mat);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+static void ggml_mul_mat_q4_0_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id = ggml_cuda_get_device();
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q4_0_RDNA2;
+        mmq_y  =  MMQ_Y_Q4_0_RDNA2;
+        nwarps = NWARPS_Q4_0_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q4_0_RDNA1;
+        mmq_y  =  MMQ_Y_Q4_0_RDNA1;
+        nwarps = NWARPS_Q4_0_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q4_0_AMPERE;
+        mmq_y  =  MMQ_Y_Q4_0_AMPERE;
+        nwarps = NWARPS_Q4_0_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q4_0_PASCAL;
+        mmq_y  =  MMQ_Y_Q4_0_PASCAL;
+        nwarps = NWARPS_Q4_0_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q4_0<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q4_0<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q4_1_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id = ggml_cuda_get_device();
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q4_1_RDNA2;
+        mmq_y  =  MMQ_Y_Q4_1_RDNA2;
+        nwarps = NWARPS_Q4_1_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q4_1_RDNA1;
+        mmq_y  =  MMQ_Y_Q4_1_RDNA1;
+        nwarps = NWARPS_Q4_1_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q4_1_AMPERE;
+        mmq_y  =  MMQ_Y_Q4_1_AMPERE;
+        nwarps = NWARPS_Q4_1_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q4_1_PASCAL;
+        mmq_y  =  MMQ_Y_Q4_1_PASCAL;
+        nwarps = NWARPS_Q4_1_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q4_1<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q4_1<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q5_0_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id = ggml_cuda_get_device();
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q5_0_RDNA2;
+        mmq_y  =  MMQ_Y_Q5_0_RDNA2;
+        nwarps = NWARPS_Q5_0_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q5_0_RDNA1;
+        mmq_y  =  MMQ_Y_Q5_0_RDNA1;
+        nwarps = NWARPS_Q5_0_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q5_0_AMPERE;
+        mmq_y  =  MMQ_Y_Q5_0_AMPERE;
+        nwarps = NWARPS_Q5_0_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q5_0_PASCAL;
+        mmq_y  =  MMQ_Y_Q5_0_PASCAL;
+        nwarps = NWARPS_Q5_0_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q5_0<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q5_0<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q5_1_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id = ggml_cuda_get_device();
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q5_1_RDNA2;
+        mmq_y  =  MMQ_Y_Q5_1_RDNA2;
+        nwarps = NWARPS_Q5_1_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q5_1_RDNA1;
+        mmq_y  =  MMQ_Y_Q5_1_RDNA1;
+        nwarps = NWARPS_Q5_1_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q5_1_AMPERE;
+        mmq_y  =  MMQ_Y_Q5_1_AMPERE;
+        nwarps = NWARPS_Q5_1_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q5_1_PASCAL;
+        mmq_y  =  MMQ_Y_Q5_1_PASCAL;
+        nwarps = NWARPS_Q5_1_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q5_1<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q5_1<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q8_0_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id = ggml_cuda_get_device();
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q8_0_RDNA2;
+        mmq_y  =  MMQ_Y_Q8_0_RDNA2;
+        nwarps = NWARPS_Q8_0_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q8_0_RDNA1;
+        mmq_y  =  MMQ_Y_Q8_0_RDNA1;
+        nwarps = NWARPS_Q8_0_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q8_0_AMPERE;
+        mmq_y  =  MMQ_Y_Q8_0_AMPERE;
+        nwarps = NWARPS_Q8_0_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q8_0_PASCAL;
+        mmq_y  =  MMQ_Y_Q8_0_PASCAL;
+        nwarps = NWARPS_Q8_0_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q8_0<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q8_0<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q2_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id = ggml_cuda_get_device();
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q2_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q2_K_RDNA2;
+        nwarps = NWARPS_Q2_K_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q2_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q2_K_RDNA1;
+        nwarps = NWARPS_Q2_K_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q2_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q2_K_AMPERE;
+        nwarps = NWARPS_Q2_K_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q2_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q2_K_PASCAL;
+        nwarps = NWARPS_Q2_K_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q2_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q2_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q3_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+#if QK_K == 256
+
+    int id = ggml_cuda_get_device();
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q3_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q3_K_RDNA2;
+        nwarps = NWARPS_Q3_K_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q3_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q3_K_RDNA1;
+        nwarps = NWARPS_Q3_K_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q3_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q3_K_AMPERE;
+        nwarps = NWARPS_Q3_K_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q3_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q3_K_PASCAL;
+        nwarps = NWARPS_Q3_K_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+#endif
+}
+
+static void ggml_mul_mat_q4_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id = ggml_cuda_get_device();
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q4_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q4_K_RDNA2;
+        nwarps = NWARPS_Q4_K_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q4_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q4_K_RDNA1;
+        nwarps = NWARPS_Q4_K_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q4_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q4_K_AMPERE;
+        nwarps = NWARPS_Q4_K_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q4_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q4_K_PASCAL;
+        nwarps = NWARPS_Q4_K_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q4_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q4_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q5_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id = ggml_cuda_get_device();
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q5_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q5_K_RDNA2;
+        nwarps = NWARPS_Q5_K_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q5_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q5_K_RDNA1;
+        nwarps = NWARPS_Q5_K_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q5_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q5_K_AMPERE;
+        nwarps = NWARPS_Q5_K_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q5_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q5_K_PASCAL;
+        nwarps = NWARPS_Q5_K_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q5_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q5_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q6_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id = ggml_cuda_get_device();
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q6_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q6_K_RDNA2;
+        nwarps = NWARPS_Q6_K_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q6_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q6_K_RDNA1;
+        nwarps = NWARPS_Q6_K_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q6_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q6_K_AMPERE;
+        nwarps = NWARPS_Q6_K_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q6_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q6_K_PASCAL;
+        nwarps = NWARPS_Q6_K_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q6_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q6_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+void ggml_cuda_op_mul_mat_q(
+    ggml_backend_cuda_context & ctx,
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, cudaStream_t stream) {
+
+    const int64_t ne00 = src0->ne[0];
+
+    const int64_t ne10 = src1->ne[0];
+    GGML_ASSERT(ne10 % QK8_1 == 0);
+
+    const int64_t ne0 = dst->ne[0];
+
+    const int64_t row_diff = row_high - row_low;
+
+    int id = ggml_cuda_get_device();
+
+    // the main device has a larger memory buffer to hold the results from all GPUs
+    // nrows_dst == nrows of the matrix that the kernel writes into
+    const int64_t nrows_dst = id == ctx.device ? ne0 : row_diff;
+
+    switch (src0->type) {
+        case GGML_TYPE_Q4_0:
+            ggml_mul_mat_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            ggml_mul_mat_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            ggml_mul_mat_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            ggml_mul_mat_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            ggml_mul_mat_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q2_K:
+            ggml_mul_mat_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q3_K:
+            ggml_mul_mat_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q4_K:
+            ggml_mul_mat_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_K:
+            ggml_mul_mat_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q6_K:
+            ggml_mul_mat_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_ddf_i);
+}
+
+bool ggml_cuda_supports_mmq(enum ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+            return true;
+        default:
+            return false;
+    }
+}
diff --git a/ggml-cuda/mmq.cuh b/ggml-cuda/mmq.cuh
new file mode 100644
index 000000000..807817c4a
--- /dev/null
+++ b/ggml-cuda/mmq.cuh
@@ -0,0 +1,9 @@
+#include "common.cuh"
+
+void ggml_cuda_op_mul_mat_q(
+    ggml_backend_cuda_context & ctx,
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, cudaStream_t stream);
+
+bool ggml_cuda_supports_mmq(enum ggml_type type);
diff --git a/ggml-cuda/mmvq.cu b/ggml-cuda/mmvq.cu
new file mode 100644
index 000000000..65cc1bcaa
--- /dev/null
+++ b/ggml-cuda/mmvq.cu
@@ -0,0 +1,404 @@
+#include "mmvq.cuh"
+#include "vecdotq.cuh"
+
+typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs);
+
+template <int ncols_y, int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
+#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+// tell the compiler to use as many registers as it wants, see nwarps definition below
+__launch_bounds__((ncols_y <= 4 ? 4 : 2)*WARP_SIZE, 1)
+#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+static __global__ void mul_mat_vec_q(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && (defined(RDNA2) || defined(RDNA3))
+    constexpr int nwarps              = 1;
+    constexpr int rows_per_cuda_block = 1;
+#else
+    constexpr int nwarps              = ncols_y <= 4 ? 4 : 2;
+    constexpr int rows_per_cuda_block = ncols_y == 1 ? 1 : 2;
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && !defined(RDNA2) && !defined(RDNA3)
+
+    const     int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
+    const     int row0 = rows_per_cuda_block*blockIdx.x;
+    const     int blocks_per_row_x = ncols_x / qk;
+    const     int blocks_per_col_y = nrows_y / QK8_1;
+    constexpr int blocks_per_iter = vdr * nwarps*WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp[ncols_y][rows_per_cuda_block] = {0.0f};
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int kbx = tid / (qi/vdr); kbx < blocks_per_row_x; kbx += blocks_per_iter) {
+        const int kby = kbx * (qk/QK8_1); // y block index that aligns with kbx
+
+        // x block quant index when casting the quants to int
+        const int kqs = vdr * (tid % (qi/vdr));
+
+#pragma unroll
+        for (int j = 0; j < ncols_y; ++j) {
+#pragma unroll
+            for (int i = 0; i < rows_per_cuda_block; ++i) {
+                tmp[j][i] += vec_dot_q_cuda(
+                    &x[kbx + (row0 + i)*blocks_per_row_x], &y[j*blocks_per_col_y + kby], kqs);
+            }
+        }
+    }
+
+    __shared__ float tmp_shared[nwarps-1 > 0 ? nwarps-1 : 1][ncols_y][rows_per_cuda_block][WARP_SIZE];
+    if (threadIdx.y > 0) {
+#pragma unroll
+        for (int j = 0; j < ncols_y; ++j) {
+#pragma unroll
+            for (int i = 0; i < rows_per_cuda_block; ++i) {
+                tmp_shared[threadIdx.y-1][j][i][threadIdx.x] = tmp[j][i];
+            }
+        }
+    }
+    __syncthreads();
+    if (threadIdx.y > 0) {
+        return;
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int j = 0; j < ncols_y; ++j) {
+#pragma unroll
+        for (int i = 0; i < rows_per_cuda_block; ++i) {
+#pragma unroll
+            for (int l = 0; l < nwarps-1; ++l) {
+                tmp[j][i] += tmp_shared[l][j][i][threadIdx.x];
+            }
+            tmp[j][i] = warp_reduce_sum(tmp[j][i]);
+        }
+
+        if (threadIdx.x < rows_per_cuda_block) {
+            dst[j*nrows_dst + row0 + threadIdx.x] = tmp[j][threadIdx.x];
+        }
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot>
+static void mul_mat_vec_q_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    GGML_ASSERT(ncols_x % qk == 0);
+    GGML_ASSERT(ncols_y <= MMVQ_MAX_BATCH_SIZE);
+
+    int id = ggml_cuda_get_device();
+
+    int64_t nwarps = 1;
+    int64_t rows_per_cuda_block = 1;
+
+    if (ggml_cuda_info().devices[id].cc < CC_RDNA2) { // NVIDIA and AMD older than RDNA2
+        switch(ncols_y) {
+            case 1:
+                nwarps = 4;
+                rows_per_cuda_block = 1;
+                break;
+            case 2:
+            case 3:
+            case 4:
+                nwarps = 4;
+                rows_per_cuda_block = 2;
+                break;
+            case 5:
+            case 6:
+            case 7:
+            case 8:
+                nwarps = 2;
+                rows_per_cuda_block = 2;
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
+    }
+    const int64_t nblocks = (nrows_x + rows_per_cuda_block - 1) / rows_per_cuda_block;
+    const dim3 block_nums(nblocks, 1, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    switch (ncols_y) {
+        case 1:
+            mul_mat_vec_q<1, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            break;
+        case 2:
+            mul_mat_vec_q<2, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            break;
+        case 3:
+            mul_mat_vec_q<3, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            break;
+        case 4:
+            mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            break;
+        case 5:
+            mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            break;
+        case 6:
+            mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            break;
+        case 7:
+            mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            break;
+        case 8:
+            mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+}
+
+static void mul_mat_vec_q4_0_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_q4_1_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK4_1, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_q5_0_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_q5_1_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_q8_0_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_q2_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_q3_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_q4_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_q5_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_q6_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_iq2_xxs_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI2_XXS, block_iq2_xxs, 1, vec_dot_iq2_xxs_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_iq2_xs_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI2_XS, block_iq2_xs, 1, vec_dot_iq2_xs_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_iq2_s_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI2_S, block_iq2_s, 1, vec_dot_iq2_s_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_iq3_xxs_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI3_XXS, block_iq3_xxs, 1, vec_dot_iq3_xxs_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_iq1_s_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI1_S, block_iq1_s, 1, vec_dot_iq1_s_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_iq1_m_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI1_S, block_iq1_m, 1, vec_dot_iq1_m_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_iq4_nl_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK4_NL, QI4_NL, block_iq4_nl, VDR_Q4_0_Q8_1_MMVQ, vec_dot_iq4_nl_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_iq4_xs_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI4_XS, block_iq4_xs, 1, vec_dot_iq4_xs_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_iq3_s_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI3_XS, block_iq3_s, 1, vec_dot_iq3_s_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+void ggml_cuda_op_mul_mat_vec_q(
+    ggml_backend_cuda_context & ctx,
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, cudaStream_t stream) {
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t row_diff = row_high - row_low;
+
+    const int64_t ne10 = src1->ne[0];
+    GGML_ASSERT(ne10 % QK8_1 == 0);
+
+    const int64_t ne0 = dst->ne[0];
+
+    int id = ggml_cuda_get_device();
+
+    // the main device has a larger memory buffer to hold the results from all GPUs
+    // nrows_dst == nrows of the matrix that the kernel writes into
+    const int64_t nrows_dst = id == ctx.device ? ne0 : row_diff;
+
+    switch (src0->type) {
+        case GGML_TYPE_Q4_0:
+            mul_mat_vec_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            mul_mat_vec_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            mul_mat_vec_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            mul_mat_vec_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            mul_mat_vec_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q2_K:
+            mul_mat_vec_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q3_K:
+            mul_mat_vec_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q4_K:
+            mul_mat_vec_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_K:
+            mul_mat_vec_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q6_K:
+            mul_mat_vec_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_IQ2_XXS:
+            mul_mat_vec_iq2_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_IQ2_XS:
+            mul_mat_vec_iq2_xs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_IQ2_S:
+            mul_mat_vec_iq2_s_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_IQ3_XXS:
+            mul_mat_vec_iq3_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_IQ1_S:
+            mul_mat_vec_iq1_s_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_IQ1_M:
+            mul_mat_vec_iq1_m_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_IQ4_NL:
+            mul_mat_vec_iq4_nl_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_IQ4_XS:
+            mul_mat_vec_iq4_xs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_IQ3_S:
+            mul_mat_vec_iq3_s_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_ddf_i);
+    GGML_UNUSED(src1_ncols);
+    GGML_UNUSED(src1_padded_row_size);
+}
diff --git a/ggml-cuda/mmvq.cuh b/ggml-cuda/mmvq.cuh
new file mode 100644
index 000000000..88c42c4b7
--- /dev/null
+++ b/ggml-cuda/mmvq.cuh
@@ -0,0 +1,7 @@
+#include "common.cuh"
+
+void ggml_cuda_op_mul_mat_vec_q(
+    ggml_backend_cuda_context & ctx,
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, cudaStream_t stream);
diff --git a/ggml-cuda/norm.cu b/ggml-cuda/norm.cu
new file mode 100644
index 000000000..86f774534
--- /dev/null
+++ b/ggml-cuda/norm.cu
@@ -0,0 +1,215 @@
+#include "norm.cuh"
+
+template <int block_size>
+static __global__ void norm_f32(const float * x, float * dst, const int ncols, const float eps) {
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
+    const int tid = threadIdx.x;
+
+    float2 mean_var = make_float2(0.f, 0.f);
+
+    for (int col = tid; col < ncols; col += block_size) {
+        const float xi = x[row*ncols + col];
+        mean_var.x += xi;
+        mean_var.y += xi * xi;
+    }
+
+    // sum up partial sums
+    mean_var = warp_reduce_sum(mean_var);
+    if (block_size > WARP_SIZE) {
+        __shared__ float2 s_sum[32];
+        int warp_id = threadIdx.x / WARP_SIZE;
+        int lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = mean_var;
+        }
+        __syncthreads();
+        mean_var = s_sum[lane_id];
+        mean_var = warp_reduce_sum(mean_var);
+    }
+
+    const float mean = mean_var.x / ncols;
+    const float var = mean_var.y / ncols - mean * mean;
+    const float inv_std = rsqrtf(var + eps);
+
+    for (int col = tid; col < ncols; col += block_size) {
+        dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_std;
+    }
+}
+
+template <int block_size>
+static __global__ void group_norm_f32(const float * x, float * dst, const int group_size, const int ne_elements, const float eps) {
+    // blockIdx.x: num_groups idx
+    // threadIdx.x: block_size idx
+    int start = blockIdx.x * group_size;
+    int end = start + group_size;
+
+    start += threadIdx.x;
+
+    if (end >= ne_elements) {
+        end = ne_elements;
+    }
+
+    float tmp = 0.0f; // partial sum for thread in warp
+
+    for (int j = start; j < end; j += block_size) {
+        tmp += x[j];
+    }
+
+    tmp = warp_reduce_sum(tmp);
+    if (block_size > WARP_SIZE) {
+        __shared__ float s_sum[32];
+        int warp_id = threadIdx.x / WARP_SIZE;
+        int lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        __syncthreads();
+        tmp = s_sum[lane_id];
+        tmp = warp_reduce_sum(tmp);
+    }
+
+    float mean = tmp / group_size;
+    tmp = 0.0f;
+
+    for (int j = start; j < end; j += block_size) {
+        float xi = x[j] - mean;
+        dst[j] = xi;
+        tmp += xi * xi;
+    }
+
+    tmp = warp_reduce_sum(tmp);
+    if (block_size > WARP_SIZE) {
+        __shared__ float s_sum[32];
+        int warp_id = threadIdx.x / WARP_SIZE;
+        int lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        __syncthreads();
+        tmp = s_sum[lane_id];
+        tmp = warp_reduce_sum(tmp);
+    }
+
+    float variance = tmp / group_size;
+    float scale = rsqrtf(variance + eps);
+    for (int j = start; j < end; j += block_size) {
+        dst[j] *= scale;
+    }
+}
+
+template <int block_size>
+static __global__ void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps) {
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
+    const int tid = threadIdx.x;
+
+    float tmp = 0.0f; // partial sum for thread in warp
+
+    for (int col = tid; col < ncols; col += block_size) {
+        const float xi = x[row*ncols + col];
+        tmp += xi * xi;
+    }
+
+    // sum up partial sums
+    tmp = warp_reduce_sum(tmp);
+    if (block_size > WARP_SIZE) {
+        __shared__ float s_sum[32];
+        int warp_id = threadIdx.x / WARP_SIZE;
+        int lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        __syncthreads();
+        tmp = s_sum[lane_id];
+        tmp = warp_reduce_sum(tmp);
+    }
+
+    const float mean = tmp / ncols;
+    const float scale = rsqrtf(mean + eps);
+
+    for (int col = tid; col < ncols; col += block_size) {
+        dst[row*ncols + col] = scale * x[row*ncols + col];
+    }
+}
+
+static void norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) {
+    GGML_ASSERT(ncols % WARP_SIZE == 0);
+    if (ncols < 1024) {
+        const dim3 block_dims(WARP_SIZE, 1, 1);
+        norm_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
+    } else {
+        const dim3 block_dims(1024, 1, 1);
+        norm_f32<1024><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
+    }
+}
+
+static void group_norm_f32_cuda(const float * x, float * dst, const int num_groups, const int group_size, const int ne_elements, cudaStream_t stream) {
+    static const float eps = 1e-6f;
+    if (group_size < 1024) {
+        const dim3 block_dims(WARP_SIZE, 1, 1);
+        group_norm_f32<WARP_SIZE><<<num_groups, block_dims, 0, stream>>>(x, dst, group_size, ne_elements, eps);
+    } else {
+        const dim3 block_dims(1024, 1, 1);
+        group_norm_f32<1024><<<num_groups, block_dims, 0, stream>>>(x, dst, group_size, ne_elements, eps);
+    }
+}
+
+static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) {
+    GGML_ASSERT(ncols % WARP_SIZE == 0);
+    if (ncols < 1024) {
+        const dim3 block_dims(WARP_SIZE, 1, 1);
+        rms_norm_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
+    } else {
+        const dim3 block_dims(1024, 1, 1);
+        rms_norm_f32<1024><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
+    }
+}
+
+void ggml_cuda_op_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    norm_f32_cuda(src0_d, dst_d, ne00, nrows, eps, stream);
+}
+
+void ggml_cuda_op_group_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    int num_groups = dst->op_params[0];
+    int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups);
+    group_norm_f32_cuda(src0_d, dst_d, num_groups * src0->ne[3], group_size, ggml_nelements(src0), stream);
+}
+
+void ggml_cuda_op_rms_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    rms_norm_f32_cuda(src0_d, dst_d, ne00, nrows, eps, stream);
+}
diff --git a/ggml-cuda/norm.cuh b/ggml-cuda/norm.cuh
new file mode 100644
index 000000000..431a8f74d
--- /dev/null
+++ b/ggml-cuda/norm.cuh
@@ -0,0 +1,7 @@
+#include "common.cuh"
+
+void ggml_cuda_op_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_group_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_rms_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/pad.cu b/ggml-cuda/pad.cu
new file mode 100644
index 000000000..aba539e8d
--- /dev/null
+++ b/ggml-cuda/pad.cu
@@ -0,0 +1,49 @@
+#include "pad.cuh"
+
+static __global__ void pad_f32(const float * x, float * dst, const int ne0, const int ne00, const int ne01, const int ne02, const int ne03) {
+    // blockIdx.z: idx of ne2*ne3, aka ne02*ne03
+    // blockIdx.y: idx of ne1
+    // blockIDx.x: idx of ne0 / BLOCK_SIZE
+    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
+    if (nidx >= ne0) {
+        return;
+    }
+
+    // operation
+    int offset_dst =
+        nidx +
+        blockIdx.y * ne0 +
+        blockIdx.z * ne0 * gridDim.y;
+    if (nidx < ne00 && blockIdx.y < ne01 && blockIdx.z < ne02*ne03) {
+        int offset_src =
+            nidx +
+            blockIdx.y * ne00 +
+            blockIdx.z * ne00 * ne01;
+        dst[offset_dst] = x[offset_src];
+    } else {
+        dst[offset_dst] = 0.0f;
+    }
+}
+
+static void pad_f32_cuda(const float * x, float * dst,
+    const int ne00, const int ne01, const int ne02, const int ne03,
+    const int ne0, const int ne1, const int ne2, const int ne3, cudaStream_t stream) {
+    int num_blocks = (ne0 + CUDA_PAD_BLOCK_SIZE - 1) / CUDA_PAD_BLOCK_SIZE;
+    dim3 gridDim(num_blocks, ne1, ne2*ne3);
+    pad_f32<<<gridDim, CUDA_PAD_BLOCK_SIZE, 0, stream>>>(x, dst, ne0, ne00, ne01, ne02, ne03);
+}
+
+void ggml_cuda_op_pad(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
+
+    pad_f32_cuda(src0_d, dst_d,
+        src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
+        dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], stream);
+}
diff --git a/ggml-cuda/pad.cuh b/ggml-cuda/pad.cuh
new file mode 100644
index 000000000..8fd386b00
--- /dev/null
+++ b/ggml-cuda/pad.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_PAD_BLOCK_SIZE 256
+
+void ggml_cuda_op_pad(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/pool2d.cu b/ggml-cuda/pool2d.cu
new file mode 100644
index 000000000..c6d51e4d6
--- /dev/null
+++ b/ggml-cuda/pool2d.cu
@@ -0,0 +1,94 @@
+#include "pool2d.cuh"
+
+template <typename Ti, typename To>
+static  __global__ void pool2d_nchw_kernel(
+        const int ih, const int iw, const int oh, const int ow,
+        const int kh, const int kw, const int sh, const int sw,
+        const int ph, const int pw, const int parallel_elements,
+        const Ti* src, To* dst, const enum ggml_op_pool op) {
+    int idx = threadIdx.x + blockIdx.x * blockDim.x;
+    if (idx >= parallel_elements) {
+        return;
+    }
+
+    const int I_HW = ih * iw;
+    const int O_HW = oh * ow;
+    const int nc = idx / O_HW;
+    const int cur_oh = idx % O_HW / ow;
+    const int cur_ow = idx % O_HW % ow;
+    const Ti* i_ptr = src + nc * I_HW;
+    To* o_ptr = dst + nc * O_HW;
+    const int start_h = cur_oh * sh - ph;
+    const int bh = max(0, start_h);
+    const int eh = min(ih, start_h + kh);
+    const int start_w = cur_ow * sw - pw;
+    const int bw = max(0, start_w);
+    const int ew = min(iw, start_w + kw);
+    const To scale = 1. / (kh * kw);
+    To res = 0;
+
+    switch (op) {
+        case GGML_OP_POOL_AVG: res = 0; break;
+        case GGML_OP_POOL_MAX: res = -FLT_MAX; break;
+        default: assert(false);
+    }
+
+    for (int i = bh; i < eh; i += 1) {
+        for (int j = bw; j < ew; j += 1) {
+#if __CUDA_ARCH__ >= 350
+            Ti cur = __ldg(i_ptr + i * iw + j);
+#else
+            Ti cur = i_ptr[i * iw + j];
+#endif
+            switch (op) {
+                case GGML_OP_POOL_AVG: res += cur * scale; break;
+                case GGML_OP_POOL_MAX: res = max(res, (To)cur); break;
+                default: assert(false);
+            }
+        }
+    }
+    o_ptr[cur_oh * ow + cur_ow] = res;
+}
+
+static void pool2d_nchw_kernel_f32_f32_cuda(
+        const int ih, const int iw, const int oh, const int ow,
+        const int kh, const int kw, const int sh, const int sw,
+        const int ph, const int pw, const int parallel_elements,
+        const float * src, float * dst, const enum ggml_op_pool op,
+        cudaStream_t stream) {
+
+    const int num_blocks = (parallel_elements + CUDA_POOL2D_BLOCK_SIZE - 1) / CUDA_POOL2D_BLOCK_SIZE;
+    dim3 block_nums(num_blocks);
+    pool2d_nchw_kernel<<<block_nums, CUDA_POOL2D_BLOCK_SIZE, 0, stream>>>(ih, iw, oh, ow, kh, kw, sh, sw, ph, pw, parallel_elements, src, dst, op);
+}
+
+void ggml_cuda_op_pool2d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int32_t * opts = (const int32_t *)dst->op_params;
+    enum ggml_op_pool op = static_cast<ggml_op_pool>(opts[0]);
+    const int k0 = opts[1];
+    const int k1 = opts[2];
+    const int s0 = opts[3];
+    const int s1 = opts[4];
+    const int p0 = opts[5];
+    const int p1 = opts[6];
+
+    const int64_t IH = src0->ne[1];
+    const int64_t IW = src0->ne[0];
+
+    const int64_t N = dst->ne[3];
+    const int64_t OC = dst->ne[2];
+    const int64_t OH = dst->ne[1];
+    const int64_t OW = dst->ne[0];
+
+    const int parallel_elements = N * OC * OH * OW;
+
+    pool2d_nchw_kernel_f32_f32_cuda(IH, IW, OH, OW, k1, k0, s1, s0, p1, p0, parallel_elements, src0_d, dst_d, op, stream);
+}
diff --git a/ggml-cuda/pool2d.cuh b/ggml-cuda/pool2d.cuh
new file mode 100644
index 000000000..7841292bc
--- /dev/null
+++ b/ggml-cuda/pool2d.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_POOL2D_BLOCK_SIZE 256
+
+void ggml_cuda_op_pool2d(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/quantize.cu b/ggml-cuda/quantize.cu
new file mode 100644
index 000000000..7578c4b6c
--- /dev/null
+++ b/ggml-cuda/quantize.cu
@@ -0,0 +1,45 @@
+#include "quantize.cuh"
+
+static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int64_t kx, const int64_t kx_padded) {
+    const int64_t ix = (int64_t)blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (ix >= kx_padded) {
+        return;
+    }
+
+    const int64_t iy = (int64_t)blockDim.y*blockIdx.y + threadIdx.y;
+
+    const int64_t i_padded = (int64_t)iy*kx_padded + ix;
+
+    block_q8_1 * y = (block_q8_1 *) vy;
+
+    const int64_t ib = i_padded / QK8_1; // block index
+    const int64_t iqs = i_padded % QK8_1; // quant index
+
+    const float xi = ix < kx ? x[iy*kx + ix] : 0.0f;
+    float amax = fabsf(xi);
+    float sum = xi;
+
+    amax = warp_reduce_max(amax);
+    sum = warp_reduce_sum(sum);
+
+    const float d = amax / 127;
+    const int8_t q = amax == 0.0f ? 0 : roundf(xi / d);
+
+    y[ib].qs[iqs] = q;
+
+    if (iqs > 0) {
+        return;
+    }
+
+    reinterpret_cast<half&>(y[ib].ds.x) = d;
+    reinterpret_cast<half&>(y[ib].ds.y) = sum;
+}
+
+void quantize_row_q8_1_cuda(const float * x, void * vy, const int64_t kx, const int64_t ky, const int64_t kx_padded, cudaStream_t stream) {
+    const int64_t block_num_x = (kx_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
+    const dim3 num_blocks(block_num_x, ky, 1);
+    const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE, 1, 1);
+    quantize_q8_1<<<num_blocks, block_size, 0, stream>>>(x, vy, kx, kx_padded);
+}
+
diff --git a/ggml-cuda/quantize.cuh b/ggml-cuda/quantize.cuh
new file mode 100644
index 000000000..b37a4752f
--- /dev/null
+++ b/ggml-cuda/quantize.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_QUANTIZE_BLOCK_SIZE 256
+
+void quantize_row_q8_1_cuda(const float * x, void * vy, const int64_t kx, const int64_t ky, const int64_t kx_padded, cudaStream_t stream);
diff --git a/ggml-cuda/rope.cu b/ggml-cuda/rope.cu
new file mode 100644
index 000000000..4b0d2e5ad
--- /dev/null
+++ b/ggml-cuda/rope.cu
@@ -0,0 +1,308 @@
+#include "rope.cuh"
+
+struct rope_corr_dims {
+    float v[4];
+};
+
+static __device__ float rope_yarn_ramp(const float low, const float high, const int i0) {
+    const float y = (i0 / 2 - low) / max(0.001f, high - low);
+    return 1.0f - min(1.0f, max(0.0f, y));
+}
+
+// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
+// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
+static __device__ void rope_yarn(
+    float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale,
+    float * cos_theta, float * sin_theta
+) {
+    // Get n-d rotational scaling corrected for extrapolation
+    float theta_interp = freq_scale * theta_extrap;
+    float theta = theta_interp;
+    if (ext_factor != 0.0f) {
+        float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor;
+        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
+
+        // Get n-d magnitude scaling corrected for interpolation
+        mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
+    }
+    *cos_theta = cosf(theta) * mscale;
+    *sin_theta = sinf(theta) * mscale;
+}
+
+// rope == RoPE == rotary positional embedding
+template<typename T, bool has_pos>
+static __global__ void rope(
+    const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
+    float ext_factor, float attn_factor, rope_corr_dims corr_dims
+) {
+    const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
+
+    if (col >= ncols) {
+        return;
+    }
+
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+    const int i = row*ncols + col;
+    const int i2 = row/p_delta_rows;
+
+    const int p = has_pos ? pos[i2] : 0;
+    const float theta_base = p*powf(freq_base, -float(col)/ncols);
+
+    float cos_theta, sin_theta;
+    rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + 1];
+
+    dst[i + 0] = x0*cos_theta - x1*sin_theta;
+    dst[i + 1] = x0*sin_theta + x1*cos_theta;
+}
+
+template<typename T, bool has_pos>
+static __global__ void rope_neox(
+    const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims
+) {
+    const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
+
+    if (col >= ncols) {
+        return;
+    }
+
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+    const int ib = col / n_dims;
+    const int ic = col % n_dims;
+
+    if (ib > 0) {
+        const int i = row*ncols + ib*n_dims + ic;
+
+        dst[i + 0] = x[i + 0];
+        dst[i + 1] = x[i + 1];
+
+        return;
+    }
+
+    const int i  = row*ncols + ib*n_dims + ic/2;
+    const int i2 = row/p_delta_rows;
+
+    float cur_rot = inv_ndims * ic - ib;
+
+    const int p = has_pos ? pos[i2] : 0;
+    const float theta_base = p*freq_scale*powf(theta_scale, col/2.0f);
+
+    float cos_theta, sin_theta;
+    rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + n_dims/2];
+
+    dst[i + 0]        = x0*cos_theta - x1*sin_theta;
+    dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
+}
+
+static __global__ void rope_glm_f32(
+    const float * x, float * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
+    int n_ctx
+) {
+    const int col = blockDim.x*blockIdx.x + threadIdx.x;
+    const int half_n_dims = ncols/4;
+
+    if (col >= half_n_dims) {
+        return;
+    }
+
+    const int row = blockDim.y*blockIdx.y + threadIdx.y;
+    const int i = row*ncols + col;
+    const int i2 = row/p_delta_rows;
+
+    const float col_theta_scale = powf(freq_base, -2.0f*col/ncols);
+     // FIXME: this is likely wrong
+    const int p = pos != nullptr ? pos[i2] : 0;
+
+    const float theta = min(p, n_ctx - 2)*freq_scale*col_theta_scale;
+    const float sin_theta = sinf(theta);
+    const float cos_theta = cosf(theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + half_n_dims];
+
+    dst[i + 0]           = x0*cos_theta - x1*sin_theta;
+    dst[i + half_n_dims] = x0*sin_theta + x1*cos_theta;
+
+    const float block_theta = ((float)max(p - n_ctx - 2, 0))*col_theta_scale;
+    const float sin_block_theta = sinf(block_theta);
+    const float cos_block_theta = cosf(block_theta);
+
+    const float x2 = x[i + half_n_dims * 2];
+    const float x3 = x[i + half_n_dims * 3];
+
+    dst[i + half_n_dims * 2] = x2*cos_block_theta - x3*sin_block_theta;
+    dst[i + half_n_dims * 3] = x2*sin_block_theta + x3*cos_block_theta;
+}
+
+
+template<typename T>
+static void rope_cuda(
+    const T * x, T * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream
+) {
+    GGML_ASSERT(ncols % 2 == 0);
+    const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
+    const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
+    const dim3 block_nums(nrows, num_blocks_x, 1);
+    if (pos == nullptr) {
+        rope<T, false><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims
+        );
+    } else {
+        rope<T, true><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims
+        );
+    }
+}
+
+template<typename T>
+static void rope_neox_cuda(
+    const T * x, T * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream
+) {
+    GGML_ASSERT(ncols % 2 == 0);
+    const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
+    const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
+    const dim3 block_nums(nrows, num_blocks_x, 1);
+
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+    const float inv_ndims = -1.0f / n_dims;
+
+    if (pos == nullptr) {
+        rope_neox<T, false><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
+            theta_scale, inv_ndims
+        );
+    } else {
+        rope_neox<T, true><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
+            theta_scale, inv_ndims
+        );
+    }
+}
+
+static void rope_glm_f32_cuda(
+    const float * x, float * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, int n_ctx, cudaStream_t stream
+) {
+    GGML_ASSERT(ncols % 4 == 0);
+    const dim3 block_dims(CUDA_ROPE_BLOCK_SIZE/4, 1, 1);
+    const int num_blocks_x = (ncols + CUDA_ROPE_BLOCK_SIZE - 1) / CUDA_ROPE_BLOCK_SIZE;
+    const dim3 block_nums(num_blocks_x, nrows, 1);
+    rope_glm_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, n_ctx);
+}
+
+static void rope_cuda_f16(
+    const half * x, half * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream) {
+
+    rope_cuda<half>(x, dst, ncols, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream);
+}
+
+static void rope_cuda_f32(
+    const float * x, float * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream) {
+
+    rope_cuda<float>(x, dst, ncols, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream);
+}
+
+static void rope_neox_cuda_f16(
+    const half * x, half * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream) {
+
+    rope_neox_cuda<half>(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream);
+}
+
+static void rope_neox_cuda_f32(
+    const float * x, float * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream
+) {
+
+    rope_neox_cuda<float>(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream);
+}
+
+void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const float * src0_d = (const float *)src0->data;
+    const float * src1_d = (const float *)src1->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32 ||  dst->type == GGML_TYPE_F16);
+    GGML_ASSERT(src0->type == dst->type);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne2 = dst->ne[2];
+    const int64_t nrows = ggml_nrows(src0);
+
+    //const int n_past      = ((int32_t *) dst->op_params)[0];
+    const int n_dims      = ((int32_t *) dst->op_params)[1];
+    const int mode        = ((int32_t *) dst->op_params)[2];
+    const int n_ctx       = ((int32_t *) dst->op_params)[3];
+    const int n_orig_ctx  = ((int32_t *) dst->op_params)[4];
+
+    // RoPE alteration for extended context
+    float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
+    memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
+    memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
+    memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
+    memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
+    memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
+    memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
+
+    const int32_t * pos = nullptr;
+    if ((mode & 1) == 0) {
+        GGML_ASSERT(src1->type == GGML_TYPE_I32);
+        GGML_ASSERT(src1->ne[0] == ne2);
+        pos = (const int32_t *) src1_d;
+    }
+
+    const bool is_neox = mode & 2;
+    const bool is_glm  = mode & 4;
+
+    rope_corr_dims corr_dims;
+    ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v);
+
+    // compute
+    if (is_glm) {
+        GGML_ASSERT(false);
+        rope_glm_f32_cuda(src0_d, dst_d, ne00, nrows, pos, freq_scale, ne01, freq_base, n_ctx, stream);
+    } else if (is_neox) {
+        if (src0->type == GGML_TYPE_F32) {
+            rope_neox_cuda_f32(
+                (const float *)src0_d, (float *)dst_d, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, stream
+            );
+        } else if (src0->type == GGML_TYPE_F16) {
+            rope_neox_cuda_f16(
+                (const half *)src0_d, (half *)dst_d, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, stream
+            );
+        } else {
+            GGML_ASSERT(false);
+        }
+    } else {
+        if (src0->type == GGML_TYPE_F32) {
+            rope_cuda_f32(
+                (const float *)src0_d, (float *)dst_d, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, stream
+            );
+        } else if (src0->type == GGML_TYPE_F16) {
+            rope_cuda_f16(
+                (const half *)src0_d, (half *)dst_d, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, stream
+            );
+        } else {
+            GGML_ASSERT(false);
+        }
+    }
+}
diff --git a/ggml-cuda/rope.cuh b/ggml-cuda/rope.cuh
new file mode 100644
index 000000000..0f787a0b2
--- /dev/null
+++ b/ggml-cuda/rope.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_ROPE_BLOCK_SIZE 256
+
+void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/scale.cu b/ggml-cuda/scale.cu
new file mode 100644
index 000000000..1405e066e
--- /dev/null
+++ b/ggml-cuda/scale.cu
@@ -0,0 +1,31 @@
+#include "scale.cuh"
+
+static __global__ void scale_f32(const float * x, float * dst, const float scale, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    dst[i] = scale * x[i];
+}
+
+static void scale_f32_cuda(const float * x, float * dst, const float scale, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE;
+    scale_f32<<<num_blocks, CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, k);
+}
+
+void ggml_cuda_op_scale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    float scale;
+    memcpy(&scale, dst->op_params, sizeof(float));
+
+    scale_f32_cuda(src0_d, dst_d, scale, ggml_nelements(src0), stream);
+}
diff --git a/ggml-cuda/scale.cuh b/ggml-cuda/scale.cuh
new file mode 100644
index 000000000..8ff75c829
--- /dev/null
+++ b/ggml-cuda/scale.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_SCALE_BLOCK_SIZE 256
+
+void ggml_cuda_op_scale(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/softmax.cu b/ggml-cuda/softmax.cu
new file mode 100644
index 000000000..ca85285a3
--- /dev/null
+++ b/ggml-cuda/softmax.cu
@@ -0,0 +1,214 @@
+#include "softmax.cuh"
+
+template <typename T>
+static __device__ __forceinline__ float t2f32(T val) {
+    return (float) val;
+}
+
+template <>
+__device__ float __forceinline__ t2f32<half>(half val) {
+    return __half2float(val);
+}
+
+template <bool vals_smem, int ncols_template, int block_size_template, typename T>
+static __global__ void soft_max_f32(const float * x, const T * mask, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) {
+    const int ncols = ncols_template == 0 ? ncols_par : ncols_template;
+
+    const int tid  = threadIdx.x;
+    const int rowx = blockIdx.x;
+    const int rowy = rowx % nrows_y; // broadcast the mask in the row dimension
+
+    const int block_size = block_size_template == 0 ? blockDim.x : block_size_template;
+
+    const int warp_id = threadIdx.x / WARP_SIZE;
+    const int lane_id = threadIdx.x % WARP_SIZE;
+
+    float slope = 1.0f;
+
+    // ALiBi
+    if (max_bias > 0.0f) {
+        const int h = rowx/nrows_y; // head index
+
+        const float base = h < n_head_log2 ? m0 : m1;
+        const int   exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+        slope = powf(base, exph);
+    }
+
+    extern __shared__ float data_soft_max_f32[];
+    float * buf_iw = data_soft_max_f32; // shared memory buffer for inter-warp communication
+    // shared memory buffer to cache values between iterations:
+    float * vals = vals_smem ? buf_iw + WARP_SIZE : dst + (int64_t)rowx*ncols;
+
+    float max_val = -INFINITY;
+
+#pragma unroll
+    for (int col0 = 0; col0 < ncols; col0 += block_size) {
+        const int col = col0 + tid;
+
+        if (ncols_template == 0 && col >= ncols) {
+            break;
+        }
+
+        const int64_t ix = (int64_t)rowx*ncols + col;
+        const int64_t iy = (int64_t)rowy*ncols + col;
+
+        const float val = x[ix]*scale + (mask ? slope*t2f32(mask[iy]) : 0.0f);
+
+        vals[col] = val;
+        max_val = max(max_val, val);
+    }
+
+    // find the max value in the block
+    max_val = warp_reduce_max(max_val);
+    if (block_size > WARP_SIZE) {
+        if (warp_id == 0) {
+            buf_iw[lane_id] = -INFINITY;
+        }
+        __syncthreads();
+
+        if (lane_id == 0) {
+            buf_iw[warp_id] = max_val;
+        }
+        __syncthreads();
+
+        max_val = buf_iw[lane_id];
+        max_val = warp_reduce_max(max_val);
+    }
+
+    float tmp = 0.0f; // partial sum
+
+#pragma unroll
+    for (int col0 = 0; col0 < ncols; col0 += block_size) {
+        const int col = col0 + tid;
+
+        if (ncols_template == 0 && col >= ncols) {
+            break;
+        }
+
+        const float val = expf(vals[col] - max_val);
+        tmp += val;
+        vals[col] = val;
+    }
+
+    // find the sum of exps in the block
+    tmp = warp_reduce_sum(tmp);
+    if (block_size > WARP_SIZE) {
+        __syncthreads();
+        if (warp_id == 0) {
+            buf_iw[lane_id] = 0.0f;
+        }
+        __syncthreads();
+
+        if (lane_id == 0) {
+            buf_iw[warp_id] = tmp;
+        }
+        __syncthreads();
+
+        tmp = buf_iw[lane_id];
+        tmp = warp_reduce_sum(tmp);
+    }
+
+    const float inv_sum = 1.0f / tmp;
+
+#pragma unroll
+    for (int col0 = 0; col0 < ncols; col0 += block_size) {
+        const int col = col0 + tid;
+
+        if (ncols_template == 0 && col >= ncols) {
+            return;
+        }
+
+        const int64_t idst = (int64_t)rowx*ncols + col;
+        dst[idst] = vals[col] * inv_sum;
+    }
+}
+
+template<typename T>
+static void soft_max_f32_cuda(const float * x, const T * mask, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, cudaStream_t stream) {
+    int nth = WARP_SIZE;
+    while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2;
+    const dim3 block_dims(nth,     1, 1);
+    const dim3 block_nums(nrows_x, 1, 1);
+    const size_t shmem = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE)*sizeof(float);
+    static_assert(CUDA_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted.");
+
+    const uint32_t n_head      = nrows_x/nrows_y;
+    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
+
+    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+    if (shmem < ggml_cuda_info().devices[ggml_cuda_get_device()].smpb) {
+        switch (ncols_x) {
+            case 32:
+                soft_max_f32<true, 32, 32><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                break;
+            case 64:
+                soft_max_f32<true, 64, 64><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                break;
+            case 128:
+                soft_max_f32<true, 128, 128><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                break;
+            case 256:
+                soft_max_f32<true, 256, 256><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                break;
+            case 512:
+                soft_max_f32<true, 512, 512><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                break;
+            case 1024:
+                soft_max_f32<true, 1024, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                break;
+            case 2048:
+                soft_max_f32<true, 2048, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                break;
+            case 4096:
+                soft_max_f32<true, 4096, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                break;
+            default:
+                soft_max_f32<true, 0, 0><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                break;
+        }
+    } else {
+        const size_t shmem_low = WARP_SIZE*sizeof(float);
+        soft_max_f32<false, 0, 0><<<block_nums, block_dims, shmem_low, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+    }
+}
+
+void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+
+    const float * src0_d = (const float *)src0->data;
+    const void  * src1_d = src1 ? (const void *)src1->data : nullptr;
+
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
+
+    const int64_t ne00    = src0->ne[0];
+    const int64_t nrows_x = ggml_nrows(src0);
+    const int64_t nrows_y = src0->ne[1];
+
+    float scale    = 1.0f;
+    float max_bias = 0.0f;
+
+    memcpy(&scale,    (float *) dst->op_params + 0, sizeof(float));
+    memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float));
+
+    const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16);
+
+    if (use_f16) {
+        const half * src1_dd = (const half *)src1_d;
+
+        soft_max_f32_cuda(src0_d, src1_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
+    } else {
+        const float * src1_dd = (const float *)src1_d;
+
+        soft_max_f32_cuda(src0_d, src1_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
+    }
+}
diff --git a/ggml-cuda/softmax.cuh b/ggml-cuda/softmax.cuh
new file mode 100644
index 000000000..4ef4ff86c
--- /dev/null
+++ b/ggml-cuda/softmax.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_SOFT_MAX_BLOCK_SIZE 1024
+
+void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/sumrows.cu b/ggml-cuda/sumrows.cu
new file mode 100644
index 000000000..82e8e875f
--- /dev/null
+++ b/ggml-cuda/sumrows.cu
@@ -0,0 +1,40 @@
+#include "sumrows.cuh"
+
+static __global__ void k_sum_rows_f32(const float * x, float * dst, const int ncols) {
+    const int row = blockIdx.x;
+    const int col = threadIdx.x;
+
+    float sum = 0.0f;
+    for (int i = col; i < ncols; i += blockDim.x) {
+        sum += x[row * ncols + i];
+    }
+
+    sum = warp_reduce_sum(sum);
+
+    if (col == 0) {
+        dst[row] = sum;
+    }
+}
+
+static void sum_rows_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    const dim3 block_dims(WARP_SIZE, 1, 1);
+    const dim3 block_nums(nrows, 1, 1);
+    k_sum_rows_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
+}
+
+void ggml_cuda_op_sum_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
+
+    const int64_t ncols = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    sum_rows_f32_cuda(src0_d, dst_d, ncols, nrows, stream);
+}
diff --git a/ggml-cuda/sumrows.cuh b/ggml-cuda/sumrows.cuh
new file mode 100644
index 000000000..e7545f83c
--- /dev/null
+++ b/ggml-cuda/sumrows.cuh
@@ -0,0 +1,3 @@
+#include "common.cuh"
+
+void ggml_cuda_op_sum_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/tsembd.cu b/ggml-cuda/tsembd.cu
new file mode 100644
index 000000000..153ddbcda
--- /dev/null
+++ b/ggml-cuda/tsembd.cu
@@ -0,0 +1,47 @@
+#include "tsembd.cuh"
+
+static __global__ void timestep_embedding_f32(const float * timesteps, float * dst, const int nb1, const int dim, const int max_period) {
+    // blockIDx.y: idx of timesteps->ne[0]
+    // blockIDx.x: idx of ((dim + 1) / 2) / BLOCK_SIZE
+    int i = blockIdx.y;
+    int j = threadIdx.x + blockIdx.x * blockDim.x;
+    float * embed_data = (float *)((char *)dst +  i*nb1);
+
+    if (dim % 2 != 0 && j == ((dim + 1) / 2)) {
+        embed_data[dim] = 0.f;
+    }
+
+    int half = dim / 2;
+    if (j >= half) {
+        return;
+    }
+
+    float timestep = timesteps[i];
+    float freq = (float)expf(-logf(max_period) * j / half);
+    float arg = timestep * freq;
+    embed_data[j] = cosf(arg);
+    embed_data[j + half] = sinf(arg);
+}
+
+static void timestep_embedding_f32_cuda(const float * x, float * dst, const int ne00, const int nb1,
+                                        const int dim, const int max_period, cudaStream_t stream) {
+    int half_ceil = (dim + 1) / 2;
+    int num_blocks = (half_ceil + CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE - 1) / CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE;
+    dim3 gridDim(num_blocks, ne00, 1);
+    timestep_embedding_f32<<<gridDim, CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE, 0, stream>>>(x, dst, nb1, dim, max_period);
+}
+
+void ggml_cuda_op_timestep_embedding(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    const int dim = dst->op_params[0];
+    const int max_period = dst->op_params[1];
+
+    timestep_embedding_f32_cuda(src0_d, dst_d, src0->ne[0], dst->nb[1], dim, max_period, stream);
+}
diff --git a/ggml-cuda/tsembd.cuh b/ggml-cuda/tsembd.cuh
new file mode 100644
index 000000000..84340e3d7
--- /dev/null
+++ b/ggml-cuda/tsembd.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE 256
+
+void ggml_cuda_op_timestep_embedding(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/unary.cu b/ggml-cuda/unary.cu
new file mode 100644
index 000000000..ac03d5c6f
--- /dev/null
+++ b/ggml-cuda/unary.cu
@@ -0,0 +1,266 @@
+#include "unary.cuh"
+
+static __global__ void gelu_f32(const float * x, float * dst, const int k) {
+    const float GELU_COEF_A    = 0.044715f;
+    const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    float xi = x[i];
+    dst[i] = 0.5f*xi*(1.0f + tanhf(SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi)));
+}
+
+static __global__ void gelu_quick_f32(const float * x, float * dst, int k) {
+    const float GELU_QUICK_COEF = -1.702f;
+    const int i  = blockDim.x*blockIdx.x + threadIdx.x;
+    if (i >= k) {
+        return;
+    }
+    dst[i] = x[i] * (1.0f / (1.0f + expf(GELU_QUICK_COEF * x[i])));
+}
+
+static __global__ void silu_f32(const float * x, float * dst, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = x[i] / (1.0f + expf(-x[i]));
+}
+
+static __global__ void tanh_f32(const float * x, float * dst, int k) {
+    const int i  = blockDim.x*blockIdx.x + threadIdx.x;
+    if (i >= k) {
+        return;
+    }
+    dst[i] = tanhf(x[i]);
+}
+
+static __global__ void relu_f32(const float * x, float * dst, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = fmaxf(x[i], 0);
+}
+
+static __global__ void sigmoid_f32(const float * x, float * dst, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = 1.0f / (1.0f + expf(-x[i]));
+}
+
+static __global__ void hardsigmoid_f32(const float * x, float * dst, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f));
+}
+
+static __global__ void hardswish_f32(const float * x, float * dst, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f));
+}
+
+static __global__ void leaky_relu_f32(const float * x, float * dst, const int k, const float negative_slope) {
+    const int i  = blockDim.x*blockIdx.x + threadIdx.x;
+    if (i >= k) {
+        return;
+    }
+    dst[i] = fmaxf(x[i], 0) + fminf(x[i], 0.0f) * negative_slope;
+}
+
+static __global__ void sqr_f32(const float * x, float * dst, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = x[i] * x[i];
+}
+
+static void gelu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE;
+    gelu_f32<<<num_blocks, CUDA_GELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
+static void gelu_quick_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE;
+    gelu_quick_f32<<<num_blocks, CUDA_GELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
+static void silu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_SILU_BLOCK_SIZE - 1) / CUDA_SILU_BLOCK_SIZE;
+    silu_f32<<<num_blocks, CUDA_SILU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
+static void tanh_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_TANH_BLOCK_SIZE - 1) / CUDA_TANH_BLOCK_SIZE;
+    tanh_f32<<<num_blocks, CUDA_TANH_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
+static void relu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE;
+    relu_f32<<<num_blocks, CUDA_RELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
+static void sigmoid_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_SIGMOID_BLOCK_SIZE - 1) / CUDA_SIGMOID_BLOCK_SIZE;
+    sigmoid_f32<<<num_blocks, CUDA_SIGMOID_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
+static void hardsigmoid_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_HARDSIGMOID_BLOCK_SIZE - 1) / CUDA_HARDSIGMOID_BLOCK_SIZE;
+    hardsigmoid_f32<<<num_blocks, CUDA_HARDSIGMOID_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
+static void hardswish_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_HARDSWISH_BLOCK_SIZE - 1) / CUDA_HARDSWISH_BLOCK_SIZE;
+    hardswish_f32<<<num_blocks, CUDA_HARDSWISH_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
+static void leaky_relu_f32_cuda(const float * x, float * dst, const int k, const float negative_slope, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE;
+    leaky_relu_f32<<<num_blocks, CUDA_RELU_BLOCK_SIZE, 0, stream>>>(x, dst, k, negative_slope);
+}
+
+static void sqr_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_SQR_BLOCK_SIZE - 1) / CUDA_SQR_BLOCK_SIZE;
+    sqr_f32<<<num_blocks, CUDA_SQR_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
+void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    gelu_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
+}
+
+void ggml_cuda_op_silu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    silu_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
+}
+
+void ggml_cuda_op_gelu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    gelu_quick_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
+}
+
+void ggml_cuda_op_tanh(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    tanh_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
+}
+
+void ggml_cuda_op_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    relu_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
+}
+
+void ggml_cuda_op_sigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    sigmoid_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
+}
+
+void ggml_cuda_op_hardsigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    hardsigmoid_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
+}
+
+void ggml_cuda_op_hardswish(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    hardswish_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
+}
+
+void ggml_cuda_op_leaky_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    float negative_slope;
+    memcpy(&negative_slope, dst->op_params, sizeof(float));
+
+    leaky_relu_f32_cuda(src0_d, dst_d, ggml_nelements(src0), negative_slope, stream);
+}
+
+void ggml_cuda_op_sqr(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    sqr_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
+}
diff --git a/ggml-cuda/unary.cuh b/ggml-cuda/unary.cuh
new file mode 100644
index 000000000..a1d07c04f
--- /dev/null
+++ b/ggml-cuda/unary.cuh
@@ -0,0 +1,30 @@
+#include "common.cuh"
+
+#define CUDA_GELU_BLOCK_SIZE 256
+#define CUDA_SILU_BLOCK_SIZE 256
+#define CUDA_TANH_BLOCK_SIZE 256
+#define CUDA_RELU_BLOCK_SIZE 256
+#define CUDA_SIGMOID_BLOCK_SIZE 256
+#define CUDA_HARDSIGMOID_BLOCK_SIZE 256
+#define CUDA_HARDSWISH_BLOCK_SIZE 256
+#define CUDA_SQR_BLOCK_SIZE 256
+
+void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_silu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_gelu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_tanh(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_sigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_hardsigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_hardswish(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_leaky_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_sqr(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/upscale.cu b/ggml-cuda/upscale.cu
new file mode 100644
index 000000000..2f62fed48
--- /dev/null
+++ b/ggml-cuda/upscale.cu
@@ -0,0 +1,48 @@
+#include "upscale.cuh"
+
+static __global__ void upscale_f32(const float * x, float * dst, const int ne00, const int ne00xne01, const int scale_factor) {
+    // blockIdx.z: idx of ne02*ne03
+    // blockIdx.y: idx of ne01*scale_factor， aka ne1
+    // blockIDx.x: idx of ne00*scale_factor / BLOCK_SIZE
+    // ne00xne01: ne00 * ne01
+    int ne0 = ne00 * scale_factor;
+    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
+    if (nidx >= ne0) {
+        return;
+    }
+    // operation
+    int i00 = nidx / scale_factor;
+    int i01 = blockIdx.y / scale_factor;
+    int offset_src =
+        i00 +
+        i01 * ne00 +
+        blockIdx.z * ne00xne01;
+    int offset_dst =
+        nidx +
+        blockIdx.y * ne0 +
+        blockIdx.z * ne0 * gridDim.y;
+    dst[offset_dst] = x[offset_src];
+}
+
+static void upscale_f32_cuda(const float * x, float * dst, const int ne00, const int ne01, const int ne02, const int ne03,
+                             const int scale_factor, cudaStream_t stream) {
+    int ne0 = (ne00 * scale_factor);
+    int num_blocks = (ne0 + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE;
+    dim3 gridDim(num_blocks, (ne01 * scale_factor), ne02*ne03);
+    upscale_f32<<<gridDim, CUDA_UPSCALE_BLOCK_SIZE, 0, stream>>>(x, dst, ne00, ne00 * ne01, scale_factor);
+}
+
+void ggml_cuda_op_upscale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
+
+    const int scale_factor = dst->op_params[0];
+
+    upscale_f32_cuda(src0_d, dst_d, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], scale_factor, stream);
+}
diff --git a/ggml-cuda/upscale.cuh b/ggml-cuda/upscale.cuh
new file mode 100644
index 000000000..d4d765230
--- /dev/null
+++ b/ggml-cuda/upscale.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_UPSCALE_BLOCK_SIZE 256
+
+void ggml_cuda_op_upscale(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/vecdotq.cuh b/ggml-cuda/vecdotq.cuh
new file mode 100644
index 000000000..86b87fa93
--- /dev/null
+++ b/ggml-cuda/vecdotq.cuh
@@ -0,0 +1,1280 @@
+#include "common.cuh"
+
+static __device__ __forceinline__ int get_int_from_int8(const int8_t * x8, const int & i32) {
+    const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
+
+    int x32 = 0;
+    x32 |= x16[0] <<  0;
+    x32 |= x16[1] << 16;
+
+    return x32;
+}
+
+static __device__ __forceinline__ int get_int_from_uint8(const uint8_t * x8, const int & i32) {
+    const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
+
+    int x32 = 0;
+    x32 |= x16[0] <<  0;
+    x32 |= x16[1] << 16;
+
+    return x32;
+}
+
+static __device__ __forceinline__ int get_int_from_int8_aligned(const int8_t * x8, const int & i32) {
+    return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
+}
+
+static __device__ __forceinline__ int get_int_from_uint8_aligned(const uint8_t * x8, const int & i32) {
+    return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
+}
+
+
+// VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
+// MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
+
+#define VDR_Q4_0_Q8_1_MMVQ 2
+#define VDR_Q4_0_Q8_1_MMQ  4
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q4_0_q8_1_impl(
+    const int * v, const int * u, const float & d4, const half2 & ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
+        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
+
+        // SIMD dot product of quantized values
+        sumi = __dp4a(vi0, u[2*i+0], sumi);
+        sumi = __dp4a(vi1, u[2*i+1], sumi);
+    }
+
+    const float2 ds8f = __half22float2(ds8);
+
+    // second part effectively subtracts 8 from each quant value
+    return d4 * (sumi * ds8f.x - (8*vdr/QI4_0) * ds8f.y);
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q4_1_Q8_1_MMVQ 2
+#define VDR_Q4_1_Q8_1_MMQ  4
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q4_1_q8_1_impl(
+    const int * v, const int * u, const half2 & dm4, const half2 & ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
+        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
+
+        // SIMD dot product of quantized values
+        sumi = __dp4a(vi0, u[2*i+0], sumi);
+        sumi = __dp4a(vi1, u[2*i+1], sumi);
+    }
+
+#ifdef GGML_CUDA_F16
+    const float2 tmp = __half22float2(__hmul2(dm4, ds8));
+    const float d4d8 = tmp.x;
+    const float m4s8 = tmp.y;
+#else
+    const float2 dm4f = __half22float2(dm4);
+    const float2 ds8f = __half22float2(ds8);
+    const float d4d8 = dm4f.x * ds8f.x;
+    const float m4s8 = dm4f.y * ds8f.y;
+#endif // GGML_CUDA_F16
+
+    // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it
+    return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1));
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q5_0_Q8_1_MMVQ 2
+#define VDR_Q5_0_Q8_1_MMQ  4
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q5_0_q8_1_impl(
+    const int * vl, const int * vh, const int * u, const float & d5, const half2 & ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
+        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
+        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
+        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
+        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
+        sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
+
+        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
+        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
+        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
+        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
+        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
+        sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
+    }
+
+    const float2 ds8f = __half22float2(ds8);
+
+    // second part effectively subtracts 16 from each quant value
+    return d5 * (sumi * ds8f.x - (16*vdr/QI5_0) * ds8f.y);
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q5_1_Q8_1_MMVQ 2
+#define VDR_Q5_1_Q8_1_MMQ  4
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q5_1_q8_1_impl(
+    const int * vl, const int * vh, const int * u, const half2 & dm5, const half2 & ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
+        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
+        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
+        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
+        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
+        sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
+
+        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
+        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
+        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
+        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
+        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
+        sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
+    }
+
+#ifdef GGML_CUDA_F16
+    const float2 tmp = __half22float2(__hmul2(dm5, ds8));
+    const float d5d8 = tmp.x;
+    const float m5s8 = tmp.y;
+#else
+    const float2 dm5f = __half22float2(dm5);
+    const float2 ds8f = __half22float2(ds8);
+    const float d5d8 = dm5f.x * ds8f.x;
+    const float m5s8 = dm5f.y * ds8f.y;
+#endif // GGML_CUDA_F16
+
+    // scale second part of sum by QI5_1 / vdr to compensate for multiple threads adding it
+    return sumi*d5d8 + m5s8 / (QI5_1 / vdr);
+
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q8_0_Q8_1_MMVQ 2
+#define VDR_Q8_0_Q8_1_MMQ 8
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q8_0_q8_1_impl(
+    const int * v, const int * u, const float & d8_0, const float & d8_1) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        // SIMD dot product of quantized values
+        sumi = __dp4a(v[i], u[i], sumi);
+    }
+
+    return d8_0*d8_1 * sumi;
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q8_1_q8_1_impl(
+    const int * v, const int * u, const half2 & dm8, const half2 & ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        // SIMD dot product of quantized values
+        sumi = __dp4a(v[i], u[i], sumi);
+    }
+
+#ifdef GGML_CUDA_F16
+    const float2 tmp = __half22float2(__hmul2(dm8, ds8));
+    const float d8d8 = tmp.x;
+    const float m8s8 = tmp.y;
+#else
+    const float2 dm8f = __half22float2(dm8);
+    const float2 ds8f = __half22float2(ds8);
+    const float d8d8 = dm8f.x * ds8f.x;
+    const float m8s8 = dm8f.y * ds8f.y;
+#endif // GGML_CUDA_F16
+
+    // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it
+    return sumi*d8d8 + m8s8 / (QI8_1 / vdr);
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q2_K_Q8_1_MMVQ 1
+#define VDR_Q2_K_Q8_1_MMQ  2
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq(
+    const int & v, const int * __restrict__ u, const uint8_t * __restrict__ scales,
+    const half2 & dm2, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR2_K; ++i) {
+        const int sc = scales[2*i];
+
+        const int vi = (v >> (2*i)) & 0x03030303;
+
+        sumf_d += d8[i] * (__dp4a(vi, u[i], 0) * (sc & 0xF)); // SIMD dot product
+
+        // fill int with 4x m
+        int m = sc >> 4;
+        m |= m <<  8;
+        m |= m << 16;
+        sumf_m += d8[i] * __dp4a(m, u[i], 0); // multiply constant q2_K part with sum of q8_1 values
+    }
+
+    const float2 dm2f = __half22float2(dm2);
+
+    return dm2f.x*sumf_d - dm2f.y*sumf_m;
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+// contiguous u/y values
+static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ scales,
+    const half2 & dm2, const float & d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi_d = 0;
+    int sumi_m = 0;
+
+#pragma unroll
+    for (int i0 = 0; i0 < QI8_1; i0 += QI8_1/2) {
+        int sumi_d_sc = 0;
+
+        const int sc = scales[i0 / (QI8_1/2)];
+
+        // fill int with 4x m
+        int m = sc >> 4;
+        m |= m <<  8;
+        m |= m << 16;
+
+#pragma unroll
+        for (int i = i0; i < i0 + QI8_1/2; ++i) {
+            sumi_d_sc = __dp4a(v[i], u[i], sumi_d_sc); // SIMD dot product
+            sumi_m    = __dp4a(m,    u[i], sumi_m); // multiply sum of q8_1 values with m
+        }
+
+        sumi_d += sumi_d_sc * (sc & 0xF);
+    }
+
+    const float2 dm2f = __half22float2(dm2);
+
+    return d8 * (dm2f.x*sumi_d - dm2f.y*sumi_m);
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q3_K_Q8_1_MMVQ 1
+#define VDR_Q3_K_Q8_1_MMQ  2
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq(
+    const int & vl, const int & vh, const int * __restrict__ u, const uint8_t * __restrict__ scales,
+    const int & scale_offset, const float & d3, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR3_K; ++i) {
+        const int isc = scale_offset + 2*i;
+
+        const int isc_low = isc % (QK_K/32);
+        const int sc_shift_low = 4 * (isc / (QK_K/32));
+        const int sc_low  = (scales[isc_low] >> sc_shift_low) & 0xF;
+
+        const int isc_high = isc % (QK_K/64);
+        const int sc_shift_high = 2 * (isc / (QK_K/64));
+        const int sc_high = ((scales[(QK_K/32) + isc_high] >> sc_shift_high) & 3) << 4;
+
+        const int sc = (sc_low | sc_high) - 32;
+
+        const int vil = (vl >> (2*i)) & 0x03030303;
+
+        const int vih = ((vh >> i) << 2) & 0x04040404;
+
+        const int vi = __vsubss4(vil, vih);
+
+        sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product
+    }
+
+    return d3 * sumf;
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+// contiguous u/y values
+static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ scales,
+    const float & d3, const float & d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i0 = 0; i0 < QR3_K*VDR_Q3_K_Q8_1_MMQ; i0 += QI8_1/2) {
+        int sumi_sc = 0;
+
+        for (int i = i0; i < i0 + QI8_1/2; ++i) {
+            sumi_sc = __dp4a(v[i], u[i], sumi_sc); // SIMD dot product
+        }
+
+        sumi += sumi_sc * scales[i0 / (QI8_1/2)];
+    }
+
+    return d3*d8 * sumi;
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q4_K_Q8_1_MMVQ 2
+#define VDR_Q4_K_Q8_1_MMQ  8
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq(
+    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
+    const uint8_t * __restrict__ m, const half2 & dm4, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR4_K; ++i) {
+        const int v0i = (v[0] >> (4*i)) & 0x0F0F0F0F;
+        const int v1i = (v[1] >> (4*i)) & 0x0F0F0F0F;
+
+        const int dot1 = __dp4a(v1i, u[2*i+1], __dp4a(v0i, u[2*i+0], 0)); // SIMD dot product
+        const int dot2 = __dp4a(0x01010101, u[2*i+1], __dp4a(0x01010101, u[2*i+0], 0)); // sum of u
+
+        sumf_d += d8[i] * (dot1 * sc[i]);
+        sumf_m += d8[i] * (dot2 * m[i]);  // multiply constant part of q4_K with sum of q8_1 values
+    }
+
+    const float2 dm4f = __half22float2(dm4);
+
+    return dm4f.x*sumf_d - dm4f.y*sumf_m;
+
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+// contiguous u/y values
+static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
+    const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR4_K*VDR_Q4_K_Q8_1_MMQ/QI8_1; ++i) {
+        int sumi_d = 0;
+
+#pragma unroll
+        for (int j = 0; j < QI8_1; ++j) {
+            sumi_d = __dp4a((v[j] >> (4*i)) & 0x0F0F0F0F, u[i*QI8_1 + j], sumi_d); // SIMD dot product
+        }
+
+        const float2 ds8f = __half22float2(ds8[i]);
+
+        sumf_d += ds8f.x * (sc[i] * sumi_d);
+        sumf_m += ds8f.y *   m[i]; // sum of q8_1 block * q4_K min val
+    }
+
+    const float2 dm4f = __half22float2(dm4);
+
+    return dm4f.x*sumf_d - dm4f.y*sumf_m;
+
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q5_K_Q8_1_MMVQ 2
+#define VDR_Q5_K_Q8_1_MMQ  8
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq(
+    const int * __restrict__ vl, const int * __restrict__ vh, const int * __restrict__ u, const uint8_t * __restrict__ sc,
+    const uint8_t * __restrict__ m, const half2 & dm5, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR5_K; ++i) {
+        const int vl0i = (vl[0] >> (4*i)) & 0x0F0F0F0F;
+        const int vl1i = (vl[1] >> (4*i)) & 0x0F0F0F0F;
+
+        const int vh0i = ((vh[0] >> i) << 4) & 0x10101010;
+        const int vh1i = ((vh[1] >> i) << 4) & 0x10101010;
+
+        const int v0i = vl0i | vh0i;
+        const int v1i = vl1i | vh1i;
+
+        const int dot1 = __dp4a(v0i, u[2*i+0], __dp4a(v1i, u[2*i+1], 0)); // SIMD dot product
+        const int dot2 = __dp4a(0x01010101, u[2*i+0], __dp4a(0x01010101, u[2*i+1], 0)); // sum of u
+
+        sumf_d += d8[i] * (dot1 * sc[i]);
+        sumf_m += d8[i] * (dot2 * m[i]);
+
+    }
+
+    const float2 dm5f = __half22float2(dm5);
+
+    return dm5f.x*sumf_d - dm5f.y*sumf_m;
+
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+// contiguous u/y values
+static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
+    const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR5_K*VDR_Q5_K_Q8_1_MMQ/QI8_1; ++i) {
+        int sumi_d = 0;
+
+#pragma unroll
+        for (int j = 0; j < QI8_1; ++j) {
+            sumi_d = __dp4a(v[i*QI8_1 + j], u[i*QI8_1 + j], sumi_d); // SIMD dot product
+        }
+
+        const float2 ds8f = __half22float2(ds8[i]);
+
+        sumf_d += ds8f.x * (sc[i] * sumi_d);
+        sumf_m += ds8f.y *   m[i]; // sum of q8_1 block * q4_K min val
+    }
+
+    const float2 dm4f = __half22float2(dm4);
+
+    return dm4f.x*sumf_d - dm4f.y*sumf_m;
+
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q6_K_Q8_1_MMVQ 1
+#define VDR_Q6_K_Q8_1_MMQ  8
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq(
+    const int & vl, const int & vh, const int * __restrict__ u, const int8_t * __restrict__ scales,
+    const float & d, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR6_K; ++i) {
+        const int sc = scales[4*i];
+
+        const int vil = (vl >> (4*i)) & 0x0F0F0F0F;
+
+        const int vih = ((vh >> (4*i)) << 4) & 0x30303030;
+
+        const int vi = __vsubss4((vil | vih), 0x20202020); // vi = (vil | vih) - 32
+
+        sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product
+    }
+
+    return d*sumf;
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+// contiguous u/y values
+static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ sc,
+    const float & d6, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+
+#pragma unroll
+    for (int i0 = 0; i0 < VDR_Q6_K_Q8_1_MMQ; i0 += 4) {
+        int2 sumi_d = {0, 0}; // 2 q6_K scales per q8_1 scale
+
+#pragma unroll
+        for (int i = i0; i < i0 + 2; ++i) {
+            sumi_d.x = __dp4a(v[2*i+0], u[2*i+0], sumi_d.x); // SIMD dot product
+            sumi_d.x = __dp4a(v[2*i+1], u[2*i+1], sumi_d.x); // SIMD dot product
+
+            sumi_d.y = __dp4a(v[2*i+4], u[2*i+4], sumi_d.y); // SIMD dot product
+            sumi_d.y = __dp4a(v[2*i+5], u[2*i+5], sumi_d.y); // SIMD dot product
+        }
+
+        sumf_d += d8[i0/4] * (sc[i0/2+0]*sumi_d.x + sc[i0/2+1]*sumi_d.y);
+    }
+
+    return d6 * sumf_d;
+
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+static __device__ __forceinline__ float vec_dot_q4_0_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq;
+
+    int v[VDR_Q4_0_Q8_1_MMVQ];
+    int u[2*VDR_Q4_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q4_0_Q8_1_MMVQ; ++i) {
+        v[i]     = get_int_from_uint8(bq4_0->qs, iqs + i);
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_0);
+    }
+
+    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMVQ>(v, u, bq4_0->d, bq8_1->ds);
+}
+
+
+static __device__ __forceinline__ float vec_dot_q4_1_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq;
+
+    int v[VDR_Q4_1_Q8_1_MMVQ];
+    int u[2*VDR_Q4_1_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q4_1_Q8_1_MMVQ; ++i) {
+        v[i]    = get_int_from_uint8_aligned(bq4_1->qs, iqs + i);
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_1);
+    }
+
+    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMVQ>(v, u, bq4_1->dm, bq8_1->ds);
+}
+
+static __device__ __forceinline__ float vec_dot_q5_0_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq;
+
+    int vl[VDR_Q5_0_Q8_1_MMVQ];
+    int vh[VDR_Q5_0_Q8_1_MMVQ];
+    int  u[2*VDR_Q5_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q5_0_Q8_1_MMVQ; ++i) {
+        vl[i]    = get_int_from_uint8(bq5_0->qs, iqs + i);
+        vh[i]    = get_int_from_uint8(bq5_0->qh, 0) >> (4 * (iqs + i));
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_0);
+    }
+
+    return vec_dot_q5_0_q8_1_impl<VDR_Q5_0_Q8_1_MMVQ>(vl, vh, u, bq5_0->d, bq8_1->ds);
+}
+
+static __device__ __forceinline__ float vec_dot_q5_1_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq;
+
+    int vl[VDR_Q5_1_Q8_1_MMVQ];
+    int vh[VDR_Q5_1_Q8_1_MMVQ];
+    int  u[2*VDR_Q5_1_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q5_1_Q8_1_MMVQ; ++i) {
+        vl[i]   = get_int_from_uint8_aligned(bq5_1->qs, iqs + i);
+        vh[i]   = get_int_from_uint8_aligned(bq5_1->qh, 0) >> (4 * (iqs + i));
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_1);
+    }
+
+    return vec_dot_q5_1_q8_1_impl<VDR_Q5_1_Q8_1_MMVQ>(vl, vh, u, bq5_1->dm, bq8_1->ds);
+}
+
+static __device__ __forceinline__ float vec_dot_q8_0_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq;
+
+    int v[VDR_Q8_0_Q8_1_MMVQ];
+    int u[VDR_Q8_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q8_0_Q8_1_MMVQ; ++i) {
+        v[i] = get_int_from_int8(bq8_0->qs, iqs + i);
+        u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+    }
+
+    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMVQ>(v, u, bq8_0->d, __low2half(bq8_1->ds));
+}
+
+static __device__ __forceinline__ float vec_dot_q2_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q2_K * bq2_K = (const block_q2_K *) vbq;
+
+    const int bq8_offset = QR2_K * (iqs / QI8_1);
+    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
+
+    const uint8_t * scales = bq2_K->scales + scale_offset;
+
+    const int v = get_int_from_uint8_aligned(bq2_K->qs, iqs);
+    int    u[QR2_K];
+    float d8[QR2_K];
+
+#pragma unroll
+    for (int i = 0; i < QR2_K; ++ i) {
+        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
+        d8[i] = __low2float(bq8_1[bq8_offset + i].ds);
+    }
+
+    return vec_dot_q2_K_q8_1_impl_mmvq(v, u, scales, bq2_K->dm, d8);
+}
+
+static __device__ __forceinline__ float vec_dot_q3_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q3_K * bq3_K = (const block_q3_K *) vbq;
+
+    const int bq8_offset = QR3_K * (iqs / (QI3_K/2));
+    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
+
+    const float d = bq3_K->d;
+
+    const int vl = get_int_from_uint8(bq3_K->qs, iqs);
+
+    // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
+    const int vh = ~get_int_from_uint8(bq3_K->hmask, iqs % (QI3_K/2)) >> bq8_offset;
+
+    int    u[QR3_K];
+    float d8[QR3_K];
+
+#pragma unroll
+    for (int i = 0; i < QR3_K; ++i) {
+        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
+        d8[i] = __low2float(bq8_1[bq8_offset + i].ds);
+    }
+
+    return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8);
+}
+
+static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+#ifndef GGML_QKK_64
+    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
+
+    int    v[2];
+    int    u[2*QR4_K];
+    float d8[QR4_K];
+
+    // iqs is in 0,2..30. bq8_offset = iqs/4 -> bq8_offset = 0, 2, 4, 6
+    const int bq8_offset = QR4_K * ((iqs/2) / (QI8_1/2));
+
+    // iqs = 0....3 -> bq8_offset = 0, want q4_offset = 0, 4, 8, 12
+    // iqs = 4....7 -> bq8_offset = 2, want q4_offset = 32, 36, 40, 44
+    // iqs = 8...11 -> bq8_offset = 4, want q4_offset = 64, 68, 72, 76
+    // iqs = 12..15 -> bq8_offset = 6, want q4_offset = 96, 100, 104, 108
+
+    const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
+    v[0] = q4[0];
+    v[1] = q4[4];
+
+    const uint16_t * scales = (const uint16_t *)bq4_K->scales;
+    uint16_t aux[2];
+    const int j = bq8_offset/2;
+    if (j < 2) {
+        aux[0] = scales[j+0] & 0x3f3f;
+        aux[1] = scales[j+2] & 0x3f3f;
+    } else {
+        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
+        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
+    }
+    const uint8_t * sc = (const uint8_t *)aux;
+    const uint8_t * m  = sc + 2;
+
+    for (int i = 0; i < QR4_K; ++i) {
+        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
+        d8[i] = __low2float(bq8i->ds);
+
+        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
+        u[2*i+0] = q8[0];
+        u[2*i+1] = q8[4];
+    }
+
+    return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8);
+
+#else
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+    uint16_t aux16[2];
+    const uint8_t * s = (const uint8_t *)aux16;
+
+    const uint16_t * a = (const uint16_t *)bq4_K->scales;
+    aux16[0] = a[0] & 0x0f0f;
+    aux16[1] = (a[0] >> 4) & 0x0f0f;
+
+    const float dall = bq4_K->dm[0];
+    const float dmin = bq4_K->dm[1];
+
+    const float d8_1 = __low2float(bq8_1[0].ds);
+    const float d8_2 = __low2float(bq8_1[1].ds);
+
+    const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
+    const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
+    const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
+    const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
+
+    const int * q4 = (const int *)bq4_K->qs + (iqs/2);
+    const int v1 = q4[0];
+    const int v2 = q4[4];
+
+    const int dot1 = __dp4a(ui2, v2 & 0x0f0f0f0f, __dp4a(ui1, v1 & 0x0f0f0f0f, 0));
+    const int dot2 = __dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, __dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0));
+    const int dot3 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0));
+    const int dot4 = __dp4a(0x01010101, ui4, __dp4a(0x01010101, ui3, 0));
+
+    sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]);
+    sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]);
+
+    return dall * sumf_d - dmin * sumf_m;
+
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+
+#endif
+}
+
+static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+#ifndef GGML_QKK_64
+    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
+
+    int   vl[2];
+    int   vh[2];
+    int    u[2*QR5_K];
+    float d8[QR5_K];
+
+    const int bq8_offset = QR5_K * ((iqs/2) / (QI8_1/2));
+    const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
+    const int * qh = (const int *)(bq5_K->qh + 4 * ((iqs/2)%4));
+
+    vl[0] = ql[0];
+    vl[1] = ql[4];
+
+    vh[0] = qh[0] >> bq8_offset;
+    vh[1] = qh[4] >> bq8_offset;
+
+    const uint16_t * scales = (const uint16_t *)bq5_K->scales;
+    uint16_t aux[2];
+    const int j = bq8_offset/2;
+    if (j < 2) {
+        aux[0] = scales[j+0] & 0x3f3f;
+        aux[1] = scales[j+2] & 0x3f3f;
+    } else {
+        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
+        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
+    }
+    const uint8_t * sc = (const uint8_t *)aux;
+    const uint8_t * m  = sc + 2;
+
+#pragma unroll
+    for (int i = 0; i < QR5_K; ++i) {
+        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
+        d8[i] = __low2float(bq8i->ds);
+
+        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
+        u[2*i+0] = q8[0];
+        u[2*i+1] = q8[4];
+    }
+
+    return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, bq5_K->dm, d8);
+
+#else
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
+
+    const int8_t * s = bq5_K->scales;
+
+    const float d = bq5_K->d;
+
+    const float d8_1 = __low2half(bq8_1[0].ds);
+    const float d8_2 = __low2half(bq8_1[1].ds);
+
+    const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
+    const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
+    const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
+    const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
+
+    const int * ql = (const int *)bq5_K->qs + (iqs/2);
+    const int vl1 = ql[0];
+    const int vl2 = ql[4];
+
+    const int step = 4 * (iqs/2); // 0, 4, 8, 12
+    const int im = step/8; // = 0 for iqs = 0, 2, = 1 for iqs = 4, 6
+    const int in = step%8; // 0, 4, 0, 4
+    const int vh = (*((const int *)(bq5_K->qh + in))) >> im;
+
+    const int v1 = (((vh << 4) & 0x10101010) ^ 0x10101010) | ((vl1 >> 0) & 0x0f0f0f0f);
+    const int v2 = (((vh << 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 0) & 0x0f0f0f0f);
+    const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f);
+    const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f);
+
+    const float sumf_d = d8_1 * (__dp4a(ui1, v1, 0) * s[0] + __dp4a(ui2, v2, 0) * s[1])
+                       + d8_2 * (__dp4a(ui3, v3, 0) * s[2] + __dp4a(ui4, v4, 0) * s[3]);
+
+    return d * sumf_d;
+
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+
+#endif
+}
+
+static __device__ __forceinline__ float vec_dot_q6_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q6_K * bq6_K = (const block_q6_K *) vbq;
+
+    const int bq8_offset = 2 * QR6_K * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/4);
+    const int scale_offset = (QI6_K/4) * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/8);
+    const int vh_shift = 2 * ((iqs % (QI6_K/2)) / (QI6_K/4));
+
+    const int vl = get_int_from_uint8(bq6_K->ql, iqs);
+    const int vh = get_int_from_uint8(bq6_K->qh, (QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4)) >> vh_shift;
+
+    const int8_t * scales = bq6_K->scales + scale_offset;
+
+    int    u[QR6_K];
+    float d8[QR6_K];
+
+#pragma unroll
+    for (int i = 0; i < QR6_K; ++i) {
+        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1);
+        d8[i] = __low2float(bq8_1[bq8_offset + 2*i].ds);
+    }
+
+    return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8);
+}
+
+static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+#if QK_K == 256
+    const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq;
+
+#if QR2_XXS == 8
+    const int ib32 = iqs;
+    const uint16_t * q2 = bq2->qs + 4*ib32;
+    const uint8_t  * aux8 = (const uint8_t *)q2;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    uint32_t aux32 = q2[2] | (q2[3] << 16);
+    int sumi = 0;
+    for (int l = 0; l < 4; ++l) {
+        const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]);
+        const uint8_t  signs = ksigns_iq2xs[aux32 & 127];
+        for (int j = 0; j < 8; ++j) {
+            sumi += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
+        }
+        q8 += 8;
+        aux32 >>= 7;
+    }
+    const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.25f;
+    return d * sumi;
+#else
+    // iqs is 0...15
+    const int ib32 = iqs/2;
+    const int il = iqs%2;
+    const uint16_t * q2 = bq2->qs + 4*ib32;
+    const uint8_t  * aux8 = (const uint8_t *)q2;
+    const uint8_t  * grid1 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+0]);
+    const uint8_t  * grid2 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+1]);
+    const uint32_t aux32 = q2[2] | (q2[3] << 16);
+    const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * __low2float(bq8_1[ib32].ds) * 0.25f;
+    const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127];
+    const uint8_t signs2 = ksigns_iq2xs[(aux32 >> (14*il + 7)) & 127];
+    const int8_t * q8 = bq8_1[ib32].qs + 16*il;
+    int sumi1 = 0, sumi2 = 0;
+    for (int j = 0; j < 8; ++j) {
+        sumi1 += q8[j+0] * grid1[j] * (signs1 & kmask_iq2xs[j] ? -1 : 1);
+        sumi2 += q8[j+8] * grid2[j] * (signs2 & kmask_iq2xs[j] ? -1 : 1);
+    }
+    return d * (sumi1 + sumi2);
+#endif
+#else
+    NO_DEVICE_CODE;
+#endif
+}
+
+static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+#if QK_K == 256
+    const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq;
+
+    const int ib32 = iqs;
+    const uint16_t * q2 = bq2->qs + 4*ib32;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    const uint8_t ls1 = bq2->scales[ib32] & 0xf;
+    const uint8_t ls2 = bq2->scales[ib32] >>  4;
+    int sumi1 = 0;
+    for (int l = 0; l < 2; ++l) {
+        const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511));
+        const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9));
+        const int grid_l = __vsub4(grid[0] ^ signs[0], signs[0]);
+        const int grid_h = __vsub4(grid[1] ^ signs[1], signs[1]);
+        sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1);
+        sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1);
+        q8 += 8;
+    }
+    int sumi2 = 0;
+    for (int l = 2; l < 4; ++l) {
+        const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511));
+        const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9));
+        const int grid_l = __vsub4(grid[0] ^ signs[0], signs[0]);
+        const int grid_h = __vsub4(grid[1] ^ signs[1], signs[1]);
+        sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2);
+        sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2);
+        q8 += 8;
+    }
+    const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f;
+    return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
+#else
+    GGML_UNUSED(ksigns64);
+    NO_DEVICE_CODE;
+#endif
+#else
+    GGML_UNUSED(ksigns64);
+    NO_DEVICE_CODE;
+#endif
+}
+
+// TODO
+static __device__ __forceinline__ float vec_dot_iq2_s_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+#if QK_K == 256
+    const block_iq2_s * bq2 = (const block_iq2_s *) vbq;
+
+    const int ib32 = iqs;
+    const int8_t  * q8 = bq8_1[ib32].qs;
+    const uint8_t * signs = bq2->qs + QK_K/8 + 4*ib32;
+    const uint8_t ls1 = bq2->scales[ib32] & 0xf;
+    const uint8_t ls2 = bq2->scales[ib32] >>  4;
+    int sumi1 = 0;
+    for (int l = 0; l < 2; ++l) {
+        const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300)));
+        const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201);
+        const uint32_t signs1 = __vcmpeq4(((signs[l] >>  4) * 0x01010101) & 0x08040201, 0x08040201);
+        const int grid_l = __vsub4(grid[0] ^ signs0, signs0);
+        const int grid_h = __vsub4(grid[1] ^ signs1, signs1);
+        sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1);
+        sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1);
+        q8 += 8;
+    }
+    int sumi2 = 0;
+    for (int l = 2; l < 4; ++l) {
+        const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300)));
+        const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201);
+        const uint32_t signs1 = __vcmpeq4(((signs[l] >>  4) * 0x01010101) & 0x08040201, 0x08040201);
+        const int grid_l = __vsub4(grid[0] ^ signs0, signs0);
+        const int grid_h = __vsub4(grid[1] ^ signs1, signs1);
+        sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2);
+        sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2);
+        q8 += 8;
+    }
+    const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f;
+    return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
+#else
+    GGML_UNUSED(ksigns64);
+    NO_DEVICE_CODE;
+#endif
+#else
+    GGML_UNUSED(ksigns64);
+    NO_DEVICE_CODE;
+#endif
+}
+
+static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+#if QK_K == 256
+    const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq;
+
+    const int ib32 = iqs;
+    const uint8_t  * q3 = bq2->qs + 8*ib32;
+    const uint16_t * gas = (const uint16_t *)(bq2->qs + QK_K/4) + 2*ib32;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    uint32_t aux32 = gas[0] | (gas[1] << 16);
+    int sumi = 0;
+    for (int l = 0; l < 4; ++l) {
+        const uint32_t * grid1 = iq3xxs_grid + q3[2*l+0];
+        const uint32_t * grid2 = iq3xxs_grid + q3[2*l+1];
+        const uint32_t * signs = (const uint32_t *)(ksigns64 + (aux32 & 127));
+        const int grid_l = __vsub4(grid1[0] ^ signs[0], signs[0]);
+        const int grid_h = __vsub4(grid2[0] ^ signs[1], signs[1]);
+        sumi = __dp4a(grid_l, *((int *)q8+0), sumi);
+        sumi = __dp4a(grid_h, *((int *)q8+1), sumi);
+        q8 += 8;
+        aux32 >>= 7;
+    }
+    const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.5f;
+    return d * sumi;
+#else
+    NO_DEVICE_CODE;
+#endif
+#else
+    NO_DEVICE_CODE;
+#endif
+}
+
+// TODO: don't use lookup table for signs
+static __device__ __forceinline__ float vec_dot_iq3_s_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+#if QK_K == 256
+    const block_iq3_s * bq2 = (const block_iq3_s *) vbq;
+
+    const int ib32 = iqs;
+    const uint8_t  * qs = bq2->qs + 8*ib32;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    int sumi = 0;
+    for (int l = 0; l < 4; ++l) {
+        const uint32_t * grid1 = iq3s_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256));
+        const uint32_t * grid2 = iq3s_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256));
+        uint32_t signs0 = __vcmpeq4(((bq2->signs[4*ib32+l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201);
+        uint32_t signs1 = __vcmpeq4(((bq2->signs[4*ib32+l] >>  4) * 0x01010101) & 0x08040201, 0x08040201);
+        const int grid_l = __vsub4(grid1[0] ^ signs0, signs0);
+        const int grid_h = __vsub4(grid2[0] ^ signs1, signs1);
+        sumi = __dp4a(grid_l, *((int *)q8+0), sumi);
+        sumi = __dp4a(grid_h, *((int *)q8+1), sumi);
+        q8 += 8;
+    }
+    const float d = (float)bq2->d * (1 + 2*((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds);
+    return d * sumi;
+#else
+    NO_DEVICE_CODE;
+#endif
+#else
+    NO_DEVICE_CODE;
+#endif
+}
+
+static __device__ __forceinline__ float vec_dot_iq1_s_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+#if QK_K == 256
+    const block_iq1_s * bq1 = (const block_iq1_s *) vbq;
+
+    const int ib32 = iqs;
+    int sumi = 0;
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    const int * q8 = (const int *)bq8_1[ib32].qs;
+    for (int l = 0; l < 4; ++l) {
+        const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8)));
+        int grid0 = grid[0] & 0x0f0f0f0f;
+        int grid1 = (grid[0] >> 4) & 0x0f0f0f0f;
+        sumi = __dp4a(q8[2*l+1], grid1, __dp4a(q8[2*l+0], grid0, sumi));
+    }
+#else
+    const int8_t * q8 = bq8_1[ib32].qs;
+    for (int l = 0; l < 4; ++l) {
+        const uint8_t * grid = (const uint8_t *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8)));
+        for (int j = 0; j < 4; ++j) {
+            sumi += q8[j] * (grid[j] & 0xf) + q8[j+4] * (grid[j] >> 4);
+        }
+        q8 += 8;
+    }
+#endif
+    const float delta = bq1->qh[ib32] & 0x8000 ? -1-IQ1S_DELTA : -1+IQ1S_DELTA;
+    const float d1q = (float)bq1->d * (2*((bq1->qh[ib32] >> 12) & 7) + 1);
+    const float d = d1q * __low2float (bq8_1[ib32].ds);
+    const float m = d1q * __high2float(bq8_1[ib32].ds);
+    return d * sumi + m * delta;
+#else
+    NO_DEVICE_CODE;
+#endif
+}
+
+static __device__ __forceinline__ float vec_dot_iq1_m_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+#if QK_K == 256
+    const block_iq1_m * bq1 = (const block_iq1_m *) vbq;
+
+    const int ib32 = iqs;
+    int   sumi[2] = {0, 0};
+    float sumf[2] = {0.f, 0.f};
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    const int * q8 = (const int *)bq8_1[ib32].qs;
+    for (int l = 0; l < 4; ++l) {
+        const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 7) << 8)));
+        int grid0 = grid[0] & 0x0f0f0f0f;
+        int grid1 = (grid[0] >> 4) & 0x0f0f0f0f;
+        sumi[l/2] = __dp4a(q8[2*l+1], grid1, __dp4a(q8[2*l+0], grid0, sumi[l/2]));
+        const float delta = (bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 0x08 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA;
+        const int sumy = __dp4a(q8[2*l+1], 0x01010101, __dp4a(q8[2*l+0], 0x01010101, 0));
+        sumf[l/2] += delta*sumy;
+    }
+#else
+    const int8_t * q8 = bq8_1[ib32].qs;
+    for (int l = 0; l < 4; ++l) {
+        const uint8_t * grid = (const uint8_t *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8)));
+        int sumy = 0;
+        for (int j = 0; j < 4; ++j) {
+            sumi[l/2] += q8[j] * (grid[j] & 0xf) + q8[j+4] * (grid[j] >> 4);
+            sumy += q8[j] + q8[j+4];
+        }
+        const float delta = (bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 0x08 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA;
+        sumf[l/2] += delta*sumy;
+        q8 += 8;
+    }
+#endif
+    iq1m_scale_t scale;
+    const uint16_t * sc = (const uint16_t *)bq1->scales;
+    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+    const float d = (float)scale.f16 * __low2float (bq8_1[ib32].ds);
+    return d * ((sumi[0] + sumf[0]) * (2*((sc[ib32/2] >> 6*(ib32%2)) & 0x7) + 1) + (sumi[1] + sumf[1]) * (2*((sc[ib32/2] >> (6*(ib32%2)+3)) & 0x7) + 1));
+#else
+    NO_DEVICE_CODE;
+#endif
+}
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+static __device__ __forceinline__ void get_int_from_table_16(const uint32_t & q4, const uint8_t * values,
+        int & val1, int & val2) {
+
+    uint32_t aux32; const uint8_t * q8 = (const uint8_t *)&aux32;
+    aux32 = q4 & 0x0f0f0f0f;
+    uint16_t v1 = values[q8[0]] | (values[q8[1]] << 8);
+    uint16_t v2 = values[q8[2]] | (values[q8[3]] << 8);
+    val1 = v1 | (v2 << 16);
+    aux32 = (q4 >> 4) & 0x0f0f0f0f;
+    v1 = values[q8[0]] | (values[q8[1]] << 8);
+    v2 = values[q8[2]] | (values[q8[3]] << 8);
+    val2 = v1 | (v2 << 16);
+}
+#endif
+
+static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_iq4_nl * bq = (const block_iq4_nl *) vbq;
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    const uint16_t * q4 = (const uint16_t *)bq->qs + 2*iqs;
+    const int32_t  * q8 = (const int32_t  *)bq8_1->qs + iqs;
+
+    const uint8_t * values = (const uint8_t *)kvalues_iq4nl;
+
+    int v1, v2;
+    int sumi1 = 0, sumi2 = 0;
+    for (int l = 0; l < VDR_Q4_0_Q8_1_MMVQ; ++l) {
+        const uint32_t aux = q4[2*l] | (q4[2*l+1] << 16);
+        get_int_from_table_16(aux, values, v1, v2);
+        sumi1 = __dp4a(v1, q8[l+0], sumi1);
+        sumi2 = __dp4a(v2, q8[l+4], sumi2);
+    }
+
+#else
+    const uint8_t * q4 = bq->qs + 4*iqs;
+    const int8_t  * q8 = bq8_1->qs + 4*iqs;
+
+    int sumi1 = 0, sumi2 = 0;
+    for (int l = 0; l < 4*VDR_Q4_0_Q8_1_MMVQ; ++l) {
+        sumi1 += q8[l+ 0] * kvalues_iq4nl[q4[l] & 0xf];
+        sumi2 += q8[l+16] * kvalues_iq4nl[q4[l] >>  4];
+    }
+#endif
+    const float d = (float)bq->d * __low2float(bq8_1->ds);
+    return d * (sumi1 + sumi2);
+}
+
+static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+#if QK_K == 256
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+
+    const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq;
+    const uint8_t * values = (const uint8_t *)kvalues_iq4nl;
+
+    // iqs is 0...7
+    const int ib32 = iqs;
+    const int32_t  * q8 = (const int *)bq8_1[ib32].qs;
+    const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32;
+    const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4);
+    const float d = (float)bq4->d * (ls - 32) * __low2float(bq8_1[ib32].ds);
+    int v1, v2;
+    int sumi1 = 0, sumi2 = 0;
+    for (int j = 0; j < 4; ++j) {
+        get_int_from_table_16(q4[j], values, v1, v2);
+        sumi1 = __dp4a(v1, q8[j+0], sumi1);
+        sumi2 = __dp4a(v2, q8[j+4], sumi2);
+    }
+    return d * (sumi1 + sumi2);
+
+#else
+    NO_DEVICE_CODE;
+#endif
+#else
+    return vec_dot_iq4_xs_q8_1(vbq, bq8_1, iqs);
+#endif
+}
diff --git a/ggml-impl.h b/ggml-impl.h
index 19df66bce..d85b152bf 100644
--- a/ggml-impl.h
+++ b/ggml-impl.h
@@ -11,6 +11,89 @@
 #include <string.h> // memcpy
 #include <math.h>   // fabsf
 
+#undef MIN
+#undef MAX
+
+#define MIN(a, b) ((a) < (b) ? (a) : (b))
+#define MAX(a, b) ((a) > (b) ? (a) : (b))
+
+/**
+ * Converts brain16 to float32.
+ *
+ * The bfloat16 floating point format has the following structure:
+ *
+ *       ┌sign
+ *       │
+ *       │   ┌exponent
+ *       │   │
+ *       │   │      ┌mantissa
+ *       │   │      │
+ *       │┌──┴───┐┌─┴───┐
+ *     0b0000000000000000 brain16
+ *
+ * Since bf16 has the same number of exponent bits as a 32bit float,
+ * encoding and decoding numbers becomes relatively straightforward.
+ *
+ *       ┌sign
+ *       │
+ *       │   ┌exponent
+ *       │   │
+ *       │   │      ┌mantissa
+ *       │   │      │
+ *       │┌──┴───┐┌─┴───────────────────┐
+ *     0b00000000000000000000000000000000 IEEE binary32
+ *
+ * For comparison, the standard fp16 format has fewer exponent bits.
+ *
+ *       ┌sign
+ *       │
+ *       │  ┌exponent
+ *       │  │
+ *       │  │    ┌mantissa
+ *       │  │    │
+ *       │┌─┴─┐┌─┴──────┐
+ *     0b0000000000000000 IEEE binary16
+ *
+ * @see IEEE 754-2008
+ */
+static inline float ggml_compute_bf16_to_fp32(ggml_bf16_t h) {
+    union {
+        float f;
+        uint32_t i;
+    } u;
+    u.i = (uint32_t)h.bits << 16;
+    return u.f;
+}
+
+/**
+ * Converts float32 to brain16.
+ *
+ * This function is binary identical to AMD Zen4 VCVTNEPS2BF16.
+ * Subnormals shall be flushed to zero, and NANs will be quiet.
+ * This code should vectorize nicely if using modern compilers.
+ */
+static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) {
+    ggml_bf16_t h;
+    union {
+        float f;
+        uint32_t i;
+    } u;
+    u.f = s;
+    if ((u.i & 0x7fffffff) > 0x7f800000) { /* nan */
+        h.bits = (u.i >> 16) | 64; /* force to quiet */
+        return h;
+    }
+    if (!(u.i & 0x7f800000)) { /* subnormal */
+        h.bits = (u.i & 0x80000000) >> 16; /* flush to zero */
+        return h;
+    }
+    h.bits = (u.i + (0x7fff + ((u.i >> 16) & 1))) >> 16;
+    return h;
+}
+
+#define GGML_FP32_TO_BF16(x) ggml_compute_fp32_to_bf16(x)
+#define GGML_BF16_TO_FP32(x) ggml_compute_bf16_to_fp32(x)
+
 #ifdef __cplusplus
 extern "C" {
 #endif
@@ -45,7 +128,7 @@ extern "C" {
 // 16-bit float
 // on Arm, we use __fp16
 // on x86, we use uint16_t
-#if defined(__ARM_NEON) && !defined(_MSC_VER)
+#if defined(__ARM_NEON)
 
 // if YCM cannot find <arm_neon.h>, make a symbolic link to it, for example:
 //
@@ -53,11 +136,279 @@ extern "C" {
 //
 #include <arm_neon.h>
 
-#define GGML_COMPUTE_FP16_TO_FP32(x) ((float) (x))
-#define GGML_COMPUTE_FP32_TO_FP16(x) (x)
+#ifdef _MSC_VER
 
-#define GGML_FP16_TO_FP32(x) ((float) (x))
-#define GGML_FP32_TO_FP16(x) (x)
+typedef uint16_t ggml_fp16_internal_t;
+
+#define ggml_vld1q_u32(w,x,y,z) { ((w) + ((uint64_t)(x) << 32)), ((y) + ((uint64_t)(z) << 32)) }
+
+#else
+
+typedef __fp16 ggml_fp16_internal_t;
+
+#define ggml_vld1q_u32(w,x,y,z) { (w), (x), (y), (z) }
+
+#endif // _MSC_VER
+
+#if !defined(__aarch64__)
+
+// 32-bit ARM compatibility
+
+// vaddvq_s16
+// vpaddq_s16
+// vpaddq_s32
+// vaddvq_s32
+// vaddvq_f32
+// vmaxvq_f32
+// vcvtnq_s32_f32
+// vzip1_u8
+// vzip2_u8
+
+inline static int32_t vaddvq_s16(int16x8_t v) {
+    return
+        (int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) +
+        (int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) +
+        (int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) +
+        (int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7);
+}
+
+inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) {
+    int16x4_t a0 = vpadd_s16(vget_low_s16(a), vget_high_s16(a));
+    int16x4_t b0 = vpadd_s16(vget_low_s16(b), vget_high_s16(b));
+    return vcombine_s16(a0, b0);
+}
+
+inline static int32x4_t vpaddq_s32(int32x4_t a, int32x4_t b) {
+    int32x2_t a0 = vpadd_s32(vget_low_s32(a), vget_high_s32(a));
+    int32x2_t b0 = vpadd_s32(vget_low_s32(b), vget_high_s32(b));
+    return vcombine_s32(a0, b0);
+}
+
+inline static int32_t vaddvq_s32(int32x4_t v) {
+    return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3);
+}
+
+inline static float vaddvq_f32(float32x4_t v) {
+    return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3);
+}
+
+inline static float vmaxvq_f32(float32x4_t v) {
+    return
+        MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)),
+            MAX(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3)));
+}
+
+inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) {
+    int32x4_t res;
+
+    res[0] = roundf(vgetq_lane_f32(v, 0));
+    res[1] = roundf(vgetq_lane_f32(v, 1));
+    res[2] = roundf(vgetq_lane_f32(v, 2));
+    res[3] = roundf(vgetq_lane_f32(v, 3));
+
+    return res;
+}
+
+inline static uint8x8_t vzip1_u8(uint8x8_t a, uint8x8_t b) {
+    uint8x8_t res;
+
+    res[0] = a[0]; res[1] = b[0];
+    res[2] = a[1]; res[3] = b[1];
+    res[4] = a[2]; res[5] = b[2];
+    res[6] = a[3]; res[7] = b[3];
+
+    return res;
+}
+
+inline static uint8x8_t vzip2_u8(uint8x8_t a, uint8x8_t b) {
+    uint8x8_t res;
+
+    res[0] = a[4]; res[1] = b[4];
+    res[2] = a[5]; res[3] = b[5];
+    res[4] = a[6]; res[5] = b[6];
+    res[6] = a[7]; res[7] = b[7];
+
+    return res;
+}
+
+// vld1q_s16_x2
+// vld1q_u8_x2
+// vld1q_u8_x4
+// vld1q_s8_x2
+// vld1q_s8_x4
+// TODO: double-check these work correctly
+
+typedef struct ggml_int16x8x2_t {
+    int16x8_t val[2];
+} ggml_int16x8x2_t;
+
+inline static ggml_int16x8x2_t ggml_vld1q_s16_x2(const int16_t * ptr) {
+    ggml_int16x8x2_t res;
+
+    res.val[0] = vld1q_s16(ptr + 0);
+    res.val[1] = vld1q_s16(ptr + 8);
+
+    return res;
+}
+
+typedef struct ggml_uint8x16x2_t {
+    uint8x16_t val[2];
+} ggml_uint8x16x2_t;
+
+inline static ggml_uint8x16x2_t ggml_vld1q_u8_x2(const uint8_t * ptr) {
+    ggml_uint8x16x2_t res;
+
+    res.val[0] = vld1q_u8(ptr + 0);
+    res.val[1] = vld1q_u8(ptr + 16);
+
+    return res;
+}
+
+typedef struct ggml_uint8x16x4_t {
+    uint8x16_t val[4];
+} ggml_uint8x16x4_t;
+
+inline static ggml_uint8x16x4_t ggml_vld1q_u8_x4(const uint8_t * ptr) {
+    ggml_uint8x16x4_t res;
+
+    res.val[0] = vld1q_u8(ptr + 0);
+    res.val[1] = vld1q_u8(ptr + 16);
+    res.val[2] = vld1q_u8(ptr + 32);
+    res.val[3] = vld1q_u8(ptr + 48);
+
+    return res;
+}
+
+typedef struct ggml_int8x16x2_t {
+    int8x16_t val[2];
+} ggml_int8x16x2_t;
+
+inline static ggml_int8x16x2_t ggml_vld1q_s8_x2(const int8_t * ptr) {
+    ggml_int8x16x2_t res;
+
+    res.val[0] = vld1q_s8(ptr + 0);
+    res.val[1] = vld1q_s8(ptr + 16);
+
+    return res;
+}
+
+typedef struct ggml_int8x16x4_t {
+    int8x16_t val[4];
+} ggml_int8x16x4_t;
+
+inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) {
+    ggml_int8x16x4_t res;
+
+    res.val[0] = vld1q_s8(ptr + 0);
+    res.val[1] = vld1q_s8(ptr + 16);
+    res.val[2] = vld1q_s8(ptr + 32);
+    res.val[3] = vld1q_s8(ptr + 48);
+
+    return res;
+}
+
+// NOTE: not tested
+inline static int8x16_t ggml_vqtbl1q_s8(int8x16_t a, uint8x16_t b) {
+    int8x16_t res;
+
+    res[ 0] = a[b[ 0]];
+    res[ 1] = a[b[ 1]];
+    res[ 2] = a[b[ 2]];
+    res[ 3] = a[b[ 3]];
+    res[ 4] = a[b[ 4]];
+    res[ 5] = a[b[ 5]];
+    res[ 6] = a[b[ 6]];
+    res[ 7] = a[b[ 7]];
+    res[ 8] = a[b[ 8]];
+    res[ 9] = a[b[ 9]];
+    res[10] = a[b[10]];
+    res[11] = a[b[11]];
+    res[12] = a[b[12]];
+    res[13] = a[b[13]];
+    res[14] = a[b[14]];
+    res[15] = a[b[15]];
+
+    return res;
+}
+
+// NOTE: not tested
+inline static uint8x16_t ggml_vqtbl1q_u8(uint8x16_t a, uint8x16_t b) {
+    uint8x16_t res;
+
+    res[ 0] = a[b[ 0]];
+    res[ 1] = a[b[ 1]];
+    res[ 2] = a[b[ 2]];
+    res[ 3] = a[b[ 3]];
+    res[ 4] = a[b[ 4]];
+    res[ 5] = a[b[ 5]];
+    res[ 6] = a[b[ 6]];
+    res[ 7] = a[b[ 7]];
+    res[ 8] = a[b[ 8]];
+    res[ 9] = a[b[ 9]];
+    res[10] = a[b[10]];
+    res[11] = a[b[11]];
+    res[12] = a[b[12]];
+    res[13] = a[b[13]];
+    res[14] = a[b[14]];
+    res[15] = a[b[15]];
+
+    return res;
+}
+
+#else
+
+#define ggml_int16x8x2_t  int16x8x2_t
+#define ggml_uint8x16x2_t uint8x16x2_t
+#define ggml_uint8x16x4_t uint8x16x4_t
+#define ggml_int8x16x2_t  int8x16x2_t
+#define ggml_int8x16x4_t  int8x16x4_t
+
+#define ggml_vld1q_s16_x2 vld1q_s16_x2
+#define ggml_vld1q_u8_x2  vld1q_u8_x2
+#define ggml_vld1q_u8_x4  vld1q_u8_x4
+#define ggml_vld1q_s8_x2  vld1q_s8_x2
+#define ggml_vld1q_s8_x4  vld1q_s8_x4
+#define ggml_vqtbl1q_s8   vqtbl1q_s8
+#define ggml_vqtbl1q_u8   vqtbl1q_u8
+
+#endif // !defined(__aarch64__)
+
+#if !defined(__ARM_FEATURE_DOTPROD)
+
+inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) {
+    const int16x8_t p0 = vmull_s8(vget_low_s8 (a), vget_low_s8 (b));
+    const int16x8_t p1 = vmull_s8(vget_high_s8(a), vget_high_s8(b));
+
+    return vaddq_s32(acc, vaddq_s32(vpaddlq_s16(p0), vpaddlq_s16(p1)));
+}
+
+#else
+
+#define ggml_vdotq_s32(a, b, c) vdotq_s32(a, b, c)
+
+#endif // !defined(__ARM_FEATURE_DOTPROD)
+
+#endif // defined(__ARM_NEON)
+
+#if defined(__ARM_NEON) && !defined(_MSC_VER)
+
+#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
+#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
+
+#define GGML_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
+
+static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
+    ggml_fp16_internal_t tmp;
+    memcpy(&tmp, &h, sizeof(ggml_fp16_t));
+    return (float)tmp;
+}
+
+static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
+    ggml_fp16_t res;
+    ggml_fp16_internal_t tmp = f;
+    memcpy(&res, &tmp, sizeof(ggml_fp16_t));
+    return res;
+}
 
 #else
 
@@ -72,7 +423,7 @@ extern "C" {
 #if defined(_MSC_VER) || defined(__MINGW32__)
 #include <intrin.h>
 #else
-#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__)
+#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__) || defined(__SSE__)
 #if !defined(__riscv)
 #include <immintrin.h>
 #endif
@@ -205,7 +556,7 @@ static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
 
 #endif // __F16C__
 
-#endif // __ARM_NEON
+#endif // defined(__ARM_NEON) && (!defined(__MSC_VER)
 
 // precomputed f32 table for f16 (256 KB)
 // defined in ggml.c, initialized in ggml_init()
@@ -214,8 +565,7 @@ extern float ggml_table_f32_f16[1 << 16];
 // On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32,
 // so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON.
 // This is also true for POWER9.
-#if !defined(GGML_FP16_TO_FP32) || !defined(GGML_FP32_TO_FP16)
-
+#if !defined(GGML_FP16_TO_FP32)
 inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
     uint16_t s;
     memcpy(&s, &f, sizeof(uint16_t));
@@ -223,8 +573,10 @@ inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
 }
 
 #define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x)
-#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
+#endif
 
+#if !defined(GGML_FP32_TO_FP16)
+#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
 #endif
 
 #define GGML_HASHTABLE_FULL ((size_t)-1)
diff --git a/ggml-kompute.cpp b/ggml-kompute.cpp
index 51c5af8ec..3f033d58b 100644
--- a/ggml-kompute.cpp
+++ b/ggml-kompute.cpp
@@ -1427,9 +1427,14 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml
         for (int i = node_start; i < node_end; ++i) {
             struct ggml_tensor * src0 = gf->nodes[i]->src[0];
             struct ggml_tensor * src1 = gf->nodes[i]->src[1];
+            struct ggml_tensor * src2 = gf->nodes[i]->src[2]; GGML_UNUSED(src2);
             struct ggml_tensor * dst = gf->nodes[i];
             GGML_ASSERT(dst->data != nullptr);
 
+            if (ggml_is_empty(dst)) {
+                continue;
+            }
+
             switch (dst->op) {
                 case GGML_OP_NONE:
                 case GGML_OP_RESHAPE:
@@ -1554,7 +1559,19 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml
                 case GGML_OP_SOFT_MAX:
                     {
                         float scale;
-                        memcpy(&scale, dst->op_params, sizeof(float));
+                        float max_bias;
+
+                        memcpy(&scale,    (float *)dst->op_params + 0, sizeof(float));
+                        memcpy(&max_bias, (float *)dst->op_params + 1, sizeof(float));
+
+#pragma message("TODO: add ggml_vk_soft_max() F16 src1 support")
+#pragma message("ref:  https://github.com/ggerganov/llama.cpp/pull/5021")
+                        GGML_ASSERT(!src1 || src1t == GGML_TYPE_F32);
+
+#pragma message("TODO: add ALiBi support")
+#pragma message("ref:  https://github.com/ggerganov/llama.cpp/pull/7192")
+                        GGML_ASSERT(max_bias == 0.0f);
+
                         ggml_vk_soft_max(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, ne01, ne02, ne03, scale);
                     } break;
                 case GGML_OP_DIAG_MASK_INF:
@@ -1927,10 +1944,10 @@ static ggml_backend_buffer_type_t ggml_backend_kompute_get_default_buffer_type(g
     return ggml_backend_kompute_buffer_type(ctx->device);
 }
 
-static bool ggml_backend_kompute_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+static ggml_status ggml_backend_kompute_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
     auto * ctx = static_cast<ggml_kompute_context *>(backend->context);
     ggml_vk_graph_compute(ctx, cgraph);
-    return true;
+    return GGML_STATUS_SUCCESS;
 }
 
 static bool ggml_backend_kompute_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
@@ -1951,13 +1968,25 @@ static struct ggml_backend_i kompute_backend_i = {
     /* .graph_plan_compute      = */ NULL,
     /* .graph_compute           = */ ggml_backend_kompute_graph_compute,
     /* .supports_op             = */ ggml_backend_kompute_supports_op,
+    /* .offload_op              = */ NULL,
+    /* .event_new               = */ NULL,
+    /* .event_free              = */ NULL,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+    /* .event_synchronize       = */ NULL,
 };
 
+static ggml_guid_t ggml_backend_kompute_guid() {
+    static ggml_guid guid = { 0x7b, 0x57, 0xdc, 0xaf, 0xde, 0x12, 0x1d, 0x49, 0xfb, 0x35, 0xfa, 0x9b, 0x18, 0x31, 0x1d, 0xca };
+    return &guid;
+}
+
 ggml_backend_t ggml_backend_kompute_init(int device) {
     GGML_ASSERT(s_kompute_context == nullptr);
     s_kompute_context = new ggml_kompute_context(device);
 
     ggml_backend_t kompute_backend = new ggml_backend {
+        /* .guid      = */ ggml_backend_kompute_guid(),
         /* .interface = */ kompute_backend_i,
         /* .context   = */ s_kompute_context,
     };
@@ -1966,7 +1995,7 @@ ggml_backend_t ggml_backend_kompute_init(int device) {
 }
 
 bool ggml_backend_is_kompute(ggml_backend_t backend) {
-    return backend && backend->iface.get_name == ggml_backend_kompute_name;
+    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_kompute_guid());
 }
 
 static ggml_backend_t ggml_backend_reg_kompute_init(const char * params, void * user_data) {
diff --git a/ggml-metal.m b/ggml-metal.m
index 956e323a0..28dec762a 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -37,13 +37,20 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_DIV_ROW,
     GGML_METAL_KERNEL_TYPE_SCALE,
     GGML_METAL_KERNEL_TYPE_SCALE_4,
+    GGML_METAL_KERNEL_TYPE_CLAMP,
     GGML_METAL_KERNEL_TYPE_TANH,
     GGML_METAL_KERNEL_TYPE_RELU,
+    GGML_METAL_KERNEL_TYPE_SIGMOID,
     GGML_METAL_KERNEL_TYPE_GELU,
+    GGML_METAL_KERNEL_TYPE_GELU_4,
     GGML_METAL_KERNEL_TYPE_GELU_QUICK,
+    GGML_METAL_KERNEL_TYPE_GELU_QUICK_4,
     GGML_METAL_KERNEL_TYPE_SILU,
-    GGML_METAL_KERNEL_TYPE_SOFT_MAX,
-    GGML_METAL_KERNEL_TYPE_SOFT_MAX_4,
+    GGML_METAL_KERNEL_TYPE_SILU_4,
+    GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16,
+    GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4,
+    GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32,
+    GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4,
     GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF,
     GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8,
     GGML_METAL_KERNEL_TYPE_GET_ROWS_F32,
@@ -61,7 +68,12 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS,
     GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS,
     GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S,
     GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS,
     GGML_METAL_KERNEL_TYPE_GET_ROWS_I32,
     GGML_METAL_KERNEL_TYPE_RMS_NORM,
     GGML_METAL_KERNEL_TYPE_GROUP_NORM,
@@ -84,7 +96,12 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32,
   //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16,
     GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32,
@@ -103,7 +120,12 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32,
@@ -119,7 +141,12 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32,
@@ -135,24 +162,39 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32,
     GGML_METAL_KERNEL_TYPE_ROPE_F32,
     GGML_METAL_KERNEL_TYPE_ROPE_F16,
-    GGML_METAL_KERNEL_TYPE_ALIBI_F32,
     GGML_METAL_KERNEL_TYPE_IM2COL_F16,
     GGML_METAL_KERNEL_TYPE_IM2COL_F32,
     GGML_METAL_KERNEL_TYPE_UPSCALE_F32,
     GGML_METAL_KERNEL_TYPE_PAD_F32,
+    GGML_METAL_KERNEL_TYPE_ARANGE_F32,
+    GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32,
     GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,
     GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC,
     GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256,
     GGML_METAL_KERNEL_TYPE_CPY_F32_F16,
     GGML_METAL_KERNEL_TYPE_CPY_F32_F32,
     GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0,
     GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0,
     GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1,
-  //GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0,
-  //GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1,
+    GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0,
+    GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1,
+    GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL,
     GGML_METAL_KERNEL_TYPE_CPY_F16_F16,
     GGML_METAL_KERNEL_TYPE_CPY_F16_F32,
     GGML_METAL_KERNEL_TYPE_CONCAT,
@@ -223,11 +265,20 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
 
 static void * ggml_metal_host_malloc(size_t n) {
     void * data = NULL;
+
+#if TARGET_OS_OSX
+    kern_return_t err = vm_allocate((vm_map_t) mach_task_self(), (void *) &data, n, VM_FLAGS_ANYWHERE);
+    if (err != KERN_SUCCESS) {
+        GGML_METAL_LOG_ERROR("%s: error: vm_allocate failed\n", __func__);
+        return NULL;
+    }
+#else
     const int result = posix_memalign((void **) &data, sysconf(_SC_PAGESIZE), n);
     if (result != 0) {
         GGML_METAL_LOG_ERROR("%s: error: posix_memalign failed\n", __func__);
         return NULL;
     }
+#endif
 
     return data;
 }
@@ -258,6 +309,11 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
     id<MTLLibrary> metal_library;
 
     // load library
+    //
+    // - first check if the library is embedded
+    // - then check if the library is in the bundle
+    // - if not found, load the source and compile it
+    // - if that fails, return NULL
     {
         NSBundle * bundle = nil;
 #ifdef SWIFT_PACKAGE
@@ -265,12 +321,21 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
 #else
         bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
 #endif
+
         NSError * error = nil;
-        NSString * libPath = [bundle pathForResource:@"default" ofType:@"metallib"];
-        if (libPath != nil) {
+
+#if GGML_METAL_EMBED_LIBRARY
+        const bool try_metallib = false;
+#else
+        const bool try_metallib = true;
+#endif
+
+        NSString * path_lib = [bundle pathForResource:@"default" ofType:@"metallib"];
+        if (try_metallib && path_lib != nil) {
             // pre-compiled library found
-            NSURL * libURL = [NSURL fileURLWithPath:libPath];
-            GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [libPath UTF8String]);
+            NSURL * libURL = [NSURL fileURLWithPath:path_lib];
+            GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [path_lib UTF8String]);
+
             metal_library = [ctx->device newLibraryWithURL:libURL error:&error];
             if (error) {
                 GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
@@ -283,38 +348,41 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
             extern const char ggml_metallib_start[];
             extern const char ggml_metallib_end[];
 
-            NSString * src  = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding];
+            NSString * src = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding];
 #else
             GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
 
-            NSString * sourcePath;
-            NSString * ggmlMetalPathResources = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
+            NSString * path_source;
+            NSString * path_resource = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
 
-            GGML_METAL_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, ggmlMetalPathResources ? [ggmlMetalPathResources UTF8String] : "nil");
+            GGML_METAL_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, path_resource ? [path_resource UTF8String] : "nil");
 
-            if (ggmlMetalPathResources) {
-                sourcePath = [ggmlMetalPathResources stringByAppendingPathComponent:@"ggml-metal.metal"];
+            if (path_resource) {
+                path_source = [path_resource stringByAppendingPathComponent:@"ggml-metal.metal"];
             } else {
-                sourcePath = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
+                path_source = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
             }
-            if (sourcePath == nil) {
+
+            if (path_source == nil) {
                 GGML_METAL_LOG_WARN("%s: error: could not use bundle path to find ggml-metal.metal, falling back to trying cwd\n", __func__);
-                sourcePath = @"ggml-metal.metal";
+                path_source = @"ggml-metal.metal";
             }
-            GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [sourcePath UTF8String]);
-            NSString * src = [NSString stringWithContentsOfFile:sourcePath encoding:NSUTF8StringEncoding error:&error];
+
+            GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [path_source UTF8String]);
+
+            NSString * src = [NSString stringWithContentsOfFile:path_source encoding:NSUTF8StringEncoding error:&error];
             if (error) {
                 GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
                 return NULL;
             }
-#endif
+#endif // GGML_METAL_EMBED_LIBRARY
 
             @autoreleasepool {
                 // dictionary of preprocessor macros
                 NSMutableDictionary * prep = [NSMutableDictionary dictionary];
 
 #ifdef GGML_QKK_64
-                prep[@"QK_K"] = @(64);
+                prep[@"GGML_QKK_64"] = @(1);
 #endif
 
                 MTLCompileOptions* options = [MTLCompileOptions new];
@@ -394,7 +462,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
         }
 
         /*
-            GGML_METAL_LOG_INFO("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \
+            GGML_METAL_LOG_INFO("%s: loaded %-40s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \
                     (int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \
                     (int) kernel->pipeline.threadExecutionWidth); \
         */
@@ -410,140 +478,182 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
                 return NULL; \
             } \
         } else { \
-            GGML_METAL_LOG_WARN("%s: skipping %-32s (not supported)\n", __func__, "kernel_"#name); \
+            GGML_METAL_LOG_WARN("%s: skipping %-40s (not supported)\n", __func__, "kernel_"#name); \
         }
 
         // simd_sum and simd_max requires MTLGPUFamilyApple7
 
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD,                       add,                    true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW,                   add_row,                true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL,                       mul,                    true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW,                   mul_row,                true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV,                       div,                    true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW,                   div_row,                true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE,                     scale,                  true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4,                   scale_4,                true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH,                      tanh,                   true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU,                      relu,                   true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU,                      gelu,                   true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK,                gelu_quick,             true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU,                      silu,                   true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX,                  soft_max,               ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_4,                soft_max_4,             ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF,             diag_mask_inf,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8,           diag_mask_inf_8,        true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F32,              get_rows_f32,           true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F16,              get_rows_f16,           true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0,             get_rows_q4_0,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1,             get_rows_q4_1,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0,             get_rows_q5_0,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1,             get_rows_q5_1,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0,             get_rows_q8_0,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K,             get_rows_q2_K,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K,             get_rows_q3_K,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K,             get_rows_q4_K,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K,             get_rows_q5_K,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K,             get_rows_q6_K,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS,          get_rows_iq2_xxs,       true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS,           get_rows_iq2_xs,        true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS,          get_rows_iq3_xxs,       true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S,            get_rows_iq1_s,         true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32,              get_rows_i32,           true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM,                  rms_norm,               ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM,                group_norm,             ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NORM,                      norm,                   true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32,            mul_mv_f32_f32,         ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16,            mul_mv_f16_f16,         ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32,            mul_mv_f16_f32,         ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW,       mul_mv_f16_f32_1row,    ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4,         mul_mv_f16_f32_l4,      ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32,           mul_mv_q4_0_f32,        ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32,           mul_mv_q4_1_f32,        ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32,           mul_mv_q5_0_f32,        ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32,           mul_mv_q5_1_f32,        ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32,           mul_mv_q8_0_f32,        ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32,           mul_mv_q2_K_f32,        ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32,           mul_mv_q3_K_f32,        ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32,           mul_mv_q4_K_f32,        ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32,           mul_mv_q5_K_f32,        ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32,           mul_mv_q6_K_f32,        ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32,        mul_mv_iq2_xxs_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32,         mul_mv_iq2_xs_f32,      ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32,        mul_mv_iq3_xxs_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32,          mul_mv_iq1_s_f32,       ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32,         mul_mv_id_f32_f32,      ctx->support_simdgroup_reduction);
-      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16,         mul_mv_id_f16_f16,      ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32,         mul_mv_id_f16_f32,      ctx->support_simdgroup_reduction);
-      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW,    mul_mv_id_f16_f32_1row, ctx->support_simdgroup_reduction);
-      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4,      mul_mv_id_f16_f32_l4,   ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32,        mul_mv_id_q4_0_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32,        mul_mv_id_q4_1_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32,        mul_mv_id_q5_0_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32,        mul_mv_id_q5_1_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32,        mul_mv_id_q8_0_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32,        mul_mv_id_q2_K_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32,        mul_mv_id_q3_K_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32,        mul_mv_id_q4_K_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32,        mul_mv_id_q5_K_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32,        mul_mv_id_q6_K_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32,     mul_mv_id_iq2_xxs_f32,  ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32,      mul_mv_id_iq2_xs_f32,   ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32,     mul_mv_id_iq3_xxs_f32,  ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32,       mul_mv_id_iq1_s_f32,    ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32,            mul_mm_f32_f32,         ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32,            mul_mm_f16_f32,         ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32,           mul_mm_q4_0_f32,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32,           mul_mm_q4_1_f32,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32,           mul_mm_q5_0_f32,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32,           mul_mm_q5_1_f32,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32,           mul_mm_q8_0_f32,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32,           mul_mm_q2_K_f32,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32,           mul_mm_q3_K_f32,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32,           mul_mm_q4_K_f32,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32,           mul_mm_q5_K_f32,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32,           mul_mm_q6_K_f32,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32,        mul_mm_iq2_xxs_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32,         mul_mm_iq2_xs_f32,      ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32,        mul_mm_iq3_xxs_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32,          mul_mm_iq1_s_f32,       ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32,         mul_mm_id_f32_f32,      ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32,         mul_mm_id_f16_f32,      ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32,        mul_mm_id_q4_0_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32,        mul_mm_id_q4_1_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32,        mul_mm_id_q5_0_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32,        mul_mm_id_q5_1_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32,        mul_mm_id_q8_0_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32,        mul_mm_id_q2_K_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32,        mul_mm_id_q3_K_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32,        mul_mm_id_q4_K_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32,        mul_mm_id_q5_K_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32,        mul_mm_id_q6_K_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32,     mul_mm_id_iq2_xxs_f32,  ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32,      mul_mm_id_iq2_xs_f32,   ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32,     mul_mm_id_iq3_xxs_f32,  ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32,       mul_mm_id_iq1_s_f32,    ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32,                  rope_f32,               true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16,                  rope_f16,               true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32,                 alibi_f32,              true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16,                im2col_f16,             true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32,                im2col_f32,             true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32,               upscale_f32,            true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32,                   pad_f32,                true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,       argsort_f32_i32_asc,    true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC,      argsort_f32_i32_desc,   true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32,            leaky_relu_f32,         true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16,               cpy_f32_f16,            true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32,               cpy_f32_f32,            true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0,              cpy_f32_q8_0,           true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0,              cpy_f32_q4_0,           true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1,              cpy_f32_q4_1,           true);
-      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0,              cpy_f32_q5_0,           true);
-      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1,              cpy_f32_q5_1,           true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16,               cpy_f16_f16,            true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32,               cpy_f16_f32,            true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT,                    concat,                 true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR,                       sqr,                    true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS,                  sum_rows,               true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD,                           add,                            true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW,                       add_row,                        true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL,                           mul,                            true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW,                       mul_row,                        true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV,                           div,                            true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW,                       div_row,                        true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE,                         scale,                          true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4,                       scale_4,                        true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP,                         clamp,                          true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH,                          tanh,                           true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU,                          relu,                           true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SIGMOID,                       sigmoid,                        true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU,                          gelu,                           true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_4,                        gelu_4,                         true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK,                    gelu_quick,                     true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK_4,                  gelu_quick_4,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU,                          silu,                           true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU_4,                        silu_4,                         true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16,                  soft_max_f16,                   ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4,                soft_max_f16_4,                 ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32,                  soft_max_f32,                   ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4,                soft_max_f32_4,                 ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF,                 diag_mask_inf,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8,               diag_mask_inf_8,                true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F32,                  get_rows_f32,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F16,                  get_rows_f16,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0,                 get_rows_q4_0,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1,                 get_rows_q4_1,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0,                 get_rows_q5_0,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1,                 get_rows_q5_1,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0,                 get_rows_q8_0,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K,                 get_rows_q2_K,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K,                 get_rows_q3_K,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K,                 get_rows_q4_K,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K,                 get_rows_q5_K,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K,                 get_rows_q6_K,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS,              get_rows_iq2_xxs,               true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS,               get_rows_iq2_xs,                true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS,              get_rows_iq3_xxs,               true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S,                get_rows_iq3_s,                 true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S,                get_rows_iq2_s,                 true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S,                get_rows_iq1_s,                 true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M,                get_rows_iq1_m,                 true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL,               get_rows_iq4_nl,                true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS,               get_rows_iq4_xs,                true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32,                  get_rows_i32,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM,                      rms_norm,                       ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM,                    group_norm,                     ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NORM,                          norm,                           true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32,                mul_mv_f32_f32,                 ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16,                mul_mv_f16_f16,                 ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32,                mul_mv_f16_f32,                 ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW,           mul_mv_f16_f32_1row,            ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4,             mul_mv_f16_f32_l4,              ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32,               mul_mv_q4_0_f32,                ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32,               mul_mv_q4_1_f32,                ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32,               mul_mv_q5_0_f32,                ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32,               mul_mv_q5_1_f32,                ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32,               mul_mv_q8_0_f32,                ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32,               mul_mv_q2_K_f32,                ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32,               mul_mv_q3_K_f32,                ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32,               mul_mv_q4_K_f32,                ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32,               mul_mv_q5_K_f32,                ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32,               mul_mv_q6_K_f32,                ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32,            mul_mv_iq2_xxs_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32,             mul_mv_iq2_xs_f32,              ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32,            mul_mv_iq3_xxs_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32,              mul_mv_iq3_s_f32,               ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32,              mul_mv_iq2_s_f32,               ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32,              mul_mv_iq1_s_f32,               ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32,              mul_mv_iq1_m_f32,               ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32,             mul_mv_iq4_nl_f32,              ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32,             mul_mv_iq4_xs_f32,              ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32,             mul_mv_id_f32_f32,              ctx->support_simdgroup_reduction);
+      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16,             mul_mv_id_f16_f16,              ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32,             mul_mv_id_f16_f32,              ctx->support_simdgroup_reduction);
+      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW,        mul_mv_id_f16_f32_1row,         ctx->support_simdgroup_reduction);
+      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4,          mul_mv_id_f16_f32_l4,           ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32,            mul_mv_id_q4_0_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32,            mul_mv_id_q4_1_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32,            mul_mv_id_q5_0_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32,            mul_mv_id_q5_1_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32,            mul_mv_id_q8_0_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32,            mul_mv_id_q2_K_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32,            mul_mv_id_q3_K_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32,            mul_mv_id_q4_K_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32,            mul_mv_id_q5_K_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32,            mul_mv_id_q6_K_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32,         mul_mv_id_iq2_xxs_f32,          ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32,          mul_mv_id_iq2_xs_f32,           ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32,         mul_mv_id_iq3_xxs_f32,          ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32,           mul_mv_id_iq3_s_f32,            ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32,           mul_mv_id_iq2_s_f32,            ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32,           mul_mv_id_iq1_s_f32,            ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32,           mul_mv_id_iq1_m_f32,            ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32,          mul_mv_id_iq4_nl_f32,           ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32,          mul_mv_id_iq4_xs_f32,           ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32,                mul_mm_f32_f32,                 ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32,                mul_mm_f16_f32,                 ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32,               mul_mm_q4_0_f32,                ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32,               mul_mm_q4_1_f32,                ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32,               mul_mm_q5_0_f32,                ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32,               mul_mm_q5_1_f32,                ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32,               mul_mm_q8_0_f32,                ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32,               mul_mm_q2_K_f32,                ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32,               mul_mm_q3_K_f32,                ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32,               mul_mm_q4_K_f32,                ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32,               mul_mm_q5_K_f32,                ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32,               mul_mm_q6_K_f32,                ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32,            mul_mm_iq2_xxs_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32,             mul_mm_iq2_xs_f32,              ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32,            mul_mm_iq3_xxs_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32,              mul_mm_iq3_s_f32,               ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32,              mul_mm_iq2_s_f32,               ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32,              mul_mm_iq1_s_f32,               ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32,              mul_mm_iq1_m_f32,               ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32,             mul_mm_iq4_nl_f32,              ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32,             mul_mm_iq4_xs_f32,              ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32,             mul_mm_id_f32_f32,              ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32,             mul_mm_id_f16_f32,              ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32,            mul_mm_id_q4_0_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32,            mul_mm_id_q4_1_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32,            mul_mm_id_q5_0_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32,            mul_mm_id_q5_1_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32,            mul_mm_id_q8_0_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32,            mul_mm_id_q2_K_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32,            mul_mm_id_q3_K_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32,            mul_mm_id_q4_K_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32,            mul_mm_id_q5_K_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32,            mul_mm_id_q6_K_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32,         mul_mm_id_iq2_xxs_f32,          ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32,          mul_mm_id_iq2_xs_f32,           ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32,         mul_mm_id_iq3_xxs_f32,          ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32,           mul_mm_id_iq3_s_f32,            ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32,           mul_mm_id_iq2_s_f32,            ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32,           mul_mm_id_iq1_s_f32,            ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32,           mul_mm_id_iq1_m_f32,            ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32,          mul_mm_id_iq4_nl_f32,           ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32,          mul_mm_id_iq4_xs_f32,           ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32,                      rope_f32,                       true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16,                      rope_f16,                       true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16,                    im2col_f16,                     true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32,                    im2col_f32,                     true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32,                   upscale_f32,                    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32,                       pad_f32,                        true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32,        timestep_embedding_f32,         true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32,                    arange_f32,                     true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,           argsort_f32_i32_asc,            true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC,          argsort_f32_i32_desc,           true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32,                leaky_relu_f32,                 true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64,        flash_attn_ext_f16_h64,         ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80,        flash_attn_ext_f16_h80,         ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96,        flash_attn_ext_f16_h96,         ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112,       flash_attn_ext_f16_h112,        ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128,       flash_attn_ext_f16_h128,        ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256,       flash_attn_ext_f16_h256,        ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128,   flash_attn_ext_vec_f16_h128,    ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256,   flash_attn_ext_vec_f16_h256,    ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16,                   cpy_f32_f16,                    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32,                   cpy_f32_f32,                    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0,                  cpy_f32_q8_0,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0,                  cpy_f32_q4_0,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1,                  cpy_f32_q4_1,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0,                  cpy_f32_q5_0,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1,                  cpy_f32_q5_1,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL,                cpy_f32_iq4_nl,                 true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16,                   cpy_f16_f16,                    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32,                   cpy_f16_f32,                    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT,                        concat,                         true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR,                           sqr,                            true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS,                      sum_rows,                       true);
     }
 
     [metal_library release];
@@ -622,6 +732,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
             switch (ggml_get_unary_op(op)) {
                 case GGML_UNARY_OP_TANH:
                 case GGML_UNARY_OP_RELU:
+                case GGML_UNARY_OP_SIGMOID:
                 case GGML_UNARY_OP_GELU:
                 case GGML_UNARY_OP_GELU_QUICK:
                 case GGML_UNARY_OP_SILU:
@@ -640,6 +751,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
         case GGML_OP_MUL:
         case GGML_OP_DIV:
         case GGML_OP_SCALE:
+        case GGML_OP_CLAMP:
         case GGML_OP_SQR:
         case GGML_OP_SUM_ROWS:
             return true;
@@ -648,7 +760,6 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
         case GGML_OP_GROUP_NORM:
             return ctx->support_simdgroup_reduction;
         case GGML_OP_NORM:
-        case GGML_OP_ALIBI:
         case GGML_OP_ROPE:
         case GGML_OP_IM2COL:
             return true;
@@ -657,9 +768,13 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
             return false;
         case GGML_OP_UPSCALE:
         case GGML_OP_PAD:
+        case GGML_OP_ARANGE:
+        case GGML_OP_TIMESTEP_EMBEDDING:
         case GGML_OP_ARGSORT:
         case GGML_OP_LEAKY_RELU:
             return true;
+        case GGML_OP_FLASH_ATTN_EXT:
+            return ctx->support_simdgroup_mm; // TODO: over-restricted for vec-kernels
         case GGML_OP_MUL_MAT:
         case GGML_OP_MUL_MAT_ID:
             return ctx->support_simdgroup_reduction &&
@@ -676,6 +791,9 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
                            case GGML_TYPE_Q8_0:
                            case GGML_TYPE_Q4_0:
                            case GGML_TYPE_Q4_1:
+                           case GGML_TYPE_Q5_0:
+                           case GGML_TYPE_Q5_1:
+                           case GGML_TYPE_IQ4_NL:
                                 return true;
                            default:
                                 return false;
@@ -695,14 +813,14 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_GET_ROWS:
             {
-                return op->ne[3] == 1;
+                return op->src[0]->type != GGML_TYPE_BF16 && op->ne[3] == 1;
             }
         default:
             return false;
     }
 }
 
-static bool ggml_metal_graph_compute(
+static enum ggml_status ggml_metal_graph_compute(
         struct ggml_metal_context * ctx,
                struct ggml_cgraph * gf) {
 
@@ -769,6 +887,10 @@ static bool ggml_metal_graph_compute(
             struct ggml_tensor * src2 = gf->nodes[i]->src[2];
             struct ggml_tensor * dst  = gf->nodes[i];
 
+            if (ggml_is_empty(dst)) {
+                continue;
+            }
+
             switch (dst->op) {
                 case GGML_OP_NONE:
                 case GGML_OP_RESHAPE:
@@ -1051,7 +1173,8 @@ static bool ggml_metal_graph_compute(
                     {
                         GGML_ASSERT(ggml_is_contiguous(src0));
 
-                        const float scale = *(const float *) dst->op_params;
+                        float scale;
+                        memcpy(&scale, dst->op_params, sizeof(scale));
 
                         int64_t n = ggml_nelements(dst);
 
@@ -1069,10 +1192,32 @@ static bool ggml_metal_graph_compute(
                         [encoder setBuffer:id_dst    offset:offs_dst  atIndex:1];
                         [encoder setBytes:&scale length:sizeof(scale) atIndex:2];
 
+                        [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                    } break;
+                case GGML_OP_CLAMP:
+                    {
+                        id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CLAMP].pipeline;
+
+                        float min;
+                        float max;
+                        memcpy(&min, ((int32_t *) dst->op_params) + 0, sizeof(float));
+                        memcpy(&max, ((int32_t *) dst->op_params) + 1, sizeof(float));
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0   offset:offs_src0 atIndex:0];
+                        [encoder setBuffer:id_dst    offset:offs_dst  atIndex:1];
+                        [encoder setBytes:&min length:sizeof(min) atIndex:2];
+                        [encoder setBytes:&max length:sizeof(max) atIndex:3];
+
+                        const int64_t n = ggml_nelements(dst);
+
                         [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                     } break;
                 case GGML_OP_UNARY:
                     switch (ggml_get_unary_op(gf->nodes[i])) {
+                        // we are not taking into account the strides, so for now require contiguous tensors
+                        GGML_ASSERT(ggml_is_contiguous(src0));
+
                         case GGML_UNARY_OP_TANH:
                             {
                                 id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TANH].pipeline;
@@ -1097,44 +1242,74 @@ static bool ggml_metal_graph_compute(
 
                                 [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                             } break;
-                        case GGML_UNARY_OP_GELU:
+                        case GGML_UNARY_OP_SIGMOID:
                             {
-                                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU].pipeline;
+                                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SIGMOID].pipeline;
 
                                 [encoder setComputePipelineState:pipeline];
                                 [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                                 [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
 
                                 const int64_t n = ggml_nelements(dst);
-                                GGML_ASSERT(n % 4 == 0);
 
-                                [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                                [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                            } break;
+                        case GGML_UNARY_OP_GELU:
+                            {
+                                int64_t n = ggml_nelements(dst);
+
+                                id<MTLComputePipelineState> pipeline = nil;
+
+                                if (n % 4 == 0) {
+                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_4].pipeline;
+                                    n /= 4;
+                                } else {
+                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU].pipeline;
+                                }
+
+                                [encoder setComputePipelineState:pipeline];
+                                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+
+                                [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                             } break;
                         case GGML_UNARY_OP_GELU_QUICK:
                             {
-                                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK].pipeline;
+                                int64_t n = ggml_nelements(dst);
+
+                                id<MTLComputePipelineState> pipeline = nil;
+
+                                if (n % 4 == 0) {
+                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK_4].pipeline;
+                                    n /= 4;
+                                } else {
+                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK].pipeline;
+                                }
 
                                 [encoder setComputePipelineState:pipeline];
                                 [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                                 [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
 
-                                const int64_t n = ggml_nelements(dst);
-                                GGML_ASSERT(n % 4 == 0);
-
-                                [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                                [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                             } break;
                         case GGML_UNARY_OP_SILU:
                             {
-                                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU].pipeline;
+                                int64_t n = ggml_nelements(dst);
+
+                                id<MTLComputePipelineState> pipeline = nil;
+
+                                if (n % 4 == 0) {
+                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU_4].pipeline;
+                                    n /= 4;
+                                } else {
+                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU].pipeline;
+                                }
 
                                 [encoder setComputePipelineState:pipeline];
                                 [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                                 [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
 
-                                const int64_t n = ggml_nelements(dst);
-                                GGML_ASSERT(n % 4 == 0);
-
-                                [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                                [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                             } break;
                         default:
                             {
@@ -1194,29 +1369,45 @@ static bool ggml_metal_graph_compute(
                     } break;
                 case GGML_OP_SOFT_MAX:
                     {
+                        GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32);
+
                         int nth = 32; // SIMD width
 
                         id<MTLComputePipelineState> pipeline = nil;
 
+                        const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16);
+
                         if (ne00%4 == 0) {
                             while (nth < ne00/4 && nth < 256) {
                                 nth *= 2;
                             }
-                            pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_4].pipeline;
+                            if (use_f16) {
+                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4].pipeline;
+                            } else {
+                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4].pipeline;
+                            }
                         } else {
                             while (nth < ne00 && nth < 1024) {
                                 nth *= 2;
                             }
-                            pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX].pipeline;
+                            if (use_f16) {
+                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16].pipeline;
+                            } else {
+                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32].pipeline;
+                            }
                         }
 
-                        const float scale    = ((float *) dst->op_params)[0];
-                        const float max_bias = ((float *) dst->op_params)[1];
+                        float scale;
+                        float max_bias;
+
+                        memcpy(&scale,    ((int32_t *) dst->op_params) + 0, sizeof(scale));
+                        memcpy(&max_bias, ((int32_t *) dst->op_params) + 1, sizeof(max_bias));
 
                         const int64_t nrows_x = ggml_nrows(src0);
                         const int64_t nrows_y = src0->ne[1];
-                        const uint32_t n_head_kv   = nrows_x/nrows_y;
-                        const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
+
+                        const uint32_t n_head      = nrows_x/nrows_y;
+                        const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
 
                         const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
                         const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
@@ -1228,20 +1419,15 @@ static bool ggml_metal_graph_compute(
                         } else {
                             [encoder setBuffer:id_src0 offset:offs_src0   atIndex:1];
                         }
-                        if (id_src2) {
-                            [encoder setBuffer:id_src2 offset:offs_src2   atIndex:2];
-                        } else {
-                            [encoder setBuffer:id_src0 offset:offs_src0   atIndex:2];
-                        }
-                        [encoder setBuffer:id_dst   offset:offs_dst          atIndex:3];
-                        [encoder setBytes:&ne00     length:sizeof(ne00)      atIndex:4];
-                        [encoder setBytes:&ne01     length:sizeof(ne01)      atIndex:5];
-                        [encoder setBytes:&ne02     length:sizeof(ne02)      atIndex:6];
-                        [encoder setBytes:&scale    length:sizeof(scale)     atIndex:7];
-                        [encoder setBytes:&max_bias length:sizeof(max_bias)  atIndex:8];
-                        [encoder setBytes:&m0       length:sizeof(m0)        atIndex:9];
-                        [encoder setBytes:&m1       length:sizeof(m1)        atIndex:10];
-                        [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:11];
+                        [encoder setBuffer:id_dst      offset:offs_dst            atIndex:2];
+                        [encoder setBytes:&ne00        length:sizeof(ne00)        atIndex:3];
+                        [encoder setBytes:&ne01        length:sizeof(ne01)        atIndex:4];
+                        [encoder setBytes:&ne02        length:sizeof(ne02)        atIndex:5];
+                        [encoder setBytes:&scale       length:sizeof(scale)       atIndex:6];
+                        [encoder setBytes:&max_bias    length:sizeof(max_bias)    atIndex:7];
+                        [encoder setBytes:&m0          length:sizeof(m0)          atIndex:8];
+                        [encoder setBytes:&m1          length:sizeof(m1)          atIndex:9];
+                        [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:10];
                         [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];
 
                         [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
@@ -1319,6 +1505,14 @@ static bool ggml_metal_graph_compute(
                             (ne11 > ne11_mm_min || (ggml_is_quantized(src0t) && ne12 > 1))) {
                             //printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12);
 
+                            // some Metal matrix data types require aligned pointers
+                            // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5)
+                            switch (src0->type) {
+                                case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break;
+                                case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8  == 0); break;
+                                default: break;
+                            }
+
                             id<MTLComputePipelineState> pipeline = nil;
 
                             switch (src0->type) {
@@ -1337,7 +1531,12 @@ static bool ggml_metal_graph_compute(
                                 case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32].pipeline; break;
                                 case GGML_TYPE_IQ2_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break;
                                 case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32].pipeline; break;
+                                case GGML_TYPE_IQ3_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32  ].pipeline; break;
+                                case GGML_TYPE_IQ2_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32  ].pipeline; break;
                                 case GGML_TYPE_IQ1_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32  ].pipeline; break;
+                                case GGML_TYPE_IQ1_M:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32  ].pipeline; break;
+                                case GGML_TYPE_IQ4_NL:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32 ].pipeline; break;
+                                case GGML_TYPE_IQ4_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32 ].pipeline; break;
                                 default: GGML_ASSERT(false && "MUL MAT-MAT not implemented");
                             }
 
@@ -1472,12 +1671,42 @@ static bool ggml_metal_graph_compute(
                                         nth1 = 16;
                                         pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32].pipeline;
                                     } break;
+                                case GGML_TYPE_IQ3_S:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ2_S:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32].pipeline;
+                                    } break;
                                 case GGML_TYPE_IQ1_S:
                                     {
                                         nth0 = 4;
                                         nth1 = 16;
                                         pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32].pipeline;
                                     } break;
+                                case GGML_TYPE_IQ1_M:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ4_NL:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ4_XS:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32].pipeline;
+                                    } break;
                                 default:
                                     {
                                         GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t);
@@ -1510,9 +1739,9 @@ static bool ggml_metal_graph_compute(
                             [encoder setBytes:&r2   length:sizeof(r2)   atIndex:17];
                             [encoder setBytes:&r3   length:sizeof(r3)   atIndex:18];
 
-                            if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 ||
-                                src0t == GGML_TYPE_Q5_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 ||
-                                src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_IQ1_S) { // || src0t == GGML_TYPE_Q4_K) {
+                            if (src0t == GGML_TYPE_Q4_0  || src0t == GGML_TYPE_Q4_1  || src0t == GGML_TYPE_Q5_0 ||
+                                src0t == GGML_TYPE_Q5_1  || src0t == GGML_TYPE_Q8_0  || src0t == GGML_TYPE_Q2_K ||
+                                src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) {
                                 [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                             }
                             else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) {
@@ -1520,11 +1749,16 @@ static bool ggml_metal_graph_compute(
                                 [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
                                 [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                             }
-                            else if (src0t == GGML_TYPE_IQ3_XXS) {
-                                const int mem_size = 256*4+128;
+                            else if (src0t == GGML_TYPE_IQ3_XXS || src0t == GGML_TYPE_IQ3_S) {
+                                const int mem_size = src0t == GGML_TYPE_IQ3_XXS ? 256*4+128 : 512*4;
                                 [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
                                 [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                             }
+                            else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS) {
+                                const int mem_size = 32*sizeof(float);
+                                [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            }
                             else if (src0t == GGML_TYPE_Q4_K) {
                                 [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                             }
@@ -1548,47 +1782,37 @@ static bool ggml_metal_graph_compute(
                     } break;
                 case GGML_OP_MUL_MAT_ID:
                     {
-                        //GGML_ASSERT(ne00 == ne10);
-                        //GGML_ASSERT(ne03 == ne13);
+                        const int n_as = src0->ne[2];
 
-                        GGML_ASSERT(src0t == GGML_TYPE_I32);
+                        // src2 = ids
+                        const int64_t  ne20 = src2->ne[0];
+                        const int64_t  ne21 = src2->ne[1];
+                        const int64_t  ne22 = src2->ne[2]; GGML_UNUSED(ne22);
+                        const int64_t  ne23 = src2->ne[3]; GGML_UNUSED(ne23);
 
-                        const int n_as = ((int32_t *) dst->op_params)[1];
+                        const uint64_t nb20 = src2->nb[0]; GGML_UNUSED(nb20);
+                        const uint64_t nb21 = src2->nb[1];
+                        const uint64_t nb22 = src2->nb[2]; GGML_UNUSED(nb22);
+                        const uint64_t nb23 = src2->nb[3]; GGML_UNUSED(nb23);
 
-                        // TODO: make this more general
-                        GGML_ASSERT(n_as <= 8);
+                        const enum ggml_type src2t = src2->type; GGML_UNUSED(src2t);
 
-                        // max size of the src1ids array in the kernel stack
-                        GGML_ASSERT(ne11 <= 512);
+                        GGML_ASSERT(src2t == GGML_TYPE_I32);
 
-                        const int64_t  ne20 = src2 ? src2->ne[0] : 0;
-                        const int64_t  ne21 = src2 ? src2->ne[1] : 0;
-                        const int64_t  ne22 = src2 ? src2->ne[2] : 0;
-                        const int64_t  ne23 = src2 ? src2->ne[3] : 0; GGML_UNUSED(ne23);
-
-                        const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20);
-                        const uint64_t nb21 = src2 ? src2->nb[1] : 0;
-                        const uint64_t nb22 = src2 ? src2->nb[2] : 0;
-                        const uint64_t nb23 = src2 ? src2->nb[3] : 0; GGML_UNUSED(nb23);
-
-                        const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t);
-
-                        GGML_ASSERT(!ggml_is_transposed(src2));
+                        GGML_ASSERT(!ggml_is_transposed(src0));
                         GGML_ASSERT(!ggml_is_transposed(src1));
 
                         GGML_ASSERT(src1t == GGML_TYPE_F32);
 
-                        const uint r2 = ne12/ne22;
-                        const uint r3 = ne13/ne23;
-
                         // find the break-even point where the matrix-matrix kernel becomes more efficient compared
                         // to the matrix-vector kernel
-                        int ne11_mm_min = n_as;
+                        // ne20 = n_used_experts
+                        // ne21 = n_rows
+                        const int dst_rows = ne20*ne21;
+                        const int dst_rows_min = n_as;
 
-                        const int idx = ((int32_t *) dst->op_params)[0];
-
-                        // batch size
-                        GGML_ASSERT(ne01 == ne11);
+                        // max size of the rowids array in the kernel shared buffer
+                        GGML_ASSERT(dst_rows <= 2048);
 
                         // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
                         // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
@@ -1597,12 +1821,20 @@ static bool ggml_metal_graph_compute(
                         //       indirect matrix multiplication
                         // !!!
                         if ([ctx->device supportsFamily:MTLGPUFamilyApple7] &&
-                            ne20 % 32 == 0 && ne20 >= 64 &&
-                            ne11 > ne11_mm_min) {
+                            ne00 % 32 == 0 && ne00 >= 64 &&
+                            dst_rows > dst_rows_min) {
+
+                            // some Metal matrix data types require aligned pointers
+                            // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5)
+                            switch (src0->type) {
+                                case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break;
+                                case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8  == 0); break;
+                                default: break;
+                            }
 
                             id<MTLComputePipelineState> pipeline = nil;
 
-                            switch (src2->type) {
+                            switch (src0->type) {
                                 case GGML_TYPE_F32:     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32    ].pipeline; break;
                                 case GGML_TYPE_F16:     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32    ].pipeline; break;
                                 case GGML_TYPE_Q4_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32   ].pipeline; break;
@@ -1618,7 +1850,12 @@ static bool ggml_metal_graph_compute(
                                 case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32].pipeline; break;
                                 case GGML_TYPE_IQ2_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32 ].pipeline; break;
                                 case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32].pipeline; break;
+                                case GGML_TYPE_IQ3_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32  ].pipeline; break;
+                                case GGML_TYPE_IQ2_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32  ].pipeline; break;
                                 case GGML_TYPE_IQ1_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32  ].pipeline; break;
+                                case GGML_TYPE_IQ1_M:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32  ].pipeline; break;
+                                case GGML_TYPE_IQ4_NL:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32 ].pipeline; break;
+                                case GGML_TYPE_IQ4_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32 ].pipeline; break;
                                 default: GGML_ASSERT(false && "MUL_MAT_ID not implemented");
                             }
 
@@ -1626,36 +1863,27 @@ static bool ggml_metal_graph_compute(
                             [encoder setBuffer:id_src0 offset:offs_src0    atIndex:0];
                             [encoder setBuffer:id_src1 offset:offs_src1    atIndex:1];
                             [encoder setBuffer:id_dst  offset:offs_dst     atIndex:2];
-                            [encoder setBytes:&nb01    length:sizeof(nb01) atIndex:3];
+                            [encoder setBuffer:id_src2 offset:offs_src2    atIndex:3];
                             [encoder setBytes:&ne20    length:sizeof(ne20) atIndex:4];
-                            [encoder setBytes:&ne22    length:sizeof(ne22) atIndex:5];
+                            [encoder setBytes:&ne21    length:sizeof(ne21) atIndex:5];
                             [encoder setBytes:&nb21    length:sizeof(nb21) atIndex:6];
-                            [encoder setBytes:&nb22    length:sizeof(nb22) atIndex:7];
-                            [encoder setBytes:&ne12    length:sizeof(ne12) atIndex:8];
-                            [encoder setBytes:&ne13    length:sizeof(ne13) atIndex:9];
-                            [encoder setBytes:&nb10    length:sizeof(nb10) atIndex:10];
-                            [encoder setBytes:&nb11    length:sizeof(nb11) atIndex:11];
-                            [encoder setBytes:&nb12    length:sizeof(nb12) atIndex:12];
-                            [encoder setBytes:&ne0     length:sizeof(ne0)  atIndex:13];
-                            [encoder setBytes:&ne1     length:sizeof(ne1)  atIndex:14];
-                            [encoder setBytes:&nb1     length:sizeof(nb1)  atIndex:15];
-                            [encoder setBytes:&r2      length:sizeof(r2)   atIndex:16];
-                            [encoder setBytes:&r3      length:sizeof(r3)   atIndex:17];
-                            [encoder setBytes:&idx     length:sizeof(idx)  atIndex:18];
-                            // TODO: how to make this an array? read Metal docs
-                            for (int j = 0; j < 8; ++j) {
-                                // NOTE: this is done like this to avoid uninitialized kernel arguments when n_as < 8
-                                struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)];
+                            [encoder setBytes:&ne00    length:sizeof(ne00) atIndex:7];
+                            [encoder setBytes:&ne02    length:sizeof(ne02) atIndex:8];
+                            [encoder setBytes:&nb01    length:sizeof(nb01) atIndex:9];
+                            [encoder setBytes:&nb02    length:sizeof(nb02) atIndex:10];
+                            [encoder setBytes:&ne11    length:sizeof(ne11) atIndex:11];
+                            [encoder setBytes:&ne12    length:sizeof(ne12) atIndex:12];
+                            [encoder setBytes:&ne13    length:sizeof(ne13) atIndex:13];
+                            [encoder setBytes:&nb10    length:sizeof(nb10) atIndex:14];
+                            [encoder setBytes:&nb11    length:sizeof(nb11) atIndex:15];
+                            [encoder setBytes:&nb12    length:sizeof(nb12) atIndex:16];
+                            [encoder setBytes:&ne0     length:sizeof(ne0)  atIndex:17];
+                            [encoder setBytes:&ne1     length:sizeof(ne1)  atIndex:18];
+                            [encoder setBytes:&nb1     length:sizeof(nb1)  atIndex:19];
 
-                                size_t offs_src_cur = 0;
-                                id<MTLBuffer> id_src_cur = ggml_metal_get_buffer(src_cur, &offs_src_cur);
+                            [encoder setThreadgroupMemoryLength:GGML_PAD(8192 + dst_rows*4/*sizeof(ushort2)*/, 16) atIndex:0];
 
-                                [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:19 + j];
-                            }
-
-                            [encoder setThreadgroupMemoryLength:8192 atIndex:0];
-
-                            [encoder dispatchThreadgroups:MTLSizeMake((ne11 + 31)/32, (ne21 + 63)/64, n_as*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
+                            [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 31)/32, (ne01 + 63)/64, n_as) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
                         } else {
                             int nth0 = 32;
                             int nth1 = 1;
@@ -1665,7 +1893,7 @@ static bool ggml_metal_graph_compute(
                             id<MTLComputePipelineState> pipeline = nil;
 
                             // use custom matrix x vector kernel
-                            switch (src2t) {
+                            switch (src0t) {
                                 case GGML_TYPE_F32:
                                     {
                                         GGML_ASSERT(src1t == GGML_TYPE_F32);
@@ -1756,12 +1984,47 @@ static bool ggml_metal_graph_compute(
                                         nth1 = 16;
                                         pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32].pipeline;
                                     } break;
+                                case GGML_TYPE_IQ3_S:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ2_S:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32].pipeline;
+                                    } break;
                                 case GGML_TYPE_IQ1_S:
                                     {
                                         nth0 = 4;
                                         nth1 = 16;
                                         pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32].pipeline;
                                     } break;
+                                case GGML_TYPE_IQ1_M:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ4_NL:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ4_XS:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                    #if QK_K == 64
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline;
+                                    #else
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32].pipeline;
+                                    #endif
+
+                                    } break;
                                 default:
                                     {
                                         GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t);
@@ -1769,80 +2032,76 @@ static bool ggml_metal_graph_compute(
                                     }
                             };
 
-                            if (ggml_is_quantized(src2t)) {
-                                GGML_ASSERT(ne20 >= nth0*nth1);
+                            if (ggml_is_quantized(src0t)) {
+                                GGML_ASSERT(ne00 >= nth0*nth1);
                             }
 
-                            const int64_t _ne1 = 1; // kernels needs a reference in constant memory
-
                             [encoder setComputePipelineState:pipeline];
                             [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                             [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
                             [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
-                            [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:3];
+                            [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3];
                             [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4];
                             [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5];
-                            [encoder setBytes:&ne22 length:sizeof(ne22) atIndex:6];
-                            [encoder setBytes:&nb20 length:sizeof(nb20) atIndex:7];
-                            [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:8];
-                            [encoder setBytes:&nb22 length:sizeof(nb22) atIndex:9];
-                            [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10];
-                            [encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:11];
-                            [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12];
-                            [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13];
-                            [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14];
-                            [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15];
-                            [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16];
-                            [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:17];
-                            [encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:18];
-                            [encoder setBytes:&nb1  length:sizeof(nb1)  atIndex:19];
-                            [encoder setBytes:&r2   length:sizeof(r2)   atIndex:20];
-                            [encoder setBytes:&r3   length:sizeof(r3)   atIndex:21];
-                            [encoder setBytes:&idx  length:sizeof(idx)  atIndex:22];
-                            // TODO: how to make this an array? read Metal docs
-                            for (int j = 0; j < 8; ++j) {
-                                // NOTE: this is done like this to avoid uninitialized kernel arguments when n_as < 8
-                                struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)];
+                            [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6];
+                            [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:7];
+                            [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:8];
+                            [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:9];
+                            [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:10];
+                            [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:11];
+                            [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:12];
+                            [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:13];
+                            [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:14];
+                            [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:15];
+                            [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:16];
+                            [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:17];
+                            [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:18];
+                            [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:19];
+                            [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:20];
+                            [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:21];
+                            [encoder setBytes:&nb1  length:sizeof(nb1)  atIndex:22];
 
-                                size_t offs_src_cur = 0;
-                                id<MTLBuffer> id_src_cur = ggml_metal_get_buffer(src_cur, &offs_src_cur);
+                            const int64_t _ne1 = 1;
+                            const int tgz = dst_rows;
 
-                                [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:23 + j];
+                            if (src0t == GGML_TYPE_Q4_0  || src0t == GGML_TYPE_Q4_1  || src0t == GGML_TYPE_Q5_0 ||
+                                src0t == GGML_TYPE_Q5_1  || src0t == GGML_TYPE_Q8_0  || src0t == GGML_TYPE_Q2_K ||
+                                src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) {
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                             }
-
-                            if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 ||
-                                src2t == GGML_TYPE_Q5_0 || src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 ||
-                                src2t == GGML_TYPE_Q2_K || src2t == GGML_TYPE_IQ1_S) { // || src2t == GGML_TYPE_Q4_K) {
-                                [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                            }
-                            else if (src2t == GGML_TYPE_IQ2_XXS || src2t == GGML_TYPE_IQ2_XS) {
-                                const int mem_size = src2t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128;
+                            else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) {
+                                const int mem_size = src0t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128;
                                 [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
-                                [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                             }
-                            else if (src2t == GGML_TYPE_IQ3_XXS) {
-                                const int mem_size = 256*4+128;
+                            else if (src0t == GGML_TYPE_IQ3_XXS || src0t == GGML_TYPE_IQ3_S) {
+                                const int mem_size = src0t == GGML_TYPE_IQ3_XXS ? 256*4+128 : 512*4;
                                 [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
-                                [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                             }
-                            else if (src2t == GGML_TYPE_Q4_K) {
-                                [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS) {
+                                const int mem_size = 32*sizeof(float);
+                                [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                             }
-                            else if (src2t == GGML_TYPE_Q3_K) {
+                            else if (src0t == GGML_TYPE_Q4_K) {
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            }
+                            else if (src0t == GGML_TYPE_Q3_K) {
 #ifdef GGML_QKK_64
-                                [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 1)/2, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
 #else
-                                [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
 #endif
                             }
-                            else if (src2t == GGML_TYPE_Q5_K) {
-                                [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            else if (src0t == GGML_TYPE_Q5_K) {
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                             }
-                            else if (src2t == GGML_TYPE_Q6_K) {
-                                [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 1)/2, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            else if (src0t == GGML_TYPE_Q6_K) {
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                             } else {
-                                const int64_t ny = (_ne1 + nrows - 1)/nrows;
-                                [encoder dispatchThreadgroups:MTLSizeMake(ne21, ny, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                const int64_t ny = (_ne1 + nrows - 1)/nrows; // = _ne1
+                                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                             }
                         }
                     } break;
@@ -1866,7 +2125,12 @@ static bool ggml_metal_graph_compute(
                             case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS].pipeline; break;
                             case GGML_TYPE_IQ2_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break;
                             case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS].pipeline; break;
+                            case GGML_TYPE_IQ3_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S  ].pipeline; break;
+                            case GGML_TYPE_IQ2_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S  ].pipeline; break;
                             case GGML_TYPE_IQ1_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S  ].pipeline; break;
+                            case GGML_TYPE_IQ1_M:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M  ].pipeline; break;
+                            case GGML_TYPE_IQ4_NL:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL ].pipeline; break;
+                            case GGML_TYPE_IQ4_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS ].pipeline; break;
                             case GGML_TYPE_I32:     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32    ].pipeline; break;
                             default: GGML_ASSERT(false && "not implemented");
                         }
@@ -1968,48 +2232,6 @@ static bool ggml_metal_graph_compute(
 
                         [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                     } break;
-                case GGML_OP_ALIBI:
-                    {
-                        GGML_ASSERT((src0t == GGML_TYPE_F32));
-
-                        const int nth = MIN(1024, ne00);
-
-                        //const int n_past = ((int32_t *) dst->op_params)[0];
-                        const int n_head = ((int32_t *) dst->op_params)[1];
-                        float max_bias;
-                        memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
-
-                        const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
-                        const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
-                        const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
-
-                        id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ALIBI_F32].pipeline;
-
-                        [encoder setComputePipelineState:pipeline];
-                        [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                        [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
-                        [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
-                        [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3];
-                        [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4];
-                        [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5];
-                        [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6];
-                        [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7];
-                        [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8];
-                        [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9];
-                        [encoder setBytes:&ne0  length:sizeof( int64_t) atIndex:10];
-                        [encoder setBytes:&ne1  length:sizeof( int64_t) atIndex:11];
-                        [encoder setBytes:&ne2  length:sizeof( int64_t) atIndex:12];
-                        [encoder setBytes:&ne3  length:sizeof( int64_t) atIndex:13];
-                        [encoder setBytes:&nb0  length:sizeof(uint64_t) atIndex:14];
-                        [encoder setBytes:&nb1  length:sizeof(uint64_t) atIndex:15];
-                        [encoder setBytes:&nb2  length:sizeof(uint64_t) atIndex:16];
-                        [encoder setBytes:&nb3  length:sizeof(uint64_t) atIndex:17];
-                        [encoder setBytes:&m0   length:sizeof(   float) atIndex:18];
-                        [encoder setBytes:&m1   length:sizeof(   float) atIndex:19];
-                        [encoder setBytes:&n_heads_log2_floor   length:sizeof(int) atIndex:20];
-
-                        [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
-                    } break;
                 case GGML_OP_ROPE:
                     {
                         GGML_ASSERT(ne10 == ne02);
@@ -2190,6 +2412,50 @@ static bool ggml_metal_graph_compute(
 
                         [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                     } break;
+                case GGML_OP_ARANGE:
+                    {
+                        GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+                        float start;
+                        float step;
+
+                        memcpy(&start, ((int32_t *) dst->op_params) + 0, sizeof(float));
+                        memcpy(&step,  ((int32_t *) dst->op_params) + 2, sizeof(float));
+
+                        id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARANGE_F32].pipeline;
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_dst  offset:offs_dst    atIndex:0];
+                        [encoder setBytes:&ne0   length:sizeof(ne0)   atIndex:1];
+                        [encoder setBytes:&start length:sizeof(start) atIndex:2];
+                        [encoder setBytes:&step  length:sizeof(step)  atIndex:3];
+
+                        const int nth = MIN(1024, ne0);
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(1, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                    } break;
+                case GGML_OP_TIMESTEP_EMBEDDING:
+                    {
+                        GGML_ASSERT(src0->type == GGML_TYPE_F32);
+
+                        const int dim        = dst->op_params[0];
+                        const int max_period = dst->op_params[1];
+
+                        const int half = dim / 2;
+
+                        id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32].pipeline;
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                        [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+                        [encoder setBytes:&nb1   length:sizeof(nb1) atIndex:2];
+                        [encoder setBytes:&dim   length:sizeof(dim) atIndex:3];
+                        [encoder setBytes:&max_period length:sizeof(max_period) atIndex:4];
+
+                        const int nth = MIN(1024, half);
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(ne00, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                    } break;
                 case GGML_OP_ARGSORT:
                     {
                         GGML_ASSERT(src0->type == GGML_TYPE_F32);
@@ -2199,20 +2465,32 @@ static bool ggml_metal_graph_compute(
 
                         enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0];
 
+                        // bitonic sort requires the number of elements to be power of 2
+                        int64_t ne00_padded = 1;
+                        while (ne00_padded < ne00) {
+                            ne00_padded *= 2;
+                        }
+
+                        // Metal kernels require the buffer size to be multiple of 16 bytes
+                        // https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/1443142-setthreadgroupmemorylength
+                        const int mem_size = GGML_PAD(ne00_padded*sizeof(int32_t), 16);
+
                         id<MTLComputePipelineState> pipeline = nil;
 
                         switch (order) {
-                            case GGML_SORT_ASC:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline;  break;
-                            case GGML_SORT_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break;
+                            case GGML_SORT_ORDER_ASC:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline;  break;
+                            case GGML_SORT_ORDER_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break;
                             default: GGML_ASSERT(false);
                         };
 
                         [encoder setComputePipelineState:pipeline];
-                        [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
-                        [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
-                        [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
+                        [encoder setBuffer:id_src0     offset:offs_src0        atIndex:0];
+                        [encoder setBuffer:id_dst      offset:offs_dst         atIndex:1];
+                        [encoder setBytes:&ne00        length:sizeof( int64_t) atIndex:2];
+                        [encoder setBytes:&ne00_padded length:sizeof( int64_t) atIndex:3];
+                        [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
 
-                        [encoder dispatchThreadgroups:MTLSizeMake(1, nrows, 1) threadsPerThreadgroup:MTLSizeMake(ne00, 1, 1)];
+                        [encoder dispatchThreadgroups:MTLSizeMake(1, nrows, 1) threadsPerThreadgroup:MTLSizeMake(ne00_padded, 1, 1)];
                     } break;
                 case GGML_OP_LEAKY_RELU:
                     {
@@ -2232,6 +2510,173 @@ static bool ggml_metal_graph_compute(
 
                         [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                     } break;
+                case GGML_OP_FLASH_ATTN_EXT:
+                    {
+                        GGML_ASSERT(ne00 % 4 == 0);
+                        GGML_ASSERT(src0->type == GGML_TYPE_F32);
+
+                        struct ggml_tensor * src3 = gf->nodes[i]->src[3];
+
+                        GGML_ASSERT(ggml_are_same_shape(src1, src2));
+                        GGML_ASSERT(src3);
+
+                        size_t offs_src3 = 0;
+
+                        id<MTLBuffer> id_src3 = src3 ? ggml_metal_get_buffer(src3, &offs_src3) : nil;
+
+                        GGML_ASSERT(!src3 || src3->type == GGML_TYPE_F16);
+                        GGML_ASSERT(!src3 || src3->ne[1] >= GGML_PAD(src0->ne[1], 8) &&
+                                "the Flash-Attention Metal kernel requires the mask to be padded to 8 and at least n_queries big");
+
+                        const int64_t  ne30 = src3 ? src3->ne[0] : 0; GGML_UNUSED(ne30);
+                      //const int64_t  ne31 = src3 ? src3->ne[1] : 0;
+                        const int64_t  ne32 = src3 ? src3->ne[2] : 0; GGML_UNUSED(ne32);
+                        const int64_t  ne33 = src3 ? src3->ne[3] : 0; GGML_UNUSED(ne33);
+
+                        const uint64_t nb30 = src3 ? src3->nb[0] : 0; GGML_UNUSED(nb30);
+                        const uint64_t nb31 = src3 ? src3->nb[1] : 0;
+                        const uint64_t nb32 = src3 ? src3->nb[2] : 0; GGML_UNUSED(nb32);
+                        const uint64_t nb33 = src3 ? src3->nb[3] : 0; GGML_UNUSED(nb33);
+
+                        const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t);
+
+                        float scale;
+                        float max_bias;
+
+                        memcpy(&scale,    ((int32_t *) dst->op_params) + 0, sizeof(scale));
+                        memcpy(&max_bias, ((int32_t *) dst->op_params) + 1, sizeof(max_bias));
+
+                        const uint32_t n_head      = src0->ne[2];
+                        const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
+
+                        const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
+                        const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+                        id<MTLComputePipelineState> pipeline = nil;
+
+                        bool use_vec_kernel = false;
+
+                        if (ne01 >= 4 || (ne00%128 != 0)) {
+                            switch (ne00) {
+                                case 64:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64 ].pipeline; break;
+                                case 80:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80 ].pipeline; break;
+                                case 96:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96 ].pipeline; break;
+                                case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112].pipeline; break;
+                                case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128].pipeline; break;
+                                case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break;
+                                default:
+                                          {
+                                              GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00);
+                                              GGML_METAL_LOG_ERROR("add template specialization for this size\n");
+                                              GGML_ASSERT(false && "add template specialization for this size");
+                                          }
+                            }
+                        } else {
+                            use_vec_kernel = true;
+
+                            switch (ne00) {
+                                case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128].pipeline; break;
+                                case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break;
+                                default:
+                                          {
+                                              GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00);
+                                              GGML_METAL_LOG_ERROR("add template specialization for this size\n");
+                                              GGML_ASSERT(false && "add template specialization for this size");
+                                          }
+                            }
+                        }
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0     offset:offs_src0           atIndex:0];
+                        [encoder setBuffer:id_src1     offset:offs_src1           atIndex:1];
+                        [encoder setBuffer:id_src2     offset:offs_src2           atIndex:2];
+                        [encoder setBuffer:id_src3     offset:offs_src3           atIndex:3];
+                        [encoder setBuffer:id_dst      offset:offs_dst            atIndex:4];
+                        [encoder setBytes:&ne00        length:sizeof( int64_t)    atIndex:5];
+                        [encoder setBytes:&ne01        length:sizeof( int64_t)    atIndex:6];
+                        [encoder setBytes:&ne02        length:sizeof( int64_t)    atIndex:7];
+                        [encoder setBytes:&ne03        length:sizeof( int64_t)    atIndex:8];
+                        [encoder setBytes:&nb00        length:sizeof(uint64_t)    atIndex:9];
+                        [encoder setBytes:&nb01        length:sizeof(uint64_t)    atIndex:10];
+                        [encoder setBytes:&nb02        length:sizeof(uint64_t)    atIndex:11];
+                        [encoder setBytes:&nb03        length:sizeof(uint64_t)    atIndex:12];
+                        [encoder setBytes:&ne10        length:sizeof( int64_t)    atIndex:13];
+                        [encoder setBytes:&ne11        length:sizeof( int64_t)    atIndex:14];
+                        [encoder setBytes:&ne12        length:sizeof( int64_t)    atIndex:15];
+                        [encoder setBytes:&ne13        length:sizeof( int64_t)    atIndex:16];
+                        [encoder setBytes:&nb10        length:sizeof(uint64_t)    atIndex:17];
+                        [encoder setBytes:&nb11        length:sizeof(uint64_t)    atIndex:18];
+                        [encoder setBytes:&nb12        length:sizeof(uint64_t)    atIndex:19];
+                        [encoder setBytes:&nb13        length:sizeof(uint64_t)    atIndex:20];
+                        [encoder setBytes:&nb31        length:sizeof(uint64_t)    atIndex:21];
+                        [encoder setBytes:&ne0         length:sizeof( int64_t)    atIndex:22];
+                        [encoder setBytes:&ne1         length:sizeof( int64_t)    atIndex:23];
+                        [encoder setBytes:&ne2         length:sizeof( int64_t)    atIndex:24];
+                        [encoder setBytes:&ne3         length:sizeof( int64_t)    atIndex:25];
+                        [encoder setBytes:&scale       length:sizeof(   float)    atIndex:26];
+                        [encoder setBytes:&max_bias    length:sizeof(   float)    atIndex:27];
+                        [encoder setBytes:&m0          length:sizeof(m0)          atIndex:28];
+                        [encoder setBytes:&m1          length:sizeof(m1)          atIndex:29];
+                        [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:30];
+
+                        if (!use_vec_kernel) {
+                            // half8x8 kernel
+                            const int64_t nqptg = 8;  // queries per threadgroup    !! sync with kernel template arguments !!
+                            const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !!
+
+                            GGML_ASSERT(nqptg <= 32);
+                            GGML_ASSERT(nqptg  % 8  == 0);
+                            GGML_ASSERT(ncpsg  % 32 == 0);
+
+                            int64_t nsgmax = 2;
+
+                            while (true) {
+                                const size_t smem = nqptg*(ne00 + 2*nsgmax*(ncpsg + nqptg))*(sizeof(float)/2);
+                                if (smem > ctx->device.maxThreadgroupMemoryLength) {
+                                    break;
+                                }
+                                nsgmax *= 2;
+                            }
+                            nsgmax /= 2;
+
+                            // simdgroups per threadgroup (a.k.a. warps)
+                            const int64_t nsg = ne01 <= nqptg ? MAX(4, MIN(nsgmax, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32))) : 4;
+
+                            const size_t smem = nqptg*(ne00 + 2*nsg*(ncpsg + nqptg))*(sizeof(float)/2);
+
+                            //printf("smem: %zu, max: %zu\n", smem, ctx->device.maxThreadgroupMemoryLength);
+                            GGML_ASSERT(smem <= ctx->device.maxThreadgroupMemoryLength);
+
+                            [encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0];
+
+                            [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)];
+                        } else {
+                            // half1x4 kernel
+                            const int64_t nqptg = 1;  // queries per threadgroup    !! sync with kernel template arguments !!
+                            const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !!
+
+                            GGML_ASSERT(nqptg <= 32);
+                            GGML_ASSERT(nqptg  % 1  == 0);
+                            GGML_ASSERT(ncpsg  % 32 == 0);
+
+                            // simdgroups per threadgroup (a.k.a. warps)
+                            const int64_t nsgt = MAX(2, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32));
+
+                            int64_t nsg = 1;
+                            while (nsg <= nsgt) {
+                                nsg *= 2;
+                            }
+                            nsg /= 2;
+
+                            const size_t smem = (nqptg*(ne00 + 2*nsg*(ncpsg + nqptg)) + nsg*ne00)*(sizeof(float)/2);
+
+                            //printf("smem: %zu, max: %zu\n", smem, ctx->device.maxThreadgroupMemoryLength);
+                            GGML_ASSERT(smem <= ctx->device.maxThreadgroupMemoryLength);
+                            [encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0];
+
+                            [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)];
+                        }
+                    } break;
                 case GGML_OP_DUP:
                 case GGML_OP_CPY:
                 case GGML_OP_CONT:
@@ -2248,13 +2693,14 @@ static bool ggml_metal_graph_compute(
                                     GGML_ASSERT(ne0 % ggml_blck_size(dst->type) == 0);
 
                                     switch (dstt) {
-                                        case GGML_TYPE_F16:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline;  break;
-                                        case GGML_TYPE_F32:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline;  break;
-                                        case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break;
-                                        case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break;
-                                        case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break;
-                                      //case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0].pipeline; break;
-                                      //case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1].pipeline; break;
+                                        case GGML_TYPE_F16:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline;  break;
+                                        case GGML_TYPE_F32:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline;  break;
+                                        case GGML_TYPE_Q8_0:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break;
+                                        case GGML_TYPE_Q4_0:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break;
+                                        case GGML_TYPE_Q4_1:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break;
+                                        case GGML_TYPE_Q5_0:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0].pipeline; break;
+                                        case GGML_TYPE_Q5_1:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1].pipeline; break;
+                                        case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL].pipeline; break;
                                         default: GGML_ASSERT(false && "not implemented");
                                     };
                                 } break;
@@ -2318,7 +2764,12 @@ static bool ggml_metal_graph_compute(
         MTLCommandBufferStatus status = [command_buffer status];
         if (status != MTLCommandBufferStatusCompleted) {
             GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status);
-            return false;
+            if (status == MTLCommandBufferStatusError) {
+                NSString * error_code = [command_buffer error].localizedDescription;
+                GGML_METAL_LOG_INFO("error: %s\n", [error_code UTF8String]);
+            }
+
+            return GGML_STATUS_FAILED;
         }
     }
 
@@ -2327,7 +2778,7 @@ static bool ggml_metal_graph_compute(
     }
 
     }
-    return true;
+    return GGML_STATUS_SUCCESS;
 }
 
 ////////////////////////////////////////////////////////////////////////////////
@@ -2374,7 +2825,11 @@ GGML_CALL static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_
     ggml_backend_metal_free_device();
 
     if (ctx->owned) {
+#if TARGET_OS_OSX
+        vm_deallocate((vm_map_t)mach_task_self(), (vm_address_t)ctx->all_data, ctx->all_size);
+#else
         free(ctx->all_data);
+#endif
     }
 
     free(ctx);
@@ -2434,10 +2889,13 @@ GGML_CALL static const char * ggml_backend_metal_buffer_type_get_name(ggml_backe
     UNUSED(buft);
 }
 
-static void ggml_backend_metal_log_allocated_size(id<MTLDevice> device) {
+static void ggml_backend_metal_log_allocated_size(id<MTLDevice> device, size_t size_aligned) {
+#ifndef GGML_METAL_NDEBUG
 #if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15)
     if (@available(macOS 10.12, iOS 16.0, *)) {
-        GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)",
+        GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, (%8.2f / %8.2f)",
+                __func__,
+                size_aligned / 1024.0 / 1024.0,
                 device.currentAllocatedSize / 1024.0 / 1024.0,
                 device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
 
@@ -2447,10 +2905,15 @@ static void ggml_backend_metal_log_allocated_size(id<MTLDevice> device) {
             GGML_METAL_LOG_INFO("\n");
         }
     } else {
-        GGML_METAL_LOG_INFO(", (%8.2f)\n", device.currentAllocatedSize / 1024.0 / 1024.0);
+        GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, (%8.2f)\n",
+                __func__,
+                size_aligned / 1024.0 / 1024.0,
+                device.currentAllocatedSize / 1024.0 / 1024.0);
     }
+#endif
 #endif
     UNUSED(device);
+    UNUSED(size_aligned);
 }
 
 GGML_CALL static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
@@ -2470,22 +2933,23 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buff
     ctx->owned = true;
     ctx->n_buffers = 1;
 
-    ctx->buffers[0].data = ctx->all_data;
-    ctx->buffers[0].size = size;
-    ctx->buffers[0].metal = [device newBufferWithBytesNoCopy:ctx->all_data
-                    length:size_aligned
-                    options:MTLResourceStorageModeShared
-                    deallocator:nil];
+    if (ctx->all_data != NULL) {
+        ctx->buffers[0].data = ctx->all_data;
+        ctx->buffers[0].size = size;
+        ctx->buffers[0].metal = [device newBufferWithBytesNoCopy:ctx->all_data
+                        length:size_aligned
+                        options:MTLResourceStorageModeShared
+                        deallocator:nil];
+    }
 
-    if (ctx->buffers[0].metal == nil) {
+    if (ctx->all_data == NULL || ctx->buffers[0].metal == nil) {
         GGML_METAL_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0);
         free(ctx);
         ggml_backend_metal_free_device();
         return NULL;
     }
 
-    GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB", __func__, size_aligned / 1024.0 / 1024.0);
-    ggml_backend_metal_log_allocated_size(device);
+    //ggml_backend_metal_log_allocated_size(device, size_aligned);
 
     return ggml_backend_buffer_init(buft, ggml_backend_metal_buffer_i, ctx, size);
 }
@@ -2572,7 +3036,7 @@ GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data,
             return false;
         }
 
-        GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB", __func__, size_aligned / 1024.0 / 1024.0);
+        ggml_backend_metal_log_allocated_size(device, size_aligned);
 
         ++ctx->n_buffers;
     } else {
@@ -2595,7 +3059,8 @@ GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data,
                 return false;
             }
 
-            GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, offs = %12ld", __func__, size_step_aligned / 1024.0 / 1024.0, i);
+            ggml_backend_metal_log_allocated_size(device, size_step_aligned);
+
             if (i + size_step < size) {
                 GGML_METAL_LOG_INFO("\n");
             }
@@ -2604,8 +3069,6 @@ GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data,
         }
     }
 
-    ggml_backend_metal_log_allocated_size(device);
-
     return ggml_backend_buffer_init(ggml_backend_metal_buffer_type(), ggml_backend_metal_buffer_i, ctx, size);
 }
 
@@ -2629,7 +3092,7 @@ GGML_CALL static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffe
     UNUSED(backend);
 }
 
-GGML_CALL static bool ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+GGML_CALL static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
     struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context;
 
     return ggml_metal_graph_compute(metal_ctx, cgraph);
@@ -2654,6 +3117,12 @@ static struct ggml_backend_i ggml_backend_metal_i = {
     /* .graph_plan_compute      = */ NULL,
     /* .graph_compute           = */ ggml_backend_metal_graph_compute,
     /* .supports_op             = */ ggml_backend_metal_supports_op,
+    /* .offload_op              = */ NULL,
+    /* .event_new               = */ NULL,
+    /* .event_free              = */ NULL,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+    /* .event_synchronize       = */ NULL,
 };
 
 void ggml_backend_metal_log_set_callback(ggml_log_callback log_callback, void * user_data) {
@@ -2661,6 +3130,11 @@ void ggml_backend_metal_log_set_callback(ggml_log_callback log_callback, void *
     ggml_metal_log_user_data = user_data;
 }
 
+static ggml_guid_t ggml_backend_metal_guid(void) {
+    static ggml_guid guid = { 0x81, 0xa1, 0x8b, 0x1e, 0x71, 0xec, 0x79, 0xed, 0x2b, 0x85, 0xdc, 0x8a, 0x61, 0x98, 0x30, 0xe6 };
+    return &guid;
+}
+
 ggml_backend_t ggml_backend_metal_init(void) {
     struct ggml_metal_context * ctx = ggml_metal_init(GGML_DEFAULT_N_THREADS);
 
@@ -2671,6 +3145,7 @@ ggml_backend_t ggml_backend_metal_init(void) {
     ggml_backend_t metal_backend = malloc(sizeof(struct ggml_backend));
 
     *metal_backend = (struct ggml_backend) {
+        /* .guid      = */ ggml_backend_metal_guid(),
         /* .interface = */ ggml_backend_metal_i,
         /* .context   = */ ctx,
     };
@@ -2679,7 +3154,7 @@ ggml_backend_t ggml_backend_metal_init(void) {
 }
 
 bool ggml_backend_is_metal(ggml_backend_t backend) {
-    return backend && backend->iface.get_name == ggml_backend_metal_name;
+    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_metal_guid());
 }
 
 void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) {
diff --git a/ggml-metal.metal b/ggml-metal.metal
index f0d77d446..7af4e8f93 100644
--- a/ggml-metal.metal
+++ b/ggml-metal.metal
@@ -1,3 +1,7 @@
+#define GGML_COMMON_DECL_METAL
+#define GGML_COMMON_IMPL_METAL
+#include "ggml-common.h"
+
 #include <metal_stdlib>
 
 using namespace metal;
@@ -6,46 +10,11 @@ using namespace metal;
 #define MIN(x, y) ((x) < (y) ? (x) : (y))
 #define SWAP(x, y) { auto tmp = (x); (x) = (y); (y) = tmp; }
 
-#define QK4_0 32
-#define QR4_0 2
-typedef struct {
-    half    d;             // delta
-    uint8_t qs[QK4_0 / 2]; // nibbles / quants
-} block_q4_0;
-
-#define QK4_1 32
-typedef struct {
-    half d;                 // delta
-    half m;                 // min
-    uint8_t qs[QK4_1 / 2];  // nibbles / quants
-} block_q4_1;
-
-#define QK5_0 32
-typedef struct {
-    half d;                // delta
-    uint8_t qh[4];         // 5-th bit of quants
-    uint8_t qs[QK5_0 / 2]; // nibbles / quants
-} block_q5_0;
-
-#define QK5_1 32
-typedef struct {
-    half d;                 // delta
-    half m;                 // min
-    uint8_t qh[4];          // 5-th bit of quants
-    uint8_t qs[QK5_1 / 2];  // nibbles / quants
-} block_q5_1;
-
-#define QK8_0 32
-typedef struct {
-    half    d;         // delta
-    int8_t  qs[QK8_0]; // quants
-} block_q8_0;
-
 #define N_SIMDWIDTH 32 // assuming SIMD group size is 32
 
 enum ggml_sort_order {
-    GGML_SORT_ASC,
-    GGML_SORT_DESC,
+    GGML_SORT_ORDER_ASC,
+    GGML_SORT_ORDER_DESC,
 };
 
 // general-purpose kernel for addition, multiplication and division of two tensors
@@ -244,6 +213,15 @@ kernel void kernel_scale_4(
     dst[tpig] = src0[tpig] * scale;
 }
 
+kernel void kernel_clamp(
+        device const float * src0,
+        device       float * dst,
+        constant     float & min,
+        constant     float & max,
+        uint tpig[[thread_position_in_grid]]) {
+    dst[tpig] = src0[tpig] < min ? min : (src0[tpig] > max ? max : src0[tpig]);
+}
+
 kernel void kernel_relu(
         device const float * src0,
         device       float * dst,
@@ -251,6 +229,13 @@ kernel void kernel_relu(
     dst[tpig] = max(0.0f, src0[tpig]);
 }
 
+kernel void kernel_sigmoid(
+        device const float * src0,
+        device       float * dst,
+        uint tpig[[thread_position_in_grid]]) {
+    dst[tpig] = 1.0f / (1.0f + exp(-src0[tpig]));
+}
+
 kernel void kernel_tanh(
         device const float * src0,
         device       float * dst,
@@ -264,6 +249,15 @@ constant float GELU_QUICK_COEF = -1.702f;
 constant float SQRT_2_OVER_PI  = 0.79788456080286535587989211986876f;
 
 kernel void kernel_gelu(
+    device const float * src0,
+    device       float * dst,
+    uint tpig[[thread_position_in_grid]]) {
+    device const float & x = src0[tpig];
+
+    dst[tpig] = 0.5f*x*(1.0f + precise::tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
+}
+
+kernel void kernel_gelu_4(
     device const float4 * src0,
     device       float4 * dst,
     uint tpig[[thread_position_in_grid]]) {
@@ -277,6 +271,15 @@ kernel void kernel_gelu(
 }
 
 kernel void kernel_gelu_quick(
+    device const float * src0,
+    device       float * dst,
+    uint tpig[[thread_position_in_grid]]) {
+    device const float & x = src0[tpig];
+
+    dst[tpig] = x*(1.0f/(1.0f+exp(GELU_QUICK_COEF*x)));
+}
+
+kernel void kernel_gelu_quick_4(
     device const float4 * src0,
     device       float4 * dst,
     uint tpig[[thread_position_in_grid]]) {
@@ -286,6 +289,14 @@ kernel void kernel_gelu_quick(
 }
 
 kernel void kernel_silu(
+        device const float * src0,
+        device       float * dst,
+        uint tpig[[thread_position_in_grid]]) {
+    device const float & x = src0[tpig];
+    dst[tpig] = x / (1.0f + exp(-x));
+}
+
+kernel void kernel_silu_4(
         device const float4 * src0,
         device       float4 * dst,
         uint tpig[[thread_position_in_grid]]) {
@@ -348,11 +359,11 @@ kernel void kernel_sum_rows(
     dst_row[0] = row_sum;
 }
 
+template<typename T>
 kernel void kernel_soft_max(
-        device const float * src0,
-        device const float * src1,
-        device const float * src2,
-        device       float * dst,
+        device const  char * src0,
+        device const  char * src1,
+        device        char * dst,
         constant   int64_t & ne00,
         constant   int64_t & ne01,
         constant   int64_t & ne02,
@@ -371,12 +382,11 @@ kernel void kernel_soft_max(
     const int64_t i02 = (tgpig - i03*ne02*ne01) / ne01;
     const int64_t i01 = (tgpig - i03*ne02*ne01 - i02*ne01);
 
-    device const float * psrc0 =         src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
-    device const float * pmask = src1 != src0 ? src1                               + i01*ne00 : nullptr;
-    device const float * ppos  = src2 != src0 ? src2                                          : nullptr;
-    device       float * pdst  =         dst  + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+    device const float * psrc0 = (device const float *) src0 + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
+    device const     T * pmask = src1 != src0 ? (device const    T *) src1         + i01*ne00 : nullptr;
+    device       float * pdst  = (device       float *) dst  + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
 
-    float slope = 0.0f;
+    float slope = 1.0f;
 
     // ALiBi
     if (max_bias > 0.0f) {
@@ -392,7 +402,7 @@ kernel void kernel_soft_max(
     float lmax = -INFINITY;
 
     for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
-        lmax = MAX(lmax, psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f));
+        lmax = MAX(lmax, psrc0[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f));
     }
 
     // find the max value in the block
@@ -417,7 +427,7 @@ kernel void kernel_soft_max(
     // parallel sum
     float lsum = 0.0f;
     for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
-        const float exp_psrc0 = exp((psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f)) - max_val);
+        const float exp_psrc0 = exp((psrc0[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f)) - max_val);
         lsum += exp_psrc0;
         pdst[i00] = exp_psrc0;
     }
@@ -452,11 +462,11 @@ kernel void kernel_soft_max(
     }
 }
 
+template<typename T>
 kernel void kernel_soft_max_4(
-        device const float * src0,
-        device const float * src1,
-        device const float * src2,
-        device       float * dst,
+        device const  char * src0,
+        device const  char * src1,
+        device        char * dst,
         constant   int64_t & ne00,
         constant   int64_t & ne01,
         constant   int64_t & ne02,
@@ -475,12 +485,11 @@ kernel void kernel_soft_max_4(
     const int64_t i02 = (tgpig - i03*ne02*ne01) / ne01;
     const int64_t i01 = (tgpig - i03*ne02*ne01 - i02*ne01);
 
-    device const float4 * psrc4 =                (device const float4 *)(src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
-    device const float4 * pmask = src1 != src0 ? (device const float4 *)(src1 +                                      i01*ne00) : nullptr;
-    device const float4 * ppos  = src2 != src0 ? (device const float4 *)(src2)                                                 : nullptr;
-    device       float4 * pdst4 =                (device       float4 *)(dst  + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
+    device const float4 * psrc4 = (device const float4 *) src0 + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00)/4;
+    device const      T * pmask = src1 != src0 ? (device const     T *) src1         + i01*ne00/4 : nullptr;
+    device       float4 * pdst4 = (device       float4 *) dst  + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00)/4;
 
-    float slope = 0.0f;
+    float slope = 1.0f;
 
     if (max_bias > 0.0f) {
         const int64_t h = i02;
@@ -495,7 +504,7 @@ kernel void kernel_soft_max_4(
     float4 lmax4 = -INFINITY;
 
     for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) {
-        lmax4 = fmax(lmax4, psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f));
+        lmax4 = fmax(lmax4, psrc4[i00]*scale + (float4)((pmask ? slope*pmask[i00] : 0.0f)));
     }
 
     const float lmax = MAX(MAX(lmax4[0], lmax4[1]), MAX(lmax4[2], lmax4[3]));
@@ -521,7 +530,7 @@ kernel void kernel_soft_max_4(
     // parallel sum
     float4 lsum4 = 0.0f;
     for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) {
-        const float4 exp_psrc4 = exp((psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f)) - max_val);
+        const float4 exp_psrc4 = exp((psrc4[i00]*scale + (float4)((pmask ? slope*pmask[i00] : 0.0f))) - max_val);
         lsum4 += exp_psrc4;
         pdst4[i00] = exp_psrc4;
     }
@@ -558,6 +567,14 @@ kernel void kernel_soft_max_4(
     }
 }
 
+typedef decltype(kernel_soft_max<float>)    kernel_soft_max_t;
+typedef decltype(kernel_soft_max_4<float4>) kernel_soft_max_4_t;
+
+template [[host_name("kernel_soft_max_f16")]]   kernel kernel_soft_max_t   kernel_soft_max<half>;
+template [[host_name("kernel_soft_max_f32")]]   kernel kernel_soft_max_t   kernel_soft_max<float>;
+template [[host_name("kernel_soft_max_f16_4")]] kernel kernel_soft_max_4_t kernel_soft_max_4<half4>;
+template [[host_name("kernel_soft_max_f32_4")]] kernel kernel_soft_max_4_t kernel_soft_max_4<float4>;
+
 kernel void kernel_diag_mask_inf(
         device const float * src0,
         device       float * dst,
@@ -897,6 +914,7 @@ void mul_vec_q_n_f32_impl(
                    int64_t   ne1,
                    uint      r2,
                    uint      r3,
+        threadgroup int8_t * shared_values,
                    uint3 tgpig, uint tiisg, uint sgitg) {
     const int nb = ne00/QK4_0;
 
@@ -973,7 +991,7 @@ kernel void kernel_mul_mv_q4_0_f32(
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
-    mul_vec_q_n_f32_impl<block_q4_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,tgpig,tiisg,sgitg);
+    mul_vec_q_n_f32_impl<block_q4_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg);
 }
 
 kernel void kernel_mul_mv_q4_1_f32(
@@ -999,7 +1017,7 @@ kernel void kernel_mul_mv_q4_1_f32(
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint tiisg[[thread_index_in_simdgroup]],
         uint sgitg[[simdgroup_index_in_threadgroup]]) {
-     mul_vec_q_n_f32_impl<block_q4_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,tgpig,tiisg,sgitg);
+     mul_vec_q_n_f32_impl<block_q4_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg);
 }
 
 kernel void kernel_mul_mv_q5_0_f32(
@@ -1025,7 +1043,7 @@ kernel void kernel_mul_mv_q5_0_f32(
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
-    mul_vec_q_n_f32_impl<block_q5_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,tgpig,tiisg,sgitg);
+    mul_vec_q_n_f32_impl<block_q5_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg);
 }
 
 kernel void kernel_mul_mv_q5_1_f32(
@@ -1051,7 +1069,7 @@ kernel void kernel_mul_mv_q5_1_f32(
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
-    mul_vec_q_n_f32_impl<block_q5_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,tgpig,tiisg,sgitg);
+    mul_vec_q_n_f32_impl<block_q5_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg);
 }
 
 
@@ -1061,18 +1079,19 @@ void kernel_mul_mv_q8_0_f32_impl(
         device const  void * src0,
         device const float * src1,
         device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant   int64_t & ne10,
-        constant   int64_t & ne12,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   uint    & r2,
-        constant   uint    & r3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint  tiisg[[thread_index_in_simdgroup]],
-        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+                   int64_t   ne00,
+                   int64_t   ne01,
+                   int64_t   ne02,
+                   int64_t   ne10,
+                   int64_t   ne12,
+                   int64_t   ne0,
+                   int64_t   ne1,
+                   uint      r2,
+                   uint      r3,
+        threadgroup int8_t * shared_values,
+                   uint3     tgpig,
+                   uint      tiisg,
+                   uint      sgitg) {
     const int nr  = N_DST;
     const int nsg = N_SIMDGROUP;
     const int nw  = N_SIMDWIDTH;
@@ -1150,7 +1169,7 @@ kernel void kernel_mul_mv_q8_0_f32(
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
-    kernel_mul_mv_q8_0_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,tgpig,tiisg,sgitg);
+    kernel_mul_mv_q8_0_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg);
 }
 
 #define N_F32_F32 4
@@ -1159,24 +1178,24 @@ void kernel_mul_mv_f32_f32_impl(
         device const  char * src0,
         device const  char * src1,
         device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant  uint64_t & nb00,
-        constant  uint64_t & nb01,
-        constant  uint64_t & nb02,
-        constant   int64_t & ne10,
-        constant   int64_t & ne11,
-        constant   int64_t & ne12,
-        constant  uint64_t & nb10,
-        constant  uint64_t & nb11,
-        constant  uint64_t & nb12,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   uint    & r2,
-        constant   uint    & r3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint  tiisg[[thread_index_in_simdgroup]]) {
+                   int64_t   ne00,
+                   int64_t   ne01,
+                   int64_t   ne02,
+                  uint64_t   nb00,
+                  uint64_t   nb01,
+                  uint64_t   nb02,
+                   int64_t   ne10,
+                   int64_t   ne11,
+                   int64_t   ne12,
+                  uint64_t   nb10,
+                  uint64_t   nb11,
+                  uint64_t   nb12,
+                   int64_t   ne0,
+                   int64_t   ne1,
+                     uint    r2,
+                     uint    r3,
+                     uint3   tgpig,
+                     uint    tiisg) {
 
     const int64_t r0 = tgpig.x;
     const int64_t rb = tgpig.y*N_F32_F32;
@@ -1429,24 +1448,24 @@ void kernel_mul_mv_f16_f32_impl(
         device const  char * src0,
         device const  char * src1,
         device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant  uint64_t & nb00,
-        constant  uint64_t & nb01,
-        constant  uint64_t & nb02,
-        constant   int64_t & ne10,
-        constant   int64_t & ne11,
-        constant   int64_t & ne12,
-        constant  uint64_t & nb10,
-        constant  uint64_t & nb11,
-        constant  uint64_t & nb12,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   uint    & r2,
-        constant   uint    & r3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint tiisg[[thread_index_in_simdgroup]]) {
+                   int64_t   ne00,
+                   int64_t   ne01,
+                   int64_t   ne02,
+                  uint64_t   nb00,
+                  uint64_t   nb01,
+                  uint64_t   nb02,
+                   int64_t   ne10,
+                   int64_t   ne11,
+                   int64_t   ne12,
+                  uint64_t   nb10,
+                  uint64_t   nb11,
+                  uint64_t   nb12,
+                   int64_t   ne0,
+                   int64_t   ne1,
+                   uint      r2,
+                   uint      r3,
+                   uint3     tgpig,
+                   uint      tiisg) {
 
     const int64_t r0 = tgpig.x;
     const int64_t rb = tgpig.y*N_F16_F32;
@@ -1579,60 +1598,6 @@ kernel void kernel_mul_mv_f16_f32_l4(
     }
 }
 
-kernel void kernel_alibi_f32(
-        device const float * src0,
-        device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant   int64_t & ne03,
-        constant  uint64_t & nb00,
-        constant  uint64_t & nb01,
-        constant  uint64_t & nb02,
-        constant  uint64_t & nb03,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   int64_t & ne2,
-        constant   int64_t & ne3,
-        constant  uint64_t & nb0,
-        constant  uint64_t & nb1,
-        constant  uint64_t & nb2,
-        constant  uint64_t & nb3,
-        constant     float & m0,
-        constant     float & m1,
-        constant       int & n_heads_log2_floor,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint3 tpitg[[thread_position_in_threadgroup]],
-        uint3   ntg[[threads_per_threadgroup]]) {
-    const int64_t i03 = tgpig[2];
-    const int64_t i02 = tgpig[1];
-    const int64_t i01 = tgpig[0];
-
-    const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
-
-    const int64_t i3 = n / (ne2*ne1*ne0);
-    const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
-    const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
-  //const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
-
-    const int64_t k = i3*ne3 + i2;
-
-    float m_k;
-    if (k < n_heads_log2_floor) {
-        m_k = pow(m0, k + 1);
-    } else {
-        m_k = pow(m1, 2 * (k - n_heads_log2_floor) + 1);
-    }
-
-    device       char * dst_row = (device char *) dst + i3*nb3 + i2*nb2 + i1*nb1;
-    device const char * src_row = (device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01;
-    for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) {
-        const  float   src_v = *(device float *)(src_row + i00*nb00);
-        device float * dst_v =  (device float *)(dst_row + i00*nb0);
-        *dst_v = i00 * m_k + src_v;
-    }
-}
-
 static float rope_yarn_ramp(const float low, const float high, const int i0) {
     const float y = (i0 / 2 - low) / max(0.001f, high - low);
     return 1.0f - min(1.0f, max(0.0f, y));
@@ -1959,11 +1924,56 @@ kernel void kernel_pad_f32(
     }
 }
 
+kernel void kernel_arange_f32(
+    device        char * dst,
+    constant   int64_t & ne0,
+    constant   float   & start,
+    constant   float   & step,
+    uint3 tgpig[[threadgroup_position_in_grid]],
+    uint3 tpitg[[thread_position_in_threadgroup]],
+    uint3   ntg[[threads_per_threadgroup]]) {
+
+    device float * dst_ptr = (device float *) dst;
+
+    for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) {
+        dst_ptr[i0] = start + step * i0;
+    }
+}
+
+kernel void kernel_timestep_embedding_f32(
+    device  const char * src0,
+    device        char * dst,
+    constant  uint64_t & nb1,
+    constant  int      & dim,
+    constant  int      & max_period,
+    uint3 tgpig[[threadgroup_position_in_grid]],
+    uint3 tpitg[[thread_position_in_threadgroup]],
+    uint3   ntg[[threads_per_threadgroup]]) {
+
+    int i = tgpig.x;
+    device float * embed_data = (device float *)(dst +  i*nb1);
+
+    int half_ = dim / 2;
+    for (int j = tpitg.x; j < half_; j += ntg.x) {
+        float timestep = ((device float *)src0)[i];
+        float freq = (float)exp(-log((float)max_period) * j / half_);
+        float arg = timestep * freq;
+        embed_data[j        ] = cos(arg);
+        embed_data[j + half_] = sin(arg);
+    }
+
+    if (dim % 2 != 0 && tpitg.x == 0) {
+        embed_data[dim] = 0.f;
+    }
+}
+
 // bitonic sort implementation following the CUDA kernels as reference
 typedef void (argsort_t)(
-        device const float * x,
-        device     int32_t * dst,
-        constant   int64_t & ncols,
+        device const float  * x,
+        device     int32_t  * dst,
+        constant   int64_t  & ncols,
+        constant   int64_t  & ncols_pad,
+        threadgroup int32_t * shared_values [[threadgroup(0)]],
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint3 tpitg[[thread_position_in_threadgroup]]);
 
@@ -1972,33 +1982,42 @@ kernel void kernel_argsort_f32_i32(
         device const float   * x,
         device       int32_t * dst,
         constant     int64_t & ncols,
+        constant     int64_t & ncols_pad,
+        threadgroup int32_t  * shared_values [[threadgroup(0)]],
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint3 tpitg[[thread_position_in_threadgroup]]) {
     // bitonic sort
     int col = tpitg[0];
     int row = tgpig[1];
 
-    if (col >= ncols) return;
+    if (col >= ncols_pad) return;
 
-    device const float   * x_row   = x   + row * ncols;
-    device       int32_t * dst_row = dst + row * ncols;
+    device const float   * x_row   = x + row * ncols;
+    threadgroup int32_t  * dst_row = shared_values;
 
     // initialize indices
-    if (col < ncols) {
-        dst_row[col] = col;
-    }
+    dst_row[col] = col;
+
     threadgroup_barrier(mem_flags::mem_threadgroup);
 
-    for (int k = 2; k <= ncols; k *= 2) {
+    for (int k = 2; k <= ncols_pad; k *= 2) {
         for (int j = k / 2; j > 0; j /= 2) {
             int ixj = col ^ j;
             if (ixj > col) {
                 if ((col & k) == 0) {
-                    if (order == GGML_SORT_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) {
+                    if (dst_row[col] >= ncols ||
+                        (dst_row[ixj] < ncols && (order == GGML_SORT_ORDER_ASC ?
+                            x_row[dst_row[col]] > x_row[dst_row[ixj]] :
+                            x_row[dst_row[col]] < x_row[dst_row[ixj]]))
+                    ) {
                         SWAP(dst_row[col], dst_row[ixj]);
                     }
                 } else {
-                    if (order == GGML_SORT_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) {
+                    if (dst_row[ixj] >= ncols ||
+                        (dst_row[col] < ncols && (order == GGML_SORT_ORDER_ASC ?
+                            x_row[dst_row[col]] < x_row[dst_row[ixj]] :
+                            x_row[dst_row[col]] > x_row[dst_row[ixj]]))
+                    ) {
                         SWAP(dst_row[col], dst_row[ixj]);
                     }
                 }
@@ -2006,10 +2025,15 @@ kernel void kernel_argsort_f32_i32(
             threadgroup_barrier(mem_flags::mem_threadgroup);
         }
     }
+
+    // copy the result to dst without the padding
+    if (col < ncols) {
+        dst[row * ncols + col] = dst_row[col];
+    }
 }
 
-template [[host_name("kernel_argsort_f32_i32_asc")]]  kernel argsort_t kernel_argsort_f32_i32<GGML_SORT_ASC>;
-template [[host_name("kernel_argsort_f32_i32_desc")]] kernel argsort_t kernel_argsort_f32_i32<GGML_SORT_DESC>;
+template [[host_name("kernel_argsort_f32_i32_asc")]]  kernel argsort_t kernel_argsort_f32_i32<GGML_SORT_ORDER_ASC>;
+template [[host_name("kernel_argsort_f32_i32_desc")]] kernel argsort_t kernel_argsort_f32_i32<GGML_SORT_ORDER_DESC>;
 
 kernel void kernel_leaky_relu_f32(
         device const float * src0,
@@ -2019,6 +2043,667 @@ kernel void kernel_leaky_relu_f32(
     dst[tpig] = src0[tpig] > 0.0f ? src0[tpig] : src0[tpig] * slope;
 }
 
+typedef void (flash_attn_ext_f16_t)(
+        device const  char * q,
+        device const  char * k,
+        device const  char * v,
+        device const  char * mask,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant   int64_t & ne03,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant  uint64_t & nb03,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant   int64_t & ne13,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant  uint64_t & nb13,
+        constant  uint64_t & nb31,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   int64_t & ne2,
+        constant   int64_t & ne3,
+        constant     float & scale,
+        constant     float & max_bias,
+        constant     float & m0,
+        constant     float & m1,
+        constant  uint32_t & n_head_log2,
+        threadgroup   half * shared,
+        uint3  tgpig[[threadgroup_position_in_grid]],
+        uint3  tpitg[[thread_position_in_threadgroup]],
+        uint3    ntg[[threads_per_threadgroup]],
+        ushort tiisg[[thread_index_in_simdgroup]],
+        ushort sgitg[[simdgroup_index_in_threadgroup]]);
+
+// ref: https://arxiv.org/pdf/2307.08691.pdf
+template<int64_t D, int64_t Q = 8, int64_t C = 32> // head size, queries per threadgroup, cache items per threadgroup
+kernel void kernel_flash_attn_ext_f16(
+        device const  char * q,
+        device const  char * k,
+        device const  char * v,
+        device const  char * mask,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant   int64_t & ne03,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant  uint64_t & nb03,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant   int64_t & ne13,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant  uint64_t & nb13,
+        constant  uint64_t & nb31,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   int64_t & ne2,
+        constant   int64_t & ne3,
+        constant     float & scale,
+        constant     float & max_bias,
+        constant     float & m0,
+        constant     float & m1,
+        constant  uint32_t & n_head_log2,
+        threadgroup   half * shared [[threadgroup(0)]],
+        uint3  tgpig[[threadgroup_position_in_grid]],
+        uint3  tpitg[[thread_position_in_threadgroup]],
+        uint3    ntg[[threads_per_threadgroup]],
+        ushort tiisg[[thread_index_in_simdgroup]],
+        ushort sgitg[[simdgroup_index_in_threadgroup]]) {
+    const short nsg = ntg.y; // number of simdgroups
+
+    const short iq3 = tgpig[2];
+    const short iq2 = tgpig[1];
+    const short iq1 = tgpig[0]*Q;
+
+    const short D4 = D/4;
+    const short D8 = D/8;
+  //const short Q8 = Q/8;
+    const short NW = N_SIMDWIDTH;
+    const short SH = (C + Q); // shared memory per simdgroup in (half)
+
+    const short T  = D + 2*nsg*SH; // shared memory size per query in (half)
+    const short TF = T/2;        // shared memory size per query in (float)
+    const short T4 = T/4;        // shared memory size per query in (half4)
+
+    threadgroup half  * sq  = (threadgroup half  *) (shared +              0*D); // holds the query data
+    threadgroup half4 * sq4 = (threadgroup half4 *) (shared +              0*D); // same as above but in half4
+    threadgroup float * ss  = (threadgroup float *) (shared + 2*sgitg*SH + 1*D); // scratch buffer for attention and diagonal matrix
+
+    // store the result for all queries in local memory in 8x8 matrices (the O matrix from the paper)
+    simdgroup_half8x8 lo[D8];
+
+    // load heads from Q to shared memory
+    for (short j = sgitg; j < Q; j += nsg) {
+        device const float4 * q4 = (device const float4 *) ((device const char *) q + ((iq1 + j)*nb01 + iq2*nb02 + iq3*nb03));
+
+        for (short i = tiisg; i < D4; i += NW) {
+            if (iq1 + j < ne01) {
+                sq4[j*T4 + i] = (half4) q4[i];
+            } else {
+                sq4[j*T4 + i] = 0.0h;
+            }
+        }
+    }
+
+    // zero out lo
+    for (short i = 0; i < D8; ++i) {
+        lo[i] = make_filled_simdgroup_matrix<half, 8>(0.0h);
+    }
+
+    // zero out shared memory SH
+    for (short j = 0; j < Q; ++j) {
+        for (short i = tiisg; i < SH; i += NW) {
+            ss[j*TF + i] = 0.0f;
+        }
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    {
+        float S[Q] = { [0 ... Q-1] = 0.0h };
+        float M[Q] = { [0 ... Q-1] = -FLT_MAX/2 };
+
+        // assume K and V are same shape
+        const short ne22 = ne12;
+        const short ne23 = ne13;
+
+        const uint nb21 = nb11;
+        const uint nb22 = nb12;
+        const uint nb23 = nb13;
+
+        // broadcast
+        const short rk2 = ne02/ne12;
+        const short rk3 = ne03/ne13;
+
+        const short rv2 = ne02/ne22;
+        const short rv3 = ne03/ne23;
+
+        // k indices
+        const short ik2 = iq2/rk2;
+        const short ik3 = iq3/rk3;
+
+        // v indices
+        const short iv2 = iq2/rv2;
+        const short iv3 = iq3/rv3;
+
+        // load the queries from shared memory into local memory
+        simdgroup_half8x8 mq[D8];
+
+        for (short i = 0; i < D8; ++i) {
+            simdgroup_load(mq[i], sq + i*8, T);
+        }
+
+        // pointer to the mask
+        device const half * mp = (device const half *) (mask + iq1*nb31);
+
+        // prepare diagonal scale matrix
+        simdgroup_float8x8 mscale(scale);
+
+        // prepare diagonal slope matrix
+        simdgroup_float8x8 mslope(1.0f);
+
+        // ALiBi
+        if (max_bias > 0.0f) {
+            const uint32_t h = iq2;
+
+            const float base = h < n_head_log2 ? m0 : m1;
+            const int   exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+            mslope = simdgroup_float8x8(pow(base, exph));
+        }
+
+        // loop over the KV cache
+        // each simdgroup handles blocks of Q rows and C columns
+        for (int ic0 = 0; ic0 < ne11; ic0 += C*nsg) {
+            const int ic = ic0 + C*sgitg;
+            if (ic >= ne11) {
+                break;
+            }
+
+            // Q*K^T
+            {
+                for (short cc = 0; cc < C/8; ++cc) {
+                    simdgroup_float8x8 mqk = make_filled_simdgroup_matrix<float, 8>(0.h);
+
+                    device const half * pk = (device const half *) ((device const char *) k + ((ic + 8*cc)*nb11 + ik2*nb12 + ik3*nb13));
+
+                    for (short i = 0; i < D8; ++i) {
+                        simdgroup_half8x8 mk;
+                        simdgroup_load(mk, pk + i*8, nb11/sizeof(half), 0, true); // transpose
+
+                        simdgroup_multiply_accumulate(mqk, mq[i], mk, mqk);
+                    }
+
+                    // mqk = mqk*scale + mask*slope
+                    simdgroup_half8x8 mm;
+                    simdgroup_load(mm, mp + ic + 8*cc, nb31/sizeof(half), 0, false);
+                    simdgroup_multiply(mm, mslope, mm);
+                    simdgroup_multiply_accumulate(mqk, mqk, mscale, mm);
+
+                    simdgroup_store(mqk, ss + 8*cc, TF, 0, false);
+                }
+            }
+
+            // used to detect blocks full of -INF
+            float smax = -INFINITY;
+
+            // online softmax
+            {
+                float ms[Q];
+
+                for (short j = 0; j < Q; ++j) {
+                    const short p = tiisg;
+
+                    const float m = M[j];
+                    const float s = ss[j*TF + p];
+
+                    smax = simd_max(max(smax, s));
+                    M[j] = simd_max(max(M[j], s));
+
+                                ms[j] = exp(m - M[j]);
+                    const float vs    = exp(s - M[j]);
+
+                    S[j] = S[j]*ms[j] + simd_sum(vs);
+
+                    // the P matrix from the paper (Q rows, C columns)
+                    ss[j*TF + p] = vs;
+                }
+
+                // create a QxQ diagonal matrix for rescaling the output
+                if (tiisg < Q) {
+                    ss[tiisg*TF + C + tiisg] = ms[tiisg];
+                }
+            }
+
+            // skip -INF blocks
+            if (smax == -INFINITY) {
+                continue;
+            }
+
+            // O = diag(ms)*O
+            {
+                simdgroup_float8x8 mm;
+                simdgroup_load(mm, ss + C, TF, 0, false);
+
+                for (short i = 0; i < D8; ++i) {
+                    simdgroup_multiply(lo[i], mm, lo[i]);
+                }
+            }
+
+            // O = O + (Q*K^T)*V
+            {
+                for (short cc = 0; cc < C/8; ++cc) {
+                    device const half * pv = (device const half *) ((device const char *) v + ((ic + 8*cc)*nb21 + iv2*nb22 + iv3*nb23));
+
+                    for (short i = 0; i < D8; ++i) {
+                        simdgroup_half8x8 mk;
+                        simdgroup_load(mk, pv + i*8, nb21/sizeof(half), 0, false);
+
+                        simdgroup_float8x8 mv;
+                        simdgroup_load(mv, ss + 8*cc, TF, 0, false);
+
+                        simdgroup_multiply_accumulate(lo[i], mv, mk, lo[i]);
+                    }
+                }
+            }
+        }
+
+        // these are needed for reducing the results from the simdgroups (reuse the ss buffer)
+        for (short j = 0; j < Q; ++j) {
+            if (tiisg == 0) {
+                ss[j*TF + 0] = S[j];
+                ss[j*TF + 1] = M[j];
+            }
+        }
+    }
+
+    // reduce the warps sequentially
+    for (short sg = 1; sg < nsg; ++sg) {
+        float S = { 0.0h };
+        float M = { -FLT_MAX/2 };
+
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+
+        // each simdgroup stores its output to shared memory, reusing sq
+        if (sgitg == sg) {
+            for (short i = 0; i < D8; ++i) {
+                simdgroup_store(lo[i], sq + i*8, T, 0, false);
+            }
+        }
+
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+
+        // the first simdgroup accumulates the results from the other simdgroups
+        if (sgitg == 0) {
+            for (short j = 0; j < Q; ++j) {
+                const float S0 = ss[j*TF +         0];
+                const float S1 = ss[j*TF + sg*SH + 0];
+
+                const float M0 = ss[j*TF +         1];
+                const float M1 = ss[j*TF + sg*SH + 1];
+
+                M = max(M0, M1);
+
+                const float ms0 = exp(M0 - M);
+                const float ms1 = exp(M1 - M);
+
+                S = S0*ms0 + S1*ms1;
+
+                if (tiisg == 0) {
+                    ss[j*TF + 0] = S;
+                    ss[j*TF + 1] = M;
+
+                    ss[j*TF + C + j        ] = ms0;
+                    ss[j*TF + C + j + sg*SH] = ms1;
+                }
+            }
+
+            // O_0 = diag(ms0)*O_0 + diag(ms1)*O_1
+            {
+                simdgroup_half8x8 t;
+                simdgroup_float8x8 ms0;
+                simdgroup_float8x8 ms1;
+
+                simdgroup_load(ms0, ss + C,         TF, 0, false);
+                simdgroup_load(ms1, ss + C + sg*SH, TF, 0, false);
+
+                for (short i = 0; i < D8; ++i) {
+                    simdgroup_load    (t, sq + i*8, T, 0, false);
+                    simdgroup_multiply(t, ms1, t);
+
+                    simdgroup_multiply_accumulate(lo[i], ms0, lo[i], t);
+                }
+            }
+        }
+    }
+
+    // store result to shared memory (reuse sq)
+    if (sgitg == 0) {
+        for (short i = 0; i < D8; ++i) {
+            simdgroup_store(lo[i], sq + i*8, T, 0, false);
+        }
+    }
+
+    device float4 * dst4 = (device float4 *) dst;
+
+    // final rescale with 1/S and store to global memory
+    if (sgitg == 0) {
+        for (short j = 0; j < Q && iq1 + j < ne01; ++j) {
+            const float S = ss[j*TF + 0];
+
+            for (short i = tiisg; i < D4; i += NW) {
+                dst4[(iq3*ne2*ne1 + iq2 + (iq1 + j)*ne1)*D4 + i] = (float4) sq4[j*T4 + i]/S;
+            }
+        }
+    }
+}
+
+template [[host_name("kernel_flash_attn_ext_f16_h64" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<64>;
+template [[host_name("kernel_flash_attn_ext_f16_h80" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<80>;
+template [[host_name("kernel_flash_attn_ext_f16_h96" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<96>;
+template [[host_name("kernel_flash_attn_ext_f16_h112")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<112>;
+template [[host_name("kernel_flash_attn_ext_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<128>;
+template [[host_name("kernel_flash_attn_ext_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<256>;
+
+template<int64_t D, int64_t Q = 1, int64_t C = 32> // head size, queries per threadgroup, cache items per threadgroup
+kernel void kernel_flash_attn_ext_vec_f16(
+        device const  char * q,
+        device const  char * k,
+        device const  char * v,
+        device const  char * mask,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant   int64_t & ne03,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant  uint64_t & nb03,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant   int64_t & ne13,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant  uint64_t & nb13,
+        constant  uint64_t & nb31,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   int64_t & ne2,
+        constant   int64_t & ne3,
+        constant     float & scale,
+        constant     float & max_bias,
+        constant     float & m0,
+        constant     float & m1,
+        constant  uint32_t & n_head_log2,
+        threadgroup   half * shared [[threadgroup(0)]],
+        uint3  tgpig[[threadgroup_position_in_grid]],
+        uint3  tpitg[[thread_position_in_threadgroup]],
+        uint3    ntg[[threads_per_threadgroup]],
+        ushort tiisg[[thread_index_in_simdgroup]],
+        ushort sgitg[[simdgroup_index_in_threadgroup]]) {
+    const short nsg = ntg.y; // number of simdgroups
+
+    const short iq3 = tgpig[2];
+    const short iq2 = tgpig[1];
+    const short iq1 = tgpig[0];
+
+    const short D4 = D/4;
+    const short NW = N_SIMDWIDTH;
+    const short SH = (C + Q); // shared memory per simdgroup in (half)
+
+    const short T  = D + 2*nsg*SH; // shared memory size per query in (half)
+
+    float slope = 1.0f;
+
+    // ALiBi
+    if (max_bias > 0.0f) {
+        const uint32_t h = iq2;
+
+        const float base = h < n_head_log2 ? m0 : m1;
+        const int   exp  = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+        slope = pow(base, exp);
+    }
+
+  //threadgroup half   * sq  = (threadgroup half   *) (shared +              0*D); // holds the query data
+    threadgroup half4  * sq4 = (threadgroup half4  *) (shared +              0*D); // same as above but in half4
+    threadgroup float  * ss  = (threadgroup float  *) (shared + 2*sgitg*SH + 1*D); // scratch buffer for attention and diagonal matrix
+    threadgroup float4 * ss4 = (threadgroup float4 *) (shared + 2*sgitg*SH + 1*D); // same as above but in half4
+    threadgroup half4  * sr4 = (threadgroup half4  *) (shared +   sgitg*D  + 1*T); // scratch buffer for the results
+
+    // store the result for all queries in local memory in 8x8 matrices (the O matrix from the paper)
+    half4 lo[D4/NW];
+
+    // load heads from Q to shared memory
+    device const float4 * q4 = (device const float4 *) ((device const char *) q + (iq1*nb01 + iq2*nb02 + iq3*nb03));
+
+    for (short i = tiisg; i < D4; i += NW) {
+        if (iq1 < ne01) {
+            sq4[i] = (half4) q4[i];
+        } else {
+            sq4[i] = 0.0h;
+        }
+    }
+
+    // zero out lo
+    for (short i = tiisg; i < D4; i += NW) {
+        lo[i/NW] = 0.0h;
+    }
+
+    // zero out shared memory SH
+    for (short i = tiisg; i < SH/4; i += NW) {
+        ss4[i] = 0.0h;
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    {
+        float S = { 0.0h };
+        float M = { -FLT_MAX/2 };
+
+        // assume K and V are same shape
+        const short ne22 = ne12;
+        const short ne23 = ne13;
+
+        const uint nb21 = nb11;
+        const uint nb22 = nb12;
+        const uint nb23 = nb13;
+
+        // broadcast
+        const short rk2 = ne02/ne12;
+        const short rk3 = ne03/ne13;
+
+        const short rv2 = ne02/ne22;
+        const short rv3 = ne03/ne23;
+
+        // k indices
+        const short ik2 = iq2 / rk2;
+        const short ik3 = iq3 / rk3;
+
+        // v indices
+        const short iv2 = iq2 / rv2;
+        const short iv3 = iq3 / rv3;
+
+        // load the queries from shared memory into local memory
+        half4 mq[D4];
+
+        for (short ii = 0; ii < D4; ii += NW) {
+            short i = ii + tiisg;
+            mq[i] = sq4[i];
+        }
+
+        // pointer to the mask
+        device const half4 * mp4 = (device const half4 *) (mask + iq1*nb31);
+
+        // loop over the KV cache
+        // each simdgroup handles blocks of Q rows and C columns
+        for (int ic0 = 0; ic0 < ne11; ic0 += C*nsg) {
+            const int ic = ic0 + C*sgitg;
+            if (ic >= ne11) {
+                break;
+            }
+
+            // Q*K^T
+            {
+#pragma unroll
+                for (short cc = 0; cc < C/4; ++cc) {
+                    float4 mqk = { 0.0h };
+
+                    device const half4 * pk4 = (device const half4 *) ((device const char *) k + ((ic + 4*cc)*nb11 + ik2*nb12 + ik3*nb13));
+
+#pragma unroll
+                    for (short ii = 0; ii < D4; ii += NW) {
+                        const short i = ii + tiisg;
+
+                        half4x4 mk;
+                        mk[0] = pk4[i + 0*(nb11/8)];
+                        mk[1] = pk4[i + 1*(nb11/8)];
+                        mk[2] = pk4[i + 2*(nb11/8)];
+                        mk[3] = pk4[i + 3*(nb11/8)];
+
+                        mqk += (float4) (mq[i] * mk);
+                    }
+
+                    // reduce the results from the threads in the simdgroup
+                    mqk += simd_shuffle_down(mqk, 16);
+                    mqk += simd_shuffle_down(mqk,  8);
+                    mqk += simd_shuffle_down(mqk,  4);
+                    mqk += simd_shuffle_down(mqk,  2);
+                    mqk += simd_shuffle_down(mqk,  1);
+
+                    // mqk = mqk*scale + mask*slope
+                    if (tiisg == 0) {
+                        float4 mm = (float4) mp4[ic/4 + cc];
+                        mqk = mqk*scale + mm*slope;
+
+                        ss4[cc] = mqk;
+                    }
+                }
+            }
+
+            // online softmax
+            {
+                const short p = tiisg;
+
+                const float m = M;
+                const float s = ss[p];
+
+                M = simd_max(max(M, s));
+
+                const float ms = exp(m - M);
+                const float vs = exp(s - M);
+
+                S = S*ms + simd_sum(vs);
+
+                // the P matrix from the paper (Q rows, C columns)
+                ss[p] = vs;
+
+                // O = diag(ms)*O
+#pragma unroll
+                for (short ii = 0; ii < D4; ii += NW) {
+                    const short i = ii + tiisg;
+                    lo[i/NW] *= ms;
+                }
+            }
+
+            // O = O + (Q*K^T)*V
+            {
+#pragma unroll
+                for (short cc = 0; cc < C/4; ++cc) {
+                    device const half4 * pv4 = (device const half4 *) ((device const char *) v + ((ic + 4*cc)*nb21 + iv2*nb22 + iv3*nb23));
+
+#pragma unroll
+                    for (short ii = 0; ii < D4; ii += NW) {
+                        const short i = ii + tiisg;
+
+                        lo[i/NW] += pv4[i + 0*(nb21/8)] * ss[4*cc + 0];
+                        lo[i/NW] += pv4[i + 1*(nb21/8)] * ss[4*cc + 1];
+                        lo[i/NW] += pv4[i + 2*(nb21/8)] * ss[4*cc + 2];
+                        lo[i/NW] += pv4[i + 3*(nb21/8)] * ss[4*cc + 3];
+                    }
+                }
+            }
+
+        }
+
+        // these are needed for reducing the results from the simdgroups (reuse the ss buffer)
+        if (tiisg == 0) {
+            ss[0] = S;
+            ss[1] = M;
+        }
+    }
+
+    // store results to shared memory
+    for (short ii = 0; ii < D4; ii += NW) {
+        short i = ii + tiisg;
+        sr4[i] = lo[ii/NW];
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // parallel reduce
+    for (short r = nsg/2; r > 0; r >>= 1) {
+        if (sgitg < r) {
+            const float S0 = ss[       0];
+            const float S1 = ss[r*SH + 0];
+
+            const float M0 = ss[       1];
+            const float M1 = ss[r*SH + 1];
+
+            const float M = max(M0, M1);
+
+            const float ms0 = exp(M0 - M);
+            const float ms1 = exp(M1 - M);
+
+            const float S = S0*ms0 + S1*ms1;
+
+            if (tiisg == 0) {
+                ss[0] = S;
+                ss[1] = M;
+            }
+
+            // O_0 = diag(ms0)*O_0 + diag(ms1)*O_1
+            for (short ii = 0; ii < D4; ii += NW) {
+                short i = ii + tiisg;
+                sr4[i] = sr4[i]*ms0 + sr4[i + r*D4]*ms1;
+            }
+        }
+
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+    }
+
+    device float4 * dst4 = (device float4 *) dst;
+
+    // final rescale with 1/S and store to global memory
+    if (sgitg == 0) {
+        const float S = ss[0];
+
+        for (short ii = 0; ii < D4; ii += NW) {
+            short i = ii + tiisg;
+            dst4[(iq3*ne2*ne1 + iq2 + (iq1)*ne1)*D4 + i] = (float4) sr4[i]/S;
+        }
+    }
+}
+
+template [[host_name("kernel_flash_attn_ext_vec_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<128>;
+template [[host_name("kernel_flash_attn_ext_vec_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<256>;
+
 kernel void kernel_cpy_f16_f16(
         device  const half * src0,
         device        half * dst,
@@ -2139,7 +2824,8 @@ kernel void kernel_cpy_f32_f16(
     for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) {
         device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
 
-        dst_data[i00] = src[0];
+        // TODO: is there a better way to handle -INFINITY?
+        dst_data[i00] = src[0] == -INFINITY ? -MAXHALF : src[0];
     }
 }
 
@@ -2376,6 +3062,242 @@ kernel void kernel_cpy_f32_q4_1(
     }
 }
 
+kernel void kernel_cpy_f32_q5_0(
+        device const float * src0,
+        device        void * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant   int64_t & ne03,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant  uint64_t & nb03,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   int64_t & ne2,
+        constant   int64_t & ne3,
+        constant  uint64_t & nb0,
+        constant  uint64_t & nb1,
+        constant  uint64_t & nb2,
+        constant  uint64_t & nb3,
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint3 tpitg[[thread_position_in_threadgroup]],
+        uint3   ntg[[threads_per_threadgroup]]) {
+    const int64_t i03 = tgpig[2];
+    const int64_t i02 = tgpig[1];
+    const int64_t i01 = tgpig[0];
+
+    const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+
+    const int64_t i3 = n / (ne2*ne1*ne0);
+    const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
+    const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
+    const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK5_0;
+
+    device block_q5_0 * dst_data = (device block_q5_0 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+
+    for (int64_t i00 = tpitg.x*QK5_0; i00 < ne00; i00 += ntg.x*QK5_0) {
+        device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
+
+        float amax = 0.0f; // absolute max
+        float max  = 0.0f;
+
+        for (int j = 0; j < QK5_0; j++) {
+            const float v = src[j];
+            if (amax < fabs(v)) {
+                amax = fabs(v);
+                max  = v;
+            }
+        }
+
+        const float d = max / -16;
+        const float id = d ? 1.0f/d : 0.0f;
+
+        dst_data[i00/QK5_0].d = d;
+
+        uint32_t qh = 0;
+        for (int j = 0; j < QK5_0/2; ++j) {
+            const float x0 = src[0       + j]*id;
+            const float x1 = src[QK5_0/2 + j]*id;
+
+            const uint8_t xi0 = MIN(31, (int8_t)(x0 + 16.5f));
+            const uint8_t xi1 = MIN(31, (int8_t)(x1 + 16.5f));
+
+            dst_data[i00/QK5_0].qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4);
+            qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
+            qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_0/2);
+        }
+        thread const uint8_t * qh8 = (thread const uint8_t *)&qh;
+        for (int j = 0; j < 4; ++j) {
+            dst_data[i00/QK5_0].qh[j] = qh8[j];
+        }
+    }
+}
+
+kernel void kernel_cpy_f32_q5_1(
+        device const float * src0,
+        device        void * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant   int64_t & ne03,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant  uint64_t & nb03,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   int64_t & ne2,
+        constant   int64_t & ne3,
+        constant  uint64_t & nb0,
+        constant  uint64_t & nb1,
+        constant  uint64_t & nb2,
+        constant  uint64_t & nb3,
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint3 tpitg[[thread_position_in_threadgroup]],
+        uint3   ntg[[threads_per_threadgroup]]) {
+    const int64_t i03 = tgpig[2];
+    const int64_t i02 = tgpig[1];
+    const int64_t i01 = tgpig[0];
+
+    const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+
+    const int64_t i3 = n / (ne2*ne1*ne0);
+    const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
+    const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
+    const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK5_1;
+
+    device block_q5_1 * dst_data = (device block_q5_1 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+
+    for (int64_t i00 = tpitg.x*QK5_1; i00 < ne00; i00 += ntg.x*QK5_1) {
+        device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
+
+        float max = src[0];
+        float min = src[0];
+
+        for (int j = 1; j < QK5_1; j++) {
+            const float v = src[j];
+            min = v < min ? v : min;
+            max = v > max ? v : max;
+        }
+
+        const float d = (max - min) / 31;
+        const float id = d ? 1.0f/d : 0.0f;
+
+        dst_data[i00/QK5_1].d = d;
+        dst_data[i00/QK5_1].m = min;
+
+        uint32_t qh = 0;
+        for (int j = 0; j < QK5_1/2; ++j) {
+            const float x0 = (src[0       + j] - min)*id;
+            const float x1 = (src[QK5_1/2 + j] - min)*id;
+
+            const uint8_t xi0 = (uint8_t)(x0 + 0.5f);
+            const uint8_t xi1 = (uint8_t)(x1 + 0.5f);
+
+            dst_data[i00/QK5_1].qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4);
+            qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
+            qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_1/2);
+        }
+        thread const uint8_t * qh8 = (thread const uint8_t *)&qh;
+        for (int j = 0; j < 4; ++j) {
+            dst_data[i00/QK5_1].qh[j] = qh8[j];
+        }
+    }
+}
+
+static inline int best_index_int8(int n, constant float * val, float x) {
+    if (x <= val[0]) return 0;
+    if (x >= val[n-1]) return n-1;
+    int ml = 0, mu = n-1;
+    while (mu-ml > 1) {
+        int mav = (ml+mu)/2;
+        if (x < val[mav]) mu = mav; else ml = mav;
+    }
+    return x - val[mu-1] < val[mu] - x ? mu-1 : mu;
+}
+
+constexpr constant static float kvalues_iq4nl_f[16] = {
+    -127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f, 1.f, 13.f, 25.f, 38.f, 53.f, 69.f, 89.f, 113.f
+};
+
+kernel void kernel_cpy_f32_iq4_nl(
+        device const float * src0,
+        device        void * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant   int64_t & ne03,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant  uint64_t & nb03,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   int64_t & ne2,
+        constant   int64_t & ne3,
+        constant  uint64_t & nb0,
+        constant  uint64_t & nb1,
+        constant  uint64_t & nb2,
+        constant  uint64_t & nb3,
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint3 tpitg[[thread_position_in_threadgroup]],
+        uint3   ntg[[threads_per_threadgroup]]) {
+    const int64_t i03 = tgpig[2];
+    const int64_t i02 = tgpig[1];
+    const int64_t i01 = tgpig[0];
+
+    const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+
+    const int64_t i3 = n / (ne2*ne1*ne0);
+    const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
+    const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
+    const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK4_NL;
+
+    device block_iq4_nl * dst_data = (device block_iq4_nl *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+
+    for (int64_t i00 = tpitg.x*QK4_NL; i00 < ne00; i00 += ntg.x*QK4_NL) {
+        device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
+
+        float amax = 0.0f; // absolute max
+        float max  = 0.0f;
+
+        for (int j = 0; j < QK4_0; j++) {
+            const float v = src[j];
+            if (amax < fabs(v)) {
+                amax = fabs(v);
+                max  = v;
+            }
+        }
+
+        const float d = max / kvalues_iq4nl_f[0];
+        const float id = d ? 1.0f/d : 0.0f;
+
+        float sumqx = 0, sumq2 = 0;
+        for (int j = 0; j < QK4_NL/2; ++j) {
+            const float x0 = src[0        + j]*id;
+            const float x1 = src[QK4_NL/2 + j]*id;
+
+            const uint8_t xi0 = best_index_int8(16, kvalues_iq4nl_f, x0);
+            const uint8_t xi1 = best_index_int8(16, kvalues_iq4nl_f, x1);
+
+            dst_data[i00/QK4_NL].qs[j] = xi0 | (xi1 << 4);
+
+            const float v0 = kvalues_iq4nl_f[xi0];
+            const float v1 = kvalues_iq4nl_f[xi1];
+            const float w0 = src[0        + j]*src[0        + j];
+            const float w1 = src[QK4_NL/2 + j]*src[QK4_NL/2 + j];
+            sumqx += w0*v0*src[j] + w1*v1*src[QK4_NL/2 + j];
+            sumq2 += w0*v0*v0 + w1*v1*v1;
+
+        }
+
+        dst_data[i00/QK4_NL].d = sumq2 > 0 ? sumqx/sumq2 : d;
+
+    }
+}
+
 kernel void kernel_concat(
     device  const char * src0,
     device  const char * src1,
@@ -2432,124 +3354,23 @@ kernel void kernel_concat(
     }
 }
 
-//============================================ k-quants ======================================================
-
-#ifndef QK_K
-#define QK_K 256
-#else
-static_assert(QK_K == 256 || QK_K == 64, "QK_K must be 256 or 64");
-#endif
-
-#if QK_K == 256
-#define K_SCALE_SIZE 12
-#else
-#define K_SCALE_SIZE 4
-#endif
-
-typedef struct {
-    uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
-    uint8_t qs[QK_K/4];      // quants
-    half d;           // super-block scale for quantized scales
-    half dmin;        // super-block scale for quantized mins
-} block_q2_K;
-// 84 bytes / block
-
-typedef struct {
-    uint8_t hmask[QK_K/8];     // quants - high bit
-    uint8_t qs[QK_K/4];        // quants - low 2 bits
-#if QK_K == 64
-    uint8_t scales[2];
-#else
-    uint8_t scales[K_SCALE_SIZE]; // scales, quantized with 6 bits
-#endif
-    half d;             // super-block scale
-} block_q3_K;
-
-#if QK_K == 64
-typedef struct {
-    half    d[2];          // super-block scales/mins
-    uint8_t scales[2];
-    uint8_t qs[QK_K/2];    // 4-bit quants
-} block_q4_K;
-#else
-typedef struct {
-    half d;             // super-block scale for quantized scales
-    half dmin;          // super-block scale for quantized mins
-    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
-    uint8_t qs[QK_K/2];        // 4--bit quants
-} block_q4_K;
-#endif
-
-#if QK_K == 64
-typedef struct {
-    half  d;                     // super-block scales/mins
-    int8_t  scales[QK_K/16];     // 8-bit block scales
-    uint8_t qh[QK_K/8];          // quants, high bit
-    uint8_t qs[QK_K/2];          // quants, low 4 bits
-} block_q5_K;
-#else
-typedef struct {
-    half d;                      // super-block scale for quantized scales
-    half dmin;                   // super-block scale for quantized mins
-    uint8_t scales[3*QK_K/64];   // scales and mins, quantized with 6 bits
-    uint8_t qh[QK_K/8];          // quants, high bit
-    uint8_t qs[QK_K/2];          // quants, low 4 bits
-} block_q5_K;
-// 176 bytes / block
-#endif
-
-typedef struct {
-    uint8_t ql[QK_K/2];      // quants, lower 4 bits
-    uint8_t qh[QK_K/4];      // quants, upper 2 bits
-    int8_t  scales[QK_K/16]; // scales, quantized with 8 bits
-    half d;                  // super-block scale
-} block_q6_K;
-// 210 bytes / block
-
-typedef struct {
-    half d;
-    uint16_t qs[QK_K/8];
-} block_iq2_xxs;
-// 66 bytes / block for QK_K = 256, so 2.0625 bpw
-
-typedef struct {
-    half d;
-    uint16_t qs[QK_K/8];
-    uint8_t  scales[QK_K/32];
-} block_iq2_xs;
-// 74 bytes / block for QK_K = 256, so 2.3125 bpw
-
-typedef struct {
-    half d;
-    uint8_t qs[3*QK_K/8];
-} block_iq3_xxs;
-// 98 bytes / block for QK_K = 256, so 3.0625 bpw
-
-typedef struct {
-    half d;
-    uint8_t qs[QK_K/8];
-    uint8_t scales[QK_K/16];
-} block_iq1_s;
-
-
-//====================================== dot products =========================
-
 void kernel_mul_mv_q2_K_f32_impl(
         device const  void * src0,
         device const float * src1,
         device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant   int64_t & ne10,
-        constant   int64_t & ne12,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   uint    & r2,
-        constant   uint    & r3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint  tiisg[[thread_index_in_simdgroup]],
-        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+                   int64_t   ne00,
+                   int64_t   ne01,
+                   int64_t   ne02,
+                   int64_t   ne10,
+                   int64_t   ne12,
+                   int64_t   ne0,
+                   int64_t   ne1,
+                   uint      r2,
+                   uint      r3,
+        threadgroup int8_t * shared_values,
+                   uint3     tgpig,
+                   uint      tiisg,
+                   uint      sgitg) {
 
     const int nb = ne00/QK_K;
     const int r0 = tgpig.x;
@@ -2709,7 +3530,7 @@ kernel void kernel_mul_mv_q2_K_f32(
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
 
-    kernel_mul_mv_q2_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg);
+    kernel_mul_mv_q2_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg);
 }
 
 #if QK_K == 256
@@ -2717,18 +3538,19 @@ void kernel_mul_mv_q3_K_f32_impl(
         device const  void * src0,
         device const float * src1,
         device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant   int64_t & ne10,
-        constant   int64_t & ne12,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   uint    & r2,
-        constant   uint    & r3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint  tiisg[[thread_index_in_simdgroup]],
-        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+                   int64_t   ne00,
+                   int64_t   ne01,
+                   int64_t   ne02,
+                   int64_t   ne10,
+                   int64_t   ne12,
+                   int64_t   ne0,
+                   int64_t   ne1,
+                   uint      r2,
+                   uint      r3,
+        threadgroup int8_t * shared_values,
+                   uint3     tgpig,
+                   uint      tiisg,
+                   uint      sgitg) {
 
     const int nb = ne00/QK_K;
 
@@ -2884,6 +3706,7 @@ void kernel_mul_mv_q3_K_f32_impl(
         constant   int64_t & ne1,
         constant   uint    & r2,
         constant   uint    & r3,
+        threadgroup int8_t * shared_values [[threadgroup(0)]],
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -2973,7 +3796,7 @@ kernel void kernel_mul_mv_q3_K_f32(
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
 
-    kernel_mul_mv_q3_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg);
+    kernel_mul_mv_q3_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg);
 }
 
 #if QK_K == 256
@@ -2981,18 +3804,19 @@ void kernel_mul_mv_q4_K_f32_impl(
         device const  void * src0,
         device const float * src1,
         device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant   int64_t & ne10,
-        constant   int64_t & ne12,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   uint    & r2,
-        constant   uint    & r3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint  tiisg[[thread_index_in_simdgroup]],
-        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+                   int64_t   ne00,
+                   int64_t   ne01,
+                   int64_t   ne02,
+                   int64_t   ne10,
+                   int64_t   ne12,
+                   int64_t   ne0,
+                   int64_t   ne1,
+                   uint      r2,
+                   uint      r3,
+        threadgroup int8_t * shared_values,
+                   uint3     tgpig,
+                   uint      tiisg,
+                   uint      sgitg) {
 
     const uint16_t kmask1 = 0x3f3f;
     const uint16_t kmask2 = 0x0f0f;
@@ -3103,6 +3927,7 @@ void kernel_mul_mv_q4_K_f32_impl(
         constant   int64_t & ne1,
         constant   uint    & r2,
         constant   uint    & r3,
+        threadgroup int8_t * shared_values [[threadgroup(0)]],
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -3211,25 +4036,26 @@ kernel void kernel_mul_mv_q4_K_f32(
         uint tiisg[[thread_index_in_simdgroup]],
         uint sgitg[[simdgroup_index_in_threadgroup]]) {
 
-    kernel_mul_mv_q4_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg);
+    kernel_mul_mv_q4_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg);
 }
 
 void kernel_mul_mv_q5_K_f32_impl(
         device const  void * src0,
         device const float * src1,
         device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant   int64_t & ne10,
-        constant   int64_t & ne12,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   uint    & r2,
-        constant   uint    & r3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint  tiisg[[thread_index_in_simdgroup]],
-        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+                   int64_t   ne00,
+                   int64_t   ne01,
+                   int64_t   ne02,
+                   int64_t   ne10,
+                   int64_t   ne12,
+                   int64_t   ne0,
+                   int64_t   ne1,
+                   uint      r2,
+                   uint      r3,
+        threadgroup int8_t * shared_values,
+                   uint3     tgpig,
+                   uint      tiisg,
+                   uint      sgitg) {
 
     const int nb = ne00/QK_K;
 
@@ -3417,25 +4243,26 @@ kernel void kernel_mul_mv_q5_K_f32(
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
 
-    kernel_mul_mv_q5_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg);
+    kernel_mul_mv_q5_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg);
 }
 
 void kernel_mul_mv_q6_K_f32_impl(
         device const  void * src0,
         device const float * src1,
         device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant   int64_t & ne10,
-        constant   int64_t & ne12,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   uint    & r2,
-        constant   uint    & r3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint  tiisg[[thread_index_in_simdgroup]],
-        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+                   int64_t   ne00,
+                   int64_t   ne01,
+                   int64_t   ne02,
+                   int64_t   ne10,
+                   int64_t   ne12,
+                   int64_t   ne0,
+                   int64_t   ne1,
+                   uint      r2,
+                   uint      r3,
+        threadgroup int8_t * shared_values,
+                   uint3     tgpig,
+                   uint      tiisg,
+                   uint      sgitg) {
 
     const uint8_t kmask1 = 0x03;
     const uint8_t kmask2 = 0x0C;
@@ -3551,406 +4378,28 @@ kernel void kernel_mul_mv_q6_K_f32(
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
 
-    kernel_mul_mv_q6_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg);
+    kernel_mul_mv_q6_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg);
 }
 
 // ======================= "True" 2-bit
 
-constexpr constant static uint64_t iq2xxs_grid[256] = {
-    0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08,
-    0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808,
-    0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819,
-    0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, 0x08080808192b0819,
-    0x08080808192b1908, 0x080808082b080808, 0x080808082b08082b, 0x080808082b082b2b,
-    0x080808082b2b082b, 0x0808081908080819, 0x0808081908081908, 0x0808081908190808,
-    0x0808081908191919, 0x0808081919080808, 0x080808192b081908, 0x080808192b192b08,
-    0x0808082b08080808, 0x0808082b0808082b, 0x0808082b082b082b, 0x0808082b2b08082b,
-    0x0808190808080819, 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819,
-    0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, 0x0808190819082b08,
-    0x08081908192b0808, 0x080819082b080819, 0x080819082b081908, 0x080819082b190808,
-    0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08,
-    0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, 0x080819192b080808,
-    0x080819192b190819, 0x0808192b08082b19, 0x0808192b08190808, 0x0808192b19080808,
-    0x0808192b2b081908, 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919,
-    0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, 0x08082b0819080819,
-    0x08082b0819081908, 0x08082b0819190808, 0x08082b081919082b, 0x08082b082b082b08,
-    0x08082b1908081908, 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908,
-    0x0819080808080819, 0x0819080808081908, 0x0819080808190808, 0x08190808082b0819,
-    0x0819080819080808, 0x08190808192b0808, 0x081908082b081908, 0x081908082b190808,
-    0x081908082b191919, 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808,
-    0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, 0x0819082b082b1908,
-    0x0819082b19081919, 0x0819190808080808, 0x0819190808082b08, 0x08191908082b0808,
-    0x08191908082b1919, 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08,
-    0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, 0x08192b0808080819,
-    0x08192b0808081908, 0x08192b0808190808, 0x08192b0819080808, 0x08192b082b080819,
-    0x08192b1908080808, 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819,
-    0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, 0x082b080819081908,
-    0x082b0808192b0819, 0x082b08082b080808, 0x082b08082b08082b, 0x082b0819082b2b19,
-    0x082b081919082b08, 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819,
-    0x082b190808081908, 0x082b190808190808, 0x082b190819080808, 0x082b19081919192b,
-    0x082b191908080808, 0x082b191919080819, 0x082b1919192b1908, 0x082b192b2b190808,
-    0x082b2b0808082b08, 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908,
-    0x1908080808080819, 0x1908080808081908, 0x1908080808190808, 0x1908080808192b08,
-    0x19080808082b0819, 0x19080808082b1908, 0x1908080819080808, 0x1908080819082b08,
-    0x190808081919192b, 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908,
-    0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, 0x19080819192b0819,
-    0x190808192b080808, 0x190808192b081919, 0x1908082b08080819, 0x1908082b08190808,
-    0x1908082b19082b08, 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808,
-    0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, 0x190819082b192b19,
-    0x190819190819082b, 0x19081919082b1908, 0x1908192b08080808, 0x19082b0808080819,
-    0x19082b0808081908, 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919,
-    0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, 0x19082b192b08082b,
-    0x19082b2b19081919, 0x19082b2b2b190808, 0x1919080808080808, 0x1919080808082b08,
-    0x1919080808190819, 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808,
-    0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, 0x191908192b2b1908,
-    0x1919082b2b190819, 0x191919082b190808, 0x191919082b19082b, 0x1919191908082b2b,
-    0x1919192b08080819, 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819,
-    0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, 0x19192b2b08082b08,
-    0x192b080808081908, 0x192b080808190808, 0x192b080819080808, 0x192b0808192b2b08,
-    0x192b081908080808, 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808,
-    0x192b190808080808, 0x192b190808081919, 0x192b191908190808, 0x192b19190819082b,
-    0x192b19192b081908, 0x192b2b081908082b, 0x2b08080808080808, 0x2b0808080808082b,
-    0x2b08080808082b2b, 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908,
-    0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819,
-    0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808,
-    0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908,
-    0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b,
-    0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808,
-    0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b,
-    0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b,
-    0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808,
-    0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19,
-    0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908,
-};
-
-constexpr constant static uint64_t iq2xs_grid[512] = {
-    0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08,
-    0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b,
-    0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919,
-    0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b,
-    0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919,
-    0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808,
-    0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, 0x080808082b190819,
-    0x080808082b191908, 0x080808082b192b19, 0x080808082b2b0808, 0x0808081908080819,
-    0x0808081908081908, 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808,
-    0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, 0x0808081908192b2b,
-    0x08080819082b0819, 0x08080819082b1908, 0x0808081919080808, 0x080808191908082b,
-    0x0808081919081919, 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908,
-    0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, 0x080808192b081908,
-    0x080808192b190808, 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b08081919,
-    0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808,
-    0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, 0x0808082b19191919,
-    0x0808082b2b080808, 0x0808082b2b082b2b, 0x0808190808080819, 0x0808190808081908,
-    0x080819080808192b, 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b,
-    0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908,
-    0x0808190819080808, 0x080819081908082b, 0x0808190819081919, 0x0808190819082b08,
-    0x0808190819190819, 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808,
-    0x080819082b080819, 0x080819082b081908, 0x080819082b190808, 0x0808191908080808,
-    0x080819190808082b, 0x0808191908081919, 0x0808191908082b08, 0x0808191908190819,
-    0x0808191908191908, 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908,
-    0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, 0x0808192b08080819,
-    0x0808192b08081908, 0x0808192b08190808, 0x0808192b082b192b, 0x0808192b19080808,
-    0x0808192b1908082b, 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b,
-    0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, 0x08082b0808190819,
-    0x08082b0808191908, 0x08082b08082b0808, 0x08082b08082b1919, 0x08082b0819080819,
-    0x08082b0819081908, 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808,
-    0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, 0x08082b1908081908,
-    0x08082b1908190808, 0x08082b1919080808, 0x08082b192b080819, 0x08082b192b082b19,
-    0x08082b2b08080808, 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b,
-    0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, 0x081908080808192b,
-    0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, 0x0819080808191919,
-    0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808,
-    0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, 0x0819080819190819,
-    0x0819080819191908, 0x08190808192b0808, 0x08190808192b2b2b, 0x081908082b080819,
-    0x081908082b081908, 0x081908082b190808, 0x0819081908080808, 0x081908190808082b,
-    0x0819081908081919, 0x0819081908082b08, 0x0819081908190819, 0x0819081908191908,
-    0x08190819082b0808, 0x0819081919080819, 0x0819081919081908, 0x0819081919190808,
-    0x081908192b080808, 0x081908192b191908, 0x081908192b19192b, 0x0819082b08080819,
-    0x0819082b08081908, 0x0819082b0808192b, 0x0819082b08190808, 0x0819082b19080808,
-    0x0819082b192b0808, 0x0819190808080808, 0x081919080808082b, 0x0819190808081919,
-    0x0819190808082b08, 0x0819190808190819, 0x0819190808191908, 0x08191908082b0808,
-    0x0819190819080819, 0x0819190819081908, 0x0819190819082b19, 0x0819190819190808,
-    0x08191908192b1908, 0x081919082b080808, 0x0819191908080819, 0x0819191908081908,
-    0x0819191908190808, 0x0819191919080808, 0x0819192b08080808, 0x0819192b08191908,
-    0x0819192b19082b19, 0x08192b0808080819, 0x08192b0808081908, 0x08192b0808190808,
-    0x08192b080819082b, 0x08192b0819080808, 0x08192b0819191908, 0x08192b082b08192b,
-    0x08192b1908080808, 0x08192b1908081919, 0x08192b19192b192b, 0x08192b2b19190819,
-    0x08192b2b2b2b2b19, 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919,
-    0x082b080808082b08, 0x082b080808082b2b, 0x082b080808190819, 0x082b080808191908,
-    0x082b0808082b0808, 0x082b080819080819, 0x082b080819081908, 0x082b080819190808,
-    0x082b08082b080808, 0x082b08082b2b0808, 0x082b081908080819, 0x082b081908081908,
-    0x082b081908190808, 0x082b081919080808, 0x082b081919082b08, 0x082b0819192b1919,
-    0x082b082b08080808, 0x082b082b082b082b, 0x082b082b2b080808, 0x082b082b2b2b2b08,
-    0x082b190808080819, 0x082b190808081908, 0x082b190808190808, 0x082b1908082b2b19,
-    0x082b190819080808, 0x082b191908080808, 0x082b191919080819, 0x082b19191919082b,
-    0x082b19192b192b19, 0x082b192b08080819, 0x082b192b08192b2b, 0x082b192b2b2b192b,
-    0x082b2b0808080808, 0x082b2b0808082b08, 0x082b2b0808082b2b, 0x082b2b08082b0808,
-    0x082b2b0819191919, 0x082b2b082b082b08, 0x082b2b082b2b082b, 0x082b2b19192b2b08,
-    0x082b2b192b190808, 0x082b2b2b08082b08, 0x082b2b2b082b0808, 0x082b2b2b2b08082b,
-    0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, 0x1908080808081908,
-    0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, 0x190808080819082b,
-    0x1908080808191919, 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908,
-    0x1908080819080808, 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08,
-    0x1908080819082b2b, 0x1908080819190819, 0x1908080819191908, 0x19080808192b0808,
-    0x19080808192b1919, 0x190808082b080819, 0x190808082b081908, 0x190808082b190808,
-    0x1908081908080808, 0x190808190808082b, 0x1908081908081919, 0x1908081908082b08,
-    0x1908081908190819, 0x1908081908191908, 0x19080819082b0808, 0x1908081919080819,
-    0x1908081919081908, 0x1908081919190808, 0x190808192b080808, 0x190808192b081919,
-    0x190808192b2b082b, 0x1908082b08080819, 0x1908082b08081908, 0x1908082b08190808,
-    0x1908082b0819082b, 0x1908082b082b2b19, 0x1908082b19080808, 0x1908190808080808,
-    0x190819080808082b, 0x1908190808081919, 0x1908190808082b08, 0x1908190808190819,
-    0x1908190808191908, 0x1908190808192b19, 0x19081908082b0808, 0x1908190819080819,
-    0x1908190819081908, 0x1908190819190808, 0x190819082b080808, 0x190819082b191908,
-    0x1908191908080819, 0x1908191908081908, 0x1908191908190808, 0x19081919082b1908,
-    0x1908191919080808, 0x190819192b192b2b, 0x1908192b08080808, 0x1908192b08082b2b,
-    0x1908192b19081908, 0x1908192b19190808, 0x19082b0808080819, 0x19082b0808081908,
-    0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, 0x19082b0819191908,
-    0x19082b08192b082b, 0x19082b1908080808, 0x19082b1908190819, 0x19082b1919081908,
-    0x19082b1919190808, 0x19082b19192b2b19, 0x19082b2b08081908, 0x1919080808080808,
-    0x191908080808082b, 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819,
-    0x1919080808191908, 0x19190808082b0808, 0x19190808082b2b08, 0x1919080819080819,
-    0x1919080819081908, 0x1919080819190808, 0x191908082b080808, 0x1919081908080819,
-    0x1919081908081908, 0x1919081908190808, 0x1919081908191919, 0x1919081919080808,
-    0x191908191908082b, 0x1919082b08080808, 0x1919082b19081908, 0x1919082b2b2b2b2b,
-    0x1919190808080819, 0x1919190808081908, 0x1919190808190808, 0x19191908082b0819,
-    0x1919190819080808, 0x19191908192b0808, 0x191919082b080819, 0x191919082b2b0819,
-    0x1919191908080808, 0x1919191908082b08, 0x191919192b080808, 0x191919192b082b08,
-    0x1919192b082b0819, 0x1919192b192b2b08, 0x1919192b2b2b0819, 0x19192b0808080808,
-    0x19192b0808191908, 0x19192b0819080819, 0x19192b0819190808, 0x19192b082b192b19,
-    0x19192b1908192b2b, 0x19192b1919080808, 0x19192b191908082b, 0x19192b2b2b081919,
-    0x192b080808080819, 0x192b080808081908, 0x192b080808190808, 0x192b080819080808,
-    0x192b080819191908, 0x192b0808192b082b, 0x192b08082b08192b, 0x192b08082b2b2b19,
-    0x192b081908080808, 0x192b082b082b1908, 0x192b082b19082b2b, 0x192b082b2b19082b,
-    0x192b190808080808, 0x192b19080819192b, 0x192b191908190808, 0x192b191919080808,
-    0x192b191919081919, 0x192b19192b2b1908, 0x192b2b0808080819, 0x192b2b08192b2b2b,
-    0x192b2b19082b1919, 0x192b2b2b0808192b, 0x192b2b2b19191908, 0x192b2b2b192b082b,
-    0x2b08080808080808, 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08,
-    0x2b08080808190819, 0x2b08080808191908, 0x2b080808082b0808, 0x2b080808082b2b2b,
-    0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808082b080808,
-    0x2b0808082b08082b, 0x2b0808082b2b2b08, 0x2b0808082b2b2b2b, 0x2b08081908080819,
-    0x2b08081908081908, 0x2b0808190808192b, 0x2b08081908190808, 0x2b08081919080808,
-    0x2b08081919190819, 0x2b08081919192b19, 0x2b08082b08080808, 0x2b08082b082b0808,
-    0x2b08082b2b080808, 0x2b08082b2b08082b, 0x2b08082b2b2b0808, 0x2b08082b2b2b2b08,
-    0x2b08190808080819, 0x2b08190808081908, 0x2b08190808190808, 0x2b0819080819082b,
-    0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, 0x2b0819082b082b19,
-    0x2b08191908080808, 0x2b08191919081908, 0x2b0819192b2b1919, 0x2b08192b08192b08,
-    0x2b08192b192b2b2b, 0x2b082b0808080808, 0x2b082b0808082b08, 0x2b082b08082b1919,
-    0x2b082b0819192b2b, 0x2b082b082b080808, 0x2b082b082b08082b, 0x2b082b082b2b2b08,
-    0x2b082b190808192b, 0x2b082b2b082b082b, 0x2b082b2b2b080808, 0x2b082b2b2b082b08,
-    0x2b082b2b2b19192b, 0x2b082b2b2b2b2b08, 0x2b19080808080819, 0x2b19080808081908,
-    0x2b19080808190808, 0x2b19080819080808, 0x2b1908081919192b, 0x2b1908082b081908,
-    0x2b19081908080808, 0x2b190819082b082b, 0x2b190819192b1908, 0x2b19082b1919192b,
-    0x2b19082b2b082b19, 0x2b19190808080808, 0x2b19190808081919, 0x2b19190819081908,
-    0x2b19190819190808, 0x2b19190819192b08, 0x2b191919082b2b19, 0x2b1919192b190808,
-    0x2b1919192b19082b, 0x2b19192b19080819, 0x2b192b0819190819, 0x2b192b082b2b192b,
-    0x2b192b1919082b19, 0x2b192b2b08191919, 0x2b192b2b192b0808, 0x2b2b080808080808,
-    0x2b2b08080808082b, 0x2b2b080808082b08, 0x2b2b080808082b2b, 0x2b2b0808082b0808,
-    0x2b2b0808082b2b2b, 0x2b2b08082b2b0808, 0x2b2b081919190819, 0x2b2b081919192b19,
-    0x2b2b08192b2b192b, 0x2b2b082b08080808, 0x2b2b082b0808082b, 0x2b2b082b08082b08,
-    0x2b2b082b082b2b2b, 0x2b2b082b2b080808, 0x2b2b082b2b2b0808, 0x2b2b190819080808,
-    0x2b2b19082b191919, 0x2b2b192b192b1919, 0x2b2b192b2b192b08, 0x2b2b2b0808082b2b,
-    0x2b2b2b08082b0808, 0x2b2b2b08082b082b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b0808,
-    0x2b2b2b082b2b2b08, 0x2b2b2b1908081908, 0x2b2b2b192b081908, 0x2b2b2b192b08192b,
-    0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b,
-};
-
-constexpr constant static uint32_t iq3xxs_grid[256] = {
-    0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414,
-    0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14,
-    0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404,
-    0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e,
-    0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c,
-    0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c,
-    0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34,
-    0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c,
-    0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c,
-    0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04,
-    0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c,
-    0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414,
-    0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434,
-    0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c,
-    0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e,
-    0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24,
-    0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24,
-    0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c,
-    0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c,
-    0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14,
-    0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414,
-    0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e,
-    0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404,
-    0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c,
-    0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c,
-    0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14,
-    0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c,
-    0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c,
-    0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14,
-    0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14,
-    0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c,
-    0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04,
-};
-
-#define NGRID_IQ1S 512
-constexpr constant static uint64_t iq1s_grid[NGRID_IQ1S] = {
-    0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000,
-    0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01,
-    0xffffff00ff000000, 0xffffff000000ff00, 0xffffff00000000ff, 0xffffff0000000100,
-    0xffffff0000010000, 0xffffff0001000000, 0xffffff01ffff00ff, 0xffffff01ff01ff00,
-    0xffffff01ff010100, 0xffffff0100000001, 0xffffff0101ffff00, 0xffffff0101ff0101,
-    0xffffff0101010100, 0xffff00ffff00ff01, 0xffff00ffff0000ff, 0xffff00ff00ff0100,
-    0xffff00ff0100ff00, 0xffff00ff010001ff, 0xffff0000ff0101ff, 0xffff000000ffff00,
-    0xffff000000000000, 0xffff00000001ff01, 0xffff000001000101, 0xffff0000010100ff,
-    0xffff0001ffff0100, 0xffff00010000ff00, 0xffff000100010101, 0xffff000101000000,
-    0xffff01ffffff0000, 0xffff01ffff01ffff, 0xffff01ffff010100, 0xffff01ff00000000,
-    0xffff01ff01ffffff, 0xffff01ff01ff0001, 0xffff01ff0101ffff, 0xffff01ff01010001,
-    0xffff0100ffffff01, 0xffff01000000ffff, 0xffff010000000100, 0xffff010001ff01ff,
-    0xffff010001000000, 0xffff0101ff000000, 0xffff0101000101ff, 0xffff010101ffff01,
-    0xffff01010101ff00, 0xff00ffffff000000, 0xff00ffff00ffff00, 0xff00ffff00000001,
-    0xff00ffff000001ff, 0xff00ffff01010000, 0xff00ff00ffff0000, 0xff00ff00ff00ff00,
-    0xff00ff00ff0000ff, 0xff00ff00ff000100, 0xff00ff00ff010001, 0xff00ff0000ff0001,
-    0xff00ff000000ffff, 0xff00ff0000000000, 0xff00ff000001ff00, 0xff00ff0000010100,
-    0xff00ff0001ff0000, 0xff00ff000100ff00, 0xff00ff0001000100, 0xff00ff01ff000000,
-    0xff00ff0100ff0000, 0xff00ff01000001ff, 0xff00ff0101010001, 0xff0000ff00000000,
-    0xff0000ff0001ff00, 0xff0000ff00010100, 0xff000000ffff0101, 0xff000000ff000000,
-    0xff000000ff01ff00, 0xff00000000ff0000, 0xff0000000000ff00, 0xff000000000000ff,
-    0xff00000000000000, 0xff00000000000001, 0xff00000000000100, 0xff0000000001ffff,
-    0xff00000000010000, 0xff00000001000000, 0xff00000001010100, 0xff000001ff00ff01,
-    0xff000001ff0100ff, 0xff00000100000000, 0xff0000010001ff00, 0xff00000101ff0100,
-    0xff0000010100ff00, 0xff0001ff00ff00ff, 0xff0001ff00000101, 0xff0001ff000100ff,
-    0xff0001ff01000000, 0xff000100ff0001ff, 0xff0001000000ff01, 0xff00010000000000,
-    0xff00010000010001, 0xff00010000010100, 0xff00010001ffff00, 0xff00010001ff0101,
-    0xff00010001010000, 0xff000101ffffffff, 0xff000101ff000101, 0xff00010101ff00ff,
-    0xff00010101000001, 0xff000101010100ff, 0xff01ffffff000101, 0xff01ffffff01ffff,
-    0xff01ffffff01ff01, 0xff01ffffff0101ff, 0xff01ffff00000000, 0xff01ffff01ff0001,
-    0xff01ffff0101ff01, 0xff01ff00ff000000, 0xff01ff0000ff0100, 0xff01ff000000ff01,
-    0xff01ff0000010000, 0xff01ff00010000ff, 0xff01ff01ff01ff00, 0xff01ff0100000101,
-    0xff0100ffffff0000, 0xff0100ffff010000, 0xff0100ff01ff00ff, 0xff0100ff01000100,
-    0xff0100ff010100ff, 0xff010000ffffff01, 0xff01000000000000, 0xff0100000101ff00,
-    0xff010001ffff00ff, 0xff010001ff000100, 0xff01000100ffff00, 0xff01000100010001,
-    0xff01000101ff0001, 0xff010001010001ff, 0xff0101ffffffffff, 0xff0101ffff01ffff,
-    0xff0101ffff010101, 0xff0101ff0000ff00, 0xff0101ff01010001, 0xff010100ff000000,
-    0xff010100ff01ff01, 0xff01010000ff0001, 0xff01010000000100, 0xff01010001000000,
-    0xff0101010100ffff, 0x00ffffff0000ff01, 0x00ffffff000000ff, 0x00ffffff00000100,
-    0x00ffffff00010000, 0x00ffff00ffff0001, 0x00ffff00ff0000ff, 0x00ffff00ff000100,
-    0x00ffff0000000000, 0x00ffff0001000100, 0x00ffff0001010001, 0x00ffff01ff00ff01,
-    0x00ffff0100ff0100, 0x00ffff010000ff00, 0x00ffff01000100ff, 0x00ffff0101ff00ff,
-    0x00ffff010101ff00, 0x00ff00ffffffffff, 0x00ff00ffffff01ff, 0x00ff00ffff000101,
-    0x00ff00ff00000000, 0x00ff00ff000101ff, 0x00ff00ff01010101, 0x00ff0000ff000000,
-    0x00ff0000ff01ffff, 0x00ff000000ff0000, 0x00ff00000000ff00, 0x00ff0000000000ff,
-    0x00ff000000000000, 0x00ff000000000001, 0x00ff000000000100, 0x00ff000000010000,
-    0x00ff000001ffff01, 0x00ff000001000000, 0x00ff0001ff000101, 0x00ff000100ffffff,
-    0x00ff000100000000, 0x00ff0001010001ff, 0x00ff01ffff000000, 0x00ff01ff0001ff00,
-    0x00ff01ff01ff0100, 0x00ff0100ff01ff01, 0x00ff010000ff00ff, 0x00ff010000ff0101,
-    0x00ff010000000000, 0x00ff010000010101, 0x00ff01000100ff00, 0x00ff010001010000,
-    0x00ff0101ffffff00, 0x00ff01010000ff01, 0x00ff010100000100, 0x00ff010101ff0000,
-    0x0000ffffffff0100, 0x0000ffffff00ff00, 0x0000ffffff0000ff, 0x0000ffffff010000,
-    0x0000ffff00000000, 0x0000ffff00010101, 0x0000ffff01ffff01, 0x0000ffff01000100,
-    0x0000ff00ff000000, 0x0000ff00ff01ff00, 0x0000ff00ff0101ff, 0x0000ff0000ff0000,
-    0x0000ff000000ff00, 0x0000ff00000000ff, 0x0000ff0000000000, 0x0000ff0000000001,
-    0x0000ff0000000100, 0x0000ff0000010000, 0x0000ff0001ffffff, 0x0000ff0001ff01ff,
-    0x0000ff0001000000, 0x0000ff000101ffff, 0x0000ff01ffff0101, 0x0000ff01ff010000,
-    0x0000ff0100000000, 0x0000ff0101000101, 0x000000ffffff0001, 0x000000ffff000000,
-    0x000000ff00ff0000, 0x000000ff0000ff00, 0x000000ff000000ff, 0x000000ff00000000,
-    0x000000ff00000001, 0x000000ff00000100, 0x000000ff00010000, 0x000000ff01000000,
-    0x000000ff0101ff00, 0x00000000ffff0000, 0x00000000ff00ff00, 0x00000000ff0000ff,
-    0x00000000ff000000, 0x00000000ff000001, 0x00000000ff000100, 0x00000000ff010000,
-    0x0000000000ffff00, 0x0000000000ff00ff, 0x0000000000ff0000, 0x0000000000ff0001,
-    0x0000000000ff0100, 0x000000000000ffff, 0x000000000000ff00, 0x000000000000ff01,
-    0x00000000000000ff, 0x0000000000000001, 0x00000000000001ff, 0x0000000000000100,
-    0x0000000000000101, 0x000000000001ff00, 0x00000000000100ff, 0x0000000000010000,
-    0x0000000000010001, 0x0000000000010100, 0x0000000001ff0000, 0x000000000100ff00,
-    0x00000000010000ff, 0x0000000001000000, 0x0000000001000001, 0x0000000001000100,
-    0x0000000001010000, 0x00000001ffff01ff, 0x00000001ff000000, 0x0000000100ff0000,
-    0x000000010000ff00, 0x00000001000000ff, 0x0000000100000000, 0x0000000100000001,
-    0x0000000100000100, 0x0000000100010000, 0x0000000101000000, 0x000001ffff00ff00,
-    0x000001ffff010001, 0x000001ffff0101ff, 0x000001ff00ffff01, 0x000001ff0000ffff,
-    0x000001ff00000000, 0x000001ff010000ff, 0x000001ff01010100, 0x00000100ffff0100,
-    0x00000100ff000000, 0x0000010000ff0000, 0x000001000000ff00, 0x00000100000000ff,
-    0x0000010000000000, 0x0000010000000001, 0x0000010000000100, 0x0000010000010000,
-    0x0000010001000000, 0x000001000101ff01, 0x00000101ffff0001, 0x00000101ff01ffff,
-    0x0000010100000000, 0x0000010101010100, 0x0001ffffff000000, 0x0001ffff00ffffff,
-    0x0001ffff00000100, 0x0001ffff0001ff00, 0x0001ffff01000000, 0x0001ff00ffffff00,
-    0x0001ff00ffff01ff, 0x0001ff00ff010000, 0x0001ff0000000000, 0x0001ff0000010001,
-    0x0001ff0001ff0000, 0x0001ff0001010100, 0x0001ff01ff0000ff, 0x0001ff01ff000001,
-    0x0001ff0100ffffff, 0x0001ff010001ffff, 0x0001ff01000101ff, 0x0001ff010100ff01,
-    0x000100ffff00ffff, 0x000100ffff00ff01, 0x000100ffff000100, 0x000100ff00000000,
-    0x000100ff000101ff, 0x000100ff01ff0101, 0x000100ff0100ffff, 0x000100ff01010101,
-    0x00010000ff000000, 0x00010000ff010100, 0x0001000000ff0000, 0x000100000000ff00,
-    0x00010000000000ff, 0x0001000000000000, 0x0001000000000001, 0x0001000000000100,
-    0x0001000000010000, 0x0001000001ffff01, 0x0001000001000000, 0x0001000100ff0101,
-    0x0001000100000000, 0x00010001010100ff, 0x000101ffffff01ff, 0x000101ffffff0101,
-    0x000101ff00010000, 0x000101ff01ff0000, 0x000101ff0100ff01, 0x00010100ffff0000,
-    0x0001010000000000, 0x000101000001ffff, 0x0001010000010101, 0x00010100010001ff,
-    0x00010101ff00ff00, 0x00010101ff010001, 0x0001010100ffffff, 0x0001010100ff01ff,
-    0x00010101000101ff, 0x0001010101ff0000, 0x000101010100ff01, 0x0001010101000101,
-    0x01ffffffffff0101, 0x01ffffffff01ffff, 0x01ffffffff01ff01, 0x01ffffffff0101ff,
-    0x01ffffffff010101, 0x01ffffff00000000, 0x01ffffff01ff01ff, 0x01ffffff01000101,
-    0x01ffffff0101ff01, 0x01ffffff010100ff, 0x01ffff000000ff00, 0x01ffff0000000001,
-    0x01ffff00000001ff, 0x01ffff0000010000, 0x01ffff0001ff0000, 0x01ffff01ffffffff,
-    0x01ffff01ffff01ff, 0x01ffff01ff000000, 0x01ffff01ff01ffff, 0x01ffff01ff0101ff,
-    0x01ffff010100ffff, 0x01ff00ffffff0000, 0x01ff00ffff010000, 0x01ff00ff00ffff01,
-    0x01ff0000ff0000ff, 0x01ff000000000000, 0x01ff00000001ff01, 0x01ff000001ffffff,
-    0x01ff000001010100, 0x01ff0001ffffff01, 0x01ff0001ff010001, 0x01ff000101ff0100,
-    0x01ff000101000001, 0x01ff0001010100ff, 0x01ff01ffff00ffff, 0x01ff01ff00010001,
-    0x01ff01ff01000000, 0x01ff01ff010101ff, 0x01ff0100ff000001, 0x01ff010000ffff00,
-    0x01ff010000000100, 0x01ff010001ff01ff, 0x01ff01000101ffff, 0x01ff0101ffff00ff,
-    0x01ff0101ffff0101, 0x01ff0101ff0101ff, 0x01ff010100010000, 0x0100ffff00ff00ff,
-    0x0100ffff00ff0001, 0x0100ffff00000100, 0x0100ffff0100ff00, 0x0100ff00ffff0000,
-    0x0100ff00ff00ffff, 0x0100ff00ff00ff01, 0x0100ff00ff000100, 0x0100ff00ff010000,
-    0x0100ff0000000000, 0x0100ff00000100ff, 0x0100ff0001ff0101, 0x0100ff0001010101,
-    0x0100ff0100ff00ff, 0x0100ff0100ff0001, 0x0100ff0100000100, 0x0100ff0100010001,
-    0x0100ff0101000000, 0x010000ffff00ff00, 0x010000ff0000ffff, 0x010000ff00000000,
-    0x010000ff010001ff, 0x010000ff01010001, 0x01000000ffffff00, 0x01000000ffff0101,
-    0x01000000ff000000, 0x01000000ff0100ff, 0x01000000ff010101, 0x0100000000ff0000,
-    0x010000000000ff00, 0x01000000000000ff, 0x0100000000000000, 0x0100000000000001,
-    0x0100000000000100, 0x0100000000010000, 0x0100000001000000, 0x0100000100000000,
-    0x01000001000101ff, 0x0100000101ffff01, 0x010001ffff000101, 0x010001ff00ff0100,
-    0x010001ff0000ff00, 0x010001ff000100ff, 0x010001ff01ffffff, 0x01000100ffff0000,
-    0x01000100ff0001ff, 0x0100010000000000, 0x010001000001ff00, 0x0100010001ff0000,
-    0x01000100010000ff, 0x0100010001000101, 0x01000101ff00ff01, 0x0100010100ff0100,
-    0x010001010000ffff, 0x0100010101010001, 0x0101ffffffff0101, 0x0101ffffff0001ff,
-    0x0101ffffff01ffff, 0x0101ffffff010101, 0x0101ffff00000000, 0x0101ffff0101ffff,
-    0x0101ffff010101ff, 0x0101ff00ff000000, 0x0101ff0000ff0100, 0x0101ff000000ff00,
-    0x0101ff0000010000, 0x0101ff00010000ff, 0x0101ff0001000001, 0x0101ff01ff010101,
-    0x0101ff0100000000, 0x0101ff010101ff00, 0x010100ffffff0000, 0x010100ffff010000,
-    0x010100ff00ff01ff, 0x010100ff000000ff, 0x010100ff00000101, 0x010100ff01ffff00,
-    0x01010000ffffff01, 0x01010000ff000100, 0x01010000ff01ff01, 0x0101000000000000,
-    0x01010000000100ff, 0x010100000101ff01, 0x01010001ffff0000, 0x01010001ff00ffff,
-    0x01010001ff010000, 0x0101000101ffffff, 0x0101000101ff01ff, 0x0101000101010101,
-    0x010101ffff01ffff, 0x010101ff00000000, 0x010101ff0001ff01, 0x010101ff0101ffff,
-    0x010101ff010101ff, 0x01010100ffffffff, 0x01010100ff000001, 0x010101000000ff00,
-    0x0101010001010000, 0x0101010100ff0001, 0x010101010001ff01, 0x010101010101ffff,
-};
-
-constexpr constant static uint8_t ksigns_iq2xs[128] = {
-      0, 129, 130,   3, 132,   5,   6, 135, 136,   9,  10, 139,  12, 141, 142,  15,
-    144,  17,  18, 147,  20, 149, 150,  23,  24, 153, 154,  27, 156,  29,  30, 159,
-    160,  33,  34, 163,  36, 165, 166,  39,  40, 169, 170,  43, 172,  45,  46, 175,
-     48, 177, 178,  51, 180,  53,  54, 183, 184,  57,  58, 187,  60, 189, 190,  63,
-    192,  65,  66, 195,  68, 197, 198,  71,  72, 201, 202,  75, 204,  77,  78, 207,
-     80, 209, 210,  83, 212,  85,  86, 215, 216,  89,  90, 219,  92, 221, 222,  95,
-     96, 225, 226,  99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111,
-    240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255,
-};
-
-constexpr constant static uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128};
-
 void kernel_mul_mv_iq2_xxs_f32_impl(
         device const  void * src0,
         device const float * src1,
         device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant   int64_t & ne10,
-        constant   int64_t & ne12,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   uint    & r2,
-        constant   uint    & r3,
-        threadgroup int8_t * shared_values [[threadgroup(0)]],
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint  tiisg[[thread_index_in_simdgroup]],
-        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+                   int64_t   ne00,
+                   int64_t   ne01,
+                   int64_t   ne02,
+                   int64_t   ne10,
+                   int64_t   ne12,
+                   int64_t   ne0,
+                   int64_t   ne1,
+                   uint      r2,
+                   uint      r3,
+        threadgroup int8_t * shared_values,
+                   uint3     tgpig,
+                   uint      tiisg,
+                   uint      sgitg) {
 
     const int nb = ne00/QK_K;
     const int r0 = tgpig.x;
@@ -3985,7 +4434,6 @@ void kernel_mul_mv_iq2_xxs_f32_impl(
         threadgroup_barrier(mem_flags::mem_threadgroup);
     }
 
-#if QK_K == 256
     const int ix = tiisg;
 
     device const float * y4 = y + 32 * ix;
@@ -4026,12 +4474,6 @@ void kernel_mul_mv_iq2_xxs_f32_impl(
 
         y4 += 32 * 32;
     }
-#else
-    (void) x;
-    (void) y;
-    (void) yl;
-    (void) nb32;
-#endif
 
     for (int row = 0; row < N_DST; ++row) {
         all_sum = simd_sum(sumf[row]);
@@ -4074,19 +4516,19 @@ void kernel_mul_mv_iq2_xs_f32_impl(
         device const  void * src0,
         device const float * src1,
         device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant   int64_t & ne10,
-        constant   int64_t & ne12,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   uint    & r2,
-        constant   uint    & r3,
-        threadgroup int8_t * shared_values [[threadgroup(0)]],
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint  tiisg[[thread_index_in_simdgroup]],
-        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+                   int64_t   ne00,
+                   int64_t   ne01,
+                   int64_t   ne02,
+                   int64_t   ne10,
+                   int64_t   ne12,
+                   int64_t   ne0,
+                   int64_t   ne1,
+                   uint      r2,
+                   uint      r3,
+        threadgroup int8_t * shared_values,
+                   uint3     tgpig,
+                   uint      tiisg,
+                   uint      sgitg) {
 
     const int nb = ne00/QK_K;
     const int r0 = tgpig.x;
@@ -4121,7 +4563,6 @@ void kernel_mul_mv_iq2_xs_f32_impl(
         threadgroup_barrier(mem_flags::mem_threadgroup);
     }
 
-#if QK_K == 256
     const int ix = tiisg;
 
     device const float * y4 = y + 32 * ix;
@@ -4172,12 +4613,6 @@ void kernel_mul_mv_iq2_xs_f32_impl(
 
         y4 += 32 * 32;
     }
-#else
-    (void) x;
-    (void) y;
-    (void) yl;
-    (void) nb32;
-#endif
 
     for (int row = 0; row < N_DST; ++row) {
         all_sum = simd_sum(sumf[row]);
@@ -4220,19 +4655,19 @@ void kernel_mul_mv_iq3_xxs_f32_impl(
         device const  void * src0,
         device const float * src1,
         device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant   int64_t & ne10,
-        constant   int64_t & ne12,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   uint    & r2,
-        constant   uint    & r3,
-        threadgroup int8_t * shared_values [[threadgroup(0)]],
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint  tiisg[[thread_index_in_simdgroup]],
-        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+                   int64_t   ne00,
+                   int64_t   ne01,
+                   int64_t   ne02,
+                   int64_t   ne10,
+                   int64_t   ne12,
+                   int64_t   ne0,
+                   int64_t   ne1,
+                   uint      r2,
+                   uint      r3,
+        threadgroup int8_t * shared_values,
+                   uint3     tgpig,
+                   uint      tiisg,
+                   uint      sgitg) {
 
     const int nb = ne00/QK_K;
     const int r0 = tgpig.x;
@@ -4267,7 +4702,6 @@ void kernel_mul_mv_iq3_xxs_f32_impl(
         threadgroup_barrier(mem_flags::mem_threadgroup);
     }
 
-#if QK_K == 256
     const int ix = tiisg;
 
     device const float * y4 = y + 32 * ix;
@@ -4311,12 +4745,6 @@ void kernel_mul_mv_iq3_xxs_f32_impl(
 
         y4 += 32 * 32;
     }
-#else
-    (void) x;
-    (void) y;
-    (void) yl;
-    (void) nb32;
-#endif
 
     for (int row = 0; row < N_DST; ++row) {
         all_sum = simd_sum(sumf[row]);
@@ -4355,22 +4783,23 @@ kernel void kernel_mul_mv_iq3_xxs_f32(
     kernel_mul_mv_iq3_xxs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
 }
 
-void kernel_mul_mv_iq1_s_f32_impl(
+void kernel_mul_mv_iq3_s_f32_impl(
         device const  void * src0,
         device const float * src1,
         device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant   int64_t & ne10,
-        constant   int64_t & ne12,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   uint    & r2,
-        constant   uint    & r3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint  tiisg[[thread_index_in_simdgroup]],
-        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+                   int64_t   ne00,
+                   int64_t   ne01,
+                   int64_t   ne02,
+                   int64_t   ne10,
+                   int64_t   ne12,
+                   int64_t   ne0,
+                   int64_t   ne1,
+                   uint      r2,
+                   uint      r3,
+        threadgroup int8_t * shared_values,
+                   uint3     tgpig,
+                   uint      tiisg,
+                   uint      sgitg) {
 
     const int nb = ne00/QK_K;
     const int r0 = tgpig.x;
@@ -4385,59 +4814,69 @@ void kernel_mul_mv_iq1_s_f32_impl(
 
     const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
 
-    device const block_iq1_s * x = (device const block_iq1_s *) src0 + ib_row + offset0;
+    device const block_iq3_s * x = (device const block_iq3_s *) src0 + ib_row + offset0;
     device const float       * y = (device const float       *) src1 + r1*ne10 + im*ne00*ne1;
 
-    float yl[16];
+    float yl[32];
     float sumf[N_DST]={0.f}, all_sum;
 
     const int nb32 = nb * (QK_K / 32);
 
-#if QK_K == 256
-    const int ix = tiisg/2;
-    const int il = tiisg%2;
+    threadgroup uint32_t * values = (threadgroup uint32_t *)shared_values;
+    {
+        int nval = 8;
+        int pos  = (32*sgitg + tiisg)*nval;
+        for (int i = 0; i < nval; ++i) values[pos + i] = iq3s_grid[pos + i];
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+    }
 
-    device const float * y4 = y + 32 * ix + 16 * il;
+    const int ix = tiisg;
 
-    for (int ib32 = ix; ib32 < nb32; ib32 += 16) {
+    device const float * y4 = y + 32 * ix;
 
-        for (int i = 0; i < 16; ++i) {
+    for (int ib32 = ix; ib32 < nb32; ib32 += 32) {
+
+        for (int i = 0; i < 32; ++i) {
             yl[i] = y4[i];
         }
 
         const int ibl = ib32 / (QK_K / 32);
         const int ib  = ib32 % (QK_K / 32);
 
-        device const block_iq1_s * xr = x + ibl;
-        device const uint8_t * qs = xr->qs + 4 * ib + 2 * il;
-        device const uint8_t * sc = xr->scales + 2 * ib + il;
-        device const half    * dh = &xr->d;
+        device const block_iq3_s * xr = x + ibl;
+        device const uint8_t * qs = xr->qs + 8 * ib;
+        device const uint8_t * qh = xr->qh + ib;
+        device const uint8_t * sc = xr->scales + (ib/2);
+        device const uint8_t * signs = xr->signs + 4 * ib;
+        device const half * dh = &xr->d;
 
         for (int row = 0; row < N_DST; row++) {
 
-            constant int8_t * grid1 = (constant int8_t *)(iq1s_grid + (qs[0] | ((sc[0] & 0x08) << 5)));
-            constant int8_t * grid2 = (constant int8_t *)(iq1s_grid + (qs[1] | ((sc[0] & 0x80) << 1)));
+            const float db = dh[0];
+            const float d = db * (1 + 2*((sc[0] >> 4*(ib%2)) & 0xf));
 
             float2 sum = {0};
-            for (int j = 0; j < 8; ++j) {
-                sum[0] += yl[j+ 0] * grid1[j];
-                sum[1] += yl[j+ 8] * grid2[j];
+            for (int l = 0; l < 4; ++l) {
+                const threadgroup uint32_t * table1 = qh[0] & kmask_iq2xs[2*l+0] ? values + 256 : values;
+                const threadgroup uint32_t * table2 = qh[0] & kmask_iq2xs[2*l+1] ? values + 256 : values;
+                const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(table1 + qs[2*l+0]);
+                const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(table2 + qs[2*l+1]);
+                for (int j = 0; j < 4; ++j) {
+                    sum[0] += yl[8*l + j + 0] * grid1[j] * select(1, -1, signs[l] & kmask_iq2xs[j+0]);
+                    sum[1] += yl[8*l + j + 4] * grid2[j] * select(1, -1, signs[l] & kmask_iq2xs[j+4]);
+                }
             }
-            sumf[row] += (float)dh[0] * (sum[0] * (2*(sc[0] & 7) + 1) + sum[1] * (2*((sc[0] >> 4) & 7) + 1));
+            sumf[row] += d * (sum[0] + sum[1]);
 
-            dh += nb*sizeof(block_iq1_s)/2;
-            qs += nb*sizeof(block_iq1_s);
-            sc += nb*sizeof(block_iq1_s);
+            dh  += nb*sizeof(block_iq3_s)/2;
+            qs  += nb*sizeof(block_iq3_s);
+            qh  += nb*sizeof(block_iq3_s);
+            sc  += nb*sizeof(block_iq3_s);
+            signs += nb*sizeof(block_iq3_s);
         }
 
-        y4 += 16 * 32;
+        y4 += 32 * 32;
     }
-#else
-    (void) x;
-    (void) y;
-    (void) yl;
-    (void) nb32;
-#endif
 
     for (int row = 0; row < N_DST; ++row) {
         all_sum = simd_sum(sumf[row]);
@@ -4447,6 +4886,558 @@ void kernel_mul_mv_iq1_s_f32_impl(
     }
 }
 
+[[host_name("kernel_mul_mv_iq3_s_f32")]]
+kernel void kernel_mul_mv_iq3_s_f32(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        threadgroup int8_t * shared_values [[threadgroup(0)]],
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    kernel_mul_mv_iq3_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
+}
+
+void kernel_mul_mv_iq2_s_f32_impl(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+                   int64_t   ne00,
+                   int64_t   ne01,
+                   int64_t   ne02,
+                   int64_t   ne10,
+                   int64_t   ne12,
+                   int64_t   ne0,
+                   int64_t   ne1,
+                   uint      r2,
+                   uint      r3,
+        threadgroup int8_t * shared_values,
+                   uint3     tgpig,
+                   uint      tiisg,
+                   uint      sgitg) {
+
+    const int nb = ne00/QK_K;
+    const int r0 = tgpig.x;
+    const int r1 = tgpig.y;
+    const int im = tgpig.z;
+
+    const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST;
+    const int ib_row = first_row * nb;
+
+    const uint i12 = im%ne12;
+    const uint i13 = im/ne12;
+
+    const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+
+    device const block_iq2_s * x = (device const block_iq2_s *) src0 + ib_row + offset0;
+    device const float       * y = (device const float       *) src1 + r1*ne10 + im*ne00*ne1;
+
+    float yl[32];
+    float sumf[N_DST]={0.f}, all_sum;
+
+    const int nb32 = nb * (QK_K / 32);
+
+    //threadgroup uint64_t * values = (threadgroup uint64_t *)shared_values;
+    //{
+    //    int nval = 32;
+    //    int pos  = (32*sgitg + tiisg)*nval;
+    //    for (int i = 0; i < nval; ++i) values[pos + i] = iq2s_grid[pos + i];
+    //    threadgroup_barrier(mem_flags::mem_threadgroup);
+    //}
+
+    const int ix = tiisg;
+
+    device const float * y4 = y + 32 * ix;
+
+    for (int ib32 = ix; ib32 < nb32; ib32 += 32) {
+
+        for (int i = 0; i < 32; ++i) {
+            yl[i] = y4[i];
+        }
+
+        const int ibl = ib32 / (QK_K / 32);
+        const int ib  = ib32 % (QK_K / 32);
+
+        device const block_iq2_s * xr = x + ibl;
+        device const uint8_t * qs = xr->qs + 4 * ib;
+        device const uint8_t * qh = xr->qh + ib;
+        device const uint8_t * sc = xr->scales + ib;
+        device const uint8_t * signs = qs + QK_K/8;
+        device const half * dh = &xr->d;
+
+        for (int row = 0; row < N_DST; row++) {
+
+            const float db = dh[0];
+            const float d1 = db * (0.5f + (sc[0] & 0xf));
+            const float d2 = db * (0.5f + (sc[0] >>  4));
+
+            float2 sum = {0};
+            for (int l = 0; l < 2; ++l) {
+                //const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(values + (qs[l+0] | ((qh[0] << (8-2*l)) & 0x300)));
+                //const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(values + (qs[l+2] | ((qh[0] << (4-2*l)) & 0x300)));
+                constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[l+0] | ((qh[0] << (8-2*l)) & 0x300)));
+                constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[l+2] | ((qh[0] << (4-2*l)) & 0x300)));
+                for (int j = 0; j < 8; ++j) {
+                    sum[0] += yl[8*l + j +  0] * grid1[j] * select(1, -1, signs[l+0] & kmask_iq2xs[j]);
+                    sum[1] += yl[8*l + j + 16] * grid2[j] * select(1, -1, signs[l+2] & kmask_iq2xs[j]);
+                }
+            }
+            sumf[row] += d1 * sum[0] + d2 * sum[1];
+
+            dh  += nb*sizeof(block_iq2_s)/2;
+            qs  += nb*sizeof(block_iq2_s);
+            qh  += nb*sizeof(block_iq2_s);
+            sc  += nb*sizeof(block_iq2_s);
+            signs += nb*sizeof(block_iq2_s);
+        }
+
+        y4 += 32 * 32;
+    }
+
+    for (int row = 0; row < N_DST; ++row) {
+        all_sum = simd_sum(sumf[row]);
+        if (tiisg == 0) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.25f;
+        }
+    }
+}
+
+[[host_name("kernel_mul_mv_iq2_s_f32")]]
+kernel void kernel_mul_mv_iq2_s_f32(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        threadgroup int8_t * shared_values [[threadgroup(0)]],
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    kernel_mul_mv_iq2_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
+}
+
+void kernel_mul_mv_iq1_s_f32_impl(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+                   int64_t   ne00,
+                   int64_t   ne01,
+                   int64_t   ne02,
+                   int64_t   ne10,
+                   int64_t   ne12,
+                   int64_t   ne0,
+                   int64_t   ne1,
+                   uint      r2,
+                   uint      r3,
+        threadgroup int8_t * shared_value,
+                   uint3     tgpig,
+                   uint      tiisg,
+                   uint      sgitg) {
+
+    const int nb = ne00/QK_K;
+    const int r0 = tgpig.x;
+    const int r1 = tgpig.y;
+    const int im = tgpig.z;
+
+    const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST;
+    const int ib_row = first_row * nb;
+
+    const uint i12 = im%ne12;
+    const uint i13 = im/ne12;
+
+    const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+    device const block_iq1_s * x = (device const block_iq1_s *) src0 + ib_row + offset0;
+    device const float       * y = (device const float       *) src1 + r1*ne10 + im*ne00*ne1;
+
+    float yl[32];
+    float sumf[N_DST]={0.f}, all_sum;
+
+    const int nb32 = nb * (QK_K / 32);
+
+    const int ix = tiisg;
+
+    device const float * y4 = y + 32 * ix;
+
+    for (int ib32 = ix; ib32 < nb32; ib32 += 32) {
+
+        float sumy = 0;
+        for (int i = 0; i < 32; ++i) {
+            yl[i] = y4[i];
+            sumy += yl[i];
+        }
+
+        const int ibl = ib32 / (QK_K / 32);
+        const int ib  = ib32 % (QK_K / 32);
+
+        device const block_iq1_s * xr = x + ibl;
+        device const uint8_t  * qs = xr->qs + 4 * ib;
+        device const uint16_t * qh = xr->qh + ib;
+        device const half     * dh = &xr->d;
+
+        for (int row = 0; row < N_DST; row++) {
+
+            constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
+            constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 5) & 0x700)));
+            constant uint8_t * grid3 = (constant uint8_t *)(iq1s_grid_gpu + (qs[2] | ((qh[0] << 2) & 0x700)));
+            constant uint8_t * grid4 = (constant uint8_t *)(iq1s_grid_gpu + (qs[3] | ((qh[0] >> 1) & 0x700)));
+
+            float sum = 0;
+            for (int j = 0; j < 4; ++j) {
+                sum += yl[j+ 0] * (grid1[j] & 0xf) + yl[j+ 4] * (grid1[j] >> 4)
+                     + yl[j+ 8] * (grid2[j] & 0xf) + yl[j+12] * (grid2[j] >> 4)
+                     + yl[j+16] * (grid3[j] & 0xf) + yl[j+20] * (grid3[j] >> 4)
+                     + yl[j+24] * (grid4[j] & 0xf) + yl[j+28] * (grid4[j] >> 4);
+            }
+            sumf[row] += (float)dh[0] * (sum + sumy * (qh[0] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA)) * (2*((qh[0] >> 12) & 7) + 1);
+
+            dh += nb*sizeof(block_iq1_s)/2;
+            qs += nb*sizeof(block_iq1_s);
+            qh += nb*sizeof(block_iq1_s)/2;
+        }
+
+        y4 += 32 * 32;
+    }
+
+    for (int row = 0; row < N_DST; ++row) {
+        all_sum = simd_sum(sumf[row]);
+        if (tiisg == 0) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum;
+        }
+    }
+}
+
+void kernel_mul_mv_iq1_m_f32_impl(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+                   int64_t   ne00,
+                   int64_t   ne01,
+                   int64_t   ne02,
+                   int64_t   ne10,
+                   int64_t   ne12,
+                   int64_t   ne0,
+                   int64_t   ne1,
+                   uint      r2,
+                   uint      r3,
+        threadgroup int8_t * shared_value,
+                   uint3     tgpig,
+                   uint      tiisg,
+                   uint      sgitg) {
+
+    const int nb = ne00/QK_K;
+    const int r0 = tgpig.x;
+    const int r1 = tgpig.y;
+    const int im = tgpig.z;
+
+    const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST;
+    const int ib_row = first_row * nb;
+
+    const uint i12 = im%ne12;
+    const uint i13 = im/ne12;
+
+    const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+    device const block_iq1_m * x = (device const block_iq1_m *) src0 + ib_row + offset0;
+    device const float       * y = (device const float       *) src1 + r1*ne10 + im*ne00*ne1;
+
+    float yl[32];
+    float sumf[N_DST]={0.f}, all_sum;
+
+    const int nb32 = nb * (QK_K / 32);
+
+    const int ix = tiisg;
+
+    device const float * y4 = y + 32 * ix;
+
+#if QK_K != 64
+    iq1m_scale_t scale;
+#endif
+
+    for (int ib32 = ix; ib32 < nb32; ib32 += 32) {
+
+        float4 sumy = {0.f};
+        for (int i = 0; i < 8; ++i) {
+            yl[i+ 0] = y4[i+ 0]; sumy[0] += yl[i+ 0];
+            yl[i+ 8] = y4[i+ 8]; sumy[1] += yl[i+ 8];
+            yl[i+16] = y4[i+16]; sumy[2] += yl[i+16];
+            yl[i+24] = y4[i+24]; sumy[3] += yl[i+24];
+        }
+
+        const int ibl = ib32 / (QK_K / 32);
+        const int ib  = ib32 % (QK_K / 32);
+
+        device const block_iq1_m * xr = x + ibl;
+        device const uint8_t  * qs = xr->qs + 4 * ib;
+        device const uint8_t  * qh = xr->qh + 2 * ib;
+        device const uint16_t * sc = (device const uint16_t *)xr->scales;
+
+        for (int row = 0; row < N_DST; row++) {
+
+#if QK_K != 64
+            scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+#endif
+
+            constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
+            constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700)));
+            constant uint8_t * grid3 = (constant uint8_t *)(iq1s_grid_gpu + (qs[2] | ((qh[1] << 8) & 0x700)));
+            constant uint8_t * grid4 = (constant uint8_t *)(iq1s_grid_gpu + (qs[3] | ((qh[1] << 4) & 0x700)));
+
+            float2 sum = {0.f};
+            for (int j = 0; j < 4; ++j) {
+                sum[0] += yl[j+ 0] * (grid1[j] & 0xf) + yl[j+ 4] * (grid1[j] >> 4)
+                        + yl[j+ 8] * (grid2[j] & 0xf) + yl[j+12] * (grid2[j] >> 4);
+                sum[1] += yl[j+16] * (grid3[j] & 0xf) + yl[j+20] * (grid3[j] >> 4)
+                        + yl[j+24] * (grid4[j] & 0xf) + yl[j+28] * (grid4[j] >> 4);
+            }
+            const float delta1 = sumy[0] * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[1] * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
+            const float delta2 = sumy[2] * (qh[1] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[3] * (qh[1] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
+#if QK_K == 64
+            const float d = (float) *((device const half *)(sc - 1));
+            sumf[row] += d * ((sum[0] + delta1) * (2*((sc[0] >> (8*(ib%2)+0)) & 0xf) + 1) +
+                              (sum[1] + delta2) * (2*((sc[0] >> (8*(ib%2)+4)) & 0xf) + 1));
+#else
+            sumf[row] += (float)scale.f16 * ((sum[0] + delta1) * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 7) + 1) +
+                                             (sum[1] + delta2) * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 7) + 1));
+#endif
+
+            sc += nb*sizeof(block_iq1_m)/2;
+            qs += nb*sizeof(block_iq1_m);
+            qh += nb*sizeof(block_iq1_m);
+        }
+
+        y4 += 32 * 32;
+    }
+
+    for (int row = 0; row < N_DST; ++row) {
+        all_sum = simd_sum(sumf[row]);
+        if (tiisg == 0) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum;
+        }
+    }
+}
+
+void kernel_mul_mv_iq4_nl_f32_impl(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+                   int64_t   ne00,
+                   int64_t   ne01,
+                   int64_t   ne02,
+                   int64_t   ne10,
+                   int64_t   ne12,
+                   int64_t   ne0,
+                   int64_t   ne1,
+                   uint      r2,
+                   uint      r3,
+        threadgroup int8_t * shared_values_i8,
+                   uint3     tgpig,
+                   uint      tiisg,
+                   uint      sgitg) {
+
+    threadgroup float * shared_values = (threadgroup float *)shared_values_i8;
+    const int nb = ne00/QK4_NL;
+    const int r0 = tgpig.x;
+    const int r1 = tgpig.y;
+    const int im = tgpig.z;
+    const int first_row = (r0 * 2 + sgitg) * 2;
+    const int ib_row = first_row * nb;
+
+    const uint i12 = im%ne12;
+    const uint i13 = im/ne12;
+
+    const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+    device const block_iq4_nl * x = (device const block_iq4_nl *) src0 + ib_row + offset0;
+    device const float        * y = (device const float        *) src1 + r1*ne10 + im*ne00*ne1;
+
+    const int ix = tiisg/2;  // 0...15
+    const int it = tiisg%2;  // 0 or 1
+
+    shared_values[tiisg] = kvalues_iq4nl_f[tiisg%16];
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    float4 yl[4];
+    float sumf[2]={0.f}, all_sum;
+
+    device const float * yb = y + ix * QK4_NL + it * 8;
+
+    uint32_t aux32[2];
+    thread const uint8_t * q8 = (thread const uint8_t *)aux32;
+
+    float4 qf1, qf2;
+
+    for (int ib = ix; ib < nb; ib += 16) {
+
+        device const float4 * y4 = (device const float4 *)yb;
+        yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5];
+
+        for (int row = 0; row < 2; ++row) {
+
+            device const block_iq4_nl & xb = x[row*nb + ib];
+            device const uint16_t * q4 = (device const uint16_t *)(xb.qs + 8*it);
+
+            float4 acc1 = {0.f}, acc2 = {0.f};
+
+            aux32[0] = q4[0] | (q4[1] << 16);
+            aux32[1] = (aux32[0] >> 4) & 0x0f0f0f0f;
+            aux32[0] &= 0x0f0f0f0f;
+            qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]};
+            qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]};
+            acc1 += yl[0] * qf1;
+            acc2 += yl[1] * qf2;
+
+            aux32[0] = q4[2] | (q4[3] << 16);
+            aux32[1] = (aux32[0] >> 4) & 0x0f0f0f0f;
+            aux32[0] &= 0x0f0f0f0f;
+            qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]};
+            qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]};
+            acc1 += yl[2] * qf1;
+            acc2 += yl[3] * qf2;
+
+            acc1 += acc2;
+
+            sumf[row] += (float)xb.d * (acc1[0] + acc1[1] + acc1[2] + acc1[3]);
+
+        }
+
+        yb += 16 * QK4_NL;
+    }
+
+    for (int row = 0; row < 2; ++row) {
+        all_sum = simd_sum(sumf[row]);
+        if (tiisg == 0) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum;
+        }
+    }
+}
+
+#if QK_K != 64
+void kernel_mul_mv_iq4_xs_f32_impl(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+                   int64_t   ne00,
+                   int64_t   ne01,
+                   int64_t   ne02,
+                   int64_t   ne10,
+                   int64_t   ne12,
+                   int64_t   ne0,
+                   int64_t   ne1,
+                   uint      r2,
+                   uint      r3,
+        threadgroup int8_t * shared_values_i8,
+                   uint3     tgpig,
+                   uint      tiisg,
+                   uint      sgitg) {
+
+    threadgroup float * shared_values = (threadgroup float *)shared_values_i8;
+    const int nb = ne00/QK_K;
+    const int r0 = tgpig.x;
+    const int r1 = tgpig.y;
+    const int im = tgpig.z;
+    const int first_row = (r0 * 2 + sgitg) * 2;
+    const int ib_row = first_row * nb;
+
+    const uint i12 = im%ne12;
+    const uint i13 = im/ne12;
+
+    const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+    device const block_iq4_xs * x = (device const block_iq4_xs *) src0 + ib_row + offset0;
+    device const float        * y = (device const float        *) src1 + r1*ne10 + im*ne00*ne1;
+
+    const int ix = tiisg/16;  // 0 or 1
+    const int it = tiisg%16;  // 0...15
+    const int ib = it/2;
+    const int il = it%2;
+
+    shared_values[tiisg] = kvalues_iq4nl_f[tiisg%16];
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    float4 yl[4];
+    float sumf[2]={0.f}, all_sum;
+
+    device const float * yb = y + ix * QK_K + ib * 32 + il * 8;
+
+    uint32_t aux32[2];
+    thread const uint8_t * q8 = (thread const uint8_t *)aux32;
+
+    float4 qf1, qf2;
+
+    for (int ibl = ix; ibl < nb; ibl += 2) {
+
+        device const float4 * y4 = (device const float4 *)yb;
+        yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5];
+
+        for (int row = 0; row < 2; ++row) {
+
+            device const block_iq4_xs & xb = x[row*nb + ibl];
+            device const uint32_t * q4 = (device const uint32_t *)(xb.qs + 16*ib + 8*il);
+
+            float4 acc1 = {0.f}, acc2 = {0.f};
+
+            aux32[0] = q4[0] & 0x0f0f0f0f;
+            aux32[1] = (q4[0] >> 4) & 0x0f0f0f0f;
+            qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]};
+            qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]};
+            acc1 += yl[0] * qf1;
+            acc2 += yl[1] * qf2;
+
+            aux32[0] = q4[1] & 0x0f0f0f0f;
+            aux32[1] = (q4[1] >> 4) & 0x0f0f0f0f;
+            qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]};
+            qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]};
+            acc1 += yl[2] * qf1;
+            acc2 += yl[3] * qf2;
+
+            acc1 += acc2;
+
+            const int ls = (((xb.scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((xb.scales_h >> 2*ib) & 3) << 4)) - 32;
+            sumf[row] += (float)xb.d * ls * (acc1[0] + acc1[1] + acc1[2] + acc1[3]);
+
+        }
+
+        yb += 2 * QK_K;
+    }
+
+    for (int row = 0; row < 2; ++row) {
+        all_sum = simd_sum(sumf[row]);
+        if (tiisg == 0) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum;
+        }
+    }
+}
+#endif
+
 [[host_name("kernel_mul_mv_iq1_s_f32")]]
 kernel void kernel_mul_mv_iq1_s_f32(
         device const  void * src0,
@@ -4472,9 +5463,98 @@ kernel void kernel_mul_mv_iq1_s_f32(
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
 
-    kernel_mul_mv_iq1_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg);
+    kernel_mul_mv_iq1_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg);
 }
 
+[[host_name("kernel_mul_mv_iq1_m_f32")]]
+kernel void kernel_mul_mv_iq1_m_f32(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    kernel_mul_mv_iq1_m_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg);
+}
+
+[[host_name("kernel_mul_mv_iq4_nl_f32")]]
+kernel void kernel_mul_mv_iq4_nl_f32(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        threadgroup int8_t * shared_values [[threadgroup(0)]],
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint tiisg[[thread_index_in_simdgroup]],
+        uint sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    kernel_mul_mv_iq4_nl_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
+}
+
+[[host_name("kernel_mul_mv_iq4_xs_f32")]]
+kernel void kernel_mul_mv_iq4_xs_f32(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        threadgroup int8_t * shared_values [[threadgroup(0)]],
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint tiisg[[thread_index_in_simdgroup]],
+        uint sgitg[[simdgroup_index_in_threadgroup]]) {
+
+#if QK_K == 64
+    kernel_mul_mv_iq4_nl_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
+#else
+    kernel_mul_mv_iq4_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
+#endif
+}
 
 //============================= templates and their specializations =============================
 
@@ -4823,19 +5903,139 @@ void dequantize_iq3_xxs(device const block_iq3_xxs * xb, short il, thread type4x
 }
 
 template <typename type4x4>
-void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & reg) {
+void dequantize_iq3_s(device const block_iq3_s * xb, short il, thread type4x4 & reg) {
     // il is 0...15 for QK_K = 256 => index of block of 32 is il/2
     const float d = xb->d;
-    device const uint8_t * qs = xb->qs + 2*il;
-    device const uint8_t * sc = xb->scales + il;
-    const float dl1 = d * (2*(sc[0] & 7) + 1);
-    const float dl2 = d * (2*((sc[0] >> 4) & 7) + 1);
-    constant int8_t * grid1 = (constant int8_t *)(iq1s_grid + (qs[0] | ((sc[0] & 0x08) << 5)));
-    constant int8_t * grid2 = (constant int8_t *)(iq1s_grid + (qs[1] | ((sc[0] & 0x80) << 1)));
-    for (int i = 0; i < 8; ++i) {
-        reg[i/4+0][i%4] = dl1 * grid1[i];
-        reg[i/4+2][i%4] = dl2 * grid2[i];
+    const int ib32 = il/2;
+    il = il%2;
+    // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
+    device const uint8_t * qs = xb->qs + 8*ib32;
+    device const uint8_t * signs = xb->signs + 4*ib32 + 2*il;
+    const uint8_t qh = xb->qh[ib32] >> 4*il;
+    const float dl = d * (1 + 2*((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf));
+    constant uint8_t * grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+0] | ((qh << 8) & 256)));
+    constant uint8_t * grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+1] | ((qh << 7) & 256)));
+    for (int i = 0; i < 4; ++i) {
+        reg[0][i] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i+0]);
+        reg[1][i] = dl * grid2[i] * select(1, -1, signs[0] & kmask_iq2xs[i+4]);
     }
+    grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+2] | ((qh << 6) & 256)));
+    grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+3] | ((qh << 5) & 256)));
+    for (int i = 0; i < 4; ++i) {
+        reg[2][i] = dl * grid1[i] * select(1, -1, signs[1] & kmask_iq2xs[i+0]);
+        reg[3][i] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i+4]);
+    }
+}
+
+template <typename type4x4>
+void dequantize_iq2_s(device const block_iq2_s * xb, short il, thread type4x4 & reg) {
+    // il is 0...15 for QK_K = 256 => index of block of 32 is il/2
+    const float d = xb->d;
+    const int ib32 = il/2;
+    il = il%2;
+    // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
+    device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
+    device const uint8_t * signs = qs + QK_K/8;
+    const uint8_t qh = xb->qh[ib32] >> 4*il;
+    const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f;
+    constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[0] | ((qh << 8) & 0x300)));
+    constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[1] | ((qh << 6) & 0x300)));
+    for (int i = 0; i < 8; ++i) {
+        reg[i/4+0][i%4] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i]);
+        reg[i/4+2][i%4] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i]);
+    }
+}
+
+template <typename type4x4>
+void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & reg) {
+    // il is 0...15 for QK_K = 256 => index of block of 32 is il/2
+    const int ib32 = il/2;
+    il = il%2;
+    const float d = xb->d;
+    device const uint8_t  * qs = xb->qs + 4*ib32 + 2*il;
+    device const uint16_t * qh = xb->qh;
+    const float dl = d * (2*((qh[ib32] >> 12) & 7) + 1);
+    const float ml = dl * (qh[ib32] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA);
+    const uint16_t h = qh[ib32] >> 6*il;
+    constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((h << 8) & 0x700)));
+    constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((h << 5) & 0x700)));
+    for (int i = 0; i < 4; ++i) {
+        reg[0][i] = dl * (grid1[i] & 0xf) + ml;
+        reg[1][i] = dl * (grid1[i] >>  4) + ml;
+        reg[2][i] = dl * (grid2[i] & 0xf) + ml;
+        reg[3][i] = dl * (grid2[i] >>  4) + ml;
+    }
+}
+
+template <typename type4x4>
+void dequantize_iq1_m(device const block_iq1_m * xb, short il, thread type4x4 & reg) {
+    // il is 0...15 for QK_K = 256 => index of block of 32 is il/2
+    const int ib32 = il/2;
+    il = il%2;
+    device const uint16_t * sc = (device const uint16_t *)xb->scales;
+#if QK_K == 64
+    const float d = xb->d;
+#else
+    iq1m_scale_t scale;
+    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+    const float d = scale.f16;
+#endif
+    device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
+    device const uint8_t * qh = xb->qh + 2*ib32 + il;
+#if QK_K == 64
+    const float dl  = d * (2*((sc[ib32/2] >> (8*(ib32%2)+4*il)) & 0xf) + 1);
+#else
+    const float dl  = d * (2*((sc[ib32/2] >> (6*(ib32%2)+3*il)) & 7) + 1);
+#endif
+    const float ml1 = dl * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
+    const float ml2 = dl * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
+    constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
+    constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700)));
+    for (int i = 0; i < 4; ++i) {
+        reg[0][i] = dl * (grid1[i] & 0xf) + ml1;
+        reg[1][i] = dl * (grid1[i] >>  4) + ml1;
+        reg[2][i] = dl * (grid2[i] & 0xf) + ml2;
+        reg[3][i] = dl * (grid2[i] >>  4) + ml2;
+    }
+}
+
+template <typename type4x4>
+void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 & reg) {
+    device const uint16_t * q4 = (device const uint16_t *)xb->qs;
+    const float d = xb->d;
+    uint32_t aux32;
+    thread const uint8_t * q8 = (thread const uint8_t *)&aux32;
+    for (int i = 0; i < 4; ++i) {
+        aux32 = ((q4[2*i] | (q4[2*i+1] << 16)) >> 4*il) & 0x0f0f0f0f;
+        reg[i][0] = d * kvalues_iq4nl_f[q8[0]];
+        reg[i][1] = d * kvalues_iq4nl_f[q8[1]];
+        reg[i][2] = d * kvalues_iq4nl_f[q8[2]];
+        reg[i][3] = d * kvalues_iq4nl_f[q8[3]];
+    }
+}
+
+template <typename type4x4>
+void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 & reg) {
+#if QK_K == 64
+    dequantize_iq4_nl(xb, il, reg);
+#else
+    // il is 0...15 for QK_K = 256 => index of block of 32 is il/2
+    const int ib32 = il/2;
+    il = il%2;
+    // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
+    device const uint32_t * q4 = (device const uint32_t *)xb->qs + 4*ib32;
+    const int ls = ((xb->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((xb->scales_h >> 2*ib32) & 3) << 4);
+    const float d = (float)xb->d * (ls - 32);
+    uint32_t aux32;
+    thread const uint8_t * q8 = (thread const uint8_t *)&aux32;
+    for (int i = 0; i < 4; ++i) {
+        aux32 = (q4[i] >> 4*il) & 0x0f0f0f0f;
+        reg[i][0] = d * kvalues_iq4nl_f[q8[0]];
+        reg[i][1] = d * kvalues_iq4nl_f[q8[1]];
+        reg[i][2] = d * kvalues_iq4nl_f[q8[2]];
+        reg[i][3] = d * kvalues_iq4nl_f[q8[3]];
+    }
+#endif
 }
 
 template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread float4x4 &)>
@@ -5101,25 +6301,25 @@ void kernel_mul_mm_impl(device const  uchar * src0,
     }
 }
 
-// same as kernel_mul_mm_impl, but src1 and dst are accessed via indices stored in src1ids
+// same as kernel_mul_mm_impl, but src1 and dst are accessed via indices stored in rowids
 template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread half4x4 &)>
 void kernel_mul_mm_id_impl(
         device const  uchar * src0,
         device const  uchar * src1,
-        thread        short * src1ids,
+        threadgroup ushort2 * rowids,
         device        float * dst,
         constant    int64_t & ne00,
         constant    int64_t & ne02,
         constant   uint64_t & nb01,
         constant   uint64_t & nb02,
+        constant    int64_t & ne11,
         constant    int64_t & ne12,
         constant   uint64_t & nb10,
         constant   uint64_t & nb11,
         constant   uint64_t & nb12,
         constant    int64_t & ne0,
                     int64_t   ne1,
-        constant       uint & r2,
-        constant       uint & r3,
+                    int64_t   ne0ne1,
         threadgroup   uchar * shared_memory,
         uint3                 tgpig[[threadgroup_position_in_grid]],
         uint                  tiitg[[thread_index_in_threadgroup]],
@@ -5130,7 +6330,6 @@ void kernel_mul_mm_id_impl(
 
     const uint r0 = tgpig.y;
     const uint r1 = tgpig.x;
-    const uint im = tgpig.z;
 
     if (r1 * BLOCK_SIZE_N >= ne1) return;
 
@@ -5148,19 +6347,16 @@ void kernel_mul_mm_id_impl(
     for (int i = 0; i < 8; i++){
         c_res[i] = make_filled_simdgroup_matrix<float, 8>(0.f);
     }
-
     short il = (tiitg % THREAD_PER_ROW);
 
-    const uint i12 = im%ne12;
-    const uint i13 = im/ne12;
-
-    uint   offset0 = (i12/r2)*nb02 + (i13/r3)*(nb02*ne02);
     ushort offset1 = il/nl;
 
-    device const block_q * x = (device const block_q *)(src0 + (r0 * BLOCK_SIZE_M + thread_row) * nb01 + offset0) + offset1;
+    threadgroup const auto & id = rowids[r1 * BLOCK_SIZE_N + thread_col];
+
+    device const block_q * x = (device const block_q *)(src0 + (r0 * BLOCK_SIZE_M + thread_row) * nb01) + offset1;
     device const float   * y = (device const float   *)(src1
-        + nb12 * im
-        + nb11 * src1ids[r1 * BLOCK_SIZE_N + thread_col]
+        + nb12 * id[1]
+        + nb11 * (id[0] % ne11)
         + nb10 * (BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL)));
 
     for (int loop_k = 0; loop_k < ne00; loop_k += BLOCK_SIZE_K) {
@@ -5189,11 +6385,11 @@ void kernel_mul_mm_id_impl(
 
         for (int ik = 0; ik < BLOCK_SIZE_K / 8; ik++) {
             for (int i = 0; i < 4; i++) {
-                simdgroup_load(ma[i],lsma + SG_MAT_SIZE * i);
+                simdgroup_load(ma[i], lsma + SG_MAT_SIZE * i);
             }
             simdgroup_barrier(mem_flags::mem_none);
             for (int i = 0; i < 2; i++) {
-                simdgroup_load(mb[i],lsmb + SG_MAT_SIZE * i);
+                simdgroup_load(mb[i], lsmb + SG_MAT_SIZE * i);
             }
 
             lsma += BLOCK_SIZE_M / SG_MAT_ROW * SG_MAT_SIZE;
@@ -5215,11 +6411,13 @@ void kernel_mul_mm_id_impl(
 
         threadgroup_barrier(mem_flags::mem_threadgroup);
 
-        device float * C = dst + (BLOCK_SIZE_M * r0) + im*ne1*ne0;
+        device float * C = dst + (BLOCK_SIZE_M * r0);
         if (sgitg == 0) {
-            for (int i = 0; i < n_rows; i++) {
-                for (int j = tiitg; j < n_cols; j += BLOCK_SIZE_N) {
-                    *(C + i + src1ids[j + r1*BLOCK_SIZE_N] * ne0) = *(temp_str + i + j * BLOCK_SIZE_M);
+            for (int j = tiitg; j < n_cols; j += BLOCK_SIZE_N) {
+                threadgroup const auto & jid = rowids[r1 * BLOCK_SIZE_N + j];
+                int joff =  jid[0] * ne0 + jid[1] * ne0ne1;
+                for (int i = 0; i < n_rows; i++) {
+                    *(C + i + joff) = *(temp_str + i + j * BLOCK_SIZE_M);
                 }
             }
         }
@@ -5270,14 +6468,18 @@ kernel void kernel_mul_mm(device const  uchar * src0,
 
 template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread half4x4 &)>
 kernel void kernel_mul_mm_id(
-        device const   uchar * ids,
+        device const   uchar * src0s,
         device const   uchar * src1,
         device         float * dst,
+        device const   uchar * ids,
+        constant     int64_t & nei0,
+        constant     int64_t & nei1,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne02,
         constant    uint64_t & nb01,
         constant    uint64_t & nb02,
+        constant     int64_t & ne11,
         constant     int64_t & ne12,
         constant     int64_t & ne13,
         constant    uint64_t & nb10,
@@ -5286,55 +6488,52 @@ kernel void kernel_mul_mm_id(
         constant     int64_t & ne0,
         constant     int64_t & ne1,
         constant    uint64_t & nb1,
-        constant        uint & r2,
-        constant        uint & r3,
-        constant         int & idx,
-        device const   uchar * src00,
-        device const   uchar * src01,
-        device const   uchar * src02,
-        device const   uchar * src03,
-        device const   uchar * src04,
-        device const   uchar * src05,
-        device const   uchar * src06,
-        device const   uchar * src07,
         threadgroup    uchar * shared_memory [[threadgroup(0)]],
         uint3                  tgpig[[threadgroup_position_in_grid]],
         uint                   tiitg[[thread_index_in_threadgroup]],
         uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const uchar * src0s[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
 
-    // expert id
-    const int32_t id = tgpig.z/(ne12*ne13);
+    const int32_t i02 = tgpig.z;
+    tgpig.z = 0;
 
-    tgpig.z = tgpig.z%(ne12*ne13);
+    device const uchar * src0 = src0s + i02*nb02;
 
-    // row indices of src1 for expert id
+    // row indices
+    threadgroup ushort2 * rowids = (threadgroup ushort2 *)(shared_memory + 8192);
+
+    // TODO: parallelize this loop
     int64_t _ne1 = 0;
-    short src1ids[512];
-
-    for (int64_t i1 = 0; i1 < ne1; i1++) {
-        if (((device int32_t *) (ids + i1*nbi1))[idx] == id) {
-            src1ids[_ne1++] = i1;
+    for (ushort ii1 = 0; ii1 < nei1; ii1++) {
+        for (ushort ii0 = 0; ii0 < nei0; ii0++) {
+            int32_t id = ((device int32_t *) (ids + ii1*nbi1))[ii0];
+            if (id == i02) {
+                //if (tiitg == 0) {
+                    rowids[_ne1] = ushort2(ii0, ii1);
+                //}
+                _ne1++;
+            }
         }
     }
 
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
     kernel_mul_mm_id_impl<block_q, nl, dequantize_func>(
-        src0s[id],
+        src0,
         src1,
-        src1ids,
+        rowids,
         dst,
         ne00,
         ne02,
         nb01,
         nb02,
+        ne11,
         ne12,
         nb10,
         nb11,
         nb12,
         ne0,
         _ne1,
-        r2,
-        r3,
+        ne0*ne1,
         shared_memory,
         tgpig,
         tiitg,
@@ -5380,247 +6579,198 @@ template [[host_name("kernel_get_rows_q6_K")]] kernel get_rows_t kernel_get_rows
 template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_t kernel_get_rows<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
 template [[host_name("kernel_get_rows_iq2_xs")]]  kernel get_rows_t kernel_get_rows<block_iq2_xs,  QK_NL, dequantize_iq2_xs>;
 template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_t kernel_get_rows<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
+template [[host_name("kernel_get_rows_iq3_s")]]   kernel get_rows_t kernel_get_rows<block_iq3_s,   QK_NL, dequantize_iq3_s>;
+template [[host_name("kernel_get_rows_iq2_s")]]   kernel get_rows_t kernel_get_rows<block_iq2_s,   QK_NL, dequantize_iq2_s>;
 template [[host_name("kernel_get_rows_iq1_s")]]   kernel get_rows_t kernel_get_rows<block_iq1_s,   QK_NL, dequantize_iq1_s>;
+template [[host_name("kernel_get_rows_iq1_m")]]   kernel get_rows_t kernel_get_rows<block_iq1_m,   QK_NL, dequantize_iq1_m>;
+template [[host_name("kernel_get_rows_iq4_nl")]]  kernel get_rows_t kernel_get_rows<block_iq4_nl,  2,     dequantize_iq4_nl>;
+#if QK_K == 64
+template [[host_name("kernel_get_rows_iq4_xs")]]  kernel get_rows_t kernel_get_rows<block_iq4_xs,  2,     dequantize_iq4_xs>;
+#else
+template [[host_name("kernel_get_rows_iq4_xs")]]  kernel get_rows_t kernel_get_rows<block_iq4_xs,  QK_NL, dequantize_iq4_xs>;
+#endif
 
 //
 // matrix-matrix multiplication
 //
 
-typedef void (mat_mm_t)(
-        device const  uchar * src0,
-        device const  uchar * src1,
-        device        float * dst,
-        constant    int64_t & ne00,
-        constant    int64_t & ne02,
-        constant   uint64_t & nb01,
-        constant   uint64_t & nb02,
-        constant    int64_t & ne12,
-        constant   uint64_t & nb10,
-        constant   uint64_t & nb11,
-        constant   uint64_t & nb12,
-        constant    int64_t & ne0,
-        constant    int64_t & ne1,
-        constant       uint & r2,
-        constant       uint & r3,
-        threadgroup   uchar *,
-        uint3, uint, uint);
+typedef decltype(kernel_mul_mm<float4x4, 1, dequantize_f32>) mat_mm_t;
 
-template [[host_name("kernel_mul_mm_f32_f32")]]  kernel mat_mm_t kernel_mul_mm<float4x4,   1,     dequantize_f32>;
-template [[host_name("kernel_mul_mm_f16_f32")]]  kernel mat_mm_t kernel_mul_mm<half4x4,    1,     dequantize_f16>;
-template [[host_name("kernel_mul_mm_q4_0_f32")]] kernel mat_mm_t kernel_mul_mm<block_q4_0, 2,     dequantize_q4_0>;
-template [[host_name("kernel_mul_mm_q4_1_f32")]] kernel mat_mm_t kernel_mul_mm<block_q4_1, 2,     dequantize_q4_1>;
-template [[host_name("kernel_mul_mm_q5_0_f32")]] kernel mat_mm_t kernel_mul_mm<block_q5_0, 2,     dequantize_q5_0>;
-template [[host_name("kernel_mul_mm_q5_1_f32")]] kernel mat_mm_t kernel_mul_mm<block_q5_1, 2,     dequantize_q5_1>;
-template [[host_name("kernel_mul_mm_q8_0_f32")]] kernel mat_mm_t kernel_mul_mm<block_q8_0, 2,     dequantize_q8_0>;
-template [[host_name("kernel_mul_mm_q2_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q2_K, QK_NL, dequantize_q2_K>;
-template [[host_name("kernel_mul_mm_q3_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q3_K, QK_NL, dequantize_q3_K>;
-template [[host_name("kernel_mul_mm_q4_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q4_K, QK_NL, dequantize_q4_K>;
-template [[host_name("kernel_mul_mm_q5_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q5_K, QK_NL, dequantize_q5_K>;
-template [[host_name("kernel_mul_mm_q6_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q6_K, QK_NL, dequantize_q6_K>;
+template [[host_name("kernel_mul_mm_f32_f32")]]     kernel mat_mm_t kernel_mul_mm<float4x4,      1,     dequantize_f32>;
+template [[host_name("kernel_mul_mm_f16_f32")]]     kernel mat_mm_t kernel_mul_mm<half4x4,       1,     dequantize_f16>;
+template [[host_name("kernel_mul_mm_q4_0_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q4_0,    2,     dequantize_q4_0>;
+template [[host_name("kernel_mul_mm_q4_1_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q4_1,    2,     dequantize_q4_1>;
+template [[host_name("kernel_mul_mm_q5_0_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q5_0,    2,     dequantize_q5_0>;
+template [[host_name("kernel_mul_mm_q5_1_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q5_1,    2,     dequantize_q5_1>;
+template [[host_name("kernel_mul_mm_q8_0_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q8_0,    2,     dequantize_q8_0>;
+template [[host_name("kernel_mul_mm_q2_K_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q2_K,    QK_NL, dequantize_q2_K>;
+template [[host_name("kernel_mul_mm_q3_K_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q3_K,    QK_NL, dequantize_q3_K>;
+template [[host_name("kernel_mul_mm_q4_K_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q4_K,    QK_NL, dequantize_q4_K>;
+template [[host_name("kernel_mul_mm_q5_K_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q5_K,    QK_NL, dequantize_q5_K>;
+template [[host_name("kernel_mul_mm_q6_K_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q6_K,    QK_NL, dequantize_q6_K>;
 template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
 template [[host_name("kernel_mul_mm_iq2_xs_f32")]]  kernel mat_mm_t kernel_mul_mm<block_iq2_xs,  QK_NL, dequantize_iq2_xs>;
 template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
+template [[host_name("kernel_mul_mm_iq3_s_f32")]]   kernel mat_mm_t kernel_mul_mm<block_iq3_s,   QK_NL, dequantize_iq3_s>;
+template [[host_name("kernel_mul_mm_iq2_s_f32")]]   kernel mat_mm_t kernel_mul_mm<block_iq2_s,   QK_NL, dequantize_iq2_s>;
 template [[host_name("kernel_mul_mm_iq1_s_f32")]]   kernel mat_mm_t kernel_mul_mm<block_iq1_s,   QK_NL, dequantize_iq1_s>;
+template [[host_name("kernel_mul_mm_iq1_m_f32")]]   kernel mat_mm_t kernel_mul_mm<block_iq1_m,   QK_NL, dequantize_iq1_m>;
+template [[host_name("kernel_mul_mm_iq4_nl_f32")]]  kernel mat_mm_t kernel_mul_mm<block_iq4_nl,  2,     dequantize_iq4_nl>;
+#if QK_K == 64
+template [[host_name("kernel_mul_mm_iq4_xs_f32")]]  kernel mat_mm_t kernel_mul_mm<block_iq4_nl,  2,     dequantize_iq4_xs>;
+#else
+template [[host_name("kernel_mul_mm_iq4_xs_f32")]]  kernel mat_mm_t kernel_mul_mm<block_iq4_xs,  QK_NL, dequantize_iq4_xs>;
+#endif
 
 //
 // indirect matrix-matrix multiplication
 //
 
-typedef void (mat_mm_id_t)(
-        device const   uchar * ids,
-        device const   uchar * src1,
-        device         float * dst,
-        constant    uint64_t & nbi1,
-        constant     int64_t & ne00,
-        constant     int64_t & ne02,
-        constant    uint64_t & nb01,
-        constant    uint64_t & nb02,
-        constant     int64_t & ne12,
-        constant     int64_t & ne13,
-        constant    uint64_t & nb10,
-        constant    uint64_t & nb11,
-        constant    uint64_t & nb12,
-        constant     int64_t & ne0,
-        constant     int64_t & ne1,
-        constant    uint64_t & nb1,
-        constant        uint & r2,
-        constant        uint & r3,
-        constant         int & idx,
-        device const   uchar * src00,
-        device const   uchar * src01,
-        device const   uchar * src02,
-        device const   uchar * src03,
-        device const   uchar * src04,
-        device const   uchar * src05,
-        device const   uchar * src06,
-        device const   uchar * src07,
-        threadgroup    uchar *,
-        uint3, uint, uint);
+typedef decltype(kernel_mul_mm_id<float4x4, 1, dequantize_f32>) mat_mm_id_t;
 
-template [[host_name("kernel_mul_mm_id_f32_f32")]]  kernel mat_mm_id_t kernel_mul_mm_id<float4x4,   1,     dequantize_f32>;
-template [[host_name("kernel_mul_mm_id_f16_f32")]]  kernel mat_mm_id_t kernel_mul_mm_id<half4x4,    1,     dequantize_f16>;
-template [[host_name("kernel_mul_mm_id_q4_0_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q4_0, 2,     dequantize_q4_0>;
-template [[host_name("kernel_mul_mm_id_q4_1_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q4_1, 2,     dequantize_q4_1>;
-template [[host_name("kernel_mul_mm_id_q5_0_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q5_0, 2,     dequantize_q5_0>;
-template [[host_name("kernel_mul_mm_id_q5_1_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q5_1, 2,     dequantize_q5_1>;
-template [[host_name("kernel_mul_mm_id_q8_0_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q8_0, 2,     dequantize_q8_0>;
-template [[host_name("kernel_mul_mm_id_q2_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q2_K, QK_NL, dequantize_q2_K>;
-template [[host_name("kernel_mul_mm_id_q3_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q3_K, QK_NL, dequantize_q3_K>;
-template [[host_name("kernel_mul_mm_id_q4_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q4_K, QK_NL, dequantize_q4_K>;
-template [[host_name("kernel_mul_mm_id_q5_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q5_K, QK_NL, dequantize_q5_K>;
-template [[host_name("kernel_mul_mm_id_q6_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q6_K, QK_NL, dequantize_q6_K>;
+template [[host_name("kernel_mul_mm_id_f32_f32")]]     kernel mat_mm_id_t kernel_mul_mm_id<float4x4,      1,     dequantize_f32>;
+template [[host_name("kernel_mul_mm_id_f16_f32")]]     kernel mat_mm_id_t kernel_mul_mm_id<half4x4,       1,     dequantize_f16>;
+template [[host_name("kernel_mul_mm_id_q4_0_f32")]]    kernel mat_mm_id_t kernel_mul_mm_id<block_q4_0,    2,     dequantize_q4_0>;
+template [[host_name("kernel_mul_mm_id_q4_1_f32")]]    kernel mat_mm_id_t kernel_mul_mm_id<block_q4_1,    2,     dequantize_q4_1>;
+template [[host_name("kernel_mul_mm_id_q5_0_f32")]]    kernel mat_mm_id_t kernel_mul_mm_id<block_q5_0,    2,     dequantize_q5_0>;
+template [[host_name("kernel_mul_mm_id_q5_1_f32")]]    kernel mat_mm_id_t kernel_mul_mm_id<block_q5_1,    2,     dequantize_q5_1>;
+template [[host_name("kernel_mul_mm_id_q8_0_f32")]]    kernel mat_mm_id_t kernel_mul_mm_id<block_q8_0,    2,     dequantize_q8_0>;
+template [[host_name("kernel_mul_mm_id_q2_K_f32")]]    kernel mat_mm_id_t kernel_mul_mm_id<block_q2_K,    QK_NL, dequantize_q2_K>;
+template [[host_name("kernel_mul_mm_id_q3_K_f32")]]    kernel mat_mm_id_t kernel_mul_mm_id<block_q3_K,    QK_NL, dequantize_q3_K>;
+template [[host_name("kernel_mul_mm_id_q4_K_f32")]]    kernel mat_mm_id_t kernel_mul_mm_id<block_q4_K,    QK_NL, dequantize_q4_K>;
+template [[host_name("kernel_mul_mm_id_q5_K_f32")]]    kernel mat_mm_id_t kernel_mul_mm_id<block_q5_K,    QK_NL, dequantize_q5_K>;
+template [[host_name("kernel_mul_mm_id_q6_K_f32")]]    kernel mat_mm_id_t kernel_mul_mm_id<block_q6_K,    QK_NL, dequantize_q6_K>;
 template [[host_name("kernel_mul_mm_id_iq2_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
 template [[host_name("kernel_mul_mm_id_iq2_xs_f32")]]  kernel mat_mm_id_t kernel_mul_mm_id<block_iq2_xs,  QK_NL, dequantize_iq2_xs>;
 template [[host_name("kernel_mul_mm_id_iq3_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
+template [[host_name("kernel_mul_mm_id_iq3_s_f32")]]   kernel mat_mm_id_t kernel_mul_mm_id<block_iq3_s,   QK_NL, dequantize_iq3_s>;
+template [[host_name("kernel_mul_mm_id_iq2_s_f32")]]   kernel mat_mm_id_t kernel_mul_mm_id<block_iq2_s,   QK_NL, dequantize_iq2_s>;
 template [[host_name("kernel_mul_mm_id_iq1_s_f32")]]   kernel mat_mm_id_t kernel_mul_mm_id<block_iq1_s,   QK_NL, dequantize_iq1_s>;
+template [[host_name("kernel_mul_mm_id_iq1_m_f32")]]   kernel mat_mm_id_t kernel_mul_mm_id<block_iq1_m,   QK_NL, dequantize_iq1_m>;
+template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]]  kernel mat_mm_id_t kernel_mul_mm_id<block_iq4_nl,  2,     dequantize_iq4_nl>;
+#if QK_K == 64
+template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]]  kernel mat_mm_id_t kernel_mul_mm_id<block_iq4_xs,  2,     dequantize_iq4_xs>;
+#else
+template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]]  kernel mat_mm_id_t kernel_mul_mm_id<block_iq4_xs,  QK_NL, dequantize_iq4_xs>;
+#endif
 
 //
 // matrix-vector multiplication
 //
 
-[[host_name("kernel_mul_mv_id_f32_f32")]]
-kernel void kernel_mul_mv_id_f32_f32(
-        device const    char * ids,
+typedef void (kernel_mul_mv_impl_t)(
+        device const  char * src0,
+        device const  char * src1,
+        device       float * dst,
+                   int64_t   ne00,
+                   int64_t   ne01,
+                   int64_t   ne02,
+                  uint64_t   nb00,
+                  uint64_t   nb01,
+                  uint64_t   nb02,
+                   int64_t   ne10,
+                   int64_t   ne11,
+                   int64_t   ne12,
+                  uint64_t   nb10,
+                  uint64_t   nb11,
+                  uint64_t   nb12,
+                   int64_t   ne0,
+                   int64_t   ne1,
+                   uint      r2,
+                   uint      r3,
+                   uint3     tgpig,
+                   uint      tiisg);
+
+typedef void (kernel_mul_mv2_impl_t)(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+                   int64_t   ne00,
+                   int64_t   ne01,
+                   int64_t   ne02,
+                   int64_t   ne10,
+                   int64_t   ne12,
+                   int64_t   ne0,
+                   int64_t   ne1,
+                   uint      r2,
+                   uint      r3,
+        threadgroup int8_t * shared_values,
+                   uint3     tgpig,
+                   uint      tiisg,
+                   uint      sgitg);
+
+template<kernel_mul_mv_impl_t impl_fn>
+void mmv_fn(
+        device const    char * src0,
         device const    char * src1,
         device         float * dst,
-        constant    uint64_t & nbi1,
-        constant     int64_t & ne00,
-        constant     int64_t & ne01,
-        constant     int64_t & ne02,
-        constant    uint64_t & nb00,
-        constant    uint64_t & nb01,
-        constant    uint64_t & nb02,
-        constant     int64_t & ne10,
-        constant     int64_t & ne11,
-        constant     int64_t & ne12,
-        constant     int64_t & ne13,
-        constant    uint64_t & nb10,
-        constant    uint64_t & nb11,
-        constant    uint64_t & nb12,
-        constant     int64_t & ne0,
-        constant     int64_t & ne1,
-        constant    uint64_t & nb1,
-        constant        uint & r2,
-        constant        uint & r3,
-        constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
-        uint3                  tgpig[[threadgroup_position_in_grid]],
-        uint                   tiitg[[thread_index_in_threadgroup]],
-        uint                   tiisg[[thread_index_in_simdgroup]],
-        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
-    const int64_t bid = tgpig.z/(ne12*ne13);
-
-    tgpig.z = tgpig.z%(ne12*ne13);
-
-    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
-
-    kernel_mul_mv_f32_f32_impl(
-        src0[id],
-        src1 + bid*nb11,
-        dst  + bid*ne0,
-        ne00,
-        ne01,
-        ne02,
-        nb00,
-        nb01,
-        nb02,
-        ne10,
-        ne11,
-        ne12,
-        nb10,
-        nb11,
-        nb12,
-        ne0,
-        ne1,
-        r2,
-        r3,
-        tgpig,
-        tiisg);
+                     int64_t   ne00,
+                     int64_t   ne01,
+                     int64_t   ne02,
+                    uint64_t   nb00,
+                    uint64_t   nb01,
+                    uint64_t   nb02,
+                     int64_t   ne10,
+                     int64_t   ne11,
+                     int64_t   ne12,
+                     int64_t   ne13,
+                    uint64_t   nb10,
+                    uint64_t   nb11,
+                    uint64_t   nb12,
+                     int64_t   ne0,
+                     int64_t   ne1,
+                    uint64_t   nb1,
+                        uint   r2,
+                        uint   r3,
+        threadgroup int8_t   * shared_values,
+        uint3                  tgpig,
+        uint                   tiitg,
+        uint                   tiisg,
+        uint                   sgitg) {
+    impl_fn(src0,src1,dst,ne00,ne01,ne02,nb00,nb01,nb02,ne10,ne11,ne12,nb10,nb11,nb12,ne0,ne1,r2,r3,tgpig,tiisg);
 }
 
-[[host_name("kernel_mul_mv_id_f16_f32")]]
-kernel void kernel_mul_mv_id_f16_f32(
-        device const    char * ids,
+template<kernel_mul_mv2_impl_t impl_fn>
+void mmv_fn(
+        device const    char * src0,
         device const    char * src1,
         device         float * dst,
-        constant    uint64_t & nbi1,
-        constant     int64_t & ne00,
-        constant     int64_t & ne01,
-        constant     int64_t & ne02,
-        constant    uint64_t & nb00,
-        constant    uint64_t & nb01,
-        constant    uint64_t & nb02,
-        constant     int64_t & ne10,
-        constant     int64_t & ne11,
-        constant     int64_t & ne12,
-        constant     int64_t & ne13,
-        constant    uint64_t & nb10,
-        constant    uint64_t & nb11,
-        constant    uint64_t & nb12,
-        constant     int64_t & ne0,
-        constant     int64_t & ne1,
-        constant    uint64_t & nb1,
-        constant        uint & r2,
-        constant        uint & r3,
-        constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
-        uint3                  tgpig[[threadgroup_position_in_grid]],
-        uint                   tiitg[[thread_index_in_threadgroup]],
-        uint                   tiisg[[thread_index_in_simdgroup]],
-        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
-    const int64_t bid = tgpig.z/(ne12*ne13);
-
-    tgpig.z = tgpig.z%(ne12*ne13);
-
-    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
-
-    kernel_mul_mv_f16_f32_impl(
-        src0[id],
-        src1 + bid*nb11,
-        dst  + bid*ne0,
-        ne00,
-        ne01,
-        ne02,
-        nb00,
-        nb01,
-        nb02,
-        ne10,
-        ne11,
-        ne12,
-        nb10,
-        nb11,
-        nb12,
-        ne0,
-        ne1,
-        r2,
-        r3,
-        tgpig,
-        tiisg);
+                     int64_t   ne00,
+                     int64_t   ne01,
+                     int64_t   ne02,
+                    uint64_t   nb00,
+                    uint64_t   nb01,
+                    uint64_t   nb02,
+                     int64_t   ne10,
+                     int64_t   ne11,
+                     int64_t   ne12,
+                     int64_t   ne13,
+                    uint64_t   nb10,
+                    uint64_t   nb11,
+                    uint64_t   nb12,
+                     int64_t   ne0,
+                     int64_t   ne1,
+                    uint64_t   nb1,
+                        uint   r2,
+                        uint   r3,
+        threadgroup int8_t   * shared_values,
+        uint3                  tgpig,
+        uint                   tiitg,
+        uint                   tiisg,
+        uint                   sgitg) {
+    impl_fn(src0,(const device float *)src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,shared_values,tgpig,tiisg,sgitg);
 }
 
-[[host_name("kernel_mul_mv_id_q8_0_f32")]]
-kernel void kernel_mul_mv_id_q8_0_f32(
-        device const    char * ids,
+typedef decltype(mmv_fn<kernel_mul_mv_f32_f32_impl>) mul_mv_impl_fn_t;
+
+template<mul_mv_impl_fn_t impl_fn>
+kernel void kernel_mul_mv_id(
+        device const    char * src0s,
         device const    char * src1,
         device         float * dst,
+        device const    char * ids,
+        constant     int64_t & nei0,
+        constant     int64_t & nei1,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -5638,868 +6788,80 @@ kernel void kernel_mul_mv_id_q8_0_f32(
         constant     int64_t & ne0,
         constant     int64_t & ne1,
         constant    uint64_t & nb1,
-        constant        uint & r2,
-        constant        uint & r3,
-        constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
-        uint3                  tgpig[[threadgroup_position_in_grid]],
-        uint                   tiitg[[thread_index_in_threadgroup]],
-        uint                   tiisg[[thread_index_in_simdgroup]],
-        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
-    const int64_t bid = tgpig.z/(ne12*ne13);
-
-    tgpig.z = tgpig.z%(ne12*ne13);
-
-    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
-
-    kernel_mul_mv_q8_0_f32_impl(
-        src0[id],
-        (device const float *) (src1 + bid*nb11),
-        dst + bid*ne0,
-        ne00,
-        ne01,
-        ne02,
-        ne10,
-        ne12,
-        ne0,
-        ne1,
-        r2,
-        r3,
-        tgpig,
-        tiisg,
-        sgitg);
-}
-
-[[host_name("kernel_mul_mv_id_q4_0_f32")]]
-kernel void kernel_mul_mv_id_q4_0_f32(
-        device const    char * ids,
-        device const    char * src1,
-        device         float * dst,
-        constant    uint64_t & nbi1,
-        constant     int64_t & ne00,
-        constant     int64_t & ne01,
-        constant     int64_t & ne02,
-        constant    uint64_t & nb00,
-        constant    uint64_t & nb01,
-        constant    uint64_t & nb02,
-        constant     int64_t & ne10,
-        constant     int64_t & ne11,
-        constant     int64_t & ne12,
-        constant     int64_t & ne13,
-        constant    uint64_t & nb10,
-        constant    uint64_t & nb11,
-        constant    uint64_t & nb12,
-        constant     int64_t & ne0,
-        constant     int64_t & ne1,
-        constant    uint64_t & nb1,
-        constant        uint & r2,
-        constant        uint & r3,
-        constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
-        uint3                  tgpig[[threadgroup_position_in_grid]],
-        uint                   tiitg[[thread_index_in_threadgroup]],
-        uint                   tiisg[[thread_index_in_simdgroup]],
-        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
-    const int64_t bid = tgpig.z/(ne12*ne13);
-
-    tgpig.z = tgpig.z%(ne12*ne13);
-
-    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
-
-    mul_vec_q_n_f32_impl<block_q4_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(
-        src0[id],
-        (device const float *) (src1 + bid*nb11),
-        dst + bid*ne0,
-        ne00,
-        ne01,
-        ne02,
-        ne10,
-        ne12,
-        ne0,
-        ne1,
-        r2,
-        r3,
-        tgpig,
-        tiisg,
-        sgitg);
-}
-
-[[host_name("kernel_mul_mv_id_q4_1_f32")]]
-kernel void kernel_mul_mv_id_q4_1_f32(
-        device const    char * ids,
-        device const    char * src1,
-        device         float * dst,
-        constant    uint64_t & nbi1,
-        constant     int64_t & ne00,
-        constant     int64_t & ne01,
-        constant     int64_t & ne02,
-        constant    uint64_t & nb00,
-        constant    uint64_t & nb01,
-        constant    uint64_t & nb02,
-        constant     int64_t & ne10,
-        constant     int64_t & ne11,
-        constant     int64_t & ne12,
-        constant     int64_t & ne13,
-        constant    uint64_t & nb10,
-        constant    uint64_t & nb11,
-        constant    uint64_t & nb12,
-        constant     int64_t & ne0,
-        constant     int64_t & ne1,
-        constant    uint64_t & nb1,
-        constant        uint & r2,
-        constant        uint & r3,
-        constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
-        uint3                  tgpig[[threadgroup_position_in_grid]],
-        uint                   tiitg[[thread_index_in_threadgroup]],
-        uint                   tiisg[[thread_index_in_simdgroup]],
-        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
-    const int64_t bid = tgpig.z/(ne12*ne13);
-
-    tgpig.z = tgpig.z%(ne12*ne13);
-
-    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
-
-    mul_vec_q_n_f32_impl<block_q4_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(
-        src0[id],
-        (device const float *) (src1 + bid*nb11),
-        dst + bid*ne0,
-        ne00,
-        ne01,
-        ne02,
-        ne10,
-        ne12,
-        ne0,
-        ne1,
-        r2,
-        r3,
-        tgpig,
-        tiisg,
-        sgitg);
-}
-
-[[host_name("kernel_mul_mv_id_q5_0_f32")]]
-kernel void kernel_mul_mv_id_q5_0_f32(
-        device const    char * ids,
-        device const    char * src1,
-        device         float * dst,
-        constant    uint64_t & nbi1,
-        constant     int64_t & ne00,
-        constant     int64_t & ne01,
-        constant     int64_t & ne02,
-        constant    uint64_t & nb00,
-        constant    uint64_t & nb01,
-        constant    uint64_t & nb02,
-        constant     int64_t & ne10,
-        constant     int64_t & ne11,
-        constant     int64_t & ne12,
-        constant     int64_t & ne13,
-        constant    uint64_t & nb10,
-        constant    uint64_t & nb11,
-        constant    uint64_t & nb12,
-        constant     int64_t & ne0,
-        constant     int64_t & ne1,
-        constant    uint64_t & nb1,
-        constant        uint & r2,
-        constant        uint & r3,
-        constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
-        uint3                  tgpig[[threadgroup_position_in_grid]],
-        uint                   tiitg[[thread_index_in_threadgroup]],
-        uint                   tiisg[[thread_index_in_simdgroup]],
-        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
-    const int64_t bid = tgpig.z/(ne12*ne13);
-
-    tgpig.z = tgpig.z%(ne12*ne13);
-
-    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
-
-    mul_vec_q_n_f32_impl<block_q5_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(
-        src0[id],
-        (device const float *) (src1 + bid*nb11),
-        dst + bid*ne0,
-        ne00,
-        ne01,
-        ne02,
-        ne10,
-        ne12,
-        ne0,
-        ne1,
-        r2,
-        r3,
-        tgpig,
-        tiisg,
-        sgitg);
-}
-
-[[host_name("kernel_mul_mv_id_q5_1_f32")]]
-kernel void kernel_mul_mv_id_q5_1_f32(
-        device const    char * ids,
-        device const    char * src1,
-        device         float * dst,
-        constant    uint64_t & nbi1,
-        constant     int64_t & ne00,
-        constant     int64_t & ne01,
-        constant     int64_t & ne02,
-        constant    uint64_t & nb00,
-        constant    uint64_t & nb01,
-        constant    uint64_t & nb02,
-        constant     int64_t & ne10,
-        constant     int64_t & ne11,
-        constant     int64_t & ne12,
-        constant     int64_t & ne13,
-        constant    uint64_t & nb10,
-        constant    uint64_t & nb11,
-        constant    uint64_t & nb12,
-        constant     int64_t & ne0,
-        constant     int64_t & ne1,
-        constant    uint64_t & nb1,
-        constant        uint & r2,
-        constant        uint & r3,
-        constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
-        uint3                  tgpig[[threadgroup_position_in_grid]],
-        uint                   tiitg[[thread_index_in_threadgroup]],
-        uint                   tiisg[[thread_index_in_simdgroup]],
-        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
-    const int64_t bid = tgpig.z/(ne12*ne13);
-
-    tgpig.z = tgpig.z%(ne12*ne13);
-
-    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
-
-    mul_vec_q_n_f32_impl<block_q5_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(
-        src0[id],
-        (device const float *) (src1 + bid*nb11),
-        dst + bid*ne0,
-        ne00,
-        ne01,
-        ne02,
-        ne10,
-        ne12,
-        ne0,
-        ne1,
-        r2,
-        r3,
-        tgpig,
-        tiisg,
-        sgitg);
-}
-
-[[host_name("kernel_mul_mv_id_q2_K_f32")]]
-kernel void kernel_mul_mv_id_q2_K_f32(
-        device const    char * ids,
-        device const    char * src1,
-        device         float * dst,
-        constant    uint64_t & nbi1,
-        constant     int64_t & ne00,
-        constant     int64_t & ne01,
-        constant     int64_t & ne02,
-        constant    uint64_t & nb00,
-        constant    uint64_t & nb01,
-        constant    uint64_t & nb02,
-        constant     int64_t & ne10,
-        constant     int64_t & ne11,
-        constant     int64_t & ne12,
-        constant     int64_t & ne13,
-        constant    uint64_t & nb10,
-        constant    uint64_t & nb11,
-        constant    uint64_t & nb12,
-        constant     int64_t & ne0,
-        constant     int64_t & ne1,
-        constant    uint64_t & nb1,
-        constant        uint & r2,
-        constant        uint & r3,
-        constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
-        uint3                  tgpig[[threadgroup_position_in_grid]],
-        uint                   tiitg[[thread_index_in_threadgroup]],
-        uint                   tiisg[[thread_index_in_simdgroup]],
-        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
-    const int64_t bid = tgpig.z/(ne12*ne13);
-
-    tgpig.z = tgpig.z%(ne12*ne13);
-
-    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
-
-    kernel_mul_mv_q2_K_f32_impl(
-        src0[id],
-        (device const float *) (src1 + bid*nb11),
-        dst + bid*ne0,
-        ne00,
-        ne01,
-        ne02,
-        ne10,
-        ne12,
-        ne0,
-        ne1,
-        r2,
-        r3,
-        tgpig,
-        tiisg,
-        sgitg);
-}
-
-[[host_name("kernel_mul_mv_id_q3_K_f32")]]
-kernel void kernel_mul_mv_id_q3_K_f32(
-        device const    char * ids,
-        device const    char * src1,
-        device         float * dst,
-        constant    uint64_t & nbi1,
-        constant     int64_t & ne00,
-        constant     int64_t & ne01,
-        constant     int64_t & ne02,
-        constant    uint64_t & nb00,
-        constant    uint64_t & nb01,
-        constant    uint64_t & nb02,
-        constant     int64_t & ne10,
-        constant     int64_t & ne11,
-        constant     int64_t & ne12,
-        constant     int64_t & ne13,
-        constant    uint64_t & nb10,
-        constant    uint64_t & nb11,
-        constant    uint64_t & nb12,
-        constant     int64_t & ne0,
-        constant     int64_t & ne1,
-        constant    uint64_t & nb1,
-        constant        uint & r2,
-        constant        uint & r3,
-        constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
-        uint3                  tgpig[[threadgroup_position_in_grid]],
-        uint                   tiitg[[thread_index_in_threadgroup]],
-        uint                   tiisg[[thread_index_in_simdgroup]],
-        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
-    const int64_t bid = tgpig.z/(ne12*ne13);
-
-    tgpig.z = tgpig.z%(ne12*ne13);
-
-    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
-
-    kernel_mul_mv_q3_K_f32_impl(
-        src0[id],
-        (device const float *) (src1 + bid*nb11),
-        dst + bid*ne0,
-        ne00,
-        ne01,
-        ne02,
-        ne10,
-        ne12,
-        ne0,
-        ne1,
-        r2,
-        r3,
-        tgpig,
-        tiisg,
-        sgitg);
-}
-
-[[host_name("kernel_mul_mv_id_q4_K_f32")]]
-kernel void kernel_mul_mv_id_q4_K_f32(
-        device const    char * ids,
-        device const    char * src1,
-        device         float * dst,
-        constant    uint64_t & nbi1,
-        constant     int64_t & ne00,
-        constant     int64_t & ne01,
-        constant     int64_t & ne02,
-        constant    uint64_t & nb00,
-        constant    uint64_t & nb01,
-        constant    uint64_t & nb02,
-        constant     int64_t & ne10,
-        constant     int64_t & ne11,
-        constant     int64_t & ne12,
-        constant     int64_t & ne13,
-        constant    uint64_t & nb10,
-        constant    uint64_t & nb11,
-        constant    uint64_t & nb12,
-        constant     int64_t & ne0,
-        constant     int64_t & ne1,
-        constant    uint64_t & nb1,
-        constant        uint & r2,
-        constant        uint & r3,
-        constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
-        uint3                  tgpig[[threadgroup_position_in_grid]],
-        uint                   tiitg[[thread_index_in_threadgroup]],
-        uint                   tiisg[[thread_index_in_simdgroup]],
-        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
-    const int64_t bid = tgpig.z/(ne12*ne13);
-
-    tgpig.z = tgpig.z%(ne12*ne13);
-
-    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
-
-    kernel_mul_mv_q4_K_f32_impl(
-        src0[id],
-        (device const float *) (src1 + bid*nb11),
-        dst + bid*ne0,
-        ne00,
-        ne01,
-        ne02,
-        ne10,
-        ne12,
-        ne0,
-        ne1,
-        r2,
-        r3,
-        tgpig,
-        tiisg,
-        sgitg);
-}
-
-[[host_name("kernel_mul_mv_id_q5_K_f32")]]
-kernel void kernel_mul_mv_id_q5_K_f32(
-        device const    char * ids,
-        device const    char * src1,
-        device         float * dst,
-        constant    uint64_t & nbi1,
-        constant     int64_t & ne00,
-        constant     int64_t & ne01,
-        constant     int64_t & ne02,
-        constant    uint64_t & nb00,
-        constant    uint64_t & nb01,
-        constant    uint64_t & nb02,
-        constant     int64_t & ne10,
-        constant     int64_t & ne11,
-        constant     int64_t & ne12,
-        constant     int64_t & ne13,
-        constant    uint64_t & nb10,
-        constant    uint64_t & nb11,
-        constant    uint64_t & nb12,
-        constant     int64_t & ne0,
-        constant     int64_t & ne1,
-        constant    uint64_t & nb1,
-        constant        uint & r2,
-        constant        uint & r3,
-        constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
-        uint3                  tgpig[[threadgroup_position_in_grid]],
-        uint                   tiitg[[thread_index_in_threadgroup]],
-        uint                   tiisg[[thread_index_in_simdgroup]],
-        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
-    const int64_t bid = tgpig.z/(ne12*ne13);
-
-    tgpig.z = tgpig.z%(ne12*ne13);
-
-    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
-
-    kernel_mul_mv_q5_K_f32_impl(
-        src0[id],
-        (device const float *) (src1 + bid*nb11),
-        dst + bid*ne0,
-        ne00,
-        ne01,
-        ne02,
-        ne10,
-        ne12,
-        ne0,
-        ne1,
-        r2,
-        r3,
-        tgpig,
-        tiisg,
-        sgitg);
-}
-
-[[host_name("kernel_mul_mv_id_q6_K_f32")]]
-kernel void kernel_mul_mv_id_q6_K_f32(
-        device const    char * ids,
-        device const    char * src1,
-        device         float * dst,
-        constant    uint64_t & nbi1,
-        constant     int64_t & ne00,
-        constant     int64_t & ne01,
-        constant     int64_t & ne02,
-        constant    uint64_t & nb00,
-        constant    uint64_t & nb01,
-        constant    uint64_t & nb02,
-        constant     int64_t & ne10,
-        constant     int64_t & ne11,
-        constant     int64_t & ne12,
-        constant     int64_t & ne13,
-        constant    uint64_t & nb10,
-        constant    uint64_t & nb11,
-        constant    uint64_t & nb12,
-        constant     int64_t & ne0,
-        constant     int64_t & ne1,
-        constant    uint64_t & nb1,
-        constant        uint & r2,
-        constant        uint & r3,
-        constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
-        uint3                  tgpig[[threadgroup_position_in_grid]],
-        uint                   tiitg[[thread_index_in_threadgroup]],
-        uint                   tiisg[[thread_index_in_simdgroup]],
-        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
-    const int64_t bid = tgpig.z/(ne12*ne13);
-
-    tgpig.z = tgpig.z%(ne12*ne13);
-
-    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
-
-    kernel_mul_mv_q6_K_f32_impl(
-        src0[id],
-        (device const float *) (src1 + bid*nb11),
-        dst + bid*ne0,
-        ne00,
-        ne01,
-        ne02,
-        ne10,
-        ne12,
-        ne0,
-        ne1,
-        r2,
-        r3,
-        tgpig,
-        tiisg,
-        sgitg);
-}
-
-[[host_name("kernel_mul_mv_id_iq2_xxs_f32")]]
-kernel void kernel_mul_mv_id_iq2_xxs_f32(
-        device const    char * ids,
-        device const    char * src1,
-        device         float * dst,
-        constant    uint64_t & nbi1,
-        constant     int64_t & ne00,
-        constant     int64_t & ne01,
-        constant     int64_t & ne02,
-        constant    uint64_t & nb00,
-        constant    uint64_t & nb01,
-        constant    uint64_t & nb02,
-        constant     int64_t & ne10,
-        constant     int64_t & ne11,
-        constant     int64_t & ne12,
-        constant     int64_t & ne13,
-        constant    uint64_t & nb10,
-        constant    uint64_t & nb11,
-        constant    uint64_t & nb12,
-        constant     int64_t & ne0,
-        constant     int64_t & ne1,
-        constant    uint64_t & nb1,
-        constant        uint & r2,
-        constant        uint & r3,
-        constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
         threadgroup int8_t   * shared_values [[threadgroup(0)]],
         uint3                  tgpig[[threadgroup_position_in_grid]],
         uint                   tiitg[[thread_index_in_threadgroup]],
         uint                   tiisg[[thread_index_in_simdgroup]],
         uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
+    const int iid1 = tgpig.z/nei0;
+    const int idx = tgpig.z%nei0;
 
-    const int64_t bid = tgpig.z/(ne12*ne13);
+    tgpig.z = 0;
 
-    tgpig.z = tgpig.z%(ne12*ne13);
+    const int32_t i02 = ((device const int32_t *) (ids + iid1*nbi1))[idx];
 
-    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    const int64_t i11 = idx % ne11;
+    const int64_t i12 = iid1;
 
-    kernel_mul_mv_iq2_xxs_f32_impl(
-        src0[id],
-        (device const float *) (src1 + bid*nb11),
-        dst + bid*ne0,
-        ne00,
-        ne01,
-        ne02,
-        ne10,
-        ne12,
-        ne0,
-        ne1,
-        r2,
-        r3,
+    const int64_t i1 = idx;
+    const int64_t i2 = i12;
+
+    device const char * src0_cur = src0s + i02*nb02;
+    device const char * src1_cur = src1 + i11*nb11 + i12*nb12;
+    device      float * dst_cur  = dst + i1*ne0 + i2*ne1*ne0;
+
+    impl_fn(
+        /* src0 */ src0_cur,
+        /* src1 */ src1_cur,
+        /* dst  */ dst_cur,
+        /* ne00 */ ne00,
+        /* ne01 */ ne01,
+        /* ne02 */ 1,//ne02,
+        /* nb00 */ nb00,
+        /* nb01 */ nb01,
+        /* nb02 */ nb02,
+        /* ne10 */ ne10,
+        /* ne11 */ 1,//ne11,
+        /* ne12 */ 1,//ne12,
+        /* ne13 */ 1,//ne13,
+        /* nb10 */ nb10,
+        /* nb11 */ nb11,
+        /* nb12 */ nb12,
+        /* ne0  */ ne0,
+        /* ne1  */ 1,//ne1,
+        /* nb1  */ nb1,
+        /* r2   */ 1,
+        /* r3   */ 1,
         shared_values,
         tgpig,
+        tiitg,
         tiisg,
         sgitg);
 }
 
-[[host_name("kernel_mul_mv_id_iq2_xs_f32")]]
-kernel void kernel_mul_mv_id_iq2_xs_f32(
-        device const    char * ids,
-        device const    char * src1,
-        device         float * dst,
-        constant    uint64_t & nbi1,
-        constant     int64_t & ne00,
-        constant     int64_t & ne01,
-        constant     int64_t & ne02,
-        constant    uint64_t & nb00,
-        constant    uint64_t & nb01,
-        constant    uint64_t & nb02,
-        constant     int64_t & ne10,
-        constant     int64_t & ne11,
-        constant     int64_t & ne12,
-        constant     int64_t & ne13,
-        constant    uint64_t & nb10,
-        constant    uint64_t & nb11,
-        constant    uint64_t & nb12,
-        constant     int64_t & ne0,
-        constant     int64_t & ne1,
-        constant    uint64_t & nb1,
-        constant        uint & r2,
-        constant        uint & r3,
-        constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
-        threadgroup int8_t   * shared_values [[threadgroup(0)]],
-        uint3                  tgpig[[threadgroup_position_in_grid]],
-        uint                   tiitg[[thread_index_in_threadgroup]],
-        uint                   tiisg[[thread_index_in_simdgroup]],
-        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
+typedef decltype(kernel_mul_mv_id<mmv_fn<kernel_mul_mv_f32_f32_impl>>) kernel_mul_mv_id_t;
 
-    const int64_t bid = tgpig.z/(ne12*ne13);
+template [[host_name("kernel_mul_mv_id_f32_f32")]]  kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_f32_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_f16_f32")]]  kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_f16_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_q8_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q8_0_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_q4_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q4_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
+template [[host_name("kernel_mul_mv_id_q4_1_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q4_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
+template [[host_name("kernel_mul_mv_id_q5_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q5_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
+template [[host_name("kernel_mul_mv_id_q5_1_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q5_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
+template [[host_name("kernel_mul_mv_id_q2_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q2_K_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_q3_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q3_K_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_q4_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q4_K_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_q5_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q5_K_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_q6_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q6_K_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_iq1_s_f32")]]   kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq1_s_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_iq1_m_f32")]]   kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq1_m_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_iq2_xxs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq2_xxs_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_iq2_xs_f32")]]  kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq2_xs_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_iq3_xxs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq3_xxs_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_iq3_s_f32")]]   kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq3_s_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_iq2_s_f32")]]   kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq2_s_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_iq4_nl_f32")]]  kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq4_nl_f32_impl>>;
+#if QK_K != 64
+template [[host_name("kernel_mul_mv_id_iq4_xs_f32")]]  kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq4_xs_f32_impl>>;
+#endif
 
-    tgpig.z = tgpig.z%(ne12*ne13);
-
-    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
-
-    kernel_mul_mv_iq2_xs_f32_impl(
-        src0[id],
-        (device const float *) (src1 + bid*nb11),
-        dst + bid*ne0,
-        ne00,
-        ne01,
-        ne02,
-        ne10,
-        ne12,
-        ne0,
-        ne1,
-        r2,
-        r3,
-        shared_values,
-        tgpig,
-        tiisg,
-        sgitg);
-}
-
-[[host_name("kernel_mul_mv_id_iq3_xxs_f32")]]
-kernel void kernel_mul_mv_id_iq3_xxs_f32(
-        device const    char * ids,
-        device const    char * src1,
-        device         float * dst,
-        constant    uint64_t & nbi1,
-        constant     int64_t & ne00,
-        constant     int64_t & ne01,
-        constant     int64_t & ne02,
-        constant    uint64_t & nb00,
-        constant    uint64_t & nb01,
-        constant    uint64_t & nb02,
-        constant     int64_t & ne10,
-        constant     int64_t & ne11,
-        constant     int64_t & ne12,
-        constant     int64_t & ne13,
-        constant    uint64_t & nb10,
-        constant    uint64_t & nb11,
-        constant    uint64_t & nb12,
-        constant     int64_t & ne0,
-        constant     int64_t & ne1,
-        constant    uint64_t & nb1,
-        constant        uint & r2,
-        constant        uint & r3,
-        constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
-        threadgroup int8_t   * shared_values [[threadgroup(0)]],
-        uint3                  tgpig[[threadgroup_position_in_grid]],
-        uint                   tiitg[[thread_index_in_threadgroup]],
-        uint                   tiisg[[thread_index_in_simdgroup]],
-        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
-    const int64_t bid = tgpig.z/(ne12*ne13);
-
-    tgpig.z = tgpig.z%(ne12*ne13);
-
-    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
-
-    kernel_mul_mv_iq3_xxs_f32_impl(
-        src0[id],
-        (device const float *) (src1 + bid*nb11),
-        dst + bid*ne0,
-        ne00,
-        ne01,
-        ne02,
-        ne10,
-        ne12,
-        ne0,
-        ne1,
-        r2,
-        r3,
-        shared_values,
-        tgpig,
-        tiisg,
-        sgitg);
-}
-
-[[host_name("kernel_mul_mv_id_iq1_s_f32")]]
-kernel void kernel_mul_mv_id_iq1_s_f32(
-        device const    char * ids,
-        device const    char * src1,
-        device         float * dst,
-        constant    uint64_t & nbi1,
-        constant     int64_t & ne00,
-        constant     int64_t & ne01,
-        constant     int64_t & ne02,
-        constant    uint64_t & nb00,
-        constant    uint64_t & nb01,
-        constant    uint64_t & nb02,
-        constant     int64_t & ne10,
-        constant     int64_t & ne11,
-        constant     int64_t & ne12,
-        constant     int64_t & ne13,
-        constant    uint64_t & nb10,
-        constant    uint64_t & nb11,
-        constant    uint64_t & nb12,
-        constant     int64_t & ne0,
-        constant     int64_t & ne1,
-        constant    uint64_t & nb1,
-        constant        uint & r2,
-        constant        uint & r3,
-        constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
-        uint3                  tgpig[[threadgroup_position_in_grid]],
-        uint                   tiitg[[thread_index_in_threadgroup]],
-        uint                   tiisg[[thread_index_in_simdgroup]],
-        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
-    const int64_t bid = tgpig.z/(ne12*ne13);
-
-    tgpig.z = tgpig.z%(ne12*ne13);
-
-    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
-
-    kernel_mul_mv_iq1_s_f32_impl(
-        src0[id],
-        (device const float *) (src1 + bid*nb11),
-        dst + bid*ne0,
-        ne00,
-        ne01,
-        ne02,
-        ne10,
-        ne12,
-        ne0,
-        ne1,
-        r2,
-        r3,
-        tgpig,
-        tiisg,
-        sgitg);
-}
diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp
index 797bee667..880a14958 100644
--- a/ggml-opencl.cpp
+++ b/ggml-opencl.cpp
@@ -1354,7 +1354,7 @@ static void ggml_cl_pool_free(cl_mem mem, size_t size) {
 }
 
 void ggml_cl_free_data(const struct ggml_tensor* tensor) {
-    if (tensor->backend != GGML_BACKEND_GPU) {
+    if (tensor->backend != GGML_BACKEND_TYPE_GPU) {
         return;
     }
 
@@ -1412,7 +1412,7 @@ static cl_int ggml_cl_h2d_tensor_2d(cl_command_queue queue, cl_mem dst, size_t o
 }
 
 static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
     const int64_t ne00 = src0->ne[0];
     const int64_t ne01 = src0->ne[1];
     const int64_t ne02 = src0->ne[2];
@@ -1476,7 +1476,7 @@ void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src
 }
 
 static void ggml_cl_add_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
     const int64_t ne00 = src0->ne[0];
     const int64_t ne01 = src0->ne[1];
     const int64_t ne02 = src0->ne[2];
@@ -1566,13 +1566,13 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
     size_t y_size;
     size_t d_size;
     cl_mem d_X;
-    if (src0->backend == GGML_BACKEND_GPU) { // NOLINT
+    if (src0->backend == GGML_BACKEND_TYPE_GPU) { // NOLINT
         d_X = (cl_mem) src0->extra;
     } else {
         d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size);
     }
-    cl_mem d_Y = src1->backend == GGML_BACKEND_GPU ? (cl_mem) src1->extra : ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
-    cl_mem d_D =  dst->backend == GGML_BACKEND_GPU ? (cl_mem)  dst->extra : ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
+    cl_mem d_Y = src1->backend == GGML_BACKEND_TYPE_GPU ? (cl_mem) src1->extra : ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
+    cl_mem d_D =  dst->backend == GGML_BACKEND_TYPE_GPU ? (cl_mem)  dst->extra : ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
 
     size_t x_offset = 0;
 
@@ -1580,7 +1580,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
         // TODO: copy src0 here when r3>1
         for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
             for (int64_t i02 = 0; i02 < ne02; i02++) {
-                if (src0->backend == GGML_BACKEND_GPU) {
+                if (src0->backend == GGML_BACKEND_TYPE_GPU) {
                     x_offset = (i03 * ne02 + i02) * x_ne;
                 } else {
                     // copy src0 to device
@@ -1589,7 +1589,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
 
                 for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) {
                     // copy src1 to device
-                    if (src1->backend == GGML_BACKEND_CPU) {
+                    if (src1->backend == GGML_BACKEND_TYPE_CPU) {
                         CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL));
                     }
 
@@ -1612,7 +1612,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
                     }
 
                     // copy dst to host
-                    if (dst->backend == GGML_BACKEND_CPU) {
+                    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
                         float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
                         CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL));
                     }
@@ -1621,13 +1621,13 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
         }
     }
 
-    if (src0->backend != GGML_BACKEND_GPU) {
+    if (src0->backend != GGML_BACKEND_TYPE_GPU) {
         ggml_cl_pool_free(d_X, x_size);
     }
-    if (src1->backend != GGML_BACKEND_GPU) {
+    if (src1->backend != GGML_BACKEND_TYPE_GPU) {
         ggml_cl_pool_free(d_Y, y_size);
     }
-    if (dst->backend != GGML_BACKEND_GPU) {
+    if (dst->backend != GGML_BACKEND_TYPE_GPU) {
         ggml_cl_pool_free(d_D, d_size);
     }
 }
@@ -1670,7 +1670,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
     size_t y_size;
     size_t d_size;
     cl_mem d_X;
-    if (src0->backend == GGML_BACKEND_GPU) { // NOLINT
+    if (src0->backend == GGML_BACKEND_TYPE_GPU) { // NOLINT
         d_X = (cl_mem) src0->extra;
     } else {
         d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size);
@@ -1687,7 +1687,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
         // TODO: copy src0 here when r3>1
         for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
             for (int64_t i02 = 0; i02 < ne02; i02++) {
-                if (src0->backend == GGML_BACKEND_GPU) {
+                if (src0->backend == GGML_BACKEND_TYPE_GPU) {
                     x_offset = (i03 * ne02 + i02) * x_ne;
                 } else {
                     // copy src0 to device
@@ -1741,7 +1741,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
                     }
 
                     // copy dst to host, then convert to float
-                    if (dst->backend == GGML_BACKEND_CPU) {
+                    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
                         CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(ggml_fp16_t) * d_ne, tmp, 1, &ev_sgemm, NULL));
                         float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
                         ggml_fp16_to_fp32_row(tmp, d, d_ne);
@@ -1753,7 +1753,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
         }
     }
 
-    if (src0->backend != GGML_BACKEND_GPU) {
+    if (src0->backend != GGML_BACKEND_TYPE_GPU) {
         ggml_cl_pool_free(d_X, x_size);
     }
     ggml_cl_pool_free(d_Y, y_size);
@@ -1798,7 +1798,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
     cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
     cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
     cl_mem d_Q;
-    if (src0->backend == GGML_BACKEND_CPU) {
+    if (src0->backend == GGML_BACKEND_TYPE_CPU) {
         d_Q = ggml_cl_pool_malloc(q_sz, &q_size);
     }
 
@@ -1817,10 +1817,10 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
         for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
             for (int64_t i02 = 0; i02 < ne02; i02++) {
                 // copy src0 to device if necessary
-                if (src0->backend == GGML_BACKEND_CPU) {
+                if (src0->backend == GGML_BACKEND_TYPE_CPU) {
                     events.emplace_back();
                     CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, events.data() + ev_idx++));
-                } else if (src0->backend == GGML_BACKEND_GPU) {
+                } else if (src0->backend == GGML_BACKEND_TYPE_GPU) {
                     d_Q = (cl_mem) src0->extra;
                 } else {
                     GGML_ASSERT(false);
@@ -1829,7 +1829,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
                 if (!mul_mat_vec) {
                     // convert src0 to fp32 on device
                     const size_t global = x_ne / global_denom;
-                    const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0;
+                    const size_t offset = src0->backend == GGML_BACKEND_TYPE_GPU ? (i03 * ne02 + i02) * x_bps : 0;
                     CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q));
                     CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X));
                     CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, &offset, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL));
@@ -1843,7 +1843,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
 
                         // compute
                         const size_t global = ne01 * local;
-                        const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0;
+                        const size_t offset = src0->backend == GGML_BACKEND_TYPE_GPU ? (i03 * ne02 + i02) * x_bps : 0;
                         const cl_int ncols = ne00;
                         events.emplace_back();
                         CL_CHECK(clSetKernelArg(*dmmv, 0, sizeof(cl_mem), &d_Q));
@@ -1895,7 +1895,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
     }
     ggml_cl_pool_free(d_Y, y_size);
     ggml_cl_pool_free(d_D, d_size);
-    if (src0->backend == GGML_BACKEND_CPU) {
+    if (src0->backend == GGML_BACKEND_TYPE_CPU) {
         ggml_cl_pool_free(d_Q, q_size);
     }
 }
@@ -1911,7 +1911,7 @@ bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tens
     if ((src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
         src1->type == GGML_TYPE_F32 &&
         dst->type == GGML_TYPE_F32 &&
-        ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_GPU)) {
+        ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_TYPE_GPU)) {
         return true;
     }
 
@@ -1993,7 +1993,7 @@ void ggml_cl_transform_tensor(void * data, ggml_tensor * tensor) {
     CL_CHECK(clFinish(queue));
 
     tensor->extra = dst;
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 }
 
 // ggml-backend
@@ -2045,7 +2045,7 @@ static void ggml_backend_opencl_buffer_init_tensor(ggml_backend_buffer_t buffer,
         ctx->sub_buffers.push_back(sub_buffer);
         tensor->extra = sub_buffer;
     }
-    tensor->backend = GGML_BACKEND_GPU;
+    tensor->backend = GGML_BACKEND_TYPE_GPU;
 }
 
 static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
@@ -2119,6 +2119,7 @@ static size_t ggml_backend_opencl_buffer_type_get_alignment(ggml_backend_buffer_
     if (alignment == (cl_uint)-1) {
         ggml_cl_init();
         clGetDeviceInfo(device, CL_DEVICE_MEM_BASE_ADDR_ALIGN, sizeof(cl_uint), &alignment, NULL);
+        alignment /= 8; // bits to bytes
     }
     return alignment;
 
@@ -2231,9 +2232,14 @@ static ggml_backend_buffer_type_t ggml_backend_opencl_get_default_buffer_type(gg
     GGML_UNUSED(backend);
 }
 
-static bool ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) {
+static ggml_status ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) {
     for (int i = 0; i < graph->n_nodes; ++i) {
         ggml_tensor * node = graph->nodes[i];
+
+        if (ggml_is_empty(node)) {
+            continue;
+        }
+
         switch (node->op) {
             case GGML_OP_MUL_MAT:
                 ggml_cl_mul_mat(node->src[0], node->src[1], node, nullptr, 0);
@@ -2246,7 +2252,7 @@ static bool ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgrap
         }
     }
 
-    return true;
+    return GGML_STATUS_SUCCESS;
 
     GGML_UNUSED(backend);
 }
diff --git a/ggml-quants.c b/ggml-quants.c
index 3319d2ccf..00334c5fe 100644
--- a/ggml-quants.c
+++ b/ggml-quants.c
@@ -1,6 +1,12 @@
+#define GGML_COMMON_IMPL_C
+#include "ggml-common.h"
+
 #include "ggml-quants.h"
 #include "ggml-impl.h"
 
+#define GGML_COMMON_IMPL_C
+#include "ggml-common.h"
+
 #include <math.h>
 #include <string.h>
 #include <assert.h>
@@ -8,49 +14,15 @@
 #include <stdlib.h> // for qsort
 #include <stdio.h>  // for GGML_ASSERT
 
-#ifdef __ARM_NEON
-
-// if YCM cannot find <arm_neon.h>, make a symbolic link to it, for example:
-//
-//   $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/
-//
-#include <arm_neon.h>
-
-#else
-
-#ifdef __wasm_simd128__
-#include <wasm_simd128.h>
-#else
-#if defined(__POWER9_VECTOR__) || defined(__powerpc64__)
-#include <altivec.h>
-#undef bool
-#define bool _Bool
-#else
-#if defined(_MSC_VER) || defined(__MINGW32__)
-#include <intrin.h>
-#else
-#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__)
-#if !defined(__riscv)
-#include <immintrin.h>
+#if defined(_MSC_VER)
+// disable "possible loss of data" to avoid warnings for hundreds of casts
+// we should just be careful :)
+#pragma warning(disable: 4244 4267)
 #endif
-#endif
-#endif
-#endif
-#endif
-#endif
-
-#ifdef __riscv_v_intrinsic
-#include <riscv_vector.h>
-#endif
-
-#undef MIN
-#undef MAX
-
-#define MIN(a, b) ((a) < (b) ? (a) : (b))
-#define MAX(a, b) ((a) > (b) ? (a) : (b))
 
 #define UNUSED GGML_UNUSED
 
+// some compilers don't provide _mm256_set_m128i, e.g. gcc 7
 #define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1)
 
 #if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__)
@@ -125,7 +97,7 @@ static inline __m256 sum_i16_pairs_float(const __m256i x) {
 }
 
 static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) {
-#if __AVXVNNI__
+#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__))
     const __m256i zero = _mm256_setzero_si256();
     const __m256i summed_pairs = _mm256_dpbusd_epi32(zero, ax, sy);
     return _mm256_cvtepi32_ps(summed_pairs);
@@ -269,208 +241,6 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128
 #endif // __AVX__ || __AVX2__ || __AVX512F__
 #endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__)
 
-#if defined(__ARM_NEON)
-
-#ifdef _MSC_VER
-
-#define ggml_vld1q_u32(w,x,y,z) { ((w) + ((uint64_t)(x) << 32)), ((y) + ((uint64_t)(z) << 32)) }
-
-#else
-
-#define ggml_vld1q_u32(w,x,y,z) { (w), (x), (y), (z) }
-
-#endif
-
-#if !defined(__aarch64__)
-
-// 64-bit compatibility
-
-// vaddvq_s16
-// vpaddq_s16
-// vpaddq_s32
-// vaddvq_s32
-// vaddvq_f32
-// vmaxvq_f32
-// vcvtnq_s32_f32
-// vzip1_u8
-// vzip2_u8
-
-inline static int32_t vaddvq_s16(int16x8_t v) {
-    return
-        (int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) +
-        (int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) +
-        (int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) +
-        (int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7);
-}
-
-inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) {
-    int16x4_t a0 = vpadd_s16(vget_low_s16(a), vget_high_s16(a));
-    int16x4_t b0 = vpadd_s16(vget_low_s16(b), vget_high_s16(b));
-    return vcombine_s16(a0, b0);
-}
-
-inline static int32x4_t vpaddq_s32(int32x4_t a, int32x4_t b) {
-    int32x2_t a0 = vpadd_s32(vget_low_s32(a), vget_high_s32(a));
-    int32x2_t b0 = vpadd_s32(vget_low_s32(b), vget_high_s32(b));
-    return vcombine_s32(a0, b0);
-}
-
-inline static int32_t vaddvq_s32(int32x4_t v) {
-    return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3);
-}
-
-inline static float vaddvq_f32(float32x4_t v) {
-    return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3);
-}
-
-inline static float vmaxvq_f32(float32x4_t v) {
-    return
-        MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)),
-            MAX(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3)));
-}
-
-inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) {
-    int32x4_t res;
-
-    res[0] = roundf(vgetq_lane_f32(v, 0));
-    res[1] = roundf(vgetq_lane_f32(v, 1));
-    res[2] = roundf(vgetq_lane_f32(v, 2));
-    res[3] = roundf(vgetq_lane_f32(v, 3));
-
-    return res;
-}
-
-inline static uint8x8_t vzip1_u8(uint8x8_t a, uint8x8_t b) {
-    uint8x8_t res;
-
-    res[0] = a[0]; res[1] = b[0];
-    res[2] = a[1]; res[3] = b[1];
-    res[4] = a[2]; res[5] = b[2];
-    res[6] = a[3]; res[7] = b[3];
-
-    return res;
-}
-
-inline static uint8x8_t vzip2_u8(uint8x8_t a, uint8x8_t b) {
-    uint8x8_t res;
-
-    res[0] = a[4]; res[1] = b[4];
-    res[2] = a[5]; res[3] = b[5];
-    res[4] = a[6]; res[5] = b[6];
-    res[6] = a[7]; res[7] = b[7];
-
-    return res;
-}
-
-// vld1q_s16_x2
-// vld1q_u8_x2
-// vld1q_u8_x4
-// vld1q_s8_x2
-// vld1q_s8_x4
-// TODO: double-check these work correctly
-
-typedef struct ggml_int16x8x2_t {
-    int16x8_t val[2];
-} ggml_int16x8x2_t;
-
-inline static ggml_int16x8x2_t ggml_vld1q_s16_x2(const int16_t * ptr) {
-    ggml_int16x8x2_t res;
-
-    res.val[0] = vld1q_s16(ptr + 0);
-    res.val[1] = vld1q_s16(ptr + 8);
-
-    return res;
-}
-
-typedef struct ggml_uint8x16x2_t {
-    uint8x16_t val[2];
-} ggml_uint8x16x2_t;
-
-inline static ggml_uint8x16x2_t ggml_vld1q_u8_x2(const uint8_t * ptr) {
-    ggml_uint8x16x2_t res;
-
-    res.val[0] = vld1q_u8(ptr + 0);
-    res.val[1] = vld1q_u8(ptr + 16);
-
-    return res;
-}
-
-typedef struct ggml_uint8x16x4_t {
-    uint8x16_t val[4];
-} ggml_uint8x16x4_t;
-
-inline static ggml_uint8x16x4_t ggml_vld1q_u8_x4(const uint8_t * ptr) {
-    ggml_uint8x16x4_t res;
-
-    res.val[0] = vld1q_u8(ptr + 0);
-    res.val[1] = vld1q_u8(ptr + 16);
-    res.val[2] = vld1q_u8(ptr + 32);
-    res.val[3] = vld1q_u8(ptr + 48);
-
-    return res;
-}
-
-typedef struct ggml_int8x16x2_t {
-    int8x16_t val[2];
-} ggml_int8x16x2_t;
-
-inline static ggml_int8x16x2_t ggml_vld1q_s8_x2(const int8_t * ptr) {
-    ggml_int8x16x2_t res;
-
-    res.val[0] = vld1q_s8(ptr + 0);
-    res.val[1] = vld1q_s8(ptr + 16);
-
-    return res;
-}
-
-typedef struct ggml_int8x16x4_t {
-    int8x16_t val[4];
-} ggml_int8x16x4_t;
-
-inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) {
-    ggml_int8x16x4_t res;
-
-    res.val[0] = vld1q_s8(ptr + 0);
-    res.val[1] = vld1q_s8(ptr + 16);
-    res.val[2] = vld1q_s8(ptr + 32);
-    res.val[3] = vld1q_s8(ptr + 48);
-
-    return res;
-}
-
-#else
-
-#define ggml_int16x8x2_t  int16x8x2_t
-#define ggml_uint8x16x2_t uint8x16x2_t
-#define ggml_uint8x16x4_t uint8x16x4_t
-#define ggml_int8x16x2_t  int8x16x2_t
-#define ggml_int8x16x4_t  int8x16x4_t
-
-#define ggml_vld1q_s16_x2 vld1q_s16_x2
-#define ggml_vld1q_u8_x2  vld1q_u8_x2
-#define ggml_vld1q_u8_x4  vld1q_u8_x4
-#define ggml_vld1q_s8_x2  vld1q_s8_x2
-#define ggml_vld1q_s8_x4  vld1q_s8_x4
-
-#endif
-
-#if !defined(__ARM_FEATURE_DOTPROD)
-
-inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) {
-    const int16x8_t p0 = vmull_s8(vget_low_s8 (a), vget_low_s8 (b));
-    const int16x8_t p1 = vmull_s8(vget_high_s8(a), vget_high_s8(b));
-
-    return vaddq_s32(acc, vaddq_s32(vpaddlq_s16(p0), vpaddlq_s16(p1)));
-}
-
-#else
-
-#define ggml_vdotq_s32(a, b, c) vdotq_s32(a, b, c)
-
-#endif
-
-#endif
-
 #if defined(__ARM_NEON) || defined(__wasm_simd128__)
 #define B1(c,s,n)  0x ## n ## c ,  0x ## n ## s
 #define B2(c,s,n) B1(c,s,n ## c), B1(c,s,n ## s)
@@ -487,7 +257,7 @@ static const uint64_t table_b2b_1[1 << 8] = { B8(10, 00) }; // (!b) << 4
 #endif
 
 // reference implementation for deterministic creation of model files
-void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k) {
+void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int64_t k) {
     static const int qk = QK4_0;
 
     assert(k % qk == 0);
@@ -524,12 +294,12 @@ void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict
     }
 }
 
-void quantize_row_q4_0(const float * restrict x, void * restrict y, int k) {
+void quantize_row_q4_0(const float * restrict x, void * restrict y, int64_t k) {
     quantize_row_q4_0_reference(x, y, k);
 }
 
 
-void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k) {
+void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int64_t k) {
     const int qk = QK4_1;
 
     assert(k % qk == 0);
@@ -566,11 +336,11 @@ void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict
     }
 }
 
-void quantize_row_q4_1(const float * restrict x, void * restrict y, int k) {
+void quantize_row_q4_1(const float * restrict x, void * restrict y, int64_t k) {
     quantize_row_q4_1_reference(x, y, k);
 }
 
-void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k) {
+void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int64_t k) {
     static const int qk = QK5_0;
 
     assert(k % qk == 0);
@@ -614,11 +384,11 @@ void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict
     }
 }
 
-void quantize_row_q5_0(const float * restrict x, void * restrict y, int k) {
+void quantize_row_q5_0(const float * restrict x, void * restrict y, int64_t k) {
     quantize_row_q5_0_reference(x, y, k);
 }
 
-void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k) {
+void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int64_t k) {
     const int qk = QK5_1;
 
     assert(k % qk == 0);
@@ -662,12 +432,12 @@ void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict
     }
 }
 
-void quantize_row_q5_1(const float * restrict x, void * restrict y, int k) {
+void quantize_row_q5_1(const float * restrict x, void * restrict y, int64_t k) {
     quantize_row_q5_1_reference(x, y, k);
 }
 
 // reference implementation for deterministic creation of model files
-void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k) {
+void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int64_t k) {
     assert(k % QK8_0 == 0);
     const int nb = k / QK8_0;
 
@@ -692,7 +462,7 @@ void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict
     }
 }
 
-void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) {
+void quantize_row_q8_0(const float * restrict x, void * restrict vy, int64_t k) {
     assert(QK8_0 == 32);
     assert(k % QK8_0 == 0);
     const int nb = k / QK8_0;
@@ -881,7 +651,7 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) {
 }
 
 // reference implementation for deterministic creation of model files
-void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int k) {
+void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int64_t k) {
     assert(QK8_1 == 32);
     assert(k % QK8_1 == 0);
     const int nb = k / QK8_1;
@@ -897,7 +667,7 @@ void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict
         const float d = amax / ((1 << 7) - 1);
         const float id = d ? 1.0f/d : 0.0f;
 
-        y[i].d = d;
+        y[i].d = GGML_FP32_TO_FP16(d);
 
         int sum = 0;
 
@@ -912,11 +682,11 @@ void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict
             sum += y[i].qs[QK8_1/2 + j];
         }
 
-        y[i].s = sum*d;
+        y[i].s = GGML_FP32_TO_FP16(sum*d);
     }
 }
 
-void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
+void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k) {
     assert(k % QK8_1 == 0);
     const int nb = k / QK8_1;
 
@@ -940,7 +710,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
         const float d = amax / ((1 << 7) - 1);
         const float id = d ? 1.0f/d : 0.0f;
 
-        y[i].d = d;
+        y[i].d = GGML_FP32_TO_FP16(d);
 
         int32x4_t accv = vdupq_n_s32(0);
 
@@ -956,7 +726,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
             accv = vaddq_s32(accv, vi);
         }
 
-        y[i].s = d * vaddvq_s32(accv);
+        y[i].s = GGML_FP32_TO_FP16(d * vaddvq_s32(accv));
     }
 #elif defined(__wasm_simd128__)
     for (int i = 0; i < nb; i++) {
@@ -979,7 +749,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
         const float d = amax / ((1 << 7) - 1);
         const float id = d ? 1.0f/d : 0.0f;
 
-        y[i].d = d;
+        y[i].d = GGML_FP32_TO_FP16(d);
 
         v128_t accv = wasm_i32x4_splat(0);
 
@@ -995,10 +765,11 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
             accv = wasm_i32x4_add(accv, vi);
         }
 
-        y[i].s = d * (wasm_i32x4_extract_lane(accv, 0) +
-                      wasm_i32x4_extract_lane(accv, 1) +
-                      wasm_i32x4_extract_lane(accv, 2) +
-                      wasm_i32x4_extract_lane(accv, 3));
+        y[i].s = GGML_FP32_TO_FP16(
+                d * (wasm_i32x4_extract_lane(accv, 0) +
+                     wasm_i32x4_extract_lane(accv, 1) +
+                     wasm_i32x4_extract_lane(accv, 2) +
+                     wasm_i32x4_extract_lane(accv, 3)));
     }
 #elif defined(__AVX2__) || defined(__AVX__)
     for (int i = 0; i < nb; i++) {
@@ -1023,7 +794,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
 
         // Quantize these floats
         const float d = maxScalar / 127.f;
-        y[i].d = d;
+        y[i].d = GGML_FP32_TO_FP16(d);
         const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f;
         const __m256 mul = _mm256_set1_ps( id );
 
@@ -1047,7 +818,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
 
 #if defined(__AVX2__)
         // Compute the sum of the quants and set y[i].s
-        y[i].s = d * hsum_i32_8(_mm256_add_epi32(_mm256_add_epi32(i0, i1), _mm256_add_epi32(i2, i3)));
+        y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_8(_mm256_add_epi32(_mm256_add_epi32(i0, i1), _mm256_add_epi32(i2, i3))));
 
         // Convert int32 to int16
         i0 = _mm256_packs_epi32( i0, i1 );	// 0, 1, 2, 3,  8, 9, 10, 11,  4, 5, 6, 7, 12, 13, 14, 15
@@ -1077,7 +848,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
         // Compute the sum of the quants and set y[i].s
         const __m128i s0 = _mm_add_epi32(_mm_add_epi32(ni0, ni1), _mm_add_epi32(ni2, ni3));
         const __m128i s1 = _mm_add_epi32(_mm_add_epi32(ni4, ni5), _mm_add_epi32(ni6, ni7));
-        y[i].s = d * hsum_i32_4(_mm_add_epi32(s0, s1));
+        y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_4(_mm_add_epi32(s0, s1)));
 
         // Convert int32 to int16
         ni0 = _mm_packs_epi32( ni0, ni1 );
@@ -1108,7 +879,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
         const float d  = amax / ((1 << 7) - 1);
         const float id = d ? 1.0f/d : 0.0f;
 
-        y[i].d = d;
+        y[i].d = GGML_FP32_TO_FP16(d);
 
         vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl);
 
@@ -1125,7 +896,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
 
         // set y[i].s
         int sum = __riscv_vmv_x_s_i16m1_i16(vwrs);
-        y[i].s = sum*d;
+        y[i].s = GGML_FP32_TO_FP16(sum*d);
     }
 #else
     GGML_UNUSED(nb);
@@ -1134,7 +905,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
 #endif
 }
 
-void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k) {
+void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int64_t k) {
     static const int qk = QK4_0;
 
     assert(k % qk == 0);
@@ -1154,7 +925,7 @@ void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int
     }
 }
 
-void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int k) {
+void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int64_t k) {
     static const int qk = QK4_1;
 
     assert(k % qk == 0);
@@ -1175,7 +946,7 @@ void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int
     }
 }
 
-void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int k) {
+void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int64_t k) {
     static const int qk = QK5_0;
 
     assert(k % qk == 0);
@@ -1201,7 +972,7 @@ void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int
     }
 }
 
-void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int k) {
+void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int64_t k) {
     static const int qk = QK5_1;
 
     assert(k % qk == 0);
@@ -1228,7 +999,7 @@ void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int
     }
 }
 
-void dequantize_row_q8_0(const block_q8_0 * restrict x, float * restrict y, int k) {
+void dequantize_row_q8_0(const block_q8_0 * restrict x, float * restrict y, int64_t k) {
     static const int qk = QK8_0;
 
     assert(k % qk == 0);
@@ -1523,7 +1294,7 @@ static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t *
 
 //========================- 2-bit (de)-quantization
 
-void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k) {
+void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
 
@@ -1600,7 +1371,7 @@ void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict
     }
 }
 
-void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k) {
+void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
 
@@ -1646,20 +1417,10 @@ void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int
     }
 }
 
-void quantize_row_q2_K(const float * restrict x, void * restrict vy, int k) {
+void quantize_row_q2_K(const float * restrict x, void * restrict vy, int64_t k) {
     quantize_row_q2_K_reference(x, vy, k);
 }
 
-size_t ggml_quantize_q2_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
-    (void)hist; // TODO: collect histograms
-
-    for (int j = 0; j < n; j += k) {
-        block_q2_K * restrict y = (block_q2_K *)dst + j/QK_K;
-        quantize_row_q2_K_reference(src + j, y, k);
-    }
-    return (n/QK_K*sizeof(block_q2_K));
-}
-
 static float make_qkx3_quants(int n, int nmax, const float * restrict x, const float * restrict weights,
         uint8_t * restrict L, float * restrict the_min, uint8_t * restrict Laux,
         float rmin, float rdelta, int nstep, bool use_mad) {
@@ -1827,7 +1588,7 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri
     float mins[QK_K/16];
     float scales[QK_K/16];
     float sw[QK_K/16];
-    float weight[QK_K/16];
+    float weight[16];
     uint8_t Ls[QK_K/16], Lm[QK_K/16];
 
     for (int i = 0; i < nb; i++) {
@@ -1837,13 +1598,42 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri
         float sigma2 = sumx2/QK_K;
         for (int j = 0; j < QK_K/16; ++j) {
             const float * restrict qw = quant_weights + QK_K * i + 16*j;
-            for (int l = 0; l < QK_K/16; ++l) weight[l] = qw[l] * sqrtf(sigma2 + x[16*j + l]*x[16*j + l]);
+            for (int l = 0; l < 16; ++l) weight[l] = qw[l] * sqrtf(sigma2 + x[16*j + l]*x[16*j + l]);
             for (int l = 0; l < QK_K/16; ++l) sw[j] += weight[l];
-            scales[j] = make_qkx3_quants(QK_K/16, 3, x + 16*j, weight, L + 16*j, &mins[j], Laux, -0.9f, 0.05f, 36, false);
+            scales[j] = make_qkx3_quants(16, 3, x + 16*j, weight, L + 16*j, &mins[j], Laux, -0.9f, 0.05f, 36, false);
         }
 
-        float dm  = make_qp_quants(QK_K/16, 15, scales, Ls, sw);
-        float mm  = make_qp_quants(QK_K/16, 15, mins,   Lm, sw);
+        float dm, mm;
+#if QK_K == 64
+        float max_scale = 0, max_min = 0;
+        for (int j = 0; j < QK_K/16; ++j) {
+            max_scale = MAX(max_scale, scales[j]);
+            max_min   = MAX(max_min,   mins[j]);
+        }
+        dm = max_scale/15;
+        mm = max_min/15;
+        if (max_scale) {
+            float id = 1/dm;
+            for (int j = 0; j < QK_K/16; ++j) {
+                int l = nearest_int(id*scales[j]);
+                Ls[j] = MAX(0, MIN(15, l));
+            }
+        } else {
+            memset(Ls, 0, QK_K/16);
+        }
+        if (max_min) {
+            float id = 1/mm;
+            for (int j = 0; j < QK_K/16; ++j) {
+                int l = nearest_int(id*mins[j]);
+                Lm[j] = MAX(0, MIN(15, l));
+            }
+        } else {
+            memset(Lm, 0, QK_K/16);
+        }
+#else
+        dm  = make_qp_quants(QK_K/16, 15, scales, Ls, sw);
+        mm  = make_qp_quants(QK_K/16, 15, mins,   Lm, sw);
+#endif
         y[i].d    = GGML_FP32_TO_FP16(dm);
         y[i].dmin = GGML_FP32_TO_FP16(mm);
         dm        = GGML_FP16_TO_FP32(y[i].d);
@@ -1883,15 +1673,14 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri
     }
 }
 
-size_t quantize_q2_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {
-    (void)hist;
+size_t quantize_q2_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     size_t row_size = ggml_row_size(GGML_TYPE_Q2_K, n_per_row);
     if (!quant_weights) {
-        quantize_row_q2_K_reference(src, dst, nrow*n_per_row);
+        quantize_row_q2_K_reference(src, dst, (int64_t)nrow*n_per_row);
     }
     else {
         char * qrow = (char *)dst;
-        for (int row = 0; row < nrow; ++row) {
+        for (int64_t row = 0; row < nrow; ++row) {
             quantize_row_q2_K_impl(src, (block_q2_K*)qrow, n_per_row, quant_weights);
             src += n_per_row;
             qrow += row_size;
@@ -1902,7 +1691,7 @@ size_t quantize_q2_K(const float * src, void * dst, int nrow, int n_per_row, int
 
 //========================= 3-bit (de)-quantization
 
-void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k) {
+void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
 
@@ -2016,7 +1805,7 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict
 }
 
 #if QK_K == 256
-void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k) {
+void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
 
@@ -2066,7 +1855,7 @@ void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int
     }
 }
 #else
-void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k) {
+void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     assert(QK_K == 64);
     const int nb = k / QK_K;
@@ -2099,21 +1888,11 @@ void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int
 }
 #endif
 
-void quantize_row_q3_K(const float * restrict x, void * restrict vy, int k) {
+void quantize_row_q3_K(const float * restrict x, void * restrict vy, int64_t k) {
     quantize_row_q3_K_reference(x, vy, k);
 }
 
-size_t ggml_quantize_q3_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
-    (void)hist; // TODO: collect histograms
-
-    for (int j = 0; j < n; j += k) {
-        block_q3_K * restrict y = (block_q3_K *)dst + j/QK_K;
-        quantize_row_q3_K_reference(src + j, y, k);
-    }
-    return (n/QK_K*sizeof(block_q3_K));
-}
-
-static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restrict y, int n_per_row, const float * restrict quant_weights) {
+static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restrict y, int64_t n_per_row, const float * restrict quant_weights) {
 #if QK_K != 256
     (void)quant_weights;
     quantize_row_q3_K_reference(x, y, n_per_row);
@@ -2202,15 +1981,14 @@ static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restri
 #endif
 }
 
-size_t quantize_q3_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {
-    (void)hist;
+size_t quantize_q3_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     size_t row_size = ggml_row_size(GGML_TYPE_Q3_K, n_per_row);
     if (!quant_weights) {
-        quantize_row_q3_K_reference(src, dst, nrow*n_per_row);
+        quantize_row_q3_K_reference(src, dst, (int64_t)nrow*n_per_row);
     }
     else {
         char * qrow = (char *)dst;
-        for (int row = 0; row < nrow; ++row) {
+        for (int64_t row = 0; row < nrow; ++row) {
             quantize_row_q3_K_impl(src, (block_q3_K*)qrow, n_per_row, quant_weights);
             src += n_per_row;
             qrow += row_size;
@@ -2221,7 +1999,7 @@ size_t quantize_q3_K(const float * src, void * dst, int nrow, int n_per_row, int
 
 // ====================== 4-bit (de)-quantization
 
-void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k) {
+void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
 
@@ -2328,7 +2106,7 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict
     }
 }
 
-void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k) {
+void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
 
@@ -2367,30 +2145,19 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int
     }
 }
 
-void quantize_row_q4_K(const float * restrict x, void * restrict vy, int k) {
+void quantize_row_q4_K(const float * restrict x, void * restrict vy, int64_t k) {
     assert(k % QK_K == 0);
     block_q4_K * restrict y = vy;
     quantize_row_q4_K_reference(x, y, k);
 }
 
-size_t ggml_quantize_q4_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
-    assert(k % QK_K == 0);
-    (void)hist; // TODO: collect histograms
-
-    for (int j = 0; j < n; j += k) {
-        block_q4_K * restrict y = (block_q4_K *)dst + j/QK_K;
-        quantize_row_q4_K_reference(src + j, y, k);
-    }
-    return (n/QK_K*sizeof(block_q4_K));
-}
-
-static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restrict y, int n_per_row, const float * quant_weights) {
+static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restrict y, int64_t n_per_row, const float * quant_weights) {
 #if QK_K != 256
     (void)quant_weights;
     quantize_row_q4_K_reference(x, y, n_per_row);
 #else
     assert(n_per_row % QK_K == 0);
-    const int nb = n_per_row / QK_K;
+    const int64_t nb = n_per_row / QK_K;
 
     uint8_t L[QK_K];
     uint8_t Laux[32];
@@ -2462,15 +2229,14 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri
 #endif
 }
 
-size_t quantize_q4_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {
-    (void)hist;
+size_t quantize_q4_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     size_t row_size = ggml_row_size(GGML_TYPE_Q4_K, n_per_row);
     if (!quant_weights) {
-        quantize_row_q4_K_reference(src, dst, nrow*n_per_row);
+        quantize_row_q4_K_reference(src, dst, (int64_t)nrow*n_per_row);
     }
     else {
         char * qrow = (char *)dst;
-        for (int row = 0; row < nrow; ++row) {
+        for (int64_t row = 0; row < nrow; ++row) {
             quantize_row_q4_K_impl(src, (block_q4_K*)qrow, n_per_row, quant_weights);
             src += n_per_row;
             qrow += row_size;
@@ -2481,9 +2247,9 @@ size_t quantize_q4_K(const float * src, void * dst, int nrow, int n_per_row, int
 
 // ====================== 5-bit (de)-quantization
 
-void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k) {
+void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int64_t k) {
     assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+    const int64_t nb = k / QK_K;
 
 #if QK_K == 256
     uint8_t L[QK_K];
@@ -2623,9 +2389,9 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict
     }
 }
 
-void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int k) {
+void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int64_t k) {
     assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+    const int64_t nb = k / QK_K;
 
     for (int i = 0; i < nb; i++) {
 
@@ -2668,30 +2434,19 @@ void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int
     }
 }
 
-void quantize_row_q5_K(const float * restrict x, void * restrict vy, int k) {
+void quantize_row_q5_K(const float * restrict x, void * restrict vy, int64_t k) {
     assert(k % QK_K == 0);
     block_q5_K * restrict y = vy;
     quantize_row_q5_K_reference(x, y, k);
 }
 
-size_t ggml_quantize_q5_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
-    assert(k % QK_K == 0);
-    (void)hist; // TODO: collect histograms
-
-    for (int j = 0; j < n; j += k) {
-        block_q5_K * restrict y = (block_q5_K *)dst + j/QK_K;
-        quantize_row_q5_K_reference(src + j, y, k);
-    }
-    return (n/QK_K*sizeof(block_q5_K));
-}
-
-static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restrict y, int n_per_row, const float * quant_weights) {
+static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restrict y, int64_t n_per_row, const float * quant_weights) {
 #if QK_K != 256
     (void)quant_weights;
     quantize_row_q5_K_reference(x, y, n_per_row);
 #else
     assert(n_per_row % QK_K == 0);
-    const int nb = n_per_row / QK_K;
+    const int64_t nb = n_per_row / QK_K;
 
     uint8_t L[QK_K];
     uint8_t Laux[32];
@@ -2783,15 +2538,14 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri
 #endif
 }
 
-size_t quantize_q5_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {
-    (void)hist;
+size_t quantize_q5_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     size_t row_size = ggml_row_size(GGML_TYPE_Q5_K, n_per_row);
     if (!quant_weights) {
-        quantize_row_q5_K_reference(src, dst, nrow*n_per_row);
+        quantize_row_q5_K_reference(src, dst, (int64_t)nrow*n_per_row);
     }
     else {
         char * qrow = (char *)dst;
-        for (int row = 0; row < nrow; ++row) {
+        for (int64_t row = 0; row < nrow; ++row) {
             quantize_row_q5_K_impl(src, (block_q5_K*)qrow, n_per_row, quant_weights);
             src += n_per_row;
             qrow += row_size;
@@ -2802,9 +2556,9 @@ size_t quantize_q5_K(const float * src, void * dst, int nrow, int n_per_row, int
 
 // ====================== 6-bit (de)-quantization
 
-void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k) {
+void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int64_t k) {
     assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+    const int64_t nb = k / QK_K;
 
     int8_t L[QK_K];
     float   scales[QK_K/16];
@@ -2884,9 +2638,9 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict
     }
 }
 
-void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k) {
+void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int64_t k) {
     assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+    const int64_t nb = k / QK_K;
 
     for (int i = 0; i < nb; i++) {
 
@@ -2931,30 +2685,19 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int
     }
 }
 
-void quantize_row_q6_K(const float * restrict x, void * restrict vy, int k) {
+void quantize_row_q6_K(const float * restrict x, void * restrict vy, int64_t k) {
     assert(k % QK_K == 0);
     block_q6_K * restrict y = vy;
     quantize_row_q6_K_reference(x, y, k);
 }
 
-size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist) {
-    assert(k % QK_K == 0);
-    (void)hist; // TODO: collect histograms
-
-    for (int j = 0; j < n; j += k) {
-        block_q6_K * restrict y = (block_q6_K *)dst + j/QK_K;
-        quantize_row_q6_K_reference(src + j, y, k);
-    }
-    return (n/QK_K*sizeof(block_q6_K));
-}
-
-static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restrict y, int n_per_row, const float * quant_weights) {
+static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restrict y, int64_t n_per_row, const float * quant_weights) {
 #if QK_K != 256
     (void)quant_weights;
     quantize_row_q6_K_reference(x, y, n_per_row);
 #else
     assert(n_per_row % QK_K == 0);
-    const int nb = n_per_row / QK_K;
+    const int64_t nb = n_per_row / QK_K;
 
     int8_t L[QK_K];
     float   scales[QK_K/16];
@@ -3037,15 +2780,14 @@ static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restri
 #endif
 }
 
-size_t quantize_q6_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {
-    (void)hist;
+size_t quantize_q6_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     size_t row_size = ggml_row_size(GGML_TYPE_Q6_K, n_per_row);
     if (!quant_weights) {
-        quantize_row_q6_K_reference(src, dst, nrow*n_per_row);
+        quantize_row_q6_K_reference(src, dst, (int64_t)nrow*n_per_row);
     }
     else {
         char * qrow = (char *)dst;
-        for (int row = 0; row < nrow; ++row) {
+        for (int64_t row = 0; row < nrow; ++row) {
             quantize_row_q6_K_impl(src, (block_q6_K*)qrow, n_per_row, quant_weights);
             src += n_per_row;
             qrow += row_size;
@@ -3054,7 +2796,7 @@ size_t quantize_q6_K(const float * src, void * dst, int nrow, int n_per_row, int
     return nrow * row_size;
 }
 
-static void quantize_row_q4_0_impl(const float * restrict x, block_q4_0 * restrict y, int n_per_row, const float * quant_weights) {
+static void quantize_row_q4_0_impl(const float * restrict x, block_q4_0 * restrict y, int64_t n_per_row, const float * quant_weights) {
     static_assert(QK4_0 == 32, "QK4_0 must be 32");
 
     if (!quant_weights) {
@@ -3069,7 +2811,7 @@ static void quantize_row_q4_0_impl(const float * restrict x, block_q4_0 * restri
     for (int j = 0; j < n_per_row; ++j) sum_x2 += x[j]*x[j];
     float sigma2 = sum_x2/n_per_row;
 
-    const int nb = n_per_row/QK4_0;
+    const int64_t nb = n_per_row/QK4_0;
     for (int ib = 0; ib < nb; ++ib) {
         const float * xb = x + QK4_0 * ib;
         const float * qw = quant_weights + QK4_0 * ib;
@@ -3082,13 +2824,14 @@ static void quantize_row_q4_0_impl(const float * restrict x, block_q4_0 * restri
     }
 }
 
-size_t quantize_q4_0(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {
+size_t quantize_q4_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     if (!quant_weights) {
-        return ggml_quantize_q4_0(src, dst, nrow*n_per_row, n_per_row, hist);
+        quantize_row_q4_0_reference(src, dst, (int64_t)nrow*n_per_row);
+        return nrow * ggml_row_size(GGML_TYPE_Q4_0, n_per_row);
     }
     size_t row_size = ggml_row_size(GGML_TYPE_Q4_0, n_per_row);
     char * qrow = (char *)dst;
-    for (int row = 0; row < nrow; ++row) {
+    for (int64_t row = 0; row < nrow; ++row) {
         quantize_row_q4_0_impl(src, (block_q4_0*)qrow, n_per_row, quant_weights);
         src += n_per_row;
         qrow += row_size;
@@ -3096,7 +2839,7 @@ size_t quantize_q4_0(const float * src, void * dst, int nrow, int n_per_row, int
     return nrow * row_size;
 }
 
-static void quantize_row_q4_1_impl(const float * restrict x, block_q4_1 * restrict y, int n_per_row, const float * quant_weights) {
+static void quantize_row_q4_1_impl(const float * restrict x, block_q4_1 * restrict y, int64_t n_per_row, const float * quant_weights) {
     static_assert(QK4_1 == 32, "QK4_1 must be 32");
 
     if (!quant_weights) {
@@ -3111,7 +2854,7 @@ static void quantize_row_q4_1_impl(const float * restrict x, block_q4_1 * restri
     for (int j = 0; j < n_per_row; ++j) sum_x2 += x[j]*x[j];
     float sigma2 = sum_x2/n_per_row;
 
-    const int nb = n_per_row/QK4_1;
+    const int64_t nb = n_per_row/QK4_1;
     for (int ib = 0; ib < nb; ++ib) {
         const float * xb = x + QK4_1 * ib;
         const float * qw = quant_weights + QK4_1 * ib;
@@ -3126,13 +2869,14 @@ static void quantize_row_q4_1_impl(const float * restrict x, block_q4_1 * restri
     }
 }
 
-size_t quantize_q4_1(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {
+size_t quantize_q4_1(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     if (!quant_weights) {
-        return ggml_quantize_q4_1(src, dst, nrow*n_per_row, n_per_row, hist);
+        quantize_row_q4_1_reference(src, dst, (int64_t)nrow*n_per_row);
+        return nrow * ggml_row_size(GGML_TYPE_Q4_1, n_per_row);
     }
     size_t row_size = ggml_row_size(GGML_TYPE_Q4_1, n_per_row);
     char * qrow = (char *)dst;
-    for (int row = 0; row < nrow; ++row) {
+    for (int64_t row = 0; row < nrow; ++row) {
         quantize_row_q4_1_impl(src, (block_q4_1*)qrow, n_per_row, quant_weights);
         src += n_per_row;
         qrow += row_size;
@@ -3140,7 +2884,7 @@ size_t quantize_q4_1(const float * src, void * dst, int nrow, int n_per_row, int
     return nrow * row_size;
 }
 
-static void quantize_row_q5_0_impl(const float * restrict x, block_q5_0 * restrict y, int n_per_row, const float * quant_weights) {
+static void quantize_row_q5_0_impl(const float * restrict x, block_q5_0 * restrict y, int64_t n_per_row, const float * quant_weights) {
     static_assert(QK5_0 == 32, "QK5_0 must be 32");
 
     if (!quant_weights) {
@@ -3155,7 +2899,7 @@ static void quantize_row_q5_0_impl(const float * restrict x, block_q5_0 * restri
     for (int j = 0; j < n_per_row; ++j) sum_x2 += x[j]*x[j];
     float sigma2 = sum_x2/n_per_row;
 
-    const int nb = n_per_row/QK5_0;
+    const int64_t nb = n_per_row/QK5_0;
     for (int ib = 0; ib < nb; ++ib) {
         const float * xb = x + QK5_0 * ib;
         const float * qw = quant_weights + QK5_0 * ib;
@@ -3179,13 +2923,14 @@ static void quantize_row_q5_0_impl(const float * restrict x, block_q5_0 * restri
     }
 }
 
-size_t quantize_q5_0(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {
+size_t quantize_q5_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     if (!quant_weights) {
-        return ggml_quantize_q5_0(src, dst, nrow*n_per_row, n_per_row, hist);
+        quantize_row_q5_0_reference(src, dst, (int64_t)nrow*n_per_row);
+        return nrow * ggml_row_size(GGML_TYPE_Q5_0, n_per_row);
     }
     size_t row_size = ggml_row_size(GGML_TYPE_Q5_0, n_per_row);
     char * qrow = (char *)dst;
-    for (int row = 0; row < nrow; ++row) {
+    for (int64_t row = 0; row < nrow; ++row) {
         quantize_row_q5_0_impl(src, (block_q5_0*)qrow, n_per_row, quant_weights);
         src += n_per_row;
         qrow += row_size;
@@ -3193,7 +2938,7 @@ size_t quantize_q5_0(const float * src, void * dst, int nrow, int n_per_row, int
     return nrow * row_size;
 }
 
-static void quantize_row_q5_1_impl(const float * restrict x, block_q5_1 * restrict y, int n_per_row, const float * quant_weights) {
+static void quantize_row_q5_1_impl(const float * restrict x, block_q5_1 * restrict y, int64_t n_per_row, const float * quant_weights) {
     static_assert(QK5_1 == 32, "QK5_1 must be 32");
 
     if (!quant_weights) {
@@ -3208,7 +2953,7 @@ static void quantize_row_q5_1_impl(const float * restrict x, block_q5_1 * restri
     for (int j = 0; j < n_per_row; ++j) sum_x2 += x[j]*x[j];
     float sigma2 = sum_x2/n_per_row;
 
-    const int nb = n_per_row/QK5_1;
+    const int64_t nb = n_per_row/QK5_1;
     for (int ib = 0; ib < nb; ++ib) {
         const float * xb = x + QK5_1 * ib;
         const float * qw = quant_weights + QK5_1 * ib;
@@ -3231,13 +2976,14 @@ static void quantize_row_q5_1_impl(const float * restrict x, block_q5_1 * restri
     }
 }
 
-size_t quantize_q5_1(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {
+size_t quantize_q5_1(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     if (!quant_weights) {
-        return ggml_quantize_q5_1(src, dst, nrow*n_per_row, n_per_row, hist);
+        quantize_row_q5_1_reference(src, dst, (int64_t)nrow*n_per_row);
+        return nrow * ggml_row_size(GGML_TYPE_Q5_1, n_per_row);
     }
     size_t row_size = ggml_row_size(GGML_TYPE_Q5_1, n_per_row);
     char * qrow = (char *)dst;
-    for (int row = 0; row < nrow; ++row) {
+    for (int64_t row = 0; row < nrow; ++row) {
         quantize_row_q5_1_impl(src, (block_q5_1*)qrow, n_per_row, quant_weights);
         src += n_per_row;
         qrow += row_size;
@@ -3245,390 +2991,18 @@ size_t quantize_q5_1(const float * src, void * dst, int nrow, int n_per_row, int
     return nrow * row_size;
 }
 
+size_t quantize_q8_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
+    (void)quant_weights; // not used
+    const size_t row_size = ggml_row_size(GGML_TYPE_Q8_0, n_per_row);
+    quantize_row_q8_0_reference(src, dst, (int64_t)nrow*n_per_row);
+    return nrow * row_size;
+}
+
 // ====================== "True" 2-bit (de)-quantization
 
-static const  uint64_t iq2xxs_grid[256] = {
-    0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08,
-    0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808,
-    0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819,
-    0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, 0x08080808192b0819,
-    0x08080808192b1908, 0x080808082b080808, 0x080808082b08082b, 0x080808082b082b2b,
-    0x080808082b2b082b, 0x0808081908080819, 0x0808081908081908, 0x0808081908190808,
-    0x0808081908191919, 0x0808081919080808, 0x080808192b081908, 0x080808192b192b08,
-    0x0808082b08080808, 0x0808082b0808082b, 0x0808082b082b082b, 0x0808082b2b08082b,
-    0x0808190808080819, 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819,
-    0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, 0x0808190819082b08,
-    0x08081908192b0808, 0x080819082b080819, 0x080819082b081908, 0x080819082b190808,
-    0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08,
-    0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, 0x080819192b080808,
-    0x080819192b190819, 0x0808192b08082b19, 0x0808192b08190808, 0x0808192b19080808,
-    0x0808192b2b081908, 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919,
-    0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, 0x08082b0819080819,
-    0x08082b0819081908, 0x08082b0819190808, 0x08082b081919082b, 0x08082b082b082b08,
-    0x08082b1908081908, 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908,
-    0x0819080808080819, 0x0819080808081908, 0x0819080808190808, 0x08190808082b0819,
-    0x0819080819080808, 0x08190808192b0808, 0x081908082b081908, 0x081908082b190808,
-    0x081908082b191919, 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808,
-    0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, 0x0819082b082b1908,
-    0x0819082b19081919, 0x0819190808080808, 0x0819190808082b08, 0x08191908082b0808,
-    0x08191908082b1919, 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08,
-    0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, 0x08192b0808080819,
-    0x08192b0808081908, 0x08192b0808190808, 0x08192b0819080808, 0x08192b082b080819,
-    0x08192b1908080808, 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819,
-    0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, 0x082b080819081908,
-    0x082b0808192b0819, 0x082b08082b080808, 0x082b08082b08082b, 0x082b0819082b2b19,
-    0x082b081919082b08, 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819,
-    0x082b190808081908, 0x082b190808190808, 0x082b190819080808, 0x082b19081919192b,
-    0x082b191908080808, 0x082b191919080819, 0x082b1919192b1908, 0x082b192b2b190808,
-    0x082b2b0808082b08, 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908,
-    0x1908080808080819, 0x1908080808081908, 0x1908080808190808, 0x1908080808192b08,
-    0x19080808082b0819, 0x19080808082b1908, 0x1908080819080808, 0x1908080819082b08,
-    0x190808081919192b, 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908,
-    0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, 0x19080819192b0819,
-    0x190808192b080808, 0x190808192b081919, 0x1908082b08080819, 0x1908082b08190808,
-    0x1908082b19082b08, 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808,
-    0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, 0x190819082b192b19,
-    0x190819190819082b, 0x19081919082b1908, 0x1908192b08080808, 0x19082b0808080819,
-    0x19082b0808081908, 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919,
-    0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, 0x19082b192b08082b,
-    0x19082b2b19081919, 0x19082b2b2b190808, 0x1919080808080808, 0x1919080808082b08,
-    0x1919080808190819, 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808,
-    0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, 0x191908192b2b1908,
-    0x1919082b2b190819, 0x191919082b190808, 0x191919082b19082b, 0x1919191908082b2b,
-    0x1919192b08080819, 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819,
-    0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, 0x19192b2b08082b08,
-    0x192b080808081908, 0x192b080808190808, 0x192b080819080808, 0x192b0808192b2b08,
-    0x192b081908080808, 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808,
-    0x192b190808080808, 0x192b190808081919, 0x192b191908190808, 0x192b19190819082b,
-    0x192b19192b081908, 0x192b2b081908082b, 0x2b08080808080808, 0x2b0808080808082b,
-    0x2b08080808082b2b, 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908,
-    0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819,
-    0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808,
-    0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908,
-    0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b,
-    0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808,
-    0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b,
-    0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b,
-    0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808,
-    0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19,
-    0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908,
-};
-
-static const uint64_t iq2xs_grid[512] = {
-    0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08,
-    0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b,
-    0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919,
-    0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b,
-    0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919,
-    0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808,
-    0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, 0x080808082b190819,
-    0x080808082b191908, 0x080808082b192b19, 0x080808082b2b0808, 0x0808081908080819,
-    0x0808081908081908, 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808,
-    0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, 0x0808081908192b2b,
-    0x08080819082b0819, 0x08080819082b1908, 0x0808081919080808, 0x080808191908082b,
-    0x0808081919081919, 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908,
-    0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, 0x080808192b081908,
-    0x080808192b190808, 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b08081919,
-    0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808,
-    0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, 0x0808082b19191919,
-    0x0808082b2b080808, 0x0808082b2b082b2b, 0x0808190808080819, 0x0808190808081908,
-    0x080819080808192b, 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b,
-    0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908,
-    0x0808190819080808, 0x080819081908082b, 0x0808190819081919, 0x0808190819082b08,
-    0x0808190819190819, 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808,
-    0x080819082b080819, 0x080819082b081908, 0x080819082b190808, 0x0808191908080808,
-    0x080819190808082b, 0x0808191908081919, 0x0808191908082b08, 0x0808191908190819,
-    0x0808191908191908, 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908,
-    0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, 0x0808192b08080819,
-    0x0808192b08081908, 0x0808192b08190808, 0x0808192b082b192b, 0x0808192b19080808,
-    0x0808192b1908082b, 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b,
-    0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, 0x08082b0808190819,
-    0x08082b0808191908, 0x08082b08082b0808, 0x08082b08082b1919, 0x08082b0819080819,
-    0x08082b0819081908, 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808,
-    0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, 0x08082b1908081908,
-    0x08082b1908190808, 0x08082b1919080808, 0x08082b192b080819, 0x08082b192b082b19,
-    0x08082b2b08080808, 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b,
-    0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, 0x081908080808192b,
-    0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, 0x0819080808191919,
-    0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808,
-    0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, 0x0819080819190819,
-    0x0819080819191908, 0x08190808192b0808, 0x08190808192b2b2b, 0x081908082b080819,
-    0x081908082b081908, 0x081908082b190808, 0x0819081908080808, 0x081908190808082b,
-    0x0819081908081919, 0x0819081908082b08, 0x0819081908190819, 0x0819081908191908,
-    0x08190819082b0808, 0x0819081919080819, 0x0819081919081908, 0x0819081919190808,
-    0x081908192b080808, 0x081908192b191908, 0x081908192b19192b, 0x0819082b08080819,
-    0x0819082b08081908, 0x0819082b0808192b, 0x0819082b08190808, 0x0819082b19080808,
-    0x0819082b192b0808, 0x0819190808080808, 0x081919080808082b, 0x0819190808081919,
-    0x0819190808082b08, 0x0819190808190819, 0x0819190808191908, 0x08191908082b0808,
-    0x0819190819080819, 0x0819190819081908, 0x0819190819082b19, 0x0819190819190808,
-    0x08191908192b1908, 0x081919082b080808, 0x0819191908080819, 0x0819191908081908,
-    0x0819191908190808, 0x0819191919080808, 0x0819192b08080808, 0x0819192b08191908,
-    0x0819192b19082b19, 0x08192b0808080819, 0x08192b0808081908, 0x08192b0808190808,
-    0x08192b080819082b, 0x08192b0819080808, 0x08192b0819191908, 0x08192b082b08192b,
-    0x08192b1908080808, 0x08192b1908081919, 0x08192b19192b192b, 0x08192b2b19190819,
-    0x08192b2b2b2b2b19, 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919,
-    0x082b080808082b08, 0x082b080808082b2b, 0x082b080808190819, 0x082b080808191908,
-    0x082b0808082b0808, 0x082b080819080819, 0x082b080819081908, 0x082b080819190808,
-    0x082b08082b080808, 0x082b08082b2b0808, 0x082b081908080819, 0x082b081908081908,
-    0x082b081908190808, 0x082b081919080808, 0x082b081919082b08, 0x082b0819192b1919,
-    0x082b082b08080808, 0x082b082b082b082b, 0x082b082b2b080808, 0x082b082b2b2b2b08,
-    0x082b190808080819, 0x082b190808081908, 0x082b190808190808, 0x082b1908082b2b19,
-    0x082b190819080808, 0x082b191908080808, 0x082b191919080819, 0x082b19191919082b,
-    0x082b19192b192b19, 0x082b192b08080819, 0x082b192b08192b2b, 0x082b192b2b2b192b,
-    0x082b2b0808080808, 0x082b2b0808082b08, 0x082b2b0808082b2b, 0x082b2b08082b0808,
-    0x082b2b0819191919, 0x082b2b082b082b08, 0x082b2b082b2b082b, 0x082b2b19192b2b08,
-    0x082b2b192b190808, 0x082b2b2b08082b08, 0x082b2b2b082b0808, 0x082b2b2b2b08082b,
-    0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, 0x1908080808081908,
-    0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, 0x190808080819082b,
-    0x1908080808191919, 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908,
-    0x1908080819080808, 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08,
-    0x1908080819082b2b, 0x1908080819190819, 0x1908080819191908, 0x19080808192b0808,
-    0x19080808192b1919, 0x190808082b080819, 0x190808082b081908, 0x190808082b190808,
-    0x1908081908080808, 0x190808190808082b, 0x1908081908081919, 0x1908081908082b08,
-    0x1908081908190819, 0x1908081908191908, 0x19080819082b0808, 0x1908081919080819,
-    0x1908081919081908, 0x1908081919190808, 0x190808192b080808, 0x190808192b081919,
-    0x190808192b2b082b, 0x1908082b08080819, 0x1908082b08081908, 0x1908082b08190808,
-    0x1908082b0819082b, 0x1908082b082b2b19, 0x1908082b19080808, 0x1908190808080808,
-    0x190819080808082b, 0x1908190808081919, 0x1908190808082b08, 0x1908190808190819,
-    0x1908190808191908, 0x1908190808192b19, 0x19081908082b0808, 0x1908190819080819,
-    0x1908190819081908, 0x1908190819190808, 0x190819082b080808, 0x190819082b191908,
-    0x1908191908080819, 0x1908191908081908, 0x1908191908190808, 0x19081919082b1908,
-    0x1908191919080808, 0x190819192b192b2b, 0x1908192b08080808, 0x1908192b08082b2b,
-    0x1908192b19081908, 0x1908192b19190808, 0x19082b0808080819, 0x19082b0808081908,
-    0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, 0x19082b0819191908,
-    0x19082b08192b082b, 0x19082b1908080808, 0x19082b1908190819, 0x19082b1919081908,
-    0x19082b1919190808, 0x19082b19192b2b19, 0x19082b2b08081908, 0x1919080808080808,
-    0x191908080808082b, 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819,
-    0x1919080808191908, 0x19190808082b0808, 0x19190808082b2b08, 0x1919080819080819,
-    0x1919080819081908, 0x1919080819190808, 0x191908082b080808, 0x1919081908080819,
-    0x1919081908081908, 0x1919081908190808, 0x1919081908191919, 0x1919081919080808,
-    0x191908191908082b, 0x1919082b08080808, 0x1919082b19081908, 0x1919082b2b2b2b2b,
-    0x1919190808080819, 0x1919190808081908, 0x1919190808190808, 0x19191908082b0819,
-    0x1919190819080808, 0x19191908192b0808, 0x191919082b080819, 0x191919082b2b0819,
-    0x1919191908080808, 0x1919191908082b08, 0x191919192b080808, 0x191919192b082b08,
-    0x1919192b082b0819, 0x1919192b192b2b08, 0x1919192b2b2b0819, 0x19192b0808080808,
-    0x19192b0808191908, 0x19192b0819080819, 0x19192b0819190808, 0x19192b082b192b19,
-    0x19192b1908192b2b, 0x19192b1919080808, 0x19192b191908082b, 0x19192b2b2b081919,
-    0x192b080808080819, 0x192b080808081908, 0x192b080808190808, 0x192b080819080808,
-    0x192b080819191908, 0x192b0808192b082b, 0x192b08082b08192b, 0x192b08082b2b2b19,
-    0x192b081908080808, 0x192b082b082b1908, 0x192b082b19082b2b, 0x192b082b2b19082b,
-    0x192b190808080808, 0x192b19080819192b, 0x192b191908190808, 0x192b191919080808,
-    0x192b191919081919, 0x192b19192b2b1908, 0x192b2b0808080819, 0x192b2b08192b2b2b,
-    0x192b2b19082b1919, 0x192b2b2b0808192b, 0x192b2b2b19191908, 0x192b2b2b192b082b,
-    0x2b08080808080808, 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08,
-    0x2b08080808190819, 0x2b08080808191908, 0x2b080808082b0808, 0x2b080808082b2b2b,
-    0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808082b080808,
-    0x2b0808082b08082b, 0x2b0808082b2b2b08, 0x2b0808082b2b2b2b, 0x2b08081908080819,
-    0x2b08081908081908, 0x2b0808190808192b, 0x2b08081908190808, 0x2b08081919080808,
-    0x2b08081919190819, 0x2b08081919192b19, 0x2b08082b08080808, 0x2b08082b082b0808,
-    0x2b08082b2b080808, 0x2b08082b2b08082b, 0x2b08082b2b2b0808, 0x2b08082b2b2b2b08,
-    0x2b08190808080819, 0x2b08190808081908, 0x2b08190808190808, 0x2b0819080819082b,
-    0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, 0x2b0819082b082b19,
-    0x2b08191908080808, 0x2b08191919081908, 0x2b0819192b2b1919, 0x2b08192b08192b08,
-    0x2b08192b192b2b2b, 0x2b082b0808080808, 0x2b082b0808082b08, 0x2b082b08082b1919,
-    0x2b082b0819192b2b, 0x2b082b082b080808, 0x2b082b082b08082b, 0x2b082b082b2b2b08,
-    0x2b082b190808192b, 0x2b082b2b082b082b, 0x2b082b2b2b080808, 0x2b082b2b2b082b08,
-    0x2b082b2b2b19192b, 0x2b082b2b2b2b2b08, 0x2b19080808080819, 0x2b19080808081908,
-    0x2b19080808190808, 0x2b19080819080808, 0x2b1908081919192b, 0x2b1908082b081908,
-    0x2b19081908080808, 0x2b190819082b082b, 0x2b190819192b1908, 0x2b19082b1919192b,
-    0x2b19082b2b082b19, 0x2b19190808080808, 0x2b19190808081919, 0x2b19190819081908,
-    0x2b19190819190808, 0x2b19190819192b08, 0x2b191919082b2b19, 0x2b1919192b190808,
-    0x2b1919192b19082b, 0x2b19192b19080819, 0x2b192b0819190819, 0x2b192b082b2b192b,
-    0x2b192b1919082b19, 0x2b192b2b08191919, 0x2b192b2b192b0808, 0x2b2b080808080808,
-    0x2b2b08080808082b, 0x2b2b080808082b08, 0x2b2b080808082b2b, 0x2b2b0808082b0808,
-    0x2b2b0808082b2b2b, 0x2b2b08082b2b0808, 0x2b2b081919190819, 0x2b2b081919192b19,
-    0x2b2b08192b2b192b, 0x2b2b082b08080808, 0x2b2b082b0808082b, 0x2b2b082b08082b08,
-    0x2b2b082b082b2b2b, 0x2b2b082b2b080808, 0x2b2b082b2b2b0808, 0x2b2b190819080808,
-    0x2b2b19082b191919, 0x2b2b192b192b1919, 0x2b2b192b2b192b08, 0x2b2b2b0808082b2b,
-    0x2b2b2b08082b0808, 0x2b2b2b08082b082b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b0808,
-    0x2b2b2b082b2b2b08, 0x2b2b2b1908081908, 0x2b2b2b192b081908, 0x2b2b2b192b08192b,
-    0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b,
-};
-
-static const uint32_t iq3xxs_grid[256] = {
-    0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414,
-    0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14,
-    0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404,
-    0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e,
-    0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c,
-    0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c,
-    0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34,
-    0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c,
-    0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c,
-    0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04,
-    0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c,
-    0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414,
-    0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434,
-    0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c,
-    0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e,
-    0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24,
-    0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24,
-    0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c,
-    0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c,
-    0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14,
-    0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414,
-    0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e,
-    0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404,
-    0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c,
-    0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c,
-    0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14,
-    0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c,
-    0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c,
-    0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14,
-    0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14,
-    0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c,
-    0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04,
-};
-
-#define NGRID_IQ2XXS 512
-static const  uint64_t iq1s_grid[NGRID_IQ2XXS] = {
-    0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000,
-    0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01,
-    0xffffff00ff000000, 0xffffff000000ff00, 0xffffff00000000ff, 0xffffff0000000100,
-    0xffffff0000010000, 0xffffff0001000000, 0xffffff01ffff00ff, 0xffffff01ff01ff00,
-    0xffffff01ff010100, 0xffffff0100000001, 0xffffff0101ffff00, 0xffffff0101ff0101,
-    0xffffff0101010100, 0xffff00ffff00ff01, 0xffff00ffff0000ff, 0xffff00ff00ff0100,
-    0xffff00ff0100ff00, 0xffff00ff010001ff, 0xffff0000ff0101ff, 0xffff000000ffff00,
-    0xffff000000000000, 0xffff00000001ff01, 0xffff000001000101, 0xffff0000010100ff,
-    0xffff0001ffff0100, 0xffff00010000ff00, 0xffff000100010101, 0xffff000101000000,
-    0xffff01ffffff0000, 0xffff01ffff01ffff, 0xffff01ffff010100, 0xffff01ff00000000,
-    0xffff01ff01ffffff, 0xffff01ff01ff0001, 0xffff01ff0101ffff, 0xffff01ff01010001,
-    0xffff0100ffffff01, 0xffff01000000ffff, 0xffff010000000100, 0xffff010001ff01ff,
-    0xffff010001000000, 0xffff0101ff000000, 0xffff0101000101ff, 0xffff010101ffff01,
-    0xffff01010101ff00, 0xff00ffffff000000, 0xff00ffff00ffff00, 0xff00ffff00000001,
-    0xff00ffff000001ff, 0xff00ffff01010000, 0xff00ff00ffff0000, 0xff00ff00ff00ff00,
-    0xff00ff00ff0000ff, 0xff00ff00ff000100, 0xff00ff00ff010001, 0xff00ff0000ff0001,
-    0xff00ff000000ffff, 0xff00ff0000000000, 0xff00ff000001ff00, 0xff00ff0000010100,
-    0xff00ff0001ff0000, 0xff00ff000100ff00, 0xff00ff0001000100, 0xff00ff01ff000000,
-    0xff00ff0100ff0000, 0xff00ff01000001ff, 0xff00ff0101010001, 0xff0000ff00000000,
-    0xff0000ff0001ff00, 0xff0000ff00010100, 0xff000000ffff0101, 0xff000000ff000000,
-    0xff000000ff01ff00, 0xff00000000ff0000, 0xff0000000000ff00, 0xff000000000000ff,
-    0xff00000000000000, 0xff00000000000001, 0xff00000000000100, 0xff0000000001ffff,
-    0xff00000000010000, 0xff00000001000000, 0xff00000001010100, 0xff000001ff00ff01,
-    0xff000001ff0100ff, 0xff00000100000000, 0xff0000010001ff00, 0xff00000101ff0100,
-    0xff0000010100ff00, 0xff0001ff00ff00ff, 0xff0001ff00000101, 0xff0001ff000100ff,
-    0xff0001ff01000000, 0xff000100ff0001ff, 0xff0001000000ff01, 0xff00010000000000,
-    0xff00010000010001, 0xff00010000010100, 0xff00010001ffff00, 0xff00010001ff0101,
-    0xff00010001010000, 0xff000101ffffffff, 0xff000101ff000101, 0xff00010101ff00ff,
-    0xff00010101000001, 0xff000101010100ff, 0xff01ffffff000101, 0xff01ffffff01ffff,
-    0xff01ffffff01ff01, 0xff01ffffff0101ff, 0xff01ffff00000000, 0xff01ffff01ff0001,
-    0xff01ffff0101ff01, 0xff01ff00ff000000, 0xff01ff0000ff0100, 0xff01ff000000ff01,
-    0xff01ff0000010000, 0xff01ff00010000ff, 0xff01ff01ff01ff00, 0xff01ff0100000101,
-    0xff0100ffffff0000, 0xff0100ffff010000, 0xff0100ff01ff00ff, 0xff0100ff01000100,
-    0xff0100ff010100ff, 0xff010000ffffff01, 0xff01000000000000, 0xff0100000101ff00,
-    0xff010001ffff00ff, 0xff010001ff000100, 0xff01000100ffff00, 0xff01000100010001,
-    0xff01000101ff0001, 0xff010001010001ff, 0xff0101ffffffffff, 0xff0101ffff01ffff,
-    0xff0101ffff010101, 0xff0101ff0000ff00, 0xff0101ff01010001, 0xff010100ff000000,
-    0xff010100ff01ff01, 0xff01010000ff0001, 0xff01010000000100, 0xff01010001000000,
-    0xff0101010100ffff, 0x00ffffff0000ff01, 0x00ffffff000000ff, 0x00ffffff00000100,
-    0x00ffffff00010000, 0x00ffff00ffff0001, 0x00ffff00ff0000ff, 0x00ffff00ff000100,
-    0x00ffff0000000000, 0x00ffff0001000100, 0x00ffff0001010001, 0x00ffff01ff00ff01,
-    0x00ffff0100ff0100, 0x00ffff010000ff00, 0x00ffff01000100ff, 0x00ffff0101ff00ff,
-    0x00ffff010101ff00, 0x00ff00ffffffffff, 0x00ff00ffffff01ff, 0x00ff00ffff000101,
-    0x00ff00ff00000000, 0x00ff00ff000101ff, 0x00ff00ff01010101, 0x00ff0000ff000000,
-    0x00ff0000ff01ffff, 0x00ff000000ff0000, 0x00ff00000000ff00, 0x00ff0000000000ff,
-    0x00ff000000000000, 0x00ff000000000001, 0x00ff000000000100, 0x00ff000000010000,
-    0x00ff000001ffff01, 0x00ff000001000000, 0x00ff0001ff000101, 0x00ff000100ffffff,
-    0x00ff000100000000, 0x00ff0001010001ff, 0x00ff01ffff000000, 0x00ff01ff0001ff00,
-    0x00ff01ff01ff0100, 0x00ff0100ff01ff01, 0x00ff010000ff00ff, 0x00ff010000ff0101,
-    0x00ff010000000000, 0x00ff010000010101, 0x00ff01000100ff00, 0x00ff010001010000,
-    0x00ff0101ffffff00, 0x00ff01010000ff01, 0x00ff010100000100, 0x00ff010101ff0000,
-    0x0000ffffffff0100, 0x0000ffffff00ff00, 0x0000ffffff0000ff, 0x0000ffffff010000,
-    0x0000ffff00000000, 0x0000ffff00010101, 0x0000ffff01ffff01, 0x0000ffff01000100,
-    0x0000ff00ff000000, 0x0000ff00ff01ff00, 0x0000ff00ff0101ff, 0x0000ff0000ff0000,
-    0x0000ff000000ff00, 0x0000ff00000000ff, 0x0000ff0000000000, 0x0000ff0000000001,
-    0x0000ff0000000100, 0x0000ff0000010000, 0x0000ff0001ffffff, 0x0000ff0001ff01ff,
-    0x0000ff0001000000, 0x0000ff000101ffff, 0x0000ff01ffff0101, 0x0000ff01ff010000,
-    0x0000ff0100000000, 0x0000ff0101000101, 0x000000ffffff0001, 0x000000ffff000000,
-    0x000000ff00ff0000, 0x000000ff0000ff00, 0x000000ff000000ff, 0x000000ff00000000,
-    0x000000ff00000001, 0x000000ff00000100, 0x000000ff00010000, 0x000000ff01000000,
-    0x000000ff0101ff00, 0x00000000ffff0000, 0x00000000ff00ff00, 0x00000000ff0000ff,
-    0x00000000ff000000, 0x00000000ff000001, 0x00000000ff000100, 0x00000000ff010000,
-    0x0000000000ffff00, 0x0000000000ff00ff, 0x0000000000ff0000, 0x0000000000ff0001,
-    0x0000000000ff0100, 0x000000000000ffff, 0x000000000000ff00, 0x000000000000ff01,
-    0x00000000000000ff, 0x0000000000000001, 0x00000000000001ff, 0x0000000000000100,
-    0x0000000000000101, 0x000000000001ff00, 0x00000000000100ff, 0x0000000000010000,
-    0x0000000000010001, 0x0000000000010100, 0x0000000001ff0000, 0x000000000100ff00,
-    0x00000000010000ff, 0x0000000001000000, 0x0000000001000001, 0x0000000001000100,
-    0x0000000001010000, 0x00000001ffff01ff, 0x00000001ff000000, 0x0000000100ff0000,
-    0x000000010000ff00, 0x00000001000000ff, 0x0000000100000000, 0x0000000100000001,
-    0x0000000100000100, 0x0000000100010000, 0x0000000101000000, 0x000001ffff00ff00,
-    0x000001ffff010001, 0x000001ffff0101ff, 0x000001ff00ffff01, 0x000001ff0000ffff,
-    0x000001ff00000000, 0x000001ff010000ff, 0x000001ff01010100, 0x00000100ffff0100,
-    0x00000100ff000000, 0x0000010000ff0000, 0x000001000000ff00, 0x00000100000000ff,
-    0x0000010000000000, 0x0000010000000001, 0x0000010000000100, 0x0000010000010000,
-    0x0000010001000000, 0x000001000101ff01, 0x00000101ffff0001, 0x00000101ff01ffff,
-    0x0000010100000000, 0x0000010101010100, 0x0001ffffff000000, 0x0001ffff00ffffff,
-    0x0001ffff00000100, 0x0001ffff0001ff00, 0x0001ffff01000000, 0x0001ff00ffffff00,
-    0x0001ff00ffff01ff, 0x0001ff00ff010000, 0x0001ff0000000000, 0x0001ff0000010001,
-    0x0001ff0001ff0000, 0x0001ff0001010100, 0x0001ff01ff0000ff, 0x0001ff01ff000001,
-    0x0001ff0100ffffff, 0x0001ff010001ffff, 0x0001ff01000101ff, 0x0001ff010100ff01,
-    0x000100ffff00ffff, 0x000100ffff00ff01, 0x000100ffff000100, 0x000100ff00000000,
-    0x000100ff000101ff, 0x000100ff01ff0101, 0x000100ff0100ffff, 0x000100ff01010101,
-    0x00010000ff000000, 0x00010000ff010100, 0x0001000000ff0000, 0x000100000000ff00,
-    0x00010000000000ff, 0x0001000000000000, 0x0001000000000001, 0x0001000000000100,
-    0x0001000000010000, 0x0001000001ffff01, 0x0001000001000000, 0x0001000100ff0101,
-    0x0001000100000000, 0x00010001010100ff, 0x000101ffffff01ff, 0x000101ffffff0101,
-    0x000101ff00010000, 0x000101ff01ff0000, 0x000101ff0100ff01, 0x00010100ffff0000,
-    0x0001010000000000, 0x000101000001ffff, 0x0001010000010101, 0x00010100010001ff,
-    0x00010101ff00ff00, 0x00010101ff010001, 0x0001010100ffffff, 0x0001010100ff01ff,
-    0x00010101000101ff, 0x0001010101ff0000, 0x000101010100ff01, 0x0001010101000101,
-    0x01ffffffffff0101, 0x01ffffffff01ffff, 0x01ffffffff01ff01, 0x01ffffffff0101ff,
-    0x01ffffffff010101, 0x01ffffff00000000, 0x01ffffff01ff01ff, 0x01ffffff01000101,
-    0x01ffffff0101ff01, 0x01ffffff010100ff, 0x01ffff000000ff00, 0x01ffff0000000001,
-    0x01ffff00000001ff, 0x01ffff0000010000, 0x01ffff0001ff0000, 0x01ffff01ffffffff,
-    0x01ffff01ffff01ff, 0x01ffff01ff000000, 0x01ffff01ff01ffff, 0x01ffff01ff0101ff,
-    0x01ffff010100ffff, 0x01ff00ffffff0000, 0x01ff00ffff010000, 0x01ff00ff00ffff01,
-    0x01ff0000ff0000ff, 0x01ff000000000000, 0x01ff00000001ff01, 0x01ff000001ffffff,
-    0x01ff000001010100, 0x01ff0001ffffff01, 0x01ff0001ff010001, 0x01ff000101ff0100,
-    0x01ff000101000001, 0x01ff0001010100ff, 0x01ff01ffff00ffff, 0x01ff01ff00010001,
-    0x01ff01ff01000000, 0x01ff01ff010101ff, 0x01ff0100ff000001, 0x01ff010000ffff00,
-    0x01ff010000000100, 0x01ff010001ff01ff, 0x01ff01000101ffff, 0x01ff0101ffff00ff,
-    0x01ff0101ffff0101, 0x01ff0101ff0101ff, 0x01ff010100010000, 0x0100ffff00ff00ff,
-    0x0100ffff00ff0001, 0x0100ffff00000100, 0x0100ffff0100ff00, 0x0100ff00ffff0000,
-    0x0100ff00ff00ffff, 0x0100ff00ff00ff01, 0x0100ff00ff000100, 0x0100ff00ff010000,
-    0x0100ff0000000000, 0x0100ff00000100ff, 0x0100ff0001ff0101, 0x0100ff0001010101,
-    0x0100ff0100ff00ff, 0x0100ff0100ff0001, 0x0100ff0100000100, 0x0100ff0100010001,
-    0x0100ff0101000000, 0x010000ffff00ff00, 0x010000ff0000ffff, 0x010000ff00000000,
-    0x010000ff010001ff, 0x010000ff01010001, 0x01000000ffffff00, 0x01000000ffff0101,
-    0x01000000ff000000, 0x01000000ff0100ff, 0x01000000ff010101, 0x0100000000ff0000,
-    0x010000000000ff00, 0x01000000000000ff, 0x0100000000000000, 0x0100000000000001,
-    0x0100000000000100, 0x0100000000010000, 0x0100000001000000, 0x0100000100000000,
-    0x01000001000101ff, 0x0100000101ffff01, 0x010001ffff000101, 0x010001ff00ff0100,
-    0x010001ff0000ff00, 0x010001ff000100ff, 0x010001ff01ffffff, 0x01000100ffff0000,
-    0x01000100ff0001ff, 0x0100010000000000, 0x010001000001ff00, 0x0100010001ff0000,
-    0x01000100010000ff, 0x0100010001000101, 0x01000101ff00ff01, 0x0100010100ff0100,
-    0x010001010000ffff, 0x0100010101010001, 0x0101ffffffff0101, 0x0101ffffff0001ff,
-    0x0101ffffff01ffff, 0x0101ffffff010101, 0x0101ffff00000000, 0x0101ffff0101ffff,
-    0x0101ffff010101ff, 0x0101ff00ff000000, 0x0101ff0000ff0100, 0x0101ff000000ff00,
-    0x0101ff0000010000, 0x0101ff00010000ff, 0x0101ff0001000001, 0x0101ff01ff010101,
-    0x0101ff0100000000, 0x0101ff010101ff00, 0x010100ffffff0000, 0x010100ffff010000,
-    0x010100ff00ff01ff, 0x010100ff000000ff, 0x010100ff00000101, 0x010100ff01ffff00,
-    0x01010000ffffff01, 0x01010000ff000100, 0x01010000ff01ff01, 0x0101000000000000,
-    0x01010000000100ff, 0x010100000101ff01, 0x01010001ffff0000, 0x01010001ff00ffff,
-    0x01010001ff010000, 0x0101000101ffffff, 0x0101000101ff01ff, 0x0101000101010101,
-    0x010101ffff01ffff, 0x010101ff00000000, 0x010101ff0001ff01, 0x010101ff0101ffff,
-    0x010101ff010101ff, 0x01010100ffffffff, 0x01010100ff000001, 0x010101000000ff00,
-    0x0101010001010000, 0x0101010100ff0001, 0x010101010001ff01, 0x010101010101ffff,
-
-};
-
-static const uint8_t ksigns_iq2xs[128] = {
-      0, 129, 130,   3, 132,   5,   6, 135, 136,   9,  10, 139,  12, 141, 142,  15,
-    144,  17,  18, 147,  20, 149, 150,  23,  24, 153, 154,  27, 156,  29,  30, 159,
-    160,  33,  34, 163,  36, 165, 166,  39,  40, 169, 170,  43, 172,  45,  46, 175,
-     48, 177, 178,  51, 180,  53,  54, 183, 184,  57,  58, 187,  60, 189, 190,  63,
-    192,  65,  66, 195,  68, 197, 198,  71,  72, 201, 202,  75, 204,  77,  78, 207,
-     80, 209, 210,  83, 212,  85,  86, 215, 216,  89,  90, 219,  92, 221, 222,  95,
-     96, 225, 226,  99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111,
-    240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255,
-};
-
-static const uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128};
-
-void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k) {
+void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int64_t k) {
     assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+    const int64_t nb = k / QK_K;
 
     uint32_t aux32[2];
     const uint8_t * aux8 = (const uint8_t *)aux32;
@@ -3654,9 +3028,9 @@ void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y
 
 // ====================== 2.3125 bpw (de)-quantization
 
-void dequantize_row_iq2_xs(const block_iq2_xs * restrict x, float * restrict y, int k) {
+void dequantize_row_iq2_xs(const block_iq2_xs * restrict x, float * restrict y, int64_t k) {
     assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+    const int64_t nb = k / QK_K;
 
     float db[2];
 
@@ -3679,11 +3053,43 @@ void dequantize_row_iq2_xs(const block_iq2_xs * restrict x, float * restrict y,
     }
 }
 
+// ====================== 2.5625 bpw (de)-quantization
+
+void dequantize_row_iq2_s(const block_iq2_s * restrict x, float * restrict y, int64_t k) {
+    assert(k % QK_K == 0);
+    const int64_t nb = k / QK_K;
+
+    float db[2];
+
+    for (int i = 0; i < nb; i++) {
+
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+        const uint8_t * qs = x[i].qs;
+        const uint8_t * qh = x[i].qh;
+        const uint8_t * signs = qs + QK_K/8;
+
+        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+            db[0] = d * (0.5f + (x[i].scales[ib32] & 0xf)) * 0.25f;
+            db[1] = d * (0.5f + (x[i].scales[ib32] >>  4)) * 0.25f;
+            for (int l = 0; l < 4; ++l) {
+                const float dl = db[l/2];
+                const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300)));
+                for (int j = 0; j < 8; ++j) {
+                    y[j] = dl * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1.f : 1.f);
+                }
+                y += 8;
+            }
+            qs += 4;
+            signs += 4;
+        }
+    }
+}
+
 // ====================== 3.0625 bpw (de)-quantization
 
-void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y, int k) {
+void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y, int64_t k) {
     assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+    const int64_t nb = k / QK_K;
 
     uint32_t aux32;
 
@@ -3711,54 +3117,187 @@ void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y
     }
 }
 
-// ====================== 1.5625 bpw (de)-quantization
+// ====================== 3.3125 bpw (de)-quantization
 
-void dequantize_row_iq1_s(const block_iq1_s * restrict x, float * restrict y, int k) {
+void dequantize_row_iq3_s(const block_iq3_s * restrict x, float * restrict y, int64_t k) {
     assert(k % QK_K == 0);
-    const int nb = k / QK_K;
-
-    float db[4];
-    uint16_t idx[4];
-    //const int8_t * grid[4];
+    const int64_t nb = k / QK_K;
 
     for (int i = 0; i < nb; i++) {
 
         const float d = GGML_FP16_TO_FP32(x[i].d);
-        const uint8_t * sc = x[i].scales;
         const uint8_t * qs = x[i].qs;
+        const uint8_t * qh = x[i].qh;
+        const uint8_t * signs = x[i].signs;
 
-        for (int i8 = 0; i8 < QK_K/8; i8 += 4) {
-            idx[0] = qs[0] | ((sc[0] & 0x08) << 5);
-            idx[1] = qs[1] | ((sc[0] & 0x80) << 1);
-            idx[2] = qs[2] | ((sc[1] & 0x08) << 5);
-            idx[3] = qs[3] | ((sc[1] & 0x80) << 1);
-            //grid[0] = (const int8_t *)(iq1s_grid + (qs[0] | ((sc[0] & 0x08) << 5)));
-            //grid[1] = (const int8_t *)(iq1s_grid + (qs[1] | ((sc[0] & 0x80) << 1)));
-            //grid[2] = (const int8_t *)(iq1s_grid + (qs[2] | ((sc[1] & 0x08) << 5)));
-            //grid[3] = (const int8_t *)(iq1s_grid + (qs[3] | ((sc[1] & 0x80) << 1)));
-            db[0] = d * (2*(sc[0] & 7) + 1);
-            db[1] = d * (2*((sc[0] >> 4) & 7) + 1);
-            db[2] = d * (2*(sc[1] & 7) + 1);
-            db[3] = d * (2*((sc[1] >> 4) & 7) + 1);
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            const float db1 = d * (1 + 2*(x[i].scales[ib32/2] & 0xf));
+            const float db2 = d * (1 + 2*(x[i].scales[ib32/2] >>  4));
             for (int l = 0; l < 4; ++l) {
-                const int8_t * grid = (const int8_t *)(iq1s_grid + idx[l]);
-                for (int j = 0; j < 8; ++j) {
-                    //y[j] = db[l] * grid[l][j];
-                    y[j] = db[l] * grid[j];
+                const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[0] << (8-2*l)) & 256)));
+                const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[0] << (7-2*l)) & 256)));
+                for (int j = 0; j < 4; ++j) {
+                    y[j+0] = db1 * grid1[j] * (signs[l] & kmask_iq2xs[j+0] ? -1.f : 1.f);
+                    y[j+4] = db1 * grid2[j] * (signs[l] & kmask_iq2xs[j+4] ? -1.f : 1.f);
                 }
                 y += 8;
             }
-            qs += 4;
-            sc += 2;
+            qs += 8;
+            signs += 4;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[1] << (8-2*l)) & 256)));
+                const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[1] << (7-2*l)) & 256)));
+                for (int j = 0; j < 4; ++j) {
+                    y[j+0] = db2 * grid1[j] * (signs[l] & kmask_iq2xs[j+0] ? -1.f : 1.f);
+                    y[j+4] = db2 * grid2[j] * (signs[l] & kmask_iq2xs[j+4] ? -1.f : 1.f);
+                }
+                y += 8;
+            }
+            qh += 2;
+            qs += 8;
+            signs += 4;
         }
     }
 }
 
+// ====================== 1.5625 bpw (de)-quantization
+
+void dequantize_row_iq1_s(const block_iq1_s * restrict x, float * restrict y, int64_t k) {
+    assert(k % QK_K == 0);
+    const int64_t nb = k / QK_K;
+
+    for (int i = 0; i < nb; i++) {
+
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+        const uint8_t  * qs = x[i].qs;
+        const uint16_t * qh = x[i].qh;
+
+        for (int ib = 0; ib < QK_K/32; ++ib) {
+            const float dl = d * (2*((qh[ib] >> 12) & 7) + 1);
+            const float delta = qh[ib] & 0x8000 ? -IQ1S_DELTA : IQ1S_DELTA;
+            for (int l = 0; l < 4; ++l) {
+                const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((qh[ib] >> 3*l) & 7) << 8)));
+                for (int j = 0; j < 8; ++j) {
+                    y[j] = dl * (grid[j] + delta);
+                }
+                y += 8;
+            }
+            qs += 4;
+        }
+    }
+}
+
+void dequantize_row_iq1_m(const block_iq1_m * restrict x, float * restrict y, int64_t k) {
+    assert(k % QK_K == 0);
+    const int64_t nb = k / QK_K;
+
+    float delta[4];
+    uint16_t idx[4];
+
+#if QK_K != 64
+    iq1m_scale_t scale;
+#endif
+
+    for (int i = 0; i < nb; i++) {
+
+        const uint16_t * sc = (const uint16_t *)x[i].scales;
+#if QK_K == 64
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+#else
+        scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+        const float d = GGML_FP16_TO_FP32(scale.f16);
+#endif
+        const uint8_t * qs = x[i].qs;
+        const uint8_t * qh = x[i].qh;
+
+        for (int ib = 0; ib < QK_K/32; ++ib) {
+#if QK_K == 64
+            const float dl1 = d * (2*((sc[ib/2] >> (8*(ib%2)+0)) & 0xf) + 1);
+            const float dl2 = d * (2*((sc[ib/2] >> (8*(ib%2)+4)) & 0xf) + 1);
+#else
+            const float dl1 = d * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1);
+            const float dl2 = d * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1);
+#endif
+            idx[0] = qs[0] | ((qh[0] << 8) & 0x700);
+            idx[1] = qs[1] | ((qh[0] << 4) & 0x700);
+            idx[2] = qs[2] | ((qh[1] << 8) & 0x700);
+            idx[3] = qs[3] | ((qh[1] << 4) & 0x700);
+            delta[0] = qh[0] & 0x08 ? -IQ1S_DELTA : IQ1S_DELTA;
+            delta[1] = qh[0] & 0x80 ? -IQ1S_DELTA : IQ1S_DELTA;
+            delta[2] = qh[1] & 0x08 ? -IQ1S_DELTA : IQ1S_DELTA;
+            delta[3] = qh[1] & 0x80 ? -IQ1S_DELTA : IQ1S_DELTA;
+            for (int l = 0; l < 2; ++l) {
+                const int8_t * grid = (const int8_t *)(iq1s_grid + idx[l]);
+                for (int j = 0; j < 8; ++j) {
+                    y[j] = dl1 * (grid[j] + delta[l]);
+                }
+                y += 8;
+            }
+            for (int l = 2; l < 4; ++l) {
+                const int8_t * grid = (const int8_t *)(iq1s_grid + idx[l]);
+                for (int j = 0; j < 8; ++j) {
+                    y[j] = dl2 * (grid[j] + delta[l]);
+                }
+                y += 8;
+            }
+            qs += 4;
+            qh += 2;
+        }
+    }
+}
+
+static const int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
+
+void dequantize_row_iq4_nl(const block_iq4_nl * restrict x, float * restrict y, int64_t k) {
+    assert(k % QK4_NL == 0);
+    const int64_t nb = k / QK4_NL;
+
+    for (int i = 0; i < nb; i++) {
+
+        const uint8_t * qs = x[i].qs;
+
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+        for (int j = 0; j < QK4_NL/2; ++j) {
+            y[j+       0] = d * kvalues_iq4nl[qs[j] & 0xf];
+            y[j+QK4_NL/2] = d * kvalues_iq4nl[qs[j] >>  4];
+        }
+        y  += QK4_NL;
+        qs += QK4_NL/2;
+    }
+}
+
+void dequantize_row_iq4_xs(const block_iq4_xs * restrict x, float * restrict y, int64_t k) {
+    assert(k % QK_K == 0);
+#if QK_K == 64
+    dequantize_row_iq4_nl((const block_iq4_nl *)x, y, k);
+#else
+    const int64_t nb = k / QK_K;
+
+    for (int i = 0; i < nb; i++) {
+
+        const uint8_t * qs = x[i].qs;
+
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+
+        for (int ib = 0; ib < QK_K/32; ++ib) {
+            const int ls = ((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4);
+            const float dl = d * (ls - 32);
+            for (int j = 0; j < 16; ++j) {
+                y[j+ 0] = dl * kvalues_iq4nl[qs[j] & 0xf];
+                y[j+16] = dl * kvalues_iq4nl[qs[j] >>  4];
+            }
+            y  += 32;
+            qs += 16;
+        }
+    }
+#endif
+}
+
 //===================================== Q8_K ==============================================
 
-void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k) {
+void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int64_t k) {
     assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+    const int64_t nb = k / QK_K;
 
     for (int i = 0; i < nb; i++) {
 
@@ -3795,9 +3334,9 @@ void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict
     }
 }
 
-void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k) {
+void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int64_t k) {
     assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+    const int64_t nb = k / QK_K;
 
     for (int i = 0; i < nb; i++) {
         for (int j = 0; j < QK_K; ++j) {
@@ -3806,7 +3345,7 @@ void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int
     }
 }
 
-void quantize_row_q8_K(const float * restrict x, void * restrict y, int k) {
+void quantize_row_q8_K(const float * restrict x, void * restrict y, int64_t k) {
     quantize_row_q8_K_reference(x, y, k);
 }
 
@@ -4256,10 +3795,10 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
             const block_q8_1 * restrict b_y0 = &vy0[i];
             const block_q8_1 * restrict b_y1 = &vy1[i];
 
-            float32x4_t summs_t = {GGML_FP16_TO_FP32(b_x0->m) * b_y0->s,
-                                   GGML_FP16_TO_FP32(b_x1->m) * b_y0->s,
-                                   GGML_FP16_TO_FP32(b_x0->m) * b_y1->s,
-                                   GGML_FP16_TO_FP32(b_x1->m) * b_y1->s};
+            float32x4_t summs_t = {GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y0->s),
+                                   GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y0->s),
+                                   GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y1->s),
+                                   GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y1->s)};
             summs0 += summs_t;
 
             const uint8x16_t m4b = vdupq_n_u8(0x0F);
@@ -4280,10 +3819,10 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
             const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16);
 
             // mmla into int32x4_t
-            float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d),
-                                 GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d),
-                                 GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d),
-                                 GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)};
+            float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*b_y0->d,
+                                 GGML_FP16_TO_FP32(b_x0->d)*b_y1->d,
+                                 GGML_FP16_TO_FP32(b_x1->d)*b_y0->d,
+                                 GGML_FP16_TO_FP32(b_x1->d)*b_y1->d};
 
             int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l)));
             int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l)));
@@ -4324,7 +3863,7 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
         const block_q8_1 * restrict y0 = &y[i + 0];
         const block_q8_1 * restrict y1 = &y[i + 1];
 
-        summs += GGML_FP16_TO_FP32(x0->m) * y0->s + GGML_FP16_TO_FP32(x1->m) * y1->s;
+        summs += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s) + GGML_FP16_TO_FP32(x1->m) * GGML_FP16_TO_FP32(y1->s);
 
         const uint8x16_t m4b = vdupq_n_u8(0x0F);
 
@@ -4347,8 +3886,8 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
         const int32x4_t p_0 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h);
         const int32x4_t p_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h);
 
-        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*y0->d);
-        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*y1->d);
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
     }
 
     *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs;
@@ -4361,9 +3900,9 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     // Main loop
     for (int i = 0; i < nb; ++i) {
         const float d0 = GGML_FP16_TO_FP32(x[i].d);
-        const float d1 = y[i].d;
+        const float d1 = GGML_FP16_TO_FP32(y[i].d);
 
-        summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s;
+        summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s);
 
         const __m256 d0v = _mm256_set1_ps( d0 );
         const __m256 d1v = _mm256_set1_ps( d1 );
@@ -4415,7 +3954,7 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
 
         int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
+        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d))*sumi + GGML_FP16_TO_FP32(x[i].m)*GGML_FP16_TO_FP32(y[i].s);
     }
 
     *s = sumf;
@@ -4433,7 +3972,7 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
             sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]);
         }
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
+        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d))*sumi + GGML_FP16_TO_FP32(x[i].m)*GGML_FP16_TO_FP32(y[i].s);
     }
 
     *s = sumf;
@@ -4769,8 +4308,8 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
 
         const uint8x16_t m4b = vdupq_n_u8(0x0F);
 
-        summs0 += GGML_FP16_TO_FP32(x0->m) * y0->s;
-        summs1 += GGML_FP16_TO_FP32(x1->m) * y1->s;
+        summs0 += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s);
+        summs1 += GGML_FP16_TO_FP32(x1->m) * GGML_FP16_TO_FP32(y1->s);
 
         // extract the 5th bit via lookup table ((b) << 4)
         memcpy(&qh0, x0->qh, sizeof(qh0));
@@ -4814,10 +4353,10 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
 
         sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
                         ggml_vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l),
-                        ggml_vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*y0->d);
+                        ggml_vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
         sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
                         ggml_vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l),
-                        ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*y1->d);
+                        ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
     }
 
     *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1;
@@ -4834,7 +4373,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
         const block_q5_1 * restrict x0 = &x[i];
         const block_q8_1 * restrict y0 = &y[i];
 
-        summs += GGML_FP16_TO_FP32(x0->m) * y0->s;
+        summs += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s);
 
         const v128_t m4b = wasm_i8x16_splat(0x0F);
 
@@ -4881,7 +4420,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
                                            wasm_i32x4_dot_i16x8(v0lfh, v1lh)),
                             wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl),
                                            wasm_i32x4_dot_i16x8(v0hfh, v1hh)))),
-                    wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * y0->d)));
+                    wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d))));
     }
 
     *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
@@ -4896,14 +4435,14 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     for (int i = 0; i < nb; i++) {
         const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d));
 
-        summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s;
+        summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s);
 
         __m256i qx = bytes_from_nibbles_32(x[i].qs);
         __m256i bxhi = bytes_from_bits_32(x[i].qh);
         bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10));
         qx = _mm256_or_si256(qx, bxhi);
 
-        const __m256 dy = _mm256_set1_ps(y[i].d);
+        const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[i].d));
         const __m256i qy = _mm256_loadu_si256((const __m256i *)y[i].qs);
 
         const __m256 q = mul_sum_us8_pairs_float(qx, qy);
@@ -4923,7 +4462,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     for (int i = 0; i < nb; i++) {
         const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d));
 
-        summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s;
+        summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s);
 
         __m256i bx_0 = bytes_from_nibbles_32(x[i].qs);
         const __m256i bxhi = bytes_from_bits_32(x[i].qh);
@@ -4937,7 +4476,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
         bxh = _mm_or_si128(bxh, bxhih);
         bx_0 = MM256_SET_M128I(bxh, bxl);
 
-        const __m256 dy = _mm256_set1_ps(y[i].d);
+        const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[i].d));
         const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[i].qs);
 
         const __m256 q = mul_sum_us8_pairs_float(bx_0, by_0);
@@ -5004,7 +4543,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
 
         int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
+        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d))*sumi + GGML_FP16_TO_FP32(x[i].m)*GGML_FP16_TO_FP32(y[i].s);
     }
 
     *s = sumf;
@@ -5028,7 +4567,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
             sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]);
         }
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
+        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d))*sumi + GGML_FP16_TO_FP32(x[i].m)*GGML_FP16_TO_FP32(y[i].s);
     }
 
     *s = sumf;
@@ -5609,8 +5148,8 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * (float)x[i].d;
-        const float dmin = -y[i].d * (float)x[i].dmin;
+        const float d    =  y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         const uint8_t * restrict q2 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
@@ -5759,8 +5298,8 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * (float)x[i].d;
-        const float dmin = -y[i].d * (float)x[i].dmin;
+        const float d    =  y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         const uint8_t * restrict q2 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
@@ -5812,7 +5351,7 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
     float sumf = 0;
 
-    int isum[4];
+    int isum[QK_K/16];
 
     for (int i = 0; i < nb; ++i) {
 
@@ -5828,14 +5367,14 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r
         const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d);
         const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
-        isum[0] = isum[1] = isum[2] = isum[3] = 0;
+        memset(isum, 0, (QK_K/16)*sizeof(int));
         for (int l =  0; l < 16; ++l) {
             isum[0] += q8[l+ 0] * ((q2[l] >> 0) & 3);
             isum[1] += q8[l+16] * ((q2[l] >> 2) & 3);
             isum[2] += q8[l+32] * ((q2[l] >> 4) & 3);
             isum[3] += q8[l+48] * ((q2[l] >> 6) & 3);
         }
-        for (int l = 0; l < 4; ++l) {
+        for (int l = 0; l < QK_K/16; ++l) {
             isum[l] *= (sc[l] & 0xF);
         }
         sumf += dall * (isum[0] + isum[1] + isum[2] + isum[3]) - dmin * summs;
@@ -6413,7 +5952,7 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
         int32_t isum = -4*(scales[0] * y[i].bsums[0] + scales[2] * y[i].bsums[1] + scales[1] * y[i].bsums[2] + scales[3] * y[i].bsums[3]);
 
-        const float d = y[i].d * (float)x[i].d;
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
 
         const uint8x16_t htmp = vcombine_u8(hbits, vshr_n_u8(hbits, 1));
         q3h.val[0] = vandq_u8(mh, vshlq_n_u8(htmp, 2));
@@ -6615,7 +6154,7 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
         int32_t isum = -4*(scales[0] * y[i].bsums[0] + scales[2] * y[i].bsums[1] + scales[1] * y[i].bsums[2] + scales[3] * y[i].bsums[3]);
 
-        const float d = y[i].d * (float)x[i].d;
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
 
         vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1);
 
@@ -7118,9 +6657,9 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r
         aux16[1] = (a[0] >> 4) & 0x0f0f;
 
         const int32_t summi = scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]);
-        sum_mins += y[i].d * (float)x[i].d[1] * summi;
+        sum_mins += y[i].d * GGML_FP16_TO_FP32(x[i].d[1]) * summi;
 
-        const float d = y[i].d * (float)x[i].d[0];
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d[0]);
 
         const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4);
 
@@ -7778,7 +7317,7 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * (float)x[i].d;
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
         const int8_t * sc = x[i].scales;
 
         const uint8_t * restrict q5 = x[i].qs;
@@ -7920,7 +7459,7 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * (float)x[i].d;
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
         const int8_t * sc = x[i].scales;
 
         const uint8_t * restrict q5 = x[i].qs;
@@ -8488,7 +8027,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d_all = (float)x[i].d;
+        const float d_all = GGML_FP16_TO_FP32(x[i].d);
 
         const uint8_t * restrict q6 = x[i].ql;
         const uint8_t * restrict qh = x[i].qh;
@@ -8659,7 +8198,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d_all = (float)x[i].d;
+        const float d_all = GGML_FP16_TO_FP32(x[i].d);
 
         const uint8_t * restrict q6 = x[i].ql;
         const uint8_t * restrict qh = x[i].qh;
@@ -8761,6 +8300,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
 #endif
 
+#if defined (__AVX2__) || defined (__ARM_NEON)
 static const int8_t keven_signs_q2xs[1024] = {
      1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1, -1,  1, -1,  1,  1,  1,  1,  1, -1, -1, -1,  1,  1,  1,  1,  1,  1,
      1,  1, -1,  1,  1,  1,  1, -1, -1,  1, -1,  1,  1,  1,  1,  1,  1, -1, -1,  1,  1,  1,  1,  1, -1, -1, -1,  1,  1,  1,  1, -1,
@@ -8795,6 +8335,7 @@ static const int8_t keven_signs_q2xs[1024] = {
      1,  1,  1, -1, -1, -1, -1,  1, -1,  1,  1, -1, -1, -1, -1, -1,  1, -1,  1, -1, -1, -1, -1, -1, -1, -1,  1, -1, -1, -1, -1,  1,
      1,  1, -1, -1, -1, -1, -1, -1, -1,  1, -1, -1, -1, -1, -1,  1,  1, -1, -1, -1, -1, -1, -1,  1, -1, -1, -1, -1, -1, -1, -1, -1,
 };
+#endif
 
 void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     assert(n % QK_K == 0);
@@ -8992,15 +8533,7 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void *
 
 #elif defined(__AVX2__)
 
-    const __m128i m4 = _mm_set1_epi8(0xf);
-    const __m128i m1 = _mm_set1_epi8(1);
-    const __m256i m511 = _mm256_set1_epi16(511);
     const __m256i mone = _mm256_set1_epi8(1);
-
-    static const uint8_t k_bit_helper[32] = {
-        0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00,
-        0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00,
-    };
     static const char block_sign_shuffle_mask_1[32] = {
         0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02,
         0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06,
@@ -9014,11 +8547,77 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void *
         0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
     };
 
-    const __m256i bit_helper = _mm256_loadu_si256((const __m256i*)k_bit_helper);
     const __m256i bit_selector_mask = _mm256_loadu_si256((const __m256i*)bit_selector_mask_bytes);
     const __m256i block_sign_shuffle_1 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_1);
     const __m256i block_sign_shuffle_2 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_2);
 
+#if QK_K == 64
+    static const uint8_t k_bit_helper[16] = {
+        0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00,
+    };
+    const __m128i bit_helper = _mm_loadu_si128((const __m128i*)k_bit_helper);
+    const __m128i m511 = _mm_set1_epi16(511);
+    typedef union {
+        __m128i vec_index;
+        uint16_t index[8];
+    } index_t;
+
+    index_t idx;
+    __m256 accumf = _mm256_setzero_ps();
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const __m128i q2_data = _mm_loadu_si128((const __m128i*)x[i].qs);
+        idx.vec_index = _mm_and_si128(q2_data, m511);
+
+        const __m128i partial_sign_bits = _mm_srli_epi16(q2_data, 9);
+        const __m128i partial_sign_bits_upper = _mm_srli_epi16(q2_data, 13);
+        const __m128i partial_sign_bits_for_counting = _mm_xor_si128(partial_sign_bits, partial_sign_bits_upper);
+
+        const __m128i odd_bits = _mm_shuffle_epi8(bit_helper, partial_sign_bits_for_counting);
+        const __m128i full_sign_bits = _mm_or_si128(partial_sign_bits, odd_bits);
+        const __m256i full_signs = MM256_SET_M128I(full_sign_bits, full_sign_bits);
+
+        const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)y[i].qs);
+        const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)(y[i].qs+32));
+
+        const __m256i q2_1 = _mm256_set_epi64x(iq2xs_grid[idx.index[3]], iq2xs_grid[idx.index[2]],
+                                               iq2xs_grid[idx.index[1]], iq2xs_grid[idx.index[0]]);
+        const __m256i q2_2 = _mm256_set_epi64x(iq2xs_grid[idx.index[7]], iq2xs_grid[idx.index[6]],
+                                               iq2xs_grid[idx.index[5]], iq2xs_grid[idx.index[4]]);
+
+        __m256i signs;
+        signs = _mm256_shuffle_epi8(full_signs, block_sign_shuffle_1);
+        signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask);
+        const __m256i q8s_1 = _mm256_sign_epi8(q8_1, _mm256_or_si256(signs, mone));
+
+        signs = _mm256_shuffle_epi8(full_signs, block_sign_shuffle_2);
+        signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask);
+        const __m256i q8s_2 = _mm256_sign_epi8(q8_2, _mm256_or_si256(signs, mone));
+
+        const __m256i dot1  = _mm256_maddubs_epi16(q2_1, q8s_1);
+        const __m256i dot2  = _mm256_maddubs_epi16(q2_2, q8s_2);
+
+        const __m256i sc1 = MM256_SET_M128I(_mm_set1_epi16(2*(x[i].scales[0] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[0] & 0xf)+1));
+        const __m256i sc2 = MM256_SET_M128I(_mm_set1_epi16(2*(x[i].scales[1] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[1] & 0xf)+1));
+
+        const __m256i sum = _mm256_add_epi32(_mm256_madd_epi16(sc1, dot1), _mm256_madd_epi16(sc2, dot2));
+
+        accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(sum), accumf);
+
+    }
+
+    *s = 0.125f * hsum_float_8(accumf);
+#else
+
+    static const uint8_t k_bit_helper[32] = {
+        0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00,
+        0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00,
+    };
+    const __m256i bit_helper = _mm256_loadu_si256((const __m256i*)k_bit_helper);
+    const __m256i m511 = _mm256_set1_epi16(511);
+    const __m128i m4 = _mm_set1_epi8(0xf);
+    const __m128i m1 = _mm_set1_epi8(1);
+
     uint64_t aux64;
 
     // somewhat hacky, but gives a significant boost in performance
@@ -9066,8 +8665,8 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void *
 
             const __m128i full_signs_l = _mm256_castsi256_si128(full_sign_bits);
             const __m128i full_signs_h = _mm256_extractf128_si256(full_sign_bits, 1);
-            const __m256i full_signs_1 = _mm256_set_m128i(full_signs_l, full_signs_l);
-            const __m256i full_signs_2 = _mm256_set_m128i(full_signs_h, full_signs_h);
+            const __m256i full_signs_1 = MM256_SET_M128I(full_signs_l, full_signs_l);
+            const __m256i full_signs_2 = MM256_SET_M128I(full_signs_h, full_signs_h);
 
             __m256i signs;
             signs = _mm256_shuffle_epi8(full_signs_1, block_sign_shuffle_1);
@@ -9107,6 +8706,7 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void *
     }
 
     *s = 0.125f * hsum_float_8(accumf);
+#endif
 
 #else
 
@@ -9148,7 +8748,210 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void *
 #endif
 }
 
-// TODO
+void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
+    assert(n % QK_K == 0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+
+    const block_iq2_s * restrict x = vx;
+    const block_q8_K  * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+#if defined(__ARM_NEON)
+
+   static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
+                                       0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03
+   };
+
+    static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,};
+
+    const ggml_uint8x16x2_t mask1 = ggml_vld1q_u8_x2(k_mask1);
+    const uint8x16_t        mask2 = vld1q_u8(k_mask2);
+    const uint8x16_t m1 = vdupq_n_u8(1);
+    const int32x4_t vzero = vdupq_n_s32(0);
+
+    uint8x16x2_t vs;
+    ggml_int8x16x4_t q2s;
+    ggml_int8x16x4_t q8b;
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+
+        const uint8_t * restrict qs = x[i].qs;
+        const uint8_t * restrict qh = x[i].qh;
+        const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8);
+        const int8_t  * restrict q8 = y[i].qs;
+
+        int sumi1 = 0, sumi2 = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            q8b = ggml_vld1q_s8_x4(q8); q8 += 64;
+            q2s.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[0] | ((qh[ib32+0] << 8) & 0x300)))),
+                                     vld1_s8((const int8_t *)(iq2s_grid + (qs[1] | ((qh[ib32+0] << 6) & 0x300)))));
+            q2s.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[2] | ((qh[ib32+0] << 4) & 0x300)))),
+                                     vld1_s8((const int8_t *)(iq2s_grid + (qs[3] | ((qh[ib32+0] << 2) & 0x300)))));
+            q2s.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[4] | ((qh[ib32+1] << 8) & 0x300)))),
+                                     vld1_s8((const int8_t *)(iq2s_grid + (qs[5] | ((qh[ib32+1] << 6) & 0x300)))));
+            q2s.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[6] | ((qh[ib32+1] << 4) & 0x300)))),
+                                     vld1_s8((const int8_t *)(iq2s_grid + (qs[7] | ((qh[ib32+1] << 2) & 0x300)))));
+            qs += 8;
+
+            vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | ((uint32_t) signs[1] << 16)));
+            vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2);
+            vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2);
+            vs.val[0] = vceqq_u8(vs.val[0], mask2);
+            vs.val[1] = vceqq_u8(vs.val[1], mask2);
+
+            q2s.val[0] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[0], m1)), q2s.val[0]);
+            q2s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[1], m1)), q2s.val[1]);
+
+            vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | ((uint32_t) signs[3] << 16)));
+            vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2);
+            vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2);
+            vs.val[0] = vceqq_u8(vs.val[0], mask2);
+            vs.val[1] = vceqq_u8(vs.val[1], mask2);
+
+            signs += 4;
+
+            q2s.val[2] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[0], m1)), q2s.val[2]);
+            q2s.val[3] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[1], m1)), q2s.val[3]);
+
+            const int32x4_t p1 = ggml_vdotq_s32(vzero, q2s.val[0], q8b.val[0]);
+            const int32x4_t p2 = ggml_vdotq_s32(vzero, q2s.val[1], q8b.val[1]);
+            const int32x4_t p3 = ggml_vdotq_s32(vzero, q2s.val[2], q8b.val[2]);
+            const int32x4_t p4 = ggml_vdotq_s32(vzero, q2s.val[3], q8b.val[3]);
+
+            sumi1 += vaddvq_s32(p1) * (1 + 2*(x[i].scales[ib32+0] & 0xf));
+            sumi2 += vaddvq_s32(p2) * (1 + 2*(x[i].scales[ib32+0] >>  4));
+            sumi1 += vaddvq_s32(p3) * (1 + 2*(x[i].scales[ib32+1] & 0xf));
+            sumi2 += vaddvq_s32(p4) * (1 + 2*(x[i].scales[ib32+1] >>  4));
+        }
+        sumf += d*(sumi1 + sumi2);
+    }
+
+    *s = 0.125f * sumf;
+
+#elif defined(__AVX2__)
+
+   static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
+                                       0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03
+   };
+
+    static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+                                        0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+    };
+
+    const __m128i m4 = _mm_set1_epi8(0xf);
+    const __m128i m1 = _mm_set1_epi8(1);
+
+    const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1);
+    const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2);
+
+    uint64_t aux64;
+
+    __m256 accumf = _mm256_setzero_ps();
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * restrict qs = x[i].qs;
+        const uint8_t * restrict qh = x[i].qh;
+        const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8);
+        const int8_t  * restrict q8 = y[i].qs;
+
+        memcpy(&aux64, x[i].scales, 8);
+        const __m128i scales8 = _mm_add_epi8(_mm_slli_epi16(_mm_and_si128(_mm_set_epi64x(aux64 >> 4, aux64), m4), 1), m1);
+        const __m256i scales16 = _mm256_cvtepi8_epi16(scales8); // 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 15
+
+        __m256i sumi1 = _mm256_setzero_si256();
+        __m256i sumi2 = _mm256_setzero_si256();
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
+            const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
+            const __m256i q2_1 = _mm256_set_epi64x(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)],
+                                                   iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)],
+                                                   iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)],
+                                                   iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]);
+            const __m256i q2_2 = _mm256_set_epi64x(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)],
+                                                   iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)],
+                                                   iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)],
+                                                   iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]);
+            qs += 8;
+
+            __m256i aux256 = _mm256_set1_epi32(signs[0] | ((uint32_t) signs[1] << 16));
+            aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2);
+            const __m256i s2_1 = _mm256_cmpeq_epi8(aux256, mask2);
+            const __m256i q8s_1 = _mm256_sub_epi8(_mm256_xor_si256(s2_1, q8_1), s2_1);
+
+            aux256 = _mm256_set1_epi32(signs[2] | ((uint32_t) signs[3] << 16));
+            aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2);
+            const __m256i s2_2 = _mm256_cmpeq_epi8(aux256, mask2);
+            const __m256i q8s_2 = _mm256_sub_epi8(_mm256_xor_si256(s2_2, q8_2), s2_2);
+
+            signs += 4;
+
+            const __m256i dot1  = _mm256_maddubs_epi16(q2_1, q8s_1); // blocks 2*ib32+0, 2*ib32+1
+            const __m256i dot2  = _mm256_maddubs_epi16(q2_2, q8s_2); // blocks 2*ib32+2, 2*ib32+3
+
+            const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_shuffle_epi8(scales16, get_scale_shuffle_k4(ib32+0)));
+            const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_shuffle_epi8(scales16, get_scale_shuffle_k4(ib32+1)));
+            sumi1 = _mm256_add_epi32(sumi1, p1);
+            sumi2 = _mm256_add_epi32(sumi2, p2);
+        }
+
+        accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf);
+
+    }
+
+    *s = 0.125f * hsum_float_8(accumf);
+
+#else
+
+    float sumf = 0;
+    for (int i = 0; i < nb; i++) {
+
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const int8_t  * q8 = y[i].qs;
+        const uint8_t * qs = x[i].qs;
+        const uint8_t * qh = x[i].qh;
+        const uint8_t * signs = qs + QK_K/8;
+
+        int bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+            int ls1 = 1 + 2*(x[i].scales[ib32] & 0xf);
+            int ls2 = 1 + 2*(x[i].scales[ib32] >>  4);
+            int sumi1 = 0, sumi2 = 0;
+            for (int l = 0; l < 2; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300)));
+                for (int j = 0; j < 8; ++j) {
+                    sumi1 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            for (int l = 2; l < 4; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300)));
+                for (int j = 0; j < 8; ++j) {
+                    sumi2 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            bsum += ls1 * sumi1 + ls2 * sumi2;
+            qs += 4;
+            signs += 4;
+        }
+
+        sumf += d * bsum;
+    }
+
+    *s = 0.125f * sumf;
+
+#endif
+
+}
+
 void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     assert(n % QK_K == 0);
     assert(nrc == 1);
@@ -9283,6 +9086,246 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void
 #endif
 }
 
+void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
+    assert(n % QK_K == 0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+
+    const block_iq3_s * restrict x = vx;
+    const block_q8_K  * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+#if defined(__ARM_NEON)
+
+    typedef union {
+        uint16x8_t vec_index;
+        uint16_t   index[8];
+    } vec_index_t;
+
+   static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
+                                       0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03
+   };
+
+    static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,};
+
+    static const int16_t k_shift[8] = {8, 7, 6, 5, 4, 3, 2, 1};
+
+    const ggml_uint8x16x2_t mask1 = ggml_vld1q_u8_x2(k_mask1);
+    const uint8x16_t        mask2 = vld1q_u8(k_mask2);
+
+    const int16x8_t  hshift = vld1q_s16(k_shift);
+    const uint16x8_t m256   = vdupq_n_u16(256);
+    const uint8x16_t m1     = vdupq_n_u8(1);
+
+    uint8x16x2_t vs;
+    ggml_int8x16x4_t q3s;
+    ggml_int8x16x4_t q8b;
+    vec_index_t idx;
+
+#if QK_K == 256
+    uint32_t scales32[2];
+    const uint8_t * scales8 = (const uint8_t *)scales32;
+#endif
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * restrict qs = x[i].qs;
+        const uint8_t * restrict qh = x[i].qh;
+        const uint16_t * restrict signs = (const uint16_t *)x[i].signs;
+        const int8_t   * restrict q8 = y[i].qs;
+
+#if QK_K == 256
+        memcpy(scales32, x[i].scales, 4);
+        scales32[1] = (((scales32[0] >> 4) & 0x0f0f0f0f) << 1) | 0x01010101;
+        scales32[0] = ((scales32[0] & 0x0f0f0f0f) << 1) | 0x01010101;
+#endif
+
+        int sumi1 = 0, sumi2 = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            q8b = ggml_vld1q_s8_x4(q8); q8 += 64;
+
+            const uint8x16_t idx_l = vld1q_u8(qs); qs += 16;
+            idx.vec_index = vorrq_u16(vmovl_u8(vget_low_u8 (idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+0]), hshift), m256));
+            const uint32x4_t aux32x4_0 = ggml_vld1q_u32(iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]],
+                                                        iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]);
+            const uint32x4_t aux32x4_1 = ggml_vld1q_u32(iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]],
+                                                        iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]);
+            idx.vec_index = vorrq_u16(vmovl_u8(vget_high_u8(idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+1]), hshift), m256));
+            const uint32x4_t aux32x4_2 = ggml_vld1q_u32(iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]],
+                                                        iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]);
+            const uint32x4_t aux32x4_3 = ggml_vld1q_u32(iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]],
+                                                        iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]);
+
+
+            vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | ((uint32_t) signs[1] << 16)));
+            vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2);
+            vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2);
+            vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1);
+            vs.val[1] = vorrq_u8(vceqq_u8(vs.val[1], mask2), m1);
+
+            q3s.val[0] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_0));
+            q3s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_1));
+
+            vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | ((uint32_t) signs[3] << 16)));
+            vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2);
+            vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2);
+            vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1);
+            vs.val[1] = vorrq_u8(vceqq_u8(vs.val[1], mask2), m1);
+
+            signs += 4;
+
+            q3s.val[2] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_2));
+            q3s.val[3] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_3));
+
+            const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]);
+            const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]);
+#if QK_K == 256
+            sumi1 += vaddvq_s32(p1) * scales8[ib32/2+0];
+            sumi2 += vaddvq_s32(p2) * scales8[ib32/2+4];
+#else
+            sumi1 += vaddvq_s32(p1) * (1 + 2*(x[i].scales[ib32/2] & 0xf));
+            sumi2 += vaddvq_s32(p2) * (1 + 2*(x[i].scales[ib32/2] >>  4));
+#endif
+        }
+        sumf += d*(sumi1 + sumi2);
+    }
+    *s = sumf;
+
+#elif defined(__AVX2__)
+
+   static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
+                                       0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03
+   };
+
+    static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+                                        0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+    };
+
+    const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1);
+    const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2);
+
+    const __m256i idx_shift = _mm256_set_epi32(1, 2, 3, 4, 5, 6, 7, 8);
+    const __m256i idx_mask  = _mm256_set1_epi32(256);
+
+    typedef union {
+        __m256i  vec[2];
+        uint32_t index[16];
+    } index_t;
+
+    index_t idx;
+
+    __m256 accumf = _mm256_setzero_ps();
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * restrict qs = x[i].qs;
+        const uint8_t * restrict qh = x[i].qh;
+        const uint16_t * restrict signs = (const uint16_t *)x[i].signs;
+        const int8_t  * restrict q8 = y[i].qs;
+        __m256i sumi1 = _mm256_setzero_si256();
+        __m256i sumi2 = _mm256_setzero_si256();
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
+            const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
+            const __m256i idx_l = _mm256_cvtepu8_epi16(_mm_loadu_si128((const __m128i *)qs)); qs += 16;
+            idx.vec[0] = _mm256_set1_epi32(qh[ib32+0]);
+            idx.vec[1] = _mm256_set1_epi32(qh[ib32+1]);
+            idx.vec[0] = _mm256_and_si256(_mm256_sllv_epi32(idx.vec[0], idx_shift), idx_mask);
+            idx.vec[1] = _mm256_and_si256(_mm256_sllv_epi32(idx.vec[1], idx_shift), idx_mask);
+            idx.vec[0] = _mm256_or_si256(idx.vec[0], _mm256_cvtepi16_epi32(_mm256_castsi256_si128(idx_l)));
+            idx.vec[1] = _mm256_or_si256(idx.vec[1], _mm256_cvtepi16_epi32(_mm256_extractf128_si256(idx_l, 1)));
+
+            // At leat on my CPU (Ryzen 7950X), using _mm256_i32gather_epi32 is slower than _mm256_set_epi32. Strange.
+            //const __m256i q2_1 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[0], 4);
+            //const __m256i q2_2 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[1], 4);
+            const __m256i q2_1 = _mm256_set_epi32(
+                    iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]],
+                    iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]]
+            );
+            const __m256i q2_2 = _mm256_set_epi32(
+                    iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]],
+                    iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[ 9]], iq3s_grid[idx.index[ 8]]
+            );
+
+            __m256i aux256 = _mm256_set1_epi32(signs[0] | (signs[1] << 16));
+            aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2);
+            const __m256i s2_1 = _mm256_cmpeq_epi8(aux256, mask2);
+            const __m256i q8s_1 = _mm256_sub_epi8(_mm256_xor_si256(s2_1, q8_1), s2_1);
+
+            aux256 = _mm256_set1_epi32(signs[2] | (signs[3] << 16));
+            aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2);
+            const __m256i s2_2 = _mm256_cmpeq_epi8(aux256, mask2);
+            const __m256i q8s_2 = _mm256_sub_epi8(_mm256_xor_si256(s2_2, q8_2), s2_2);
+
+            signs += 4;
+
+            const __m256i dot1  = _mm256_maddubs_epi16(q2_1, q8s_1);
+            const __m256i dot2  = _mm256_maddubs_epi16(q2_2, q8s_2);
+            const uint16_t ls1 = x[i].scales[ib32/2] & 0xf;
+            const uint16_t ls2 = x[i].scales[ib32/2] >>  4;
+            const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1));
+            const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1));
+            sumi1 = _mm256_add_epi32(sumi1, p1);
+            sumi2 = _mm256_add_epi32(sumi2, p2);
+        }
+
+        accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf);
+
+    }
+
+    *s = hsum_float_8(accumf);
+
+#else
+
+    float sumf = 0.f;
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * restrict qs = x[i].qs;
+        const uint8_t * restrict qh = x[i].qh;
+        const uint8_t * restrict signs = x[i].signs;
+        const int8_t  * restrict q8 = y[i].qs;
+        int32_t bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            const uint32_t ls1 = 2*(x[i].scales[ib32/2] & 0xf) + 1;
+            const uint32_t ls2 = 2*(x[i].scales[ib32/2] >>  4) + 1;
+            int32_t sumi = 0;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256)));
+                const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256)));
+                for (int j = 0; j < 4; ++j) {
+                    sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1);
+                    sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            qs += 8;
+            signs += 4;
+            bsum += sumi * ls1;
+            sumi = 0;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256)));
+                const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256)));
+                for (int j = 0; j < 4; ++j) {
+                    sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1);
+                    sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            qs += 8;
+            signs += 4;
+            bsum += sumi * ls2;
+        }
+        sumf += d * bsum;
+    }
+    *s = sumf;
+#endif
+}
+
+
 #ifdef __AVX2__
 static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) {
     const __m256i ax = _mm256_sign_epi8(x, x);
@@ -9291,7 +9334,7 @@ static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) {
 }
 #endif
 
-void ggml_vec_dot_iq1_s_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
+void ggml_vec_dot_iq1_s_q8_K  (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     assert(n % QK_K == 0);
     assert(nrc == 1);
     UNUSED(nrc);
@@ -9306,153 +9349,591 @@ void ggml_vec_dot_iq1_s_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const
 
 #if defined __ARM_NEON
 
-    const uint8x16_t m8 = vdupq_n_u8(0x08);
-    const uint8x16_t m7 = vdupq_n_u8(0x07);
-    const uint8x16_t m1 = vdupq_n_u8(0x01);
-    const int32x4_t vzero = vdupq_n_s32(0);
-
-    uint16_t gindex[8];
-    uint16x8x2_t vindex;
-    int8x16x4_t q1b;
-    int8x16x4_t q8b;
-    uint16x8x4_t scales;
-    int32x4x2_t sumi;
-    int32x4x2_t dotq;
+    ggml_int8x16x4_t q1b;
+    ggml_int8x16x4_t q8b;
 
     float sumf = 0;
     for (int i = 0; i < nb; ++i) {
 
-        const int8_t  * q8 = y[i].qs;
-        const uint8_t * qs = x[i].qs;
-        const uint8_t * sc = x[i].scales;
+        const int8_t   * q8 = y[i].qs;
+        const uint8_t  * qs = x[i].qs;
+        const uint16_t * qh = x[i].qh;
 
-        sumi.val[0] = sumi.val[1] = vzero;
+        int sumi1 = 0, sumi2 = 0, sumi3 = 0;
 
-        for (int i128 = 0; i128 < QK_K/128; ++i128) {
-            const uint8x16_t ql = vld1q_u8(qs); qs += 16;
-            const uint8x8_t tm1 = vld1_u8 (sc); sc +=  8;
-            const uint8x8_t tm2 = vshr_n_u8(tm1, 4);
-            const uint8x16_t qh = vcombine_u8(vzip1_u8(tm1, tm2), vzip2_u8(tm1, tm2));
-            const uint8x16_t hbit = vandq_u8(qh, m8);
-            vindex.val[0] = vorrq_u16(vmovl_u8(vget_low_u8 (ql)), vshlq_n_u16(vmovl_u8(vget_low_u8 (hbit)), 5));
-            vindex.val[1] = vorrq_u16(vmovl_u8(vget_high_u8(ql)), vshlq_n_u16(vmovl_u8(vget_high_u8(hbit)), 5));
-            const uint8x16_t scales8 = vorrq_u8(vshlq_n_u8(vandq_u8(qh, m7), 1), m1);
-            scales.val[0] = vmovl_u8(vget_low_u8 (scales8));
-            scales.val[1] = vmovl_u8(vget_high_u8 (scales8));
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
 
-            for (int l = 0; l < 2; ++l) {
-                vst1q_u16(gindex+0, vindex.val[l]);
-                q1b.val[0] = vcombine_s8(vld1_s8((const void *)(iq1s_grid+gindex[0])), vld1_s8((const void *)(iq1s_grid+gindex[1])));
-                q1b.val[1] = vcombine_s8(vld1_s8((const void *)(iq1s_grid+gindex[2])), vld1_s8((const void *)(iq1s_grid+gindex[3])));
-                q1b.val[2] = vcombine_s8(vld1_s8((const void *)(iq1s_grid+gindex[4])), vld1_s8((const void *)(iq1s_grid+gindex[5])));
-                q1b.val[3] = vcombine_s8(vld1_s8((const void *)(iq1s_grid+gindex[6])), vld1_s8((const void *)(iq1s_grid+gindex[7])));
-                q8b = ggml_vld1q_s8_x4(q8); q8 += 64;
+            q1b.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[0] | ((qh[ib+0] << 8) & 0x700)))),
+                                     vld1_s8((const int8_t *)(iq1s_grid + (qs[1] | ((qh[ib+0] << 5) & 0x700)))));
+            q1b.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[2] | ((qh[ib+0] << 2) & 0x700)))),
+                                     vld1_s8((const int8_t *)(iq1s_grid + (qs[3] | ((qh[ib+0] >> 1) & 0x700)))));
+            q1b.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[4] | ((qh[ib+1] << 8) & 0x700)))),
+                                     vld1_s8((const int8_t *)(iq1s_grid + (qs[5] | ((qh[ib+1] << 5) & 0x700)))));
+            q1b.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[6] | ((qh[ib+1] << 2) & 0x700)))),
+                                     vld1_s8((const int8_t *)(iq1s_grid + (qs[7] | ((qh[ib+1] >> 1) & 0x700)))));
+            qs += 8;
 
-                dotq.val[0] = vpaddq_s32(ggml_vdotq_s32(vzero, q1b.val[0], q8b.val[0]), ggml_vdotq_s32(vzero, q1b.val[1], q8b.val[1]));
-                dotq.val[1] = vpaddq_s32(ggml_vdotq_s32(vzero, q1b.val[2], q8b.val[2]), ggml_vdotq_s32(vzero, q1b.val[3], q8b.val[3]));
+            q8b = ggml_vld1q_s8_x4(q8); q8 += 64;
+
+            const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q1b.val[0], q8b.val[0]), q1b.val[1], q8b.val[1]);
+            const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q1b.val[2], q8b.val[2]), q1b.val[3], q8b.val[3]);
+
+            const int ls1 = 2*((qh[ib+0] >> 12) & 7) + 1;
+            const int ls2 = 2*((qh[ib+1] >> 12) & 7) + 1;
+            sumi1 += vaddvq_s32(p1) * ls1;
+            sumi2 += vaddvq_s32(p2) * ls2;
+            sumi3 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * ls1 * (qh[ib+0] & 0x8000 ? -1 : 1)
+                   + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * ls2 * (qh[ib+1] & 0x8000 ? -1 : 1);
 
-                sumi.val[0] = vmlaq_s32(sumi.val[0], dotq.val[0], vreinterpretq_s32_u32(vmovl_u16(vget_low_u16 (scales.val[l]))));
-                sumi.val[1] = vmlaq_s32(sumi.val[1], dotq.val[1], vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(scales.val[l]))));
-            }
         }
 
-        sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * vaddvq_s32(vaddq_s32(sumi.val[0], sumi.val[1]));
+        sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * (sumi1 + sumi2 + IQ1S_DELTA * sumi3);
     }
 
     *s = sumf;
 
 #elif defined __AVX2__
 
-    const __m128i m8 = _mm_set1_epi8(0x08);
-    const __m128i m7 = _mm_set1_epi8(0x07);
-    const __m128i m1 = _mm_set1_epi8(0x01);
-    const __m128i shuffle_h = _mm_set_epi8(15, 7, 14, 6, 13, 5, 12, 4, 11, 3, 10, 2, 9, 1, 8, 0);
-    const __m128i shuffle_s[4] = {
-        _mm_set_epi32(0x03030303, 0x02020202, 0x01010101, 0x00000000),
-        _mm_set_epi32(0x07070707, 0x06060606, 0x05050505, 0x04040404),
-        _mm_set_epi32(0x0b0b0b0b, 0x0a0a0a0a, 0x09090909, 0x08080808),
-        _mm_set_epi32(0x0f0f0f0f, 0x0e0e0e0e, 0x0d0d0d0d, 0x0c0c0c0c)
-    };
-
-    uint64_t aux64;
-
-    __m256i v_gindex;
-    const uint16_t * gindex = (const uint16_t *)&v_gindex;
-
     __m256 accum = _mm256_setzero_ps();
+    float accum1 = 0;
     for (int i = 0; i < nb; ++i) {
 
-        const int8_t  * q8 = y[i].qs;
-        const uint8_t * qs = x[i].qs;
-        const uint8_t * sc = x[i].scales;
+        const int8_t   * q8 = y[i].qs;
+        const uint8_t  * qs = x[i].qs;
+        const uint16_t * qh = x[i].qh;
 
         __m256i sumi = _mm256_setzero_si256();
-        for (int i128 = 0; i128 < QK_K/128; ++i128) {
-            const __m128i ql = _mm_loadu_si128((const __m128i*)qs); qs += 16;
-            memcpy(&aux64, sc, 8); sc += 8;
-            const __m128i qh = _mm_shuffle_epi8(_mm_set_epi64x(aux64 >> 4, aux64), shuffle_h);
-            const __m256i hbit = _mm256_cvtepu8_epi16(_mm_and_si128(qh, m8));
-            v_gindex = _mm256_or_si256(_mm256_cvtepu8_epi16(ql), _mm256_slli_epi16(hbit, 5));
-            const __m128i scales = _mm_or_si128(_mm_slli_epi16(_mm_and_si128(qh, m7), 1), m1);
+        int sumi1 = 0;
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
+            const __m256i q1b_1 = _mm256_set_epi64x(iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)],
+                                                    iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)]);
+            const __m256i q1b_2 = _mm256_set_epi64x(iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)],
+                                                    iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)]);
+            qs += 8;
+            const __m256i q8b_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+            const __m256i q8b_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
 
-            for (int i32 = 0; i32 < 4; ++i32) {
-                const __m256i q8b = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
-                const __m256i q1b = _mm256_set_epi64x(iq1s_grid[gindex[4*i32+3]], iq1s_grid[gindex[4*i32+2]],
-                                                      iq1s_grid[gindex[4*i32+1]], iq1s_grid[gindex[4*i32+0]]);
-                const __m256i dot = mul_add_epi8(q1b, q8b);
-                const __m256i s16 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, shuffle_s[i32]));
-                const __m256i p   = _mm256_madd_epi16(s16, dot);
-                sumi = _mm256_add_epi32(sumi, p);
-            }
+            const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1);
+            const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2);
+            const int16_t ls1 = 2*((qh[ib+0] >> 12) & 7) + 1;
+            const int16_t ls2 = 2*((qh[ib+1] >> 12) & 7) + 1;
+            const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(ls1));
+            const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(ls2));
 
+            sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p1, p2));
+            sumi1 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * (qh[ib+0] & 0x8000 ? -1 : 1) * ls1
+                   + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2;
         }
 
-        accum = _mm256_fmadd_ps(_mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d)), _mm256_cvtepi32_ps(sumi), accum);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        accum = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(sumi), accum);
+        accum1 += d * sumi1;
 
     }
 
-    *s = hsum_float_8(accum);
+    *s = hsum_float_8(accum) + IQ1S_DELTA * accum1;
 
 #else
 
-    int db[4];
-    uint16_t idx[4];
-
     float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
+    for (int i = 0; i < nb; i++) {
 
-        const int8_t  * q8 = y[i].qs;
-        const uint8_t * qs = x[i].qs;
-        const uint8_t * sc = x[i].scales;
+        const int8_t   * q8 = y[i].qs;
+        const uint8_t  * qs = x[i].qs;
+        const uint16_t * qh = x[i].qh;
 
-        int sumi = 0;
-        for (int i32 = 0; i32 < QK_K/32; ++i32) {
-            idx[0] = qs[0] | ((sc[0] & 0x08) << 5);
-            idx[1] = qs[1] | ((sc[0] & 0x80) << 1);
-            idx[2] = qs[2] | ((sc[1] & 0x08) << 5);
-            idx[3] = qs[3] | ((sc[1] & 0x80) << 1);
-            db[0] = (2*(sc[0] & 7) + 1);
-            db[1] = (2*((sc[0] >> 4) & 7) + 1);
-            db[2] = (2*(sc[1] & 7) + 1);
-            db[3] = (2*((sc[1] >> 4) & 7) + 1);
+        int sumi = 0, sumi1 = 0;
+        for (int ib = 0; ib < QK_K/32; ++ib) {
+            const int ls = 2*((qh[ib] >> 12) & 7) + 1;
+            const int delta = qh[ib] & 0x8000 ? -1 : 1;
+            int lsum = 0;
             for (int l = 0; l < 4; ++l) {
-                const int8_t * grid = (const int8_t *)(iq1s_grid + idx[l]);
-                int suml = 0;
-                for (int j = 0; j < 8; ++j) suml += q8[j] * grid[j];
-                sumi += db[l] * suml;
+                const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((qh[ib] >> 3*l) & 7) << 8)));
+                for (int j = 0; j < 8; ++j) {
+                    lsum += q8[j] * grid[j];
+                }
                 q8 += 8;
             }
+            sumi  += ls * lsum;
+            sumi1 += ls * delta * (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]);
             qs += 4;
-            sc += 2;
         }
 
-        sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * sumi;
+        sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * (sumi + IQ1S_DELTA * sumi1);
     }
 
     *s = sumf;
 
 #endif
+}
 
+void ggml_vec_dot_iq1_m_q8_K  (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
+    assert(n % QK_K == 0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+
+    const block_iq1_m * restrict x = vx;
+    const block_q8_K  * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+#if QK_K != 64
+    iq1m_scale_t scale;
+#endif
+
+#if defined __ARM_NEON
+
+#if QK_K == 64
+    const int32x4_t mask  = vdupq_n_s32(0xf);
+#else
+    const int32x4_t mask  = vdupq_n_s32(0x7);
+#endif
+    const int32x4_t mone  = vdupq_n_s32(1);
+    const int32x4_t mzero = vdupq_n_s32(0);
+
+    ggml_int8x16x4_t deltas;
+    deltas.val[0] = vcombine_s8(vdup_n_s8(+1), vdup_n_s8(+1));
+    deltas.val[1] = vcombine_s8(vdup_n_s8(-1), vdup_n_s8(+1));
+    deltas.val[2] = vcombine_s8(vdup_n_s8(+1), vdup_n_s8(-1));
+    deltas.val[3] = vcombine_s8(vdup_n_s8(-1), vdup_n_s8(-1));
+
+    ggml_int8x16x4_t q1b;
+    ggml_int8x16x4_t q8b;
+
+    uint32_t aux32;
+    const uint8_t * aux8 = (const uint8_t *)&aux32;
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+
+        const int8_t   * q8 = y[i].qs;
+        const uint8_t  * qs = x[i].qs;
+        const uint8_t  * qh = x[i].qh;
+        const uint16_t * sc = (const uint16_t *)x[i].scales;
+
+#if QK_K != 64
+        scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+#endif
+
+        int32x4_t sumi1 = mzero;
+        int32x4_t sumi2 = mzero;
+
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
+
+            q1b.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[0] | ((qh[0] << 8) & 0x700)))),
+                                     vld1_s8((const int8_t *)(iq1s_grid + (qs[1] | ((qh[0] << 4) & 0x700)))));
+            q1b.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[2] | ((qh[1] << 8) & 0x700)))),
+                                     vld1_s8((const int8_t *)(iq1s_grid + (qs[3] | ((qh[1] << 4) & 0x700)))));
+            q1b.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[4] | ((qh[2] << 8) & 0x700)))),
+                                     vld1_s8((const int8_t *)(iq1s_grid + (qs[5] | ((qh[2] << 4) & 0x700)))));
+            q1b.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[6] | ((qh[3] << 8) & 0x700)))),
+                                     vld1_s8((const int8_t *)(iq1s_grid + (qs[7] | ((qh[3] << 4) & 0x700)))));
+
+            q8b = ggml_vld1q_s8_x4(q8); q8 += 64;
+
+            const int32x4_t p1 = vpaddq_s32(ggml_vdotq_s32(mzero, q1b.val[0], q8b.val[0]), ggml_vdotq_s32(mzero, q1b.val[1], q8b.val[1]));
+            const int32x4_t p2 = vpaddq_s32(ggml_vdotq_s32(mzero, q1b.val[2], q8b.val[2]), ggml_vdotq_s32(mzero, q1b.val[3], q8b.val[3]));
+            const int32x4_t p12 = vpaddq_s32(p1, p2);
+
+            const uint32_t * qh32 = (const uint32_t *)qh; // we are 4-byte aligned, so we can do that
+            aux32 = ((qh32[0] >> 3) & 0x01010101) | ((qh32[0] >> 6) & 0x02020202);
+
+            const int32x4_t p3 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[0]], q8b.val[0]), ggml_vdotq_s32(mzero, deltas.val[aux8[1]], q8b.val[1]));
+            const int32x4_t p4 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[2]], q8b.val[2]), ggml_vdotq_s32(mzero, deltas.val[aux8[3]], q8b.val[3]));
+            const int32x4_t p34 = vpaddq_s32(p3, p4);
+
+#if QK_K == 64
+            int32x4_t scales_4 = ggml_vld1q_u32(sc[0] >> 0, sc[0] >> 4, sc[0] >> 8, sc[0] >> 12);
+#else
+            int32x4_t scales_4 = ggml_vld1q_u32(sc[ib/2] >> 0, sc[ib/2] >> 3, sc[ib/2] >> 6, sc[ib/2] >> 9);
+#endif
+            scales_4 = vaddq_s32(vshlq_n_s32(vandq_s32(scales_4, mask), 1), mone);
+
+            sumi1 = vmlaq_s32(sumi1, scales_4, p12);
+            sumi2 = vmlaq_s32(sumi2, scales_4, p34);
+
+            qs += 8; qh += 4;
+
+        }
+
+#if QK_K == 64
+        sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2));
+#else
+        sumf += y[i].d * GGML_FP16_TO_FP32(scale.f16) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2));
+#endif
+    }
+
+    *s = sumf;
+
+#elif defined __AVX2__
+
+#if QK_K == 64
+    const __m256i mask = _mm256_set1_epi16(0xf);
+#else
+    const __m256i mask = _mm256_set1_epi16(0x7);
+#endif
+    const __m256i mone = _mm256_set1_epi16(1);
+
+    __m256 accum1 = _mm256_setzero_ps();
+    __m256 accum2 = _mm256_setzero_ps();
+    for (int i = 0; i < nb; ++i) {
+
+        const int8_t   * q8 = y[i].qs;
+        const uint8_t  * qs = x[i].qs;
+        const uint8_t  * qh = x[i].qh;
+        const uint16_t * sc = (const uint16_t *)x[i].scales;
+
+#if QK_K != 64
+        scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+#endif
+
+        __m256i sumi1 = _mm256_setzero_si256();
+        __m256i sumi2 = _mm256_setzero_si256();
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
+            const __m256i q1b_1 = _mm256_set_epi64x(
+                    iq1s_grid[qs[3] | (((uint16_t)qh[1] << 4) & 0x700)], iq1s_grid[qs[2] | (((uint16_t)qh[1] << 8) & 0x700)],
+                    iq1s_grid[qs[1] | (((uint16_t)qh[0] << 4) & 0x700)], iq1s_grid[qs[0] | (((uint16_t)qh[0] << 8) & 0x700)]
+            );
+            const __m256i q1b_2 = _mm256_set_epi64x(
+                    iq1s_grid[qs[7] | (((uint16_t)qh[3] << 4) & 0x700)], iq1s_grid[qs[6] | (((uint16_t)qh[3] << 8) & 0x700)],
+                    iq1s_grid[qs[5] | (((uint16_t)qh[2] << 4) & 0x700)], iq1s_grid[qs[4] | (((uint16_t)qh[2] << 8) & 0x700)]
+            );
+            const __m256i q8b_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+            const __m256i q8b_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+
+            const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1);
+            const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2);
+
+            const __m256i delta1 = _mm256_set_epi64x(qh[1] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
+                                                     qh[1] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101,
+                                                     qh[0] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
+                                                     qh[0] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101);
+            const __m256i delta2 = _mm256_set_epi64x(qh[3] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
+                                                     qh[3] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101,
+                                                     qh[2] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
+                                                     qh[2] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101);
+
+            const __m256i dot3 = mul_add_epi8(delta1, q8b_1);
+            const __m256i dot4 = mul_add_epi8(delta2, q8b_2);
+#if QK_K == 64
+            __m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[0] >>  4), _mm_set1_epi16(sc[0] >> 0));
+            __m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[0] >> 12), _mm_set1_epi16(sc[0] >> 8));
+#else
+            __m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 3), _mm_set1_epi16(sc[ib/2] >> 0));
+            __m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 9), _mm_set1_epi16(sc[ib/2] >> 6));
+#endif
+            scale1 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale1, mask), 1), mone);
+            scale2 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale2, mask), 1), mone);
+            const __m256i p1 = _mm256_madd_epi16(dot1, scale1);
+            const __m256i p2 = _mm256_madd_epi16(dot2, scale2);
+            const __m256i p3 = _mm256_madd_epi16(dot3, scale1);
+            const __m256i p4 = _mm256_madd_epi16(dot4, scale2);
+
+            sumi1 = _mm256_add_epi32(sumi1, _mm256_add_epi32(p1, p2));
+            sumi2 = _mm256_add_epi32(sumi2, _mm256_add_epi32(p3, p4));
+
+            qs += 8; qh += 4;
+        }
+
+#if QK_K == 64
+        const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d));
+#else
+        const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(scale.f16));
+#endif
+        accum1 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi1), accum1);
+        accum2 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi2), accum2);
+
+    }
+
+    *s = hsum_float_8(accum1) + IQ1M_DELTA * hsum_float_8(accum2);
+
+#else
+
+    int sum1[2], sum2[2], delta[4];
+
+    float sumf = 0;
+    for (int i = 0; i < nb; i++) {
+
+        const int8_t   * q8 = y[i].qs;
+        const uint8_t  * qs = x[i].qs;
+        const uint8_t  * qh = x[i].qh;
+        const uint16_t * sc = (const uint16_t *)x[i].scales;
+
+#if QK_K != 64
+        scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+#endif
+
+        int sumi1 = 0, sumi2 = 0;
+        for (int ib = 0; ib < QK_K/32; ++ib) {
+            delta[0] = qh[0] & 0x08 ? -1 : 1;
+            delta[1] = qh[0] & 0x80 ? -1 : 1;
+            delta[2] = qh[1] & 0x08 ? -1 : 1;
+            delta[3] = qh[1] & 0x80 ? -1 : 1;
+            sum1[0] = sum1[1] = sum2[0] = sum2[1] = 0;
+            for (int l = 0; l < 4; ++l) {
+                const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((uint16_t)qh[l/2] << (8 - 4*(l%2))) & 0x700)));
+                int lsum1 = 0, lsum2 = 0;
+                for (int j = 0; j < 8; ++j) {
+                    lsum1 += q8[j] * grid[j];
+                    lsum2 += q8[j];
+                }
+                q8 += 8;
+                sum1[l/2] += lsum1;
+                sum2[l/2] += lsum2*delta[l];
+            }
+#if QK_K == 64
+            const int ls1 = 2*((sc[0] >> (8*(ib%2)+0)) & 0xf) + 1;
+            const int ls2 = 2*((sc[0] >> (8*(ib%2)+4)) & 0xf) + 1;
+#else
+            const int ls1 = 2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1;
+            const int ls2 = 2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1;
+#endif
+            sumi1 += sum1[0] * ls1 + sum1[1] * ls2;
+            sumi2 += sum2[0] * ls1 + sum2[1] * ls2;
+            qs += 4;
+            qh += 2;
+        }
+
+#if QK_K == 64
+        sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2);
+#else
+        sumf += GGML_FP16_TO_FP32(scale.f16) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2);
+#endif
+    }
+
+    *s = sumf;
+
+#endif
+}
+
+void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+    assert(n % QK4_NL == 0);
+    static_assert(QK4_NL == QK8_0, "QK4_NL and QK8_0 must be the same");
+
+    const block_iq4_nl * restrict x = vx;
+    const block_q8_0   * restrict y = vy;
+
+    const int nb = n / QK4_NL;
+
+#if defined __ARM_NEON
+    const int8x16_t values = vld1q_s8(kvalues_iq4nl);
+    const uint8x16_t m4b = vdupq_n_u8(0x0f);
+    uint8x16x2_t q4bits;
+    int8x16x4_t q4b;
+    int8x16x4_t q8b;
+    int32x4_t prod_1, prod_2;
+
+    float sumf = 0;
+
+    for (int ib = 0; ib < nb; ib += 2) {
+
+        q4bits.val[0] = vld1q_u8(x[ib+0].qs);
+        q4bits.val[1] = vld1q_u8(x[ib+1].qs);
+        q8b.val[0]    = vld1q_s8(y[ib+0].qs);
+        q8b.val[1]    = vld1q_s8(y[ib+0].qs + 16);
+        q8b.val[2]    = vld1q_s8(y[ib+1].qs);
+        q8b.val[3]    = vld1q_s8(y[ib+1].qs + 16);
+
+        q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits.val[0], m4b));
+        q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4));
+        q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits.val[1], m4b));
+        q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4));
+
+        prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]);
+        prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]);
+
+        sumf +=
+            GGML_FP16_TO_FP32(x[ib+0].d) * GGML_FP16_TO_FP32(y[ib+0].d) * vaddvq_s32(prod_1) +
+            GGML_FP16_TO_FP32(x[ib+1].d) * GGML_FP16_TO_FP32(y[ib+1].d) * vaddvq_s32(prod_2);
+    }
+
+    *s = sumf;
+
+#elif defined __AVX2__
+
+    const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl);
+    const __m128i m4b  = _mm_set1_epi8(0x0f);
+    const __m256i mone = _mm256_set1_epi16(1);
+
+    __m256 accum1 = _mm256_setzero_ps();
+    __m256 accum2 = _mm256_setzero_ps();
+    for (int ib = 0; ib < nb; ib += 2) {
+        const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)x[0].qs);
+        const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)x[1].qs);
+        const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)y[0].qs);
+        const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)y[1].qs);
+        const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)),
+                                              _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)));
+        const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)),
+                                              _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b)));
+        const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1);
+        const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2);
+        const __m256i p_1 = _mm256_madd_epi16(p16_1, mone);
+        const __m256i p_2 = _mm256_madd_epi16(p16_2, mone);
+        accum1 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[0].d)*GGML_FP16_TO_FP32(x[0].d)),
+                _mm256_cvtepi32_ps(p_1), accum1);
+        accum2 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[1].d)*GGML_FP16_TO_FP32(x[1].d)),
+                _mm256_cvtepi32_ps(p_2), accum2);
+
+        y += 2;
+        x += 2;
+    }
+
+    *s = hsum_float_8(_mm256_add_ps(accum1, accum2));
+
+#else
+    float sumf = 0;
+    for (int ib = 0; ib < nb; ++ib) {
+        const float d = GGML_FP16_TO_FP32(y[ib].d)*GGML_FP16_TO_FP32(x[ib].d);
+        int sumi1 = 0, sumi2 = 0;
+        for (int j = 0; j < QK4_NL/2; ++j) {
+            sumi1 += y[ib].qs[j+       0] * kvalues_iq4nl[x[ib].qs[j] & 0xf];
+            sumi2 += y[ib].qs[j+QK4_NL/2] * kvalues_iq4nl[x[ib].qs[j] >>  4];
+        }
+        sumf += d * (sumi1 + sumi2);
+    }
+    *s = sumf;
+#endif
+}
+
+void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+    assert(n % QK_K == 0);
+#if QK_K == 64
+    ggml_vec_dot_iq4_nl_q8_0(n, s, bs, vx, bx, vy, by, nrc);
+#else
+
+    const block_iq4_xs * restrict x = vx;
+    const block_q8_K   * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+#if defined __ARM_NEON
+    const int8x16_t values = vld1q_s8(kvalues_iq4nl);
+    const uint8x16_t m4b = vdupq_n_u8(0x0f);
+    ggml_uint8x16x2_t q4bits;
+    ggml_int8x16x4_t q4b;
+    ggml_int8x16x4_t q8b;
+    int32x4_t prod_1, prod_2;
+
+    float sumf = 0;
+
+    for (int ibl = 0; ibl < nb; ++ibl) {
+
+        const int8_t  * q8 = y[ibl].qs;
+        const uint8_t * q4 = x[ibl].qs;
+        uint16_t h = x[ibl].scales_h;
+
+        int sumi1 = 0, sumi2 = 0;
+        for (int ib = 0; ib < QK_K/64; ++ib) {
+
+            q4bits = ggml_vld1q_u8_x2(q4); q4 += 32;
+            q8b    = ggml_vld1q_s8_x4(q8); q8 += 64;
+
+            q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits.val[0], m4b));
+            q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4));
+            q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits.val[1], m4b));
+            q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4));
+
+            prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]);
+            prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]);
+
+            int ls1 = ((x[ibl].scales_l[ib] & 0xf) | ((h << 4) & 0x30)) - 32;
+            int ls2 = ((x[ibl].scales_l[ib] >>  4) | ((h << 2) & 0x30)) - 32;
+            h >>= 4;
+            sumi1 += vaddvq_s32(prod_1) * ls1;
+            sumi2 += vaddvq_s32(prod_2) * ls2;
+
+        }
+
+        sumf += GGML_FP16_TO_FP32(x[ibl].d) * y[ibl].d * (sumi1 + sumi2);
+    }
+
+    *s = sumf;
+
+#elif defined __AVX2__
+
+    const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl);
+    const __m128i m4b  = _mm_set1_epi8(0x0f);
+
+    __m256 accum = _mm256_setzero_ps();
+    for (int ibl = 0; ibl < nb; ++ibl) {
+        const uint8_t * qs = x[ibl].qs;
+        const int8_t  * q8 = y[ibl].qs;
+        uint16_t sh = x[ibl].scales_h;
+        __m256i sumi1 = _mm256_setzero_si256();
+        __m256i sumi2 = _mm256_setzero_si256();
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
+            const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)qs);  qs += 16;
+            const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)qs);  qs += 16;
+            const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
+            const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
+            const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)),
+                                                  _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)));
+            const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)),
+                                                  _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b)));
+            const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1);
+            const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2);
+            const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32;
+            const int16_t ls2 = ((x[ibl].scales_l[ib/2] >>  4) | ((sh << 2) & 0x30)) - 32;
+            sh >>= 4;
+            const __m256i p_1 = _mm256_madd_epi16(p16_1, _mm256_set1_epi16(ls1));
+            const __m256i p_2 = _mm256_madd_epi16(p16_2, _mm256_set1_epi16(ls2));
+            sumi1 = _mm256_add_epi32(p_1, sumi1);
+            sumi2 = _mm256_add_epi32(p_2, sumi2);
+        }
+        accum = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d),
+                _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accum);
+    }
+
+    *s = hsum_float_8(accum);
+
+#else
+    float sumf = 0;
+    for (int ibl = 0; ibl < nb; ++ibl) {
+        const float d4d8 = GGML_FP16_TO_FP32(x[ibl].d) * y[ibl].d;
+        uint16_t h = x[ibl].scales_h;
+        const uint8_t * qs = x[ibl].qs;
+        const int8_t  * q8 = y[ibl].qs;
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
+            const uint8_t ls1 = (x[ibl].scales_l[ib/2] & 0xf) | ((h << 4) & 0x30);
+            const uint8_t ls2 = (x[ibl].scales_l[ib/2] >>  4) | ((h << 2) & 0x30);
+            h >>= 4;
+            const float d1 = d4d8*(ls1 - 32);
+            const float d2 = d4d8*(ls2 - 32);
+            int sumi1 = 0, sumi2 = 0;
+            for (int j = 0; j < 16; ++j) {
+                sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
+                sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >>  4];
+            }
+            sumf += d1 * (sumi1 + sumi2);
+            qs += 16;
+            q8 += 32;
+            sumi1 = sumi2 = 0;
+            for (int j = 0; j < 16; ++j) {
+                sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
+                sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >>  4];
+            }
+            sumf += d2 * (sumi1 + sumi2);
+            qs += 16;
+            q8 += 32;
+        }
+    }
+    *s = sumf;
+#endif
+#endif
 }
 
 // ================================ IQ2 quantization =============================================
@@ -9463,22 +9944,25 @@ typedef struct {
     uint16_t * neighbours;
 } iq2_entry_t;
 
-static iq2_entry_t iq2_data[3] = {
+static iq2_entry_t iq2_data[4] = {
+    {NULL, NULL, NULL},
     {NULL, NULL, NULL},
     {NULL, NULL, NULL},
     {NULL, NULL, NULL},
 };
 
 static inline int iq2_data_index(enum ggml_type type) {
-    GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S);
+    GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M || type == GGML_TYPE_IQ2_S);
     return type == GGML_TYPE_IQ2_XXS ? 0 :
-           type == GGML_TYPE_IQ2_XS  ? 1 : 2;
+           type == GGML_TYPE_IQ2_XS  ? 1 :
+           type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M ? 2 : 3;
 }
 
 static inline int iq2_grid_size(enum ggml_type type) {
-    GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S);
+    GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M || type == GGML_TYPE_IQ2_S);
     return type == GGML_TYPE_IQ2_XXS ? 256 :
-           type == GGML_TYPE_IQ2_XS  ? 512 : 512;
+           type == GGML_TYPE_IQ2_XS  ? 512 :
+           type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M ? NGRID_IQ1S : 1024;
 }
 
 static int iq2_compare_func(const void * left, const void * right) {
@@ -9545,50 +10029,214 @@ void iq2xs_init_impl(enum ggml_type type) {
         40962, 40968, 40970, 40992, 41002, 41120, 41297, 41305, 41382, 41472, 41474, 41480, 41514, 41600, 41632, 42048,
         42133, 42597, 42648, 43018, 43040, 43042, 43048, 43168, 43176, 43268, 43396, 43398, 43560, 43562, 43665, 43690,
     };
-    static const uint16_t kgrid_1bit_512[512] = {
-           10,    33,    41,    85,   132,   134,   160,   162,   277,   337,   340,   345,   357,   405,   516,   545,
-          553,   598,   641,   650,   681,  1042,  1044,  1097,  1169,  1176,  1320,  1345,  1365,  1378,  1434,  1444,
-         1545,  1617,  1642,  1685,  2053,  2080,  2089,  2133,  2176,  2182,  2208,  2214,  2306,  2384,  2393,  2440,
-         2453,  2581,  2664,  2690,  2721,  4117,  4161,  4182,  4184,  4261,  4357,  4369,  4372,  4377,  4390,  4422,
-         4432,  4437,  4449,  4457,  4485,  4497,  4505,  4629,  4677,  4696,  4774,  5205,  5217,  5225,  5386,  5397,
-         5409,  5445,  5457,  5460,  5461,  5462,  5465,  5472,  5477,  5525,  5545,  5650,  5668,  5717,  5729,  5769,
-         5777,  6212,  6234,  6244,  6293,  6424,  6482,  6485,  6502,  6505,  6529,  6538,  6565,  6656,  6682,  6788,
-         6806,  6820,  8218,  8224,  8226,  8232,  8277,  8326,  8354,  8469,  8521,  8530,  8549,  8596,  8737,  8794,
-         9221,  9253,  9348,  9369,  9380,  9474,  9557,  9633,  9732,  9753,  9793,  9830,  9862,  9880, 10240, 10272,
-        10282, 10321, 10406, 10517, 10530, 10566, 10585, 10645, 10896, 16466, 16468, 16473, 16485, 16646, 16660, 16665,
-        16725, 16793, 16806, 16914, 16969, 16977, 16996, 17028, 17057, 17408, 17416, 17434, 17493, 17512, 17578, 17685,
-        17696, 17733, 17745, 17748, 17749, 17750, 17753, 17765, 17794, 17813, 17946, 17984, 18005, 18072, 18453, 18529,
-        18569, 18722, 18756, 18762, 18773, 18794, 18833, 18853, 18945, 19026, 19033, 19077, 20489, 20497, 20500, 20517,
-        20565, 20586, 20610, 20633, 20757, 20769, 20776, 20805, 20817, 20820, 20821, 20822, 20825, 20837, 20864, 20872,
-        20885, 20896, 21002, 21029, 21077, 21146, 21510, 21525, 21573, 21585, 21588, 21589, 21590, 21593, 21605, 21653,
-        21665, 21765, 21777, 21780, 21781, 21782, 21785, 21797, 21825, 21828, 21829, 21830, 21833, 21840, 21841, 21842,
-        21844, 21846, 21848, 21849, 21850, 21857, 21860, 21861, 21862, 21865, 21893, 21905, 21908, 21909, 21910, 21913,
-        21925, 22024, 22037, 22085, 22097, 22100, 22101, 22102, 22105, 22117, 22165, 22545, 22566, 22568, 22594, 22608,
-        22613, 22676, 22697, 22793, 22805, 22853, 22865, 22868, 22869, 22870, 22873, 22885, 22933, 22946, 23046, 23072,
-        23125, 23209, 24597, 24640, 24665, 24673, 24725, 24833, 24840, 24869, 24917, 24934, 24965, 25001, 25108, 25110,
-        25152, 25184, 25192, 25234, 25616, 25618, 25625, 25685, 25704, 25738, 25744, 25770, 25877, 25897, 25925, 25937,
-        25940, 25941, 25942, 25945, 25957, 25986, 26005, 26186, 26197, 26276, 26632, 26634, 26725, 26757, 26770, 26885,
-        26965, 26976, 26986, 27032, 27153, 27174, 27200, 27208, 27240, 27269, 27282, 27290, 32778, 32800, 32802, 32808,
-        32810, 32853, 32904, 32922, 32930, 32932, 33105, 33110, 33112, 33125, 33157, 33280, 33288, 33301, 33312, 33320,
-        33424, 33797, 33829, 33858, 34068, 34133, 34146, 34176, 34217, 34306, 34342, 34441, 34454, 34468, 34832, 34918,
-        34965, 34984, 35094, 35137, 35161, 35208, 35232, 35332, 35338, 35368, 35429, 36932, 36934, 36953, 37009, 37125,
-        37136, 37138, 37145, 37157, 37205, 37220, 37258, 37290, 37444, 37446, 37465, 37478, 37525, 37905, 37968, 37973,
-        38040, 38054, 38145, 38154, 38165, 38180, 38186, 38213, 38225, 38228, 38229, 38230, 38233, 38245, 38293, 38485,
-        38504, 38530, 38938, 38985, 38993, 39012, 39040, 39173, 39192, 39253, 39265, 39301, 39316, 39322, 39442, 39497,
-        39504, 39590, 40970, 40984, 40992, 41002, 41045, 41120, 41128, 41237, 41289, 41297, 41317, 41364, 41366, 41514,
-        41557, 41633, 41989, 42021, 42056, 42068, 42074, 42113, 42242, 42265, 42274, 42325, 42340, 42402, 42501, 42512,
-        42533, 42624, 42632, 42666, 43040, 43093, 43106, 43168, 43176, 43264, 43286, 43345, 43429, 43590, 43618, 43680,
+    static const uint16_t kgrid_1bit_2048[NGRID_IQ1S] = {
+            0,     2,     5,     8,    10,    17,    21,    32,    34,    40,    42,    69,    81,    84,    86,   101,
+          128,   130,   136,   138,   149,   160,   162,   168,   170,   260,   261,   273,   276,   278,   281,   282,
+          293,   321,   326,   329,   338,   341,   346,   353,   356,   358,   360,   389,   401,   404,   406,   421,
+          512,   514,   520,   522,   533,   544,   546,   552,   554,   581,   593,   601,   612,   617,   640,   642,
+          648,   650,   657,   661,   665,   672,   674,   680,   682,  1041,  1044,  1046,  1061,  1089,  1097,  1109,
+         1114,  1124,  1125,  1169,  1177,  1189,  1281,  1284,  1285,  1286,  1301,  1304,  1306,  1321,  1344,  1349,
+         1354,  1360,  1361,  1364,  1365,  1366,  1369,  1376,  1378,  1381,  1384,  1386,  1409,  1425,  1429,  1432,
+         1434,  1441,  1444,  1445,  1446,  1449,  1556,  1561,  1601,  1604,  1616,  1618,  1621,  1624,  1632,  1633,
+         1638,  1641,  1669,  1681,  1684,  1689,  2048,  2050,  2056,  2058,  2069,  2080,  2082,  2088,  2090,  2117,
+         2129,  2134,  2149,  2176,  2178,  2184,  2186,  2197,  2208,  2210,  2216,  2218,  2309,  2321,  2324,  2329,
+         2340,  2341,  2369,  2384,  2385,  2389,  2401,  2404,  2409,  2449,  2452,  2454,  2457,  2469,  2560,  2562,
+         2568,  2570,  2581,  2592,  2594,  2600,  2602,  2629,  2641,  2649,  2657,  2661,  2688,  2690,  2693,  2696,
+         2698,  2709,  2720,  2722,  2728,  2730,  4112,  4113,  4116,  4121,  4132,  4133,  4161,  4164,  4176,  4181,
+         4184,  4193,  4196,  4197,  4201,  4241,  4244,  4246,  4257,  4261,  4353,  4356,  4358,  4361,  4368,  4370,
+         4373,  4376,  4385,  4388,  4393,  4421,  4426,  4432,  4433,  4434,  4436,  4437,  4438,  4441,  4448,  4453,
+         4484,  4498,  4501,  4513,  4516,  4625,  4628,  4630,  4645,  4672,  4678,  4681,  4690,  4693,  4696,  4698,
+         4708,  4710,  4741,  4753,  4756,  4758,  4773,  5121,  5126,  5129,  5140,  5141,  5144,  5145,  5153,  5158,
+         5185,  5189,  5190,  5192,  5194,  5201,  5204,  5205,  5206,  5209,  5218,  5221,  5224,  5252,  5257,  5264,
+         5268,  5269,  5272,  5273,  5274,  5281,  5284,  5285,  5289,  5378,  5381,  5386,  5393,  5396,  5397,  5398,
+         5401,  5408,  5410,  5413,  5416,  5418,  5441,  5444,  5445,  5446,  5457,  5458,  5460,  5461,  5462,  5465,
+         5466,  5473,  5476,  5477,  5478,  5481,  5504,  5506,  5508,  5509,  5512,  5514,  5520,  5521,  5524,  5525,
+         5526,  5529,  5530,  5536,  5538,  5541,  5633,  5636,  5637,  5638,  5653,  5654,  5656,  5658,  5665,  5670,
+         5696,  5698,  5700,  5701,  5704,  5706,  5713,  5717,  5718,  5720,  5721,  5729,  5732,  5733,  5736,  5737,
+         5738,  5766,  5770,  5778,  5781,  5796,  5801,  6161,  6166,  6181,  6209,  6212,  6214,  6217,  6224,  6229,
+         6232,  6234,  6240,  6241,  6244,  6246,  6249,  6277,  6289,  6292,  6309,  6416,  6418,  6421,  6426,  6433,
+         6437,  6466,  6468,  6469,  6472,  6481,  6484,  6485,  6486,  6489,  6490,  6496,  6501,  6506,  6537,  6545,
+         6546,  6549,  6552,  6561,  6566,  6569,  6665,  6678,  6692,  6694,  6724,  6726,  6729,  6736,  6738,  6741,
+         6744,  6753,  6758,  6761,  6789,  6801,  6806,  6810,  8192,  8194,  8200,  8202,  8213,  8224,  8226,  8229,
+         8232,  8234,  8261,  8273,  8281,  8289,  8293,  8320,  8322,  8328,  8330,  8341,  8352,  8354,  8357,  8360,
+         8362,  8453,  8465,  8468,  8473,  8485,  8514,  8516,  8521,  8533,  8536,  8538,  8545,  8548,  8549,  8550,
+         8581,  8592,  8598,  8601,  8613,  8705,  8712,  8714,  8721,  8725,  8736,  8738,  8744,  8746,  8773,  8785,
+         8790,  8793,  8805,  8833,  8840,  8842,  8849,  8853,  8864,  8866,  8872,  8874,  9221,  9236,  9238,  9241,
+         9253,  9284,  9285,  9286,  9289,  9298,  9301,  9304,  9306,  9318,  9349,  9361,  9364,  9369,  9377,  9381,
+         9481,  9493,  9505,  9513,  9536,  9541,  9544,  9553,  9556,  9557,  9561,  9570,  9573,  9576,  9609,  9616,
+         9620,  9621,  9624,  9626,  9633,  9636,  9638,  9641,  9733,  9744,  9746,  9753,  9765,  9793,  9801,  9813,
+         9824,  9825,  9833,  9860,  9862,  9872,  9882, 10240, 10242, 10248, 10250, 10261, 10272, 10274, 10280, 10282,
+        10309, 10321, 10324, 10341, 10368, 10370, 10376, 10378, 10400, 10402, 10408, 10410, 10505, 10513, 10516, 10521,
+        10533, 10566, 10569, 10578, 10581, 10593, 10596, 10598, 10601, 10629, 10640, 10646, 10649, 10660, 10661, 10752,
+        10754, 10760, 10762, 10784, 10786, 10792, 10794, 10821, 10833, 10838, 10841, 10853, 10880, 10882, 10888, 10890,
+        10901, 10912, 10914, 10920, 10922, 16389, 16401, 16406, 16421, 16457, 16466, 16469, 16472, 16474, 16481, 16484,
+        16486, 16532, 16537, 16545, 16550, 16640, 16641, 16644, 16646, 16649, 16658, 16661, 16662, 16664, 16666, 16673,
+        16678, 16681, 16709, 16712, 16714, 16721, 16724, 16725, 16726, 16729, 16730, 16741, 16744, 16746, 16769, 16772,
+        16774, 16784, 16786, 16789, 16800, 16801, 16802, 16901, 16913, 16916, 16918, 16933, 16961, 16978, 16981, 16986,
+        16996, 17001, 17033, 17044, 17061, 17409, 17429, 17433, 17449, 17477, 17480, 17482, 17489, 17492, 17493, 17494,
+        17505, 17506, 17509, 17512, 17514, 17537, 17542, 17545, 17552, 17554, 17557, 17568, 17569, 17577, 17665, 17666,
+        17669, 17674, 17681, 17684, 17685, 17686, 17689, 17696, 17701, 17706, 17729, 17732, 17733, 17734, 17737, 17744,
+        17745, 17748, 17749, 17750, 17752, 17753, 17761, 17764, 17765, 17766, 17769, 17794, 17796, 17797, 17800, 17809,
+        17812, 17813, 17814, 17817, 17818, 17829, 17832, 17834, 17921, 17925, 17929, 17940, 17941, 17944, 17946, 17953,
+        17956, 17961, 17984, 17986, 17989, 17992, 18000, 18001, 18002, 18005, 18006, 18009, 18018, 18021, 18024, 18049,
+        18053, 18058, 18068, 18069, 18081, 18084, 18086, 18437, 18449, 18453, 18458, 18469, 18498, 18505, 18512, 18517,
+        18520, 18529, 18532, 18534, 18537, 18565, 18577, 18580, 18582, 18585, 18597, 18689, 18693, 18694, 18698, 18704,
+        18708, 18709, 18712, 18721, 18724, 18726, 18752, 18757, 18762, 18769, 18770, 18772, 18773, 18774, 18777, 18784,
+        18786, 18789, 18790, 18794, 18822, 18825, 18834, 18837, 18838, 18840, 18849, 18852, 18854, 18857, 18966, 19012,
+        19014, 19017, 19029, 19032, 19034, 19044, 19049, 19092, 19109, 20481, 20484, 20485, 20486, 20489, 20498, 20501,
+        20506, 20513, 20516, 20521, 20544, 20549, 20552, 20561, 20564, 20565, 20566, 20569, 20581, 20584, 20614, 20617,
+        20629, 20632, 20640, 20641, 20646, 20649, 20741, 20744, 20745, 20746, 20753, 20756, 20757, 20758, 20760, 20761,
+        20768, 20773, 20774, 20776, 20778, 20801, 20804, 20805, 20806, 20809, 20816, 20817, 20818, 20820, 20821, 20822,
+        20824, 20825, 20826, 20833, 20836, 20837, 20838, 20841, 20866, 20869, 20881, 20884, 20885, 20886, 20889, 20896,
+        20901, 20906, 20993, 20998, 21010, 21013, 21018, 21025, 21028, 21058, 21061, 21066, 21073, 21076, 21077, 21078,
+        21081, 21090, 21093, 21125, 21136, 21138, 21141, 21145, 21146, 21156, 21508, 21509, 21521, 21524, 21525, 21526,
+        21528, 21529, 21537, 21541, 21544, 21546, 21569, 21572, 21573, 21574, 21577, 21578, 21584, 21585, 21588, 21589,
+        21590, 21592, 21593, 21594, 21601, 21602, 21604, 21605, 21606, 21609, 21632, 21640, 21642, 21649, 21652, 21653,
+        21654, 21657, 21665, 21668, 21669, 21674, 21761, 21762, 21764, 21765, 21766, 21769, 21776, 21777, 21778, 21780,
+        21781, 21782, 21785, 21786, 21793, 21796, 21797, 21798, 21801, 21824, 21825, 21826, 21828, 21829, 21830, 21832,
+        21833, 21840, 21841, 21842, 21844, 21845, 21846, 21848, 21849, 21850, 21856, 21857, 21860, 21861, 21862, 21864,
+        21865, 21866, 21889, 21892, 21893, 21897, 21898, 21904, 21905, 21908, 21909, 21910, 21912, 21913, 21921, 21924,
+        21925, 21926, 21929, 22016, 22017, 22018, 22020, 22022, 22024, 22025, 22033, 22036, 22037, 22040, 22041, 22048,
+        22049, 22050, 22052, 22053, 22054, 22056, 22057, 22081, 22085, 22086, 22088, 22089, 22090, 22096, 22097, 22098,
+        22100, 22101, 22102, 22104, 22105, 22106, 22113, 22116, 22117, 22121, 22146, 22149, 22150, 22152, 22153, 22154,
+        22161, 22165, 22170, 22178, 22181, 22182, 22184, 22185, 22532, 22533, 22534, 22537, 22544, 22549, 22552, 22561,
+        22570, 22597, 22600, 22602, 22609, 22612, 22613, 22614, 22616, 22617, 22624, 22626, 22628, 22629, 22658, 22665,
+        22672, 22674, 22677, 22680, 22689, 22697, 22785, 22786, 22789, 22794, 22801, 22804, 22805, 22806, 22809, 22821,
+        22849, 22852, 22853, 22854, 22857, 22864, 22865, 22866, 22868, 22869, 22870, 22872, 22873, 22874, 22881, 22884,
+        22885, 22886, 22889, 22913, 22917, 22921, 22929, 22932, 22933, 22934, 22936, 22937, 22949, 23044, 23048, 23061,
+        23066, 23072, 23077, 23078, 23081, 23109, 23112, 23113, 23121, 23125, 23126, 23128, 23129, 23138, 23141, 23144,
+        23146, 23169, 23178, 23186, 23189, 23190, 23192, 23194, 23201, 24581, 24596, 24598, 24601, 24613, 24644, 24656,
+        24661, 24662, 24664, 24666, 24673, 24676, 24678, 24681, 24705, 24726, 24741, 24833, 24836, 24838, 24841, 24850,
+        24853, 24865, 24866, 24870, 24873, 24901, 24905, 24913, 24917, 24918, 24921, 24933, 24934, 24938, 24964, 24970,
+        24978, 24981, 24993, 24998, 25001, 25105, 25110, 25113, 25152, 25153, 25158, 25173, 25174, 25176, 25184, 25221,
+        25233, 25238, 25253, 25617, 25618, 25621, 25622, 25626, 25633, 25638, 25641, 25664, 25666, 25669, 25672, 25674,
+        25681, 25684, 25685, 25686, 25689, 25690, 25696, 25698, 25701, 25732, 25733, 25737, 25744, 25746, 25748, 25749,
+        25750, 25752, 25754, 25761, 25764, 25769, 25861, 25864, 25866, 25873, 25877, 25878, 25881, 25924, 25925, 25926,
+        25929, 25936, 25937, 25940, 25941, 25942, 25945, 25953, 25956, 25957, 25958, 25961, 25990, 25993, 25994, 26001,
+        26005, 26006, 26009, 26010, 26018, 26021, 26022, 26024, 26114, 26121, 26133, 26144, 26150, 26152, 26153, 26176,
+        26181, 26184, 26186, 26193, 26196, 26197, 26198, 26200, 26202, 26208, 26213, 26216, 26240, 26242, 26245, 26250,
+        26260, 26262, 26264, 26265, 26272, 26276, 26278, 26282, 26646, 26649, 26661, 26689, 26706, 26709, 26714, 26721,
+        26729, 26757, 26769, 26776, 26790, 26881, 26884, 26896, 26901, 26913, 26916, 26918, 26921, 26944, 26945, 26949,
+        26950, 26952, 26961, 26964, 26965, 26966, 26969, 26976, 26981, 26986, 27010, 27012, 27018, 27029, 27041, 27044,
+        27045, 27049, 27153, 27158, 27160, 27201, 27204, 27209, 27216, 27221, 27224, 27226, 27236, 27237, 27241, 27270,
+        27284, 27288, 27290, 27302, 32768, 32770, 32776, 32778, 32800, 32802, 32808, 32810, 32837, 32848, 32849, 32852,
+        32854, 32857, 32869, 32896, 32898, 32904, 32906, 32917, 32928, 32930, 32936, 32938, 33029, 33041, 33044, 33046,
+        33049, 33061, 33089, 33092, 33097, 33104, 33106, 33109, 33110, 33112, 33113, 33124, 33126, 33129, 33157, 33161,
+        33172, 33174, 33177, 33189, 33280, 33282, 33288, 33290, 33301, 33312, 33314, 33320, 33322, 33361, 33364, 33369,
+        33381, 33408, 33410, 33416, 33418, 33429, 33440, 33442, 33448, 33450, 33812, 33817, 33857, 33860, 33873, 33877,
+        33882, 33889, 33892, 33897, 33940, 33945, 34049, 34057, 34066, 34069, 34074, 34086, 34089, 34112, 34113, 34117,
+        34120, 34129, 34132, 34133, 34134, 34137, 34138, 34149, 34150, 34152, 34154, 34177, 34180, 34182, 34185, 34192,
+        34194, 34197, 34200, 34214, 34321, 34326, 34329, 34341, 34369, 34372, 34377, 34378, 34384, 34389, 34393, 34394,
+        34401, 34406, 34410, 34437, 34449, 34458, 34468, 34816, 34818, 34824, 34826, 34837, 34848, 34850, 34856, 34858,
+        34881, 34885, 34897, 34900, 34905, 34917, 34921, 34944, 34946, 34952, 34954, 34965, 34976, 34978, 34984, 34986,
+        35077, 35078, 35089, 35092, 35094, 35109, 35137, 35140, 35142, 35145, 35152, 35154, 35157, 35162, 35169, 35172,
+        35205, 35222, 35225, 35237, 35328, 35330, 35336, 35338, 35349, 35360, 35362, 35368, 35370, 35397, 35409, 35412,
+        35414, 35456, 35458, 35464, 35466, 35477, 35488, 35490, 35496, 35498, 36869, 36881, 36886, 36888, 36889, 36901,
+        36929, 36934, 36937, 36949, 36952, 36954, 36969, 36970, 36997, 37009, 37012, 37014, 37017, 37029, 37121, 37124,
+        37126, 37129, 37136, 37141, 37144, 37146, 37153, 37156, 37158, 37161, 37184, 37189, 37200, 37201, 37204, 37205,
+        37206, 37209, 37218, 37221, 37252, 37254, 37266, 37269, 37272, 37281, 37284, 37286, 37289, 37381, 37393, 37396,
+        37401, 37413, 37444, 37446, 37449, 37456, 37458, 37461, 37464, 37478, 37481, 37509, 37524, 37526, 37545, 37889,
+        37892, 37894, 37904, 37909, 37912, 37926, 37952, 37962, 37969, 37972, 37973, 37974, 37976, 37977, 37984, 37985,
+        37986, 37989, 38020, 38022, 38034, 38036, 38037, 38040, 38049, 38057, 38144, 38149, 38152, 38154, 38160, 38161,
+        38164, 38165, 38166, 38169, 38177, 38181, 38185, 38186, 38209, 38212, 38213, 38214, 38217, 38224, 38225, 38226,
+        38228, 38229, 38230, 38232, 38233, 38234, 38241, 38244, 38245, 38246, 38249, 38273, 38277, 38280, 38289, 38290,
+        38292, 38293, 38294, 38297, 38298, 38304, 38306, 38309, 38312, 38314, 38401, 38404, 38416, 38421, 38425, 38432,
+        38438, 38441, 38469, 38472, 38473, 38481, 38482, 38485, 38486, 38489, 38501, 38504, 38530, 38532, 38537, 38538,
+        38546, 38548, 38549, 38564, 38566, 38569, 38917, 38934, 38937, 38949, 38977, 38982, 38992, 38994, 38997, 38998,
+        39002, 39012, 39013, 39045, 39057, 39062, 39065, 39077, 39172, 39174, 39177, 39184, 39186, 39189, 39192, 39194,
+        39200, 39201, 39204, 39206, 39232, 39234, 39237, 39240, 39242, 39249, 39252, 39253, 39254, 39257, 39266, 39269,
+        39270, 39274, 39297, 39300, 39312, 39314, 39317, 39322, 39329, 39334, 39429, 39445, 39461, 39492, 39494, 39497,
+        39504, 39509, 39512, 39521, 39557, 39569, 39572, 39573, 39574, 40960, 40962, 40968, 40970, 40981, 40992, 40994,
+        41000, 41002, 41029, 41041, 41044, 41046, 41049, 41088, 41090, 41096, 41098, 41109, 41120, 41122, 41128, 41130,
+        41221, 41225, 41233, 41236, 41238, 41241, 41242, 41286, 41289, 41297, 41301, 41304, 41306, 41313, 41316, 41349,
+        41360, 41362, 41366, 41369, 41474, 41480, 41482, 41488, 41497, 41506, 41512, 41514, 41541, 41553, 41558, 41561,
+        41573, 41600, 41602, 41608, 41610, 41621, 41632, 41634, 41640, 41642, 42009, 42021, 42049, 42052, 42064, 42068,
+        42069, 42072, 42074, 42081, 42085, 42086, 42088, 42089, 42117, 42246, 42249, 42256, 42258, 42261, 42264, 42278,
+        42281, 42306, 42309, 42321, 42324, 42325, 42326, 42329, 42341, 42346, 42369, 42372, 42373, 42374, 42377, 42386,
+        42389, 42392, 42501, 42513, 42518, 42522, 42529, 42533, 42564, 42566, 42570, 42578, 42581, 42582, 42584, 42592,
+        42594, 42630, 42640, 42645, 42646, 42649, 42657, 42660, 42662, 43008, 43010, 43016, 43018, 43040, 43042, 43048,
+        43050, 43089, 43092, 43094, 43097, 43136, 43138, 43144, 43146, 43157, 43168, 43170, 43176, 43178, 43269, 43284,
+        43289, 43297, 43301, 43329, 43344, 43349, 43354, 43361, 43366, 43369, 43408, 43414, 43520, 43522, 43528, 43530,
+        43552, 43554, 43560, 43562, 43601, 43604, 43606, 43648, 43650, 43656, 43658, 43669, 43680, 43682, 43688, 43690,
+    };
+    static const uint16_t kgrid_2bit_1024[1024] = {
+            0,     2,     5,     8,    10,    17,    20,    22,    25,    32,    34,    37,    40,    65,    68,    70,
+           73,    80,    82,    85,    88,    97,   100,   102,   105,   128,   130,   133,   136,   145,   148,   160,
+          165,   170,   257,   260,   262,   265,   272,   274,   277,   280,   289,   292,   320,   322,   325,   328,
+          337,   340,   342,   345,   352,   357,   360,   385,   388,   400,   402,   405,   417,   420,   512,   514,
+          517,   520,   529,   532,   544,   554,   577,   580,   582,   585,   592,   597,   640,   645,   650,   660,
+          674,  1025,  1028,  1030,  1033,  1040,  1042,  1045,  1048,  1057,  1060,  1062,  1065,  1088,  1090,  1093,
+         1096,  1098,  1105,  1108,  1110,  1113,  1120,  1122,  1125,  1153,  1156,  1158,  1161,  1168,  1173,  1176,
+         1185,  1188,  1280,  1282,  1285,  1288,  1290,  1297,  1300,  1302,  1305,  1312,  1317,  1320,  1345,  1348,
+         1350,  1353,  1360,  1362,  1365,  1368,  1377,  1380,  1408,  1410,  1413,  1416,  1425,  1428,  1440,  1537,
+         1540,  1542,  1545,  1552,  1557,  1600,  1605,  1608,  1617,  1620,  1632,  1665,  1668,  1680,  2048,  2050,
+         2053,  2056,  2065,  2068,  2070,  2073,  2080,  2085,  2090,  2113,  2116,  2118,  2121,  2128,  2130,  2133,
+         2136,  2145,  2148,  2176,  2181,  2196,  2218,  2305,  2308,  2320,  2322,  2325,  2328,  2337,  2368,  2373,
+         2376,  2385,  2388,  2400,  2433,  2448,  2560,  2577,  2580,  2594,  2600,  2602,  2640,  2713,  4097,  4100,
+         4102,  4105,  4112,  4114,  4117,  4120,  4129,  4132,  4134,  4160,  4162,  4165,  4168,  4177,  4180,  4182,
+         4185,  4192,  4194,  4197,  4200,  4225,  4228,  4230,  4240,  4245,  4248,  4257,  4260,  4352,  4354,  4357,
+         4360,  4362,  4369,  4372,  4374,  4377,  4384,  4386,  4389,  4392,  4417,  4420,  4422,  4425,  4432,  4434,
+         4437,  4440,  4449,  4452,  4480,  4482,  4485,  4488,  4497,  4500,  4609,  4612,  4617,  4624,  4629,  4641,
+         4644,  4672,  4677,  4689,  4692,  4737,  4740,  4752,  5120,  5122,  5125,  5128,  5137,  5140,  5142,  5145,
+         5152,  5157,  5160,  5185,  5188,  5190,  5193,  5200,  5202,  5205,  5208,  5217,  5220,  5248,  5250,  5253,
+         5256,  5265,  5268,  5280,  5377,  5380,  5382,  5385,  5392,  5394,  5397,  5400,  5409,  5412,  5440,  5442,
+         5445,  5448,  5457,  5460,  5472,  5505,  5508,  5520,  5632,  5637,  5640,  5649,  5652,  5664,  5697,  5700,
+         5712,  5760,  5802,  6145,  6148,  6150,  6153,  6160,  6165,  6168,  6177,  6208,  6210,  6213,  6216,  6225,
+         6228,  6240,  6273,  6276,  6400,  6402,  6405,  6408,  6417,  6420,  6432,  6465,  6468,  6480,  6505,  6562,
+         6660,  6672,  6720,  6742,  8192,  8194,  8197,  8200,  8209,  8212,  8214,  8217,  8224,  8229,  8234,  8257,
+         8260,  8272,  8274,  8277,  8292,  8320,  8330,  8340,  8362,  8449,  8452,  8464,  8466,  8469,  8481,  8512,
+         8514,  8517,  8529,  8532,  8544,  8577,  8580,  8592,  8704,  8714,  8738,  8744,  8746,  8772,  8784,  8840,
+         8842,  8872,  9217,  9220,  9222,  9225,  9232,  9237,  9240,  9249,  9252,  9280,  9282,  9285,  9288,  9297,
+         9300,  9312,  9345,  9348,  9360,  9472,  9477,  9480,  9489,  9492,  9504,  9537,  9540,  9552,  9574,  9600,
+         9729,  9732,  9744,  9792,  9817, 10240, 10245, 10257, 10260, 10305, 10308, 10320, 10378, 10410, 10497, 10500,
+        10512, 10645, 10762, 10786, 10852, 10888, 10890, 16385, 16388, 16390, 16393, 16400, 16402, 16405, 16408, 16410,
+        16417, 16420, 16422, 16448, 16450, 16453, 16456, 16458, 16465, 16468, 16470, 16473, 16480, 16482, 16485, 16513,
+        16516, 16528, 16533, 16536, 16545, 16548, 16640, 16642, 16645, 16648, 16657, 16660, 16662, 16665, 16672, 16674,
+        16677, 16705, 16708, 16710, 16713, 16720, 16722, 16725, 16728, 16737, 16740, 16768, 16770, 16773, 16776, 16785,
+        16788, 16800, 16897, 16900, 16912, 16914, 16917, 16920, 16932, 16960, 16965, 16968, 16977, 16980, 16992, 17025,
+        17028, 17408, 17410, 17413, 17416, 17418, 17425, 17428, 17430, 17433, 17440, 17442, 17445, 17448, 17473, 17476,
+        17478, 17481, 17488, 17490, 17493, 17496, 17505, 17508, 17536, 17538, 17541, 17544, 17553, 17556, 17568, 17665,
+        17668, 17670, 17673, 17680, 17682, 17685, 17688, 17697, 17700, 17728, 17730, 17733, 17736, 17745, 17748, 17760,
+        17770, 17793, 17796, 17808, 17920, 17922, 17925, 17928, 17937, 17940, 17952, 17985, 17988, 18000, 18048, 18085,
+        18433, 18436, 18441, 18448, 18450, 18453, 18456, 18465, 18468, 18496, 18498, 18501, 18504, 18513, 18516, 18528,
+        18564, 18576, 18688, 18690, 18693, 18696, 18705, 18708, 18720, 18753, 18756, 18768, 18816, 18838, 18945, 18948,
+        18960, 19008, 20480, 20482, 20485, 20488, 20497, 20500, 20502, 20505, 20512, 20514, 20517, 20520, 20545, 20548,
+        20550, 20553, 20560, 20562, 20565, 20568, 20577, 20580, 20608, 20610, 20613, 20616, 20625, 20628, 20737, 20740,
+        20742, 20745, 20752, 20754, 20757, 20760, 20769, 20772, 20800, 20802, 20805, 20808, 20817, 20820, 20832, 20865,
+        20868, 20880, 20992, 20997, 21000, 21009, 21012, 21024, 21057, 21060, 21072, 21097, 21120, 21505, 21508, 21510,
+        21513, 21520, 21522, 21525, 21528, 21537, 21540, 21568, 21570, 21573, 21576, 21585, 21588, 21600, 21633, 21636,
+        21648, 21760, 21762, 21765, 21768, 21777, 21780, 21792, 21825, 21828, 21840, 21888, 22017, 22020, 22032, 22054,
+        22080, 22528, 22530, 22533, 22536, 22545, 22548, 22560, 22593, 22596, 22608, 22618, 22656, 22785, 22788, 22800,
+        22848, 23040, 23065, 23173, 23208, 24577, 24580, 24582, 24592, 24594, 24597, 24600, 24609, 24612, 24640, 24645,
+        24648, 24657, 24660, 24672, 24708, 24720, 24832, 24834, 24837, 24840, 24849, 24852, 24864, 24897, 24900, 24912,
+        24960, 24985, 25092, 25104, 25152, 25174, 25249, 25600, 25605, 25608, 25617, 25620, 25632, 25665, 25668, 25680,
+        25728, 25857, 25860, 25872, 25920, 25930, 25960, 26002, 26112, 26260, 26625, 26628, 26640, 26725, 26776, 26880,
+        26922, 27202, 27297, 32768, 32770, 32773, 32776, 32785, 32788, 32793, 32800, 32805, 32833, 32836, 32848, 32850,
+        32853, 32856, 32865, 32896, 32901, 32913, 32916, 33025, 33028, 33033, 33040, 33042, 33045, 33048, 33057, 33060,
+        33088, 33090, 33093, 33096, 33105, 33108, 33153, 33156, 33168, 33193, 33280, 33285, 33290, 33297, 33300, 33345,
+        33348, 33360, 33793, 33796, 33798, 33801, 33808, 33810, 33813, 33816, 33825, 33856, 33858, 33861, 33864, 33873,
+        33876, 33888, 33921, 33924, 33936, 34048, 34050, 34053, 34056, 34065, 34068, 34080, 34113, 34116, 34128, 34176,
+        34186, 34305, 34308, 34320, 34345, 34368, 34816, 34821, 34833, 34836, 34881, 34884, 34896, 34978, 35073, 35076,
+        35136, 35173, 35362, 35416, 35418, 35458, 35490, 36865, 36868, 36873, 36880, 36882, 36885, 36888, 36900, 36928,
+        36930, 36933, 36936, 36945, 36948, 36960, 36993, 36996, 37008, 37120, 37125, 37137, 37140, 37185, 37188, 37200,
+        37210, 37377, 37380, 37392, 37440, 37542, 37888, 37890, 37893, 37896, 37905, 37908, 37920, 37953, 37956, 37968,
+        38016, 38038, 38145, 38148, 38160, 38208, 38296, 38305, 38400, 38470, 38500, 38913, 38916, 38928, 38950, 38976,
+        39081, 39168, 39241, 39250, 39568, 40960, 40965, 40970, 40980, 40994, 41002, 41025, 41028, 41040, 41122, 41130,
+        41280, 41317, 41474, 41482, 41506, 41512, 41514, 41602, 41608, 41610, 41640, 41985, 41988, 42000, 42048, 42121,
+        42148, 42240, 42265, 42577, 43018, 43048, 43170, 43348, 43398, 43528, 43530, 43552, 43554, 43560, 43656, 43690,
     };
 
     const int kmap_size = 43692;
-    const int nwant = type == GGML_TYPE_IQ1_S ? 3 : 2;
+    //const int nwant = type == GGML_TYPE_IQ1_S ? 3 : 2;
+    const int nwant = type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M ? 3 : type == GGML_TYPE_IQ2_S ? 1 : 2;
     const uint16_t * kgrid = type == GGML_TYPE_IQ2_XXS ? kgrid_2bit_256 :
-                             type == GGML_TYPE_IQ2_XS  ? kgrid_2bit_512 : kgrid_1bit_512;
+                             type == GGML_TYPE_IQ2_XS  ? kgrid_2bit_512 :
+                             type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M ? kgrid_1bit_2048 : kgrid_2bit_1024;
     uint64_t * kgrid_q2xs;
     int      * kmap_q2xs;
     uint16_t * kneighbors_q2xs;
 
-    printf("================================================================= %s(grid_size = %d)\n", __func__, grid_size);
+    //printf("================================================================= %s(grid_size = %d)\n", __func__, grid_size);
     uint64_t * the_grid = (uint64_t *)malloc(grid_size*sizeof(uint64_t));
     for (int k = 0; k < grid_size; ++k) {
         int8_t * pos = (int8_t *)(the_grid + k);
@@ -9643,7 +10291,7 @@ void iq2xs_init_impl(enum ggml_type type) {
         }
         num_neighbors += n;
     }
-    printf("%s: %d neighbours in total\n", __func__, num_neighbors);
+    //printf("%s: %d neighbours in total\n", __func__, num_neighbors);
     kneighbors_q2xs = (uint16_t *)malloc((num_neighbors + num_not_in_map)*sizeof(uint16_t));
     iq2_data[gindex].neighbours = kneighbors_q2xs;
     int counter = 0;
@@ -9680,7 +10328,7 @@ void iq2xs_init_impl(enum ggml_type type) {
 }
 
 void iq2xs_free_impl(enum ggml_type type) {
-    GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S);
+    GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M || type == GGML_TYPE_IQ2_S);
     const int gindex = iq2_data_index(type);
     if (iq2_data[gindex].grid) {
         free(iq2_data[gindex].grid);       iq2_data[gindex].grid = NULL;
@@ -9713,7 +10361,7 @@ static int iq2_find_best_neighbour(const uint16_t * restrict neighbours, const u
     return grid_index;
 }
 
-static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) {
+static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict vy, int64_t n, const float * restrict quant_weights) {
 
     const int gindex = iq2_data_index(GGML_TYPE_IQ2_XXS);
 
@@ -9729,7 +10377,7 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict
 
     const int kMaxQ = 3;
 
-    const int nbl = n/256;
+    const int64_t nbl = n/QK_K;
 
     block_iq2_xxs * y = vy;
 
@@ -9886,7 +10534,7 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict
     }
 }
 
-static void quantize_row_iq2_xs_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) {
+static void quantize_row_iq2_xs_impl(const float * restrict x, void * restrict vy, int64_t n, const float * restrict quant_weights) {
 
     const int gindex = iq2_data_index(GGML_TYPE_IQ2_XS);
 
@@ -9902,7 +10550,7 @@ static void quantize_row_iq2_xs_impl(const float * restrict x, void * restrict v
 
     const int kMaxQ = 3;
 
-    const int nbl = n/256;
+    const int64_t nbl = n/QK_K;
 
     block_iq2_xs * y = vy;
 
@@ -10066,12 +10714,11 @@ static void quantize_row_iq2_xs_impl(const float * restrict x, void * restrict v
     }
 }
 
-size_t quantize_iq2_xxs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {
-    (void)hist;
+size_t quantize_iq2_xxs(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     GGML_ASSERT(n_per_row%QK_K == 0);
-    int nblock = n_per_row/QK_K;
+    int64_t nblock = n_per_row/QK_K;
     char * qrow = (char *)dst;
-    for (int row = 0; row < nrow; ++row) {
+    for (int64_t row = 0; row < nrow; ++row) {
         quantize_row_iq2_xxs_impl(src, qrow, n_per_row, quant_weights);
         src += n_per_row;
         qrow += nblock*sizeof(block_iq2_xxs);
@@ -10079,12 +10726,11 @@ size_t quantize_iq2_xxs(const float * src, void * dst, int nrow, int n_per_row,
     return nrow * nblock * sizeof(block_iq2_xxs);
 }
 
-size_t quantize_iq2_xs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {
-    (void)hist;
+size_t quantize_iq2_xs(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     GGML_ASSERT(n_per_row%QK_K == 0);
-    int nblock = n_per_row/QK_K;
+    int64_t nblock = n_per_row/QK_K;
     char * qrow = (char *)dst;
-    for (int row = 0; row < nrow; ++row) {
+    for (int64_t row = 0; row < nrow; ++row) {
         quantize_row_iq2_xs_impl(src, qrow, n_per_row, quant_weights);
         src += n_per_row;
         qrow += nblock*sizeof(block_iq2_xs);
@@ -10102,14 +10748,15 @@ typedef struct {
     uint16_t * neighbours;
 } iq3_entry_t;
 
-static iq3_entry_t iq3_data[1] = {
+static iq3_entry_t iq3_data[2] = {
+    {NULL, NULL, NULL},
     {NULL, NULL, NULL},
 };
 
 static inline int iq3_data_index(int grid_size) {
     (void)grid_size;
-    GGML_ASSERT(grid_size == 256);
-    return 0;
+    GGML_ASSERT(grid_size == 256 || grid_size == 512);
+    return grid_size == 256 ? 0 : 1;
 }
 
 static int iq3_compare_func(const void * left, const void * right) {
@@ -10141,14 +10788,49 @@ void iq3xs_init_impl(int grid_size) {
          3185,  3215,  3252,  3288,  3294,  3364,  3397,  3434,  3483,  3523,  3537,  3587,  3589,  3591,  3592,  3610,
          3626,  3670,  3680,  3722,  3749,  3754,  3776,  3789,  3803,  3824,  3857,  3873,  3904,  3906,  3924,  3992,
     };
+    static const uint16_t kgrid_512[512] = {
+            0,     1,     2,     5,     7,     8,     9,    10,    12,    14,    16,    17,    21,    27,    32,    34,
+           37,    39,    41,    43,    48,    50,    57,    60,    63,    64,    65,    66,    68,    72,    73,    77,
+           80,    83,    87,    89,    93,   100,   113,   117,   122,   128,   129,   133,   135,   136,   139,   142,
+          145,   149,   152,   156,   162,   165,   167,   169,   171,   184,   187,   195,   201,   205,   208,   210,
+          217,   219,   222,   228,   232,   234,   247,   249,   253,   256,   267,   271,   273,   276,   282,   288,
+          291,   297,   312,   322,   324,   336,   338,   342,   347,   353,   357,   359,   374,   379,   390,   393,
+          395,   409,   426,   441,   448,   450,   452,   464,   466,   470,   475,   488,   492,   512,   513,   514,
+          516,   520,   521,   523,   525,   527,   528,   530,   537,   540,   542,   556,   558,   561,   570,   576,
+          577,   579,   582,   584,   588,   593,   600,   603,   609,   616,   618,   632,   638,   640,   650,   653,
+          655,   656,   660,   666,   672,   675,   685,   688,   698,   705,   708,   711,   712,   715,   721,   727,
+          728,   732,   737,   754,   760,   771,   773,   778,   780,   793,   795,   802,   806,   808,   812,   833,
+          840,   843,   849,   856,   858,   873,   912,   916,   919,   932,   934,   961,   963,   968,   970,   977,
+          989,   993,  1010,  1016,  1024,  1025,  1027,  1029,  1031,  1032,  1034,  1036,  1038,  1041,  1043,  1047,
+         1048,  1050,  1057,  1059,  1061,  1064,  1066,  1079,  1080,  1083,  1085,  1088,  1090,  1096,  1099,  1103,
+         1106,  1109,  1113,  1116,  1122,  1129,  1153,  1156,  1159,  1169,  1171,  1176,  1183,  1185,  1195,  1199,
+         1209,  1212,  1216,  1218,  1221,  1225,  1234,  1236,  1241,  1243,  1250,  1256,  1270,  1281,  1287,  1296,
+         1299,  1306,  1309,  1313,  1338,  1341,  1348,  1353,  1362,  1375,  1376,  1387,  1400,  1408,  1410,  1415,
+         1425,  1453,  1457,  1477,  1481,  1494,  1496,  1507,  1512,  1538,  1545,  1547,  1549,  1551,  1554,  1561,
+         1563,  1565,  1570,  1572,  1575,  1577,  1587,  1593,  1601,  1603,  1605,  1612,  1617,  1619,  1632,  1648,
+         1658,  1662,  1664,  1674,  1680,  1690,  1692,  1704,  1729,  1736,  1740,  1745,  1747,  1751,  1752,  1761,
+         1763,  1767,  1773,  1787,  1795,  1801,  1806,  1810,  1817,  1834,  1840,  1844,  1857,  1864,  1866,  1877,
+         1882,  1892,  1902,  1915,  1934,  1953,  1985,  1987,  2000,  2002,  2013,  2048,  2052,  2058,  2064,  2068,
+         2071,  2074,  2081,  2088,  2104,  2114,  2119,  2121,  2123,  2130,  2136,  2141,  2147,  2153,  2157,  2177,
+         2179,  2184,  2189,  2193,  2203,  2208,  2223,  2226,  2232,  2244,  2249,  2251,  2256,  2258,  2265,  2269,
+         2304,  2306,  2324,  2335,  2336,  2361,  2373,  2375,  2385,  2418,  2443,  2460,  2480,  2504,  2509,  2520,
+         2531,  2537,  2562,  2568,  2572,  2578,  2592,  2596,  2599,  2602,  2614,  2620,  2625,  2627,  2629,  2634,
+         2641,  2650,  2682,  2688,  2697,  2707,  2712,  2718,  2731,  2754,  2759,  2760,  2775,  2788,  2793,  2805,
+         2811,  2817,  2820,  2832,  2842,  2854,  2890,  2902,  2921,  2923,  2978,  3010,  3012,  3026,  3081,  3083,
+         3085,  3097,  3099,  3120,  3136,  3152,  3159,  3188,  3210,  3228,  3234,  3245,  3250,  3256,  3264,  3276,
+         3281,  3296,  3349,  3363,  3378,  3392,  3395,  3420,  3440,  3461,  3488,  3529,  3531,  3584,  3588,  3591,
+         3600,  3602,  3614,  3616,  3628,  3634,  3650,  3657,  3668,  3683,  3685,  3713,  3716,  3720,  3726,  3729,
+         3736,  3753,  3778,  3802,  3805,  3819,  3841,  3845,  3851,  3856,  3880,  3922,  3938,  3970,  3993,  4032,
+    };
+
     const int kmap_size = 4096;
-    const int nwant = 2;
-    const uint16_t * kgrid = kgrid_256;
+    const int nwant = grid_size == 256 ? 2 : 3;
+    const uint16_t * kgrid = grid_size == 256 ? kgrid_256 : kgrid_512;
     uint32_t * kgrid_q3xs;
     int      * kmap_q3xs;
     uint16_t * kneighbors_q3xs;
 
-    printf("================================================================= %s(grid_size = %d)\n", __func__, grid_size);
+    //printf("================================================================= %s(grid_size = %d)\n", __func__, grid_size);
     uint32_t * the_grid = (uint32_t *)malloc(grid_size*sizeof(uint32_t));
     for (int k = 0; k < grid_size; ++k) {
         int8_t * pos = (int8_t *)(the_grid + k);
@@ -10203,7 +10885,7 @@ void iq3xs_init_impl(int grid_size) {
         }
         num_neighbors += n;
     }
-    printf("%s: %d neighbours in total\n", __func__, num_neighbors);
+    //printf("%s: %d neighbours in total\n", __func__, num_neighbors);
     kneighbors_q3xs = (uint16_t *)malloc((num_neighbors + num_not_in_map)*sizeof(uint16_t));
     iq3_data[gindex].neighbours = kneighbors_q3xs;
     int counter = 0;
@@ -10240,7 +10922,7 @@ void iq3xs_init_impl(int grid_size) {
 }
 
 void iq3xs_free_impl(int grid_size) {
-    GGML_ASSERT(grid_size == 256);
+    GGML_ASSERT(grid_size == 256 || grid_size == 512);
     const int gindex = iq3_data_index(grid_size);
     if (iq3_data[gindex].grid) {
         free(iq3_data[gindex].grid);       iq3_data[gindex].grid = NULL;
@@ -10273,9 +10955,10 @@ static int iq3_find_best_neighbour(const uint16_t * restrict neighbours, const u
     return grid_index;
 }
 
-static void quantize_row_iq3_xxs_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) {
+static void quantize_row_iq3_xxs_impl(int grid_size, const float * restrict x, void * restrict vy, int64_t n,
+        const float * restrict quant_weights) {
 
-    const int gindex = iq3_data_index(256);
+    const int gindex = iq3_data_index(grid_size);
 
     const uint32_t * kgrid_q3xs      = iq3_data[gindex].grid;
     const int      * kmap_q3xs       = iq3_data[gindex].map;
@@ -10289,9 +10972,23 @@ static void quantize_row_iq3_xxs_impl(const float * restrict x, void * restrict
 
     const int kMaxQ = 8;
 
-    const int nbl = n/256;
+    const int64_t nbl = n/QK_K;
 
-    block_iq3_xxs * y = vy;
+    ggml_fp16_t * dh;
+    uint8_t * qs;
+    int block_size;
+    if (grid_size == 256) {
+        block_iq3_xxs * y = vy;
+        dh = &y->d;
+        qs = y->qs;
+        block_size = sizeof(block_iq3_xxs);
+    } else {
+        block_iq3_s * y = vy;
+        dh = &y->d;
+        qs = y->qs;
+        block_size = sizeof(block_iq3_s);
+    }
+    int quant_size = block_size - sizeof(ggml_fp16_t);
 
     float scales[QK_K/32];
     float weight[32];
@@ -10302,20 +10999,21 @@ static void quantize_row_iq3_xxs_impl(const float * restrict x, void * restrict
     bool   is_on_grid[8];
     bool   is_on_grid_aux[8];
     uint8_t block_signs[8];
-    uint8_t q3[3*(QK_K/8)];
+    uint8_t q3[3*(QK_K/8)+QK_K/32];
     uint32_t * scales_and_signs = (uint32_t *)(q3 + QK_K/4);
+    uint8_t  * qh = q3 + 3*(QK_K/8);
 
     for (int ibl = 0; ibl < nbl; ++ibl) {
 
-        y[ibl].d = GGML_FP32_TO_FP16(0.f);
-        memset(q3, 0, 3*QK_K/8);
+        dh[0] = GGML_FP32_TO_FP16(0.f);
+        memset(q3, 0, 3*QK_K/8+QK_K/32);
 
         float max_scale = 0;
 
         const float * xbl = x + QK_K*ibl;
         float sumx2 = 0;
         for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i];
-        float sigma2 = sumx2/QK_K;
+        float sigma2 = 2*sumx2/QK_K;
 
         for (int ib = 0; ib < QK_K/32; ++ib) {
             const float * xb = xbl + 32*ib;
@@ -10433,7 +11131,13 @@ static void quantize_row_iq3_xxs_impl(const float * restrict x, void * restrict
                     printf("\n");
                     GGML_ASSERT(false);
                 }
-                q3[8*ib+k] = grid_index;
+                if (grid_size == 256) {
+                    q3[8*ib+k] = grid_index;
+                } else {
+                    q3[8*ib+k] = grid_index & 255;
+                    qh[ib] |= ((grid_index >> 8) << k);
+                }
+
             }
             scales_and_signs[ib] = block_signs[0] | (block_signs[1] << 7) | (block_signs[2] << 14) | (block_signs[3] << 21);
             GGML_ASSERT(scale >= 0);
@@ -10442,90 +11146,268 @@ static void quantize_row_iq3_xxs_impl(const float * restrict x, void * restrict
         }
 
         if (!max_scale) {
-            memset(y[ibl].qs, 0, 3*QK_K/8);
+            memset(qs, 0, quant_size);
+            dh += block_size/sizeof(ggml_fp16_t);
+            qs += block_size;
             continue;
         }
 
         float d = max_scale/31;
-        y[ibl].d = GGML_FP32_TO_FP16(d);
+        dh[0] = GGML_FP32_TO_FP16(d * 1.0125f);  // small improvement via this fudge factor
         float id = 1/d;
-        float sumqx = 0, sumq2 = 0;
         for (int ib = 0; ib < QK_K/32; ++ib) {
             int l = nearest_int(0.5f*(id*scales[ib]-1));
             l = MAX(0, MIN(15, l));
             scales_and_signs[ib] |= ((uint32_t)l << 28);
-            if (false) {
-                const float * xb = xbl + 32*ib;
-                if (quant_weights) {
-                    const float * qw = quant_weights + QK_K*ibl + 32*ib;
-                    for (int i = 0; i < 32; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
-                } else {
-                    for (int i = 0; i < 32; ++i) weight[i] = xb[i]*xb[i];
-                }
-                const float db = 0.25f * d * (1 + 2*l);
-                for (int k = 0; k < 8; ++k) {
-                    const int8_t * signs = keven_signs_q2xs + 8*((scales_and_signs[ib] >> 7*(k/2)) & 127) + 4*(k%2);
-                    const float * xk = xb + 4*k;
-                    const float * wk = weight + 4*k;
-                    //const uint8_t * grid = (const uint8_t *)(kgrid_q3xs + q3[8*ib+k]);
-                    const uint8_t * grid = (const uint8_t *)(iq3xxs_grid + q3[8*ib+k]);
-                    float best_mse = 0; int best_index = q3[8*ib+k];
-                    for (int j = 0; j < 4; ++j) {
-                        float diff = db * grid[j] * signs[j] - xk[j];
-                        best_mse += wk[j] * diff * diff;
-                    }
-                    for (int idx = 0; idx < 256; ++idx) {
-                        //grid = (const uint8_t *)(kgrid_q3xs + idx);
-                        grid = (const uint8_t *)(iq3xxs_grid + idx);
-                        float mse = 0;
-                        for (int j = 0; j < 4; ++j) {
-                            float diff = db * grid[j] * signs[j] - xk[j];
-                            mse += wk[j] * diff * diff;
-                        }
-                        if (mse < best_mse) {
-                            best_mse = mse; best_index = idx;
-                        }
-                    }
-                    q3[8*ib+k] = best_index;
-                    //grid = (const uint8_t *)(kgrid_q3xs + best_index);
-                    grid = (const uint8_t *)(iq3xxs_grid + best_index);
-                    for (int j = 0; j < 4; ++j) {
-                        float q = db * grid[j] * signs[j];
-                        sumqx += wk[j] * q * xk[j];
-                        sumq2 += wk[j] * q * q;
-                    }
-                }
-                if (sumq2 > 0) y[ibl].d = GGML_FP32_TO_FP16(d*sumqx/sumq2);
-            }
         }
-        memcpy(y[ibl].qs, q3, 3*QK_K/8);
+        memcpy(qs, q3, quant_size);
+
+        dh += block_size/sizeof(ggml_fp16_t);
+        qs += block_size;
+
     }
 }
 
-size_t quantize_iq3_xxs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {
-    (void)hist;
+size_t quantize_iq3_xxs(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     GGML_ASSERT(n_per_row%QK_K == 0);
-    int nblock = n_per_row/QK_K;
+    int64_t nblock = n_per_row/QK_K;
     char * qrow = (char *)dst;
-    for (int row = 0; row < nrow; ++row) {
-        quantize_row_iq3_xxs_impl(src, qrow, n_per_row, quant_weights);
+    for (int64_t row = 0; row < nrow; ++row) {
+        quantize_row_iq3_xxs_impl(256, src, qrow, n_per_row, quant_weights);
         src += n_per_row;
         qrow += nblock*sizeof(block_iq3_xxs);
     }
     return nrow * nblock * sizeof(block_iq3_xxs);
 }
 
-void quantize_row_iq3_xxs(const float * restrict x, void * restrict vy, int k) {
+void quantize_row_iq3_xxs(const float * restrict x, void * restrict vy, int64_t k) {
     assert(k % QK_K == 0);
     block_iq3_xxs * restrict y = vy;
     quantize_row_iq3_xxs_reference(x, y, k);
 }
 
-void quantize_row_iq3_xxs_reference(const float * restrict x, block_iq3_xxs * restrict y, int k) {
+void quantize_row_iq3_xxs_reference(const float * restrict x, block_iq3_xxs * restrict y, int64_t k) {
     assert(k % QK_K == 0);
-    quantize_row_iq3_xxs_impl(x, y, k, NULL);
+    quantize_row_iq3_xxs_impl(256, x, y, k, NULL);
 }
 
+static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, void * restrict vy, int n,
+        const float * restrict quant_weights,
+        float   * scales,
+        float   * weight,
+        float   * xval,
+        int8_t  * L,
+        int8_t  * Laux,
+        float   * waux,
+        bool    * is_on_grid,
+        bool    * is_on_grid_aux,
+        uint8_t * block_signs) {
+
+    const int gindex = iq3_data_index(512);
+
+    const uint32_t * kgrid_q3xs      = iq3_data[gindex].grid;
+    const int      * kmap_q3xs       = iq3_data[gindex].map;
+    const uint16_t * kneighbors_q3xs = iq3_data[gindex].neighbours;
+
+    //GGML_ASSERT(quant_weights   && "missing quantization weights");
+    GGML_ASSERT(kgrid_q3xs      && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(kmap_q3xs       && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(kneighbors_q3xs && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(n%QK_K == 0);
+
+    const int kMaxQ = 8;
+
+    const int64_t nbl = n/QK_K;
+
+    block_iq3_s * y = vy;
+
+    const int bs4 = block_size/4;
+    const int bs8 = block_size/8;
+
+    for (int ibl = 0; ibl < nbl; ++ibl) {
+
+        memset(&y[ibl], 0, sizeof(block_iq3_s));
+        y[ibl].d = GGML_FP32_TO_FP16(0.f);
+
+        uint8_t * qs = y[ibl].qs;
+        uint8_t * qh = y[ibl].qh;
+        uint8_t * signs = y[ibl].signs;
+
+        float max_scale = 0;
+
+        const float * xbl = x + QK_K*ibl;
+        float sumx2 = 0;
+        for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i];
+        float sigma2 = 2*sumx2/QK_K;
+
+        for (int ib = 0; ib < QK_K/block_size; ++ib) {
+            const float * xb = xbl + block_size*ib;
+            if (quant_weights) {
+                const float * qw = quant_weights + QK_K*ibl + block_size*ib;
+                for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
+            } else {
+                for (int i = 0; i < block_size; ++i) weight[i] = xb[i]*xb[i];
+            }
+            for (int i = 0; i < block_size; ++i) waux[i] = sqrtf(weight[i]);
+            for (int k = 0; k < bs8; ++k) {
+                uint8_t s = 0;
+                for (int i = 0; i < 8; ++i) {
+                    if (xb[8*k + i] >= 0) xval[8*k + i] = xb[8*k + i];
+                    else {
+                        xval[8*k + i] = -xb[8*k + i]; s |= (1 << i);
+                    }
+                }
+                block_signs[k] = s;
+            }
+            float max = xval[0];
+            for (int i = 1; i < block_size; ++i) max = MAX(max, xval[i]);
+            if (!max) {
+                scales[ib] = 0;
+                continue;
+            }
+            float best = 0;
+            float scale = max/(2*kMaxQ-1);
+            for (int k = 0; k < bs4; ++k) is_on_grid[k] = false;
+            for (int is = -9; is <= 9; ++is) {
+                float id = (2*kMaxQ-1+is*0.2f)/max;
+                float this_scale = 1/id;
+                for (int k = 0; k < bs4; ++k) {
+                    for (int i = 0; i < 4; ++i) {
+                        int l = nearest_int(0.5f*(id*xval[4*k+i]-1));
+                        Laux[4*k+i] = MAX(0, MIN(kMaxQ-1, l));
+                    }
+                    uint16_t u = 0;
+                    for (int i = 0; i < 4; ++i) u |= (Laux[4*k+i] << 3*i);
+                    int grid_index = kmap_q3xs[u];
+                    is_on_grid_aux[k] = true;
+                    if (grid_index < 0) {
+                        is_on_grid_aux[k] = false;
+                        const uint16_t * neighbours = kneighbors_q3xs - kmap_q3xs[u] - 1;
+                        grid_index = iq3_find_best_neighbour(neighbours, kgrid_q3xs, xval + 4*k, waux + 4*k, this_scale, Laux + 4*k);
+                    }
+                }
+                float sumqx = 0, sumq2 = 0;
+                for (int i = 0; i < block_size; ++i) {
+                    float w = weight[i];
+                    float q = 2*Laux[i] + 1;
+                    sumqx += w*xval[i]*q;
+                    sumq2 += w*q*q;
+                }
+                if (sumq2 > 0 && sumqx*sumqx > best*sumq2) {
+                    scale = sumqx/sumq2; best = scale*sumqx;
+                    for (int i = 0; i < block_size; ++i) L[i] = Laux[i];
+                    for (int k = 0; k < bs4; ++k) is_on_grid[k] = is_on_grid_aux[k];
+                }
+            }
+            int n_not_ongrid = 0;
+            for (int k = 0; k < bs4; ++k) if (!is_on_grid[k]) ++n_not_ongrid;
+            if (n_not_ongrid > 0 && scale > 0) {
+                float id = 1/scale;
+                for (int k = 0; k < bs4; ++k) {
+                    //if (is_on_grid[k]) continue;
+                    uint16_t u = 0;
+                    for (int i = 0; i < 4; ++i) {
+                        int l = nearest_int(0.5f*(id*xval[4*k+i]-1));
+                        l = MAX(0, MIN(kMaxQ-1, l));
+                        u |= (l << 3*i);
+                    }
+                    int grid_index = kmap_q3xs[u];
+                    if (grid_index < 0) {
+                        const uint16_t * neighbours = kneighbors_q3xs - kmap_q3xs[u] - 1;
+                        grid_index = iq3_find_best_neighbour(neighbours, kgrid_q3xs, xval + 4*k, waux + 4*k, scale, L + 4*k);
+                    }
+                    const int8_t * pg = (const int8_t *)(kgrid_q3xs + grid_index);
+                    for (int i = 0; i < 4; ++i) L[4*k+i] = (pg[i] - 1)/2;
+                }
+                float sumqx = 0, sumq2 = 0;
+                for (int i = 0; i < block_size; ++i) {
+                    float w = weight[i];
+                    float q = 2*L[i] + 1;
+                    sumqx += w*xval[i]*q;
+                    sumq2 += w*q*q;
+                }
+                if (sumq2 > 0) scale = sumqx/sumq2;
+            }
+            if (scale < 0) {
+                // This should never happen, but just in case, flip scale so that it is positive (we use uint's to encode the scale)
+                // and correspondingly flip quant signs.
+                scale = -scale;
+                for (int k = 0; k < bs8; ++k) block_signs[k] = ~block_signs[k];
+            }
+            for (int k = 0; k < bs4; ++k) {
+                uint16_t u = 0;
+                for (int i = 0; i < 4; ++i) u |= (L[4*k+i] << 3*i);
+                int grid_index = kmap_q3xs[u];
+                if (grid_index < 0) {
+                    printf("Oops: found point %u not on grid:", u);
+                    for (int i = 0; i < 4; ++i) printf(" %d", L[4*k+i]);
+                    printf("\n");
+                    GGML_ASSERT(false);
+                }
+                qs[k] = grid_index & 255;
+                qh[(ib*bs4+k)/8] |= ((grid_index >> 8) << ((ib*bs4+k)%8));
+            }
+            qs += bs4;
+            for (int k = 0; k < bs8; ++k) signs[k] = block_signs[k];
+            signs += bs8;
+            GGML_ASSERT(scale >= 0);
+            scales[ib] = scale;
+            max_scale = MAX(max_scale, scale);
+        }
+
+        if (!max_scale) {
+            continue;
+        }
+
+        float d = max_scale/31;
+        y[ibl].d = GGML_FP32_TO_FP16(d * 1.033f);
+        float id = 1/d;
+        for (int ib = 0; ib < QK_K/block_size; ib += 2) {
+            int l1 = nearest_int(0.5f*(id*scales[ib+0]-1));
+            l1 = MAX(0, MIN(15, l1));
+            int l2 = nearest_int(0.5f*(id*scales[ib+1]-1));
+            l2 = MAX(0, MIN(15, l2));
+            y[ibl].scales[ib/2] = l1 | (l2 << 4);
+        }
+
+    }
+}
+
+#define IQ3S_BLOCK_SIZE 32
+size_t quantize_iq3_s(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
+    GGML_ASSERT(n_per_row%QK_K == 0);
+    int64_t nblock = n_per_row/QK_K;
+    float scales[QK_K/IQ3S_BLOCK_SIZE];
+    float weight[IQ3S_BLOCK_SIZE];
+    float xval[IQ3S_BLOCK_SIZE];
+    int8_t L[IQ3S_BLOCK_SIZE];
+    int8_t Laux[IQ3S_BLOCK_SIZE];
+    float  waux[IQ3S_BLOCK_SIZE];
+    bool   is_on_grid[IQ3S_BLOCK_SIZE/4];
+    bool   is_on_grid_aux[IQ3S_BLOCK_SIZE/4];
+    uint8_t block_signs[IQ3S_BLOCK_SIZE/8];
+    char * qrow = (char *)dst;
+    for (int64_t row = 0; row < nrow; ++row) {
+        quantize_row_iq3_s_impl(IQ3S_BLOCK_SIZE, src, qrow, n_per_row, quant_weights,
+                scales, weight, xval, L, Laux, waux, is_on_grid, is_on_grid_aux, block_signs);
+        src += n_per_row;
+        qrow += nblock*sizeof(block_iq3_s);
+    }
+    return nrow * nblock * sizeof(block_iq3_s);
+}
+
+void quantize_row_iq3_s(const float * restrict x, void * restrict vy, int64_t k) {
+    assert(k % QK_K == 0);
+    block_iq3_s * restrict y = vy;
+    quantize_row_iq3_s_reference(x, y, k);
+}
+
+void quantize_row_iq3_s_reference(const float * restrict x, block_iq3_s * restrict y, int64_t k) {
+    assert(k % QK_K == 0);
+    quantize_iq3_s(x, y, 1, k, NULL);
+}
+
+
 // =================================== 1.5 bpw ===================================================
 
 static int iq1_find_best_neighbour(const uint16_t * restrict neighbours, const uint64_t * restrict grid,
@@ -10588,13 +11470,80 @@ static int iq1_find_best_neighbour(const uint16_t * restrict neighbours, const u
     return grid_index;
 }
 
+static int iq1_find_best_neighbour2(const uint16_t * restrict neighbours, const uint64_t * restrict grid,
+        const float * restrict xval, const float * restrict weight, float scale, const float * restrict xg, int8_t * restrict L, int ngrid) {
+    int num_neighbors = neighbours[0];
+    GGML_ASSERT(num_neighbors > 0);
+    float best_score = FLT_MAX;
+    int grid_index = -1;
+    for (int j = 1; j <= num_neighbors; ++j) {
+        const int8_t * pg = (const int8_t *)(grid + neighbours[j]);
+        float d2 = 0;
+        for (int i = 0; i < 8; ++i) {
+            float q = xg[(pg[i] - 1)/2];
+            float w = weight[i];
+            float diff = scale*q - xval[i];
+            d2 += w*diff*diff;
+        }
+        if (d2 < best_score) {
+            best_score = d2;
+            grid_index = neighbours[j];
+        }
+    }
+    if (grid_index < 0) {
+        for (int i = 0; i < ngrid; ++i) {
+            const int8_t * grid_i = (const int8_t *)(grid + i);
+            float d2 = 0;
+            for (int j = 0; j < 8; ++j) {
+                float w = weight[j];
+                float q = xg[(grid_i[j] - 1)/2];
+                float diff = scale*q - xval[i];
+                d2 += w*diff*diff;
+            }
+            if (d2 < best_score) {
+                best_score = d2;
+                grid_index = i;
+            }
+        }
+    }
+    if (grid_index < 0) {
+        printf("Oops, did not find grid point\n");
+        printf("Have %d neighbours\n", num_neighbors);
+        for (int j = 1; j <= num_neighbors; ++j) {
+            const int8_t * pg = (const int8_t *)(grid + neighbours[j]);
+            float sumqx = 0, sumq2 = 0;
+            for (int i = 0; i < 8; ++i) {
+                float q = xg[(pg[i] - 1)/2];
+                float w = weight[i];
+                sumqx += w*q*xval[i];
+                sumq2 += w*q*q;
+            }
+            printf("    neighbour %d: sumqx = %g sumq2 = %g\n", j, (double)sumqx, (double)sumq2);
+        }
+    }
+    GGML_ASSERT(grid_index >= 0);
+    const int8_t * pg = (const int8_t *)(grid + grid_index);
+    for (int i = 0; i < 8; ++i) L[i] = (pg[i] - 1)/2;
+    return grid_index;
+}
+
 static int iq1_sort_helper(const void * left, const void * right) {
     const float * l = left;
     const float * r = right;
     return *l < *r ? -1 : *l > *r ? 1 : 0;
 }
 
-static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) {
+#define IQ1S_BLOCK_SIZE 32
+#define IQ1M_BLOCK_SIZE 16
+static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy, int64_t n, const float * restrict quant_weights,
+        float    * scales,
+        float    * weight,
+        float    * sumx,
+        float    * sumw,
+        float    * pairs,
+        int8_t   * L,
+        uint16_t * index,
+        int8_t   * shifts) {
 
     const int gindex = iq2_data_index(GGML_TYPE_IQ1_S);
 
@@ -10608,41 +11557,40 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
     GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?");
     GGML_ASSERT(n%QK_K == 0);
 
-    const int nbl = n/256;
-
     block_iq1_s * y = vy;
 
-    float  scales[QK_K/8];
-    float  weight[8];
-    int8_t L[8];
-    float  sumx[9];
-    float  sumw[9];
-    float  pairs[16];
+    const int64_t nbl = n/QK_K;
+
+    const int block_size = IQ1S_BLOCK_SIZE;
+
+    const float x_p[3] = {-1 + IQ1S_DELTA,  IQ1S_DELTA, 1 + IQ1S_DELTA};
+    const float x_m[3] = {-1 - IQ1S_DELTA, -IQ1S_DELTA, 1 - IQ1S_DELTA};
+
+
     int * idx = (int *)(pairs + 1);
-    uint8_t hbit[QK_K/8];
 
     for (int ibl = 0; ibl < nbl; ++ibl) {
 
         y[ibl].d = GGML_FP32_TO_FP16(0.f);
         memset(y[ibl].qs, 0, QK_K/8);
-        memset(y[ibl].scales, 0, QK_K/16);
+        memset(y[ibl].qh, 0, QK_K/16);
 
         float max_scale = 0;
 
         const float * xbl = x + QK_K*ibl;
         float sumx2 = 0;
         for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i];
-        float sigma2 = sumx2/QK_K;
+        float sigma2 = 2*sumx2/QK_K;
 
-        for (int ib = 0; ib < QK_K/8; ++ib) {
-            const float * xb = xbl + 8*ib;
-            const float * qw = quant_weights + QK_K*ibl + 8*ib;
-            for (int i = 0; i < 8; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
+        for (int ib = 0; ib < QK_K/block_size; ++ib) {
+            const float * xb = xbl + block_size*ib;
+            const float * qw = quant_weights + QK_K*ibl + block_size*ib;
+            for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
             float max = fabsf(xb[0]);
-            for (int i = 1; i < 8; ++i) max = MAX(max, fabsf(xb[i]));
+            for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i]));
             if (!max) {
                 scales[ib] = 0;
-                memset(L, 1, 8);
+                memset(L, 1, block_size);
                 continue;
             }
             // Here we solve exactly the sum of squared difference (SSD) weighted minimization problem.
@@ -10651,81 +11599,1095 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
             // in ascending order, compute Si = sum[weight[j] xb[j], j = 0...i] and
             // Wi = sum[weight[j], j = 0...i], and use these to quckly get get the optimum scale
             // for each possible and score for each split.
-            for (int j = 0; j < 8; ++j) {
+            for (int j = 0; j < block_size; ++j) {
                 pairs[2*j] = xb[j];
                 idx[2*j] = j;
             }
-            qsort(pairs, 8, 2*sizeof(float), iq1_sort_helper);
+            qsort(pairs, block_size, 2*sizeof(float), iq1_sort_helper);
             {
                 sumx[0] = sumw[0] = 0;
-                for (int j = 0; j < 8; ++j) {
+                for (int j = 0; j < block_size; ++j) {
                     int i = idx[2*j];
                     sumx[j+1] = sumx[j] + weight[i]*xb[i];
                     sumw[j+1] = sumw[j] + weight[i];
                 }
             }
             float best_score = 0, scale = max;
-            int besti1 = 0, besti2 = 0;
-            for (int i1 = 0; i1 <= 8; ++i1) {
-                for (int i2 = i1; i2 <= 8; ++i2) {
-                    float sumqx = -(sumx[i1] - sumx[0]) + (sumx[8] - sumx[i2]);
-                    float sumq2 =  (sumw[i1] - sumw[0]) + (sumw[8] - sumw[i2]);
+            int besti1 = -1, besti2 = -1, best_shift = 0;
+            for (int i1 = 0; i1 <= block_size; ++i1) {
+                for (int i2 = i1; i2 <= block_size; ++i2) {
+                    float sumqx = (sumx[i1] - sumx[0])*x_p[0] + (sumx[i2] - sumx[i1])*x_p[1] + (sumx[block_size] - sumx[i2])*x_p[2];
+                    float sumq2 = (sumw[i1] - sumw[0])*x_p[0]*x_p[0] + (sumw[i2] - sumw[i1])*x_p[1]*x_p[1] + (sumw[block_size] - sumw[i2])*x_p[2]*x_p[2];
                     if (sumq2 > 0 && sumqx*sumqx > best_score*sumq2) {
                         scale = sumqx/sumq2; best_score = scale*sumqx;
-                        besti1 = i1; besti2 = i2;
+                        besti1 = i1; besti2 = i2; best_shift = 1;
+                    }
+                    sumqx = (sumx[i1] - sumx[0])*x_m[0] + (sumx[i2] - sumx[i1])*x_m[1] + (sumx[block_size] - sumx[i2])*x_m[2];
+                    sumq2 = (sumw[i1] - sumw[0])*x_m[0]*x_m[0] + (sumw[i2] - sumw[i1])*x_m[1]*x_m[1] + (sumw[block_size] - sumw[i2])*x_m[2]*x_m[2];
+                    if (sumq2 > 0 && sumqx*sumqx > best_score*sumq2) {
+                        scale = sumqx/sumq2; best_score = scale*sumqx;
+                        besti1 = i1; besti2 = i2; best_shift = -1;
                     }
                 }
             }
+            GGML_ASSERT(besti1 >= 0 && besti2 >= 0 && best_shift != 0);
             for (int j =      0; j < besti1; ++j) L[idx[2*j]] = 0;
             for (int j = besti1; j < besti2; ++j) L[idx[2*j]] = 1;
-            for (int j = besti2; j <      8; ++j) L[idx[2*j]] = 2;
+            for (int j = besti2; j < block_size; ++j) L[idx[2*j]] = 2;
             if (scale < 0) {
-                for (int j = 0; j < 8; ++j) L[j] = 2 - L[j];
+                for (int j = 0; j < block_size; ++j) L[j] = 2 - L[j];
+                scale = -scale; best_shift = -best_shift;
+            }
+            bool all_on_grid = true;
+            const float * xx = best_shift == 1 ? x_p : x_m;
+            for (int k = 0; k < block_size/8; ++k) {
+                uint16_t u = 0;
+                for (int j = 0; j < 8; ++j) u |= (L[8*k+j] << 2*j);
+                int grid_index = kmap_q2xs[u];
+                if (grid_index < 0) {
+                    all_on_grid = false;
+                    const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1;
+                    grid_index = iq1_find_best_neighbour2(neighbours, kgrid_q2xs, xb + 8*k, weight + 8*k, scale, xx, L + 8*k, NGRID_IQ1S);
+                    GGML_ASSERT(grid_index >= 0);
+                }
+                index[k] = grid_index;
+            }
+            if (!all_on_grid) {
+                float sumqx = 0, sumq2 = 0;
+                for (int k = 0; k < block_size/8; ++k) {
+                    const int8_t * pg = (const int8_t *)(kgrid_q2xs + index[k]);
+                    for (int j = 0; j < 8; ++j) {
+                        float w = weight[8*k + j];
+                        float q = xx[(pg[j] - 1)/2];
+                        sumqx += w*q*xb[8*k+j];
+                        sumq2 += w*q*q;
+                    }
+                }
+                if (sumqx > 0 && sumq2 > 0) scale = sumqx/sumq2;
+            }
+            uint16_t h = 0;
+            for (int k = 0; k < block_size/8; ++k) {
+                y[ibl].qs[(block_size/8)*ib + k] = index[k] & 255;
+                h |= (index[k] >> 8) << 3*k;
+            }
+            y[ibl].qh[ib] = h;
+            GGML_ASSERT(scale >= 0);
+            scales[ib] = scale;
+            shifts[ib] = best_shift;
+            max_scale = MAX(max_scale, scale);
+        }
+
+        if (!max_scale) {
+            continue;
+        }
+
+        float d = max_scale/15;
+        y[ibl].d = GGML_FP32_TO_FP16(d*1.125f); // 1.125f is another fudge factor. Don't ask me why it is needed.
+        float id = 1/d;
+        for (int ib = 0; ib < QK_K/block_size; ++ib) {
+            int l = nearest_int(0.5f*(id*scales[ib]-1));
+            l = MAX(0, MIN(7, l));
+            if (shifts[ib] == -1) l |= 8;
+            y[ibl].qh[ib] |= (l << 12);
+        }
+    }
+}
+
+size_t quantize_iq1_s(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
+    GGML_ASSERT(n_per_row%QK_K == 0);
+    float  scales[QK_K/IQ1S_BLOCK_SIZE];
+    float  weight[IQ1S_BLOCK_SIZE];
+    int8_t L[IQ1S_BLOCK_SIZE];
+    float  sumx[IQ1S_BLOCK_SIZE+1];
+    float  sumw[IQ1S_BLOCK_SIZE+1];
+    float  pairs[2*IQ1S_BLOCK_SIZE];
+    uint16_t index[IQ1S_BLOCK_SIZE/8];
+    int8_t shifts[QK_K/IQ1S_BLOCK_SIZE];
+    int64_t nblock = n_per_row/QK_K;
+    char * qrow = (char *)dst;
+    for (int64_t row = 0; row < nrow; ++row) {
+        quantize_row_iq1_s_impl(src, qrow, n_per_row, quant_weights, scales, weight, sumx, sumw, pairs, L, index, shifts);
+        src += n_per_row;
+        qrow += nblock*sizeof(block_iq1_s);
+    }
+    return nrow * nblock * sizeof(block_iq1_s);
+}
+
+static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy, int64_t n, const float * restrict quant_weights,
+        float    * scales,
+        float    * weight,
+        float    * pairs,
+        int8_t   * L,
+        uint16_t * index,
+        int8_t   * shifts) {
+
+    const int gindex = iq2_data_index(GGML_TYPE_IQ1_M);
+
+    const uint64_t * kgrid_q2xs      = iq2_data[gindex].grid;
+    const int      * kmap_q2xs       = iq2_data[gindex].map;
+    const uint16_t * kneighbors_q2xs = iq2_data[gindex].neighbours;
+
+    //GGML_ASSERT(quant_weights   && "missing quantization weights");
+    GGML_ASSERT(kgrid_q2xs      && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(kmap_q2xs       && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(n%QK_K == 0);
+
+    block_iq1_m * y = vy;
+
+    const int64_t nbl = n/QK_K;
+
+    const int block_size = IQ1M_BLOCK_SIZE;
+
+    const float x_p[3] = {-1 + IQ1M_DELTA,  IQ1M_DELTA, 1 + IQ1M_DELTA};
+    const float x_m[3] = {-1 - IQ1M_DELTA, -IQ1M_DELTA, 1 - IQ1M_DELTA};
+    const uint8_t masks[4] = {0x00, 0x80, 0x08, 0x88};
+
+    int * idx = (int *)(pairs + 1);
+
+    float sumqx[4], sumq2[4];
+
+    iq1m_scale_t s;
+    const float * xx;
+
+    for (int ibl = 0; ibl < nbl; ++ibl) {
+
+#if QK_K == 64
+        y[ibl].d = GGML_FP32_TO_FP16(0.f);
+#endif
+        memset(y[ibl].qs, 0, QK_K/8);
+        memset(y[ibl].qh, 0, QK_K/16);
+        memset(y[ibl].scales, 0, QK_K/32);
+
+        float max_scale = 0;
+
+        const float * xbl = x + QK_K*ibl;
+        float sumx2 = 0;
+        for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i];
+        float sigma2 = 2*sumx2/QK_K;
+
+        for (int ib = 0; ib < QK_K/block_size; ++ib) {
+            const float * xb = xbl + block_size*ib;
+            if (quant_weights) {
+                const float * qw = quant_weights + QK_K*ibl + block_size*ib;
+                for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
+            } else {
+                for (int i = 0; i < block_size; ++i) weight[i] = xb[i]*xb[i];
+            }
+            float max = fabsf(xb[0]);
+            for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i]));
+            if (!max) {
+                scales[ib] = 0;
+                memset(L, 1, block_size);
+                continue;
+            }
+            // Here we solve exactly the sum of squared difference (SSD) weighted minimization problem.
+            // With just 3 allowed quant values (-1, 0, 1), we can search exhaustively for the two
+            // boundaries that split the weights xb[i] into 3 groups. To do so, we sort the weights
+            // in ascending order, compute Si = sum[weight[j] xb[j], j = 0...i] and
+            // Wi = sum[weight[j], j = 0...i], and use these to quckly get get the optimum scale
+            // for each possible and score for each split.
+            for (int j = 0; j < block_size; ++j) {
+                pairs[2*j] = xb[j];
+                idx[2*j] = j;
+            }
+            qsort(pairs, block_size, 2*sizeof(float), iq1_sort_helper);
+            float best_score = 0, scale = max;
+            int besti1 = -1, besti2 = -1, best_k = -1;
+            // 0: +, +
+            // 1: +, -
+            // 2: -, +
+            // 3: -, -
+            for (int i1 = 0; i1 <= block_size; ++i1) {
+                for (int i2 = i1; i2 <= block_size; ++i2) {
+                    memset(sumqx, 0, 4*sizeof(float));
+                    memset(sumq2, 0, 4*sizeof(float));
+                    for (int j = 0; j < i1; ++j) {
+                        int i = idx[2*j];
+                        if (i < block_size/2) {
+                            sumqx[0] += weight[i]*x_p[0]*xb[i];
+                            sumqx[1] += weight[i]*x_p[0]*xb[i];
+                            sumqx[2] += weight[i]*x_m[0]*xb[i];
+                            sumqx[3] += weight[i]*x_m[0]*xb[i];
+                            sumq2[0] += weight[i]*x_p[0]*x_p[0];
+                            sumq2[1] += weight[i]*x_p[0]*x_p[0];
+                            sumq2[2] += weight[i]*x_m[0]*x_m[0];
+                            sumq2[3] += weight[i]*x_m[0]*x_m[0];
+                        } else {
+                            sumqx[0] += weight[i]*x_p[0]*xb[i];
+                            sumqx[2] += weight[i]*x_p[0]*xb[i];
+                            sumqx[1] += weight[i]*x_m[0]*xb[i];
+                            sumqx[3] += weight[i]*x_m[0]*xb[i];
+                            sumq2[0] += weight[i]*x_p[0]*x_p[0];
+                            sumq2[2] += weight[i]*x_p[0]*x_p[0];
+                            sumq2[1] += weight[i]*x_m[0]*x_m[0];
+                            sumq2[3] += weight[i]*x_m[0]*x_m[0];
+                        }
+                    }
+                    for (int j = i1; j < i2; ++j) {
+                        int i = idx[2*j];
+                        if (i < block_size/2) {
+                            sumqx[0] += weight[i]*x_p[1]*xb[i];
+                            sumqx[1] += weight[i]*x_p[1]*xb[i];
+                            sumqx[2] += weight[i]*x_m[1]*xb[i];
+                            sumqx[3] += weight[i]*x_m[1]*xb[i];
+                            sumq2[0] += weight[i]*x_p[1]*x_p[1];
+                            sumq2[1] += weight[i]*x_p[1]*x_p[1];
+                            sumq2[2] += weight[i]*x_m[1]*x_m[1];
+                            sumq2[3] += weight[i]*x_m[1]*x_m[1];
+                        } else {
+                            sumqx[0] += weight[i]*x_p[1]*xb[i];
+                            sumqx[2] += weight[i]*x_p[1]*xb[i];
+                            sumqx[1] += weight[i]*x_m[1]*xb[i];
+                            sumqx[3] += weight[i]*x_m[1]*xb[i];
+                            sumq2[0] += weight[i]*x_p[1]*x_p[1];
+                            sumq2[2] += weight[i]*x_p[1]*x_p[1];
+                            sumq2[1] += weight[i]*x_m[1]*x_m[1];
+                            sumq2[3] += weight[i]*x_m[1]*x_m[1];
+                        }
+                    }
+                    for (int j = i2; j < block_size; ++j) {
+                        int i = idx[2*j];
+                        if (i < block_size/2) {
+                            sumqx[0] += weight[i]*x_p[2]*xb[i];
+                            sumqx[1] += weight[i]*x_p[2]*xb[i];
+                            sumqx[2] += weight[i]*x_m[2]*xb[i];
+                            sumqx[3] += weight[i]*x_m[2]*xb[i];
+                            sumq2[0] += weight[i]*x_p[2]*x_p[2];
+                            sumq2[1] += weight[i]*x_p[2]*x_p[2];
+                            sumq2[2] += weight[i]*x_m[2]*x_m[2];
+                            sumq2[3] += weight[i]*x_m[2]*x_m[2];
+                        } else {
+                            sumqx[0] += weight[i]*x_p[2]*xb[i];
+                            sumqx[2] += weight[i]*x_p[2]*xb[i];
+                            sumqx[1] += weight[i]*x_m[2]*xb[i];
+                            sumqx[3] += weight[i]*x_m[2]*xb[i];
+                            sumq2[0] += weight[i]*x_p[2]*x_p[2];
+                            sumq2[2] += weight[i]*x_p[2]*x_p[2];
+                            sumq2[1] += weight[i]*x_m[2]*x_m[2];
+                            sumq2[3] += weight[i]*x_m[2]*x_m[2];
+                        }
+                    }
+                    for (int k = 0; k < 4; ++k) {
+                        if (sumq2[k] > 0 && sumqx[k]*sumqx[k] > best_score*sumq2[k]) {
+                            scale = sumqx[k]/sumq2[k]; best_score = scale*sumqx[k];
+                            besti1 = i1; besti2 = i2; best_k = k;
+                        }
+                    }
+                }
+            }
+            GGML_ASSERT(besti1 >= 0 && besti2 >= 0 && best_k >= 0);
+            for (int j =      0; j < besti1; ++j) L[idx[2*j]] = 0;
+            for (int j = besti1; j < besti2; ++j) L[idx[2*j]] = 1;
+            for (int j = besti2; j < block_size; ++j) L[idx[2*j]] = 2;
+            if (scale < 0) {
+                for (int j = 0; j < block_size; ++j) L[j] = 2 - L[j];
                 scale = -scale;
+                best_k = best_k == 0 ? 3 : best_k == 1 ? 2 : best_k == 2 ? 1 : 0;
             }
-            // Now we check if the solution found above corresponds to a grid point and, if not, use a neighbouring
-            // grid point that minimizes SSD.
-            uint16_t u = 0;
-            for (int j = 0; j < 8; ++j) u |= (L[j] << 2*j);
-            int grid_index = kmap_q2xs[u];
-            if (grid_index < 0) {
-                const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1;
-                grid_index = iq1_find_best_neighbour(neighbours, kgrid_q2xs, xb, weight, &scale, L, NGRID_IQ2XXS);
-                GGML_ASSERT(grid_index >= 0);
+            bool all_on_grid = true;
+            for (int k = 0; k < block_size/8; ++k) {
+                if (k == 0) xx = best_k < 2 ? x_p : x_m;
+                else xx = best_k%2 == 0 ? x_p : x_m;
+                uint16_t u = 0;
+                for (int j = 0; j < 8; ++j) u |= (L[8*k+j] << 2*j);
+                int grid_index = kmap_q2xs[u];
+                if (grid_index < 0) {
+                    all_on_grid = false;
+                    const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1;
+                    grid_index = iq1_find_best_neighbour2(neighbours, kgrid_q2xs, xb + 8*k, weight + 8*k, scale, xx, L + 8*k, NGRID_IQ1S);
+                    GGML_ASSERT(grid_index >= 0);
+                }
+                index[k] = grid_index;
+            }
+            if (!all_on_grid) {
+                float sumqx_f = 0, sumq2_f = 0;
+                for (int k = 0; k < block_size/8; ++k) {
+                    if (k == 0) xx = best_k < 2 ? x_p : x_m;
+                    else xx = best_k%2 == 0 ? x_p : x_m;
+                    const int8_t * pg = (const int8_t *)(kgrid_q2xs + index[k]);
+                    for (int j = 0; j < 8; ++j) {
+                        float w = weight[8*k + j];
+                        float q = xx[(pg[j] - 1)/2];
+                        sumqx_f += w*q*xb[8*k+j];
+                        sumq2_f += w*q*q;
+                    }
+                }
+                if (sumqx_f > 0 && sumq2_f > 0) scale = sumqx_f/sumq2_f;
+            }
+            y[ibl].qs[2*ib + 0] = index[0] & 255;
+            y[ibl].qs[2*ib + 1] = index[1] & 255;
+            y[ibl].qh[ib] = (index[0] >> 8) | ((index[1] >> 8) << 4);
+            GGML_ASSERT(scale >= 0);
+            scales[ib] = scale;
+            shifts[ib] = best_k;
+            max_scale = MAX(max_scale, scale);
+        }
+
+        if (!max_scale) {
+            continue;
+        }
+
+        uint16_t * sc = (uint16_t *)y[ibl].scales;
+#if QK_K == 64
+        float d = max_scale/31;
+#else
+        float d = max_scale/15;
+#endif
+        float id = 1/d;
+        float sumqx_f = 0, sumq2_f = 0;
+        for (int ib = 0; ib < QK_K/block_size; ++ib) {
+            int l = nearest_int(0.5f*(id*scales[ib+0]-1));
+#if QK_K == 64
+            l = MAX(0, MIN(15, l));
+            sc[ib/4] |= (l << 4*(ib%4));
+#else
+            l = MAX(0, MIN(7, l));
+            sc[ib/4] |= (l << 3*(ib%4));
+#endif
+            y[ibl].qh[ib] |= masks[shifts[ib]];
+            const float * xb = xbl + block_size*ib;
+            if (quant_weights) {
+                const float * qw = quant_weights + QK_K*ibl + block_size*ib;
+                for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
+            } else {
+                for (int i = 0; i < block_size; ++i) weight[i] = xb[i]*xb[i];
+            }
+            for (int k = 0; k < block_size/8; ++k) {
+                if (k == 0) xx = shifts[ib] < 2 ? x_p : x_m;
+                else xx = shifts[ib]%2 == 0 ? x_p : x_m;
+                const int8_t * pg = (const int8_t *)(kgrid_q2xs + y[ibl].qs[2*ib+k] + ((y[ibl].qh[ib] << (8 - 4*k)) & 0x700));
+                for (int j = 0; j < 8; ++j) {
+                    float w = weight[8*k + j];
+                    float q = xx[(pg[j] - 1)/2]*(2*l+1);
+                    sumqx_f += w*q*xb[8*k+j];
+                    sumq2_f += w*q*q;
+                }
+            }
+        }
+        if (sumq2_f > 0) d = sumqx_f/sumq2_f;
+        s.f16 = GGML_FP32_TO_FP16(d*1.1125f); // 1.1125f is another fudge factor. Don't ask me why it is needed.
+#if QK_K == 64
+        y[ibl].d = s.f16;
+#else
+        sc[0] |= ((s.u16 & 0x000f) << 12);
+        sc[1] |= ((s.u16 & 0x00f0) <<  8);
+        sc[2] |= ((s.u16 & 0x0f00) <<  4);
+        sc[3] |= ((s.u16 & 0xf000) <<  0);
+#endif
+    }
+}
+
+size_t quantize_iq1_m(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
+    GGML_ASSERT(n_per_row%QK_K == 0);
+    float  scales[QK_K/IQ1M_BLOCK_SIZE];
+    float  weight[IQ1M_BLOCK_SIZE];
+    int8_t L[IQ1M_BLOCK_SIZE];
+    float  pairs[2*IQ1M_BLOCK_SIZE];
+    uint16_t index[IQ1M_BLOCK_SIZE/8];
+    int8_t shifts[QK_K/IQ1M_BLOCK_SIZE];
+    int64_t nblock = n_per_row/QK_K;
+    char * qrow = (char *)dst;
+    for (int64_t row = 0; row < nrow; ++row) {
+        quantize_row_iq1_m_impl(src, qrow, n_per_row, quant_weights, scales, weight, pairs, L, index, shifts);
+        src += n_per_row;
+        qrow += nblock*sizeof(block_iq1_m);
+    }
+    return nrow * nblock * sizeof(block_iq1_m);
+}
+
+// ============================ 4-bit non-linear quants
+
+static inline int best_index_int8(int n, const int8_t * val, float x) {
+    if (x <= val[0]) return 0;
+    if (x >= val[n-1]) return n-1;
+    int ml = 0, mu = n-1;
+    while (mu-ml > 1) {
+        int mav = (ml+mu)/2;
+        if (x < val[mav]) mu = mav; else ml = mav;
+    }
+    return x - val[mu-1] < val[mu] - x ? mu-1 : mu;
+}
+
+static void quantize_row_iq4_nl_impl(const int super_block_size, const int block_size, const float * restrict x,
+        ggml_fp16_t * dh, uint8_t * q4, uint16_t * scales_h, uint8_t * scales_l,
+        float * scales, float * weight, uint8_t * L,
+        const int8_t * values,
+        const float * quant_weights,
+        const int ntry) {
+
+    float sigma2 = 0;
+    for (int j = 0; j < super_block_size; ++j) sigma2 += x[j]*x[j];
+    sigma2 *= 2.f/super_block_size;
+
+    memset(q4, 0, super_block_size/2);
+    dh[0] = GGML_FP32_TO_FP16(0.f);
+
+    float max_scale = 0, amax_scale = 0;
+    for (int ib = 0; ib < super_block_size/block_size; ++ib) {
+        const float * xb = x + ib*block_size;
+        uint8_t * Lb = L + ib*block_size;
+        if (quant_weights) {
+            const float * qw = quant_weights + ib*block_size;
+            for (int j = 0; j < block_size; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]);
+        } else {
+            for (int j = 0; j < block_size; ++j) weight[j] = xb[j]*xb[j];
+        }
+        float amax = 0, max = 0;
+        for (int j = 0; j < block_size; ++j) {
+            float ax = fabsf(xb[j]);
+            if (ax > amax) {
+                amax = ax; max = xb[j];
+            }
+        }
+        if (!amax) {
+            scales[ib] = 0;
+            continue;
+        }
+        float d = ntry > 0 ? -max/values[0] : max/values[0];
+        float id = 1/d;
+        float sumqx = 0, sumq2 = 0;
+        for (int j = 0; j < block_size; ++j) {
+            float al = id*xb[j];
+            int l = best_index_int8(16, values, al);
+            Lb[j] = l;
+            float q = values[l];
+            float w = weight[j];
+            sumqx += w*q*xb[j];
+            sumq2 += w*q*q;
+        }
+        d = sumqx/sumq2;
+        float best = d*sumqx;
+        for (int itry = -ntry; itry <= ntry; ++itry) {
+            id = (itry + values[0])/max;
+            sumqx = sumq2 = 0;
+            for (int j = 0; j < block_size; ++j) {
+                float al = id*xb[j];
+                int l = best_index_int8(16, values, al);
+                float q = values[l];
+                float w = weight[j];
+                sumqx += w*q*xb[j];
+                sumq2 += w*q*q;
+            }
+            if (sumq2 > 0 && sumqx*sumqx > best*sumq2) {
+                d = sumqx/sumq2; best = d * sumqx;
+            }
+        }
+        scales[ib] = d;
+        float abs_d = fabsf(d);
+        if (abs_d > amax_scale) {
+            amax_scale = abs_d; max_scale = d;
+        }
+    }
+
+    if (super_block_size/block_size > 1) {
+        int nb = super_block_size/block_size;
+        memset(scales_h, 0, ((nb+7)/8)*sizeof(uint16_t));
+        float d = -max_scale/32;
+        dh[0] = GGML_FP32_TO_FP16(d);
+        float id = d ? 1/d : 0.f;
+        for (int ib = 0; ib < super_block_size/block_size; ++ib) {
+            int l = nearest_int(id*scales[ib]);
+            l = MAX(-32, MIN(31, l));
+            float dl = d * l;
+            float idl = dl ? 1/dl : 0.f;
+            uint8_t * Lb = L + ib*block_size;
+            const float * xb = x + ib*block_size;
+            for (int j = 0; j < block_size; ++j) {
+                Lb[j] = best_index_int8(16, values, idl*xb[j]);
+            }
+            l += 32;
+            uint8_t l_l = l & 0xf;
+            uint8_t l_h = l >>  4;
+            if (ib%2 == 0) scales_l[ib/2] = l_l;
+            else scales_l[ib/2] |= (l_l << 4);
+            scales_h[ib/8] |= (l_h << 2*(ib%8));
+        }
+    } else {
+        dh[0] = GGML_FP32_TO_FP16(scales[0]);
+        if (ntry > 0) {
+            float id = scales[0] ? 1/scales[0] : 0;
+            for (int j = 0; j < super_block_size; ++j) {
+                L[j] = best_index_int8(16, values, id*x[j]);
+            }
+        }
+    }
+
+    for (int i = 0; i < super_block_size/32; ++i) {
+        for (int j = 0; j < 16; ++j) {
+            q4[16*i + j] = L[32*i + j] | (L[32*i + 16 + j] << 4);
+        }
+    }
+}
+
+size_t quantize_iq4_nl(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
+    GGML_ASSERT(n_per_row%QK4_NL == 0);
+    int64_t nblock = n_per_row/QK4_NL;
+    char * qrow = (char *)dst;
+    uint8_t L[QK4_NL];
+    float weight[QK4_NL];
+    uint16_t unused_h;
+    uint8_t * unused_l = NULL;
+    float scale;
+    for (int64_t row = 0; row < nrow; ++row) {
+        block_iq4_nl * iq4 = (block_iq4_nl *)qrow;
+        for (int ibl = 0; ibl < nblock; ++ibl) {
+            const float * qw = quant_weights ? quant_weights + QK4_NL*ibl : NULL;
+            quantize_row_iq4_nl_impl(QK4_NL, 32, src + QK4_NL*ibl, &iq4[ibl].d, iq4[ibl].qs, &unused_h, unused_l,
+                    &scale, weight, L, kvalues_iq4nl, qw, 7);
+        }
+        src += n_per_row;
+        qrow += nblock*sizeof(block_iq4_nl);
+    }
+    return nrow * nblock * sizeof(block_iq4_nl);
+}
+
+void quantize_row_iq4_nl(const float * restrict x, void * restrict vy, int64_t k) {
+    GGML_ASSERT(k%QK4_NL == 0);
+    int64_t nblock = k/QK4_NL;
+    uint8_t L[QK4_NL];
+    float weight[QK4_NL];
+    uint16_t unused_h;
+    uint8_t * unused_l = NULL;
+    float scale;
+    block_iq4_nl * iq4 = (block_iq4_nl *)vy;
+    for (int ibl = 0; ibl < nblock; ++ibl) {
+        quantize_row_iq4_nl_impl(QK4_NL, 32, x + QK4_NL*ibl, &iq4[ibl].d, iq4[ibl].qs, &unused_h, unused_l,
+                &scale, weight, L, kvalues_iq4nl, NULL, -1);
+    }
+}
+
+void quantize_row_iq4_nl_reference(const float * restrict x, block_iq4_nl * restrict y, int64_t k) {
+    assert(k % QK4_NL == 0);
+    quantize_row_iq4_nl(x, y, k);
+}
+
+size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
+#if QK_K == 64
+    return quantize_iq4_nl(src, dst, nrow, n_per_row, quant_weights);
+#else
+    GGML_ASSERT(n_per_row%QK_K == 0);
+    int64_t nblock = n_per_row/QK_K;
+    char * qrow = (char *)dst;
+    uint8_t L[QK_K];
+    float weight[32];
+    float scales[QK_K/32];
+    for (int64_t row = 0; row < nrow; ++row) {
+        block_iq4_xs * iq4 = (block_iq4_xs *)qrow;
+        for (int ibl = 0; ibl < nblock; ++ibl) {
+            const float * qw = quant_weights ? quant_weights + QK_K*ibl : NULL;
+            quantize_row_iq4_nl_impl(QK_K, 32, src + QK_K*ibl, &iq4[ibl].d, iq4[ibl].qs, &iq4[ibl].scales_h, iq4[ibl].scales_l,
+                    scales, weight, L, kvalues_iq4nl, qw, 7);
+        }
+        src += n_per_row;
+        qrow += nblock*sizeof(block_iq4_xs);
+    }
+    return nrow * nblock * sizeof(block_iq4_xs);
+#endif
+}
+
+void quantize_row_iq4_xs(const float * restrict x, void * restrict vy, int64_t k) {
+    assert(k % QK_K == 0);
+    block_iq4_xs * restrict y = vy;
+    quantize_row_iq4_xs_reference(x, y, k);
+}
+
+void quantize_row_iq4_xs_reference(const float * restrict x, block_iq4_xs * restrict y, int64_t k) {
+    assert(k % QK_K == 0);
+    quantize_iq4_xs(x, y, 1, k, NULL);
+}
+
+// =============================== 2.5625 bpw
+
+static void quantize_row_iq2_s_impl(const float * restrict x, void * restrict vy, int64_t n, const float * restrict quant_weights) {
+
+    const int gindex = iq2_data_index(GGML_TYPE_IQ2_S);
+
+    const uint64_t * kgrid_q2xs      = iq2_data[gindex].grid;
+    const int      * kmap_q2xs       = iq2_data[gindex].map;
+    const uint16_t * kneighbors_q2xs = iq2_data[gindex].neighbours;
+
+    GGML_ASSERT(kmap_q2xs       && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(kgrid_q2xs      && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(n%QK_K == 0);
+
+    const int kMaxQ = 3;
+
+    const int64_t nbl = n/QK_K;
+
+    block_iq2_s * y = vy;
+
+    float scales[QK_K/16];
+    float weight[16];
+    float xval[16];
+    int8_t L[16];
+    int8_t Laux[16];
+    float  waux[16];
+    bool   is_on_grid[2];
+    bool   is_on_grid_aux[2];
+    uint8_t block_signs[2];
+
+    for (int ibl = 0; ibl < nbl; ++ibl) {
+
+        memset(&y[ibl], 0, sizeof(block_iq2_s));
+        y[ibl].d = GGML_FP32_TO_FP16(0.f);
+
+        float max_scale = 0;
+
+        const float * xbl = x + QK_K*ibl;
+        float sumx2 = 0;
+        for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i];
+        float sigma2 = 2*sumx2/QK_K;
+
+        for (int ib = 0; ib < QK_K/16; ++ib) {
+            const float * xb = xbl + 16*ib;
+            if (quant_weights) {
+                const float * qw = quant_weights + QK_K*ibl + 16*ib;
+                for (int i = 0; i < 16; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
+            } else {
+                for (int i = 0; i < 16; ++i) weight[i] = 0.25f*sigma2 + xb[i]*xb[i];
+            }
+            for (int i = 0; i < 16; ++i) waux[i] = sqrtf(weight[i]);
+            for (int k = 0; k < 2; ++k) {
+                uint8_t s = 0;
+                for (int i = 0; i < 8; ++i) {
+                    if (xb[8*k + i] >= 0) xval[8*k + i] = xb[8*k + i];
+                    else {
+                        xval[8*k + i] = -xb[8*k + i]; s |= (1 << i);
+                    }
+                }
+                block_signs[k] = s;
+            }
+            float max = xval[0];
+            for (int i = 1; i < 16; ++i) max = MAX(max, xval[i]);
+            if (!max) {
+                scales[ib] = 0;
+                continue;
+            }
+            float best = 0;
+            float scale = max/(2*kMaxQ-1);
+            is_on_grid[0] = is_on_grid[1] = true;
+            for (int is = -9; is <= 9; ++is) {
+                float id = (2*kMaxQ-1+is*0.1f)/max;
+                float this_scale = 1/id;
+                for (int k = 0; k < 2; ++k) {
+                    for (int i = 0; i < 8; ++i) {
+                        int l = nearest_int(0.5f*(id*xval[8*k+i]-1));
+                        Laux[8*k+i] = MAX(0, MIN(kMaxQ-1, l));
+                    }
+                    uint16_t u = 0;
+                    for (int i = 0; i < 8; ++i) u |= (Laux[8*k+i] << 2*i);
+                    int grid_index = kmap_q2xs[u];
+                    is_on_grid_aux[k] = true;
+                    if (grid_index < 0) {
+                        is_on_grid_aux[k] = false;
+                        const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1;
+                        grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, this_scale, Laux + 8*k);
+                    }
+                }
+                float sumqx = 0, sumq2 = 0;
+                for (int i = 0; i < 16; ++i) {
+                    float w = weight[i];
+                    float q = 2*Laux[i] + 1;
+                    sumqx += w*xval[i]*q;
+                    sumq2 += w*q*q;
+                }
+                if (sumq2 > 0 && sumqx*sumqx > best*sumq2) {
+                    scale = sumqx/sumq2; best = scale*sumqx;
+                    for (int i = 0; i < 16; ++i) L[i] = Laux[i];
+                    for (int k = 0; k <  2; ++k) is_on_grid[k] = is_on_grid_aux[k];
+                }
+            }
+            int n_not_ongrid = 0;
+            for (int k = 0; k < 2; ++k) if (!is_on_grid[k]) ++n_not_ongrid;
+            if (n_not_ongrid > 0 && scale > 0) {
+                float id = 1/scale;
+                for (int k = 0; k < 2; ++k) {
+                    if (is_on_grid[k]) continue;
+                    uint16_t u = 0;
+                    for (int i = 0; i < 8; ++i) {
+                        int l = nearest_int(0.5f*(id*xval[8*k+i]-1));
+                        l = MAX(0, MIN(kMaxQ-1, l));
+                        u |= (l << 2*i);
+                        L[8*k + i] = l;
+                    }
+                    int grid_index = kmap_q2xs[u];
+                    if (grid_index < 0) {
+                        const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1;
+                        grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, scale, L + 8*k);
+                    }
+                }
+                float sumqx = 0, sumq2 = 0;
+                for (int i = 0; i < 16; ++i) {
+                    float w = weight[i];
+                    float q = 2*L[i] + 1;
+                    sumqx += w*xval[i]*q;
+                    sumq2 += w*q*q;
+                }
+                if (sumq2 > 0) scale = sumqx/sumq2;
+            }
+            if (scale < 0) {
+                scale = -scale;
+                for (int k = 0; k < 2; ++k) block_signs[k] = ~block_signs[k];
+            }
+            for (int k = 0; k < 2; ++k) {
+                uint16_t u = 0;
+                for (int i = 0; i < 8; ++i) u |= (L[8*k+i] << 2*i);
+                int grid_index = kmap_q2xs[u];
+                if (grid_index < 0) {
+                    printf("Oops: found point %u not on grid:", u);
+                    for (int i = 0; i < 8; ++i) printf(" %d", L[8*k+i]);
+                    printf("\n");
+                    GGML_ASSERT(false);
+                }
+                const int i8 = 2*ib + k;
+                y[ibl].qs[i8] = grid_index & 255;
+                y[ibl].qh[i8/4] |= ((grid_index >> 8) << 2*(i8%4));
+                y[ibl].qs[QK_K/8 + i8] = block_signs[k];
             }
-            y[ibl].qs[ib] = grid_index & 255;
-            hbit[ib] = grid_index >> 8;
             GGML_ASSERT(scale >= 0);
             scales[ib] = scale;
             max_scale = MAX(max_scale, scale);
         }
 
         if (!max_scale) {
-            memset(y[ibl].qs, 0, QK_K/8);
             continue;
         }
 
-        float d = max_scale/15;
-        y[ibl].d = GGML_FP32_TO_FP16(d*1.085f); // 1.085f is another fudge factor. Don't ask me why it is needed.
+        float d = max_scale/31;
+        y[ibl].d = GGML_FP32_TO_FP16(d * 0.9875f);
         float id = 1/d;
-        for (int ib = 0; ib < QK_K/8; ++ib) {
+        for (int ib = 0; ib < QK_K/16; ++ib) {
             int l = nearest_int(0.5f*(id*scales[ib]-1));
-            l = MAX(0, MIN(7, l));
-            if (hbit[ib]) l |= 8;
-            y[ibl].scales[ib/2] |= (l << 4*(ib%2));
+            l = MAX(0, MIN(15, l));
+            if (ib%2 == 0) y[ibl].scales[ib/2] = l;
+            else y[ibl].scales[ib/2] |= (l << 4);
         }
     }
 }
 
-size_t quantize_iq1_s(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {
-    (void)hist;
+size_t quantize_iq2_s(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     GGML_ASSERT(n_per_row%QK_K == 0);
-    int nblock = n_per_row/QK_K;
+    int64_t nblock = n_per_row/QK_K;
     char * qrow = (char *)dst;
-    for (int row = 0; row < nrow; ++row) {
-        quantize_row_iq1_s_impl(src, qrow, n_per_row, quant_weights);
+    for (int64_t row = 0; row < nrow; ++row) {
+        quantize_row_iq2_s_impl(src, qrow, n_per_row, quant_weights);
         src += n_per_row;
-        qrow += nblock*sizeof(block_iq1_s);
+        qrow += nblock*sizeof(block_iq2_s);
     }
-    return nrow * nblock * sizeof(block_iq1_s);
+    return nrow * nblock * sizeof(block_iq2_s);
+}
+
+void quantize_row_iq2_s_reference(const float * restrict x, block_iq2_s * restrict y, int64_t k) {
+    assert(k % QK_K == 0);
+    quantize_iq2_s(x, y, 1, k, NULL);
+}
+
+void quantize_row_iq2_s(const float * restrict x, void * restrict vy, int64_t k) {
+    assert(k % QK_K == 0);
+    block_iq2_s * restrict y = vy;
+    quantize_row_iq2_s_reference(x, y, k);
+}
+
+static bool validate_float(float f, size_t i) {
+    if (isinf(f)) {
+        fprintf(stderr, "ggml_validate_row_data: found inf value at block %zu\n", i);
+        return false;
+    }
+
+    if (isnan(f)) {
+        fprintf(stderr, "ggml_validate_row_data: found nan value at block %zu\n", i);
+        return false;
+    }
+
+    return true;
+}
+
+static bool isinf_fp16(ggml_fp16_t f) {
+    return (f & 0x7c00) == 0x7c00 && (f & 0x03ff) == 0;
+}
+
+static bool isnan_fp16(ggml_fp16_t f) {
+    return (f & 0x7c00) == 0x7c00 && (f & 0x03ff) != 0;
+}
+
+static bool validate_fp16(ggml_fp16_t f, size_t i) {
+    if (isinf_fp16(f)) {
+        fprintf(stderr, "ggml_validate_row_data: found inf value at block %zu\n", i);
+        return false;
+    }
+
+    if (isnan_fp16(f)) {
+        fprintf(stderr, "ggml_validate_row_data: found nan value at block %zu\n", i);
+        return false;
+    }
+
+    return true;
+}
+
+#define VALIDATE_ROW_DATA_D_F16_IMPL(type, data, nb) \
+    const type * q = (const type *) (data); \
+    for (size_t i = 0; i < (nb); ++i) { \
+        if (!validate_fp16(q[i].d, i)) { \
+            return false; \
+        } \
+    }
+
+#define VALIDATE_ROW_DATA_DM_F16_IMPL(type, data, nb, d, m) \
+    const type * q = (const type *) (data); \
+    for (size_t i = 0; i < (nb); ++i) { \
+        if (!validate_fp16(q[i].d, i) || !validate_fp16(q[i].m, i)) { \
+            return false; \
+        } \
+    }
+
+bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbytes) {
+    if (type < 0 || type >= GGML_TYPE_COUNT) {
+        fprintf(stderr, "%s: invalid type %d\n", __func__, type);
+        return false;
+    }
+
+    if (nbytes % ggml_type_size(type) != 0) {
+        fprintf(stderr, "%s: invalid size %zu for type %d\n", __func__, nbytes, type);
+        return false;
+    }
+
+    const size_t nb = nbytes/ggml_type_size(type);
+
+    switch (type) {
+        case GGML_TYPE_BF16:
+            {
+                int nans = 0;
+                int infs = 0;
+                const unsigned short * f = (const unsigned short *) data;
+                for (size_t i = 0; i < nb; ++i) {
+                    nans += (f[i] & 0x7fff) > 0x7f80;
+                    infs += (f[i] & 0x7fff) == 0x7f80;
+                }
+                if (nans) {
+                    fprintf(stderr, "%s: found %d NaNs in row of %zu BF16 values\n", __func__, nans, nb);
+                    return false;
+                }
+                if (infs) {
+                    fprintf(stderr, "%s: found %d infinities in row of %zu BF16 values\n", __func__, infs, nb);
+                    return false;
+                }
+            } break;
+        case GGML_TYPE_F16:
+            {
+                const ggml_fp16_t * f = (const ggml_fp16_t *) data;
+                size_t i = 0;
+#if defined(__AVX2__)
+                for (; i + 15 < nb; i += 16) {
+                    __m256i v = _mm256_loadu_si256((const __m256i *)(f + i));
+                    __m256i vexp = _mm256_and_si256(v, _mm256_set1_epi16(0x7c00));
+                    __m256i cmp = _mm256_cmpeq_epi16(vexp, _mm256_set1_epi16(0x7c00));
+                    int mask = _mm256_movemask_epi8(cmp);
+                    if (mask) {
+                        for (size_t j = 0; j < 16; ++j) {
+                            if (!validate_fp16(f[i + j], i + j)) {
+                                return false;
+                            }
+                        }
+                        GGML_UNREACHABLE();
+                    }
+                }
+#elif defined(__ARM_NEON)
+                for (; i + 7 < nb; i += 8) {
+                    uint16x8_t v = vld1q_u16(f + i);
+                    uint16x8_t vexp = vandq_u16(v, vdupq_n_u16(0x7c00));
+                    uint16x8_t cmp = vceqq_u16(vexp, vdupq_n_u16(0x7c00));
+                    uint64_t mask = vget_lane_u64(vreinterpret_u64_u8(vshrn_n_u16(cmp, 4)), 0);
+                    if (mask) {
+                        for (size_t j = 0; j < 8; ++j) {
+                            if (!validate_fp16(f[i + j], i + j)) {
+                                return false;
+                            }
+                        }
+                        GGML_UNREACHABLE();
+                    }
+                }
+#endif
+                for (; i < nb; ++i) {
+                    if (!validate_fp16(f[i], i)) {
+                        return false;
+                    }
+                }
+            } break;
+        case GGML_TYPE_F32:
+            {
+                const float * f = (const float *) data;
+                size_t i = 0;
+#if defined(__AVX2__)
+                for (; i + 7 < nb; i += 8) {
+                    __m256i v = _mm256_loadu_si256((const __m256i *)(f + i));
+                    __m256i vexp = _mm256_and_si256(v, _mm256_set1_epi32(0x7f800000));
+                    __m256i cmp = _mm256_cmpeq_epi32(vexp, _mm256_set1_epi32(0x7f800000));
+                    int mask = _mm256_movemask_epi8(cmp);
+                    if (mask) {
+                        for (size_t j = 0; j < 8; ++j) {
+                            if (!validate_float(f[i + j], i + j)) {
+                                return false;
+                            }
+                        }
+                        GGML_UNREACHABLE();
+                    }
+                }
+#elif defined(__ARM_NEON)
+                for (; i + 3 < nb; i += 4) {
+                    uint32x4_t v = vld1q_u32((const uint32_t *)f + i);
+                    uint32x4_t vexp = vandq_u32(v, vdupq_n_u32(0x7f800000));
+                    uint32x4_t cmp = vceqq_u32(vexp, vdupq_n_u32(0x7f800000));
+                    uint64_t mask = vget_lane_u64(vreinterpret_u64_u16(vshrn_n_u32(cmp, 8)), 0);
+                    if (mask) {
+                        for (size_t j = 0; j < 4; ++j) {
+                            if (!validate_float(f[i + j], i + j)) {
+                                return false;
+                            }
+                        }
+                        GGML_UNREACHABLE();
+                    }
+                }
+#endif
+                for (; i < nb; ++i) {
+                    if (!validate_float(f[i], i)) {
+                        return false;
+                    }
+                }
+            } break;
+        case GGML_TYPE_F64:
+            {
+                const double * f = (const double *) data;
+                for (size_t i = 0; i < nb; ++i) {
+                    if (!validate_float(f[i], i)) {
+                        return false;
+                    }
+                }
+            } break;
+        case GGML_TYPE_Q4_0:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_q4_0, data, nb);
+            } break;
+        case GGML_TYPE_Q4_1:
+            {
+                VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_1, data, nb, d, m);
+            } break;
+        case GGML_TYPE_Q5_0:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_q5_0, data, nb);
+            } break;
+        case GGML_TYPE_Q5_1:
+            {
+                VALIDATE_ROW_DATA_DM_F16_IMPL(block_q5_1, data, nb, d, m);
+            } break;
+        case GGML_TYPE_Q8_0:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_q8_0, data, nb);
+            } break;
+        case GGML_TYPE_Q2_K:
+            {
+                VALIDATE_ROW_DATA_DM_F16_IMPL(block_q2_K, data, nb, d, dmin);
+            } break;
+        case GGML_TYPE_Q3_K:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_q3_K, data, nb);
+            } break;
+        case GGML_TYPE_Q4_K:
+            {
+            #ifdef GGML_QKK_64
+                VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d[0], d[1]);
+            #else
+                VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d, dmin);
+            #endif
+            } break;
+        case GGML_TYPE_Q5_K:
+            {
+            #ifdef GGML_QKK_64
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_q5_K, data, nb);
+            #else
+                VALIDATE_ROW_DATA_DM_F16_IMPL(block_q5_K, data, nb, d, dmin);
+            #endif
+            } break;
+        case GGML_TYPE_Q6_K:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_q6_K, data, nb);
+            } break;
+        case GGML_TYPE_Q8_K:
+            {
+                const block_q8_K * q = (const block_q8_K *) data;
+                for (size_t i = 0; i < nb; ++i) {
+                    if (!validate_float(q[i].d, i)) {
+                        return false;
+                    }
+                }
+            } break;
+        case GGML_TYPE_IQ1_S:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq1_s, data, nb);
+            } break;
+        case GGML_TYPE_IQ1_M:
+            {
+                const block_iq1_m * q = (const block_iq1_m *) data;
+                for (size_t i = 0; i < nb; ++i) {
+                #if QK_K == 64
+                    if (!validate_fp16(q[i].d, i)) {
+                        return false;
+                    }
+                #else
+                    iq1m_scale_t scale;
+                    const uint16_t * sc = (const uint16_t *)q[i].scales;
+                    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+                    if (!validate_fp16(scale.f16, i)) {
+                        return false;
+                    }
+                #endif
+                }
+            } break;
+        case GGML_TYPE_IQ2_XXS:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_xxs, data, nb);
+            } break;
+        case GGML_TYPE_IQ2_XS:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_xs, data, nb);
+            } break;
+        case GGML_TYPE_IQ2_S:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_s, data, nb);
+            } break;
+        case GGML_TYPE_IQ3_XXS:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq3_xxs, data, nb);
+            } break;
+
+        case GGML_TYPE_IQ3_S:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq3_s, data, nb);
+            } break;
+        case GGML_TYPE_IQ4_XS:
+        #if QK_K != 64
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_xs, data, nb);
+            } break;
+        #endif
+        // with QK_K == 64, iq4_xs is iq4_nl
+        case GGML_TYPE_IQ4_NL:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_nl, data, nb);
+            } break;
+        case GGML_TYPE_I8:
+        case GGML_TYPE_I16:
+        case GGML_TYPE_I32:
+        case GGML_TYPE_I64:
+            // nothing to validate
+            break;
+        default:
+            {
+                fprintf(stderr, "%s: invalid type %d\n", __func__, type);
+                return false;
+            }
+    }
+
+    return true;
 }
diff --git a/ggml-quants.h b/ggml-quants.h
index ad381cfab..4d436a8f0 100644
--- a/ggml-quants.h
+++ b/ggml-quants.h
@@ -1,256 +1,81 @@
 #pragma once
 
-#include "ggml-impl.h"
+#define GGML_COMMON_DECL_C
+#include "ggml-common.h"
+
+#include "ggml.h"
 
 // GGML internal header
 
-#include <stdint.h>
-#include <stddef.h>
-
-#define QK4_0 32
-typedef struct {
-    ggml_fp16_t d;          // delta
-    uint8_t qs[QK4_0 / 2];  // nibbles / quants
-} block_q4_0;
-static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
-
-#define QK4_1 32
-typedef struct {
-    ggml_fp16_t d;          // delta
-    ggml_fp16_t m;          // min
-    uint8_t qs[QK4_1 / 2];  // nibbles / quants
-} block_q4_1;
-static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_fp16_t) + QK4_1 / 2, "wrong q4_1 block size/padding");
-
-#define QK5_0 32
-typedef struct {
-    ggml_fp16_t d;         // delta
-    uint8_t qh[4];         // 5-th bit of quants
-    uint8_t qs[QK5_0 / 2]; // nibbles / quants
-} block_q5_0;
-static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
-
-#define QK5_1 32
-typedef struct {
-    ggml_fp16_t d;         // delta
-    ggml_fp16_t m;         // min
-    uint8_t qh[4];         // 5-th bit of quants
-    uint8_t qs[QK5_1 / 2]; // nibbles / quants
-} block_q5_1;
-static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
-
-#define QK8_0 32
-typedef struct {
-    ggml_fp16_t d;         // delta
-    int8_t  qs[QK8_0];     // quants
-} block_q8_0;
-static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
-
-#define QK8_1 32
-typedef struct {
-    float d;               // delta
-    float s;               // d * sum(qs[i])
-    int8_t  qs[QK8_1];     // quants
-} block_q8_1;
-static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block size/padding");
-
-//
-// Super-block quantization structures
-//
-
-// Super-block size
-#ifdef GGML_QKK_64
-#define QK_K 64
-#define K_SCALE_SIZE 4
-#else
-#define QK_K 256
-#define K_SCALE_SIZE 12
-#endif
-
-// 2-bit quantization
-// weight is represented as x = a * q + b
-// 16 blocks of 16 elements each
-// Effectively 2.625 bits per weight
-typedef struct {
-    uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
-    uint8_t qs[QK_K/4];      // quants
-    ggml_fp16_t d;           // super-block scale for quantized scales
-    ggml_fp16_t dmin;        // super-block scale for quantized mins
-} block_q2_K;
-static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
-
-// 3-bit quantization
-// weight is represented as x = a * q
-// 16 blocks of 16 elements each
-// Effectively 3.4375 bits per weight
-#ifdef GGML_QKK_64
-typedef struct {
-    uint8_t hmask[QK_K/8];     // quants - high bit
-    uint8_t qs[QK_K/4];        // quants - low 2 bits
-    uint8_t scales[2];
-    ggml_fp16_t d;             // super-block scale
-} block_q3_K;
-static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding");
-#else
-typedef struct {
-    uint8_t hmask[QK_K/8];     // quants - high bit
-    uint8_t qs[QK_K/4];        // quants - low 2 bits
-    uint8_t scales[12];        // scales, quantized with 6 bits
-    ggml_fp16_t d;             // super-block scale
-} block_q3_K;
-static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding");
-#endif
-
-// 4-bit quantization
-// 8 blocks of 32 elements each
-// weight is represented as x = a * q + b
-// Effectively 4.5 bits per weight
-#ifdef GGML_QKK_64
-typedef struct {
-    ggml_fp16_t d[2];          // super-block scales/mins
-    uint8_t scales[2];         // 4-bit block scales/mins
-    uint8_t qs[QK_K/2];        // 4--bit quants
-} block_q4_K;
-static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding");
-#else
-typedef struct {
-    ggml_fp16_t d;             // super-block scale for quantized scales
-    ggml_fp16_t dmin;          // super-block scale for quantized mins
-    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
-    uint8_t qs[QK_K/2];        // 4--bit quants
-} block_q4_K;
-static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding");
-#endif
-
-// 5-bit quantization
-// 8 blocks of 32 elements each
-// weight is represented as x = a * q + b
-// Effectively 5.5 bits per weight
-#ifdef GGML_QKK_64
-typedef struct {
-    ggml_fp16_t d;               // super-block scale
-    int8_t  scales[QK_K/16];     // 8-bit block scales
-    uint8_t qh[QK_K/8];          // quants, high bit
-    uint8_t qs[QK_K/2];          // quants, low 4 bits
-} block_q5_K;
-static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
-#else
-typedef struct {
-    ggml_fp16_t d;               // super-block scale for quantized scales
-    ggml_fp16_t dmin;            // super-block scale for quantized mins
-    uint8_t scales[K_SCALE_SIZE];   // scales and mins, quantized with 6 bits
-    uint8_t qh[QK_K/8];          // quants, high bit
-    uint8_t qs[QK_K/2];          // quants, low 4 bits
-} block_q5_K;
-static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
-#endif
-
-// 6-bit quantization
-// weight is represented as x = a * q
-// 16 blocks of 16 elements each
-// Effectively 6.5625 bits per weight
-typedef struct {
-    uint8_t ql[QK_K/2];      // quants, lower 4 bits
-    uint8_t qh[QK_K/4];      // quants, upper 2 bits
-    int8_t  scales[QK_K/16]; // scales, quantized with 8 bits
-    ggml_fp16_t d;           // super-block scale
-} block_q6_K;
-static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + QK_K / 16 + 3*QK_K/4, "wrong q6_K block size/padding");
-
-// This is only used for intermediate quantization and dot products
-typedef struct {
-    float   d;              // delta
-    int8_t  qs[QK_K];       // quants
-    int16_t bsums[QK_K/16]; // sum of quants in groups of 16
-} block_q8_K;
-static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_K block size/padding");
-
-// (Almost) "true" 2-bit quantization.
-// Due to the need to use blocks as per ggml design, it ends up using
-// 2.0625 bpw because of the 16-bit scale for each block of 256.
-typedef struct {
-    ggml_fp16_t d;
-    uint16_t qs[QK_K/8];
-} block_iq2_xxs;
-static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t), "wrong iq2_xxs block size/padding");
-
-// 2.3125 bpw quants
-typedef struct {
-    ggml_fp16_t d;
-    uint16_t qs[QK_K/8];
-    uint8_t  scales[QK_K/32];
-} block_iq2_xs;
-static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding");
-
-// (Almost) "true" 3-bit quantization.
-// Due to the need to use blocks as per ggml design, it ends up using
-// 3.0625 bpw because of the 16-bit scale for each block of 256.
-typedef struct {
-    ggml_fp16_t d;
-    uint8_t qs[3*QK_K/8];
-} block_iq3_xxs;
-static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding");
-
-typedef struct {
-    ggml_fp16_t d;
-    uint8_t qs[QK_K/8];
-    uint8_t scales[QK_K/16];
-} block_iq1_s;
-static_assert(sizeof(block_iq1_s) == sizeof(ggml_fp16_t) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding");
-
 #ifdef __cplusplus
 extern "C" {
 #endif
 
 // Quantization
-void quantize_row_q4_0_reference(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int k);
-void quantize_row_q4_1_reference(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int k);
-void quantize_row_q5_0_reference(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int k);
-void quantize_row_q5_1_reference(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int k);
-void quantize_row_q8_0_reference(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int k);
-void quantize_row_q8_1_reference(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int k);
+void quantize_row_q4_0_reference(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int64_t k);
+void quantize_row_q4_1_reference(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int64_t k);
+void quantize_row_q5_0_reference(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int64_t k);
+void quantize_row_q5_1_reference(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int64_t k);
+void quantize_row_q8_0_reference(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int64_t k);
+void quantize_row_q8_1_reference(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int64_t k);
 
-void quantize_row_q2_K_reference(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int k);
-void quantize_row_q3_K_reference(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int k);
-void quantize_row_q4_K_reference(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int k);
-void quantize_row_q5_K_reference(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int k);
-void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int k);
-void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int k);
-void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int k);
+void quantize_row_q2_K_reference(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int64_t k);
+void quantize_row_q3_K_reference(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int64_t k);
+void quantize_row_q4_K_reference(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int64_t k);
+void quantize_row_q5_K_reference(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int64_t k);
+void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int64_t k);
+void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int64_t k);
 
-void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
-void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
-void quantize_row_q5_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
-void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
-void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
-void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq4_nl_reference (const float * GGML_RESTRICT x, block_iq4_nl  * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq4_xs_reference (const float * GGML_RESTRICT x, block_iq4_xs  * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq3_s_reference  (const float * GGML_RESTRICT x, block_iq3_s   * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq2_s_reference  (const float * GGML_RESTRICT x, block_iq2_s   * GGML_RESTRICT y, int64_t k);
 
-void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
-void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
-void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
-void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
-void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
-void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
-void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q5_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+
+void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+
+void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq3_s  (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq2_s  (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 
 // Dequantization
-void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
-void dequantize_row_q4_1(const block_q4_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
-void dequantize_row_q5_0(const block_q5_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
-void dequantize_row_q5_1(const block_q5_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
-void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
-//void dequantize_row_q8_1(const block_q8_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_q4_1(const block_q4_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_q5_0(const block_q5_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_q5_1(const block_q5_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+//void dequantize_row_q8_1(const block_q8_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
 
-void dequantize_row_q2_K(const block_q2_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
-void dequantize_row_q3_K(const block_q3_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
-void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
-void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
-void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
-void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
-void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
-void dequantize_row_iq2_xs (const block_iq2_xs  * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
-void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
-void dequantize_row_iq1_s  (const block_iq1_s   * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_q2_K(const block_q2_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_q3_K(const block_q3_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+
+void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_iq2_xs (const block_iq2_xs  * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_iq2_s  (const block_iq2_s   * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_iq1_s  (const block_iq1_s   * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_iq1_m  (const block_iq1_m   * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_iq4_nl (const block_iq4_nl  * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_iq4_xs (const block_iq4_xs  * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_iq3_s  (const block_iq3_s   * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
 
 // Dot product
 void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
@@ -264,27 +89,38 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
 void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+
 void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq2_s_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_iq1_s_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq1_m_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq4_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq3_s_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 
-//
 // Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization")
-//
-size_t quantize_iq2_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
-size_t quantize_iq2_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
-size_t quantize_iq3_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
-size_t quantize_iq1_s  (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
-size_t quantize_q2_K   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
-size_t quantize_q3_K   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
-size_t quantize_q4_K   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
-size_t quantize_q5_K   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
-size_t quantize_q6_K   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
-size_t quantize_q4_0   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
-size_t quantize_q4_1   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
-size_t quantize_q5_0   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
-size_t quantize_q5_1   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_iq2_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_iq2_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_iq2_s  (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_iq3_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_iq1_s  (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_iq1_m  (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_iq4_nl (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_iq4_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_iq3_s  (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+
+size_t quantize_q2_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q3_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q4_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q5_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q6_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q4_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q4_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q5_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q5_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q8_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
 
 void iq2xs_init_impl(enum ggml_type type);
 void iq2xs_free_impl(enum ggml_type type);
diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp
index cd4b3a1e1..724070eb9 100644
--- a/ggml-sycl.cpp
+++ b/ggml-sycl.cpp
@@ -16,6 +16,7 @@
 #include <cinttypes>
 #include <cstddef>
 #include <cstdint>
+#include <cstdlib>
 #include <float.h>
 #include <limits>
 #include <stdint.h>
@@ -24,10 +25,9 @@
 #include <cmath>
 #include <iostream>
 #include <fstream>
-
 #include <stdio.h>
 #include <stdlib.h>
-
+#include <regex>
 
 #include <sycl/sycl.hpp>
 #include <sycl/half_type.hpp>
@@ -82,6 +82,30 @@ Following definition copied from DPCT head files, which are used by ggml-sycl.cp
 #define __dpct_noinline__ __attribute__((noinline))
 #endif
 
+
+std::string get_device_type_name(const sycl::device &Device) {
+    auto DeviceType = Device.get_info<sycl::info::device::device_type>();
+    switch (DeviceType) {
+    case sycl::info::device_type::cpu:
+        return "cpu";
+    case sycl::info::device_type::gpu:
+        return "gpu";
+    case sycl::info::device_type::host:
+        return "host";
+    case sycl::info::device_type::accelerator:
+        return "acc";
+    default:
+        return "unknown";
+    }
+}
+
+std::string get_device_backend_and_type(const sycl::device &device) {
+    std::stringstream device_type;
+    sycl::backend backend = device.get_backend();
+    device_type <<  backend << ":" << get_device_type_name(device);
+    return device_type.str();
+}
+
 namespace dpct
 {
     typedef sycl::queue *queue_ptr;
@@ -202,24 +226,29 @@ namespace dpct
             // Version string has the following format:
             // a. OpenCL<space><major.minor><space><vendor-specific-information>
             // b. <major.minor>
+            // c. <AmdGcnArchName> e.g gfx1030
             std::string ver;
             ver = dev.get_info<sycl::info::device::version>();
             std::string::size_type i = 0;
-            while (i < ver.size())
-            {
-                if (isdigit(ver[i]))
-                    break;
-                i++;
+            while (i < ver.size()) {
+              if (isdigit(ver[i]))
+                break;
+              i++;
             }
             major = std::stoi(&(ver[i]));
-            while (i < ver.size())
-            {
-                if (ver[i] == '.')
-                    break;
-                i++;
+            while (i < ver.size()) {
+              if (ver[i] == '.')
+                break;
+              i++;
+            }
+            if (i < ver.size()) {
+              // a. and b.
+              i++;
+              minor = std::stoi(&(ver[i]));
+            } else {
+              // c.
+              minor = 0;
             }
-            i++;
-            minor = std::stoi(&(ver[i]));
         }
 
         template <typename tag, typename T>
@@ -661,26 +690,29 @@ namespace dpct
         /// \param [out] total_memory The number of bytes of total memory on the SYCL device.
         void get_memory_info(size_t &free_memory, size_t &total_memory)
         {
+            total_memory = get_device_info().get_global_mem_size();
+            const char *warning_info = "get_memory_info: [warning] ext_intel_free_memory is not "
+                                 "supported (export/set ZES_ENABLE_SYSMAN=1 to support), "
+                                 "use total memory as free memory";
 #if (defined(__SYCL_COMPILER_VERSION) && __SYCL_COMPILER_VERSION >= 20221105)
             if (!has(sycl::aspect::ext_intel_free_memory))
             {
-                std::cerr << "get_memory_info: ext_intel_free_memory is not supported." << std::endl;
-                free_memory = 0;
+                std::cerr << warning_info << std::endl;
+                free_memory = total_memory;
             }
             else
             {
                 free_memory = get_info<sycl::ext::intel::info::device::free_memory>();
             }
 #else
-            std::cerr << "get_memory_info: ext_intel_free_memory is not supported." << std::endl;
-            free_memory = 0;
+            std::cerr << warning_info << std::endl;
+            free_memory = total_memory;
 #if defined(_MSC_VER) && !defined(__clang__)
 #pragma message("Querying the number of bytes of free memory is not supported")
 #else
 #warning "Querying the number of bytes of free memory is not supported"
 #endif
 #endif
-            total_memory = get_device_info().get_global_mem_size();
         }
 
         void get_device_info(device_info &out) const
@@ -708,11 +740,7 @@ namespace dpct
 
         sycl::queue &default_queue()
         {
-#ifdef DPCT_USM_LEVEL_NONE
-            return out_of_order_queue();
-#else
             return in_order_queue();
-#endif // DPCT_USM_LEVEL_NONE
         }
 
         void queues_wait_and_throw()
@@ -731,22 +759,28 @@ namespace dpct
 
         sycl::queue *create_queue(bool enable_exception_handler = false)
         {
-#ifdef DPCT_USM_LEVEL_NONE
-            return create_out_of_order_queue(enable_exception_handler);
-#else
             return create_in_order_queue(enable_exception_handler);
-#endif // DPCT_USM_LEVEL_NONE
         }
 
-        sycl::queue *create_in_order_queue(bool enable_exception_handler = false)
-        {
+        sycl::queue *create_queue(sycl::context context, sycl::device device,
+                                bool enable_exception_handler = false) {
+            return create_in_order_queue(context, device, enable_exception_handler);
+        }
+
+        sycl::queue *create_in_order_queue(bool enable_exception_handler = false) {
             std::lock_guard<mutex_type> lock(m_mutex);
             return create_queue_impl(enable_exception_handler,
-                                     sycl::property::queue::in_order());
+                                    sycl::property::queue::in_order());
         }
 
-        sycl::queue *create_out_of_order_queue(bool enable_exception_handler = false)
-        {
+        sycl::queue *create_in_order_queue(sycl::context context, sycl::device device,
+                                        bool enable_exception_handler = false) {
+            std::lock_guard<mutex_type> lock(m_mutex);
+            return create_queue_impl(context, device, enable_exception_handler,
+                                    sycl::property::queue::in_order());
+        }
+
+        sycl::queue *create_out_of_order_queue(bool enable_exception_handler = false) {
             std::lock_guard<mutex_type> lock(m_mutex);
             return create_queue_impl(enable_exception_handler);
         }
@@ -809,6 +843,25 @@ namespace dpct
             return _queues.back().get();
         }
 
+        template <class... Properties>
+        sycl::queue *create_queue_impl(sycl::context context, sycl::device device,
+                                    bool enable_exception_handler,
+                                    Properties... properties) {
+            sycl::async_handler eh = {};
+            if (enable_exception_handler) {
+                eh = exception_handler;
+            }
+            _queues.push_back(std::make_shared<sycl::queue>(
+                context, device, eh,
+                sycl::property_list(
+        #ifdef DPCT_PROFILING_ENABLED
+                    sycl::property::queue::enable_profiling(),
+        #endif
+                    properties...)));
+
+            return _queues.back().get();
+        }
+
         void get_version(int &major, int &minor) const
         {
             detail::get_version(*this, major, minor);
@@ -905,17 +958,67 @@ namespace dpct
 
     private:
         mutable std::recursive_mutex m_mutex;
+        static bool compare_dev(sycl::device &device1, sycl::device &device2)
+        {
+            dpct::device_info prop1;
+            dpct::get_device_info(prop1, device1);
+            dpct::device_info prop2;
+            dpct::get_device_info(prop2, device2);
+            return prop1.get_max_compute_units() > prop2.get_max_compute_units();
+        }
+        static int convert_backend_index(std::string & backend) {
+            if (backend == "ext_oneapi_level_zero:gpu") return 0;
+            if (backend == "opencl:gpu") return 1;
+            if (backend == "ext_oneapi_cuda:gpu") return 2;
+            if (backend == "ext_oneapi_hip:gpu") return 3;
+            if (backend == "opencl:cpu") return 4;
+            if (backend == "opencl:acc") return 5;
+            printf("convert_backend_index: can't handle backend=%s\n", backend.c_str());
+            GGML_ASSERT(false);
+        }
+        static bool compare_backend(std::string &backend1, std::string &backend2) {
+            return convert_backend_index(backend1) < convert_backend_index(backend2);
+        }
         dev_mgr()
         {
             sycl::device default_device =
                 sycl::device(sycl::default_selector_v);
             _devs.push_back(std::make_shared<device_ext>(default_device));
 
-            std::vector<sycl::device> sycl_all_devs =
-                sycl::device::get_devices(sycl::info::device_type::all);
+            std::vector<sycl::device> sycl_all_devs;
             // Collect other devices except for the default device.
             if (default_device.is_cpu())
                 _cpu_device = 0;
+
+            auto Platforms = sycl::platform::get_platforms();
+            // Keep track of the number of devices per backend
+            std::map<sycl::backend, size_t> DeviceNums;
+            std::map<std::string, std::vector<sycl::device>> backend_devices;
+
+            while (!Platforms.empty()) {
+                auto Platform = Platforms.back();
+                Platforms.pop_back();
+                auto devices = Platform.get_devices();
+                std::string backend_type = get_device_backend_and_type(devices[0]);
+                for (const auto &device : devices) {
+                    backend_devices[backend_type].push_back(device);
+                }
+            }
+
+            std::vector<std::string> keys;
+            for(auto it = backend_devices.begin(); it != backend_devices.end(); ++it) {
+                keys.push_back(it->first);
+            }
+            std::sort(keys.begin(), keys.end(), compare_backend);
+
+            for (auto &key : keys) {
+                std::vector<sycl::device> devs = backend_devices[key];
+                std::sort(devs.begin(), devs.end(), compare_dev);
+                for (const auto &dev : devs) {
+                    sycl_all_devs.push_back(dev);
+                }
+            }
+
             for (auto &dev : sycl_all_devs)
             {
                 if (dev == default_device)
@@ -964,11 +1067,6 @@ namespace dpct
         static pointer_access_attribute get_pointer_attribute(sycl::queue &q,
                                                               const void *ptr)
         {
-#ifdef DPCT_USM_LEVEL_NONE
-            return mem_mgr::instance().is_device_ptr(ptr)
-                       ? pointer_access_attribute::device_only
-                       : pointer_access_attribute::host_only;
-#else
             switch (sycl::get_pointer_type(ptr, q.get_context()))
             {
             case sycl::usm::alloc::unknown:
@@ -979,7 +1077,6 @@ namespace dpct
             case sycl::usm::alloc::host:
                 return pointer_access_attribute::host_device;
             }
-#endif
         }
 
         template <typename ArgT>
@@ -1162,11 +1259,7 @@ namespace dpct
 
         static inline void *dpct_malloc(size_t size, sycl::queue &q)
         {
-#ifdef DPCT_USM_LEVEL_NONE
-            return mem_mgr::instance().mem_alloc(size * sizeof(byte_t));
-#else
             return sycl::malloc_device(size, q.get_device(), q.get_context());
-#endif // DPCT_USM_LEVEL_NONE
         }
 
 #define PITCH_DEFAULT_ALIGN(x) (((x) + 31) & ~(0x1F))
@@ -1190,25 +1283,7 @@ namespace dpct
         static inline sycl::event dpct_memset(sycl::queue &q, void *dev_ptr,
                                               valueT value, size_t size)
         {
-#ifdef DPCT_USM_LEVEL_NONE
-            auto &mm = mem_mgr::instance();
-            assert(mm.is_device_ptr(dev_ptr));
-            auto alloc = mm.translate_ptr(dev_ptr);
-            size_t offset = (valueT *)dev_ptr - (valueT *)alloc.alloc_ptr;
-
-            return q.submit([&](sycl::handler &cgh)
-                            {
-    auto r = sycl::range<1>(size);
-    auto o = sycl::id<1>(offset);
-    auto new_buffer = alloc.buffer.reinterpret<valueT>(
-        sycl::range<1>(alloc.size / sizeof(valueT)));
-    sycl::accessor<valueT, 1, sycl::access_mode::write,
-                sycl::access::target::device>
-        acc(new_buffer, cgh, r, o);
-    cgh.fill(acc, value); });
-#else
             return q.fill(dev_ptr, value, size);
-#endif // DPCT_USM_LEVEL_NONE
         }
 
         /**
@@ -1302,72 +1377,8 @@ namespace dpct
         {
             if (!size)
                 return sycl::event{};
-#ifdef DPCT_USM_LEVEL_NONE
-            auto &mm = mem_mgr::instance();
-            auto real_direction = deduce_memcpy_direction(q, to_ptr, from_ptr, direction);
-
-            switch (real_direction)
-            {
-            case host_to_host:
-                return q.submit([&](sycl::handler &cgh)
-                                {
-    cgh.depends_on(dep_events);
-    cgh.host_task([=] { std::memcpy(to_ptr, from_ptr, size); }); });
-            case host_to_device:
-            {
-                auto alloc = mm.translate_ptr(to_ptr);
-                size_t offset = (byte_t *)to_ptr - alloc.alloc_ptr;
-                return q.submit([&](sycl::handler &cgh)
-                                {
-    cgh.depends_on(dep_events);
-    auto r = sycl::range<1>(size);
-    auto o = sycl::id<1>(offset);
-    sycl::accessor<byte_t, 1, sycl::access_mode::write,
-                        sycl::access::target::device>
-        acc(alloc.buffer, cgh, r, o);
-    cgh.copy(from_ptr, acc); });
-            }
-            case device_to_host:
-            {
-                auto alloc = mm.translate_ptr(from_ptr);
-                size_t offset = (byte_t *)from_ptr - alloc.alloc_ptr;
-                return q.submit([&](sycl::handler &cgh)
-                                {
-    cgh.depends_on(dep_events);
-    auto r = sycl::range<1>(size);
-    auto o = sycl::id<1>(offset);
-    sycl::accessor<byte_t, 1, sycl::access_mode::read,
-                        sycl::access::target::device>
-        acc(alloc.buffer, cgh, r, o);
-    cgh.copy(acc, to_ptr); });
-            }
-            case device_to_device:
-            {
-                auto to_alloc = mm.translate_ptr(to_ptr);
-                auto from_alloc = mm.translate_ptr(from_ptr);
-                size_t to_offset = (byte_t *)to_ptr - to_alloc.alloc_ptr;
-                size_t from_offset = (byte_t *)from_ptr - from_alloc.alloc_ptr;
-                return q.submit([&](sycl::handler &cgh)
-                                {
-    cgh.depends_on(dep_events);
-    auto r = sycl::range<1>(size);
-    auto to_o = sycl::id<1>(to_offset);
-    auto from_o = sycl::id<1>(from_offset);
-    sycl::accessor<byte_t, 1, sycl::access_mode::write,
-                        sycl::access::target::device>
-        to_acc(to_alloc.buffer, cgh, r, to_o);
-    sycl::accessor<byte_t, 1, sycl::access_mode::read,
-                        sycl::access::target::device>
-        from_acc(from_alloc.buffer, cgh, r, from_o);
-    cgh.copy(from_acc, to_acc); });
-            }
-            default:
-                throw std::runtime_error("dpct_memcpy: invalid direction value");
-            }
-#else
             return q.memcpy(to_ptr, from_ptr, size, dep_events);
             GGML_UNUSED(direction);
-#endif // DPCT_USM_LEVEL_NONE
         }
 
         // Get actual copy range and make sure it will not exceed range.
@@ -1507,45 +1518,15 @@ namespace dpct
                 break;
             }
             case device_to_device:
-#ifdef DPCT_USM_LEVEL_NONE
-            {
-                auto &mm = mem_mgr::instance();
-                auto to_alloc = mm.translate_ptr(to_surface);
-                auto from_alloc = mm.translate_ptr(from_surface);
-                size_t to_offset = (byte_t *)to_surface - to_alloc.alloc_ptr;
-                size_t from_offset = (byte_t *)from_surface - from_alloc.alloc_ptr;
-                event_list.push_back(q.submit([&](sycl::handler &cgh)
-                                              {
-    cgh.depends_on(dep_events);
-    auto to_o = sycl::id<1>(to_offset);
-    auto from_o = sycl::id<1>(from_offset);
-    sycl::accessor<byte_t, 1, sycl::access_mode::write,
-                        sycl::access::target::device>
-        to_acc(to_alloc.buffer, cgh,
-                get_copy_range(size, to_slice, to_range.get(0)), to_o);
-    sycl::accessor<byte_t, 1, sycl::access_mode::read,
-                        sycl::access::target::device>
-        from_acc(from_alloc.buffer, cgh,
-                get_copy_range(size, from_slice, from_range.get(0)), from_o);
-    cgh.parallel_for<class dpct_memcpy_3d_detail_usmnone>(
-        size,
-        [=](sycl::id<3> id) {
-            to_acc[get_offset(id, to_slice, to_range.get(0))] =
-                from_acc[get_offset(id, from_slice, from_range.get(0))];
-        }); }));
-            }
-#else
-                event_list.push_back(q.submit([&](sycl::handler &cgh)
-                                              {
-    cgh.depends_on(dep_events);
-    cgh.parallel_for<class dpct_memcpy_3d_detail>(
-        size,
-        [=](sycl::id<3> id) {
-            to_surface[get_offset(id, to_slice, to_range.get(0))] =
-                from_surface[get_offset(id, from_slice, from_range.get(0))];
-        }); }));
-#endif
-            break;
+                event_list.push_back(q.submit([&](sycl::handler &cgh){
+                cgh.depends_on(dep_events);
+                cgh.parallel_for<class dpct_memcpy_3d_detail>(
+                    size,
+                    [=](sycl::id<3> id) {
+                        to_surface[get_offset(id, to_slice, to_range.get(0))] =
+                            from_surface[get_offset(id, from_slice, from_range.get(0))];
+                    }); }));
+                break;
             default:
                 throw std::runtime_error("dpct_memcpy: invalid direction value");
             }
@@ -1643,11 +1624,7 @@ namespace dpct
         {
             if (ptr)
             {
-#ifdef DPCT_USM_LEVEL_NONE
-                detail::mem_mgr::instance().mem_free(ptr);
-#else
                 sycl::free(ptr, q.get_context());
-#endif // DPCT_USM_LEVEL_NONE
             }
         }
 
@@ -1655,11 +1632,7 @@ namespace dpct
         inline auto get_memory(const void *x)
         {
             T *new_x = reinterpret_cast<T *>(const_cast<void *>(x));
-#ifdef DPCT_USM_LEVEL_NONE
-            return dpct::get_buffer<std::remove_cv_t<T>>(new_x);
-#else
             return new_x;
-#endif
         }
 
         template <typename T>
@@ -1691,24 +1664,6 @@ namespace dpct
                               const void *alpha, const void *a, int lda, const void *b,
                               int ldb, const void *beta, void *c, int ldc)
         {
-#ifndef __INTEL_MKL__
-            GGML_UNUSED(q);
-            GGML_UNUSED(a_trans);
-            GGML_UNUSED(b_trans);
-            GGML_UNUSED(m);
-            GGML_UNUSED(n);
-            GGML_UNUSED(k);
-            GGML_UNUSED(alpha);
-            GGML_UNUSED(a);
-            GGML_UNUSED(lda);
-            GGML_UNUSED(b);
-            GGML_UNUSED(ldb);
-            GGML_UNUSED(beta);
-            GGML_UNUSED(c);
-            GGML_UNUSED(ldc);
-            throw std::runtime_error("The oneAPI Math Kernel Library (oneMKL) Interfaces "
-                                     "Project does not support this API.");
-#else
             Ts alpha_value = dpct::get_value(reinterpret_cast<const Ts *>(alpha), q);
             Ts beta_value = dpct::get_value(reinterpret_cast<const Ts *>(beta), q);
             auto data_a = get_memory<const Ta>(a);
@@ -1717,7 +1672,6 @@ namespace dpct
             oneapi::mkl::blas::column_major::gemm(
                 q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda,
                 data_b, ldb, beta_value, data_c, ldc);
-#endif
         }
 
         template <typename VecT, class BinaryOperation, class = void>
@@ -2111,72 +2065,8 @@ namespace dpct
     {
         if (!size)
             return sycl::event{};
-#ifdef DPCT_USM_LEVEL_NONE
-        auto &mm = mem_mgr::instance();
-        auto real_direction = deduce_memcpy_direction(q, to_ptr, from_ptr, direction);
-
-        switch (real_direction)
-        {
-        case host_to_host:
-            return q.submit([&](sycl::handler &cgh)
-                            {
-        cgh.depends_on(dep_events);
-        cgh.host_task([=] { std::memcpy(to_ptr, from_ptr, size); }); });
-        case host_to_device:
-        {
-            auto alloc = mm.translate_ptr(to_ptr);
-            size_t offset = (byte_t *)to_ptr - alloc.alloc_ptr;
-            return q.submit([&](sycl::handler &cgh)
-                            {
-        cgh.depends_on(dep_events);
-        auto r = sycl::range<1>(size);
-        auto o = sycl::id<1>(offset);
-        sycl::accessor<byte_t, 1, sycl::access_mode::write,
-                            sycl::access::target::device>
-            acc(alloc.buffer, cgh, r, o);
-        cgh.copy(from_ptr, acc); });
-        }
-        case device_to_host:
-        {
-            auto alloc = mm.translate_ptr(from_ptr);
-            size_t offset = (byte_t *)from_ptr - alloc.alloc_ptr;
-            return q.submit([&](sycl::handler &cgh)
-                            {
-        cgh.depends_on(dep_events);
-        auto r = sycl::range<1>(size);
-        auto o = sycl::id<1>(offset);
-        sycl::accessor<byte_t, 1, sycl::access_mode::read,
-                            sycl::access::target::device>
-            acc(alloc.buffer, cgh, r, o);
-        cgh.copy(acc, to_ptr); });
-        }
-        case device_to_device:
-        {
-            auto to_alloc = mm.translate_ptr(to_ptr);
-            auto from_alloc = mm.translate_ptr(from_ptr);
-            size_t to_offset = (byte_t *)to_ptr - to_alloc.alloc_ptr;
-            size_t from_offset = (byte_t *)from_ptr - from_alloc.alloc_ptr;
-            return q.submit([&](sycl::handler &cgh)
-                            {
-        cgh.depends_on(dep_events);
-        auto r = sycl::range<1>(size);
-        auto to_o = sycl::id<1>(to_offset);
-        auto from_o = sycl::id<1>(from_offset);
-        sycl::accessor<byte_t, 1, sycl::access_mode::write,
-                            sycl::access::target::device>
-            to_acc(to_alloc.buffer, cgh, r, to_o);
-        sycl::accessor<byte_t, 1, sycl::access_mode::read,
-                            sycl::access::target::device>
-            from_acc(from_alloc.buffer, cgh, r, from_o);
-        cgh.copy(from_acc, to_acc); });
-        }
-        default:
-            throw std::runtime_error("dpct_memcpy: invalid direction value");
-        }
-#else
         return q.memcpy(to_ptr, from_ptr, size, dep_events);
         GGML_UNUSED(direction);
-#endif // DPCT_USM_LEVEL_NONE
     }
 
     // Get actual copy range and make sure it will not exceed range.
@@ -2316,34 +2206,6 @@ namespace dpct
             break;
         }
         case device_to_device:
-#ifdef DPCT_USM_LEVEL_NONE
-        {
-            auto &mm = mem_mgr::instance();
-            auto to_alloc = mm.translate_ptr(to_surface);
-            auto from_alloc = mm.translate_ptr(from_surface);
-            size_t to_offset = (byte_t *)to_surface - to_alloc.alloc_ptr;
-            size_t from_offset = (byte_t *)from_surface - from_alloc.alloc_ptr;
-            event_list.push_back(q.submit([&](sycl::handler &cgh)
-                                          {
-        cgh.depends_on(dep_events);
-        auto to_o = sycl::id<1>(to_offset);
-        auto from_o = sycl::id<1>(from_offset);
-        sycl::accessor<byte_t, 1, sycl::access_mode::write,
-                            sycl::access::target::device>
-            to_acc(to_alloc.buffer, cgh,
-                    get_copy_range(size, to_slice, to_range.get(0)), to_o);
-        sycl::accessor<byte_t, 1, sycl::access_mode::read,
-                            sycl::access::target::device>
-            from_acc(from_alloc.buffer, cgh,
-                    get_copy_range(size, from_slice, from_range.get(0)), from_o);
-        cgh.parallel_for<class dpct_memcpy_3d_detail_usmnone>(
-            size,
-            [=](sycl::id<3> id) {
-                to_acc[get_offset(id, to_slice, to_range.get(0))] =
-                    from_acc[get_offset(id, from_slice, from_range.get(0))];
-            }); }));
-        }
-#else
             event_list.push_back(q.submit([&](sycl::handler &cgh)
                                           {
         cgh.depends_on(dep_events);
@@ -2353,7 +2215,6 @@ namespace dpct
                 to_surface[get_offset(id, to_slice, to_range.get(0))] =
                     from_surface[get_offset(id, from_slice, from_range.get(0))];
             }); }));
-#endif
         break;
         default:
             throw std::runtime_error("dpct_memcpy: invalid direction value");
@@ -2450,6 +2311,7 @@ namespace dpct
                                           lda, b, ldb, beta, c, ldc);
             break;
         }
+#ifdef __INTEL_MKL__
         case detail::get_type_combination_id(
             library_data_t::real_bfloat16, library_data_t::real_bfloat16,
             library_data_t::real_float, library_data_t::real_float):
@@ -2511,6 +2373,7 @@ namespace dpct
                 q, a_trans, b_trans, m, n, k, &alpha_float, a, lda, b, ldb, &beta_float, c, ldc);
             break;
         }
+#endif // __INTEL_MKL__
         default:
             throw std::runtime_error("the combination of data type is unsupported");
         }
@@ -2544,9 +2407,6 @@ namespace dpct
                            void *c[], library_data_t c_type, int ldc,
                            int batch_size, library_data_t scaling_type)
     {
-#ifdef DPCT_USM_LEVEL_NONE
-        throw std::runtime_error("this API is unsupported when USM level is none");
-#else
         if (scaling_type == library_data_t::real_float &&
             c_type == library_data_t::complex_float)
         {
@@ -2681,7 +2541,6 @@ namespace dpct
         default:
             throw std::runtime_error("the combination of data type is unsupported");
         }
-#endif
     }
 
     /// Computes a batch of matrix-matrix product with general matrices.
@@ -2862,11 +2721,237 @@ namespace dpct
     using err0 = detail::generic_error_type<struct err0_tag, int>;
     using err1 = detail::generic_error_type<struct err1_tag, int>;
 
+    static inline void dpct_free(void *ptr, sycl::queue &q = get_default_queue()) {
+        detail::dpct_free(ptr, q);
+    }
+
+    /// dpct accessor used as device function parameter.
+    template <class T, memory_region Memory, size_t Dimension> class accessor;
+    template <class T, memory_region Memory> class accessor<T, Memory, 3> {
+    public:
+        using memory_t = detail::memory_traits<Memory, T>;
+        using element_t = typename memory_t::element_t;
+        using pointer_t = typename memory_t::pointer_t;
+        using accessor_t = typename memory_t::template accessor_t<3>;
+        accessor(pointer_t data, const sycl::range<3> &in_range)
+            : _data(data), _range(in_range) {}
+        template <memory_region M = Memory>
+        accessor(typename std::enable_if<M != local, const accessor_t>::type &acc)
+            : accessor(acc, acc.get_range()) {}
+        accessor(const accessor_t &acc, const sycl::range<3> &in_range)
+            : accessor(acc.get_pointer(), in_range) {}
+        accessor<T, Memory, 2> operator[](size_t index) const {
+            sycl::range<2> sub(_range.get(1), _range.get(2));
+            return accessor<T, Memory, 2>(_data + index * sub.size(), sub);
+        }
+
+        pointer_t get_ptr() const { return _data; }
+
+    private:
+        pointer_t _data;
+        sycl::range<3> _range;
+    };
+    template <class T, memory_region Memory> class accessor<T, Memory, 2> {
+    public:
+        using memory_t = detail::memory_traits<Memory, T>;
+        using element_t = typename memory_t::element_t;
+        using pointer_t = typename memory_t::pointer_t;
+        using accessor_t = typename memory_t::template accessor_t<2>;
+        accessor(pointer_t data, const sycl::range<2> &in_range)
+            : _data(data), _range(in_range) {}
+        template <memory_region M = Memory>
+        accessor(typename std::enable_if<M != local, const accessor_t>::type &acc)
+            : accessor(acc, acc.get_range()) {}
+        accessor(const accessor_t &acc, const sycl::range<2> &in_range)
+            : accessor(acc.get_pointer(), in_range) {}
+
+        pointer_t operator[](size_t index) const {
+            return _data + _range.get(1) * index;
+        }
+
+        pointer_t get_ptr() const { return _data; }
+
+    private:
+        pointer_t _data;
+        sycl::range<2> _range;
+    };
+
+    namespace detail {
+        /// Device variable with address space of shared, global or constant.
+        template <class T, memory_region Memory, size_t Dimension> class device_memory {
+        public:
+            using accessor_t =
+                typename detail::memory_traits<Memory,
+                                            T>::template accessor_t<Dimension>;
+            using value_t = typename detail::memory_traits<Memory, T>::value_t;
+            using dpct_accessor_t = dpct::accessor<T, Memory, Dimension>;
+
+            device_memory() : device_memory(sycl::range<Dimension>(1)) {}
+
+            /// Constructor of 1-D array with initializer list
+            device_memory(const sycl::range<Dimension> &in_range,
+                        std::initializer_list<value_t> &&init_list)
+                : device_memory(in_range) {
+                assert(init_list.size() <= in_range.size());
+                _host_ptr = (value_t *)std::malloc(_size);
+                std::memset(_host_ptr, 0, _size);
+                std::memcpy(_host_ptr, init_list.begin(), init_list.size() * sizeof(T));
+            }
+
+            /// Constructor of 2-D array with initializer list
+            template <size_t D = Dimension>
+            device_memory(
+                const typename std::enable_if<D == 2, sycl::range<2>>::type &in_range,
+                std::initializer_list<std::initializer_list<value_t>> &&init_list)
+                : device_memory(in_range) {
+                assert(init_list.size() <= in_range[0]);
+                _host_ptr = (value_t *)std::malloc(_size);
+                std::memset(_host_ptr, 0, _size);
+                auto tmp_data = _host_ptr;
+                for (auto sub_list : init_list) {
+                    assert(sub_list.size() <= in_range[1]);
+                    std::memcpy(tmp_data, sub_list.begin(),
+                                sub_list.size() * sizeof(T));
+                    tmp_data += in_range[1];
+                }
+            }
+
+            /// Constructor with range
+            device_memory(const sycl::range<Dimension> &range_in)
+                : _size(range_in.size() * sizeof(T)), _range(range_in),
+                _reference(false), _host_ptr(nullptr), _device_ptr(nullptr) {
+                static_assert(
+                    (Memory == global) || (Memory == constant) || (Memory == shared),
+                    "device memory region should be global, constant or shared");
+                // Make sure that singleton class mem_mgr and dev_mgr will destruct
+                // later than this.
+                detail::mem_mgr::instance();
+                dev_mgr::instance();
+            }
+
+            /// Constructor with range
+            template <class... Args>
+            device_memory(Args... Arguments)
+                : device_memory(sycl::range<Dimension>(Arguments...)) {}
+
+            ~device_memory() {
+                if (_device_ptr && !_reference)
+                    dpct::dpct_free(_device_ptr);
+                if (_host_ptr)
+                    std::free(_host_ptr);
+            }
+
+            /// Allocate memory with default queue, and init memory if has initial
+            /// value.
+            void init() { init(dpct::get_default_queue()); }
+            /// Allocate memory with specified queue, and init memory if has initial
+            /// value.
+            void init(sycl::queue &q) {
+                if (_device_ptr)
+                    return;
+                if (!_size)
+                    return;
+                allocate_device(q);
+                if (_host_ptr)
+                    detail::dpct_memcpy(q, _device_ptr, _host_ptr, _size,
+                                        host_to_device);
+            }
+
+            /// The variable is assigned to a device pointer.
+            void assign(value_t *src, size_t size) {
+                this->~device_memory();
+                new (this) device_memory(src, size);
+            }
+
+            /// Get memory pointer of the memory object, which is virtual pointer when
+            /// usm is not used, and device pointer when usm is used.
+            value_t *get_ptr() { return get_ptr(get_default_queue()); }
+            /// Get memory pointer of the memory object, which is virtual pointer when
+            /// usm is not used, and device pointer when usm is used.
+            value_t *get_ptr(sycl::queue &q) {
+                init(q);
+                return _device_ptr;
+            }
+
+            /// Get the device memory object size in bytes.
+            size_t get_size() { return _size; }
+
+            template <size_t D = Dimension>
+            typename std::enable_if<D == 1, T>::type &operator[](size_t index) {
+                init();
+                return _device_ptr[index];
+            }
+
+            /// Get dpct::accessor with dimension info for the device memory object
+            /// when usm is used and dimension is greater than 1.
+            template <size_t D = Dimension>
+            typename std::enable_if<D != 1, dpct_accessor_t>::type
+            get_access(sycl::handler &cgh) {
+                return dpct_accessor_t((T *)_device_ptr, _range);
+            }
+
+        private:
+            device_memory(value_t *memory_ptr, size_t size)
+                : _size(size), _range(size / sizeof(T)), _reference(true),
+                _device_ptr(memory_ptr) {}
+
+            void allocate_device(sycl::queue &q) {
+        #ifndef DPCT_USM_LEVEL_NONE
+                if (Memory == shared) {
+                    _device_ptr = (value_t *)sycl::malloc_shared(_size, q.get_device(),
+                                                                q.get_context());
+                    return;
+                }
+        #ifdef SYCL_EXT_ONEAPI_USM_DEVICE_READ_ONLY
+                if (Memory == constant) {
+                    _device_ptr = (value_t *)sycl::malloc_device(
+                        _size, q.get_device(), q.get_context(),
+                        sycl::ext::oneapi::property::usm::device_read_only());
+                    return;
+                }
+        #endif
+        #endif
+                _device_ptr = (value_t *)detail::dpct_malloc(_size, q);
+            }
+
+            size_t _size;
+            sycl::range<Dimension> _range;
+            bool _reference;
+            value_t *_host_ptr;
+            value_t *_device_ptr;
+        };
+        template <class T, memory_region Memory>
+        class device_memory<T, Memory, 0> : public device_memory<T, Memory, 1> {
+        public:
+            using base = device_memory<T, Memory, 1>;
+            using value_t = typename base::value_t;
+            using accessor_t =
+                typename detail::memory_traits<Memory, T>::template accessor_t<0>;
+
+            /// Constructor with initial value.
+            device_memory(const value_t &val) : base(sycl::range<1>(1), {val}) {}
+
+            /// Default constructor
+            device_memory() : base(1) {}
+        };
+        } // namespace detail
+
+    template <class T, size_t Dimension>
+    using global_memory = detail::device_memory<T, global, Dimension>;
+    template <class T, size_t Dimension>
+    using constant_memory = detail::device_memory<T, constant, Dimension>;
+    template <class T, size_t Dimension>
+    using shared_memory = detail::device_memory<T, shared, Dimension>;
+
+
 } // COPY from DPCT head files
 
+#define GGML_COMMON_DECL_SYCL
+#define GGML_COMMON_IMPL_SYCL
+#include "ggml-common.h"
 
 static int g_ggml_sycl_debug=0;
-#define GGML_SYCL_DEBUG(...) do{if(g_ggml_sycl_debug) printf(__VA_ARGS__);}while(0)
+#define GGML_SYCL_DEBUG(...) do{if(g_ggml_sycl_debug) fprintf(stderr, __VA_ARGS__);}while(0)
 
 #define CHECK_TRY_ERROR(expr)                                                  \
   [&]() {                                                                      \
@@ -2906,6 +2991,20 @@ static int g_work_group_size = 0;
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
+// dmmv = dequantize_mul_mat_vec
+#ifndef GGML_SYCL_DMMV_X
+#define GGML_SYCL_DMMV_X 32
+#endif
+#ifndef GGML_SYCL_MMV_Y
+#define GGML_SYCL_MMV_Y 1
+#endif
+
+enum ggml_sycl_backend_gpu_mode {
+    SYCL_UNSET_GPU_MODE = -1,
+    SYCL_SINGLE_GPU_MODE = 0,
+    SYCL_MUL_GPU_MODE
+};
+
 static_assert(sizeof(sycl::half) == sizeof(ggml_fp16_t), "wrong fp16 size");
 
 static void crash(){
@@ -2939,18 +3038,19 @@ typedef float dfloat; // dequantize float
 typedef sycl::float2 dfloat2;
 #endif //GGML_SYCL_F16
 
+#define MMVQ_MAX_BATCH_SIZE  8
+
+static const int8_t kvalues_iq4nl[16]={-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
+
 bool   ggml_sycl_loaded(void);
 void * ggml_sycl_host_malloc(size_t size);
 void   ggml_sycl_host_free(void * ptr);
 bool   ggml_sycl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
-void   ggml_sycl_set_tensor_split(const float * tensor_split);
-void   ggml_sycl_transform_tensor(void * data, struct ggml_tensor * tensor);
 void   ggml_sycl_free_data(struct ggml_tensor * tensor);
 void   ggml_sycl_assign_buffers(struct ggml_tensor * tensor);
 void   ggml_sycl_assign_buffers_no_scratch(struct ggml_tensor * tensor);
 void   ggml_sycl_assign_buffers_force_inplace(struct ggml_tensor * tensor);
 void   ggml_sycl_assign_buffers_no_alloc(struct ggml_tensor * tensor);
-void   ggml_sycl_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset);
 void   ggml_sycl_copy_to_device(struct ggml_tensor * tensor);
 void   ggml_sycl_set_main_device(int main_device);
 void   ggml_sycl_set_mul_mat_q(bool mul_mat_q);
@@ -2963,6 +3063,14 @@ int    get_main_device();
 void   print_ggml_tensor(const char*name, struct ggml_tensor *src);
 void   log_tensor_with_cnt(const char* name, struct ggml_tensor * src, int stop_cnt);
 
+void dev2dev_memcpy(sycl::queue &q_dst, sycl::queue &q_src, void *ptr_dst,
+                    const void *ptr_src, size_t size) {
+    char *host_buf = (char *)malloc(size);
+    q_src.memcpy(host_buf, (const char *)ptr_src, size).wait();
+    q_dst.memcpy((char *)ptr_dst, host_buf, size).wait();
+    free(host_buf);
+}
+
 static __dpct_inline__ int get_int_from_int8(const int8_t *x8, const int &i32) {
     const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
 
@@ -3016,66 +3124,6 @@ typedef void (*ggml_sycl_op_flatten_t)(const ggml_tensor *src0,
                                        const float *src1_dd, float *dst_dd,
                                        const dpct::queue_ptr &main_stream);
 
-// QK = number of values after dequantization
-// QR = QK / number of values before dequantization
-// QI = number of 32 bit integers before dequantization
-
-#define QK4_0 32
-#define QR4_0 2
-#define QI4_0 (QK4_0 / (4 * QR4_0))
-typedef struct dpct_type_471834 {
-    sycl::half d;           // delta
-    uint8_t qs[QK4_0 / 2];  // nibbles / quants
-} block_q4_0;
-static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
-
-#define QK4_1 32
-#define QR4_1 2
-#define QI4_1 (QK4_1 / (4 * QR4_1))
-typedef struct dpct_type_143705 {
-    sycl::half2 dm;         // dm.x = delta, dm.y = min
-    uint8_t qs[QK4_1 / 2];  // nibbles / quants
-} block_q4_1;
-static_assert(sizeof(block_q4_1) == sizeof(ggml_fp16_t) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding");
-
-#define QK5_0 32
-#define QR5_0 2
-#define QI5_0 (QK5_0 / (4 * QR5_0))
-typedef struct dpct_type_673649 {
-    sycl::half d;           // delta
-    uint8_t qh[4];          // 5-th bit of quants
-    uint8_t qs[QK5_0 / 2];  // nibbles / quants
-} block_q5_0;
-static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
-
-#define QK5_1 32
-#define QR5_1 2
-#define QI5_1 (QK5_1 / (4 * QR5_1))
-typedef struct dpct_type_135589 {
-    sycl::half2 dm;         // dm.x = delta, dm.y = min
-    uint8_t qh[4];          // 5-th bit of quants
-    uint8_t qs[QK5_1 / 2];  // nibbles / quants
-} block_q5_1;
-static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
-
-#define QK8_0 32
-#define QR8_0 1
-#define QI8_0 (QK8_0 / (4 * QR8_0))
-typedef struct dpct_type_122878 {
-    sycl::half d;           // delta
-    int8_t  qs[QK8_0];      // quants
-} block_q8_0;
-static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
-
-#define QK8_1 32
-#define QR8_1 1
-#define QI8_1 (QK8_1 / (4 * QR8_1))
-typedef struct dpct_type_143721 {
-    sycl::half2 ds;         // ds.x = delta, ds.y = sum
-    int8_t  qs[QK8_0];      // quants
-} block_q8_1;
-static_assert(sizeof(block_q8_1) == 2*sizeof(ggml_fp16_t) + QK8_0, "wrong q8_1 block size/padding");
-
 typedef float (*vec_dot_q_sycl_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs);
 typedef void (*allocate_tiles_sycl_t)(int **x_ql, sycl::half2 **x_dm,
                                       int **x_qh, int **x_sc);
@@ -3092,87 +3140,6 @@ typedef float (*vec_dot_q_mul_mat_sycl_t)(
     const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ms,
     const int &i, const int &j, const int &k);
 
-//================================= k-quants
-
-#ifdef GGML_QKK_64
-#define QK_K 64
-#define K_SCALE_SIZE 4
-#else
-#define QK_K 256
-#define K_SCALE_SIZE 12
-#endif
-
-#define QR2_K 4
-#define QI2_K (QK_K / (4*QR2_K))
-typedef struct dpct_type_619598 {
-    uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
-    uint8_t qs[QK_K/4];      // quants
-    sycl::half2 dm;          // super-block scale for quantized scales/mins
-} block_q2_K;
-static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
-
-#define QR3_K 4
-#define QI3_K (QK_K / (4*QR3_K))
-typedef struct dpct_type_138576 {
-    uint8_t hmask[QK_K/8];     // quants - high bit
-    uint8_t qs[QK_K/4];        // quants - low 2 bits
-#ifdef GGML_QKK_64
-    uint8_t scales[2]; // scales, quantized with 8 bits
-#else
-    uint8_t scales[K_SCALE_SIZE]; // scales, quantized with 6 bits
-#endif
-    sycl::half d; // super-block scale
-} block_q3_K;
-//static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + K_SCALE_SIZE, "wrong q3_K block size/padding");
-
-#define QR4_K 2
-#define QI4_K (QK_K / (4*QR4_K))
-#ifdef GGML_QKK_64
-typedef struct {
-    half    dm[2];             // super-block scales/mins
-    uint8_t scales[2];         // 4-bit block scales/mins
-    uint8_t qs[QK_K/2];        // 4--bit quants
-} block_q4_K;
-static_assert(sizeof(block_q4_K) == sizeof(half2) + QK_K/2 + 2, "wrong q4_K block size/padding");
-#else
-typedef struct dpct_type_154943 {
-    sycl::half2 dm;            // super-block scale for quantized scales/mins
-    uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits
-    uint8_t qs[QK_K/2];        // 4--bit quants
-} block_q4_K;
-static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2, "wrong q4_K block size/padding");
-#endif
-
-#define QR5_K 2
-#define QI5_K (QK_K / (4*QR5_K))
-#ifdef GGML_QKK_64
-typedef struct {
-    half d;                  // super-block scale
-    int8_t scales[QK_K/16];  // block scales
-    uint8_t qh[QK_K/8];      // quants, high bit
-    uint8_t qs[QK_K/2];      // quants, low 4 bits
-} block_q5_K;
-static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
-#else
-typedef struct dpct_type_866817 {
-    sycl::half2 dm;               // super-block scale for quantized scales/mins
-    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
-    uint8_t qh[QK_K/8];           // quants, high bit
-    uint8_t qs[QK_K/2];           // quants, low 4 bits
-} block_q5_K;
-static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
-#endif
-
-#define QR6_K 2
-#define QI6_K (QK_K / (4*QR6_K))
-typedef struct dpct_type_107281 {
-    uint8_t ql[QK_K/2];   // quants, lower 4 bits
-    uint8_t qh[QK_K/4];   // quants, upper 2 bits
-    int8_t  scales[QK_K/16]; // scales
-    sycl::half d;            // delta
-} block_q6_K;
-static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_K block size/padding");
-
 #define WARP_SIZE 32
 #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
 
@@ -3180,13 +3147,13 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_
 #define SYCL_SILU_BLOCK_SIZE 256
 #define SYCL_TANH_BLOCK_SIZE 256
 #define SYCL_RELU_BLOCK_SIZE 256
+#define SYCL_HARDSIGMOID_BLOCK_SIZE 256
+#define SYCL_HARDSWISH_BLOCK_SIZE 256
 #define SYCL_SQR_BLOCK_SIZE 256
 #define SYCL_CPY_BLOCK_SIZE 32
 #define SYCL_SCALE_BLOCK_SIZE 256
 #define SYCL_CLAMP_BLOCK_SIZE 256
 #define SYCL_ROPE_BLOCK_SIZE 256
-#define SYCL_SOFT_MAX_BLOCK_SIZE 1024
-#define SYCL_ALIBI_BLOCK_SIZE 32
 #define SYCL_DIAG_MASK_INF_BLOCK_SIZE 32
 #define SYCL_QUANTIZE_BLOCK_SIZE 256
 #define SYCL_DEQUANTIZE_BLOCK_SIZE 256
@@ -3196,6 +3163,7 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_
 #define SYCL_PAD_BLOCK_SIZE 256
 #define SYCL_ACC_BLOCK_SIZE 256
 #define SYCL_IM2COL_BLOCK_SIZE 256
+#define SYCL_POOL2D_BLOCK_SIZE 256
 
 // dmmv = dequantize_mul_mat_vec
 #ifndef GGML_SYCL_DMMV_X
@@ -3218,8 +3186,7 @@ static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUA
 #define MUL_MAT_SRC1_COL_STRIDE 128
 
 #define MAX_STREAMS 8
-static dpct::queue_ptr g_syclStreams[GGML_SYCL_MAX_DEVICES][MAX_STREAMS] = {
-    {0}};
+static dpct::queue_ptr g_syclStreams[GGML_SYCL_MAX_DEVICES][MAX_STREAMS] = {{0}};
 
 struct ggml_tensor_extra_gpu {
     void * data_device[GGML_SYCL_MAX_DEVICES]; // 1 pointer for each device for split tensors
@@ -3228,33 +3195,141 @@ struct ggml_tensor_extra_gpu {
               [MAX_STREAMS]; // events for synchronizing multiple GPUs
 };
 
-inline dpct::err0 ggml_sycl_set_device(const int device) try {
-    int current_device;
+class sycl_gpu_mgr {
+    public:
+        std::vector<int> gpus;
+        std::vector<sycl::device> devices;
+        sycl::queue *first_queue;
+        sycl::context co_ctx;
+        int max_compute_units = 0;
+        int work_group_size = 0;
+        std::string gpus_list = "";
 
-    SYCL_CHECK(CHECK_TRY_ERROR(
-        current_device = dpct::dev_mgr::instance().current_device_id()));
+        /*
+        Use all GPUs with same top max compute units
+        */
+        sycl_gpu_mgr() {
+            detect_sycl_gpu_list_with_max_cu();
+            get_allow_gpus();
+            create_context_with_gpus();
+        }
 
-    // GGML_SYCL_DEBUG("ggml_sycl_set_device device=%d, current_device=%d\n", device, current_device);
-    if (device == current_device) {
-        return 0;
-    }
+        /*
+        Only use the assigned GPU
+        */
+        sycl_gpu_mgr(int main_gpu_id) {
+            sycl::device device = dpct::dev_mgr::instance().get_device(main_gpu_id);
+            dpct::device_info prop;
+            dpct::get_device_info(prop, device);
+            gpus.push_back(main_gpu_id);
+            devices.push_back(device);
+            work_group_size = prop.get_max_work_group_size();
+            max_compute_units = prop.get_max_compute_units();
 
-    return CHECK_TRY_ERROR(dpct::select_device(device));
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  crash();
-  std::exit(1);
-}
+            get_allow_gpus();
+            create_context_with_gpus();
+        }
 
+        void create_context_with_gpus() {
+            sycl::context ctx = sycl::context(devices);
+            assert(gpus.size() > 0);
+            first_queue = dpct::get_current_device().create_queue(ctx, devices[0]);
+            co_ctx = first_queue->get_context();
+        }
+
+        sycl::context &get_co_ctx() { return co_ctx; }
+
+        void get_allow_gpus() {
+            gpus_list = "";
+            for (size_t i = 0; i < gpus.size(); ++i) {
+                gpus_list += std::to_string(gpus[i]);
+                gpus_list += ",";
+            }
+            if (gpus_list.length() > 1) {
+                gpus_list.pop_back();
+            }
+        }
+
+        bool is_allowed_gpu(int device_id) {
+            return std::find(gpus.begin(), gpus.end(), device_id) != gpus.end();
+        }
+
+        void detect_sycl_gpu_list_with_max_cu() try {
+            int device_count = dpct::dev_mgr::instance().device_count();
+
+            for (int id = 0; id < device_count; id++) {
+                sycl::device device = dpct::dev_mgr::instance().get_device(id);
+                if (!device.is_gpu())
+                    continue;
+                dpct::device_info prop;
+                dpct::get_device_info(prop, device);
+                if (max_compute_units < prop.get_max_compute_units())
+                    max_compute_units = prop.get_max_compute_units();
+            }
+
+            for (int id = 0; id < device_count; id++) {
+                sycl::device device = dpct::dev_mgr::instance().get_device(id);
+                if (!device.is_gpu())
+                    continue;
+                dpct::device_info prop;
+                dpct::get_device_info(prop, device);
+                if (max_compute_units == prop.get_max_compute_units() &&
+                    is_ext_oneapi_device(device)) {
+                    gpus.push_back(id);
+                    devices.push_back(device);
+                    work_group_size = prop.get_max_work_group_size();
+                }
+            }
+            return;
+        } catch (sycl::exception const &exc) {
+            std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+                    << ", line:" << __LINE__ << std::endl;
+            std::exit(1);
+        }
+
+        int get_gpu_count() { return (int)gpus.size(); }
+
+        int get_index(int id) {
+            for (int i = 0; i < (int)gpus.size(); i++) {
+                if (gpus[i] == id)
+                    return i;
+            }
+            printf("miss to get device index by id=%d\n", id);
+            GGML_ASSERT(false);
+        }
+
+        int get_next_index(int id) {
+            int cur_index = get_index(id);
+            for (int i = cur_index + 1; i < (int)gpus.size(); i++) {
+                if (gpus[i] == id)
+                    return i;
+            }
+            GGML_ASSERT(false);
+        }
+
+        bool is_ext_oneapi_device(const sycl::device &dev) {
+            sycl::backend dev_backend = dev.get_backend();
+            if (dev_backend == sycl::backend::ext_oneapi_level_zero ||
+                dev_backend == sycl::backend::ext_oneapi_cuda ||
+                dev_backend == sycl::backend::ext_oneapi_hip)
+                return true;
+            return false;
+        }
+};
+
+static sycl_gpu_mgr *g_sycl_gpu_mgr = NULL;
 static int g_device_count = -1;
 static int g_all_sycl_device_count = -1;
 static int g_main_device = -1;
-static int g_main_device_index = -1;
+static int g_main_device_id = -1;
+static bool g_ggml_backend_sycl_buffer_type_initialized = false;
+
+static std::array<float, GGML_SYCL_MAX_DEVICES> g_default_tensor_split = {};
 
 static float g_tensor_split[GGML_SYCL_MAX_DEVICES] = {0};
 
+static ggml_sycl_backend_gpu_mode g_ggml_sycl_backend_gpu_mode = SYCL_UNSET_GPU_MODE;
+
 struct sycl_device_capabilities {
     int     cc;                 // compute capability
     bool    vmm;                // virtual memory support
@@ -3268,8 +3343,6 @@ struct sycl_device_id2index {
     int index;
 };
 
-static sycl_device_id2index g_sycl_device_id2index[GGML_SYCL_MAX_DEVICES] = { {-1} };
-
 static void * g_scratch_buffer = nullptr;
 static size_t g_scratch_size = 0; // disabled by default
 static size_t g_scratch_offset = 0;
@@ -3290,6 +3363,63 @@ static void bad_arch(const sycl::stream &stream_ct1) {
     (void) bad_arch; // suppress unused function warning
 }
 
+/*
+device_index: device index from 0 to n (continue numbers).
+    It is used for device select/set in SYCL backend internal data structure.
+*/
+void check_allow_gpu_index(const int device_index) {
+    if (device_index >= g_device_count) {
+        char error_buf[256];
+        snprintf(error_buf, sizeof(error_buf),
+                 "%s error: device_index:%d is out of range: [0-%d]", __func__,
+                 device_index, g_device_count - 1);
+        fprintf(stderr, "%s\n", error_buf);
+        assert(false);
+    }
+}
+
+/*
+device_id: device ID is shown by ggml_backend_sycl_print_sycl_devices().
+    It is only used to set current working device.
+*/
+void check_allow_gpu_id(const int device_id) {
+    if (!g_sycl_gpu_mgr->is_allowed_gpu(device_id)) {
+        char error_buf[256];
+        snprintf(error_buf, sizeof(error_buf),
+                 "error: cannot set device=%d, which is not allowed. Please "
+                 "set GPU ID in: [%s]",
+                 device_id, g_sycl_gpu_mgr->gpus_list.c_str());
+        fprintf(stderr, "%s\n", error_buf);
+        throw std::invalid_argument(error_buf);
+    }
+}
+
+int get_current_device_id() {
+    return dpct::dev_mgr::instance().current_device_id();
+}
+
+inline dpct::err0 ggml_sycl_set_device(const int device) try {
+
+    int device_id = g_sycl_gpu_mgr->gpus[device];
+    check_allow_gpu_id(device_id);
+
+    int current_device_id;
+    SYCL_CHECK(CHECK_TRY_ERROR(current_device_id = get_current_device_id()));
+
+    // GGML_SYCL_DEBUG("ggml_sycl_set_device device_id=%d,
+    // current_device_id=%d\n", device, current_device);
+    if (device_id == current_device_id) {
+        return 0;
+    }
+
+    return CHECK_TRY_ERROR(dpct::select_device(device_id));
+} catch (sycl::exception const &exc) {
+    std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+              << ", line:" << __LINE__ << std::endl;
+    crash();
+    std::exit(1);
+}
+
 void log_ggml_var_device(const char*name, float *src, size_t total_elements, bool src_on_device){
     if(!g_ggml_sycl_debug) return;
     if(!src){
@@ -3302,25 +3432,55 @@ void log_ggml_var_device(const char*name, float *src, size_t total_elements, boo
 
     size_t total_size = total_elements*sizeof(float);
     float *local_buf = NULL;
-    // printf("total_size %d2, src_on_device %d\n", total_size, src_on_device);
     if(src_on_device) {
         local_buf = (float *) ggml_sycl_host_malloc(total_size);
-        // printf("local buf %p size %d bytes\n", local_buf, total_size);
         ggml_sycl_set_device(g_main_device);
-        dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0];
-        main_stream->memcpy(local_buf, src, total_size);
+        dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
+        main_stream->memcpy(local_buf, src, total_size).wait();
     }
     else {
         local_buf = (float *)src;
-        // printf("local buf from src-> data %p\n", local_buf);
     }
 
     std::ofstream logfile;
     logfile.open(filename);
-    // printf("local buf element %d\n", total_elements);
     for(size_t i=0; i<total_elements; i++){
+        logfile << local_buf[i] <<" ";
+        if((i+1)%20 ==0) logfile <<std::endl;
+    }
+    logfile <<std::endl;
+    logfile.close();
+
+    if(src_on_device) ggml_sycl_host_free(local_buf);
+}
+
+void log_ggml_var_device_fp16(const char*name, sycl::half *src, size_t total_elements, bool src_on_device){
+    if(!g_ggml_sycl_debug) return;
+    if(!src){
+        printf("GGML Tensor:%s skip to save for NULL pointer\n", name);
+        return;
+    }
+    char filename[1024];
+    sprintf(filename, "%s.txt", name);
+    printf("GGML Tensor:%s save to %s\n", name, filename);
+
+    size_t total_size = total_elements*sizeof(sycl::half);
+    sycl::half *local_buf = NULL;
+    if(src_on_device) {
+        local_buf = (sycl::half *) ggml_sycl_host_malloc(total_size);
+        ggml_sycl_set_device(g_main_device);
+        dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
+        main_stream->memcpy(local_buf, src, total_size).wait();
+    }
+    else {
+        local_buf = (sycl::half *)src;
+    }
+
+    std::ofstream logfile;
+    logfile.open(filename);
+    for(size_t i=0; i<total_elements; i++){
+        logfile << local_buf[i] <<" ";
         if((i+1)%20 ==0) logfile <<std::endl;
-        else logfile << local_buf[i] <<" ";
     }
     logfile <<std::endl;
     logfile.close();
@@ -3338,11 +3498,11 @@ void print_ggml_tensor(const char*name, struct ggml_tensor *src){
 
     size_t total_elements = ggml_nelements(src);
 
-    const bool src_on_device = src->backend == GGML_BACKEND_GPU || src->backend == GGML_BACKEND_GPU_SPLIT;
+    const bool src_on_device = src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
     float *src_data =NULL;
     if(src_on_device) {
         ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *)  src->extra;
-        src_data = (float*)src_extra->data_device[g_main_device_index];
+        src_data = (float*)src_extra->data_device[g_main_device];
     }
     else {
         src_data = (float *)src->data;
@@ -3359,10 +3519,6 @@ void log_tensor_with_cnt(const char* name, struct ggml_tensor * src, int stop_cn
     sprintf(filename, "%s_%07d", name, log_file_name_idx);
     log_file_name_idx++;
     print_ggml_tensor(filename, src);
-    // print_ggml_tensor("ggml_sycl_rms_norm_src0", (ggml_tensor *)src0);
-    // print_ggml_tensor("ggml_sycl_rms_norm_src1", (ggml_tensor *)src1);
-    // int *ptr = NULL;
-    // *ptr = 0;
 }
 
 static __dpct_inline__ float warp_reduce_sum(float x,
@@ -3583,6 +3739,28 @@ static void relu_f32(const float * x, float * dst, const int k,
     dst[i] = sycl::fmax((float)(x[i]), (float)0);
 }
 
+static void hardsigmoid_f32(const float * x, float * dst, const int k,
+                            const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f));
+}
+
+static void hardswish_f32(const float * x, float * dst, const int k,
+                          const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = x[i] * sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f));
+}
+
 static void leaky_relu_f32(const float *x, float *dst, const int k, const float negative_slope,
                            const sycl::nd_item<3> &item_ct1) {
     const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
@@ -3943,6 +4121,66 @@ static __dpct_inline__ void dequantize_q8_0(const void *vx, const int ib,
 #endif // GGML_SYCL_F16
 }
 
+template<typename dst_t>
+static void dequantize_block_q4_0(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32,
+                                  const sycl::nd_item<3> &item_ct1) {
+
+    const int i = item_ct1.get_group(2);
+
+    // assume 32 threads
+    const int tid = item_ct1.get_local_id(2);
+    const int il  = tid/8;
+    const int ir  = tid%8;
+    const int ib = 8*i + ir;
+    if (ib >= nb32) {
+        return;
+    }
+
+    dst_t * y = yy + 256*i + 32*ir + 4*il;
+
+    const block_q4_0 * x = (const block_q4_0 *)vx + ib;
+    const float d = sycl::vec<sycl::half, 1>(x->d)
+                        .convert<float, sycl::rounding_mode::automatic>()[0];
+    const float dm = -8*d;
+
+    const uint8_t * q = x->qs + 4*il;
+
+    for (int l = 0; l < 4; ++l) {
+        y[l+ 0] = d * (q[l] & 0xF) + dm;
+        y[l+16] = d * (q[l] >>  4) + dm;
+    }
+}
+
+template<typename dst_t>
+static void dequantize_block_q4_1(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32,
+                                  const sycl::nd_item<3> &item_ct1) {
+
+    const int i = item_ct1.get_group(2);
+
+    // assume 32 threads
+    const int tid = item_ct1.get_local_id(2);
+    const int il  = tid/8;
+    const int ir  = tid%8;
+    const int ib = 8*i + ir;
+    if (ib >= nb32) {
+        return;
+    }
+
+    dst_t * y = yy + 256*i + 32*ir + 4*il;
+
+    const block_q4_1 * x = (const block_q4_1 *)vx + ib;
+    const sycl::float2 d =
+        x->dm.convert<float, sycl::rounding_mode::automatic>();
+
+    const uint8_t * q = x->qs + 4*il;
+
+    for (int l = 0; l < 4; ++l) {
+        y[l + 0] = d.x() * (q[l] & 0xF) + d.y();
+        y[l + 16] = d.x() * (q[l] >> 4) + d.y();
+    }
+}
+
+
 //================================== k-quants
 
 template<typename dst_t>
@@ -3972,8 +4210,9 @@ static void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restri
     const int il = tid%16;  // 0...15
     const uint8_t q = x[i].qs[il] >> (2*is);
     dst_t * y = yy + i*QK_K + 16*is + il;
-    float dall = __low2half(x[i].dm);
-    float dmin = __high2half(x[i].dm);
+
+    float dall = x[i].dm[0];
+    float dmin = x[i].dm[1];
     y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
     y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4);
 #endif
@@ -4012,7 +4251,7 @@ static void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restri
 
     for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));
 #else
-    const int tid = threadIdx.x;
+    const int tid = item_ct1.get_local_id(2);
     const int is  = tid/16;  // 0 or 1
     const int il  = tid%16;  // 0...15
     const int im  = il/8;    // 0...1
@@ -4078,7 +4317,7 @@ static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restri
         y[l +32] = d2 * (q[l] >>  4) - m2;
     }
 #else
-    const int tid = threadIdx.x;
+    const int tid = item_ct1.get_local_id(2);
     const uint8_t * q = x[i].qs;
     dst_t * y = yy + i*QK_K;
     const float d = (float)x[i].dm[0];
@@ -4123,7 +4362,7 @@ static void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restri
     y[32] = d2 * ((ql[ 0] >>  4) + (qh[ 0] & hm ? 16 : 0)) - m2;
     y[33] = d2 * ((ql[ 1] >>  4) + (qh[ 1] & hm ? 16 : 0)) - m2;
 #else
-    const int tid = threadIdx.x;
+    const int tid = item_ct1.get_local_id(2);
     const uint8_t q = x[i].qs[tid];
     const int im = tid/8;  // 0...3
     const int in = tid%8;  // 0...7
@@ -4165,7 +4404,7 @@ static void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restri
 #else
 
     // assume 32 threads
-    const int tid = threadIdx.x;
+    const int tid = item_ct1.get_local_id(2);
     const int ip  = tid/16;         // 0 or 1
     const int il  = tid - 16*ip;    // 0...15
 
@@ -4182,6 +4421,257 @@ static void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restri
 #endif
 }
 
+template<typename dst_t>
+static void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                     const sycl::nd_item<3> &item_ct1,
+                                     const uint64_t *iq2xxs_grid_ptr,
+                                     const uint8_t *ksigns_iq2xs_ptr,
+                                     const uint8_t *kmask_iq2xs_ptr) {
+
+    const int i = item_ct1.get_group(2);
+    const block_iq2_xxs * x = (const block_iq2_xxs  *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint16_t * q2 = x[i].qs + 4*ib;
+    const uint8_t  * aux8 = (const uint8_t *)q2;
+    const uint8_t  * grid = (const uint8_t *)(iq2xxs_grid_ptr + aux8[il]);
+    const uint32_t aux32 = q2[2] | (q2[3] << 16);
+    const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f;
+    const uint8_t signs = ksigns_iq2xs_ptr[(aux32 >> 7*il) & 127];
+    for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs_ptr[j] ? -1.f : 1.f);
+#else
+    assert(false);
+#endif
+
+}
+
+template<typename dst_t>
+static void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                    const sycl::nd_item<3> &item_ct1,
+                                    const uint64_t *iq2xs_grid,
+                                    const uint8_t *ksigns_iq2xs,
+                                    const uint8_t *kmask_iq2xs) {
+
+    const int i = item_ct1.get_group(2);
+    const block_iq2_xs * x = (const block_iq2_xs *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint16_t * q2 = x[i].qs + 4*ib;
+    const uint8_t  * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511));
+    const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
+    const uint8_t signs = ksigns_iq2xs[q2[il] >> 9];
+    for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
+#else
+    assert(false);
+#endif
+
+}
+
+template <typename dst_t>
+__dpct_inline__ static void
+dequantize_block_iq2_s(const void *__restrict__ vx, dst_t *__restrict__ yy,
+                       const sycl::nd_item<3> &item_ct1) {
+
+    const int i = item_ct1.get_group(2);
+    const block_iq2_s * x = (const block_iq2_s *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint8_t * grid = (const uint8_t *)(iq2s_grid + (x[i].qs[4*ib+il] | ((x[i].qh[ib] << (8-2*il)) & 0x300)));
+    const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
+    const uint8_t signs = x[i].qs[QK_K/8+4*ib+il];
+#pragma unroll
+    for (int j = 0; j < 8; ++j)
+        y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
+#else
+    assert(false);
+
+#endif
+
+}
+
+template<typename dst_t>
+static void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                     const sycl::nd_item<3> &item_ct1,
+                                     const uint32_t *iq3xxs_grid,
+                                     const uint8_t *ksigns_iq2xs,
+                                     const uint8_t *kmask_iq2xs) {
+
+    const int i = item_ct1.get_group(2);
+    const block_iq3_xxs * x = (const block_iq3_xxs  *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint8_t  * q3 = x[i].qs + 8*ib;
+    const uint16_t * gas = (const uint16_t *)(x[i].qs + QK_K/4) + 2*ib;
+    const uint8_t  * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*il+0]);
+    const uint8_t  * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*il+1]);
+    const uint32_t aux32 = gas[0] | (gas[1] << 16);
+    const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.5f;
+    const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
+    for (int j = 0; j < 4; ++j) {
+        y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
+        y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
+    }
+#else
+    assert(false);
+#endif
+
+}
+
+template <typename dst_t>
+__dpct_inline__ static void
+dequantize_block_iq3_s(const void *__restrict__ vx, dst_t *__restrict__ yy,
+                       const sycl::nd_item<3> &item_ct1,
+                       const uint8_t *kmask_iq2xs, const uint32_t *iq3s_grid) {
+
+    const int i = item_ct1.get_group(2);
+    const block_iq3_s * x = (const block_iq3_s *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint8_t * qs = x[i].qs + 8*ib;
+    const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256)));
+    const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256)));
+    const float d = (float)x[i].d * (1 + 2*((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf));
+    const uint8_t signs = x[i].signs[4*ib + il];
+#pragma unroll
+    for (int j = 0; j < 4; ++j) {
+        y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
+        y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
+    }
+#else
+    assert(false);
+#endif
+
+}
+
+template <typename dst_t>
+__dpct_inline__ static void
+dequantize_block_iq1_s(const void *__restrict__ vx, dst_t *__restrict__ yy,
+                       const sycl::nd_item<3> &item_ct1,
+                       const uint32_t *iq1s_grid_gpu) {
+
+    const int i = item_ct1.get_group(2);
+    const block_iq1_s * x = (const block_iq1_s  *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const float delta = x[i].qh[ib] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA;
+    const float d = (float)x[i].d * (2*((x[i].qh[ib] >> 12) & 7) + 1);
+    uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32;
+    grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[ib] >> 3*il) & 7) << 8)];
+    grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f;
+    grid32[0] &= 0x0f0f0f0f;
+#pragma unroll
+    for (int j = 0; j < 8; ++j) {
+        y[j] = d * (q[j] + delta);
+    }
+#else
+    assert(false);
+#endif
+
+}
+
+template <typename dst_t>
+__dpct_inline__ static void
+dequantize_block_iq1_m(const void *__restrict__ vx, dst_t *__restrict__ yy,
+                       const sycl::nd_item<3> &item_ct1,
+                       const uint32_t *iq1s_grid_gpu) {
+
+    const int i = item_ct1.get_group(2);
+    const block_iq1_m * x = (const block_iq1_m  *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint16_t * sc = (const uint16_t *)x[i].scales;
+    iq1m_scale_t scale;
+    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+    const int ib16 = 2*ib + il/2; // sc[ib16/4] >> 3*(ib16%4) -> sc[ib/2] >> 3*((2*ib+il/2)%4);
+    const float d = (float)scale.f16 * (2*((sc[ib16/4] >> 3*(ib16%4)) & 0x7) + 1);
+    const float delta = x[i].qh[2*ib+il/2] & (0x08 << 4*(il%2)) ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA;
+    uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32;
+    grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[2*ib+il/2] >> 4*(il%2)) & 7) << 8)];
+    grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f;
+    grid32[0] &= 0x0f0f0f0f;
+#pragma unroll
+    for (int j = 0; j < 8; ++j) {
+        y[j] = d * (q[j] + delta);
+    }
+#else
+    assert(false);
+#endif
+
+}
+
+template <typename dst_t>
+__dpct_inline__ static void
+dequantize_block_iq4_nl(const void *__restrict__ vx, dst_t *__restrict__ yy,
+                        const sycl::nd_item<3> &item_ct1) {
+
+    const int i = item_ct1.get_group(2);
+    const block_iq4_nl * x = (const block_iq4_nl *) vx + i*(QK_K/QK4_NL);
+
+    const int tid = item_ct1.get_local_id(2);
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
+    const uint8_t  * q4 = x[ib].qs + 4*il;
+    const float d = (float)x[ib].d;
+#pragma unroll
+    for (int j = 0; j < 4; ++j) {
+        y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf];
+        y[j+16] = d * kvalues_iq4nl[q4[j] >>  4];
+    }
+
+}
+
+
+template <typename dst_t>
+__dpct_inline__ static void
+dequantize_block_iq4_xs(const void *__restrict__ vx, dst_t *__restrict__ yy,
+                        const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_group(2);
+    const block_iq4_xs * x = (const block_iq4_xs *)vx;
+
+    const int tid = item_ct1.get_local_id(2);
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
+    const uint8_t  * q4 = x[i].qs + 16*ib + 4*il;
+    const float d = (float)x[i].d * ((((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4)) - 32);
+#pragma unroll
+    for (int j = 0; j < 4; ++j) {
+        y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf];
+        y[j+16] = d * kvalues_iq4nl[q4[j] >>  4];
+    }
+}
+
+
+
 /*
 DPCT1110:4: The total declared local variable size in device function
 dequantize_mul_mat_vec_q2_k exceeds 128 bytes and may cause high register
@@ -4260,13 +4750,16 @@ static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx,
 
     }
 #else
-    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15 or 0...7
-    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0....1 or 0...3
+    const int tid = item_ct1.get_local_id(2) /
+                    (2 * K_QUANTS_PER_ITERATION); // 0...15 or 0...7
+    const int ix = item_ct1.get_local_id(2) %
+                   (2 * K_QUANTS_PER_ITERATION); // 0....1 or 0...3
     const int offset = tid * K_QUANTS_PER_ITERATION;
 
     uint32_t uaux[2];
     const uint8_t * d = (const uint8_t *)uaux;
 
+
     for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
 
         const float   * y = yy + i * QK_K + offset;
@@ -4276,7 +4769,8 @@ static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx,
         uaux[0] = s[0] & 0x0f0f0f0f;
         uaux[1] = (s[0] >> 4) & 0x0f0f0f0f;
 
-        const float2 dall = __half22float2(x[i].dm);
+        const sycl::float2 dall =
+            x[i].dm.convert<float, sycl::rounding_mode::automatic>();
 
         float sum1 = 0, sum2 = 0;
         for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
@@ -4287,8 +4781,9 @@ static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx,
                   + y[l+48] * d[3] * ((ql >> 6) & 3);
             sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7];
         }
-        tmp += dall.x * sum1 - dall.y * sum2;
+        tmp += dall.x() * sum1 - dall.y() * sum2;
     }
+
 #endif
 
     // sum up partial sums and write back result
@@ -4383,8 +4878,8 @@ static void dequantize_mul_mat_vec_q3_k(const void *__restrict__ vx,
     }
 #else
 
-    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15 or 0...7
-    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0....1 or 0...3
+    const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION);  // 0...15 or 0...7
+    const int ix  = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION);  // 0....1 or 0...3
     const int offset = tid * K_QUANTS_PER_ITERATION;         // 0...15 or 0...14
     const int in = offset/8;                                 // 0 or 1
     const int im = offset%8;                                 // 0...7
@@ -4533,8 +5028,8 @@ static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx,
 
     }
 #else
-    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15
-    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);
+    const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION);  // 0...15
+    const int ix  = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION);
 
     const int step = tid * K_QUANTS_PER_ITERATION;
 
@@ -4674,8 +5169,8 @@ static void dequantize_mul_mat_vec_q5_k(const void *__restrict__ vx,
     }
 
 #else
-    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15
-    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);
+    const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION);  // 0...15
+    const int ix  = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION);
     const int step = tid * K_QUANTS_PER_ITERATION;
     const int im = step/8;
     const int in = step%8;
@@ -4783,8 +5278,8 @@ static void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const floa
 
 #else
 
-    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...7
-    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0...3
+    const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION);  // 0...7
+    const int ix  = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION);  // 0...3
 
     const int step = tid * K_QUANTS_PER_ITERATION;
 
@@ -4964,8 +5459,8 @@ static void k_get_rows_float(
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
 static void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k,
                              const sycl::nd_item<3> &item_ct1) {
-    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                  2 * item_ct1.get_local_id(2);
+    const int i = 2 * (item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                       item_ct1.get_local_id(2));
 
     if (i >= k) {
         return;
@@ -4984,6 +5479,21 @@ static void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__
     y[iybs + iqs + y_offset] = v.y();
 }
 
+template <typename src_t, typename dst_t>
+static void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int k,
+                          const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+
+    const src_t * x = (src_t *) vx;
+
+    y[i] = x[i];
+}
+
 // VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
 // MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
 
@@ -6402,8 +6912,8 @@ vec_dot_q4_K_q8_1(const void *__restrict__ vbq,
     const float dall = bq4_K->dm[0];
     const float dmin = bq4_K->dm[1];
 
-    const float d8_1 = __low2float(bq8_1[0].ds);
-    const float d8_2 = __low2float(bq8_1[1].ds);
+    const float d8_1 = bq8_1[0].ds[0];
+    const float d8_2 = bq8_1[1].ds[1];
 
     const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
     const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
@@ -6414,10 +6924,10 @@ vec_dot_q4_K_q8_1(const void *__restrict__ vbq,
     const int v1 = q4[0];
     const int v2 = q4[4];
 
-    const int dot1 = __dp4a(ui2, v2 & 0x0f0f0f0f, __dp4a(ui1, v1 & 0x0f0f0f0f, 0));
-    const int dot2 = __dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, __dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0));
-    const int dot3 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0));
-    const int dot4 = __dp4a(0x01010101, ui4, __dp4a(0x01010101, ui3, 0));
+    const int dot1 = dpct::dp4a(ui2, v2 & 0x0f0f0f0f, dpct::dp4a(ui1, v1 & 0x0f0f0f0f, 0));
+    const int dot2 = dpct::dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, dpct::dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0));
+    const int dot3 = dpct::dp4a(0x01010101, ui2, dpct::dp4a(0x01010101, ui1, 0));
+    const int dot4 = dpct::dp4a(0x01010101, ui4, dpct::dp4a(0x01010101, ui3, 0));
 
     sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]);
     sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]);
@@ -6586,8 +7096,8 @@ vec_dot_q5_K_q8_1(const void *__restrict__ vbq,
 
     const float d = bq5_K->d;
 
-    const float d8_1 = __low2half(bq8_1[0].ds);
-    const float d8_2 = __low2half(bq8_1[1].ds);
+    const float d8_1 = bq8_1[0].ds[0];
+    const float d8_2 = bq8_1[1].ds[1];
 
     const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
     const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
@@ -6608,8 +7118,8 @@ vec_dot_q5_K_q8_1(const void *__restrict__ vbq,
     const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f);
     const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f);
 
-    const float sumf_d = d8_1 * (__dp4a(ui1, v1, 0) * s[0] + __dp4a(ui2, v2, 0) * s[1])
-                       + d8_2 * (__dp4a(ui3, v3, 0) * s[2] + __dp4a(ui4, v4, 0) * s[3]);
+    const float sumf_d = d8_1 * (dpct::dp4a(ui1, v1, 0) * s[0] + dpct::dp4a(ui2, v2, 0) * s[1])
+                       + d8_2 * (dpct::dp4a(ui3, v3, 0) * s[2] + dpct::dp4a(ui4, v4, 0) * s[3]);
 
     return d * sumf_d;
 
@@ -6865,6 +7375,368 @@ static __dpct_inline__ float vec_dot_q6_K_q8_1_mul_mat(
     return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]);
 }
 
+
+static __dpct_inline__ float
+vec_dot_iq2_xxs_q8_1(const void *__restrict__ vbq,
+                     const block_q8_1 *__restrict__ bq8_1, const int &iqs,
+                     const uint64_t *iq2xxs_grid, const uint8_t *ksigns_iq2xs,
+                     const uint8_t *kmask_iq2xs) {
+#if QK_K == 256
+    const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq;
+
+#if QR2_XXS == 8
+    const int ib32 = iqs;
+    const uint16_t * q2 = bq2->qs + 4*ib32;
+    const uint8_t  * aux8 = (const uint8_t *)q2;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    uint32_t aux32 = q2[2] | (q2[3] << 16);
+    int sumi = 0;
+    for (int l = 0; l < 4; ++l) {
+        const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]);
+        const uint8_t  signs = ksigns_iq2xs[aux32 & 127];
+        for (int j = 0; j < 8; ++j) {
+            sumi += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
+        }
+        q8 += 8;
+        aux32 >>= 7;
+    }
+    const float d = (float)bq2->d * (0.5f + aux32) * bq8_1[ib32].ds[0] * 0.25f;
+    return d * sumi;
+#else
+    // iqs is 0...15
+    const int ib32 = iqs/2;
+    const int il = iqs%2;
+    const uint16_t * q2 = bq2->qs + 4*ib32;
+    const uint8_t  * aux8 = (const uint8_t *)q2;
+    const uint8_t  * grid1 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+0]);
+    const uint8_t  * grid2 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+1]);
+    const uint32_t aux32 = q2[2] | (q2[3] << 16);
+    const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * bq8_1[ib32].ds[0] * 0.25f;
+    const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127];
+    const uint8_t signs2 = ksigns_iq2xs[(aux32 >> (14*il + 7)) & 127];
+    const int8_t * q8 = bq8_1[ib32].qs + 16*il;
+    int sumi1 = 0, sumi2 = 0;
+    for (int j = 0; j < 8; ++j) {
+        sumi1 += q8[j+0] * grid1[j] * (signs1 & kmask_iq2xs[j] ? -1 : 1);
+        sumi2 += q8[j+8] * grid2[j] * (signs2 & kmask_iq2xs[j] ? -1 : 1);
+    }
+    return d * (sumi1 + sumi2);
+#endif
+#else
+    assert(false);
+    return 0.f;
+#endif
+}
+
+static __dpct_inline__ float
+vec_dot_iq2_xs_q8_1(const void *__restrict__ vbq,
+                    const block_q8_1 *__restrict__ bq8_1, const int &iqs,
+                    const uint64_t *iq2xs_grid, const uint64_t *ksigns64) {
+#if DPCT_COMPATIBILITY_TEMP >=                                                 \
+    MIN_CC_DP4A // lowest compute capability for integer intrinsics
+#if QK_K == 256
+    const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq;
+
+    const int ib32 = iqs;
+    const uint16_t * q2 = bq2->qs + 4*ib32;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    const uint8_t ls1 = bq2->scales[ib32] & 0xf;
+    const uint8_t ls2 = bq2->scales[ib32] >>  4;
+    int sumi1 = 0;
+    for (int l = 0; l < 2; ++l) {
+        const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511));
+        const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9));
+        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
+            grid[0] ^ signs[0], signs[0], std::minus<>());
+        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
+            grid[1] ^ signs[1], signs[1], std::minus<>());
+        sumi1 = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi1);
+        sumi1 = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi1);
+        q8 += 8;
+    }
+    int sumi2 = 0;
+    for (int l = 2; l < 4; ++l) {
+        const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511));
+        const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9));
+        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
+            grid[0] ^ signs[0], signs[0], std::minus<>());
+        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
+            grid[1] ^ signs[1], signs[1], std::minus<>());
+        sumi2 = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi2);
+        sumi2 = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi2);
+        q8 += 8;
+    }
+    const float d = (float)bq2->d * bq8_1[ib32].ds[0] * 0.25f;
+    return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
+#else
+    assert(false);
+    return 0.f;
+#endif
+#else
+    assert(false);
+    return 0.f;
+#endif
+}
+
+static __dpct_inline__ float
+vec_dot_iq2_s_q8_1(const void *__restrict__ vbq,
+                   const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+#if QK_K == 256
+    const block_iq2_s * bq2 = (const block_iq2_s *) vbq;
+
+    const int ib32 = iqs;
+    const int8_t  * q8 = bq8_1[ib32].qs;
+    const uint8_t * signs = bq2->qs + QK_K/8 + 4*ib32;
+    const uint8_t ls1 = bq2->scales[ib32] & 0xf;
+    const uint8_t ls2 = bq2->scales[ib32] >>  4;
+    int sumi1 = 0;
+    for (int l = 0; l < 2; ++l) {
+        const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300)));
+        const uint32_t signs0 = dpct::vectorized_binary<sycl::uchar4>(
+            ((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201,
+            std::equal_to<>());
+        const uint32_t signs1 = dpct::vectorized_binary<sycl::uchar4>(
+            ((signs[l] >> 4) * 0x01010101) & 0x08040201, 0x08040201,
+            std::equal_to<>());
+        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
+            grid[0] ^ signs0, signs0, std::minus<>());
+        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
+            grid[1] ^ signs1, signs1, std::minus<>());
+        sumi1 = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi1);
+        sumi1 = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi1);
+        q8 += 8;
+    }
+    int sumi2 = 0;
+    for (int l = 2; l < 4; ++l) {
+        const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300)));
+        const uint32_t signs0 = dpct::vectorized_binary<sycl::uchar4>(
+            ((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201,
+            std::equal_to<>());
+        const uint32_t signs1 = dpct::vectorized_binary<sycl::uchar4>(
+            ((signs[l] >> 4) * 0x01010101) & 0x08040201, 0x08040201,
+            std::equal_to<>());
+        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
+            grid[0] ^ signs0, signs0, std::minus<>());
+        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
+            grid[1] ^ signs1, signs1, std::minus<>());
+        sumi2 = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi2);
+        sumi2 = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi2);
+        q8 += 8;
+    }
+    const float d = (float)bq2->d * bq8_1[ib32].ds[0] * 0.25f;
+    return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
+#else
+    assert(false);
+#endif
+}
+
+static __dpct_inline__ float
+vec_dot_iq3_xxs_q8_1(const void *__restrict__ vbq,
+                     const block_q8_1 *__restrict__ bq8_1, const int &iqs,
+                     const uint32_t *iq3xxs_grid, const uint64_t *ksigns64) {
+#if DPCT_COMPATIBILITY_TEMP >=                                                 \
+    MIN_CC_DP4A // lowest compute capability for integer intrinsics
+#if QK_K == 256
+    const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq;
+
+    const int ib32 = iqs;
+    const uint8_t  * q3 = bq2->qs + 8*ib32;
+    const uint16_t * gas = (const uint16_t *)(bq2->qs + QK_K/4) + 2*ib32;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    uint32_t aux32 = gas[0] | (gas[1] << 16);
+    int sumi = 0;
+    for (int l = 0; l < 4; ++l) {
+        const uint32_t * grid1 = iq3xxs_grid + q3[2*l+0];
+        const uint32_t * grid2 = iq3xxs_grid + q3[2*l+1];
+        const uint32_t * signs = (const uint32_t *)(ksigns64 + (aux32 & 127));
+        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
+            grid1[0] ^ signs[0], signs[0], std::minus<>());
+        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
+            grid2[0] ^ signs[1], signs[1], std::minus<>());
+        sumi = dpct::dp4a(grid_l, *((int *)q8 + 0), sumi);
+        sumi = dpct::dp4a(grid_h, *((int *)q8 + 1), sumi);
+        q8 += 8;
+        aux32 >>= 7;
+    }
+    const float d = (float)bq2->d * (0.5f + aux32) * bq8_1[ib32].ds[0] * 0.5f;
+    return d * sumi;
+#else
+    assert(false);
+    return 0.f;
+#endif
+#else
+    assert(false);
+    return 0.f;
+#endif
+}
+
+static __dpct_inline__ float
+vec_dot_iq3_s_q8_1(const void *__restrict__ vbq,
+                   const block_q8_1 *__restrict__ bq8_1, const int &iqs,
+                   const uint32_t *iq3s_grid) {
+#if QK_K == 256
+    const block_iq3_s * bq2 = (const block_iq3_s *) vbq;
+
+    const int ib32 = iqs;
+    const uint8_t  * qs = bq2->qs + 8*ib32;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    int sumi = 0;
+    for (int l = 0; l < 4; ++l) {
+        const uint32_t * grid1 = iq3s_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256));
+        const uint32_t * grid2 = iq3s_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256));
+        uint32_t signs0 = dpct::vectorized_binary<sycl::uchar4>(
+            ((bq2->signs[4 * ib32 + l] & 0xf) * 0x01010101) & 0x08040201,
+            0x08040201, std::equal_to<>());
+        uint32_t signs1 = dpct::vectorized_binary<sycl::uchar4>(
+            ((bq2->signs[4 * ib32 + l] >> 4) * 0x01010101) & 0x08040201,
+            0x08040201, std::equal_to<>());
+        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
+            grid1[0] ^ signs0, signs0, std::minus<>());
+        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
+            grid2[0] ^ signs1, signs1, std::minus<>());
+        sumi = dpct::dp4a(grid_l, *((int *)q8 + 0), sumi);
+        sumi = dpct::dp4a(grid_h, *((int *)q8 + 1), sumi);
+        q8 += 8;
+    }
+    const float d =
+        (float)bq2->d *
+        (1 + 2 * ((bq2->scales[ib32 / 2] >> 4 * (ib32 % 2)) & 0xf)) *
+        bq8_1[ib32].ds[0];
+    return d * sumi;
+#else
+    assert(false);
+#endif
+}
+
+static __dpct_inline__ float
+vec_dot_iq1_s_q8_1(const void *__restrict__ vbq,
+                   const block_q8_1 *__restrict__ bq8_1, const int &iqs,
+                   const uint32_t *iq1s_grid_gpu) {
+#if QK_K == 256
+    const block_iq1_s * bq1 = (const block_iq1_s *) vbq;
+
+    const int ib32 = iqs;
+    int sumi = 0;
+    const int * q8 = (const int *)bq8_1[ib32].qs;
+    for (int l = 0; l < 4; ++l) {
+        const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8)));
+        int grid0 = grid[0] & 0x0f0f0f0f;
+        int grid1 = (grid[0] >> 4) & 0x0f0f0f0f;
+        sumi = dpct::dp4a(q8[2 * l + 1], grid1,
+                          dpct::dp4a(q8[2 * l + 0], grid0, sumi));
+    }
+
+    const float delta = bq1->qh[ib32] & 0x8000 ? -1-IQ1S_DELTA : -1+IQ1S_DELTA;
+    const float d1q = (float)bq1->d * (2*((bq1->qh[ib32] >> 12) & 7) + 1);
+    const float d = d1q * bq8_1[ib32].ds[0];
+    const float m = d1q * bq8_1[ib32].ds[1];
+    return d * sumi + m * delta;
+#else
+    assert(false);
+#endif
+}
+
+static __dpct_inline__ float
+vec_dot_iq1_m_q8_1(const void *__restrict__ vbq,
+                   const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+#if QK_K == 256
+    const block_iq1_m * bq1 = (const block_iq1_m *) vbq;
+
+    const int ib32 = iqs;
+    int   sumi[2] = {0, 0};
+    float sumf[2] = {0.f, 0.f};
+
+    const int * q8 = (const int *)bq8_1[ib32].qs;
+    for (int l = 0; l < 4; ++l) {
+        const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 7) << 8)));
+        int grid0 = grid[0] & 0x0f0f0f0f;
+        int grid1 = (grid[0] >> 4) & 0x0f0f0f0f;
+        sumi[l / 2] = dpct::dp4a(q8[2 * l + 1], grid1,
+                                 dpct::dp4a(q8[2 * l + 0], grid0, sumi[l / 2]));
+        const float delta = (bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 0x08 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA;
+        const int sumy = dpct::dp4a(q8[2 * l + 1], 0x01010101,
+                                    dpct::dp4a(q8[2 * l + 0], 0x01010101, 0));
+        sumf[l/2] += delta*sumy;
+    }
+
+    iq1m_scale_t scale;
+    const uint16_t * sc = (const uint16_t *)bq1->scales;
+    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+    const float d = (float)scale.f16 * bq8_1[ib32].ds[0];
+    return d * ((sumi[0] + sumf[0]) * (2*((sc[ib32/2] >> 6*(ib32%2)) & 0x7) + 1) + (sumi[1] + sumf[1]) * (2*((sc[ib32/2] >> (6*(ib32%2)+3)) & 0x7) + 1));
+#else
+    assert(false);
+#endif
+}
+
+static __dpct_inline__ void get_int_from_table_16(const uint32_t &q4,
+                                                  const uint8_t *values,
+                                                  int &val1, int &val2) {
+
+    uint32_t aux32; const uint8_t * q8 = (const uint8_t *)&aux32;
+    aux32 = q4 & 0x0f0f0f0f;
+    uint16_t v1 = values[q8[0]] | (values[q8[1]] << 8);
+    uint16_t v2 = values[q8[2]] | (values[q8[3]] << 8);
+    val1 = v1 | (v2 << 16);
+    aux32 = (q4 >> 4) & 0x0f0f0f0f;
+    v1 = values[q8[0]] | (values[q8[1]] << 8);
+    v2 = values[q8[2]] | (values[q8[3]] << 8);
+    val2 = v1 | (v2 << 16);
+}
+
+
+static __dpct_inline__ float
+vec_dot_iq4_nl_q8_1(const void *__restrict__ vbq,
+                    const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+    const block_iq4_nl * bq = (const block_iq4_nl *) vbq;
+
+    const uint16_t * q4 = (const uint16_t *)bq->qs + 2*iqs;
+    const int32_t  * q8 = (const int32_t  *)bq8_1->qs + iqs;
+
+    const uint8_t * values = (const uint8_t *)kvalues_iq4nl;
+
+    int v1, v2;
+    int sumi1 = 0, sumi2 = 0;
+    for (int l = 0; l < VDR_Q4_0_Q8_1_MMVQ; ++l) {
+        const uint32_t aux = q4[2*l] | (q4[2*l+1] << 16);
+        get_int_from_table_16(aux, values, v1, v2);
+        sumi1 = dpct::dp4a(v1, q8[l + 0], sumi1);
+        sumi2 = dpct::dp4a(v2, q8[l + 4], sumi2);
+    }
+
+    const float d = (float)bq->d * bq8_1->ds[0];
+    return d * (sumi1 + sumi2);
+}
+
+
+static __dpct_inline__ float
+vec_dot_iq4_xs_q8_1(const void *__restrict__ vbq,
+                    const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+#if QK_K == 256
+    const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq;
+    const uint8_t * values = (const uint8_t *)kvalues_iq4nl;
+
+    // iqs is 0...7
+    const int ib32 = iqs;
+    const int32_t  * q8 = (const int *)bq8_1[ib32].qs;
+    const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32;
+    const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4);
+    const float d = (float)bq4->d * (ls - 32) * bq8_1[ib32].ds[0];
+    int v1, v2;
+    int sumi1 = 0, sumi2 = 0;
+    for (int j = 0; j < 4; ++j) {
+        get_int_from_table_16(q4[j], values, v1, v2);
+        sumi1 = dpct::dp4a(v1, q8[j + 0], sumi1);
+        sumi2 = dpct::dp4a(v2, q8[j + 4], sumi2);
+    }
+    return d * (sumi1 + sumi2);
+#else
+    assert(false);
+#endif
+}
+
 template <int qk, int qr, int qi, bool need_sum, typename block_q_t, int mmq_x,
           int mmq_y, int nwarps, load_tiles_sycl_t load_tiles, int vdr,
           vec_dot_q_mul_mat_sycl_t vec_dot>
@@ -7457,25 +8329,26 @@ static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict_
     const int blocks_per_row = ncols / qk;
     const int blocks_per_warp = vdr * WARP_SIZE / qi;
 
-// partial sum for each thread
+    const int qi_vdr = (qi / vdr); // N_threads processing 1 qk block
+
+    // partial sum for each thread
     float tmp = 0.0f;
 
     const block_q_t  * x = (const block_q_t  *) vx;
     const block_q8_1 * y = (const block_q8_1 *) vy;
 
-    for (int i = 0; i < blocks_per_row; i += blocks_per_warp) {
-        const int ibx = row * blocks_per_row + i +
-                        item_ct1.get_local_id(2) / (qi / vdr); // x block index
+    for (int i = item_ct1.get_local_id(2) / qi_vdr; i < blocks_per_row;
+         i += blocks_per_warp) {
+      const int ibx = row * blocks_per_row + i; // x block index
 
-        const int iby = (i + item_ct1.get_local_id(2) / (qi / vdr)) *
-                        (qk / QK8_1); // y block index that aligns with ibx
+      const int iby = i * (qk / QK8_1); // y block index that aligns with ibx
 
-        const int iqs =
-            vdr *
-            (item_ct1.get_local_id(2) %
-             (qi / vdr)); // x block quant index when casting the quants to int
+      const int iqs =
+          vdr *
+          (item_ct1.get_local_id(2) -
+           i * qi_vdr); // x block quant index when casting the quants to int
 
-        tmp += vec_dot_q_sycl(&x[ibx], &y[iby], iqs);
+      tmp += vec_dot_q_sycl(&x[ibx], &y[iby], iqs);
     }
 
     // sum up partial sums and write back result
@@ -7490,6 +8363,440 @@ static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict_
     }
 }
 
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq2_xxs_q8_1(const void *__restrict__ vx,
+                                       const void *__restrict__ vy,
+                                       float *__restrict__ dst, const int ncols,
+                                       const int nrows,
+                                       const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq2_xxs_q8_1(&x[ibx], &y[iby], iqs, iq2xxs_grid, ksigns_iq2xs, kmask_iq2xs);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq2_xs_q8_1(const void *__restrict__ vx,
+                                      const void *__restrict__ vy,
+                                      float *__restrict__ dst, const int ncols,
+                                      const int nrows,
+                                      const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq2_xs_q8_1(&x[ibx], &y[iby], iqs, iq2xs_grid, ksigns64);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq2_s_q8_1(const void *__restrict__ vx,
+                                     const void *__restrict__ vy,
+                                     float *__restrict__ dst, const int ncols,
+                                     const int nrows,
+                                     const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq2_s_q8_1(&x[ibx], &y[iby], iqs);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq3_xxs_q8_1(const void *__restrict__ vx,
+                                       const void *__restrict__ vy,
+                                       float *__restrict__ dst, const int ncols,
+                                       const int nrows,
+                                       const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq3_xxs_q8_1(&x[ibx], &y[iby], iqs, iq3xxs_grid, ksigns64);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq3_s_q8_1(const void *__restrict__ vx,
+                                     const void *__restrict__ vy,
+                                     float *__restrict__ dst, const int ncols,
+                                     const int nrows,
+                                     const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq3_s_q8_1(&x[ibx], &y[iby], iqs, iq3s_grid);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq1_s_q8_1(const void *__restrict__ vx,
+                                     const void *__restrict__ vy,
+                                     float *__restrict__ dst, const int ncols,
+                                     const int nrows,
+                                     const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq1_s_q8_1(&x[ibx], &y[iby], iqs, iq1s_grid_gpu);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq1_m_q8_1(const void *__restrict__ vx,
+                                     const void *__restrict__ vy,
+                                     float *__restrict__ dst, const int ncols,
+                                     const int nrows,
+                                     const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq1_m_q8_1(&x[ibx], &y[iby], iqs);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq4_nl_q8_1(const void *__restrict__ vx,
+                                      const void *__restrict__ vy,
+                                      float *__restrict__ dst, const int ncols,
+                                      const int nrows,
+                                      const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq4_nl_q8_1(&x[ibx], &y[iby], iqs);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq4_xs_q8_1(const void *__restrict__ vx,
+                                      const void *__restrict__ vy,
+                                      float *__restrict__ dst, const int ncols,
+                                      const int nrows,
+                                      const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq4_xs_q8_1(&x[ibx], &y[iby], iqs);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
 static void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows,
                                    const sycl::nd_item<3> &item_ct1) {
@@ -7695,7 +9002,7 @@ static void cpy_1_f16_f16(const char * cxi, char * cdsti) {
 
 static void cpy_1_f16_f32(const char * cxi, char * cdsti) {
     const sycl::half *xi = (const sycl::half *)cxi;
-    float *dsti = (float *)cdsti;
+    float * dsti = (float *) cdsti;
 
     *dsti = *xi;
 }
@@ -8008,32 +9315,6 @@ static void rope_glm_f32(
     dst[i + half_n_dims * 3] = x2*sin_block_theta + x3*cos_block_theta;
 }
 
-static void alibi_f32(const float * x, float * dst, const int ncols, const int k_rows,
-                                 const int n_heads_log2_floor, const float m0, const float m1,
-                                 const sycl::nd_item<3> &item_ct1) {
-    const int col = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                    item_ct1.get_local_id(2);
-
-    if (col >= ncols) {
-        return;
-    }
-
-    const int row = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
-                    item_ct1.get_local_id(1);
-    const int i = row*ncols + col;
-
-    const int k = row/k_rows;
-
-    float m_k;
-    if (k < n_heads_log2_floor) {
-        m_k = dpct::pow(m0, k + 1);
-    } else {
-        m_k = dpct::pow(m1, 2 * (k - n_heads_log2_floor) + 1);
-    }
-
-    dst[i] = col * m_k + x[i];
-}
-
 static void k_sum_rows_f32(const float * x, float * dst, const int ncols,
                            const sycl::nd_item<3> &item_ct1) {
     const int row = item_ct1.get_group(1);
@@ -8051,64 +9332,71 @@ static void k_sum_rows_f32(const float * x, float * dst, const int ncols,
     }
 }
 
+
 template<typename T>
-static inline void swap(T & a, T & b) {
+static inline void ggml_sycl_swap(T & a, T & b) {
     T tmp = a;
     a = b;
     b = tmp;
 }
 
-template<ggml_sort_order order>
-static void k_argsort_f32_i32(const float * x, int * dst, const int ncols,
-                              const sycl::nd_item<3> &item_ct1) {
+template <ggml_sort_order order>
+__dpct_inline__ static void
+k_argsort_f32_i32(const float *x, int *dst, const int ncols, int ncols_pad,
+                  const sycl::nd_item<3> &item_ct1, uint8_t *dpct_local) {
     // bitonic sort
     int col = item_ct1.get_local_id(2);
     int row = item_ct1.get_group(1);
 
-    if (col >= ncols) return;
+    if (col >= ncols_pad) {
+        return;
+    }
 
     const float * x_row = x + row * ncols;
-    int * dst_row = dst + row * ncols;
+    auto dst_row = (int *)dpct_local;
 
     // initialize indices
-    if (col < ncols) {
-        dst_row[col] = col;
-    }
-    /*
-    DPCT1065:58: Consider replacing sycl::nd_item::barrier() with
-    sycl::nd_item::barrier(sycl::access::fence_space::local_space) for better
-    performance if there is no access to global memory.
-    */
-    item_ct1.barrier();
+    dst_row[col] = col;
 
-    for (int k = 2; k <= ncols; k *= 2) {
+    item_ct1.barrier(sycl::access::fence_space::local_space);
+
+    for (int k = 2; k <= ncols_pad; k *= 2) {
         for (int j = k / 2; j > 0; j /= 2) {
             int ixj = col ^ j;
             if (ixj > col) {
                 if ((col & k) == 0) {
-                    if (order == GGML_SORT_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) {
-                        swap(dst_row[col], dst_row[ixj]);
+                    if (dst_row[col] >= ncols ||
+                        (dst_row[ixj] < ncols && (order == GGML_SORT_ORDER_ASC ?
+                            x_row[dst_row[col]] > x_row[dst_row[ixj]] :
+                            x_row[dst_row[col]] < x_row[dst_row[ixj]]))
+                    ) {
+                        ggml_sycl_swap(dst_row[col], dst_row[ixj]);
                     }
                 } else {
-                    if (order == GGML_SORT_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) {
-                        swap(dst_row[col], dst_row[ixj]);
+                    if (dst_row[ixj] >= ncols ||
+                        (dst_row[col] < ncols && (order == GGML_SORT_ORDER_ASC ?
+                            x_row[dst_row[col]] < x_row[dst_row[ixj]] :
+                            x_row[dst_row[col]] > x_row[dst_row[ixj]]))
+                    ) {
+                        ggml_sycl_swap(dst_row[col], dst_row[ixj]);
                     }
                 }
             }
             /*
-            DPCT1118:11: SYCL group functions and algorithms must be encountered
+            DPCT1118:1: SYCL group functions and algorithms must be encountered
             in converged control flow. You may need to adjust the code.
             */
-            /*
-            DPCT1065:59: Consider replacing sycl::nd_item::barrier() with
-            sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
-            better performance if there is no access to global memory.
-            */
-            item_ct1.barrier();
+            item_ct1.barrier(sycl::access::fence_space::local_space);
         }
     }
+
+    // copy the result to dst without the padding
+    if (col < ncols) {
+        dst[row * ncols + col] = dst_row[col];
+    }
 }
 
+
 static void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past,
                               const sycl::nd_item<3> &item_ct1) {
     const int col = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
@@ -8126,23 +9414,51 @@ static void diag_mask_inf_f32(const float * x, float * dst, const int ncols, con
     dst[i] = x[i] - (col > n_past + row % rows_per_channel) * FLT_MAX;
 }
 
-static void soft_max_f32(const float * x, const float * y, float * dst, const int ncols, const int nrows_y, const float scale,
-                         const sycl::nd_item<3> &item_ct1, float *buf) {
+
+template <bool vals_smem, int ncols_template, int block_size_template>
+static void soft_max_f32(const float * x, const float * mask, float * dst, const int ncols_par,
+                         const int nrows_y, const float scale, const float max_bias, const float m0,
+                         const float m1, uint32_t n_head_log2, const sycl::nd_item<3> &item_ct1, float *buf) {
+    const int ncols = ncols_template == 0 ? ncols_par : ncols_template;
+
     const int tid = item_ct1.get_local_id(2);
     const int rowx = item_ct1.get_group(2);
     const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension
 
-    const int block_size = item_ct1.get_local_range(2);
+    const int block_size = block_size_template == 0 ? item_ct1.get_local_range(2) : block_size_template;
 
     const int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
     const int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
 
+    float slope = 1.0f;
+
+    // ALiBi
+    if (max_bias > 0.0f) {
+        const uint32_t h = rowx/nrows_y; // head index
+
+        const float base = h < n_head_log2 ? m0 : m1;
+        const int   exp  = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+        slope = sycl::pow(base, float(exp));
+    }
+
+    float * vals = vals_smem ? buf + WARP_SIZE : dst + rowx*ncols;
     float max_val = -INFINITY;
 
-    for (int col = tid; col < ncols; col += block_size) {
+    for (int col0 = 0; col0 < ncols; col0 += block_size) {
+        const int col = col0 + tid;
+
+        if (ncols_template == 0 && col >= ncols) {
+            break;
+        }
+
         const int ix = rowx*ncols + col;
         const int iy = rowy*ncols + col;
-        max_val = sycl::max(max_val, x[ix] * scale + (y ? y[iy] : 0.0f));
+
+        const float val = x[ix]*scale + (mask ? slope*mask[iy] : 0.0f);
+
+        vals[col] = val;
+        max_val = sycl::max(max_val, val);
     }
 
     // find the max value in the block
@@ -8151,30 +9467,12 @@ static void soft_max_f32(const float * x, const float * y, float * dst, const in
         if (warp_id == 0) {
             buf[lane_id] = -INFINITY;
         }
-        /*
-        DPCT1118:12: SYCL group functions and algorithms must be encountered in
-        converged control flow. You may need to adjust the code.
-        */
-        /*
-        DPCT1065:60: Consider replacing sycl::nd_item::barrier() with
-        sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
-        better performance if there is no access to global memory.
-        */
-        item_ct1.barrier();
+        item_ct1.barrier(sycl::access::fence_space::local_space);
 
         if (lane_id == 0) {
             buf[warp_id] = max_val;
         }
-        /*
-        DPCT1118:13: SYCL group functions and algorithms must be encountered in
-        converged control flow. You may need to adjust the code.
-        */
-        /*
-        DPCT1065:61: Consider replacing sycl::nd_item::barrier() with
-        sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
-        better performance if there is no access to global memory.
-        */
-        item_ct1.barrier();
+        item_ct1.barrier(sycl::access::fence_space::local_space);
 
         max_val = buf[lane_id];
         max_val = warp_reduce_max(max_val, item_ct1);
@@ -8182,13 +9480,16 @@ static void soft_max_f32(const float * x, const float * y, float * dst, const in
 
     float tmp = 0.f;
 
-    for (int col = tid; col < ncols; col += block_size) {
-        const int ix = rowx*ncols + col;
-        const int iy = rowy*ncols + col;
-        const float val =
-            sycl::native::exp((x[ix] * scale + (y ? y[iy] : 0.0f)) - max_val);
+#pragma unroll
+    for (int col0 = 0; col0 < ncols; col0 += block_size) {
+        const int col = col0 + tid;
+                if (ncols_template == 0 && col >= ncols) {
+            break;
+        }
+
+        const float val = sycl::native::exp(vals[col] - max_val);
         tmp += val;
-        dst[ix] = val;
+        vals[col] = val;
     }
 
     // find the sum of exps in the block
@@ -8197,40 +9498,29 @@ static void soft_max_f32(const float * x, const float * y, float * dst, const in
         if (warp_id == 0) {
             buf[lane_id] = 0.f;
         }
-        /*
-        DPCT1118:14: SYCL group functions and algorithms must be encountered in
-        converged control flow. You may need to adjust the code.
-        */
-        /*
-        DPCT1065:62: Consider replacing sycl::nd_item::barrier() with
-        sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
-        better performance if there is no access to global memory.
-        */
-        item_ct1.barrier();
+        item_ct1.barrier(sycl::access::fence_space::local_space);
 
         if (lane_id == 0) {
             buf[warp_id] = tmp;
         }
-        /*
-        DPCT1118:15: SYCL group functions and algorithms must be encountered in
-        converged control flow. You may need to adjust the code.
-        */
-        /*
-        DPCT1065:63: Consider replacing sycl::nd_item::barrier() with
-        sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
-        better performance if there is no access to global memory.
-        */
-        item_ct1.barrier();
+        item_ct1.barrier(sycl::access::fence_space::local_space);
 
         tmp = buf[lane_id];
         tmp = warp_reduce_sum(tmp, item_ct1);
     }
 
-    const float inv_tmp = 1.f / tmp;
+    const float inv_sum = 1.f / tmp;
 
-    for (int col = tid; col < ncols; col += block_size) {
-        const int i = rowx*ncols + col;
-        dst[i] *= inv_tmp;
+#pragma unroll
+    for (int col0 = 0; col0 < ncols; col0 += block_size) {
+        const int col = col0 + tid;
+
+        if (ncols_template == 0 && col >= ncols) {
+            return;
+        }
+
+        const int idst = rowx*ncols + col;
+        dst[idst] = vals[col] * inv_sum;
     }
 }
 
@@ -8295,6 +9585,62 @@ static void im2col_kernel(const float *x, T *dst, int offset_delta,
     }
 }
 
+template <typename Ti, typename To>
+static  void pool2d_nchw_kernel(
+        const int ih, const int iw, const int oh, const int ow,
+        const int kh, const int kw, const int sh, const int sw,
+        const int ph, const int pw, const int parallel_elements,
+        const Ti* src, To* dst, const enum ggml_op_pool op,
+        const sycl::nd_item<3> &item_ct1) {
+        int idx = item_ct1.get_local_id(2) +
+                  item_ct1.get_group(2) * item_ct1.get_local_range(2);
+        if (idx >= parallel_elements) {
+            return;
+        }
+
+        const int I_HW = ih * iw;
+        const int O_HW = oh * ow;
+        const int nc = idx / O_HW;
+        const int cur_oh = idx % O_HW / ow;
+        const int cur_ow = idx % O_HW % ow;
+        const Ti* i_ptr = src + nc * I_HW;
+        To* o_ptr = dst + nc * O_HW;
+        const int start_h = cur_oh * sh - ph;
+        const int bh = sycl::max(0, start_h);
+        const int eh = sycl::min(ih, start_h + kh);
+        const int start_w = cur_ow * sw - pw;
+        const int bw = sycl::max(0, start_w);
+        const int ew = sycl::min(iw, start_w + kw);
+
+        To res = 0;
+
+        switch (op) {
+            case GGML_OP_POOL_AVG: res = 0; break;
+            case GGML_OP_POOL_MAX: res = -FLT_MAX; break;
+        }
+
+        for (int i = bh; i < eh; i += 1) {
+            for (int j = bw; j < ew; j += 1) {
+#if DPCT_COMPATIBILITY_TEMP >= 350
+                /*
+                DPCT1098:106: The '*' expression is used instead of the __ldg
+                call. These two expressions do not provide the exact same
+                functionality. Check the generated code for potential precision
+                and/or performance issues.
+                */
+                Ti cur = *(i_ptr + i * iw + j);
+#else
+                Ti cur = i_ptr[i * iw + j];
+#endif
+                switch (op) {
+                    case GGML_OP_POOL_AVG: res += (cur / (kh * kw)); break;
+                    case GGML_OP_POOL_MAX: res = sycl::max(res, (To)cur); break;
+                }
+            }
+        }
+        o_ptr[cur_oh * ow + cur_ow] = res;
+}
+
 template <int qk, int qr, dequantize_kernel_t dq>
 static void get_rows_sycl(const ggml_tensor *src0, const ggml_tensor *src1,
                           ggml_tensor *dst, const void *src0_dd,
@@ -8583,6 +9929,30 @@ static void relu_f32_sycl(const float *x, float *dst, const int k,
         });
 }
 
+static void hardsigmoid_f32_sycl(const float *x, float *dst, const int k,
+                                 dpct::queue_ptr stream) {
+    const int num_blocks = (k + SYCL_HARDSIGMOID_BLOCK_SIZE - 1) / SYCL_HARDSIGMOID_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            hardsigmoid_f32(x, dst, k, item_ct1);
+        });
+}
+
+static void hardswish_f32_sycl(const float *x, float *dst, const int k,
+                               dpct::queue_ptr stream) {
+    const int num_blocks = (k + SYCL_HARDSWISH_BLOCK_SIZE - 1) / SYCL_HARDSWISH_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            hardswish_f32(x, dst, k, item_ct1);
+        });
+}
+
 static void leaky_relu_f32_sycl(const float *x, float *dst, const int k,
                                 const float negative_slope,
                                 dpct::queue_ptr stream) {
@@ -8809,11 +10179,10 @@ template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
 static void dequantize_block_sycl(const void *__restrict__ vx,
                                   dst_t *__restrict__ y, const int k,
                                   dpct::queue_ptr stream) {
-    const int num_blocks = (k + SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / SYCL_DEQUANTIZE_BLOCK_SIZE;
+    const int num_blocks = (k + 2*SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / (2*SYCL_DEQUANTIZE_BLOCK_SIZE);
     {
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
-
         stream->parallel_for(
             sycl::nd_range<3>(
                 sycl::range<3>(1, 1, num_blocks) *
@@ -8842,7 +10211,18 @@ static void dequantize_row_q2_K_sycl(const void *vx, dst_t *y, const int k,
                              });
     }
 #else
-    dequantize_block_q2_K<<<nb, 32, 0, stream>>>(vx, y);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q2_K(vx, y, item_ct1);
+                             });
+    }
+
 #endif
 }
 
@@ -8863,10 +10243,57 @@ static void dequantize_row_q3_K_sycl(const void *vx, dst_t *y, const int k,
                              });
     }
 #else
-    dequantize_block_q3_K<<<nb, 32, 0, stream>>>(vx, y);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q3_K(vx, y, item_ct1);
+                             });
+    }
 #endif
 }
 
+template <typename dst_t>
+static void dequantize_row_q4_0_sycl(const void *vx, dst_t *y, const int k,
+                                     dpct::queue_ptr stream) {
+    const int nb32 = k / 32;
+    const int nb = (k + 255) / 256;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q4_0(vx, y, nb32, item_ct1);
+                             });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_q4_1_sycl(const void *vx, dst_t *y, const int k,
+                                     dpct::queue_ptr stream) {
+    const int nb32 = k / 32;
+    const int nb = (k + 255) / 256;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q4_1(vx, y, nb32, item_ct1);
+                             });
+    }
+}
+
+
 template <typename dst_t>
 static void dequantize_row_q4_K_sycl(const void *vx, dst_t *y, const int k,
                                      dpct::queue_ptr stream) {
@@ -8901,7 +10328,18 @@ static void dequantize_row_q5_K_sycl(const void *vx, dst_t *y, const int k,
                              });
     }
 #else
-    dequantize_block_q5_K<<<nb, 32, 0, stream>>>(vx, y);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q5_K(vx, y, item_ct1);
+                             });
+    }
+
 #endif
 }
 
@@ -8922,11 +10360,236 @@ static void dequantize_row_q6_K_sycl(const void *vx, dst_t *y, const int k,
                              });
     }
 #else
-    dequantize_block_q6_K<<<nb, 32, 0, stream>>>(vx, y);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q6_K(vx, y, item_ct1);
+                             });
+    }
+
 #endif
 }
 
-static to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type) {
+template <typename dst_t>
+static void dequantize_row_iq1_s_sycl(const void *vx, dst_t *y, const int k,
+                                        dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq1_s(
+                                     vx, y, item_ct1, iq1s_grid_gpu
+                                     );
+                             });
+        });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_iq1_m_sycl(const void *vx, dst_t *y, const int k,
+                                        dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq1_m(
+                                     vx, y, item_ct1, iq1s_grid_gpu
+                                     );
+                             });
+        });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_iq2_xxs_sycl(const void *vx, dst_t *y, const int k,
+                                        dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq2_xxs(
+                                     vx, y, item_ct1, iq2xxs_grid,
+                                     ksigns_iq2xs, kmask_iq2xs);
+                             });
+        });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_iq2_xs_sycl(const void *vx, dst_t *y, const int k,
+                                       dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq2_xs(
+                                     vx, y, item_ct1, iq2xs_grid,
+                                     ksigns_iq2xs, kmask_iq2xs);
+                             });
+        });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_iq2_s_sycl(const void *vx, dst_t *y, const int k,
+                                      dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq2_s(vx, y, item_ct1);
+                             });
+        });
+    }
+}
+
+
+template <typename dst_t>
+static void dequantize_row_iq3_xxs_sycl(const void *vx, dst_t *y, const int k,
+                                        dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq3_xxs(
+                                     vx, y, item_ct1, iq3xxs_grid,
+                                     ksigns_iq2xs, kmask_iq2xs);
+                             });
+        });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_iq3_s_sycl(const void *vx, dst_t *y, const int k,
+                                        dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq3_s(
+                                     vx, y, item_ct1, kmask_iq2xs, iq3s_grid);
+                             });
+        });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_iq4_xs_sycl(const void *vx, dst_t *y, const int k,
+                                       dpct::queue_ptr stream) {
+    const int nb = (k + QK_K - 1) / QK_K;
+#if QK_K == 64
+    dequantize_row_iq4_nl_sycl(vx, y, k, stream);
+#else
+      {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                  cgh.parallel_for(
+                      sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                            sycl::range<3>(1, 1, 32),
+                                        sycl::range<3>(1, 1, 32)),
+                      [=](sycl::nd_item<3> item_ct1) {
+                            dequantize_block_iq4_xs(vx, y, item_ct1);
+                      });
+            });
+      }
+#endif
+}
+
+
+template <typename dst_t>
+static void dequantize_row_iq4_nl_sycl(const void *vx, dst_t *y, const int k,
+                                       dpct::queue_ptr stream) {
+    const int nb = (k + QK_K - 1) / QK_K;
+      {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                  cgh.parallel_for(
+                      sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                            sycl::range<3>(1, 1, 32),
+                                        sycl::range<3>(1, 1, 32)),
+                      [=](sycl::nd_item<3> item_ct1) {
+                            dequantize_block_iq4_nl(vx, y, item_ct1);
+                      });
+            });
+      }
+}
+
+
+
+template <typename src_t, typename dst_t>
+static void convert_unary_sycl(const void *__restrict__ vx,
+                               dst_t *__restrict__ y, const int k,
+                               dpct::queue_ptr stream) {
+    const int num_blocks = (k + SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / SYCL_DEQUANTIZE_BLOCK_SIZE;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(
+                sycl::range<3>(1, 1, num_blocks) *
+                    sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE),
+                sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                convert_unary<src_t>(vx, y, k, item_ct1);
+            });
+    }
+}
+
+
+static to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type) try {
+    int id;
     switch (type) {
         case GGML_TYPE_Q4_0:
             return dequantize_block_sycl<QK4_0, QR4_0, dequantize_q4_0>;
@@ -8948,19 +10611,42 @@ static to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type) {
             return dequantize_row_q5_K_sycl;
         case GGML_TYPE_Q6_K:
             return dequantize_row_q6_K_sycl;
+        case GGML_TYPE_IQ1_S:
+            return dequantize_row_iq1_s_sycl;
+        case GGML_TYPE_IQ1_M:
+            return dequantize_row_iq1_m_sycl;
+        case GGML_TYPE_IQ2_XXS:
+            return dequantize_row_iq2_xxs_sycl;
+        case GGML_TYPE_IQ2_XS:
+            return dequantize_row_iq2_xs_sycl;
+        case GGML_TYPE_IQ2_S:
+            return dequantize_row_iq2_s_sycl;
+        case GGML_TYPE_IQ3_XXS:
+            return dequantize_row_iq3_xxs_sycl;
+        case GGML_TYPE_IQ3_S:
+            return dequantize_row_iq3_s_sycl;
+        case GGML_TYPE_IQ4_XS:
+            return dequantize_row_iq4_xs_sycl;
+        case GGML_TYPE_IQ4_NL:
+            return dequantize_row_iq4_nl_sycl;
         case GGML_TYPE_F32:
-            return dequantize_block_sycl<1, 1, convert_f32>;
+            return convert_unary_sycl<float>;
         default:
             return nullptr;
     }
 }
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
 
 static to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type) {
     switch (type) {
         case GGML_TYPE_Q4_0:
-            return dequantize_block_sycl<QK4_0, QR4_0, dequantize_q4_0>;
+            return dequantize_row_q4_0_sycl;
         case GGML_TYPE_Q4_1:
-            return dequantize_block_sycl<QK4_1, QR4_1, dequantize_q4_1>;
+            return dequantize_row_q4_1_sycl;
         case GGML_TYPE_Q5_0:
             return dequantize_block_sycl<QK5_0, QR5_0, dequantize_q5_0>;
         case GGML_TYPE_Q5_1:
@@ -8977,8 +10663,26 @@ static to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type) {
             return dequantize_row_q5_K_sycl;
         case GGML_TYPE_Q6_K:
             return dequantize_row_q6_K_sycl;
+        case GGML_TYPE_IQ1_S:
+            return dequantize_row_iq1_s_sycl;
+        case GGML_TYPE_IQ1_M:
+            return dequantize_row_iq1_m_sycl;
+        case GGML_TYPE_IQ2_XXS:
+            return dequantize_row_iq2_xxs_sycl;
+        case GGML_TYPE_IQ2_XS:
+            return dequantize_row_iq2_xs_sycl;
+        case GGML_TYPE_IQ2_S:
+            return dequantize_row_iq2_s_sycl;
+        case GGML_TYPE_IQ3_XXS:
+            return dequantize_row_iq3_xxs_sycl;
+        case GGML_TYPE_IQ3_S:
+            return dequantize_row_iq3_s_sycl;
+        case GGML_TYPE_IQ4_XS:
+            return dequantize_row_iq4_xs_sycl;
+        case GGML_TYPE_IQ4_NL:
+            return dequantize_row_iq4_nl_sycl;
         case GGML_TYPE_F16:
-            return dequantize_block_sycl<1, 1, convert_f16>;
+            return convert_unary_sycl<sycl::half>;
         default:
             return nullptr;
     }
@@ -9188,6 +10892,7 @@ static void convert_mul_mat_vec_f16_sycl(const void *vx, const dfloat *y,
     }
 }
 
+
 static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy,
                                        float *dst, const int ncols,
                                        const int nrows,
@@ -9196,13 +10901,20 @@ static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy,
     const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    stream->parallel_for(
-        sycl::nd_range<3>(block_nums * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-            mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ,
-                          vec_dot_q4_0_q8_1>(vx, vy, dst, ncols, nrows,
-                                             item_ct1);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q<QK4_0, QI4_0, block_q4_0,
+                                      VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
+    }
 }
 
 static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy,
@@ -9213,13 +10925,20 @@ static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy,
     const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    stream->parallel_for(
-        sycl::nd_range<3>(block_nums * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-            mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ,
-                          vec_dot_q4_1_q8_1>(vx, vy, dst, ncols, nrows,
-                                             item_ct1);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q<QK4_0, QI4_1, block_q4_1,
+                                      VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
+    }
 }
 
 static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy,
@@ -9230,13 +10949,20 @@ static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy,
     const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    stream->parallel_for(
-        sycl::nd_range<3>(block_nums * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-            mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ,
-                          vec_dot_q5_0_q8_1>(vx, vy, dst, ncols, nrows,
-                                             item_ct1);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q<QK5_0, QI5_0, block_q5_0,
+                                      VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
+    }
 }
 
 static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy,
@@ -9247,13 +10973,20 @@ static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy,
     const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    stream->parallel_for(
-        sycl::nd_range<3>(block_nums * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-            mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ,
-                          vec_dot_q5_1_q8_1>(vx, vy, dst, ncols, nrows,
-                                             item_ct1);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q<QK5_1, QI5_1, block_q5_1,
+                                      VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
+    }
 }
 
 static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy,
@@ -9264,13 +10997,20 @@ static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy,
     const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    stream->parallel_for(
-        sycl::nd_range<3>(block_nums * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-            mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ,
-                          vec_dot_q8_0_q8_1>(vx, vy, dst, ncols, nrows,
-                                             item_ct1);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q<QK8_0, QI8_0, block_q8_0,
+                                      VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
+    }
 }
 
 static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy,
@@ -9281,13 +11021,20 @@ static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy,
     const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    stream->parallel_for(
-        sycl::nd_range<3>(block_nums * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-            mul_mat_vec_q<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ,
-                          vec_dot_q2_K_q8_1>(vx, vy, dst, ncols, nrows,
-                                             item_ct1);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q<QK_K, QI2_K, block_q2_K,
+                                      VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
+    }
 }
 
 static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy,
@@ -9298,13 +11045,20 @@ static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy,
     const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    stream->parallel_for(
-        sycl::nd_range<3>(block_nums * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-            mul_mat_vec_q<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ,
-                          vec_dot_q3_K_q8_1>(vx, vy, dst, ncols, nrows,
-                                             item_ct1);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q<QK_K, QI3_K, block_q3_K,
+                                      VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
+    }
 }
 
 static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy,
@@ -9315,13 +11069,20 @@ static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy,
     const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    stream->parallel_for(
-        sycl::nd_range<3>(block_nums * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-            mul_mat_vec_q<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ,
-                          vec_dot_q4_K_q8_1>(vx, vy, dst, ncols, nrows,
-                                             item_ct1);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q<QK_K, QI4_K, block_q4_K,
+                                      VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
+    }
 }
 
 static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy,
@@ -9332,13 +11093,20 @@ static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy,
     const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    stream->parallel_for(
-        sycl::nd_range<3>(block_nums * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-            mul_mat_vec_q<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ,
-                          vec_dot_q5_K_q8_1>(vx, vy, dst, ncols, nrows,
-                                             item_ct1);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q<QK_K, QI5_K, block_q5_K,
+                                      VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
+    }
 }
 
 static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy,
@@ -9349,31 +11117,231 @@ static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy,
     const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    stream->parallel_for(
-        sycl::nd_range<3>(block_nums * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-            mul_mat_vec_q<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ,
-                          vec_dot_q6_K_q8_1>(vx, vy, dst, ncols, nrows,
-                                             item_ct1);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q<QK_K, QI6_K, block_q6_K,
+                                      VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
-}
-
-int get_device_index_by_id(int id){
-    int res = g_sycl_device_id2index[id].index;
-    // GGML_SYCL_DEBUG("get_device_index_by_id id=%d device_index=%d\n", id, res);
-    GGML_ASSERT(res>=0);
-    return res;
-}
-
-int get_device_id_by_index(int index){
-    int res = g_device_caps[index].device_id;
-    GGML_ASSERT(res>=0);
-    return res;
+    }
 }
 
 
-int get_current_device_index(){
-    return get_device_index_by_id(dpct::dev_mgr::instance().current_device_id());
+static void mul_mat_vec_iq2_xxs_q8_1_sycl(const void *vx, const void *vy,
+                                          float *dst, const int ncols,
+                                          const int nrows,
+                                          dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q_iq2_xxs_q8_1<QK_K, QI2_XXS, block_iq2_xxs, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy,
+                                         float *dst, const int ncols,
+                                         const int nrows,
+                                         dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+            auto iq2xs_grid_ptr_ct1 = &iq2xs_grid[0];
+            auto ksigns64_ptr_ct1 = &ksigns64[0];
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q_iq2_xs_q8_1<QK_K, QI2_XS, block_iq2_xs, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_iq2_s_q8_1_sycl(const void *vx, const void *vy,
+                                         float *dst, const int ncols,
+                                         const int nrows,
+                                         dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+            auto iq2xs_grid_ptr_ct1 = &iq2xs_grid[0];
+            auto ksigns64_ptr_ct1 = &ksigns64[0];
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q_iq2_s_q8_1<QK_K, QI2_S, block_iq2_s, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy,
+                                          float *dst, const int ncols,
+                                          const int nrows,
+                                          dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+            auto iq3xxs_grid_ptr_ct1 = &iq3xxs_grid[0];
+            auto ksigns64_ptr_ct1 = &ksigns64[0];
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q_iq3_xxs_q8_1<QK_K, QI3_XXS, block_iq3_xxs, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy,
+                                          float *dst, const int ncols,
+                                          const int nrows,
+                                          dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+            auto iq3s_grid_ptr_ct1 = &iq3s_grid[0];
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q_iq3_s_q8_1<QK_K, QI3_XS, block_iq3_s, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy,
+                                          float *dst, const int ncols,
+                                          const int nrows,
+                                          dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+            auto iq1s_grid_ptr_ct1 = &iq1s_grid_gpu[0];
+            auto ksigns64_ptr_ct1 = &ksigns64[0];
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q_iq1_s_q8_1<QK_K, QI1_S, block_iq1_s, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_iq1_m_q8_1_sycl(const void *vx, const void *vy,
+                                          float *dst, const int ncols,
+                                          const int nrows,
+                                          dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q_iq1_m_q8_1<QK_K, QI1_S, block_iq1_m, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_iq4_nl_q8_1_sycl(const void *vx, const void *vy,
+                                          float *dst, const int ncols,
+                                          const int nrows,
+                                          dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK4_NL == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q_iq4_nl_q8_1<QK4_NL, QI4_NL, block_iq4_nl, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_iq4_xs_q8_1_sycl(const void *vx, const void *vy,
+                                          float *dst, const int ncols,
+                                          const int nrows,
+                                          dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q_iq4_xs_q8_1<QK_K, QI4_XS, block_iq4_xs, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
 }
 
 static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy,
@@ -9384,7 +11352,7 @@ static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy,
 
     int id;
     SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_index()));
+        CHECK_TRY_ERROR(id = get_current_device_id()));
     const int compute_capability = g_device_caps[id].cc;
 
     int mmq_x, mmq_y, nwarps;
@@ -9499,7 +11467,7 @@ static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy,
 
     int id;
     SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_index()));
+        CHECK_TRY_ERROR(id = get_current_device_id()));
     const int compute_capability = g_device_caps[id].cc;
 
     int mmq_x, mmq_y, nwarps;
@@ -9614,7 +11582,7 @@ static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy,
 
     int id;
     SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_index()));
+        CHECK_TRY_ERROR(id = get_current_device_id()));
     const int compute_capability = g_device_caps[id].cc;
 
     int mmq_x, mmq_y, nwarps;
@@ -9729,7 +11697,7 @@ static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy,
 
     int id;
     SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_index()));
+        CHECK_TRY_ERROR(id = get_current_device_id()));
     const int compute_capability = g_device_caps[id].cc;
 
     int mmq_x, mmq_y, nwarps;
@@ -9844,7 +11812,7 @@ static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy,
 
     int id;
     SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_index()));
+        CHECK_TRY_ERROR(id = get_current_device_id()));
     const int compute_capability = g_device_caps[id].cc;
 
     int mmq_x, mmq_y, nwarps;
@@ -9959,7 +11927,7 @@ static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy,
 
     int id;
     SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_index()));
+        CHECK_TRY_ERROR(id = get_current_device_id()));
     const int compute_capability = g_device_caps[id].cc;
 
     int mmq_x, mmq_y, nwarps;
@@ -10082,7 +12050,7 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy,
 
     int id;
     SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_index()));
+        CHECK_TRY_ERROR(id = get_current_device_id()));
     const int compute_capability = g_device_caps[id].cc;
 
     int mmq_x, mmq_y, nwarps;
@@ -10210,7 +12178,7 @@ static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy,
 
     int id;
     SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_index()));
+        CHECK_TRY_ERROR(id = get_current_device_id()));
     const int compute_capability = g_device_caps[id].cc;
 
     int mmq_x, mmq_y, nwarps;
@@ -10331,7 +12299,7 @@ static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy,
 
     int id;
     SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_index()));
+        CHECK_TRY_ERROR(id = get_current_device_id()));
     const int compute_capability = g_device_caps[id].cc;
 
     int mmq_x, mmq_y, nwarps;
@@ -10452,7 +12420,7 @@ static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy,
 
     int id;
     SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_index()));
+        CHECK_TRY_ERROR(id = get_current_device_id()));
     const int compute_capability = g_device_caps[id].cc;
 
     int mmq_x, mmq_y, nwarps;
@@ -10608,6 +12576,31 @@ static void ggml_mul_mat_vec_nc_f16_f32_sycl(
     }
 }
 
+static void
+ggml_cpy_f16_f32_sycl(const char *cx, char *cdst, const int ne, const int ne00,
+                      const int ne01, const int ne02, const int nb00,
+                      const int nb01, const int nb02, const int nb03,
+                      const int ne10, const int ne11, const int ne12,
+                      const int nb10, const int nb11, const int nb12,
+                      const int nb13, dpct::queue_ptr stream) {
+
+    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                                  sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                cpy_f32_f16<cpy_1_f16_f32>(cx, cdst, ne, ne00, ne01, ne02, nb00,
+                                           nb01, nb02, nb03, ne10, ne11, ne12,
+                                           nb10, nb11, nb12, nb13, item_ct1);
+            });
+    }
+}
+
 static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne,
                                   const int ne00, const int ne01,
                                   const int ne02, const int nb00,
@@ -10944,20 +12937,6 @@ static void rope_glm_f32_sycl(const float *x, float *dst, int ncols, int nrows,
                          });
 }
 
-static void alibi_f32_sycl(const float *x, float *dst, const int ncols,
-                           const int nrows, const int k_rows,
-                           const int n_heads_log2_floor, const float m0,
-                           const float m1, dpct::queue_ptr stream) {
-    const sycl::range<3> block_dims(1, 1, SYCL_ALIBI_BLOCK_SIZE);
-    const int num_blocks_x = (ncols + SYCL_ALIBI_BLOCK_SIZE - 1) / (SYCL_ALIBI_BLOCK_SIZE);
-    const sycl::range<3> block_nums(1, nrows, num_blocks_x);
-    stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                         [=](sycl::nd_item<3> item_ct1) {
-                             alibi_f32(x, dst, ncols, k_rows,
-                                       n_heads_log2_floor, m0, m1, item_ct1);
-                         });
-}
-
 static void sum_rows_f32_sycl(const float *x, float *dst, const int ncols,
                               const int nrows, dpct::queue_ptr stream) {
     const sycl::range<3> block_dims(1, 1, WARP_SIZE);
@@ -10969,36 +12948,54 @@ static void sum_rows_f32_sycl(const float *x, float *dst, const int ncols,
                              });
 }
 
+static int next_power_of_2(int x) {
+    int n = 1;
+    while (n < x) {
+        n *= 2;
+    }
+    return n;
+}
+
 static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols,
                                  const int nrows, ggml_sort_order order,
                                  dpct::queue_ptr stream) {
     // bitonic sort requires ncols to be power of 2
-    GGML_ASSERT((ncols & (ncols - 1)) == 0);
+    const int ncols_pad = next_power_of_2(ncols);
 
-    const sycl::range<3> block_dims(1, 1, ncols);
+    const sycl::range<3> block_dims(1, 1, ncols_pad);
     const sycl::range<3> block_nums(1, nrows, 1);
-    if (order == GGML_SORT_ASC) {
-        /*
-        DPCT1049:44: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        stream->parallel_for(
-            sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) {
-                k_argsort_f32_i32<GGML_SORT_ASC>(x, dst, ncols, item_ct1);
-            });
-    } else if (order == GGML_SORT_DESC) {
-        /*
-        DPCT1049:45: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        stream->parallel_for(
-            sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) {
-                k_argsort_f32_i32<GGML_SORT_DESC>(x, dst, ncols, item_ct1);
-            });
+    const size_t shared_mem = ncols_pad * sizeof(int);
+
+    // GGML_ASSERT(shared_mem <= ggml_cuda_info().devices[ggml_cuda_get_device()].smpb);
+
+    if (order == GGML_SORT_ORDER_ASC) {
+        stream->submit([&](sycl::handler &cgh) {
+            sycl::local_accessor<uint8_t, 1> dpct_local_acc_ct1(
+                sycl::range<1>(shared_mem), cgh);
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1) {
+                    k_argsort_f32_i32<GGML_SORT_ORDER_ASC>(
+                        x, dst, ncols, ncols_pad, item_ct1,
+                        dpct_local_acc_ct1.get_multi_ptr<sycl::access::decorated::no>()
+                            .get());
+                });
+        });
+    } else if (order == GGML_SORT_ORDER_DESC) {
+        stream->submit([&](sycl::handler &cgh) {
+            sycl::local_accessor<uint8_t, 1> dpct_local_acc_ct1(
+                sycl::range<1>(shared_mem), cgh);
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1) {
+                    k_argsort_f32_i32<GGML_SORT_ORDER_DESC>(
+                        x, dst, ncols, ncols_pad, item_ct1,
+                        dpct_local_acc_ct1.get_multi_ptr<sycl::access::decorated::no>()
+                            .get());
+                });
+        });
     } else {
         GGML_ASSERT(false);
     }
@@ -11019,37 +13016,106 @@ static void diag_mask_inf_f32_sycl(const float *x, float *dst,
                          });
 }
 
-static void soft_max_f32_sycl(const float *x, const float *y, float *dst,
-                              const int ncols_x, const int nrows_x,
-                              const int nrows_y, const float scale,
-                              dpct::queue_ptr stream) {
-    int nth = WARP_SIZE;
-    while (nth < ncols_x && nth < SYCL_SOFT_MAX_BLOCK_SIZE) nth *= 2;
-    const sycl::range<3> block_dims(1, 1, nth);
-    const sycl::range<3> block_nums(1, 1, nrows_x);
-    /*
-    DPCT1049:46: The work-group size passed to the SYCL kernel may exceed the
-    limit. To get the device limit, query info::device::max_work_group_size.
-    Adjust the work-group size if needed.
-    */
+template <bool vals_smem, int ncols_template, int block_size_template>
+static void soft_max_f32_submitter(const float * x, const float * mask, float * dst, const int ncols_par,
+                                   const int nrows_y, const float scale, const float max_bias, const float m0,
+                                   const float m1, uint32_t n_head_log2, sycl::range<3> block_nums, sycl::range<3> block_dims,
+                                   const size_t n_local_scratch, dpct::queue_ptr stream) {
     stream->submit([&](sycl::handler &cgh) {
-        /*
-        DPCT1101:96: 'SYCL_SOFT_MAX_BLOCK_SIZE/WARP_SIZE' expression was
-        replaced with a value. Modify the code to use the original expression,
-        provided in comments, if it is correct.
-        */
-        sycl::local_accessor<float, 1> buf_acc_ct1(
-            sycl::range<1>(32 /*SYCL_SOFT_MAX_BLOCK_SIZE/WARP_SIZE*/), cgh);
+        sycl::local_accessor<float, 1> local_buf_acc(n_local_scratch, cgh);
 
         cgh.parallel_for(
             sycl::nd_range<3>(block_nums * block_dims, block_dims),
             [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-                soft_max_f32(x, y, dst, ncols_x, nrows_y, scale, item_ct1,
-                             buf_acc_ct1.get_pointer());
+                soft_max_f32<vals_smem, ncols_template, block_size_template>(x, mask, dst, ncols_par,
+                                                                             nrows_y, scale, max_bias, m0,
+                                                                             m1, n_head_log2, item_ct1,
+                                                                             local_buf_acc.get_pointer());
             });
     });
 }
 
+static void soft_max_f32_sycl(const float * x, const float * mask,
+                              float * dst, const int ncols_x, const int nrows_x,
+                              const int nrows_y, const float scale, const float max_bias,
+                              dpct::queue_ptr stream) {
+    int nth = WARP_SIZE;
+    int max_block_size = g_work_group_size;
+    while (nth < ncols_x && nth < max_block_size) nth *= 2;
+    if (nth>max_block_size) nth = max_block_size;
+
+    const sycl::range<3> block_dims(1, 1, nth);
+    const sycl::range<3> block_nums(1, 1, nrows_x);
+    const size_t n_local_scratch = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE);
+
+    const uint32_t n_head_kv   = nrows_x/nrows_y;
+    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
+
+    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+    const size_t local_mem_size = stream->get_device().get_info<sycl::info::device::local_mem_size>();
+    if (n_local_scratch*sizeof(float) < local_mem_size) {
+        if (ncols_x > max_block_size) {
+            soft_max_f32_submitter<true, 0, 0>(x, mask, dst, ncols_x, nrows_y, scale,
+                                               max_bias, m0, m1, n_head_log2, block_nums,
+                                               block_dims, n_local_scratch, stream);
+            return;
+        }
+        switch (ncols_x) {
+            case 32:
+                soft_max_f32_submitter<true, 32, 32>(x, mask, dst, ncols_x, nrows_y, scale,
+                                                     max_bias, m0, m1, n_head_log2, block_nums,
+                                                     block_dims, n_local_scratch, stream);
+                break;
+            case 64:
+                soft_max_f32_submitter<true, 64, 64>(x, mask, dst, ncols_x, nrows_y, scale,
+                                                     max_bias, m0, m1, n_head_log2, block_nums,
+                                                     block_dims, n_local_scratch, stream);
+                break;
+            case 128:
+                soft_max_f32_submitter<true, 128, 128>(x, mask, dst, ncols_x, nrows_y, scale,
+                                                       max_bias, m0, m1, n_head_log2, block_nums,
+                                                       block_dims, n_local_scratch, stream);
+                break;
+            case 256:
+                soft_max_f32_submitter<true, 256, 256>(x, mask, dst, ncols_x, nrows_y, scale,
+                                                       max_bias, m0, m1, n_head_log2, block_nums,
+                                                       block_dims, n_local_scratch, stream);
+                break;
+            case 512:
+                soft_max_f32_submitter<true, 512, 512>(x, mask, dst, ncols_x, nrows_y, scale,
+                                                       max_bias, m0, m1, n_head_log2, block_nums,
+                                                       block_dims, n_local_scratch, stream);
+                break;
+            case 1024:
+                soft_max_f32_submitter<true, 1024, 1024>(x, mask, dst, ncols_x, nrows_y, scale,
+                                                         max_bias, m0, m1, n_head_log2, block_nums,
+                                                         block_dims, n_local_scratch, stream);
+                break;
+            case 2048:
+                soft_max_f32_submitter<true, 2048, 1024>(x, mask, dst, ncols_x, nrows_y, scale,
+                                                         max_bias, m0, m1, n_head_log2, block_nums,
+                                                         block_dims, n_local_scratch, stream);
+                break;
+            case 4096:
+                soft_max_f32_submitter<true, 4096, 1024>(x, mask, dst, ncols_x, nrows_y, scale,
+                                                         max_bias, m0, m1, n_head_log2, block_nums,
+                                                         block_dims, n_local_scratch, stream);
+                break;
+            default:
+                soft_max_f32_submitter<true, 0, 0>(x, mask, dst, ncols_x, nrows_y, scale,
+                                                   max_bias, m0, m1, n_head_log2, block_nums,
+                                                   block_dims, n_local_scratch, stream);
+                break;
+        }
+    } else {
+        soft_max_f32_submitter<false, 0, 0>(x, mask, dst, ncols_x, nrows_y, scale,
+                                            max_bias, m0, m1, n_head_log2, block_nums,
+                                            block_dims, WARP_SIZE, stream);
+    }
+}
+
 template <typename T>
 static void im2col_sycl(const float *x, T *dst, int IW, int IH,
                                 int OW, int OH, int KW, int KH, int IC,
@@ -11103,12 +13169,9 @@ struct sycl_buffer {
 static sycl_buffer g_sycl_buffer_pool[GGML_SYCL_MAX_DEVICES][MAX_SYCL_BUFFERS];
 static size_t g_sycl_pool_size[GGML_SYCL_MAX_DEVICES] = {0};
 
-static void *ggml_sycl_pool_malloc_leg(size_t size, size_t *actual_size) try {
+static void *ggml_sycl_pool_malloc_leg(int device_index, size_t size, size_t *actual_size) try {
     scoped_spin_lock lock(g_sycl_pool_lock);
-    int id;
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_index()));
-    // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg index %d\n", id);
+    // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg device_index %d size=%lu\n", device_index, size);
 #ifdef DEBUG_SYCL_MALLOC
     int nnz = 0;
     size_t max_size = 0;
@@ -11116,7 +13179,7 @@ static void *ggml_sycl_pool_malloc_leg(size_t size, size_t *actual_size) try {
     size_t best_diff = 1ull << 36;
     int ibest = -1;
     for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) {
-        sycl_buffer& b = g_sycl_buffer_pool[id][i];
+        sycl_buffer& b = g_sycl_buffer_pool[device_index][i];
         if (b.ptr != nullptr) {
 #ifdef DEBUG_SYCL_MALLOC
             ++nnz;
@@ -11132,7 +13195,7 @@ static void *ggml_sycl_pool_malloc_leg(size_t size, size_t *actual_size) try {
                         *actual_size = b.size;
                         b.ptr = nullptr;
                         b.size = 0;
-                        // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 1 %p\n", ptr);
+                        // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 1 %p and rm in pool\n", ptr);
                         return ptr;
                     }
                 }
@@ -11140,30 +13203,30 @@ static void *ggml_sycl_pool_malloc_leg(size_t size, size_t *actual_size) try {
         }
     }
     if (ibest >= 0) {
-        sycl_buffer& b = g_sycl_buffer_pool[id][ibest];
+        sycl_buffer& b = g_sycl_buffer_pool[device_index][ibest];
         void * ptr = b.ptr;
         *actual_size = b.size;
         b.ptr = nullptr;
         b.size = 0;
-        // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 2 %p\n", ptr);
+        // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 2 %p and rm in pool\n", ptr);
         return ptr;
     }
     void * ptr;
     size_t look_ahead_size = (size_t) (1.05 * size);
     look_ahead_size = 256 * ((look_ahead_size + 255)/256);
 
-    const dpct::queue_ptr stream = g_syclStreams[id][0];
+    const dpct::queue_ptr stream = g_syclStreams[device_index][0];
     SYCL_CHECK(
         CHECK_TRY_ERROR(ptr = (void *)sycl::malloc_device(
                              look_ahead_size, *stream)));
     *actual_size = look_ahead_size;
-    g_sycl_pool_size[id] += look_ahead_size;
+    g_sycl_pool_size[device_index] += look_ahead_size;
 
 #ifdef DEBUG_SYCL_MALLOC
     fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz,
             (uint32_t)(max_size/1024/1024), (uint32_t)(g_sycl_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024));
 #endif
-    // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return %p\n", ptr);
+    // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg look_ahead_size=%lu, return %p\n", look_ahead_size, ptr);
     return ptr;
 }
 catch (sycl::exception const &exc) {
@@ -11172,15 +13235,11 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
-static void ggml_sycl_pool_free_leg(void *ptr, size_t size) try {
+static void ggml_sycl_pool_free_leg(int device_index, void *ptr, size_t size) try {
     scoped_spin_lock lock(g_sycl_pool_lock);
-    int id;
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_index()));
-
-    const dpct::queue_ptr stream = g_syclStreams[id][0];
+    const dpct::queue_ptr stream = g_syclStreams[device_index][0];
     for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) {
-        sycl_buffer& b = g_sycl_buffer_pool[id][i];
+        sycl_buffer& b = g_sycl_buffer_pool[device_index][i];
         if (b.ptr == nullptr) {
             b.ptr = ptr;
             b.size = size;
@@ -11189,7 +13248,7 @@ static void ggml_sycl_pool_free_leg(void *ptr, size_t size) try {
     }
     fprintf(stderr, "WARNING: sycl buffer pool full, increase MAX_SYCL_BUFFERS\n");
     SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, *stream)));
-    g_sycl_pool_size[id] -= size;
+    g_sycl_pool_size[device_index] -= size;
 }
 catch (sycl::exception const &exc) {
   std::cerr << exc.what() << "Exception caught at file:" << __FILE__
@@ -11206,7 +13265,8 @@ DPCT1082:64: Migration of CUmemGenericAllocationHandle type is not supported.
 static dpct::device_ptr g_sycl_pool_addr[GGML_SYCL_MAX_DEVICES] = {0};
 static size_t g_sycl_pool_used[GGML_SYCL_MAX_DEVICES] = {0};
 
-static void *ggml_sycl_pool_malloc_vmm(size_t size, size_t *actual_size) try {
+static void *ggml_sycl_pool_malloc_vmm(int device_index, size_t size, size_t *actual_size) try {
+    GGML_UNUSED(device_index);
     GGML_UNUSED(size);
     GGML_UNUSED(actual_size);
     return NULL;
@@ -11217,20 +13277,16 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
-static void ggml_sycl_pool_free_vmm(void *ptr, size_t size) try {
+static void ggml_sycl_pool_free_vmm(int device_index, void *ptr, size_t size) try {
     scoped_spin_lock lock(g_sycl_pool_lock);
-    int id;
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(id = dpct::dev_mgr::instance().current_device_id()));
-
 #ifdef DEBUG_SYCL_MALLOC
-    printf("sycl pool[%d]: freed %llu bytes at %llx\n", id, (unsigned long long) size, ptr);
+    printf("sycl pool[%d]: freed %llu bytes at %llx\n", device_index, (unsigned long long) size, ptr);
 #endif
 
-    g_sycl_pool_used[id] -= size;
+    g_sycl_pool_used[device_index] -= size;
 
     // all deallocations must be in reverse order of the allocations
-    GGML_ASSERT(ptr == (void *) (g_sycl_pool_addr[id] + g_sycl_pool_used[id]));
+    GGML_ASSERT(ptr == (void *) (g_sycl_pool_addr[device_index] + g_sycl_pool_used[device_index]));
 }
 catch (sycl::exception const &exc) {
   std::cerr << exc.what() << "Exception caught at file:" << __FILE__
@@ -11238,14 +13294,11 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
-static void *ggml_sycl_pool_malloc(size_t size, size_t *actual_size) try {
-    int id;
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_index()));
-    if (g_device_caps[id].vmm) {
-        return ggml_sycl_pool_malloc_vmm(size, actual_size);
+static void *ggml_sycl_pool_malloc(int device_index, size_t size, size_t *actual_size) try {
+    if (g_device_caps[device_index].vmm) {
+        return ggml_sycl_pool_malloc_vmm(device_index, size, actual_size);
     } else {
-        return ggml_sycl_pool_malloc_leg(size, actual_size);
+        return ggml_sycl_pool_malloc_leg(device_index, size, actual_size);
     }
 }
 catch (sycl::exception const &exc) {
@@ -11254,14 +13307,11 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
-static void ggml_sycl_pool_free(void *ptr, size_t size) try {
-    int id;
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_index()));
-    if (g_device_caps[id].vmm) {
-        ggml_sycl_pool_free_vmm(ptr, size);
+static void ggml_sycl_pool_free(int device_index, void *ptr, size_t size) try {
+    if (g_device_caps[device_index].vmm) {
+        ggml_sycl_pool_free_vmm(device_index, ptr, size);
     } else {
-        ggml_sycl_pool_free_leg(ptr, size);
+        ggml_sycl_pool_free_leg(device_index, ptr, size);
     }
 }
 catch (sycl::exception const &exc) {
@@ -11273,14 +13323,18 @@ catch (sycl::exception const &exc) {
 
 template<typename T>
 struct sycl_pool_alloc {
+    int device_index = -1;
+    int device_id = -1;
     T * ptr = nullptr;
     size_t actual_size = 0;
 
     // size is in number of elements
     T * alloc(size_t size) {
         GGML_ASSERT(ptr == nullptr);
-        ptr = (T *) ggml_sycl_pool_malloc(size * sizeof(T), &this->actual_size);
-        // GGML_SYCL_DEBUG("alloc %lu return %p actual size=%lu\n", size * sizeof(T), ptr, this->actual_size);
+        device_id = get_current_device_id();
+        device_index = g_sycl_gpu_mgr->get_index(device_id);
+        ptr = (T *) ggml_sycl_pool_malloc(device_index, size * sizeof(T), &this->actual_size);
+        // GGML_SYCL_DEBUG("sycl_pool_alloc %lu return %p actual size=%lu\n", size * sizeof(T), ptr, this->actual_size);
         return ptr;
     }
 
@@ -11290,7 +13344,7 @@ struct sycl_pool_alloc {
 
     ~sycl_pool_alloc() {
         if (ptr != nullptr) {
-            ggml_sycl_pool_free(ptr, actual_size);
+            ggml_sycl_pool_free(device_index, ptr, actual_size);
         }
     }
 
@@ -11311,159 +13365,117 @@ bool ggml_sycl_loaded(void) {
     return g_sycl_loaded;
 }
 
-void ggml_backend_sycl_print_sycl_devices(){
-    int device_count = dpct::dev_mgr::instance().device_count();
-    fprintf(stderr, "found %d SYCL devices:\n", device_count);
-    for (int id = 0; id < device_count; ++id) {
-        dpct::device_info prop;
-        SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info(
-            prop, dpct::dev_mgr::instance().get_device(id))));
-        sycl::device cur_device = dpct::dev_mgr::instance().get_device(id);
-        fprintf(stderr, "  Device %d: %s,\tcompute capability %d.%d,\n\tmax compute_units %d,\tmax work group size %d,\tmax sub group size %d,\tglobal mem size %lu\n", id,
-                prop.get_name(), prop.get_major_version(),
-                prop.get_minor_version(),
-                prop.get_max_compute_units(),
-                prop.get_max_work_group_size(),
-                prop.get_max_sub_group_size(),
-                prop.get_global_mem_size()
-                );
-    }
-    // fprintf(stderr, "\n");
+void print_device_detail(int id, sycl::device &device, std::string device_type) {
+
+    dpct::device_info prop;
+    SYCL_CHECK(CHECK_TRY_ERROR(
+        dpct::get_device_info(prop, device)));
+
+    std::string version;
+    version += std::to_string(prop.get_major_version());
+    version += ".";
+    version += std::to_string(prop.get_minor_version());
+
+    device_type = std::regex_replace(device_type, std::regex("ext_oneapi_"), "");
+    std::string name = std::string(prop.get_name());
+    name = std::regex_replace(name, std::regex("\\(R\\)"), "");
+    name = std::regex_replace(name, std::regex("\\(TM\\)"), "");
+
+    auto global_mem_size = prop.get_global_mem_size()/1000000;
+
+    fprintf(stderr, "|%2d|%19s|%39s|%7s|%7d|%8d|%5d|%6luM|%21s|\n", id, device_type.c_str(),
+            name.c_str(), version.c_str(), prop.get_max_compute_units(),
+            prop.get_max_work_group_size(), prop.get_max_sub_group_size(),
+            global_mem_size, device.get_info<sycl::info::device::driver_version>().c_str());
 }
 
-int get_sycl_env(const char* env_name, int default_val){
-    char * user_device_string = getenv(env_name);
+void ggml_backend_sycl_print_sycl_devices() {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_print_sycl_devices\n");
+    int device_count = dpct::dev_mgr::instance().device_count();
+    std::map<std::string, size_t> DeviceNums;
+    fprintf(stderr, "found %d SYCL devices:\n", device_count);
+    fprintf(stderr, "|  |                   |                                       |       |Max    |        |Max  |Global |                     |\n");
+    fprintf(stderr, "|  |                   |                                       |       |compute|Max work|sub  |mem    |                     |\n");
+    fprintf(stderr, "|ID|        Device Type|                                   Name|Version|units  |group   |group|size   |       Driver version|\n");
+    fprintf(stderr, "|--|-------------------|---------------------------------------|-------|-------|--------|-----|-------|---------------------|\n");
+    for (int id = 0; id < device_count; ++id) {
+        sycl::device device = dpct::dev_mgr::instance().get_device(id);
+        sycl::backend backend = device.get_backend();
+        std::string backend_type = get_device_backend_and_type(device);
+        int type_id=DeviceNums[backend_type]++;
+        std::stringstream device_type;
+        device_type << "[" <<  backend_type << ":" << std::to_string(type_id) << "]";
+        print_device_detail(id, device, device_type.str());
+    }
+}
+
+void print_gpu_device_list() {
+    GGML_ASSERT(g_sycl_gpu_mgr);
+
+    char* hint=NULL;
+    if (g_ggml_sycl_backend_gpu_mode == SYCL_SINGLE_GPU_MODE) {
+        hint = "use %d SYCL GPUs: [%s] with Max compute units:%d\n";
+    } else {
+        hint = "detect %d SYCL GPUs: [%s] with top Max compute units:%d\n";
+    }
+    fprintf(stderr, hint,
+        g_sycl_gpu_mgr->get_gpu_count(),
+        g_sycl_gpu_mgr->gpus_list.c_str(),
+        g_sycl_gpu_mgr->max_compute_units);
+}
+
+int get_sycl_env(const char *env_name, int default_val) {
+    char *user_device_string = getenv(env_name);
     int user_number = default_val;
 
     unsigned n;
-    if (user_device_string != NULL && sscanf(user_device_string, " %u", &n) == 1) {
-            user_number = (int)n;
-        } else {
-            user_number=default_val;
-        }
+    if (user_device_string != NULL &&
+        sscanf(user_device_string, " %u", &n) == 1) {
+        user_number = (int)n;
+    } else {
+        user_number = default_val;
+    }
     return user_number;
 }
 
-int get_work_group_size(int user_device_id){
+int get_work_group_size(int user_device_id) {
     dpct::device_info prop;
-    dpct::get_device_info(
-        prop,
-        dpct::dev_mgr::instance().get_device(user_device_id));
+    dpct::get_device_info(prop,
+                          dpct::dev_mgr::instance().get_device(user_device_id));
     return prop.get_max_work_group_size();
 }
 
-void ggml_init_sycl() try {
+static void ggml_init_sycl() try {
     static bool initialized = false;
 
     if (!initialized) {
+        fprintf(stderr, "[SYCL] call ggml_init_sycl\n");
         g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0);
 
-        printf("GGML_SYCL_DEBUG=%d\n", g_ggml_sycl_debug);
-
-        int user_device_id = get_sycl_env("GGML_SYCL_DEVICE", 0);
-
-        if (CHECK_TRY_ERROR(g_all_sycl_device_count =
-                                 dpct::dev_mgr::instance().device_count()) !=
-            0) {
-            initialized = true;
-            g_sycl_loaded = false;
-            return;
-        }
-        GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES);
-        int64_t total_vram = 0;
+        fprintf(stderr, "%s: GGML_SYCL_DEBUG: %d\n", __func__, g_ggml_sycl_debug);
 
 #if defined(GGML_SYCL_F16)
-        fprintf(stderr, "%s: GGML_SYCL_F16:   yes\n", __func__);
+        fprintf(stderr, "%s: GGML_SYCL_F16: yes\n", __func__);
 #else
-        fprintf(stderr, "%s: GGML_SYCL_F16:   no\n", __func__);
+        fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__);
 #endif
 
-
+/* NOT REMOVE, keep it for next optimize for XMX.
 #if defined(SYCL_USE_XMX)
         fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__);
 #else
         fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__);
 #endif
+*/
+
+        if (CHECK_TRY_ERROR(g_all_sycl_device_count =
+                            dpct::dev_mgr::instance().device_count()) != 0) {
+            initialized = true;
+            g_sycl_loaded = false;
+            return;
+        }
+        GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES);
         ggml_backend_sycl_print_sycl_devices();
-        for (int id = 0; id < GGML_SYCL_MAX_DEVICES; ++id) {
-            g_sycl_device_id2index[id].index = -1;
-            g_device_caps[id].vmm = 0;
-            g_device_caps[id].device_id = -1;
-            g_device_caps[id].cc = 0;
-            g_tensor_split[id] = 0;
-        }
-
-        int device_inx = -1;
-        for (int id = 0; id < g_all_sycl_device_count; ++id) {
-            if(id!=user_device_id) continue;
-
-            device_inx++;
-
-            g_device_caps[device_inx].vmm = 0;
-            g_device_caps[device_inx].device_id = id;
-            g_sycl_device_id2index[id].index = device_inx;
-
-            dpct::device_info prop;
-            SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info(
-                prop, dpct::dev_mgr::instance().get_device(id))));
-
-            g_tensor_split[device_inx] = total_vram;
-            total_vram += prop.get_global_mem_size();
-
-            g_device_caps[device_inx].cc =
-                100 * prop.get_major_version() + 10 * prop.get_minor_version();
-
-        }
-        device_inx = -1;
-        for (int id = 0; id < g_all_sycl_device_count; ++id) {
-            if(id!=user_device_id) continue;
-            device_inx++;
-            g_tensor_split[device_inx] /= total_vram;
-        }
-
-        device_inx = -1;
-        for (int id = 0; id < g_all_sycl_device_count; ++id) {
-            if(id!=user_device_id) continue;
-            device_inx++;
-            SYCL_CHECK(ggml_sycl_set_device(id));
-
-            // create sycl streams
-            for (int is = 0; is < MAX_STREAMS; ++is) {
-                /*
-                DPCT1025:88: The SYCL queue is created ignoring the flag and
-                priority options.
-                */
-                SYCL_CHECK(CHECK_TRY_ERROR(
-                    g_syclStreams[device_inx][is] =
-                        dpct::get_current_device().create_queue()));
-            }
-
-            const dpct::queue_ptr stream = g_syclStreams[device_inx][0];
-            // create sycl handle
-            SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[device_inx] =
-                                              stream));
-            /*
-            DPCT1027:89: The call to syclSetMathMode was replaced with 0
-            because this functionality is redundant in SYCL.
-            */
-            SYCL_CHECK(0);
-        }
-
-        // configure logging to stdout
-        // SYCL_CHECK(syclLoggerConfigure(1, 1, 0, nullptr));
-
-        //hardcode, force set to 1 device
-        g_device_count = 1;
-        ggml_sycl_set_main_device(user_device_id);
-        ggml_sycl_set_device(user_device_id);
-        g_work_group_size = get_work_group_size(user_device_id);
-        // fprintf(stderr, "Using Device %d\n", user_device_id);
-
-        // for (int id = 0; id < g_all_sycl_device_count; ++id) {
-        //     GGML_SYCL_DEBUG("id=%d  g_device_caps[%d].device_id=%d g_sycl_device_id2index[%d].index=%d ", id, id,
-        //     g_device_caps[id].device_id, id, g_sycl_device_id2index[id].index);
-        // }
-
         initialized = true;
         g_sycl_loaded = true;
     }
@@ -11474,29 +13486,61 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
+void ggml_init_by_gpus(int device_count) try {
+    g_device_count = device_count;
+    g_work_group_size = g_sycl_gpu_mgr->work_group_size;
 
-void ggml_sycl_set_tensor_split(const float * tensor_split) {
-    if (tensor_split == nullptr) {
-        return;
+    int64_t total_vram = 0;
+
+    print_gpu_device_list();
+
+    for (int id = 0; id < GGML_SYCL_MAX_DEVICES; ++id) {
+        g_device_caps[id].vmm = 0;
+        g_device_caps[id].device_id = -1;
+        g_device_caps[id].cc = 0;
+        g_tensor_split[id] = 0;
+        g_default_tensor_split[id] = 0;
     }
-    bool all_zero = true;
+
     for (int i = 0; i < g_device_count; ++i) {
-        if (tensor_split[i] != 0.0f) {
-            all_zero = false;
-            break;
+        int device_id = g_sycl_gpu_mgr->gpus[i];
+        g_device_caps[i].vmm = 0;
+
+        dpct::device_info prop;
+        SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info(
+            prop, dpct::dev_mgr::instance().get_device(device_id))));
+
+        g_default_tensor_split[i] = total_vram;
+        total_vram += prop.get_global_mem_size();
+
+        g_device_caps[i].cc =
+            100 * prop.get_major_version() + 10 * prop.get_minor_version();
+    }
+
+    for (int i = 0; i < g_device_count; ++i) {
+        g_default_tensor_split[i] /= total_vram;
+    }
+
+    for (int i = 0; i < g_device_count; ++i) {
+        SYCL_CHECK(ggml_sycl_set_device(i));
+
+        // create sycl streams
+        for (int is = 0; is < MAX_STREAMS; ++is) {
+            SYCL_CHECK(CHECK_TRY_ERROR(
+                g_syclStreams[i][is] =
+                    dpct::get_current_device().create_queue(
+                        g_sycl_gpu_mgr->get_co_ctx(), dpct::get_current_device())));
         }
+
+        const dpct::queue_ptr stream = g_syclStreams[i][0];
+        // create sycl handle
+        SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[i] = stream));
     }
-    if (all_zero) {
-        return;
-    }
-    float split_sum = 0.0f;
-    for (int i = 0; i < g_device_count; ++i) {
-        g_tensor_split[i] = split_sum;
-        split_sum += tensor_split[i];
-    }
-    for (int i = 0; i < g_device_count; ++i) {
-        g_tensor_split[i] /= split_sum;
-    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
 }
 
 void *ggml_sycl_host_malloc(size_t size) try {
@@ -11508,28 +13552,14 @@ void *ggml_sycl_host_malloc(size_t size) try {
     //allow to use dpct::get_in_order_queue() for host malloc
     dpct::err0 err = CHECK_TRY_ERROR(
         ptr = (void *)sycl::malloc_host(size, dpct::get_in_order_queue()));
-    /*
-    DPCT1000:82: Error handling if-stmt was detected but could not be rewritten.
-    */
+
     if (err != 0) {
         // clear the error
-        /*
-        DPCT1026:83: The call to syclGetLastError was removed because this
-        functionality is redundant in SYCL.
-        */
-        /*
-        DPCT1001:81: The statement could not be removed.
-        */
         fprintf(
             stderr,
             "WARNING: failed to allocate %.2f MB of pinned memory: %s\n",
-            /*
-            DPCT1009:84: SYCL uses exceptions to report errors and does not use
-            the error codes. The original code was commented out and a warning
-            string was inserted. You need to rewrite this code.
-            */
             size / 1024.0 / 1024.0,
-            "syclGetErrorString is not supported" /*syclGetErrorString(err)*/);
+            "syclGetErrorString is not supported");
         return nullptr;
     }
 
@@ -11559,17 +13589,17 @@ static dpct::err0 ggml_sycl_cpy_tensor_2d(void *dst,
 
     dpct::memcpy_direction kind;
     char * src_ptr;
-    if (src->backend == GGML_BACKEND_CPU) {
+    if (src->backend == GGML_BACKEND_TYPE_CPU) {
         kind = dpct::host_to_device;
         src_ptr = (char *) src->data;
-        // GGML_SYCL_DEBUG("ggml_sycl_cpy_tensor_2d  GGML_BACKEND_CPU src_ptr %p\n", src_ptr);
-    } else if (src->backend == GGML_BACKEND_GPU || src->backend == GGML_BACKEND_GPU_SPLIT) {
-        GGML_ASSERT(src->backend != GGML_BACKEND_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1]));
+        // GGML_SYCL_DEBUG("ggml_sycl_cpy_tensor_2d  GGML_BACKEND_TYPE_CPU src_ptr %p\n", src_ptr);
+    } else if (src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT) {
+        GGML_ASSERT(src->backend != GGML_BACKEND_TYPE_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1]));
         kind = dpct::device_to_device;
         ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra;
         int id;
         SYCL_CHECK(CHECK_TRY_ERROR(
-            id = get_current_device_index()));
+            id = get_current_device_id()));
         // GGML_SYCL_DEBUG("current device index %d\n", id);
         src_ptr = (char *) extra->data_device[id];
     } else {
@@ -11803,7 +13833,6 @@ inline void ggml_sycl_op_tanh(const ggml_tensor *src0, const ggml_tensor *src1,
 
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
     tanh_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
 
     (void) src1;
@@ -11826,6 +13855,37 @@ inline void ggml_sycl_op_relu(const ggml_tensor *src0, const ggml_tensor *src1,
     (void) src1_dd;
 }
 
+static void ggml_sycl_op_hardsigmoid(const ggml_tensor *src0,
+                                     const ggml_tensor *src1, ggml_tensor *dst,
+                                     const float *src0_dd, const float *src1_dd,
+                                     float *dst_dd,
+                                     const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    hardsigmoid_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+static void ggml_sycl_op_hardswish(const ggml_tensor *src0,
+                                   const ggml_tensor *src1, ggml_tensor *dst,
+                                   const float *src0_dd, const float *src1_dd,
+                                   float *dst_dd, const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    hardswish_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
 inline void ggml_sycl_op_leaky_relu(const ggml_tensor *src0,
                                     const ggml_tensor *src1, ggml_tensor *dst,
                                     const float *src0_dd, const float *src1_dd,
@@ -11994,11 +14054,11 @@ inline void ggml_sycl_op_mul_mat_q(
 
     int device_id;
     SYCL_CHECK(
-        CHECK_TRY_ERROR(device_id = dpct::dev_mgr::instance().current_device_id()));
+        CHECK_TRY_ERROR(device_id = get_current_device_id()));
 
     // the main device has a larger memory buffer to hold the results from all GPUs
     // nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into
-    const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && device_id == g_main_device ? ne0 : row_diff;
+    const int64_t nrows_dst = dst->backend == GGML_BACKEND_TYPE_GPU && device_id == g_main_device ? ne0 : row_diff;
 
     switch (src0->type) {
         case GGML_TYPE_Q4_0:
@@ -12046,16 +14106,16 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
-static int64_t get_row_rounding(ggml_type type) {
+static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_SYCL_MAX_DEVICES> & tensor_split) {
     int64_t min_compute_capability = INT_MAX;
     int64_t max_compute_capability = INT_MIN;
-    for (int64_t id = 0; id < g_device_count; ++id) {
-        if (g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) {
-            if (min_compute_capability > g_device_caps[id].cc) {
-                min_compute_capability = g_device_caps[id].cc;
+    for (int i = 0; i < g_device_count; ++i) {
+        if (tensor_split[i] < (i + 1 < g_device_count ? tensor_split[i + 1] : 1.0f)) {
+            if (min_compute_capability > g_device_caps[i].cc) {
+                min_compute_capability = g_device_caps[i].cc;
             }
-            if (max_compute_capability < g_device_caps[id].cc) {
-                max_compute_capability = g_device_caps[id].cc;
+            if (max_compute_capability < g_device_caps[i].cc) {
+                max_compute_capability = g_device_caps[i].cc;
             }
         }
     }
@@ -12075,12 +14135,23 @@ static int64_t get_row_rounding(ggml_type type) {
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q5_K:
+        case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ4_NL:
+            return max_compute_capability >= VER_GEN9 ? 128 : 64;
+        case GGML_TYPE_IQ3_S:
             return max_compute_capability >= VER_GEN9 ? 128 : 64;
         case GGML_TYPE_Q6_K:
             return 64;
         default:
             GGML_ASSERT(false);
     }
+
 }
 
 inline void ggml_sycl_op_mul_mat_vec_q(
@@ -12090,11 +14161,20 @@ inline void ggml_sycl_op_mul_mat_vec_q(
     const int64_t src1_ncols, const int64_t src1_padded_row_size,
     const dpct::queue_ptr &stream) {
 
-    GGML_ASSERT(ggml_nrows(src1) == 1);
+    const int64_t ne10 = src1->ne[0];
+    GGML_ASSERT(ne10 % QK8_1 == 0);
 
     const int64_t ne00 = src0->ne[0];
     const int64_t row_diff = row_high - row_low;
 
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+
+    // the main device has a larger memory buffer to hold the results from all GPUs
+    // nrows_dst == nrows of the matrix that the kernel writes into
+    const int64_t nrows_dst = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne00 : row_diff;
+
     switch (src0->type) {
         case GGML_TYPE_Q4_0:
             mul_mat_vec_q4_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
@@ -12126,6 +14206,33 @@ inline void ggml_sycl_op_mul_mat_vec_q(
         case GGML_TYPE_Q6_K:
             mul_mat_vec_q6_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
             break;
+        case GGML_TYPE_IQ1_S:
+            mul_mat_vec_iq1_s_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ1_M:
+            mul_mat_vec_iq1_m_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ2_XXS:
+            mul_mat_vec_iq2_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ2_XS:
+            mul_mat_vec_iq2_xs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ2_S:
+            mul_mat_vec_iq2_s_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ3_XXS:
+            mul_mat_vec_iq3_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ3_S:
+            mul_mat_vec_iq3_s_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ4_NL:
+            mul_mat_vec_iq4_nl_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ4_XS:
+            mul_mat_vec_iq4_xs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
         default:
             GGML_ASSERT(false);
             break;
@@ -12138,6 +14245,7 @@ inline void ggml_sycl_op_mul_mat_vec_q(
     (void) src1_padded_row_size;
 }
 
+
 inline void ggml_sycl_op_dequantize_mul_mat_vec(
     const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
     const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
@@ -12145,10 +14253,11 @@ inline void ggml_sycl_op_dequantize_mul_mat_vec(
     const int64_t src1_ncols, const int64_t src1_padded_row_size,
     const dpct::queue_ptr &stream) {
 
-    GGML_TENSOR_BINARY_OP_LOCALS
-
+    const int64_t ne00 = src0->ne[0];
     const int64_t row_diff = row_high - row_low;
 
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
     // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
 #ifdef GGML_SYCL_F16
     sycl_pool_alloc<sycl::half> src1_dfloat_a;
@@ -12160,15 +14269,10 @@ inline void ggml_sycl_op_dequantize_mul_mat_vec(
         src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16;
 
     if (src1_convert_f16) {
-        if (src1->type == GGML_TYPE_F16) {
-            src1_dfloat = (sycl::half *)src1->data + src1_padded_row_size;
-        } else {
-            src1_dfloat = src1_dfloat_a.alloc(ne00);
-            ggml_cpy_f32_f16_sycl((const char *)src1_ddf_i, (char *)src1_dfloat,
-                                  ne00, ne00, ne01, ne02, nb00, nb01, nb02,
-                                  nb03, ne10, ne11, ne12, nb10, nb11, nb12,
-                                  nb13, stream);
-        }
+        src1_dfloat = src1_dfloat_a.alloc(ne00);
+        const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type);
+        GGML_ASSERT(to_fp16_sycl != nullptr);
+        to_fp16_sycl(src1_ddf_i, src1_dfloat, ne00, stream);
     }
 #else
     const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion
@@ -12209,6 +14313,7 @@ inline void ggml_sycl_op_dequantize_mul_mat_vec(
             convert_mul_mat_vec_f16_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
             break;
         default:
+            printf("ggml_sycl_op_dequantize_mul_mat_vec unsupported GGML_TYPE %d\n", src0->type);
             GGML_ASSERT(false);
             break;
     }
@@ -12239,27 +14344,22 @@ inline void ggml_sycl_op_mul_mat_sycl(
     const int64_t row_diff = row_high - row_low;
 
     int id;
-    int device_id = dpct::dev_mgr::instance().current_device_id();
     SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_index()));
+        CHECK_TRY_ERROR(id = get_current_device_id()));
 
     // the main device has a larger memory buffer to hold the results from all GPUs
     // ldc == nrows of the matrix that cuBLAS writes into
-    int ldc = dst->backend == GGML_BACKEND_GPU && device_id == g_main_device ? ne0 : row_diff;
+    int ldc = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne0 : row_diff;
 
 #ifdef GGML_SYCL_F16
     bool use_fp16 = true;  // TODO(Yu) SYCL capability check
 #else
     bool use_fp16 = false;
 #endif
-    // if (compute_capability >= VER_GEN9 && (src0->type == GGML_TYPE_F16 ||
-    // ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff ==
-    // src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) {
     if ((src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
         use_fp16 && ggml_is_contiguous(src0) && row_diff == src0->ne[1] &&
         dst->op_params[0] == GGML_PREC_DEFAULT) {
 
-        // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32
         // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp16 path\n");
         sycl_pool_alloc<sycl::half> src0_as_f16;
         if (src0->type != GGML_TYPE_F16) {
@@ -12288,7 +14388,6 @@ inline void ggml_sycl_op_mul_mat_sycl(
 
         const sycl::half alpha_f16 = 1.0f;
         const sycl::half beta_f16 = 0.0f;
-
         SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[id] = stream));
         SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm(
             *g_sycl_handles[id], oneapi::mkl::transpose::trans,
@@ -12297,21 +14396,28 @@ inline void ggml_sycl_op_mul_mat_sycl(
             src1_ptr, dpct::library_data_t::real_half, ne10, &beta_f16,
             dst_f16.get(), dpct::library_data_t::real_half, ldc,
             dpct::library_data_t::real_half)));
-
+        g_sycl_handles[id]->wait();
         const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16);
         to_fp32_sycl(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream);
     }
     else {
         // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp32 path\n");
         sycl_pool_alloc<float> src0_ddq_as_f32;
-
+        sycl_pool_alloc<float> src1_ddq_as_f32;
         if (src0->type != GGML_TYPE_F32) {
             const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src0->type);
             GGML_ASSERT(to_fp32_sycl != nullptr);
             src0_ddq_as_f32.alloc(row_diff*ne00);
             to_fp32_sycl(src0_dd_i, src0_ddq_as_f32.get(), row_diff*ne00, stream);
         }
+        if (src1->type != GGML_TYPE_F32) {
+            const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src1->type);
+            GGML_ASSERT(to_fp32_sycl != nullptr);
+            src1_ddq_as_f32.alloc(src1_ncols*ne10);
+            to_fp32_sycl(src1_ddf_i, src1_ddq_as_f32.get(), src1_ncols*ne10, stream);
+        }
         const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32.get();
+        const float * src1_ddf1_i = src1->type == GGML_TYPE_F32 ? (const float *) src1_ddf_i : src1_ddq_as_f32.get();
 
         const float alpha = 1.0f;
         const float beta = 0.0f;
@@ -12321,10 +14427,10 @@ inline void ggml_sycl_op_mul_mat_sycl(
             *g_sycl_handles[id], oneapi::mkl::transpose::trans,
             oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10,
             dpct::get_value(&alpha, *g_sycl_handles[id]), src0_ddf_i, ne00,
-            src1_ddf_i, ne10, dpct::get_value(&beta, *g_sycl_handles[id]),
+            src1_ddf1_i, ne10, dpct::get_value(&beta, *g_sycl_handles[id]),
             dst_dd_i, ldc)));
+        g_sycl_handles[id]->wait();
     }
-
     (void) dst;
     (void) src1_ddq_i;
     (void) src1_padded_row_size;
@@ -12415,31 +14521,43 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1,
     (void) src1_dd;
 }
 
-inline void ggml_sycl_op_alibi(const ggml_tensor *src0, const ggml_tensor *src1,
-                               ggml_tensor *dst, const float *src0_dd,
-                               const float *src1_dd, float *dst_dd,
-                               const dpct::queue_ptr &main_stream) {
+static void ggml_sycl_op_pool2d(const ggml_tensor *src0,
+                                const ggml_tensor *src1, ggml_tensor *dst,
+                                const float *src0_dd, const float *src1_dd,
+                                float *dst_dd, const dpct::queue_ptr &main_stream) {
 
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
-    GGML_TENSOR_LOCALS_3(int64_t, ne0, src0, ne);
-    const int64_t nrows = ggml_nrows(src0);
+    const int32_t * opts = (const int32_t *)dst->op_params;
+    enum ggml_op_pool op = static_cast<ggml_op_pool>(opts[0]);
+    const int k0 = opts[1];
+    const int k1 = opts[2];
+    const int s0 = opts[3];
+    const int s1 = opts[4];
+    const int p0 = opts[5];
+    const int p1 = opts[6];
 
-    //const int n_past = ((int32_t *) dst->op_params)[0];
-    const int n_head = ((int32_t *) dst->op_params)[1];
-    float max_bias;
-    memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
+    const int64_t IH = src0->ne[1];
+    const int64_t IW = src0->ne[0];
 
-    //GGML_ASSERT(ne01 + n_past == ne00);
-    GGML_ASSERT(n_head == ne02);
+    const int64_t N = dst->ne[3];
+    const int64_t OC = dst->ne[2];
+    const int64_t OH = dst->ne[1];
+    const int64_t OW = dst->ne[0];
 
-    const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
-
-    const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
-    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
-
-    alibi_f32_sycl(src0_dd, dst_dd, ne00, nrows, ne01, n_heads_log2_floor, m0, m1, main_stream);
+    const int parallel_elements = N * OC * OH * OW;
+    const int num_blocks = (parallel_elements + SYCL_POOL2D_BLOCK_SIZE - 1) / SYCL_POOL2D_BLOCK_SIZE;
+    sycl::range<3> block_nums(1, 1, num_blocks);
+    main_stream->parallel_for(
+        sycl::nd_range<3>(block_nums *
+                              sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            pool2d_nchw_kernel(IH, IW, OH, OW, k1, k0, s1, s0, p1, p0,
+                               parallel_elements, src0_dd, dst_dd, op,
+                               item_ct1);
+        });
 
     (void) src1;
     (void) src1_dd;
@@ -12557,18 +14675,22 @@ inline void ggml_sycl_op_soft_max(const ggml_tensor *src0,
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
+#pragma message("TODO: add ggml_sycl_op_soft_max() F16 src1 support")
+#pragma message("ref:  https://github.com/ggerganov/llama.cpp/pull/5021")
     GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
 
     const int64_t ne00 = src0->ne[0];
     const int64_t nrows_x = ggml_nrows(src0);
-    const int64_t nrows_y = src1 ? ggml_nrows(src1) : 1;
+    const int64_t nrows_y = src0->ne[1];
 
     float scale = 1.0f;
-    memcpy(&scale, dst->op_params, sizeof(float));
+    float max_bias = 0.0f;
 
-    soft_max_f32_sycl(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, main_stream);
+    memcpy(&scale, dst->op_params + 0, sizeof(float));
+    memcpy(&max_bias, dst->op_params + 1, sizeof(float));
 
-    (void) dst;
+    soft_max_f32_sycl(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00,
+                      nrows_x, nrows_y, scale, max_bias, main_stream);
 }
 
 inline void ggml_sycl_op_scale(const ggml_tensor *src0, const ggml_tensor *src1,
@@ -12627,16 +14749,16 @@ static void ggml_sycl_op_flatten(const ggml_tensor *src0,
     const bool use_src1 = src1 != nullptr;
     const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1;
 
-    GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_GPU_SPLIT);
-    GGML_ASSERT(              dst->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(              dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
 
     ggml_tensor_extra_gpu * src0_extra =            (ggml_tensor_extra_gpu *) src0->extra;
     ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
     ggml_tensor_extra_gpu * dst_extra  =            (ggml_tensor_extra_gpu *)  dst->extra;
 
-    const bool src0_on_device =             src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT;
-    const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_GPU;
-    const bool  dst_on_device =              dst->backend == GGML_BACKEND_GPU;
+    const bool src0_on_device =             src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
+    const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_TYPE_GPU;
+    const bool  dst_on_device =              dst->backend == GGML_BACKEND_TYPE_GPU;
 
     // dd = data device
     float * src0_ddf = nullptr;
@@ -12648,12 +14770,12 @@ static void ggml_sycl_op_flatten(const ggml_tensor *src0,
     sycl_pool_alloc<float>  dst_f;
 
     ggml_sycl_set_device(g_main_device);
-    dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0];
-    // GGML_SYCL_DEBUG("g_main_device_index=%d, main_stream=%p src0_on_device=%d, src1_on_device=%d, dst_on_device=%d\n",
-        // g_main_device_index, main_stream, src0_on_device, src1_on_device, dst_on_device);
+    dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
+    // GGML_SYCL_DEBUG("g_main_device=%d, main_stream=%p src0_on_device=%d, src1_on_device=%d, dst_on_device=%d\n",
+        // g_main_device, main_stream, src0_on_device, src1_on_device, dst_on_device);
 
     if (src0_on_device) {
-        src0_ddf = (float *) src0_extra->data_device[g_main_device_index];
+        src0_ddf = (float *) src0_extra->data_device[g_main_device];
     } else {
         src0_ddf = src0_f.alloc(ggml_nelements(src0));
         // GGML_SYCL_DEBUG("before ggml_sycl_cpy_tensor_2d src0_ddf=%p, src0=%p\n", src0_ddf, src0);
@@ -12662,15 +14784,14 @@ static void ggml_sycl_op_flatten(const ggml_tensor *src0,
 
     if (use_src1) {
         if (src1_on_device) {
-            src1_ddf = (float *) src1_extra->data_device[g_main_device_index];
+            src1_ddf = (float *) src1_extra->data_device[g_main_device];
         } else {
             src1_ddf = src1_f.alloc(ggml_nelements(src1));
             SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src1_ddf, src1, 0, 0, 0, nrows1, main_stream));
         }
     }
     if (dst_on_device) {
-        dst_ddf = (float *) dst_extra->data_device[g_main_device_index];
-        // printf("zjy dst_ddf=%p main_stream=%p g_main_device_index=%d\n", dst_ddf, main_stream, g_main_device_index);
+        dst_ddf = (float *) dst_extra->data_device[g_main_device];
     } else {
         dst_ddf = dst_f.alloc(ggml_nelements(dst));
     }
@@ -12688,10 +14809,10 @@ static void ggml_sycl_op_flatten(const ggml_tensor *src0,
     // copy dst to host if necessary
     if (!dst_on_device) {
         SYCL_CHECK(CHECK_TRY_ERROR(
-            main_stream->memcpy(dst->data, dst_ddf, ggml_nbytes(dst))));
+            main_stream->memcpy(dst->data, dst_ddf, ggml_nbytes(dst)).wait()));
     }
 
-    if (dst->backend == GGML_BACKEND_CPU) {
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
         SYCL_CHECK(CHECK_TRY_ERROR(
             dpct::get_current_device().queues_wait_and_throw()));
     }
@@ -12714,21 +14835,19 @@ static void ggml_sycl_set_peer_access(const int n_tokens) {
     }
 
 #ifdef NDEBUG
-    for (int id = 0; id < g_device_count; ++id) {
-        SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id)));
+    for (int i = 0; i < g_device_count; ++i) {
+        SYCL_CHECK(ggml_sycl_set_device(i));
         // SYCL_CHECK(syclDeviceSynchronize());
     }
 
-    for (int id = 0; id < g_device_count; ++id) {
-        SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id)));
-        int device_id = g_device_caps[id].device_id;
+    for (int i = 0; i < g_device_count; ++i) {
+        SYCL_CHECK(ggml_sycl_set_device(i));
 
         for (int id_other = 0; id_other < g_device_count; ++id_other) {
-            int device_id_other = g_device_caps[id_other].device_id;
-            if (device_id == id_other) {
+            if (i == id_other) {
                 continue;
             }
-            if (device_id != g_main_device && device_id_other != g_main_device) {
+            if (i != g_main_device && id_other != g_main_device) {
                 continue;
             }
 
@@ -12748,6 +14867,10 @@ static void ggml_sycl_set_peer_access(const int n_tokens) {
     peer_access_enabled = enable_peer_access;
 }
 
+struct ggml_backend_sycl_split_buffer_type_context {
+    std::array<float, GGML_SYCL_MAX_DEVICES> tensor_split;
+};
+
 static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
                                  const ggml_tensor *src1, ggml_tensor *dst,
                                  ggml_sycl_op_mul_mat_t op,
@@ -12766,8 +14889,9 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
     const int nb2 = dst->nb[2];
     const int nb3 = dst->nb[3];
 
-    GGML_ASSERT(dst->backend != GGML_BACKEND_GPU_SPLIT);
-    GGML_ASSERT(src1->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1));
 
     GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0);
 
@@ -12782,91 +14906,101 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
     ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
     ggml_tensor_extra_gpu *  dst_extra = (ggml_tensor_extra_gpu *)  dst->extra;
 
-    const bool src0_on_device = src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT;
+    const bool src0_on_device = src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
     const bool src0_is_contiguous = ggml_is_contiguous(src0);
     const bool src1_is_contiguous = ggml_is_contiguous(src1);
 
     int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING);
 
-    const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
+    const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
     GGML_ASSERT(!(split && ne02 > 1));
     GGML_ASSERT(!(split && ne03 > 1));
     GGML_ASSERT(!(split && ne02 < ne12));
 
-    // dd = data device
-    char  *  src0_dd[GGML_SYCL_MAX_DEVICES] = {nullptr};
-    float * src1_ddf[GGML_SYCL_MAX_DEVICES] = {nullptr}; // float
-    char  * src1_ddq[GGML_SYCL_MAX_DEVICES] = {nullptr}; // q8_1
-    float *   dst_dd[GGML_SYCL_MAX_DEVICES] = {nullptr};
+    std::array<float, GGML_SYCL_MAX_DEVICES> tensor_split;
+    if (split) {
+        // TODO: check that src0->buffer->buft is a split buffer type, replace GGML_BACKEND_TYPE_GPU_SPLIT check
+        // GGML_ASSERT(src0->buffer != nullptr && src0->buffer->buft == ...);
+        ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *) src0->buffer->buft->context;
+        tensor_split = buft_ctx->tensor_split;
+    }
 
-    // as = actual size
-    size_t  src0_as[GGML_SYCL_MAX_DEVICES] = {0};
-    size_t src1_asf[GGML_SYCL_MAX_DEVICES] = {0};
-    size_t src1_asq[GGML_SYCL_MAX_DEVICES] = {0};
-    size_t   dst_as[GGML_SYCL_MAX_DEVICES] = {0};
+    struct dev_data {
+        sycl_pool_alloc<char> src0_dd_alloc;
+        sycl_pool_alloc<float> src1_ddf_alloc;
+        sycl_pool_alloc<char> src1_ddq_alloc;
+        sycl_pool_alloc<float> dst_dd_alloc;
 
-    int64_t  row_low[GGML_SYCL_MAX_DEVICES];
-    int64_t row_high[GGML_SYCL_MAX_DEVICES];
+        char *src0_dd = nullptr;
+        float *src1_ddf = nullptr; // float
+        char *src1_ddq = nullptr;  // q8_1
+        float *dst_dd = nullptr;
+
+        int64_t row_low;
+        int64_t row_high;
+    };
+
+    dev_data dev[GGML_SYCL_MAX_DEVICES];
 
     int used_devices = 0;
+    dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
 
-    for (int64_t id = 0; id < g_device_count; ++id) {
+    for (int i = 0; i < g_device_count; ++i) {
         // by default, use all rows
-        row_low[id]  = 0;
-        row_high[id] = ne01;
+        dev[i].row_low  = 0;
+        dev[i].row_high = ne01;
 
         // for multi GPU, get the row boundaries from tensor split
         // and round to mul_mat_q tile sizes
         if (split) {
-            const int64_t rounding = get_row_rounding(src0->type);
+            const int64_t rounding = get_row_rounding(src0->type, tensor_split);
 
-            if (id != 0) {
-                row_low[id]  = ne01*g_tensor_split[id];
-                if (row_low[id] < ne01) {
-                    row_low[id] -= row_low[id] % rounding;
+            if (i != 0) {
+                dev[i].row_low  = ne01*tensor_split[i];
+                if (dev[i].row_low < ne01) {
+                    dev[i].row_low -= dev[i].row_low % rounding;
                 }
             }
 
-            if (id != g_device_count - 1) {
-                row_high[id]  = ne01*g_tensor_split[id + 1];
-                if (row_high[id] < ne01) {
-                    row_high[id] -= row_high[id] % rounding;
+            if (i != g_device_count - 1) {
+                dev[i].row_high  = ne01*tensor_split[i + 1];
+                if (dev[i].row_high < ne01) {
+                    dev[i].row_high -= dev[i].row_high % rounding;
                 }
             }
         }
     }
-    for (int64_t id = 0; id < g_device_count; ++id) {
 
-        if ((!split && id != g_main_device_index) || row_low[id] == row_high[id]) {
+    for (int i = 0; i < g_device_count; ++i) {
+        if ((!split && i != g_main_device) || dev[i].row_low == dev[i].row_high) {
             continue;
         }
 
         used_devices++;
 
-        const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device_index;
-        const bool  dst_on_device =  dst->backend == GGML_BACKEND_GPU && id == g_main_device_index;
+        const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device;
+        const bool  dst_on_device =  dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device;
 
-        ggml_sycl_set_device(get_device_id_by_index(id));
-        const dpct::queue_ptr stream = g_syclStreams[id][0];
+        ggml_sycl_set_device(i);
+        dpct::queue_ptr stream = g_syclStreams[i][0];
 
         if (src0_on_device && src0_is_contiguous) {
-            src0_dd[id] = (char *) src0_extra->data_device[id];
+            dev[i].src0_dd = (char *) src0_extra->data_device[i];
         } else {
-            // const size_t size_src0_ddq = split ? (row_high[id]-row_low[id])*ne00 * src0_ts/src0_bs : ggml_nbytes(src0);
-            src0_dd[id] = (char *) ggml_sycl_pool_malloc(ggml_nbytes(src0), &src0_as[id]);
+            dev[i].src0_dd = dev[i].src0_dd_alloc.alloc(ggml_nbytes(src0));
         }
 
         if (src1_on_device && src1_is_contiguous) {
-            src1_ddf[id] = (float *) src1_extra->data_device[id];
+            dev[i].src1_ddf = (float *) src1_extra->data_device[i];
         } else {
-            src1_ddf[id] = (float *) ggml_sycl_pool_malloc(ggml_nbytes(src1), &src1_asf[id]);
+            dev[i].src1_ddf = dev[i].src1_ddf_alloc.alloc(ggml_nelements(src1));
         }
 
         if (convert_src1_to_q8_1) {
-            src1_ddq[id] = (char *) ggml_sycl_pool_malloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs, &src1_asq[id]);
+            dev[i].src1_ddq = dev[i].src1_ddq_alloc.alloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs);
 
             if (src1_on_device && src1_is_contiguous) {
-                quantize_row_q8_1_sycl(src1_ddf[id], src1_ddq[id], ne10, nrows1, src1_padded_col_size, stream);
+                quantize_row_q8_1_sycl(dev[i].src1_ddf, dev[i].src1_ddq, ne10, nrows1, src1_padded_col_size, stream);
                 /*
                 DPCT1010:90: SYCL uses exceptions to report errors and does not
                 use the error codes. The call was replaced with 0. You need to
@@ -12877,25 +15011,25 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
         }
 
         if (dst_on_device) {
-            dst_dd[id] = (float *) dst_extra->data_device[id];
+            dev[i].dst_dd = (float *) dst_extra->data_device[i];
         } else {
-            const size_t size_dst_ddf = split ? (row_high[id]-row_low[id])*ne1*sizeof(float) : ggml_nbytes(dst);
-            dst_dd[id] = (float *) ggml_sycl_pool_malloc(size_dst_ddf, &dst_as[id]);
+            const size_t size_dst_ddf = split ? (dev[i].row_high - dev[i].row_low)*ne1 : ggml_nelements(dst);
+            dev[i].dst_dd = dev[i].dst_dd_alloc.alloc(size_dst_ddf);
         }
     }
 
     // if multiple devices are used they need to wait for the main device
     // here an event is recorded that signals that the main device has finished calculating the input data
     if (split && used_devices > 1) {
-        SYCL_CHECK(ggml_sycl_set_device(g_main_device));
+        ggml_sycl_set_device(g_main_device);
         /*
         DPCT1024:91: The original code returned the error code that was further
         consumed by the program logic. This original code was replaced with 0.
         You may need to rewrite the program logic consuming the error code.
         */
         SYCL_CHECK(CHECK_TRY_ERROR(
-            *src0_extra->events[g_main_device_index][0] =
-                g_syclStreams[g_main_device_index][0]->ext_oneapi_submit_barrier()));
+            *src0_extra->events[g_main_device][0] =
+                g_syclStreams[g_main_device][0]->ext_oneapi_submit_barrier()));
     }
 
     const int64_t src1_col_stride = split && used_devices > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11;
@@ -12903,22 +15037,27 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
         const int64_t is = split ? (src1_col_0/src1_col_stride) % MAX_STREAMS : 0;
         const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride;
 
-        for (int64_t id = 0; id < g_device_count; ++id) {
-            if ((!split && id != g_main_device_index) || row_low[id] == row_high[id]) {
+        for (int i = 0; i < g_device_count; ++i) {
+            if ((!split && i != g_main_device) || dev[i].row_low == dev[i].row_high) {
                 continue;
             }
 
-            const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device_index;
-            const bool  dst_on_device =  dst->backend == GGML_BACKEND_GPU && id == g_main_device_index;
-            const int64_t row_diff = row_high[id] - row_low[id];
+            const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device;
+            const bool  dst_on_device =  dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device;
+            const int64_t row_diff = dev[i].row_high - dev[i].row_low;
 
-            ggml_sycl_set_device(get_device_id_by_index(id));
-            const dpct::queue_ptr stream = g_syclStreams[id][is];
+            ggml_sycl_set_device(i);
+            dpct::queue_ptr stream = g_syclStreams[i][is];
 
             // wait for main GPU data if necessary
-            if (split && (id != g_main_device_index || is != 0)) {
+            if (split && (i != g_main_device || is != 0)) {
+                /*
+                DPCT1009:163: SYCL uses exceptions to report errors and does not
+                use the error codes. The original code was commented out and a
+                warning string was inserted. You need to rewrite this code.
+                */
                 SYCL_CHECK(CHECK_TRY_ERROR(stream->ext_oneapi_submit_barrier(
-                    {*src0_extra->events[g_main_device_index][0]})));
+                    {*src0_extra->events[g_main_device][0]})));
             }
 
             for (int64_t i0 = 0; i0 < ne13*ne12; ++i0) {
@@ -12928,42 +15067,44 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
                 const size_t src1_ddq_i_offset = (i0*ne11 + src1_col_0) * src1_padded_col_size*q8_1_ts/q8_1_bs;
 
                 // for split tensors the data begins at i0 == i0_offset_low
-                char  *  src0_dd_i =  src0_dd[id] + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs;
-                float * src1_ddf_i = src1_ddf[id] + (i0*ne11 + src1_col_0) * ne10;
-                char  * src1_ddq_i = src1_ddq[id] +  src1_ddq_i_offset;
-                float *   dst_dd_i =   dst_dd[id] + (i0*ne1  + src1_col_0) * (dst_on_device ? ne0 : row_diff);
+                char  *  src0_dd_i =  dev[i].src0_dd + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs;
+                float * src1_ddf_i = dev[i].src1_ddf + (i0*ne11 + src1_col_0) * ne10;
+                char  * src1_ddq_i = dev[i].src1_ddq +  src1_ddq_i_offset;
+                float *   dst_dd_i =   dev[i].dst_dd + (i0*ne1  + src1_col_0) * (dst_on_device ? ne0 : row_diff);
 
                 // the main device memory buffer can be on VRAM scratch, with space for all partial results
                 // in that case an offset on dst_ddf_i is needed
-                if (dst->backend == GGML_BACKEND_GPU && id == g_main_device_index) {
-                    dst_dd_i += row_low[id]; // offset is 0 if no tensor split
+                if (dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device) {
+                    dst_dd_i += dev[i].row_low; // offset is 0 if no tensor split
                 }
 
                 // copy src0, src1 to device if necessary
-                if (src1->backend == GGML_BACKEND_GPU && src1_is_contiguous) {
-                    if (id != g_main_device_index) {
+                if (src1->backend == GGML_BACKEND_TYPE_GPU && src1_is_contiguous) {
+                    if (i != g_main_device) {
                         if (convert_src1_to_q8_1) {
-                            char * src1_ddq_i_source = src1_ddq[g_main_device_index] + src1_ddq_i_offset;
-                            SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(
+                            char * src1_ddq_i_source = dev[g_main_device].src1_ddq + src1_ddq_i_offset;
+                          SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(
                                 src1_ddq_i, src1_ddq_i_source,
                                 src1_ncols * src1_padded_col_size * q8_1_ts /
-                                    q8_1_bs)));
+                                    q8_1_bs).wait()));
                         } else {
-                            float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device_index];
+
+                            float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device];
                             src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10;
-                            SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(
+
+                            SYCL_CHECK(CHECK_TRY_ERROR(dev2dev_memcpy(*stream, *main_stream,
                                 src1_ddf_i, src1_ddf_i_source,
                                 src1_ncols * ne10 * sizeof(float))));
                         }
                     }
-                } else if (src1->backend == GGML_BACKEND_CPU || (src1_on_device && !src1_is_contiguous)) {
+                } else if (src1->backend == GGML_BACKEND_TYPE_CPU || (src1_on_device && !src1_is_contiguous)) {
                     SYCL_CHECK(ggml_sycl_cpy_tensor_2d(
                                    src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream));
                 } else {
                     GGML_ASSERT(false);
                 }
 
-                if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_CPU || !src1_is_contiguous)) {
+                if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_TYPE_CPU || !src1_is_contiguous)) {
                     quantize_row_q8_1_sycl(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream);
                     /*
                     DPCT1010:92: SYCL uses exceptions to report errors and does
@@ -12974,14 +15115,14 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
                 }
 
                 if (src1_col_0 == 0 && (!src0_on_device || !src0_is_contiguous) && i02 % i02_divisor == 0) {
-                    SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, row_low[id], row_high[id], stream));
+                    SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, dev[i].row_low, dev[i].row_high, stream));
                 }
                 if (src1->type == GGML_TYPE_F16) {
                     src1_padded_col_size = (i0 * ne11 + src1_col_0) * ne10;
                 }
                 // do the computation
-                op(src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i,
-                   row_low[id], row_high[id], src1_ncols, src1_padded_col_size, stream);
+                SYCL_CHECK(CHECK_TRY_ERROR(op(src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i,
+                    dev[i].row_low, dev[i].row_high, src1_ncols, src1_padded_col_size, stream)));
                 /*
                 DPCT1010:93: SYCL uses exceptions to report errors and does not
                 use the error codes. The call was replaced with 0. You need to
@@ -12993,11 +15134,11 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
                 if (!dst_on_device) {
                     void * dst_off_device;
                     dpct::memcpy_direction kind;
-                    if (dst->backend == GGML_BACKEND_CPU) {
+                    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
                         dst_off_device = dst->data;
                         kind = dpct::device_to_host;
-                    } else if (dst->backend == GGML_BACKEND_GPU) {
-                        dst_off_device = dst_extra->data_device[g_main_device_index];
+                    } else if (dst->backend == GGML_BACKEND_TYPE_GPU) {
+                        dst_off_device = dst_extra->data_device[g_main_device];
                         kind = dpct::device_to_device;
                     } else {
                         GGML_ASSERT(false);
@@ -13010,23 +15151,41 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
                         // If dst is a vector with ne0 == 1 then you don't have to do this but it still produces correct results.
                         float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3);
                         GGML_ASSERT(dst->nb[1] == ne0*sizeof(float));
-                        dhf_dst_i += src1_col_0*ne0 + row_low[id];
-                        SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy(
-                            dhf_dst_i, ne0 * sizeof(float), dst_dd_i,
-                            row_diff * sizeof(float), row_diff * sizeof(float),
-                            src1_ncols, kind, *stream)));
+                        dhf_dst_i += src1_col_0*ne0 + dev[i].row_low;
+
+                        //todo, dirty solution. Need be updated when device2device memcpy() is supported.
+                        if (kind == dpct::device_to_device) {
+                            size_t dst_size = ggml_nbytes_pad(dst);
+                            float *host_buf = (float *)malloc(dst_size);
+                            SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy(
+                                host_buf, ne0 * sizeof(float), dst_dd_i,
+                                row_diff * sizeof(float), row_diff * sizeof(float),
+                                src1_ncols, dpct::device_to_host, *stream)));
+                            dpct::dev_mgr::instance().get_device(g_sycl_gpu_mgr->gpus[i]).queues_wait_and_throw();
+                            SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy(
+                                dhf_dst_i, ne0 * sizeof(float), host_buf,
+                                row_diff * sizeof(float), row_diff * sizeof(float),
+                                src1_ncols, dpct::host_to_device, *main_stream)));
+                            dpct::dev_mgr::instance().get_device(g_sycl_gpu_mgr->gpus[g_main_device]).queues_wait_and_throw();
+                            free(host_buf);
+                        } else {
+                            SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy(
+                                dhf_dst_i, ne0 * sizeof(float), dst_dd_i,
+                                row_diff * sizeof(float), row_diff * sizeof(float),
+                                src1_ncols, kind, *stream)));
+                        }
                     } else {
                         float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3);
                         GGML_ASSERT(dst->nb[1] == ne0*sizeof(float));
                         dhf_dst_i += src1_col_0*ne0;
                         SYCL_CHECK(CHECK_TRY_ERROR(
                             stream->memcpy(dhf_dst_i, dst_dd_i,
-                                           src1_ncols * ne0 * sizeof(float))));
+                                           src1_ncols * ne0 * sizeof(float)).wait()));
                     }
                 }
 
                 // add event for the main device to wait on until other device is done
-                if (split && (id != g_main_device_index || is != 0)) {
+                if (split && (i != g_main_device || is != 0)) {
                     /*
                     DPCT1024:94: The original code returned the error code that
                     was further consumed by the program logic. This original
@@ -13034,53 +15193,32 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
                     program logic consuming the error code.
                     */
                     SYCL_CHECK(CHECK_TRY_ERROR(
-                        *src0_extra->events[id][is] =
+                        *src0_extra->events[i][is] =
                             stream->ext_oneapi_submit_barrier()));
                 }
             }
         }
     }
 
-    for (int64_t id = 0; id < g_device_count; ++id) {
-        if ((!split && id != g_main_device_index) || row_low[id] == row_high[id]) {
-            continue;
-        }
-        SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id)));
-
-        // free buffers again when done
-        if (dst_as[id] > 0) {
-            ggml_sycl_pool_free(dst_dd[id], dst_as[id]);
-        }
-        if (src1_asq[id] > 0) {
-            ggml_sycl_pool_free(src1_ddq[id], src1_asq[id]);
-        }
-        if (src1_asf[id] > 0) {
-            ggml_sycl_pool_free(src1_ddf[id], src1_asf[id]);
-        }
-        if (src0_as[id] > 0) {
-            ggml_sycl_pool_free(src0_dd[id], src0_as[id]);
-        }
-    }
-
     // main device waits for all other devices to be finished
     if (split && g_device_count > 1) {
         int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE;
         is_max = is_max <= MAX_STREAMS ? is_max : MAX_STREAMS;
 
-        SYCL_CHECK(ggml_sycl_set_device(g_main_device));
-        for (int64_t id = 0; id < g_device_count; ++id) {
-            if (row_low[id] == row_high[id]) {
+        ggml_sycl_set_device(g_main_device);
+        for (int i = 0; i < g_device_count; ++i) {
+            if (dev[i].row_low == dev[i].row_high) {
                 continue;
             }
             for (int64_t is = 0; is < is_max; ++is) {
                 SYCL_CHECK(CHECK_TRY_ERROR(
-                    g_syclStreams[g_main_device_index][0]->ext_oneapi_submit_barrier(
-                        {*src0_extra->events[id][is]})));
+                    g_syclStreams[g_main_device][0]->ext_oneapi_submit_barrier(
+                        {*src0_extra->events[i][is]})));
             }
         }
     }
 
-    if (dst->backend == GGML_BACKEND_CPU) {
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
         SYCL_CHECK(ggml_sycl_set_device(g_main_device));
         SYCL_CHECK(CHECK_TRY_ERROR(
             dpct::get_current_device().queues_wait_and_throw()));
@@ -13092,110 +15230,132 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
+
 static void ggml_sycl_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
     ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_repeat);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 static void ggml_sycl_get_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
     ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_get_rows);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 static void ggml_sycl_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
     ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_add);
-    // log_tensor_with_cnt("log_ggml_sycl_add_src0", (struct ggml_tensor *) src0, 6);
-    // log_tensor_with_cnt("log_ggml_sycl_add_src1", (struct ggml_tensor *)src1, 6);
-    // log_tensor_with_cnt("log_ggml_sycl_add_dst", dst, 6);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 static void ggml_sycl_acc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
     ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_acc);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 static void ggml_sycl_mul(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
     ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_mul);
-    // log_tensor_with_cnt("log_ggml_sycl_mul_src0", (struct ggml_tensor *)src0, 6);
-    // log_tensor_with_cnt("log_ggml_sycl_mul_src1", (struct ggml_tensor *)src1, 6);
-    // log_tensor_with_cnt("log_ggml_sycl_mul_dst", dst, 6);
-
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 static void ggml_sycl_div(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
     ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_div);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 static void ggml_sycl_gelu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
     ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_gelu);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 static void ggml_sycl_silu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
     ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_silu);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 static void ggml_sycl_gelu_quick(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
     ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_gelu_quick);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 static void ggml_sycl_tanh(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
     ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_tanh);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 static void ggml_sycl_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
     ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_relu);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_hardsigmoid(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_hardsigmoid);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_hardswish(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_hardswish);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 static void ggml_sycl_leaky_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
     ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_leaky_relu);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 static void ggml_sycl_sqr(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
     ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_sqr);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 static void ggml_sycl_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
     ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_norm);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 static void ggml_sycl_group_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
     ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_group_norm);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 static void ggml_sycl_concat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
     ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_concat);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 static void ggml_sycl_upscale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
     ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_upscale);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 static void ggml_sycl_pad(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
     ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_pad);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 
 static void ggml_sycl_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
     ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_rms_norm);
-    // log_tensor_with_cnt("log_ggml_sycl_rms_norm_src0", (struct ggml_tensor *)src0, 6);
-    // log_tensor_with_cnt("log_ggml_sycl_rms_norm_src1", (struct ggml_tensor *)src1, 6);
-    // log_tensor_with_cnt("log_ggml_sycl_rms_norm_dst", dst, 6);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 bool ggml_sycl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
@@ -13217,7 +15377,7 @@ static void ggml_sycl_mul_mat_vec_p021(const ggml_tensor *src0,
                                        const ggml_tensor *src1,
                                        ggml_tensor *dst) try {
     GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
-    GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
     GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation
     GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
@@ -13230,16 +15390,16 @@ static void ggml_sycl_mul_mat_vec_p021(const ggml_tensor *src0,
     const int64_t ne12 = src1->ne[2];
 
     SYCL_CHECK(ggml_sycl_set_device(g_main_device));
-    dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0];
+    dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
 
     ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    void * src0_ddq = src0_extra->data_device[g_main_device_index];
+    void * src0_ddq = src0_extra->data_device[g_main_device];
 
     ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    float * src1_ddf = (float *) src1_extra->data_device[g_main_device_index];
+    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
 
     ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
-    float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index];
+    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
 
     ggml_mul_mat_p021_f16_f32_sycl(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream);
 }
@@ -13255,7 +15415,7 @@ static void ggml_sycl_mul_mat_vec_nc(const ggml_tensor *src0,
     GGML_ASSERT(!ggml_is_transposed(src0));
     GGML_ASSERT(!ggml_is_transposed(src1));
     GGML_ASSERT(!ggml_is_permuted(src0));
-    GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
 
@@ -13269,16 +15429,16 @@ static void ggml_sycl_mul_mat_vec_nc(const ggml_tensor *src0,
     const int64_t ne12 = src1->ne[2];
 
     SYCL_CHECK(ggml_sycl_set_device(g_main_device));
-    dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0];
+    dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
 
     ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    void * src0_ddq = src0_extra->data_device[g_main_device_index];
+    void * src0_ddq = src0_extra->data_device[g_main_device];
 
     ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    float * src1_ddf = (float *) src1_extra->data_device[g_main_device_index];
+    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
 
     ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
-    float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index];
+    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
 
     const int64_t row_stride_x = nb01 / sizeof(sycl::half);
     const int64_t channel_stride_x = nb02 / sizeof(sycl::half);
@@ -13311,87 +15471,90 @@ static void k_compute_batched_ptrs(const sycl::half *src0_as_f16,
     int64_t i03 = i13 / r3;
     int64_t i02 = i12 / r2;
 
-    ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02   + i03*nb03;
-    ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2;
-    ptrs_dst[0*ne23 + i12 + i13*ne12] = (      char *)         dst + i12*nbd2   + i13*nbd3;
+    ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03;
+    ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12 + i13*nb13;
+    ptrs_dst[0*ne23 + i12 + i13*ne12] = (      char *)         dst + i12*nbd2 + i13*nbd3;
 }
 
-static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0,
-                                                 const ggml_tensor *src1,
-                                                 ggml_tensor *dst) try {
+static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0,
+                                             const ggml_tensor *src1,
+                                             ggml_tensor *dst) try {
     GGML_ASSERT(!ggml_is_transposed(src0));
     GGML_ASSERT(!ggml_is_transposed(src1));
-
-    GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
 
-    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne);
+    GGML_TENSOR_BINARY_OP_LOCALS
 
-    GGML_TENSOR_LOCALS(int64_t, nb0, src0, nb);
-
-    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);
-
-    GGML_TENSOR_LOCALS(int64_t, nb1, src1, nb);
-
-    const int64_t ne1 = ggml_nelements(src1);
-    const int64_t ne  = ggml_nelements(dst);
+    const int64_t ne_dst = ggml_nelements(dst);
 
     SYCL_CHECK(ggml_sycl_set_device(g_main_device));
-    dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0];
+    dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
+
+    bool no_mixed_dtypes = main_stream->get_backend() == sycl::backend::ext_oneapi_cuda ||
+                           main_stream->get_backend() == sycl::backend::ext_oneapi_hip;
 
     SYCL_CHECK(
-        CHECK_TRY_ERROR(g_sycl_handles[g_main_device_index] = main_stream));
+        CHECK_TRY_ERROR(g_sycl_handles[g_main_device] = main_stream));
 
     ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    void * src0_ddq = src0_extra->data_device[g_main_device_index];
+    void * src0_ddq = src0_extra->data_device[g_main_device];
     sycl::half *src0_as_f16 = (sycl::half *)src0_ddq;
 
     ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    float * src1_ddf = (float *) src1_extra->data_device[g_main_device_index];
+    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
 
     ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
-    float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index];
+    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
 
     // convert src1 to fp16
-    const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type);
-    GGML_ASSERT(to_fp16_sycl != nullptr);
-
-    sycl_pool_alloc<sycl::half> src1_as_f16(ne1);
-    to_fp16_sycl(src1_ddf, src1_as_f16.get(), ne1, main_stream);
+    sycl_pool_alloc<sycl::half> src1_f16_alloc;
+    if (src1->type != GGML_TYPE_F16) {
+        const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type);
+        const int64_t ne_src1 = ggml_nelements(src1);
+        src1_f16_alloc.alloc(ne_src1);
+        GGML_ASSERT(to_fp16_sycl != nullptr);
+        to_fp16_sycl(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream);
+    }
+    sycl::half *src1_f16 = src1->type == GGML_TYPE_F16 ? (sycl::half *)src1_ddf
+                                                       : src1_f16_alloc.get();
 
     sycl_pool_alloc<sycl::half> dst_f16;
     char * dst_t;
 
-    dpct::library_data_t cu_compute_type = dpct::library_data_t::real_half;
-    dpct::library_data_t cu_data_type = dpct::library_data_t::real_half;
+    dpct::library_data_t cu_compute_type = dpct::library_data_t::real_float;
+    dpct::library_data_t cu_data_type = dpct::library_data_t::real_float;
+    if (no_mixed_dtypes) {
+        cu_compute_type = dpct::library_data_t::real_half;
+        cu_data_type = dpct::library_data_t::real_half;
+    }
 
     // dst strides
     size_t nbd2 = dst->nb[2];
     size_t nbd3 = dst->nb[3];
 
+    const float alpha_f32 = 1.0f;
+    const float beta_f32 = 0.0f;
+
     const sycl::half alpha_f16 = 1.0f;
     const sycl::half beta_f16 = 0.0f;
 
-    const float alpha_f32 = 1.0f;
-    const float beta_f32  = 0.0f;
+    const void * alpha = &alpha_f32;
+    const void * beta  = &beta_f32;
+    if (no_mixed_dtypes) {
+        alpha = &alpha_f16;
+        beta  = &beta_f16;
+    }
 
-    const void * alpha = &alpha_f16;
-    const void * beta  = &beta_f16;
+    // TODO: Renable (dst->op_params[0] =! GGML_PREC_DEFAULT) pathway
+    // when oneMKL open source supports half, half, float, float: datatypes
 
-    if (dst->op_params[0] == GGML_PREC_DEFAULT) {
-        dst_t = (char *) dst_f16.alloc(ne);
+    dst_t = (char *) dst_ddf;
+    if (no_mixed_dtypes) {
+        dst_t = (char *) dst_f16.alloc(ne_dst);
 
         nbd2 /= sizeof(float) / sizeof(sycl::half);
         nbd3 /= sizeof(float) / sizeof(sycl::half);
-    } else {
-        dst_t = (char *) dst_ddf;
-
-        cu_compute_type = dpct::library_data_t::real_float;
-        cu_data_type = dpct::library_data_t::real_float;
-
-        alpha = &alpha_f32;
-        beta  = &beta_f32;
     }
 
     GGML_ASSERT(ne12 % ne02 == 0);
@@ -13401,40 +15564,18 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0,
     const int64_t r2 = ne12/ne02;
     const int64_t r3 = ne13/ne03;
 
-#if 0
-    // use syclGemmEx
-    {
-        for (int i13 = 0; i13 < ne13; ++i13) {
-            for (int i12 = 0; i12 < ne12; ++i12) {
-                int i03 = i13 / r3;
-                int i02 = i12 / r2;
-
-                SYCL_CHECK(
-                        syclGemmEx(g_sycl_handles[g_main_device_index], CUBLAS_OP_T, CUBLAS_OP_N,
-                            ne01, ne11, ne10,
-                            alpha, (const char *) src0_as_f16 + i02*src0->nb[2]   + i03*src0->nb[3]  , SYCL_R_16F,   nb01/sizeof(half),
-                                   (const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, SYCL_R_16F,   nb11/sizeof(float),
-                            beta,  (      char *)       dst_t + i12*nbd2          + i13*nbd3,          cu_data_type, ne01,
-                            cu_compute_type,
-                            CUBLAS_GEMM_DEFAULT_TENSOR_OP));
-            }
-        }
-    }
-#else
     if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) {
         // there is no broadcast and src0, src1 are contiguous across dims 2, 3
-        // use syclGemmStridedBatchedEx
         SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch(
-            *g_sycl_handles[g_main_device_index], oneapi::mkl::transpose::trans,
+            *g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans,
             oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha,
             (const char *)src0_as_f16, dpct::library_data_t::real_half,
-            nb01 / sizeof(sycl::half), src0->nb[2] / sizeof(sycl::half),
-            (const char *)src1_as_f16.get(), dpct::library_data_t::real_half,
-            nb11 / sizeof(float), src1->nb[2] / sizeof(float), beta,
-            (char *)dst_t, cu_data_type, ne01, dst->nb[2] / sizeof(float),
+            nb01 / nb00, nb02 / nb00,
+            (const char *)src1_f16, dpct::library_data_t::real_half,
+            nb11 / nb10, nb12 / nb10, beta,
+            (char *)dst_t, cu_data_type, ne01, nb2 / nb0,
             ne12 * ne13, cu_compute_type)));
     } else {
-        // use syclGemmBatchedEx
         const int ne23 = ne12*ne13;
 
         sycl_pool_alloc<const void *> ptrs_src(2*ne23);
@@ -13451,43 +15592,35 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0,
                                          {sycl::aspect::fp16});
 
             main_stream->submit([&](sycl::handler &cgh) {
-                const sycl::half *src1_as_f16_get_ct1 = src1_as_f16.get();
-                const void **ptrs_src_get_ct3 = ptrs_src.get();
-                void **ptrs_dst_get_ct4 = ptrs_dst.get();
-
+                const void **ptrs_src_get = ptrs_src.get();
+                void **ptrs_dst_get = ptrs_dst.get();
+                size_t nb12_scaled = src1->type == GGML_TYPE_F16 ? nb12 : nb12 / 2;
+                size_t nb13_scaled = src1->type == GGML_TYPE_F16 ? nb13 : nb13 / 2;
                 cgh.parallel_for(sycl::nd_range<3>(block_dims, block_dims),
                                  [=](sycl::nd_item<3> item_ct1) {
                                      k_compute_batched_ptrs(
-                                         src0_as_f16, src1_as_f16_get_ct1,
-                                         dst_t, ptrs_src_get_ct3,
-                                         ptrs_dst_get_ct4, ne12, ne13, ne23,
-                                         nb02, nb03, nb12, nb13, nbd2, nbd3, r2,
-                                         r3, item_ct1);
+                                         src0_as_f16, src1_f16,
+                                         dst_t, ptrs_src_get,
+                                         ptrs_dst_get, ne12, ne13, ne23,
+                                         nb02, nb03, nb12_scaled, nb13_scaled,
+                                         nbd2, nbd3, r2, r3, item_ct1);
                                  });
             });
         }
-        /*
-        DPCT1010:95: SYCL uses exceptions to report errors and does not use the
-        error codes. The call was replaced with 0. You need to rewrite this
-        code.
-        */
-        SYCL_CHECK(0);
-
         SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch(
-            *g_sycl_handles[g_main_device_index], oneapi::mkl::transpose::trans,
+            *g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans,
             oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha,
             (const void **)(ptrs_src.get() + 0 * ne23),
-            dpct::library_data_t::real_half, nb01 / sizeof(sycl::half),
+            dpct::library_data_t::real_half, nb01 / nb00,
             (const void **)(ptrs_src.get() + 1 * ne23),
-            dpct::library_data_t::real_half, nb11 / sizeof(float), beta,
+            dpct::library_data_t::real_half, nb11 / nb10, beta,
             (void **)(ptrs_dst.get() + 0 * ne23), cu_data_type, ne01, ne23,
             cu_compute_type)));
     }
-#endif
 
-    if (dst->op_params[0] == GGML_PREC_DEFAULT) {
+    if (no_mixed_dtypes) {
         const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16);
-        to_fp32_sycl(dst_f16.get(), dst_ddf, ne, main_stream);
+        to_fp32_sycl(dst_f16.get(), dst_ddf, ne_dst, main_stream);
     }
 }
 catch (sycl::exception const &exc) {
@@ -13496,18 +15629,19 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
+
 static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     const bool all_on_device =
-        (src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) &&
-        (src1->backend == GGML_BACKEND_GPU) &&
-        ( dst->backend == GGML_BACKEND_GPU);
+        (src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT) &&
+        (src1->backend == GGML_BACKEND_TYPE_GPU) &&
+        ( dst->backend == GGML_BACKEND_TYPE_GPU);
 
-    const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
+    const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
 
     int64_t min_compute_capability = INT_MAX;
-    for (int64_t id = 0; id < g_device_count; ++id) {
-        if (min_compute_capability > g_device_caps[id].cc && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) {
-            min_compute_capability = g_device_caps[id].cc;
+    for (int i = 0; i < g_device_count; ++i) {
+        if (min_compute_capability > g_device_caps[i].cc && g_tensor_split[i] < (i + 1 < g_device_count ? g_tensor_split[i + 1] : 1.0f)) {
+            min_compute_capability = g_device_caps[i].cc;
         }
     }
 
@@ -13533,10 +15667,10 @@ static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
         // KQV single-batch
         // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_nc\n");
         ggml_sycl_mul_mat_vec_nc(src0, src1, dst);
-    } else if (!split && all_on_device && use_xmx && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
+    } else if (!split && all_on_device && use_xmx && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
         // KQ + KQV multi-batch
-        // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_mat_batched_sycl\n");
-        ggml_sycl_mul_mat_mat_batched_sycl(src0, src1, dst);
+        // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_batched_sycl\n");
+        ggml_sycl_mul_mat_batched_sycl(src0, src1, dst);
     } else if (src0->type == GGML_TYPE_F32) {
         // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat\n");
         ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false);
@@ -13546,11 +15680,17 @@ static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
 #ifdef GGML_SYCL_FORCE_DMMV
             const bool use_mul_mat_vec_q = false;
 #else
-            const bool use_mul_mat_vec_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type) && ggml_nrows(src1) == 1;
+            bool use_mul_mat_vec_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type);
+            use_mul_mat_vec_q = use_mul_mat_vec_q ||
+                (src0->type == GGML_TYPE_IQ2_XXS) || (src0->type == GGML_TYPE_IQ2_XS) || (src0->type == GGML_TYPE_IQ2_S) ||
+                (src0->type == GGML_TYPE_IQ3_XXS) || (src0->type == GGML_TYPE_IQ3_S) ||
+                (src0->type == GGML_TYPE_IQ4_NL) || (src0->type == GGML_TYPE_IQ4_XS) ||
+                (src0->type == GGML_TYPE_IQ1_S) || (src0->type == GGML_TYPE_IQ1_M);
+
+
 #endif // GGML_SYCL_FORCE_DMMV
 
             if (use_mul_mat_vec_q) {
-                // NOTE: this kernel does not support ggml_nrows(src1) > 1
                 // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_vec_q path\n");
                 ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_vec_q, true);
             } else {
@@ -13600,13 +15740,13 @@ static __global__ void k_compute_batched_ptrs_id(
         src0_f16 = (half *) srcs_ar[i];
     } else {
         src0_f16 = src0_as_f16;
-        if (threadIdx.x == 0 && threadIdx.y == 0) {
+        if (item_ct1.get_local_id(2) == 0 && threadIdx.y == 0) {
             const to_fp16_sycl_t to_fp16 = ggml_get_to_fp16_sycl(src0_type);
             to_fp16(srcs_ar[i], src0_f16, src0_ne, syclStreamFireAndForget);
         }
     }
 
-    int i13 = blockIdx.x * blockDim.x + threadIdx.x;
+    int i13 = blockIdx.x * blockDim.x + item_ct1.get_local_id(2);
     int i12 = blockIdx.y * blockDim.y + threadIdx.y;
 
     if (i13 >= ne13 || i12 >= ne12) {
@@ -13631,7 +15771,7 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) {
     GGML_ASSERT(!ggml_is_transposed(src00));
     GGML_ASSERT(!ggml_is_transposed(src1));
 
-    GGML_ASSERT(src00->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(src00->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
 
     GGML_TENSOR_LOCALS(int64_t, ne0, src00, ne);
@@ -13648,30 +15788,30 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) {
     const int64_t ne  = ggml_nelements(dst);
 
     SYCL_CHECK(ggml_sycl_set_device(g_main_device));
-    syclStream_t main_stream = g_syclStreams[g_main_device_index][0];
+    syclStream_t main_stream = g_syclStreams[g_main_device][0];
 
-    SYCL_CHECK(syclSetStream(g_sycl_handles[g_main_device_index], main_stream));
+    SYCL_CHECK(syclSetStream(g_sycl_handles[g_main_device], main_stream));
 
     //ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    //void * src0_ddq = src0_extra->data_device[g_main_device_index];
+    //void * src0_ddq = src0_extra->data_device[g_main_device];
     //half * src0_as_f16 = (half *) src0_ddq;
 
     ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    float * src1_ddf = (float *) src1_extra->data_device[g_main_device_index];
+    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
 
     ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
-    float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index];
+    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
 
     // convert src1 to fp16
     const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type);
     GGML_ASSERT(to_fp16_sycl != nullptr);
 
     size_t src1_as = 0;
-    half * src1_as_f16 = (half *) ggml_sycl_pool_malloc(ne1 * sizeof(half), &src1_as);
+    half * src1_as_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, ne1 * sizeof(half), &src1_as);
     to_fp16_sycl(src1_ddf, src1_as_f16, ne1, main_stream);
 
     size_t dst_as = 0;
-    half * dst_f16 = (half *) ggml_sycl_pool_malloc(ne * sizeof(half), &dst_as);
+    half * dst_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, ne * sizeof(half), &dst_as);
 
     GGML_ASSERT(ne12 % ne02 == 0);
     GGML_ASSERT(ne13 % ne03 == 0);
@@ -13692,14 +15832,14 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) {
     size_t ptrs_src_s = 0;
     size_t ptrs_dst_s = 0;
 
-    ptrs_src = (const void **) ggml_sycl_pool_malloc(2*ne23*sizeof(void *), &ptrs_src_s);
-    ptrs_dst = (      void **) ggml_sycl_pool_malloc(1*ne23*sizeof(void *), &ptrs_dst_s);
+    ptrs_src = (const void **) ggml_sycl_pool_malloc(g_main_device, 2*ne23*sizeof(void *), &ptrs_src_s);
+    ptrs_dst = (      void **) ggml_sycl_pool_malloc(g_main_device, 1*ne23*sizeof(void *), &ptrs_dst_s);
 
     int64_t src0_ne = ggml_nelements(src00);
     half * src0_as_f16 = nullptr;
     size_t src0_as = 0;
     if (src00->type != GGML_TYPE_F16) {
-        src0_as_f16 = (half *) ggml_sycl_pool_malloc(src0_ne * sizeof(half), &src0_as);
+        src0_as_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, src0_ne * sizeof(half), &src0_as);
     }
 
     static_assert(GGML_MAX_SRC == 6, "GGML_MAX_SRC == 6");
@@ -13714,16 +15854,16 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) {
             r2, r3,
             src00->type, src0_as_f16, src0_ne,
             src1_as_f16, dst_f16,
-            (const int *)((ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device_index], id,
-            dst->src[2] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[2]->extra)->data_device[g_main_device_index] : nullptr,
-            dst->src[3] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[3]->extra)->data_device[g_main_device_index] : nullptr,
-            dst->src[4] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[4]->extra)->data_device[g_main_device_index] : nullptr,
-            dst->src[5] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[5]->extra)->data_device[g_main_device_index] : nullptr
+            (const int *)((ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device], id,
+            dst->src[2] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[2]->extra)->data_device[g_main_device] : nullptr,
+            dst->src[3] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[3]->extra)->data_device[g_main_device] : nullptr,
+            dst->src[4] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[4]->extra)->data_device[g_main_device] : nullptr,
+            dst->src[5] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[5]->extra)->data_device[g_main_device] : nullptr
     );
     SYCL_CHECK(syclGetLastError());
 
     SYCL_CHECK(
-    syclGemmBatchedEx(g_sycl_handles[g_main_device_index], CUBLAS_OP_T, CUBLAS_OP_N,
+    syclGemmBatchedEx(g_sycl_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
             ne01, ne11, ne10,
             &alpha_f16, (const void **) (ptrs_src + 0*ne23), SYCL_R_16F, ne00,
                         (const void **) (ptrs_src + 1*ne23), SYCL_R_16F, ne10,
@@ -13733,104 +15873,105 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) {
             CUBLAS_GEMM_DEFAULT_TENSOR_OP));
 
     if (src0_as != 0) {
-        ggml_sycl_pool_free(src0_as_f16, src0_as);
+        ggml_sycl_pool_free(g_main_device, src0_as_f16, src0_as);
     }
     if (ptrs_src_s != 0) {
-        ggml_sycl_pool_free(ptrs_src, ptrs_src_s);
+        ggml_sycl_pool_free(g_main_device, ptrs_src, ptrs_src_s);
     }
     if (ptrs_dst_s != 0) {
-        ggml_sycl_pool_free(ptrs_dst, ptrs_dst_s);
+        ggml_sycl_pool_free(g_main_device, ptrs_dst, ptrs_dst_s);
     }
 
     const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16);
     to_fp32_sycl(dst_f16, dst_ddf, ne, main_stream);
 
-    ggml_sycl_pool_free(src1_as_f16, src1_as);
-    ggml_sycl_pool_free(dst_f16, dst_as);
+    ggml_sycl_pool_free(g_main_device, src1_as_f16, src1_as);
+    ggml_sycl_pool_free(g_main_device, dst_f16, dst_as);
 }
 #endif
 
 static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
                                  const ggml_tensor *src1,
                                  ggml_tensor *dst) try {
-#if 0
-    ggml_sycl_mul_mat_id_sycl(dst);
-    // TODO: mmq/mmv support
-#endif
+    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT &&
+                "mul_mat_id does not support split buffers");
+    const ggml_tensor *ids = dst->src[2];
+    const dpct::queue_ptr stream = g_syclStreams[g_main_device][0];
 
-    const int64_t nb11 = src1->nb[1];
-    const int64_t nb1  =  dst->nb[1];
+    const size_t nb11 = src1->nb[1];
+    const size_t nb1 = dst->nb[1];
 
-    const struct ggml_tensor * ids = src0;
-    const int32_t id = ((int32_t *) dst->op_params)[0];
-    const int32_t n_as = ((int32_t *) dst->op_params)[1];
+    const int32_t id = ((int32_t *)dst->op_params)[0];
+    const int32_t n_as = src0->ne[2];
 
     std::vector<char> ids_host(ggml_nbytes(ids));
+    const char *ids_dev = (const char *)ids->data;
 
-    const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0];
+    SYCL_CHECK(CHECK_TRY_ERROR(
+        stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids))));
+    SYCL_CHECK(CHECK_TRY_ERROR(stream->wait()));
 
-    if (ids->backend == GGML_BACKEND_GPU) {
-        const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device_index];
-        SYCL_CHECK(CHECK_TRY_ERROR(
-            stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids))));
-        SYCL_CHECK(CHECK_TRY_ERROR(stream->wait()));
-    } else {
-        memcpy(ids_host.data(), ids->data, ggml_nbytes(ids));
-    }
-
-    const ggml_tensor_extra_gpu * src1_extra = (const ggml_tensor_extra_gpu *) src1->extra;
-    const ggml_tensor_extra_gpu * dst_extra = (const ggml_tensor_extra_gpu *) dst->extra;
+    const ggml_tensor_extra_gpu *src0_extra =
+        (const ggml_tensor_extra_gpu *)src0->extra;
+    const ggml_tensor_extra_gpu *src1_extra =
+        (const ggml_tensor_extra_gpu *)src1->extra;
+    const ggml_tensor_extra_gpu *dst_extra =
+        (const ggml_tensor_extra_gpu *)dst->extra;
 
+    ggml_tensor_extra_gpu src0_row_extra;
     ggml_tensor_extra_gpu src1_row_extra;
     ggml_tensor_extra_gpu dst_row_extra;
 
+    ggml_tensor src0_row = *src0;
     ggml_tensor src1_row = *src1;
     ggml_tensor dst_row = *dst;
 
-    src1_row.backend = GGML_BACKEND_GPU;
-    dst_row.backend  = GGML_BACKEND_GPU;
+    src1_row.backend = GGML_BACKEND_TYPE_GPU;
+    dst_row.backend  = GGML_BACKEND_TYPE_GPU;
 
+    src0_row.extra = &src0_row_extra;
     src1_row.extra = &src1_row_extra;
     dst_row.extra = &dst_row_extra;
 
-    char * src1_original = src1->backend == GGML_BACKEND_CPU ?
-        (char *) src1->data : (char *) src1_extra->data_device[g_main_device_index];
-    char * dst_original  =  dst->backend == GGML_BACKEND_CPU ?
-        (char *)  dst->data : (char *)  dst_extra->data_device[g_main_device_index];
+    char *src0_original = src1->backend == GGML_BACKEND_TYPE_CPU
+                              ? (char *)src0->data
+                              : (char *)src0_extra->data_device[g_main_device];
+    char *src1_original = src1->backend == GGML_BACKEND_TYPE_CPU
+                              ? (char *)src1->data
+                              : (char *)src1_extra->data_device[g_main_device];
+    char *dst_original = dst->backend == GGML_BACKEND_TYPE_CPU
+                             ? (char *)dst->data
+                             : (char *)dst_extra->data_device[g_main_device];
+
+    src0_row.ne[2] = 1;
+    src0_row.ne[3] = 1;
+    src0_row.nb[3] = src0->nb[2];
 
     if (src1->ne[1] == 1) {
-        GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
-        GGML_ASSERT(dst->backend  == GGML_BACKEND_GPU);
-
         for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
-            //int32_t row_id;
-            //SYCL_CHECK(syclMemcpyAsync(&row_id, ids_dev + i01*ids->nb[1] + id*ids->nb[0], sizeof(int32_t), syclMemcpyDeviceToHost, g_syclStreams[g_main_device][0]));
-            //SYCL_CHECK(syclStreamSynchronize(g_syclStreams[g_main_device][0]));
-
-            const int32_t row_id = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
+            const int32_t row_id =
+                *(const int32_t *)(ids_host.data() + i01 * ids->nb[1] +
+                                   id * ids->nb[0]);
 
             GGML_ASSERT(row_id >= 0 && row_id < n_as);
 
-            const struct ggml_tensor * src0_row = dst->src[row_id + 2];
+            src0_row_extra.data_device[g_main_device] =
+                src0_original + row_id * src0->nb[2];
+            src1_row_extra.data_device[g_main_device] =
+                src1_original + i01 * src1->nb[1];
+            dst_row_extra.data_device[g_main_device] =
+                dst_original + i01 * dst->nb[1];
 
-            src1_row_extra.data_device[g_main_device_index] = src1_original + i01*src1->nb[1];
-            src1_row.data = (char *) src1->data + i01*src1->nb[1]; // TODO why is this set?
-
-            dst_row_extra.data_device[g_main_device_index] = dst_original + i01*dst->nb[1];
-            dst_row.data = (char *) dst->data + i01*dst->nb[1]; // TODO why is this set?
-
-            ggml_sycl_mul_mat(src0_row, &src1_row, &dst_row);
+            ggml_sycl_mul_mat(&src0_row, &src1_row, &dst_row);
         }
     } else {
         sycl_pool_alloc<char> src1_contiguous(sizeof(float)*ggml_nelements(src1));
         sycl_pool_alloc<char>  dst_contiguous(sizeof(float)*ggml_nelements(dst));
 
-        src1_row_extra.data_device[g_main_device_index] = src1_contiguous.get();
-        dst_row_extra.data_device[g_main_device_index]  =  dst_contiguous.get();
+        src1_row_extra.data_device[g_main_device] = src1_contiguous.get();
+        dst_row_extra.data_device[g_main_device]  =  dst_contiguous.get();
 
         for (int32_t row_id = 0; row_id < n_as; ++row_id) {
-            const struct ggml_tensor * src0_row = dst->src[row_id + 2];
-
             int64_t num_src1_rows = 0;
             for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
                 const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
@@ -13851,6 +15992,9 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
                 continue;
             }
 
+            src0_row_extra.data_device[g_main_device] =
+                src0_original + row_id * src0->nb[2];
+
             src1_row.ne[1] = num_src1_rows;
             dst_row.ne[1] = num_src1_rows;
 
@@ -13862,7 +16006,7 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
             dst_row.nb[2] = num_src1_rows*nb1;
             dst_row.nb[3] = num_src1_rows*nb1;
 
-            ggml_sycl_mul_mat(src0_row, &src1_row, &dst_row);
+            ggml_sycl_mul_mat(&src0_row, &src1_row, &dst_row);
 
             num_src1_rows = 0;
             for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
@@ -13882,7 +16026,7 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
         }
     }
 
-    if (dst->backend == GGML_BACKEND_CPU) {
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
         SYCL_CHECK(CHECK_TRY_ERROR(stream->wait()));
     }
 }
@@ -13905,8 +16049,8 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1,
     const int64_t ne = ggml_nelements(src0);
     GGML_ASSERT(ne == ggml_nelements(src1));
 
-    GGML_ASSERT(src0->backend == GGML_BACKEND_GPU);
-    GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
+    GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
 
     GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX);
     GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX);
@@ -13914,13 +16058,13 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1,
     GGML_TENSOR_BINARY_OP_LOCALS;
 
     SYCL_CHECK(ggml_sycl_set_device(g_main_device));
-    dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0];
+    dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
 
     const ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
     const ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
 
-    char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index];
-    char * src1_ddc = (char *) src1_extra->data_device[g_main_device_index];
+    char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
+    char * src1_ddc = (char *) src1_extra->data_device[g_main_device];
 
     if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
         ggml_cpy_f32_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
@@ -13932,6 +16076,8 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1,
         ggml_cpy_f32_q4_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
         ggml_cpy_f32_q4_1_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
+        ggml_cpy_f16_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
     } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
         ggml_cpy_f16_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
     } else if (src0->type == GGML_TYPE_I16 && src1->type == GGML_TYPE_I16) {
@@ -13971,8 +16117,8 @@ static void ggml_sycl_rope(const ggml_tensor * src0, const ggml_tensor * src1, g
     ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_rope);
 }
 
-static void ggml_sycl_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_alibi);
+static void ggml_sycl_pool2d(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_pool2d);
 }
 
 static void ggml_sycl_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@@ -14001,112 +16147,25 @@ static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_spl
     return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]);
 }
 
-void ggml_sycl_transform_tensor(void *data, struct ggml_tensor *tensor) try {
-    const int64_t nrows = ggml_nrows(tensor);
-
-    const int64_t ne0 = tensor->ne[0];
-
-    const size_t nb1 = tensor->nb[1];
-
-    ggml_backend_type backend = tensor->backend;
-    ggml_tensor_extra_gpu * extra = new struct ggml_tensor_extra_gpu;
-    memset(extra, 0, sizeof(*extra));
-
-    for (int64_t id = 0; id < g_device_count; ++id) {
-        if (backend == GGML_BACKEND_GPU && id != g_main_device_index) {
-            continue;
-        }
-        ggml_sycl_set_device(get_device_id_by_index(id));
-        const dpct::queue_ptr stream = g_syclStreams[id][0];
-
-        int64_t row_low, row_high;
-        if (backend == GGML_BACKEND_GPU) {
-            row_low = 0;
-            row_high = nrows;
-        } else if (backend == GGML_BACKEND_GPU_SPLIT) {
-            const int64_t rounding = get_row_rounding(tensor->type);
-
-            row_low = id == 0 ? 0 : nrows*g_tensor_split[id];
-            row_low -= row_low % rounding;
-
-            if (id == g_device_count - 1) {
-                row_high = nrows;
-            } else {
-                row_high = nrows*g_tensor_split[id + 1];
-                row_high -= row_high % rounding;
-            }
-        } else {
-            GGML_ASSERT(false);
-        }
-        if (row_low == row_high) {
-            continue;
-        }
-
-        int64_t nrows_split = row_high - row_low;
-
-        const size_t offset_split = row_low*nb1;
-        size_t size = ggml_nbytes_split(tensor, nrows_split);
-        const size_t original_size = size;
-
-        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
-        if (ne0 % MATRIX_ROW_PADDING != 0) {
-            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
-        }
-
-        char * buf;
-        SYCL_CHECK(CHECK_TRY_ERROR(buf = (char *)sycl::malloc_device(
-                                        size, *stream)));
-        char * buf_host = (char *)data + offset_split;
-
-        // set padding to 0 to avoid possible NaN values
-        if (size > original_size) {
-            SYCL_CHECK(CHECK_TRY_ERROR(
-                (*stream)
-                .memset(buf + original_size, 0, size - original_size)
-                .wait()));
-        }
-
-        SYCL_CHECK(CHECK_TRY_ERROR((*stream)
-                                    .memcpy(buf, buf_host, original_size)
-                                    .wait()));
-
-        extra->data_device[id] = buf;
-
-        if (backend == GGML_BACKEND_GPU_SPLIT) {
-            for (int64_t is = 0; is < MAX_STREAMS; ++is) {
-                SYCL_CHECK(CHECK_TRY_ERROR(extra->events[id][is] =
-                                                new sycl::event()));
-            }
-        }
-    }
-
-    tensor->extra = extra;
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
 void ggml_sycl_free_data(struct ggml_tensor *tensor) try {
-    if (!tensor || !tensor->extra || (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) ) {
+    if (!tensor || !tensor->extra || (tensor->backend != GGML_BACKEND_TYPE_GPU && tensor->backend != GGML_BACKEND_TYPE_GPU_SPLIT) ) {
         return;
     }
 
     ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
 
-    for (int64_t id = 0; id < g_device_count; ++id) {
-        const dpct::queue_ptr stream = g_syclStreams[id][0];
-        if (extra->data_device[id] != nullptr) {
-            SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id)));
-            SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(extra->data_device[id], *stream)));
+    for (int i = 0; i < g_device_count; ++i) {
+        const dpct::queue_ptr stream = g_syclStreams[i][0];
+        if (extra->data_device[i] != nullptr) {
+            SYCL_CHECK(ggml_sycl_set_device(i));
+            SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(extra->data_device[i], *stream)));
         }
 
         for (int64_t is = 0; is < MAX_STREAMS; ++is) {
-            if (extra->events[id][is] != nullptr) {
-                SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id)));
+            if (extra->events[i][is] != nullptr) {
+                SYCL_CHECK(ggml_sycl_set_device(i));
                 SYCL_CHECK(CHECK_TRY_ERROR(
-                    dpct::destroy_event(extra->events[id][is])));
+                    dpct::destroy_event(extra->events[i][is])));
             }
         }
     }
@@ -14142,15 +16201,15 @@ static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor,
         return;
     }
 
-    tensor->backend = GGML_BACKEND_GPU;
+    tensor->backend = GGML_BACKEND_TYPE_GPU;
 
-    if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_CPU) {
+    if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_TYPE_CPU) {
         const ggml_op src0_op = tensor->src[0]->op;
         if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW || src0_op == GGML_OP_PERMUTE) {
             ggml_sycl_assign_buffers_impl(tensor->src[0], scratch, force_inplace, no_alloc);
         }
     }
-    if (tensor->op == GGML_OP_CPY && tensor->src[1]->backend == GGML_BACKEND_CPU) {
+    if (tensor->op == GGML_OP_CPY && tensor->src[1]->backend == GGML_BACKEND_TYPE_CPU) {
         ggml_sycl_assign_buffers_impl(tensor->src[1], scratch, force_inplace, no_alloc);
     }
 
@@ -14166,22 +16225,22 @@ static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor,
     const size_t size = ggml_nbytes(tensor);
 
     SYCL_CHECK(ggml_sycl_set_device(g_main_device));
-    const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0];
+    const dpct::queue_ptr stream = g_syclStreams[g_main_device][0];
 
-    if (inplace && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) {
+    if (inplace && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) {
         ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra;
-        char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index];
+        char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
         size_t offset = 0;
         if (tensor->op == GGML_OP_VIEW) {
             memcpy(&offset, tensor->op_params, sizeof(size_t));
         }
         extra = ggml_sycl_alloc_temp_tensor_extra();
-        extra->data_device[g_main_device_index] = src0_ddc + offset;
+        extra->data_device[g_main_device] = src0_ddc + offset;
     } else if (tensor->op == GGML_OP_CPY) {
         ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu * ) tensor->src[1]->extra;
-        void * src1_ddv = src1_extra->data_device[g_main_device_index];
+        void * src1_ddv = src1_extra->data_device[g_main_device];
         extra = ggml_sycl_alloc_temp_tensor_extra();
-        extra->data_device[g_main_device_index] = src1_ddv;
+        extra->data_device[g_main_device] = src1_ddv;
     } else if (scratch) {
         GGML_ASSERT(size <= g_scratch_size);
         if (g_scratch_offset + size > g_scratch_size) {
@@ -14196,7 +16255,7 @@ static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor,
             g_scratch_buffer = data;
         }
         extra = ggml_sycl_alloc_temp_tensor_extra();
-        extra->data_device[g_main_device_index] = data + g_scratch_offset;
+        extra->data_device[g_main_device] = data + g_scratch_offset;
 
         g_scratch_offset += size;
 
@@ -14209,44 +16268,7 @@ static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor,
             (*stream).memset(data, 0, size).wait()));
         extra = new ggml_tensor_extra_gpu;
         memset(extra, 0, sizeof(*extra));
-        extra->data_device[g_main_device_index] = data;
-    }
-
-    tensor->extra = extra;
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-void ggml_sycl_assign_scratch_offset(struct ggml_tensor *tensor,
-                                     size_t offset) try {
-    if (g_scratch_size == 0) {
-        return;
-    }
-    if (g_scratch_buffer == nullptr) {
-        ggml_sycl_set_device(g_main_device);
-        const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0];
-        SYCL_CHECK(
-            CHECK_TRY_ERROR(g_scratch_buffer = (void *)sycl::malloc_device(
-                                 g_scratch_size, *stream)));
-    }
-
-    ggml_tensor_extra_gpu * extra = ggml_sycl_alloc_temp_tensor_extra();
-
-    const bool inplace = tensor->view_src != nullptr;
-
-    if (inplace && (tensor->view_src->backend == GGML_BACKEND_GPU || tensor->view_src->backend == GGML_BACKEND_GPU_SPLIT)) {
-        ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->view_src->extra;
-        char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index];
-        size_t view_offset = 0;
-        if (tensor->op == GGML_OP_VIEW) {
-            memcpy(&view_offset, tensor->op_params, sizeof(size_t));
-        }
-        extra->data_device[g_main_device_index] = src0_ddc + view_offset;
-    } else {
-        extra->data_device[g_main_device_index] = (char *) g_scratch_buffer + offset;
+        extra->data_device[g_main_device] = data;
     }
 
     tensor->extra = extra;
@@ -14258,14 +16280,14 @@ catch (sycl::exception const &exc) {
 }
 
 void ggml_sycl_copy_to_device(struct ggml_tensor *tensor) try {
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
     GGML_ASSERT(ggml_is_contiguous(tensor));
 
     ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
     SYCL_CHECK(ggml_sycl_set_device(g_main_device));
-    const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0];
+    const dpct::queue_ptr stream = g_syclStreams[g_main_device][0];
     SYCL_CHECK(CHECK_TRY_ERROR((*stream)
-                                    .memcpy(extra->data_device[g_main_device_index],
+                                    .memcpy(extra->data_device[g_main_device],
                                             tensor->data, ggml_nbytes(tensor))
                                     .wait()));
 }
@@ -14292,21 +16314,17 @@ void ggml_sycl_assign_buffers_force_inplace(struct ggml_tensor * tensor) {
 }
 
 void ggml_sycl_set_main_device(const int main_device) try {
+    if (g_main_device == main_device) return;
+    check_allow_gpu_index(main_device);
+    g_main_device = main_device;
+    g_main_device_id = g_sycl_gpu_mgr->gpus[main_device];
 
-    if (main_device >= g_all_sycl_device_count) {
-        fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n",
-                main_device, g_all_sycl_device_count, g_main_device);
-        return;
-    }
-
-    if (g_main_device != main_device && g_device_count >= 1) {
-        g_main_device = main_device;
-        g_main_device_index = get_device_index_by_id(g_main_device);
+    if (g_ggml_sycl_debug) {
         dpct::device_info prop;
         SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info(
-            prop, dpct::dev_mgr::instance().get_device(g_main_device))));
+            prop, dpct::dev_mgr::instance().get_device(g_main_device_id))));
         fprintf(stderr, "Using device %d (%s) as main device\n",
-                g_main_device, prop.get_name());
+                g_main_device_id, prop.get_name());
     }
 }
 catch (sycl::exception const &exc) {
@@ -14329,7 +16347,7 @@ void ggml_sycl_free_scratch() try {
         return;
     }
     ggml_sycl_set_device(g_main_device);
-    const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0];
+    const dpct::queue_ptr stream = g_syclStreams[g_main_device][0];
 
     SYCL_CHECK(CHECK_TRY_ERROR(
         sycl::free(g_scratch_buffer, *stream)));
@@ -14345,9 +16363,9 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_
     if (!g_sycl_loaded) return false;
 
     ggml_sycl_func_t func;
-    const bool any_on_device = tensor->backend == GGML_BACKEND_GPU
-        || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT))
-        || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU);
+    const bool any_on_device = tensor->backend == GGML_BACKEND_TYPE_GPU
+        || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT))
+        || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_TYPE_GPU);
 
     if (!any_on_device && tensor->op != GGML_OP_MUL_MAT && tensor->op != GGML_OP_MUL_MAT_ID) {
         return false;
@@ -14401,6 +16419,12 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_
                 case GGML_UNARY_OP_RELU:
                     func = ggml_sycl_relu;
                     break;
+                case GGML_UNARY_OP_HARDSIGMOID:
+                    func = ggml_sycl_hardsigmoid;
+                    break;
+                case GGML_UNARY_OP_HARDSWISH:
+                    func = ggml_sycl_hardswish;
+                    break;
                 default:
                     return false;
             }
@@ -14469,12 +16493,12 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_
         case GGML_OP_ROPE:
             func = ggml_sycl_rope;
             break;
-        case GGML_OP_ALIBI:
-            func = ggml_sycl_alibi;
-            break;
         case GGML_OP_IM2COL:
             func = ggml_sycl_im2col;
             break;
+        case GGML_OP_POOL_2D:
+            func = ggml_sycl_pool2d;
+            break;
         case GGML_OP_SUM_ROWS:
             func = ggml_sycl_sum_rows;
             break;
@@ -14485,14 +16509,14 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_
             return false;
     }
 
-    if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT) {
+    if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT) {
         ggml_sycl_set_peer_access(tensor->src[1]->ne[1]);
     }
 
     if (params->ith != 0) {
         return true;
     }
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return true;
     }
     func(tensor->src[0], tensor->src[1], tensor);
@@ -14500,27 +16524,16 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_
 }
 
 GGML_API GGML_CALL void   ggml_sycl_get_gpu_list(int *id_list, int max_len) try {
-    int max_compute_units = -1;
-    for(int i=0;i<max_len;i++) id_list[i] = 0;
+    GGML_SYCL_DEBUG("[SYCL] call ggml_sycl_get_gpu_list\n");
+    for(int i=0;i<max_len;i++) id_list[i] = -1;
 
-    int device_count = dpct::dev_mgr::instance().device_count();
-
-    for(int id=0; id< device_count; id++){
-        sycl::device device = dpct::dev_mgr::instance().get_device(id);
-        if (!device.is_gpu()) continue;
-        dpct::device_info prop;
-        dpct::get_device_info(prop, device);
-        if(max_compute_units < prop.get_max_compute_units()) max_compute_units = prop.get_max_compute_units();
+    if (!g_sycl_gpu_mgr) {
+        g_sycl_gpu_mgr = new sycl_gpu_mgr();
     }
 
-    for(int id=0;id< device_count;id++){
-        sycl::device device = dpct::dev_mgr::instance().get_device(id);
-        if (!device.is_gpu()) continue;
-        dpct::device_info prop;
-        dpct::get_device_info(prop, device);
-        if(max_compute_units == prop.get_max_compute_units() && prop.get_major_version() == 1 ){
-            id_list[id] = 1;
-        }
+    for (int i=0;i< g_sycl_gpu_mgr->gpus.size();i++){
+        if (i>=max_len) break;
+        id_list[i] = g_sycl_gpu_mgr->gpus[i];
     }
     return;
 }
@@ -14546,9 +16559,11 @@ catch (sycl::exception const &exc) {
 
 GGML_API GGML_CALL void ggml_sycl_get_device_description(int device, char *description,
                                       size_t description_size) try {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_sycl_get_device_description\n");
     dpct::device_info prop;
+    int device_id = g_sycl_gpu_mgr->gpus[device];
     SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info(
-        prop, dpct::dev_mgr::instance().get_device(device))));
+        prop, dpct::dev_mgr::instance().get_device(device_id))));
     snprintf(description, description_size, "%s", prop.get_name());
 }
 catch (sycl::exception const &exc) {
@@ -14557,17 +16572,37 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
+GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free,
+                                                   size_t *total) try {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_memory\n");
+    ggml_sycl_set_device(device);
+
+    /*
+    DPCT1009:218: SYCL uses exceptions to report errors and does not use the
+    error codes. The original code was commented out and a warning string was
+    inserted. You need to rewrite this code.
+    */
+    /*
+    DPCT1106:217: 'cudaMemGetInfo' was migrated with the Intel extensions for
+    device information which may not be supported by all compilers or runtimes.
+    You may need to adjust the code.
+    */
+   int device_id = g_sycl_gpu_mgr->gpus[device];
+    SYCL_CHECK(CHECK_TRY_ERROR(
+        dpct::dev_mgr::instance().get_device(device_id).get_memory_info(*free, *total)));
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
 ////////////////////////////////////////////////////////////////////////////////
 
 // backend interface
 
 #define UNUSED GGML_UNUSED
 
-struct ggml_backend_sycl_context {
-    int device;
-    std::string name;
-};
-
 // sycl buffer
 
 struct ggml_backend_sycl_buffer_context {
@@ -14577,7 +16612,12 @@ struct ggml_backend_sycl_buffer_context {
     size_t temp_tensor_extra_index = 0;
     std::string name;
 
-     ggml_backend_sycl_buffer_context(int device, void * dev_ptr) : device(device), dev_ptr(dev_ptr) {}
+     ggml_backend_sycl_buffer_context(int device, void * dev_ptr) :
+        device(device), dev_ptr(dev_ptr) {
+            check_allow_gpu_index(device);
+            int id = g_sycl_gpu_mgr->gpus[device];
+            name = (GGML_SYCL_NAME + std::to_string(id));
+        }
 
     ~ ggml_backend_sycl_buffer_context() {
         delete[] temp_tensor_extras;
@@ -14608,10 +16648,9 @@ GGML_CALL static bool ggml_backend_buffer_is_sycl(ggml_backend_buffer_t buffer)
 
 static void
 ggml_backend_sycl_buffer_free_buffer(ggml_backend_buffer_t buffer) try {
-     ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
+    ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
     ggml_sycl_set_device(ctx->device);
-    int device_index = get_device_index_by_id(ctx->device);
-    const dpct::queue_ptr stream = g_syclStreams[device_index][0];
+    const dpct::queue_ptr stream = g_syclStreams[ctx->device][0];
 
     SYCL_CHECK(
         CHECK_TRY_ERROR(sycl::free(ctx->dev_ptr, *stream)));
@@ -14624,13 +16663,14 @@ catch (sycl::exception const &exc) {
 }
 
 static void * ggml_backend_sycl_buffer_get_base(ggml_backend_buffer_t buffer) {
-     ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
+    ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
     return ctx->dev_ptr;
 }
 
-static void ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer,
-                                                 ggml_tensor *tensor) try {
-     ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
+GGML_CALL static void
+ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer,
+                                     ggml_tensor *tensor) try {
+    ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context;
 
     if (tensor->view_src != NULL && tensor->view_offs == 0) {
         assert(tensor->view_src->buffer->buft == buffer->buft);
@@ -14642,27 +16682,20 @@ static void ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer,
     ggml_tensor_extra_gpu * extra = ctx->ggml_sycl_alloc_temp_tensor_extra();
 
     extra->data_device[ctx->device] = tensor->data;
-
-    tensor->backend = GGML_BACKEND_GPU;
+    tensor->backend = GGML_BACKEND_TYPE_GPU;
     tensor->extra = extra;
 
     if (ggml_is_quantized(tensor->type)) {
         // initialize padding to 0 to avoid possible NaN values
-        int64_t row_low = 0;
-        int64_t row_high = ggml_nrows(tensor);
-        int64_t nrows_split = row_high - row_low;
-
-        size_t original_size = ggml_nbytes_split(tensor, nrows_split);
+        size_t original_size = ggml_nbytes(tensor);
         size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor);
 
         if (padded_size > original_size && tensor->view_src == nullptr) {
             SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[ctx->device][0]->memset(
                 (char *)tensor->data + original_size, 0,
-                padded_size - original_size)));
+                padded_size - original_size).wait()));
         }
     }
-
-    UNUSED(buffer);
 }
 catch (sycl::exception const &exc) {
   std::cerr << exc.what() << "Exception caught at file:" << __FILE__
@@ -14674,20 +16707,21 @@ static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer,
                                                 ggml_tensor *tensor,
                                                 const void *data, size_t offset,
                                                 size_t size) try {
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 
-     ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
+    ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
 
     ggml_sycl_set_device(ctx->device);
-    int device_index = get_device_index_by_id(ctx->device);
-    const dpct::queue_ptr stream = g_syclStreams[device_index][0];
+    const dpct::queue_ptr stream = g_syclStreams[ctx->device][0];
     SYCL_CHECK(
-        CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw()));
-
+        CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw()));
+    char* host_buf = (char*)malloc(size);
+    memcpy(host_buf, data, size);
     SYCL_CHECK(
         CHECK_TRY_ERROR((*stream)
-                             .memcpy((char *)tensor->data + offset, data, size)
+                             .memcpy((char *)tensor->data + offset, host_buf, size)
                              .wait()));
+    free(host_buf);
 }
 catch (sycl::exception const &exc) {
   std::cerr << exc.what() << "Exception caught at file:" << __FILE__
@@ -14699,16 +16733,15 @@ static void ggml_backend_sycl_buffer_get_tensor(ggml_backend_buffer_t buffer,
                                                 const ggml_tensor *tensor,
                                                 void *data, size_t offset,
                                                 size_t size) try {
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 
-     ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
+    ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
 
     ggml_sycl_set_device(ctx->device);
-    int device_index = get_device_index_by_id(ctx->device);
-    const dpct::queue_ptr stream = g_syclStreams[device_index][0];
+    const dpct::queue_ptr stream = g_syclStreams[ctx->device][0];
 
     SYCL_CHECK(
-        CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw()));
+        CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw()));
 
     SYCL_CHECK(CHECK_TRY_ERROR(
         (*stream)
@@ -14721,13 +16754,73 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
+GGML_CALL static bool
+ggml_backend_sycl_buffer_cpy_tensor(ggml_backend_buffer_t buffer,
+                                    const ggml_tensor *src,
+                                    ggml_tensor *dst) try {
+    if (ggml_backend_buffer_is_sycl(src->buffer)) {
+        ggml_backend_sycl_buffer_context * src_ctx = (ggml_backend_sycl_buffer_context *)src->buffer->context;
+        ggml_backend_sycl_buffer_context * dst_ctx = (ggml_backend_sycl_buffer_context *)buffer->context;
+
+        ggml_sycl_set_device(src_ctx->device);
+        /*
+        DPCT1009:198: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            dpct::dev_mgr::instance().get_device(src_ctx->device).queues_wait_and_throw()));
+        ggml_sycl_set_device(dst_ctx->device);
+        /*
+        DPCT1009:199: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw()));
+        /*
+        DPCT1009:200: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+
+        dpct::queue_ptr stream_dst = g_syclStreams[dst_ctx->device][0];
+        dpct::queue_ptr stream_src = g_syclStreams[src_ctx->device][0];
+        size_t size = ggml_nbytes(src);
+
+        //todo. it's dirty solutino to walkaroud known issue:device2device cross GPUs.
+        dev2dev_memcpy(*stream_dst, *stream_src, dst->data, src->data, size);
+
+//todo, it's known issue：error in device2device cross GPUs. reused when the issue is fixed. DON"T remove
+#if 0
+        SYCL_CHECK(CHECK_TRY_ERROR((*stream).memcpy(
+            (char *)dst->data, (const char *)src->data, size).wait()));
+
+        /*
+        DPCT1009:201: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw()));
+#endif
+        return true;
+    }
+    return false;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+
 static void ggml_backend_sycl_buffer_clear(ggml_backend_buffer_t buffer,
                                            uint8_t value) try {
      ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
 
     ggml_sycl_set_device(ctx->device);
-    int device_index = get_device_index_by_id(ctx->device);
-    const dpct::queue_ptr stream = g_syclStreams[device_index][0];
+    const dpct::queue_ptr stream = g_syclStreams[ctx->device][0];
     SYCL_CHECK(
         CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw()));
 
@@ -14748,7 +16841,7 @@ static struct ggml_backend_buffer_i ggml_backend_sycl_buffer_interface = {
     /* .init_tensor     = */ ggml_backend_sycl_buffer_init_tensor,
     /* .set_tensor      = */ ggml_backend_sycl_buffer_set_tensor,
     /* .get_tensor      = */ ggml_backend_sycl_buffer_get_tensor,
-    /* .cpy_tensor      = */ NULL,
+    /* .cpy_tensor      = */ ggml_backend_sycl_buffer_cpy_tensor,
     /* .clear           = */ ggml_backend_sycl_buffer_clear,
     /* .reset           = */ NULL,
 };
@@ -14759,28 +16852,28 @@ struct ggml_backend_sycl_buffer_type_context {
     std::string name;
 };
 
+struct ggml_backend_sycl_context {
+    int device;
+    std::string name;
+};
+
 GGML_CALL static const char * ggml_backend_sycl_buffer_type_name(ggml_backend_buffer_type_t buft) {
     ggml_backend_sycl_buffer_type_context * ctx = (ggml_backend_sycl_buffer_type_context *)buft->context;
 
     return ctx->name.c_str();
 }
-
-static ggml_backend_buffer_t
+GGML_CALL static ggml_backend_buffer_t
 ggml_backend_sycl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft,
                                            size_t size) try {
-    int device = (int) (intptr_t) buft->context;
-
-    ggml_sycl_set_device(device);
-    int device_index = get_device_index_by_id(device);
-    const dpct::queue_ptr stream = g_syclStreams[device_index][0];
+    ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context;
+    ggml_sycl_set_device(buft_ctx->device);
+    const dpct::queue_ptr stream = g_syclStreams[buft_ctx->device][0];
     size = std::max(size, (size_t)1); // syclMalloc returns null for size 0
 
     void * dev_ptr;
     SYCL_CHECK(CHECK_TRY_ERROR(dev_ptr = (void *)sycl::malloc_device(
                                     size, *stream)));
-
-     ggml_backend_sycl_buffer_context * ctx = new  ggml_backend_sycl_buffer_context(device, dev_ptr);
-
+    ggml_backend_sycl_buffer_context * ctx = new  ggml_backend_sycl_buffer_context(buft_ctx->device, dev_ptr);
     return ggml_backend_buffer_init(buft, ggml_backend_sycl_buffer_interface, ctx, size);
 }
 catch (sycl::exception const &exc) {
@@ -14789,9 +16882,8 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
-static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+GGML_CALL static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
     return 128;
-
     UNUSED(buft);
 }
 
@@ -14801,13 +16893,8 @@ static size_t ggml_backend_sycl_buffer_type_get_max_size(ggml_backend_buffer_typ
     UNUSED(buft);
 }
 
-static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
-    int64_t row_low = 0;
-    int64_t row_high = ggml_nrows(tensor);
-    int64_t nrows_split = row_high - row_low;
-
-    size_t size = ggml_nbytes_split(tensor, nrows_split);
-
+GGML_CALL static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
+    size_t size = ggml_nbytes(tensor);
     int64_t ne0 = tensor->ne[0];
 
     if (ggml_is_quantized(tensor->type)) {
@@ -14821,10 +16908,13 @@ static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_t
     UNUSED(buft);
 }
 
-static bool ggml_backend_sycl_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
-    return ggml_backend_is_sycl(backend);
-
-    UNUSED(buft);
+GGML_CALL static bool ggml_backend_sycl_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
+    if (!ggml_backend_is_sycl(backend)) {
+        return false;
+    }
+    ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context;
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
+    return buft_ctx->device == sycl_ctx->device;
 }
 
 static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = {
@@ -14837,22 +16927,413 @@ static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = {
     /* .is_host          = */ nullptr,
 };
 
-ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) {
+ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device_index) {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_buffer_type\n");
+
+    if (device_index>=g_device_count or device_index<0) {
+        printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n",
+            device_index, g_device_count-1);
+        GGML_ASSERT(device_index<g_device_count);
+    }
     static struct ggml_backend_buffer_type ggml_backend_sycl_buffer_types[GGML_SYCL_MAX_DEVICES];
 
-    static bool ggml_backend_sycl_buffer_type_initialized = false;
-
-    if (!ggml_backend_sycl_buffer_type_initialized) {
-        for (int i = 0; i < GGML_SYCL_MAX_DEVICES; i++) {
+    if (!g_ggml_backend_sycl_buffer_type_initialized) {
+        for (int i = 0; i < g_device_count; i++) {
             ggml_backend_sycl_buffer_types[i] = {
                 /* .iface    = */ ggml_backend_sycl_buffer_type_interface,
-                /* .context  = */ (ggml_backend_buffer_type_context_t) (intptr_t) i,
+                /* .context  = */ new ggml_backend_sycl_buffer_type_context{i, GGML_SYCL_NAME + std::to_string(g_sycl_gpu_mgr->gpus[i])},
             };
         }
-        ggml_backend_sycl_buffer_type_initialized = true;
+        g_ggml_backend_sycl_buffer_type_initialized = true;
+    }
+    return &ggml_backend_sycl_buffer_types[device_index];
+}
+
+// sycl split buffer type
+static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array<float, GGML_SYCL_MAX_DEVICES> & tensor_split, int id) {
+    const int64_t nrows = ggml_nrows(tensor);
+    const int64_t rounding = get_row_rounding(tensor->type, tensor_split);
+
+    *row_low = id == 0 ? 0 : nrows*tensor_split[id];
+    *row_low -= *row_low % rounding;
+    if (id == g_device_count - 1) {
+        *row_high = nrows;
+    } else {
+        *row_high = nrows*tensor_split[id + 1];
+        *row_high -= *row_high % rounding;
+    }
+}
+
+struct ggml_backend_sycl_split_buffer_context {
+    ~ggml_backend_sycl_split_buffer_context() try {
+        for (ggml_tensor_extra_gpu * extra : tensor_extras) {
+            for (int i = 0; i < g_device_count; ++i) {
+                // int id = g_sycl_gpu_mgr->gpus[i];
+                for (int64_t is = 0; is < MAX_STREAMS; ++is) {
+                    if (extra->events[i][is] != nullptr) {
+                        /*
+                        DPCT1009:206: SYCL uses exceptions to report errors and
+                        does not use the error codes. The original code was
+                        commented out and a warning string was inserted. You
+                        need to rewrite this code.
+                        */
+                        SYCL_CHECK(CHECK_TRY_ERROR(
+                            dpct::destroy_event(extra->events[i][is])));
+                    }
+                }
+                if (extra->data_device[i] != nullptr) {
+                    /*
+                    DPCT1009:207: SYCL uses exceptions to report errors and does
+                    not use the error codes. The original code was commented out
+                    and a warning string was inserted. You need to rewrite this
+                    code.
+                    */
+                    ggml_sycl_set_device(i);
+                    SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(
+                        extra->data_device[i], *g_syclStreams[i][0])));
+                }
+            }
+            delete extra;
+        }
+    }
+    catch (sycl::exception const &exc) {
+      std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+                << ", line:" << __LINE__ << std::endl;
+      std::exit(1);
     }
 
-    return &ggml_backend_sycl_buffer_types[device];
+    std::vector<ggml_tensor_extra_gpu *> tensor_extras;
+};
+
+GGML_CALL static const char * ggml_backend_sycl_split_buffer_get_name(ggml_backend_buffer_t buffer) {
+    return GGML_SYCL_NAME "_Split";
+
+    UNUSED(buffer);
+}
+
+// unused at the moment
+//static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) {
+//    return buffer->iface.get_name == ggml_backend_sycl_split_buffer_get_name;
+//}
+
+GGML_CALL static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context;
+    delete ctx;
+}
+
+GGML_CALL static void * ggml_backend_sycl_split_buffer_get_base(ggml_backend_buffer_t buffer) {
+    // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced
+    return (void *)0x1000;
+
+    UNUSED(buffer);
+}
+
+GGML_CALL static void
+ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer,
+                                           ggml_tensor *tensor) try {
+    GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported
+
+    ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context;
+    ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context;
+
+    const int64_t ne0 = tensor->ne[0];
+
+    ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{};
+
+    ctx->tensor_extras.push_back(extra);
+
+    for (int i = 0; i < g_device_count; ++i) {
+        // int id = g_sycl_gpu_mgr->gpus[i];
+        int64_t row_low, row_high;
+        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i);
+
+        int64_t nrows_split = row_high - row_low;
+        if (nrows_split == 0) {
+            continue;
+        }
+
+        size_t size = ggml_nbytes_split(tensor, nrows_split);
+        const size_t original_size = size;
+
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
+
+        // FIXME: do not crash if cudaMalloc fails
+        // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first
+        ggml_sycl_set_device(i);
+        char * buf;
+        /*
+        DPCT1009:208: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        SYCL_CHECK(CHECK_TRY_ERROR(buf = (char *)sycl::malloc_device(
+                                        size, *g_syclStreams[i][0])));
+
+        // set padding to 0 to avoid possible NaN values
+        if (size > original_size) {
+            /*
+            DPCT1009:209: SYCL uses exceptions to report errors and does not use
+            the error codes. The original code was commented out and a warning
+            string was inserted. You need to rewrite this code.
+            */
+            SYCL_CHECK(CHECK_TRY_ERROR(
+                (*g_syclStreams[i][0])
+                    .memset(buf + original_size, 0, size - original_size)
+                    .wait()));
+        }
+
+        extra->data_device[i] = buf;
+
+        for (int64_t is = 0; is < MAX_STREAMS; ++is) {
+            /*
+            DPCT1009:210: SYCL uses exceptions to report errors and does not use
+            the error codes. The original code was commented out and a warning
+            string was inserted. You need to rewrite this code.
+            */
+            SYCL_CHECK(
+                CHECK_TRY_ERROR(extra->events[i][is] = new sycl::event()));
+        }
+    }
+    tensor->backend = GGML_BACKEND_TYPE_GPU_SPLIT;
+    tensor->extra = extra;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL static void
+ggml_backend_sycl_split_buffer_set_tensor(ggml_backend_buffer_t buffer,
+                                          ggml_tensor *tensor, const void *data,
+                                          size_t offset, size_t size) try {
+    // split tensors must always be set in their entirety at once
+    GGML_ASSERT(offset == 0);
+    GGML_ASSERT(size == ggml_nbytes(tensor));
+
+    ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context;
+
+    const int64_t ne0 = tensor->ne[0];
+    const size_t nb1 = tensor->nb[1];
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra;
+
+    for (int i = 0; i < g_device_count; ++i) {
+        // int id = g_sycl_gpu_mgr->gpus[i];
+        int64_t row_low, row_high;
+        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i);
+
+        int64_t nrows_split = row_high - row_low;
+        if (nrows_split == 0) {
+            continue;
+        }
+
+        const size_t offset_split = row_low*nb1;
+        size_t size = ggml_nbytes_split(tensor, nrows_split);
+        const size_t original_size = size;
+
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
+
+        const char * buf_host = (const char *)data + offset_split;
+        /*
+        DPCT1009:211: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        ggml_sycl_set_device(i);
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            (*g_syclStreams[i][0])
+                .memcpy(extra->data_device[i], buf_host, original_size)
+                .wait()));
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL static void
+ggml_backend_sycl_split_buffer_get_tensor(ggml_backend_buffer_t buffer,
+                                          const ggml_tensor *tensor, void *data,
+                                          size_t offset, size_t size) try {
+    // split tensors must always be set in their entirety at once
+    GGML_ASSERT(offset == 0);
+    GGML_ASSERT(size == ggml_nbytes(tensor));
+
+    ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context;
+
+    const int64_t ne0 = tensor->ne[0];
+    const size_t nb1 = tensor->nb[1];
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra;
+
+    for (int i = 0; i < g_device_count; ++i) {
+        // int id = g_sycl_gpu_mgr->gpus[i];
+        int64_t row_low, row_high;
+        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i);
+
+        int64_t nrows_split = row_high - row_low;
+        if (nrows_split == 0) {
+            continue;
+        }
+
+        const size_t offset_split = row_low*nb1;
+        size_t size = ggml_nbytes_split(tensor, nrows_split);
+        const size_t original_size = size;
+
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
+
+        char * buf_host = (char *)data + offset_split;
+        /*
+        DPCT1009:212: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        ggml_sycl_set_device(i);
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            (*g_syclStreams[i][0])
+                .memcpy(buf_host, extra->data_device[i], original_size)
+                .wait()));
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL static void ggml_backend_sycl_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    UNUSED(buffer);
+    UNUSED(value);
+}
+
+static struct ggml_backend_buffer_i ggml_backend_sycl_split_buffer_interface = {
+    /* .get_name        = */ ggml_backend_sycl_split_buffer_get_name,
+    /* .free_buffer     = */ ggml_backend_sycl_split_buffer_free_buffer,
+    /* .get_base        = */ ggml_backend_sycl_split_buffer_get_base,
+    /* .init_tensor     = */ ggml_backend_sycl_split_buffer_init_tensor,
+    /* .set_tensor      = */ ggml_backend_sycl_split_buffer_set_tensor,
+    /* .get_tensor      = */ ggml_backend_sycl_split_buffer_get_tensor,
+    /* .cpy_tensor      = */ NULL,
+    /* .clear           = */ ggml_backend_sycl_split_buffer_clear,
+    /* .reset           = */ NULL,
+};
+
+GGML_CALL static const char * ggml_backend_sycl_split_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    return GGML_SYCL_NAME "_Split";
+
+    UNUSED(buft);
+}
+
+GGML_CALL static ggml_backend_buffer_t ggml_backend_sycl_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point
+    // instead, we allocate them for each tensor separately in init_tensor
+    // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated,
+    // as returned by get_alloc_size. this limit is enforced during tensor allocation by ggml-alloc, so it must be correct.
+    ggml_backend_sycl_split_buffer_context * ctx = new ggml_backend_sycl_split_buffer_context();
+
+    return ggml_backend_buffer_init(buft, ggml_backend_sycl_split_buffer_interface, ctx, size);
+}
+
+GGML_CALL static size_t ggml_backend_sycl_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+    return 128;
+    UNUSED(buft);
+}
+
+GGML_CALL static size_t ggml_backend_sycl_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
+    ggml_backend_sycl_split_buffer_type_context * ctx = (ggml_backend_sycl_split_buffer_type_context *)buft->context;
+
+    size_t total_size = 0;
+
+    const int64_t ne0 = tensor->ne[0];
+
+    for (int i = 0; i < g_device_count; ++i) {
+        // int id = g_sycl_gpu_mgr->gpus[i];
+        int64_t row_low, row_high;
+        get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, i);
+
+        int64_t nrows_split = row_high - row_low;
+        if (nrows_split == 0) {
+            continue;
+        }
+
+        total_size += ggml_nbytes_split(tensor, nrows_split);
+
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            total_size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
+    }
+
+    return total_size;
+}
+
+GGML_CALL static bool ggml_backend_sycl_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
+    return ggml_backend_is_sycl(backend);
+
+    UNUSED(buft);
+}
+
+GGML_CALL static bool ggml_backend_sycl_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
+    return false;
+
+    UNUSED(buft);
+}
+
+static ggml_backend_buffer_type_i ggml_backend_sycl_split_buffer_type_interface = {
+    /* .get_name         = */ ggml_backend_sycl_split_buffer_type_name,
+    /* .alloc_buffer     = */ ggml_backend_sycl_split_buffer_type_alloc_buffer,
+    /* .get_alignment    = */ ggml_backend_sycl_split_buffer_type_get_alignment,
+    /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
+    /* .get_alloc_size   = */ ggml_backend_sycl_split_buffer_type_get_alloc_size,
+    /* .supports_backend = */ ggml_backend_sycl_split_buffer_type_supports_backend,
+    /* .is_host          = */ ggml_backend_sycl_split_buffer_type_is_host,
+};
+
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split) {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_split_buffer_type\n");
+    ggml_init_sycl();
+    // FIXME: this is not thread safe
+    static std::map<std::array<float, GGML_SYCL_MAX_DEVICES>, struct ggml_backend_buffer_type> buft_map;
+
+    std::array<float, GGML_SYCL_MAX_DEVICES> tensor_split_arr = {};
+
+    bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_SYCL_MAX_DEVICES, [](float x) { return x == 0.0f; });
+    if (all_zero) {
+        tensor_split_arr = g_default_tensor_split;
+    } else {
+        float split_sum = 0.0f;
+        for (int i = 0; i < g_device_count; ++i) {
+            // int id = g_sycl_gpu_mgr->gpus[i];
+            tensor_split_arr[i] = split_sum;
+            split_sum += tensor_split[i];
+        }
+        for (int i = 0; i < g_device_count; ++i) {
+            // int id = g_sycl_gpu_mgr->gpus[i];
+            tensor_split_arr[i] /= split_sum;
+        }
+    }
+
+    auto it = buft_map.find(tensor_split_arr);
+    if (it != buft_map.end()) {
+        return &it->second;
+    }
+
+    struct ggml_backend_buffer_type buft {
+        /* .iface   = */ ggml_backend_sycl_split_buffer_type_interface,
+        /* .context = */ new ggml_backend_sycl_split_buffer_type_context{tensor_split_arr},
+    };
+
+    auto result = buft_map.emplace(tensor_split_arr, buft);
+    return &result.first->second;
 }
 
 // host buffer type
@@ -14884,12 +17365,14 @@ static ggml_backend_buffer_t ggml_backend_sycl_host_buffer_type_alloc_buffer(ggm
     // FIXME: this is a hack to avoid having to implement a new buffer type
     ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
     buffer->buft = buft;
+    buffer->iface.get_name = ggml_backend_sycl_host_buffer_name;
     buffer->iface.free_buffer = ggml_backend_sycl_host_buffer_free_buffer;
 
     return buffer;
 }
 
 ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type() {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_host_buffer_type\n");
     static struct ggml_backend_buffer_type ggml_backend_sycl_buffer_type_host = {
         /* .iface    = */ {
             /* .get_name         = */ ggml_backend_sycl_host_buffer_type_name,
@@ -14908,40 +17391,35 @@ ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type() {
 
 // backend
 
-struct ggml_backend_context_sycl {
-    int device;
-};
+GGML_CALL static const char * ggml_backend_sycl_name(ggml_backend_t backend) {
 
-static const char * ggml_backend_sycl_name(ggml_backend_t backend) {
-    return GGML_SYCL_NAME;
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
 
-    UNUSED(backend);
+    return sycl_ctx->name.c_str();
 }
 
-static void ggml_backend_sycl_free(ggml_backend_t backend) {
-    ggml_backend_context_sycl * sycl_ctx = (ggml_backend_context_sycl *)backend->context;
+GGML_CALL static void ggml_backend_sycl_free(ggml_backend_t backend) {
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
 
     delete sycl_ctx;
     delete backend;
 }
 
-static ggml_backend_buffer_type_t ggml_backend_sycl_get_default_buffer_type(ggml_backend_t backend) {
-    ggml_backend_context_sycl * sycl_ctx = (ggml_backend_context_sycl *)backend->context;
 
+GGML_CALL static ggml_backend_buffer_type_t ggml_backend_sycl_get_default_buffer_type(ggml_backend_t backend) {
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
     return ggml_backend_sycl_buffer_type(sycl_ctx->device);
 }
 
-static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend,
+GGML_CALL static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend,
                                                ggml_tensor *tensor,
                                                const void *data, size_t offset,
                                                size_t size) try {
-    ggml_backend_context_sycl * sycl_ctx = (ggml_backend_context_sycl *)backend->context;
-
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
     GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type");
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
-
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
     SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy(
-        (char *)tensor->data + offset, data, size)));
+        (char *)tensor->data + offset, data, size).wait()));
 }
 catch (sycl::exception const &exc) {
   std::cerr << exc.what() << "Exception caught at file:" << __FILE__
@@ -14949,17 +17427,38 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
-static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend,
+GGML_CALL static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend,
                                                const ggml_tensor *tensor,
                                                void *data, size_t offset,
                                                size_t size) try {
-    ggml_backend_context_sycl * sycl_ctx = (ggml_backend_context_sycl *)backend->context;
-
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
     GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type");
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
-
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
     SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy(
-        data, (const char *)tensor->data + offset, size)));
+        data, (const char *)tensor->data + offset, size).wait()));
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL static bool ggml_backend_sycl_cpy_tensor_async(ggml_backend_t backend,
+                                                         const ggml_tensor *src,
+                                                         ggml_tensor *dst) try {
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
+    if (dst->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && ggml_backend_buffer_is_sycl(src->buffer)) {
+        /*
+        DPCT1009:215: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy(
+            dst->data, src->data, ggml_nbytes(dst)).wait()));
+        return true;
+    }
+
+    return false;
 }
 catch (sycl::exception const &exc) {
   std::cerr << exc.what() << "Exception caught at file:" << __FILE__
@@ -14968,8 +17467,7 @@ catch (sycl::exception const &exc) {
 }
 
 static void ggml_backend_sycl_synchronize(ggml_backend_t backend) try {
-    ggml_backend_context_sycl * sycl_ctx = (ggml_backend_context_sycl *)backend->context;
-
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
     SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->wait()));
 
     UNUSED(backend);
@@ -14980,96 +17478,50 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
-static ggml_backend_graph_plan_t ggml_backend_sycl_graph_plan_create(ggml_backend_t backend, const ggml_cgraph * cgraph) {
-    GGML_ASSERT(!"not implemented");
-
-    return nullptr;
-
-    UNUSED(backend);
-    UNUSED(cgraph);
-}
-
-static void ggml_backend_sycl_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
-    GGML_ASSERT(!"not implemented");
-
-    UNUSED(backend);
-    UNUSED(plan);
-}
-
-static void ggml_backend_sycl_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
-    GGML_ASSERT(!"not implemented");
-
-    UNUSED(backend);
-    UNUSED(plan);
-}
-
-static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
-    ggml_backend_context_sycl * sycl_ctx = (ggml_backend_context_sycl *)backend->context;
-
+GGML_CALL static ggml_status ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
     ggml_sycl_set_main_device(sycl_ctx->device);
 
     ggml_compute_params params = {};
-    params.type = GGML_TASK_COMPUTE;
+    params.type = GGML_TASK_TYPE_COMPUTE;
     params.ith = 0;
     for (int i = 0; i < cgraph->n_nodes; i++) {
         ggml_tensor * node = cgraph->nodes[i];
-
-        if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE)
+        if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
             continue;
-
-        assert(node->backend == GGML_BACKEND_GPU);
+        }
+#ifndef NDEBUG
+        assert(node->backend == GGML_BACKEND_TYPE_GPU || node->backend == GGML_BACKEND_TYPE_GPU_SPLIT);
         assert(node->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device));
         assert(node->extra != nullptr);
 
         for (int j = 0; j < GGML_MAX_SRC; j++) {
             if (node->src[j] != nullptr) {
-                assert(node->src[j]->backend == GGML_BACKEND_GPU);
+                assert(node->src[j]->backend == GGML_BACKEND_TYPE_GPU || node->src[j]->backend == GGML_BACKEND_TYPE_GPU_SPLIT);
                 assert(node->src[j]->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device));
                 assert(node->src[j]->extra != nullptr);
             }
         }
-
+#endif
         bool ok = ggml_sycl_compute_forward(&params, node);
         if (!ok) {
             fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
         }
         GGML_ASSERT(ok);
-
-#if 0
-        if (node->type == GGML_TYPE_F32) {
-            syclDeviceSynchronize();
-            std::vector<float> tmp(ggml_nelements(node), 0.0f);
-            syclMemcpy(tmp.data(), node->data, ggml_nelements(node)*sizeof(float), syclMemcpyDeviceToHost);
-            printf("\n%s (%s) (%s %s) (%s %s): ", node->name, ggml_op_name(node->op),
-                ggml_type_name(node->src[0]->type),
-                node->src[1] ? ggml_type_name(node->src[1]->type) : "none",
-                node->src[0]->name,
-                node->src[1] ? node->src[1]->name : "none");
-            double sum = 0.0;
-            double sq_sum = 0.0;
-            for (int i = 0; i < ggml_nelements(node); i++) {
-                printf("%f ", tmp[i]);
-                sum += tmp[i];
-                sq_sum += tmp[i]*tmp[i];
-            }
-            printf("\n");
-            printf("sum: %f, ", sum);
-            printf("sq_sum: %f\n", sq_sum);
-        }
-#endif
     }
 
-    UNUSED(backend);
-    return true;
+    return GGML_STATUS_SUCCESS;
 }
 
-static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
+GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
     switch (op->op) {
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(op)) {
                 case GGML_UNARY_OP_GELU:
                 case GGML_UNARY_OP_SILU:
                 case GGML_UNARY_OP_RELU:
+                case GGML_UNARY_OP_HARDSIGMOID:
+                case GGML_UNARY_OP_HARDSWISH:
                 case GGML_UNARY_OP_GELU_QUICK:
                 case GGML_UNARY_OP_TANH:
                     return true;
@@ -15092,14 +17544,16 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten
                 if (a->ne[3] != b->ne[3]) {
                     return false;
                 }
-
-                if (a->type == GGML_TYPE_IQ2_XXS) {
-                    return false;
+                ggml_type a_type = a->type;
+                if (a_type == GGML_TYPE_IQ4_NL  || a_type == GGML_TYPE_IQ4_XS ||
+                    a_type == GGML_TYPE_IQ3_XXS || a_type == GGML_TYPE_IQ3_S  ||
+                    a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ2_S ||
+                    a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ1_M
+                    ) {
+                    if (b->ne[1] == 1 && ggml_nrows(b) > 1) {
+                        return false;
+                    }
                 }
-                if (a->type == GGML_TYPE_IQ2_XS) {
-                    return false;
-                }
-
                 return true;
             } break;
         case GGML_OP_GET_ROWS:
@@ -15139,25 +17593,24 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten
                 if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
                     return true;
                 }
+                if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
+                    return true;
+                }
                 return false;
             } break;
         case GGML_OP_CONCAT:
             {
                 ggml_type src0_type = op->src[0]->type;
-                if (src0_type == GGML_TYPE_F32) {
-                    return true;
-                } else {
-                    return false;
-                }
+                return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
             } break;
+        case GGML_OP_DUP:
         case GGML_OP_NONE:
         case GGML_OP_RESHAPE:
+        case GGML_OP_REPEAT:
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
         case GGML_OP_TRANSPOSE:
         case GGML_OP_NORM:
-        case GGML_OP_REPEAT:
-        case GGML_OP_DUP:
         case GGML_OP_ADD:
         case GGML_OP_MUL:
         case GGML_OP_DIV:
@@ -15169,8 +17622,8 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_SOFT_MAX:
         case GGML_OP_ROPE:
-        case GGML_OP_ALIBI:
         case GGML_OP_IM2COL:
+        case GGML_OP_POOL_2D:
         case GGML_OP_SUM_ROWS:
         case GGML_OP_ARGSORT:
         case GGML_OP_ACC:
@@ -15186,37 +17639,55 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten
     UNUSED(backend);
 }
 
+GGML_CALL static bool ggml_backend_sycl_offload_op(ggml_backend_t backend, const ggml_tensor * op) {
+    const int min_batch_size = 32;
+    return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS && op->op != GGML_OP_MUL_MAT_ID;
+    GGML_UNUSED(backend);
+}
+
+
 static ggml_backend_i ggml_backend_sycl_interface = {
     /* .get_name                = */ ggml_backend_sycl_name,
     /* .free                    = */ ggml_backend_sycl_free,
     /* .get_default_buffer_type = */ ggml_backend_sycl_get_default_buffer_type,
     /* .set_tensor_async        = */ ggml_backend_sycl_set_tensor_async,
     /* .get_tensor_async        = */ ggml_backend_sycl_get_tensor_async,
-    /* .cpy_tensor_async        = */ NULL,
+    /* .cpy_tensor_async        = */ NULL, //ggml_backend_sycl_cpy_tensor_async, // TODO: update for the new interface
     /* .synchronize             = */ ggml_backend_sycl_synchronize,
-    /* .graph_plan_create       = */ ggml_backend_sycl_graph_plan_create,
-    /* .graph_plan_free         = */ ggml_backend_sycl_graph_plan_free,
-    /* .graph_plan_compute      = */ ggml_backend_sycl_graph_plan_compute,
+    /* .graph_plan_create       = */ NULL,
+    /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_compute      = */ NULL,
     /* .graph_compute           = */ ggml_backend_sycl_graph_compute,
     /* .supports_op             = */ ggml_backend_sycl_supports_op,
+    /* .offload_op              = */ ggml_backend_sycl_offload_op,
+    /* .event_new               = */ NULL,
+    /* .event_free              = */ NULL,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+    /* .event_synchronize       = */ NULL,
 };
 
-ggml_backend_t ggml_backend_sycl_init(int device) {
-    ggml_init_sycl(); // TODO: remove from ggml.c
+static ggml_guid_t ggml_backend_sycl_guid() {
+    static ggml_guid guid = { 0x58, 0x05, 0x13, 0x8f, 0xcd, 0x3a, 0x61, 0x9d, 0xe7, 0xcd, 0x98, 0xa9, 0x03, 0xfd, 0x7c, 0x53 };
+    return &guid;
+}
 
-    if (device < 0 || device >= ggml_sycl_get_device_count()) {
-        fprintf(stderr, "%s: error: invalid device %d\n", __func__, device);
-        return nullptr;
-    }
+GGML_CALL ggml_backend_t ggml_backend_sycl_init(int device) {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_init\n");
+    ggml_init_sycl();
+
+    check_allow_gpu_index(device);
 
     // not strictly necessary, but it may reduce the overhead of the first graph_compute
     ggml_sycl_set_main_device(device);
-
-    ggml_backend_context_sycl * ctx = new ggml_backend_context_sycl {
-        /* .device = */ device
+    int id = g_sycl_gpu_mgr->gpus[device];
+    ggml_backend_sycl_context * ctx = new ggml_backend_sycl_context {
+        /* .device = */ device,
+        /* .name   = */ GGML_SYCL_NAME + std::to_string(id),
     };
 
     ggml_backend_t sycl_backend = new ggml_backend {
+        /* .guid      = */ ggml_backend_sycl_guid(),
         /* .interface = */ ggml_backend_sycl_interface,
         /* .context   = */ ctx
     };
@@ -15225,25 +17696,76 @@ ggml_backend_t ggml_backend_sycl_init(int device) {
 }
 
 bool ggml_backend_is_sycl(ggml_backend_t backend) {
-    return backend->iface.get_name == ggml_backend_sycl_name;
+    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_sycl_guid());
 }
 
-static ggml_backend_t ggml_backend_reg_sycl_init(const char * params, void * user_data) {
+GGML_CALL int ggml_backend_sycl_get_device_count() {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_count\n");
+    if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr();
+    return g_sycl_gpu_mgr->get_gpu_count();
+}
+
+GGML_CALL static ggml_backend_t ggml_backend_reg_sycl_init(const char * params, void * user_data) {
     ggml_backend_t sycl_backend = ggml_backend_sycl_init((int) (intptr_t) user_data);
     return sycl_backend;
 
     UNUSED(params);
 }
 
+GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id) {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_index\n");
+    return g_sycl_gpu_mgr->get_index(device_id);
+}
+
+GGML_API GGML_CALL int ggml_backend_sycl_get_device_id(int device_index) {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_id\n");
+    return g_sycl_gpu_mgr->gpus[device_index];
+}
+
+GGML_API GGML_CALL void ggml_backend_sycl_set_single_device_mode(int main_gpu_id) {
+    ggml_init_sycl();
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_set_single_device_mode\n");
+    fprintf(stderr, "ggml_backend_sycl_set_single_device: use single device: [%d]\n", main_gpu_id);
+    GGML_ASSERT(main_gpu_id<g_all_sycl_device_count);
+
+    if (g_sycl_gpu_mgr) {
+        delete g_sycl_gpu_mgr;
+    }
+    g_sycl_gpu_mgr = new sycl_gpu_mgr(main_gpu_id);
+    g_ggml_sycl_backend_gpu_mode = SYCL_SINGLE_GPU_MODE;
+    ggml_init_by_gpus(g_sycl_gpu_mgr->get_gpu_count());
+    g_ggml_backend_sycl_buffer_type_initialized = false;
+}
+
+GGML_API GGML_CALL void ggml_backend_sycl_set_mul_device_mode() {
+    ggml_init_sycl();
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_set_mul_device_mode\n");
+
+    if (g_ggml_sycl_backend_gpu_mode == SYCL_MUL_GPU_MODE) {
+        return;
+    }
+
+    fprintf(stderr, "ggml_backend_sycl_set_mul_device_mode: true\n");
+
+    if (g_sycl_gpu_mgr) {
+        delete g_sycl_gpu_mgr;
+    }
+    g_sycl_gpu_mgr = new sycl_gpu_mgr();
+    g_ggml_sycl_backend_gpu_mode = SYCL_MUL_GPU_MODE;
+    ggml_init_by_gpus(g_sycl_gpu_mgr->get_gpu_count());
+    g_ggml_backend_sycl_buffer_type_initialized = false;
+}
+
 extern "C" int ggml_backend_sycl_reg_devices();
 
 int ggml_backend_sycl_reg_devices() {
-    int device_count = ggml_sycl_get_device_count();
-
-    for (int i = 0; i < device_count; i++) {
+    ggml_backend_sycl_set_mul_device_mode();
+    assert(g_device_count>0);
+    for (int i = 0; i < g_device_count; i++) {
+        int id = g_sycl_gpu_mgr->gpus[i];
         char name[128];
-        snprintf(name, sizeof(name), "%s%d", GGML_SYCL_NAME, i);
+        snprintf(name, sizeof(name), "%s%d", GGML_SYCL_NAME, id);
         ggml_backend_register(name, ggml_backend_reg_sycl_init, ggml_backend_sycl_buffer_type(i), (void *) (intptr_t) i);
     }
-    return device_count;
+    return g_device_count;
 }
diff --git a/ggml-sycl.h b/ggml-sycl.h
index 891f2d00a..a9f776fc1 100644
--- a/ggml-sycl.h
+++ b/ggml-sycl.h
@@ -13,17 +13,37 @@
 extern "C" {
 #endif
 
-#define GGML_SYCL_MAX_DEVICES       16
+#define GGML_SYCL_MAX_DEVICES       48
 #define GGML_SYCL_NAME "SYCL"
 
-GGML_API void   ggml_init_sycl(void);
-GGML_API bool   ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
+// backend API
 GGML_API ggml_backend_t ggml_backend_sycl_init(int device);
+
+// devide buffer
 GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device);
+
+// split tensor buffer that splits matrices by rows across multiple devices
+GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split);
+
+// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
 GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void);
+
 GGML_API void   ggml_backend_sycl_print_sycl_devices(void);
 GGML_API GGML_CALL void   ggml_sycl_get_gpu_list(int *id_list, int max_len);
 GGML_API GGML_CALL void   ggml_sycl_get_device_description(int device, char *description, size_t description_size);
+GGML_API GGML_CALL int   ggml_backend_sycl_get_device_count();
+GGML_API GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total);
+GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id);
+
+// TODO: these are temporary
+//       ref: https://github.com/ggerganov/llama.cpp/pull/6022#issuecomment-1992615670
+GGML_API GGML_CALL int ggml_backend_sycl_get_device_id(int device_index);
+GGML_API GGML_CALL void ggml_backend_sycl_set_single_device_mode(int main_gpu_id);
+GGML_API GGML_CALL void ggml_backend_sycl_set_mul_device_mode();
+
+// SYCL doesn't support registering host memory, keep here for reference
+// GGML_API GGML_CALL bool ggml_backend_sycl_register_host_buffer(void * buffer, size_t size);
+// GGML_API GGML_CALL void ggml_backend_sycl_unregister_host_buffer(void * buffer);
 #ifdef  __cplusplus
 }
 #endif
diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp
index e5e7a8414..d3a2fcf38 100644
--- a/ggml-vulkan-shaders.hpp
+++ b/ggml-vulkan-shaders.hpp
@@ -2,50 +2,1847 @@
 
 unsigned char add_f32_data[] = {
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+0x06,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x07,0x02,0x00,0x00,
+0x06,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x08,0x02,0x00,0x00,0xf6,0x01,0x00,0x00,0x07,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x09,0x02,0x00,0x00,
+0x04,0x02,0x00,0x00,0x08,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x50,0x01,0x00,0x00,0x51,0x01,0x00,0x00,0x4b,0x01,0x00,0x00,
+0x34,0x01,0x00,0x00,0x09,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x52,0x01,0x00,0x00,0x51,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x49,0x02,0x00,0x00,
+0x17,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x4a,0x02,0x00,0x00,0x49,0x02,0x00,0x00,
+0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4b,0x02,0x00,0x00,
+0xca,0x01,0x00,0x00,0x4a,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x4c,0x02,0x00,0x00,0x17,0x00,0x00,0x00,
+0xb1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x4d,0x02,0x00,0x00,0x4c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x4e,0x02,0x00,0x00,0x4b,0x02,0x00,0x00,
+0x4d,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x50,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x51,0x02,0x00,0x00,
+0x50,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x52,0x02,0x00,0x00,0xdd,0x01,0x00,0x00,0x51,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x53,0x02,0x00,0x00,
+0x17,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x54,0x02,0x00,0x00,0x53,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x55,0x02,0x00,0x00,
+0x52,0x02,0x00,0x00,0x54,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x56,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,
+0x55,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x58,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x59,0x02,0x00,0x00,
+0x58,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5a,0x02,0x00,0x00,0xec,0x01,0x00,0x00,0x59,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x5b,0x02,0x00,0x00,
+0x17,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5d,0x02,0x00,0x00,
+0x5a,0x02,0x00,0x00,0x5c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5e,0x02,0x00,0x00,0x56,0x02,0x00,0x00,
+0x5d,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x60,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x61,0x02,0x00,0x00,
+0x60,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x62,0x02,0x00,0x00,0xf6,0x01,0x00,0x00,0x61,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x63,0x02,0x00,0x00,
+0x17,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x63,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x65,0x02,0x00,0x00,
+0x62,0x02,0x00,0x00,0x64,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x66,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,
+0x65,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x01,0x00,0x00,
+0x5b,0x01,0x00,0x00,0x56,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
+0x66,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x5c,0x01,0x00,0x00,0x5b,0x01,0x00,0x00,0x81,0x00,0x05,0x00,
+0x14,0x00,0x00,0x00,0x5d,0x01,0x00,0x00,0x52,0x01,0x00,0x00,
+0x5c,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x01,0x00,0x00,
+0x5e,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
+0x47,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x5e,0x01,0x00,0x00,
+0x5d,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x62,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x62,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t add_f32_len = 4276;
+
+unsigned char argsort_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x2b,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0x33,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x04,0x00,0x00,
 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x24,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x2b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x31,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x31,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x31,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x81,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x82,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x82,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x82,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x84,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x84,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x11,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x22,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x23,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x25,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x26,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x1e,0x00,0x04,0x00,
+0x31,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x32,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x31,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x32,0x00,0x00,0x00,
+0x33,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x34,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x14,0x00,0x02,0x00,0x37,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
+0x08,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x78,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x80,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x81,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x82,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x83,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x83,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x8e,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0x00,0x04,0x00,0x00,
+0x2c,0x00,0x06,0x00,0x22,0x00,0x00,0x00,0x06,0x01,0x00,0x00,
+0x05,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x07,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,0x25,0x00,0x00,0x00,
+0x08,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x08,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x26,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x34,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
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+0xd9,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x80,0x00,0x00,0x00,
+0xdb,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0xb8,0x00,0x05,0x00,
+0x37,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,
+0xdb,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc6,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xdd,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x16,0x00,0x00,0x00,
+0xe3,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0xe2,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0xe4,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x8e,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x80,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x16,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0xee,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x8e,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
+0x84,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x80,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
+0xf1,0x00,0x00,0x00,0xba,0x00,0x05,0x00,0x37,0x00,0x00,0x00,
+0xf3,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xc6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xc6,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x37,0x00,0x00,0x00,
+0x29,0x01,0x00,0x00,0xdc,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
+0xf3,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xf6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x29,0x01,0x00,0x00,0xf5,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xf5,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xfd,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x16,0x00,0x00,0x00,0x1c,0x01,0x00,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,
+0x1c,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x16,0x00,0x00,0x00,
+0x20,0x01,0x00,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0xfd,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x21,0x01,0x00,0x00,0x20,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x1c,0x01,0x00,0x00,0x21,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x20,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xf6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf6,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x77,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x77,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x70,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x70,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x50,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x62,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x62,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x02,0x01,0x00,0x00,0x27,0x01,0x00,0x00,0x50,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x61,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x56,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x56,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x26,0x01,0x00,0x00,
+0x50,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x53,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x55,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x07,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x07,0x01,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t argsort_f32_len = 3752;
+
+unsigned char clamp_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xb6,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
+0xe4,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xc7,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xd6,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xd7,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xd7,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xd7,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd9,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xd9,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xe1,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xe2,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xe2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xe2,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe4,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xe4,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0a,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x1e,0x00,0x16,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x5a,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
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+0x17,0x00,0x00,0x00,0xa5,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xa6,0x01,0x00,0x00,0xa5,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa7,0x01,0x00,0x00,0x86,0x01,0x00,0x00,
+0xa6,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa8,0x01,0x00,0x00,0xa3,0x01,0x00,0x00,0xa7,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xaa,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xab,0x01,0x00,0x00,0xaa,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xac,0x01,0x00,0x00,
+0x95,0x01,0x00,0x00,0xab,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xad,0x01,0x00,0x00,0xa8,0x01,0x00,0x00,
+0xac,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0xaf,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb0,0x01,0x00,0x00,
+0xaf,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb1,0x01,0x00,0x00,0x9f,0x01,0x00,0x00,0xb0,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x01,0x00,0x00,
+0xad,0x01,0x00,0x00,0xb1,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
+0xb2,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0xef,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0xb8,0x00,0x05,0x00,0xcf,0x00,0x00,0x00,
+0xf2,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xf5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xf2,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
+0xf8,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xf5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xf8,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0xef,0x00,0x00,0x00,
+0xfb,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
+0xfb,0x00,0x00,0x00,0xba,0x00,0x05,0x00,0xcf,0x00,0x00,0x00,
+0xfd,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
+0xa9,0x00,0x06,0x00,0x11,0x00,0x00,0x00,0xb5,0x01,0x00,0x00,
+0xfd,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xf5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xf5,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x11,0x00,0x00,0x00,
+0xb4,0x01,0x00,0x00,0xf1,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
+0xb5,0x01,0x00,0x00,0xf8,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0xde,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0xe4,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x07,0x01,0x00,0x00,0xb4,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0b,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x0b,0x01,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t clamp_f32_len = 3512;
+
+unsigned char cpy_f16_f16_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x98,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
 0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
 0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
 0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
 0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
 0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x20,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc7,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xd5,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xd6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xd6,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x20,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x22,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x22,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x24,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x25,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x25,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x25,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x27,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x27,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2c,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x2d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2f,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2f,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xd6,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xd8,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd8,0x00,0x00,0x00,
 0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0xe1,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xe2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xe2,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xe2,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xe4,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe4,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xef,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x1e,0x00,0x16,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
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+0x17,0x00,0x00,0x00,0x8a,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0x5a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x8b,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8c,0x01,0x00,0x00,0x6b,0x01,0x00,0x00,
+0x8b,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8d,0x01,0x00,0x00,0x88,0x01,0x00,0x00,0x8c,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x90,0x01,0x00,0x00,0x8f,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x91,0x01,0x00,0x00,
+0x7a,0x01,0x00,0x00,0x90,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x92,0x01,0x00,0x00,0x8d,0x01,0x00,0x00,
+0x91,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x94,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,
+0x94,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x96,0x01,0x00,0x00,0x84,0x01,0x00,0x00,0x95,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x97,0x01,0x00,0x00,
+0x92,0x01,0x00,0x00,0x96,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0xe9,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0x97,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xd4,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0xe9,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
+0xd8,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xf0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xf0,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t cpy_f16_f16_len = 3252;
+
+unsigned char cpy_f32_f16_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x9a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc7,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xd5,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xd6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xd6,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xd6,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xd8,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd8,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xe1,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xe2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xe2,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xe2,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xe4,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe4,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xf1,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x1e,0x00,0x16,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x60,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x72,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0xc5,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0xc6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0xc6,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xc8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0xc9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0xcf,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0xd4,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0xd5,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0xd6,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0xd7,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0xd7,0x00,0x00,0x00,
+0xd8,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0xe1,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0xe2,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0xe3,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xe2,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0xe3,0x00,0x00,0x00,
+0xe4,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0xe9,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0xed,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xd4,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xef,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2c,0x00,0x06,0x00,0xc5,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
+0xef,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
+0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xf2,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,0xc8,0x00,0x00,0x00,
+0xf3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf3,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0xc9,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
+0xae,0x00,0x05,0x00,0xcf,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xcb,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xd2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xd0,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd1,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xf2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd2,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x01,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x01,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x03,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x03,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x05,0x01,0x00,0x00,
+0x02,0x01,0x00,0x00,0x04,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0x76,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x07,0x01,0x00,0x00,0x06,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x05,0x01,0x00,0x00,
+0x07,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x09,0x01,0x00,0x00,0xcb,0x00,0x00,0x00,0x08,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,
+0x09,0x01,0x00,0x00,0x02,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x10,0x01,0x00,0x00,0x0d,0x01,0x00,0x00,
+0x04,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x13,0x01,0x00,0x00,0x10,0x01,0x00,0x00,0x07,0x01,0x00,0x00,
+0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x16,0x01,0x00,0x00,
+0xcb,0x00,0x00,0x00,0x13,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x1b,0x01,0x00,0x00,0x04,0x01,0x00,0x00,
+0x07,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1c,0x01,0x00,0x00,0x16,0x01,0x00,0x00,0x1b,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x20,0x01,0x00,0x00,
+0x1c,0x01,0x00,0x00,0x04,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x23,0x01,0x00,0x00,0x20,0x01,0x00,0x00,
+0x07,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x28,0x01,0x00,0x00,0x16,0x01,0x00,0x00,0x23,0x01,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2b,0x01,0x00,0x00,
+0x28,0x01,0x00,0x00,0x07,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x34,0x01,0x00,0x00,0x2b,0x01,0x00,0x00,
+0x07,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x35,0x01,0x00,0x00,0x28,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x37,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x38,0x01,0x00,0x00,0x37,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x39,0x01,0x00,0x00,
+0x09,0x01,0x00,0x00,0x38,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x3b,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3c,0x01,0x00,0x00,0x3b,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3d,0x01,0x00,0x00,0x1c,0x01,0x00,0x00,
+0x3c,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x3e,0x01,0x00,0x00,0x39,0x01,0x00,0x00,0x3d,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x40,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x41,0x01,0x00,0x00,0x40,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x42,0x01,0x00,0x00,
+0x2b,0x01,0x00,0x00,0x41,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x43,0x01,0x00,0x00,0x3e,0x01,0x00,0x00,
+0x42,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x45,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x46,0x01,0x00,0x00,
+0x45,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x47,0x01,0x00,0x00,0x35,0x01,0x00,0x00,0x46,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x48,0x01,0x00,0x00,
+0x43,0x01,0x00,0x00,0x47,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
+0x48,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x52,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x53,0x01,0x00,0x00,
+0x52,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x54,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x55,0x01,0x00,0x00,
+0x54,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x56,0x01,0x00,0x00,0x53,0x01,0x00,0x00,0x55,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x57,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x58,0x01,0x00,0x00,0x57,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x59,0x01,0x00,0x00,
+0x56,0x01,0x00,0x00,0x58,0x01,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5a,0x01,0x00,0x00,0xcb,0x00,0x00,0x00,
+0x59,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5e,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,0x53,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x61,0x01,0x00,0x00,
+0x5e,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x64,0x01,0x00,0x00,0x61,0x01,0x00,0x00,
+0x58,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x67,0x01,0x00,0x00,0xcb,0x00,0x00,0x00,0x64,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6c,0x01,0x00,0x00,
+0x55,0x01,0x00,0x00,0x58,0x01,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6d,0x01,0x00,0x00,0x67,0x01,0x00,0x00,
+0x6c,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x71,0x01,0x00,0x00,0x6d,0x01,0x00,0x00,0x55,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x74,0x01,0x00,0x00,
+0x71,0x01,0x00,0x00,0x58,0x01,0x00,0x00,0x82,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x79,0x01,0x00,0x00,0x67,0x01,0x00,0x00,
+0x74,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x7c,0x01,0x00,0x00,0x79,0x01,0x00,0x00,0x58,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x85,0x01,0x00,0x00,
+0x7c,0x01,0x00,0x00,0x58,0x01,0x00,0x00,0x82,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x86,0x01,0x00,0x00,0x79,0x01,0x00,0x00,
+0x85,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x88,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x89,0x01,0x00,0x00,
+0x88,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8a,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,0x89,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x8c,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,0x8c,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8e,0x01,0x00,0x00,
+0x6d,0x01,0x00,0x00,0x8d,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,
+0x8e,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x91,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x92,0x01,0x00,0x00,
+0x91,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x93,0x01,0x00,0x00,0x7c,0x01,0x00,0x00,0x92,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x94,0x01,0x00,0x00,
+0x8f,0x01,0x00,0x00,0x93,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x96,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0x66,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x97,0x01,0x00,0x00,0x96,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x98,0x01,0x00,0x00,0x86,0x01,0x00,0x00,
+0x97,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x99,0x01,0x00,0x00,0x94,0x01,0x00,0x00,0x98,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0xe9,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
+0xe4,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x99,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
+0xea,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0xd4,0x00,0x00,0x00,
+0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0xed,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xee,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xf2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf2,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t cpy_f32_f16_len = 3284;
+
+unsigned char cpy_f32_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x97,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
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+0x19,0x01,0x00,0x00,0x13,0x01,0x00,0x00,0x18,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,
+0x19,0x01,0x00,0x00,0x01,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x20,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,
+0x04,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x25,0x01,0x00,0x00,0x13,0x01,0x00,0x00,0x20,0x01,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x28,0x01,0x00,0x00,
+0x25,0x01,0x00,0x00,0x04,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x31,0x01,0x00,0x00,0x28,0x01,0x00,0x00,
+0x04,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x32,0x01,0x00,0x00,0x25,0x01,0x00,0x00,0x31,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x34,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x35,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x36,0x01,0x00,0x00,
+0x06,0x01,0x00,0x00,0x35,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x38,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x39,0x01,0x00,0x00,0x38,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,0x19,0x01,0x00,0x00,
+0x39,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x3b,0x01,0x00,0x00,0x36,0x01,0x00,0x00,0x3a,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x3d,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x3e,0x01,0x00,0x00,0x3d,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3f,0x01,0x00,0x00,
+0x28,0x01,0x00,0x00,0x3e,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x40,0x01,0x00,0x00,0x3b,0x01,0x00,0x00,
+0x3f,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x42,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x43,0x01,0x00,0x00,
+0x42,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x44,0x01,0x00,0x00,0x32,0x01,0x00,0x00,0x43,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x45,0x01,0x00,0x00,
+0x40,0x01,0x00,0x00,0x44,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
+0x45,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x4f,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x50,0x01,0x00,0x00,
+0x4f,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x51,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x52,0x01,0x00,0x00,
+0x51,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x53,0x01,0x00,0x00,0x50,0x01,0x00,0x00,0x52,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x54,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x55,0x01,0x00,0x00,0x54,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x56,0x01,0x00,0x00,
+0x53,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x57,0x01,0x00,0x00,0xcb,0x00,0x00,0x00,
+0x56,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5b,0x01,0x00,0x00,0x57,0x01,0x00,0x00,0x50,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5e,0x01,0x00,0x00,
+0x5b,0x01,0x00,0x00,0x52,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x61,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,
+0x55,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x64,0x01,0x00,0x00,0xcb,0x00,0x00,0x00,0x61,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x01,0x00,0x00,
+0x52,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6a,0x01,0x00,0x00,0x64,0x01,0x00,0x00,
+0x69,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6e,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,0x52,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x71,0x01,0x00,0x00,
+0x6e,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x82,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x76,0x01,0x00,0x00,0x64,0x01,0x00,0x00,
+0x71,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x79,0x01,0x00,0x00,0x76,0x01,0x00,0x00,0x55,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x82,0x01,0x00,0x00,
+0x79,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x82,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x83,0x01,0x00,0x00,0x76,0x01,0x00,0x00,
+0x82,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x85,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x86,0x01,0x00,0x00,
+0x85,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x87,0x01,0x00,0x00,0x57,0x01,0x00,0x00,0x86,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x89,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x8a,0x01,0x00,0x00,0x89,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8b,0x01,0x00,0x00,
+0x6a,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8c,0x01,0x00,0x00,0x87,0x01,0x00,0x00,
+0x8b,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x8e,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,
+0x8e,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x90,0x01,0x00,0x00,0x79,0x01,0x00,0x00,0x8f,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x91,0x01,0x00,0x00,
+0x8c,0x01,0x00,0x00,0x90,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x93,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0x66,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x94,0x01,0x00,0x00,0x93,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,0x83,0x01,0x00,0x00,
+0x94,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x96,0x01,0x00,0x00,0x91,0x01,0x00,0x00,0x95,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0xe8,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
+0xe3,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x96,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
+0xe9,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xe8,0x00,0x00,0x00,
+0xeb,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
+0xdf,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xeb,0x00,0x00,0x00,
+0xea,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xef,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xef,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t cpy_f32_f32_len = 3232;
+
+unsigned char dequant_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x1f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x13,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x13,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x13,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x13,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x13,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x13,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x2a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x32,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x33,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x33,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x33,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x35,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x35,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x44,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
 0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
 0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
 0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
@@ -55,114 +1852,1021 @@ unsigned char add_f32_data[] = {
 0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,
-0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
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+0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,
+0x6e,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,
+0x70,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x3d,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x71,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x13,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,
+0x7b,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x79,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,
+0x7c,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x28,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x7e,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x14,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x39,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x31,0x00,0x00,0x00,0x89,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
+0x89,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,
+0x8b,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x8b,0x00,0x00,0x00,
+0x8a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x93,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x15,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
+0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x93,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0x96,0x00,0x00,0x00,
+0x95,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,
+0x97,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x3d,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x98,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x16,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,
+0xa2,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0xa0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,
+0xa3,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x28,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xa5,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x17,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x39,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x31,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
+0xb0,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0xad,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xb2,0x00,0x00,0x00,
+0xb1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xba,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x18,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,
+0xbe,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x3d,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xbf,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x19,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,
+0xc9,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,
+0xca,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x28,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xcc,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x1a,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x39,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x31,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
+0xd7,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,
+0xd9,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0xd4,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xd9,0x00,0x00,0x00,
+0xd8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe1,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1b,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
+0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
+0xe3,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,
+0xe5,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x3d,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xe6,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x1c,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0xee,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,
+0xf1,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x28,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xf3,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x39,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x31,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,0xff,0x00,0x00,0x00,
+0xfe,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0xfb,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x00,0x01,0x00,0x00,
+0xff,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x08,0x01,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,
+0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x08,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,
+0x0a,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,
+0x0c,0x01,0x00,0x00,0x0b,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x3d,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x0d,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x45,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x45,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
 };
-const uint64_t add_f32_len = 1456;
+const uint64_t dequant_f32_len = 3200;
 
-unsigned char clamp_f32_data[] = {
+unsigned char dequant_q2_K_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x55,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x10,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
 0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x5e,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
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+0xfa,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x67,0x00,0x00,0x00,
+0xfb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x6f,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
+0xfb,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x6c,0x00,0x00,0x00,
+0xfd,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0xde,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xfd,0x00,0x00,0x00,
+0xfc,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0x09,0x01,0x00,0x00,
+0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0a,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x11,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x03,0x01,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x07,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x0a,0x01,0x00,0x00,0x01,0x01,0x00,0x00,
+0x07,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x07,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x01,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x01,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t dequant_q2_K_len = 4032;
+
+unsigned char dequant_q3_K_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x3f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x77,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x30,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x71,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x73,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x73,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x73,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x73,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x74,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x75,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x75,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x75,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x77,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x04,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x06,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2c,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x14,0x00,0x02,0x00,0x11,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x1e,0x00,0x07,0x00,0x1e,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x1f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0x26,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x55,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x1c,0x00,0x04,0x00,0x6f,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x6e,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x70,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
+0x71,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x72,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x1e,0x00,0x06,0x00,0x73,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
+0x6f,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x74,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x75,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x76,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x75,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x76,0x00,0x00,0x00,
+0x77,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x7c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0x81,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0xdb,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0xdf,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xee,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x03,0x01,0x00,0x00,0x72,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x04,0x01,0x00,0x00,0x03,0x01,0x00,0x00,0x20,0x00,0x04,0x00,
+0x05,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x04,0x01,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x05,0x01,0x00,0x00,0x06,0x01,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,
+0x14,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,0x6e,0x00,0x00,0x00,
+0x2b,0x01,0x00,0x00,0x2b,0x01,0x00,0x00,0x2a,0x00,0x03,0x00,
+0x11,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,0x29,0x00,0x03,0x00,
+0x11,0x00,0x00,0x00,0x32,0x01,0x00,0x00,0x36,0x00,0x05,0x00,
+0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x2d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfb,0x00,0x03,0x00,0x09,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x2e,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x0a,0x00,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x35,0x01,0x00,0x00,
+0x09,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,0x2a,0x01,0x00,0x00,
+0x0d,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x35,0x01,0x00,0x00,0x10,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
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+0x7c,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x77,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
+0xcd,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xd2,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
+0xd3,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,
+0x21,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
+0x8d,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0xca,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xae,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xae,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x21,0x00,0x00,0x00,
+0x36,0x01,0x00,0x00,0xc1,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x95,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x95,0x00,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x21,0x00,0x00,0x00,0x37,0x01,0x00,0x00,
+0xa8,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x36,0x01,0x00,0x00,
+0xae,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x6b,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x6b,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x21,0x00,0x00,0x00,0x38,0x01,0x00,0x00,0x8f,0x00,0x00,0x00,
+0x6a,0x00,0x00,0x00,0x37,0x01,0x00,0x00,0x95,0x00,0x00,0x00,
+0x72,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
+0x38,0x01,0x00,0x00,0x41,0x00,0x07,0x00,0xdf,0x00,0x00,0x00,
+0xe0,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x72,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0xdb,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,
+0xe1,0x00,0x00,0x00,0x72,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0xe6,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
+0x21,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
+0xe7,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,0xdb,0x00,0x00,0x00,
+0xe9,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
+0xdb,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,
+0xe9,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xed,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
+0xee,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf3,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
+0xf1,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xfa,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xfa,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x39,0x01,0x00,0x00,0x44,0x00,0x00,0x00,
+0x6b,0x00,0x00,0x00,0x28,0x01,0x00,0x00,0xfb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x01,0x01,0x00,0x00,
+0x44,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
+0x11,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x39,0x01,0x00,0x00,
+0x01,0x01,0x00,0x00,0xf6,0x00,0x04,0x00,0xfc,0x00,0x00,0x00,
+0xfb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x02,0x01,0x00,0x00,0xfb,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xfb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0xf4,0x00,0x00,0x00,
+0x39,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x0e,0x01,0x00,0x00,0xf7,0x00,0x00,0x00,0x39,0x01,0x00,0x00,
+0x41,0x00,0x08,0x00,0x7c,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,
+0x77,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x26,0x00,0x00,0x00,0x0e,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x10,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x12,0x01,0x00,0x00,
+0x10,0x01,0x00,0x00,0x5d,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x13,0x01,0x00,0x00,0x12,0x01,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x14,0x01,0x00,0x00,
+0x13,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0x15,0x01,0x00,0x00,0x14,0x01,0x00,0x00,0x8b,0x00,0x00,0x00,
+0x72,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0x16,0x01,0x00,0x00,
+0x15,0x01,0x00,0x00,0x72,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0x17,0x01,0x00,0x00,0x16,0x01,0x00,0x00,0x41,0x00,0x08,0x00,
+0x7c,0x00,0x00,0x00,0x1a,0x01,0x00,0x00,0x77,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x39,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
+0x1b,0x01,0x00,0x00,0x1a,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0x1b,0x01,0x00,0x00,
+0x57,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,
+0xab,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x20,0x01,0x00,0x00,
+0x1f,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0xa9,0x00,0x06,0x00,
+0x21,0x00,0x00,0x00,0x21,0x01,0x00,0x00,0x20,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
+0x21,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x17,0x01,0x00,0x00,
+0x21,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,0xdb,0x00,0x00,0x00,
+0x23,0x01,0x00,0x00,0x22,0x01,0x00,0x00,0x85,0x00,0x05,0x00,
+0xdb,0x00,0x00,0x00,0x24,0x01,0x00,0x00,0xea,0x00,0x00,0x00,
+0x23,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x72,0x00,0x00,0x00,
+0x25,0x01,0x00,0x00,0x24,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0xdf,0x00,0x00,0x00,0x26,0x01,0x00,0x00,0x06,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x26,0x01,0x00,0x00,0x25,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x28,0x01,0x00,0x00,0x39,0x01,0x00,0x00,
+0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xfa,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xfc,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2a,0x01,0x00,0x00,
+0x35,0x01,0x00,0x00,0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x11,0x00,0x00,0x00,0x3e,0x01,0x00,0x00,
+0x2f,0x01,0x00,0x00,0x0a,0x00,0x00,0x00,0x32,0x01,0x00,0x00,
+0x2c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x33,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x3e,0x01,0x00,0x00,
+0x2d,0x01,0x00,0x00,0x33,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x33,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x2d,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x2d,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t dequant_q3_K_len = 4804;
+
+unsigned char dequant_q4_0_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x83,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x48,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x49,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x49,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x4b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x64,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x64,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x64,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x66,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x66,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x94,0x00,0x00,0x00,
 0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
 0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
 0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
@@ -173,3186 +2877,389 @@ unsigned char clamp_f32_data[] = {
 0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
 0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
 0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x02,0x00,0x1a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x21,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x22,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x26,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x29,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x2a,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x2b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x2f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x30,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x49,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
-0x49,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x4c,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x4d,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0xae,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x1d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x1c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4c,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x1d,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x30,0x00,0x00,0x00,
-0x31,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x31,0x00,0x00,0x00,0xb8,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x33,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x36,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x33,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
-0x39,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x35,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x36,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x39,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x30,0x00,0x00,0x00,
-0x3c,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0x3c,0x00,0x00,0x00,0xba,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0xa9,0x00,0x06,0x00,0x11,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x36,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x36,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x11,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x26,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x48,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4c,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x4c,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t clamp_f32_len = 1448;
-
-unsigned char cpy_f16_f16_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xaa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x0e,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x0e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x0e,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x0e,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x0e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x0e,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x0e,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x0e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x0e,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x0e,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x0e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x0e,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x93,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x94,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x94,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x94,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x96,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x96,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9c,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x9d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x9d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x9f,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x9f,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x07,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x1e,0x00,0x0e,0x00,0x0e,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1e,0x00,0x07,0x00,
+0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
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-0x8b,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x13,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x97,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x99,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x99,0x00,0x00,0x00,
-0x91,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xa1,0x00,0x00,0x00,
-0xa2,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x56,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x92,0x00,0x00,0x00,
-0xa3,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0xa1,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,0x96,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xa4,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa8,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xa8,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t cpy_f16_f16_len = 2480;
-
-unsigned char cpy_f32_f16_data[] = {
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-0x9c,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x92,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
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-0xa7,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x9b,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xa7,0x00,0x00,0x00,
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-0xf8,0x00,0x02,0x00,0xab,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t cpy_f32_f16_len = 2524;
-
-unsigned char cpy_f32_f32_data[] = {
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+0x46,0x00,0x00,0x00,0x6d,0x01,0x00,0x00,0x5f,0x01,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x46,0x00,0x00,0x00,0x6e,0x01,0x00,0x00,
+0x6d,0x01,0x00,0x00,0x2d,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
+0x42,0x00,0x00,0x00,0x6f,0x01,0x00,0x00,0x6e,0x01,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,0x71,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x6f,0x01,0x00,0x00,0x57,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x45,0x00,0x00,0x00,0x72,0x01,0x00,0x00,0x71,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,0x73,0x01,0x00,0x00,
+0x66,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x6a,0x01,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x73,0x01,0x00,0x00,0x72,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x95,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x95,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
 
 };
-const uint64_t cpy_f32_f32_len = 2472;
-
-unsigned char dequant_f16_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x86,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
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-};
-const uint64_t dequant_f16_len = 1816;
-
-unsigned char dequant_q2_K_data[] = {
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-0xe2,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x00,0x01,0x00,0x00,
-0xff,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,
-0x29,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0a,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x11,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,0x06,0x01,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0x2f,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x0a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x0d,0x01,0x00,0x00,0x04,0x01,0x00,0x00,
-0x0a,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x0a,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x04,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x04,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t dequant_q2_K_len = 4056;
-
-unsigned char dequant_q3_K_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x42,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x23,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x33,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x71,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x73,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x75,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x77,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x77,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x77,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x77,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x78,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x79,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x06,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x07,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x07,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x07,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x09,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x09,0x01,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2f,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x14,0x00,0x02,0x00,
-0x11,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x15,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x1e,0x00,0x06,0x00,
-0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x24,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x24,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x26,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x37,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x5a,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x70,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x71,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x73,0x00,0x00,0x00,
-0x52,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x75,0x00,0x00,0x00,0x52,0x00,0x00,0x00,
-0x74,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x76,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x77,0x00,0x00,0x00,
-0x71,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
-0x76,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x78,0x00,0x00,0x00,
-0x77,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x79,0x00,0x00,0x00,
-0x78,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x7a,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x7a,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x7f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x52,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x84,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xd3,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0xde,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0xe2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
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-0x3b,0x00,0x04,0x00,0x08,0x01,0x00,0x00,0x09,0x01,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
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-0x15,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,0x72,0x00,0x00,0x00,
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-0x11,0x00,0x00,0x00,0x35,0x01,0x00,0x00,0x36,0x00,0x05,0x00,
-0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x30,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfb,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x31,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x31,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x0a,0x00,0x00,0x00,
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-0x0d,0x00,0x00,0x00,0xb1,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x38,0x01,0x00,0x00,0x10,0x00,0x00,0x00,
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-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
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-0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
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-0x7c,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
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-0x52,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
-0xd3,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0xd5,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
-0xd6,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
-0x37,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd9,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb1,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb1,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x39,0x01,0x00,0x00,0xc4,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0xd9,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x97,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x97,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,
-0xaa,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x39,0x01,0x00,0x00,
-0xb1,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x6f,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x6f,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x3b,0x01,0x00,0x00,0x91,0x00,0x00,0x00,
-0x6e,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,0x97,0x00,0x00,0x00,
-0x72,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
-0x3b,0x01,0x00,0x00,0x41,0x00,0x07,0x00,0xe2,0x00,0x00,0x00,
-0xe3,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x76,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0xde,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
-0xe4,0x00,0x00,0x00,0x72,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xe9,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
-0xea,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,0xde,0x00,0x00,0x00,
-0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0xde,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
-0xec,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
-0xf1,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
-0xf3,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf6,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0xf4,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xfd,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xfd,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,0x49,0x00,0x00,0x00,
-0x6f,0x00,0x00,0x00,0x2b,0x01,0x00,0x00,0xfe,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x04,0x01,0x00,0x00,
-0x49,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0xb1,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0x3c,0x01,0x00,0x00,
-0x04,0x01,0x00,0x00,0xf6,0x00,0x04,0x00,0xff,0x00,0x00,0x00,
-0xfe,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x05,0x01,0x00,0x00,0xfe,0x00,0x00,0x00,0xff,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xfe,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,0xf7,0x00,0x00,0x00,
-0x3c,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x11,0x01,0x00,0x00,0xfa,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,
-0x41,0x00,0x08,0x00,0x7f,0x00,0x00,0x00,0x12,0x01,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x11,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x52,0x00,0x00,0x00,0x13,0x01,0x00,0x00,0x12,0x01,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x52,0x00,0x00,0x00,0x15,0x01,0x00,0x00,
-0x13,0x01,0x00,0x00,0x62,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x16,0x01,0x00,0x00,0x15,0x01,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x17,0x01,0x00,0x00,
-0x16,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x18,0x01,0x00,0x00,0x17,0x01,0x00,0x00,0x8e,0x00,0x00,0x00,
-0x72,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0x19,0x01,0x00,0x00,
-0x18,0x01,0x00,0x00,0x72,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x1a,0x01,0x00,0x00,0x19,0x01,0x00,0x00,0x41,0x00,0x08,0x00,
-0x7f,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0x7b,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x3c,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
-0x1e,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x52,0x00,0x00,0x00,0x20,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,
-0x5c,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x21,0x01,0x00,0x00,0x20,0x01,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x21,0x01,0x00,0x00,
-0xab,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x23,0x01,0x00,0x00,
-0x22,0x01,0x00,0x00,0x09,0x00,0x00,0x00,0xa9,0x00,0x06,0x00,
-0x06,0x00,0x00,0x00,0x24,0x01,0x00,0x00,0x23,0x01,0x00,0x00,
-0x09,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x25,0x01,0x00,0x00,0x1a,0x01,0x00,0x00,
-0x24,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,0xde,0x00,0x00,0x00,
-0x26,0x01,0x00,0x00,0x25,0x01,0x00,0x00,0x85,0x00,0x05,0x00,
-0xde,0x00,0x00,0x00,0x27,0x01,0x00,0x00,0xed,0x00,0x00,0x00,
-0x26,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x76,0x00,0x00,0x00,
-0x28,0x01,0x00,0x00,0x27,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0xe2,0x00,0x00,0x00,0x29,0x01,0x00,0x00,0x09,0x01,0x00,0x00,
-0x09,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x29,0x01,0x00,0x00,0x28,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x01,0x00,0x00,0x3c,0x01,0x00,0x00,
-0x29,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xfd,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xff,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2d,0x01,0x00,0x00,
-0x38,0x01,0x00,0x00,0x29,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x11,0x00,0x00,0x00,0x41,0x01,0x00,0x00,
-0x32,0x01,0x00,0x00,0x0a,0x00,0x00,0x00,0x35,0x01,0x00,0x00,
-0x2f,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x36,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x41,0x01,0x00,0x00,
-0x30,0x01,0x00,0x00,0x36,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x36,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x30,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x30,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t dequant_q3_K_len = 4828;
-
-unsigned char dequant_q4_0_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x19,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x55,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x14,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x14,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x50,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x51,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x53,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x53,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x53,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x55,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x75,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x76,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x76,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x76,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x78,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x78,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
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-0x4e,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,
-0x71,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,
-0xb7,0x02,0x00,0x00,0xc7,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,0x67,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,0xba,0x02,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xc2,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0xbb,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,0x6b,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,
-0xbb,0x02,0x00,0x00,0x50,0x00,0x05,0x00,0x63,0x00,0x00,0x00,
-0xbd,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,
-0x83,0x00,0x05,0x00,0x63,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xbd,0x02,0x00,0x00,0xa5,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,
-0x63,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xb5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc5,0x02,0x00,0x00,0x80,0x00,0x00,0x00,0x15,0x03,0x00,0x00,
-0x51,0x00,0x05,0x00,0x4a,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,
-0xbf,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x4d,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0x57,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,
-0x78,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xc9,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,
-0x80,0x00,0x00,0x00,0x16,0x03,0x00,0x00,0x51,0x00,0x05,0x00,
-0x4a,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0xd2,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x57,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,0x78,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xd3,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x4d,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,0x58,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,
-0xdc,0x02,0x00,0x00,0x41,0x00,0x08,0x00,0x5f,0x00,0x00,0x00,
-0xde,0x02,0x00,0x00,0x55,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x17,0x03,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xde,0x02,0x00,0x00,0x71,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0xe0,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0xe1,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,
-0x67,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,
-0xe2,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,
-0x6b,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,
-0xe4,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0x50,0x00,0x05,0x00,
-0x63,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
-0xe4,0x02,0x00,0x00,0x83,0x00,0x05,0x00,0x63,0x00,0x00,0x00,
-0xe6,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0xa5,0x00,0x00,0x00,
-0x8e,0x00,0x05,0x00,0x63,0x00,0x00,0x00,0xe7,0x02,0x00,0x00,
-0xe6,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xed,0x02,0x00,0x00,0x80,0x00,0x00,0x00,
-0x17,0x03,0x00,0x00,0x51,0x00,0x05,0x00,0x4a,0x00,0x00,0x00,
-0xef,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0xf0,0x02,0x00,0x00,
-0xef,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x57,0x00,0x00,0x00,
-0xf1,0x02,0x00,0x00,0x78,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0xed,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xf1,0x02,0x00,0x00,
-0xf0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf8,0x02,0x00,0x00,0x80,0x00,0x00,0x00,0x18,0x03,0x00,0x00,
-0x51,0x00,0x05,0x00,0x4a,0x00,0x00,0x00,0xf9,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x4d,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,0xf9,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0x57,0x00,0x00,0x00,0xfb,0x02,0x00,0x00,
-0x78,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0xf8,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xfb,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9b,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t dequant_q4_0_len = 8856;
+const uint64_t dequant_q4_0_len = 5184;
 
 unsigned char dequant_q4_1_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x59,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
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-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
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-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
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-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x52,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
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-0x68,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
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-0x68,0x00,0x00,0x00,0xce,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,
-0xc4,0x02,0x00,0x00,0x81,0x00,0x05,0x00,0x68,0x00,0x00,0x00,
-0xcf,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,0xce,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,
-0x85,0x00,0x00,0x00,0x53,0x03,0x00,0x00,0x51,0x00,0x05,0x00,
-0x4a,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0xd8,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x57,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,0x7d,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xd9,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,0x85,0x00,0x00,0x00,
-0x54,0x03,0x00,0x00,0x51,0x00,0x05,0x00,0x4a,0x00,0x00,0x00,
-0xe1,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0xe2,0x02,0x00,0x00,
-0xe1,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x57,0x00,0x00,0x00,
-0xe3,0x02,0x00,0x00,0x7d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0xe0,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xe3,0x02,0x00,0x00,
-0xe2,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0xec,0x02,0x00,0x00,0x58,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x4a,0x00,0x00,0x00,0xed,0x02,0x00,0x00,0xec,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0xef,0x02,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,
-0xf0,0x02,0x00,0x00,0xef,0x02,0x00,0x00,0x41,0x00,0x08,0x00,
-0x64,0x00,0x00,0x00,0xf1,0x02,0x00,0x00,0x55,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
-0x55,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,
-0xf2,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,0x71,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,0xf2,0x02,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,
-0xf3,0x02,0x00,0x00,0x6c,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
-0x4a,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0xf6,0x02,0x00,0x00,
-0xf3,0x02,0x00,0x00,0x70,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
-0x4a,0x00,0x00,0x00,0xf7,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,
-0x50,0x00,0x05,0x00,0x68,0x00,0x00,0x00,0xf8,0x02,0x00,0x00,
-0xf5,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,0x8e,0x00,0x05,0x00,
-0x68,0x00,0x00,0x00,0xf9,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,
-0xed,0x02,0x00,0x00,0x50,0x00,0x05,0x00,0x68,0x00,0x00,0x00,
-0xfa,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,
-0x81,0x00,0x05,0x00,0x68,0x00,0x00,0x00,0xfb,0x02,0x00,0x00,
-0xf9,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x01,0x03,0x00,0x00,0x85,0x00,0x00,0x00,
-0x55,0x03,0x00,0x00,0x51,0x00,0x05,0x00,0x4a,0x00,0x00,0x00,
-0x03,0x03,0x00,0x00,0xfb,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x04,0x03,0x00,0x00,
-0x03,0x03,0x00,0x00,0x41,0x00,0x06,0x00,0x57,0x00,0x00,0x00,
-0x05,0x03,0x00,0x00,0x7d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x01,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x05,0x03,0x00,0x00,
-0x04,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0c,0x03,0x00,0x00,0x85,0x00,0x00,0x00,0x56,0x03,0x00,0x00,
-0x51,0x00,0x05,0x00,0x4a,0x00,0x00,0x00,0x0d,0x03,0x00,0x00,
-0xfb,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x4d,0x00,0x00,0x00,0x0e,0x03,0x00,0x00,0x0d,0x03,0x00,0x00,
-0x41,0x00,0x06,0x00,0x57,0x00,0x00,0x00,0x0f,0x03,0x00,0x00,
-0x7d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0c,0x03,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x0f,0x03,0x00,0x00,0x0e,0x03,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x18,0x03,0x00,0x00,
-0x58,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,
-0x19,0x03,0x00,0x00,0x18,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x4d,0x00,0x00,0x00,0x1b,0x03,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,
-0x1b,0x03,0x00,0x00,0x41,0x00,0x08,0x00,0x64,0x00,0x00,0x00,
-0x1d,0x03,0x00,0x00,0x55,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x57,0x03,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,0x1e,0x03,0x00,0x00,
-0x1d,0x03,0x00,0x00,0x71,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x1f,0x03,0x00,0x00,0x1e,0x03,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x20,0x03,0x00,0x00,0x1f,0x03,0x00,0x00,
-0x6c,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,
-0x21,0x03,0x00,0x00,0x20,0x03,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x22,0x03,0x00,0x00,0x1f,0x03,0x00,0x00,
-0x70,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,
-0x23,0x03,0x00,0x00,0x22,0x03,0x00,0x00,0x50,0x00,0x05,0x00,
-0x68,0x00,0x00,0x00,0x24,0x03,0x00,0x00,0x21,0x03,0x00,0x00,
-0x23,0x03,0x00,0x00,0x8e,0x00,0x05,0x00,0x68,0x00,0x00,0x00,
-0x25,0x03,0x00,0x00,0x24,0x03,0x00,0x00,0x19,0x03,0x00,0x00,
-0x50,0x00,0x05,0x00,0x68,0x00,0x00,0x00,0x26,0x03,0x00,0x00,
-0x1c,0x03,0x00,0x00,0x1c,0x03,0x00,0x00,0x81,0x00,0x05,0x00,
-0x68,0x00,0x00,0x00,0x27,0x03,0x00,0x00,0x25,0x03,0x00,0x00,
-0x26,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x2d,0x03,0x00,0x00,0x85,0x00,0x00,0x00,0x57,0x03,0x00,0x00,
-0x51,0x00,0x05,0x00,0x4a,0x00,0x00,0x00,0x2f,0x03,0x00,0x00,
-0x27,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x4d,0x00,0x00,0x00,0x30,0x03,0x00,0x00,0x2f,0x03,0x00,0x00,
-0x41,0x00,0x06,0x00,0x57,0x00,0x00,0x00,0x31,0x03,0x00,0x00,
-0x7d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x2d,0x03,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x31,0x03,0x00,0x00,0x30,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x38,0x03,0x00,0x00,
-0x85,0x00,0x00,0x00,0x58,0x03,0x00,0x00,0x51,0x00,0x05,0x00,
-0x4a,0x00,0x00,0x00,0x39,0x03,0x00,0x00,0x27,0x03,0x00,0x00,
-0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x3a,0x03,0x00,0x00,0x39,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
-0x57,0x00,0x00,0x00,0x3b,0x03,0x00,0x00,0x7d,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x38,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x3b,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xa0,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t dequant_q4_1_len = 9704;
-
-unsigned char dequant_q4_K_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xb1,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x85,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
@@ -3360,29 +3267,970 @@ unsigned char dequant_q4_K_data[] = {
 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
 0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
-0x50,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x48,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x49,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x49,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x49,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x4b,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x4b,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x66,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x66,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x66,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x68,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x68,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x96,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
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+0x35,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,
+0x37,0x01,0x00,0x00,0x68,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x2e,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x37,0x01,0x00,0x00,
+0x36,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x3f,0x01,0x00,0x00,0x41,0x00,0x00,0x00,0x81,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x41,0x01,0x00,0x00,
+0x40,0x00,0x00,0x00,0x81,0x01,0x00,0x00,0x41,0x00,0x08,0x00,
+0x73,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x4e,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,
+0x41,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x46,0x00,0x00,0x00,
+0x43,0x01,0x00,0x00,0x42,0x01,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x44,0x01,0x00,0x00,0x43,0x01,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x45,0x01,0x00,0x00,
+0x44,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x2c,0x00,0x00,0x00,
+0x46,0x01,0x00,0x00,0x45,0x01,0x00,0x00,0x78,0x00,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x47,0x01,0x00,0x00,
+0x46,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,
+0x49,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x47,0x01,0x00,0x00,0x59,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,
+0x49,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,
+0x4b,0x01,0x00,0x00,0x68,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x3f,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x4b,0x01,0x00,0x00,
+0x4a,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x4d,0x01,0x00,0x00,0x41,0x00,0x00,0x00,0x82,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x46,0x00,0x00,0x00,0x50,0x01,0x00,0x00,
+0x42,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x46,0x00,0x00,0x00,
+0x51,0x01,0x00,0x00,0x50,0x01,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x52,0x01,0x00,0x00,
+0x51,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,
+0x54,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x52,0x01,0x00,0x00,0x59,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x55,0x01,0x00,0x00,
+0x54,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,
+0x56,0x01,0x00,0x00,0x68,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x4d,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x56,0x01,0x00,0x00,
+0x55,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5e,0x01,0x00,0x00,0x41,0x00,0x00,0x00,0x83,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x60,0x01,0x00,0x00,
+0x40,0x00,0x00,0x00,0x83,0x01,0x00,0x00,0x41,0x00,0x08,0x00,
+0x73,0x00,0x00,0x00,0x61,0x01,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x4e,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,
+0x60,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x46,0x00,0x00,0x00,
+0x62,0x01,0x00,0x00,0x61,0x01,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x63,0x01,0x00,0x00,0x62,0x01,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x64,0x01,0x00,0x00,
+0x63,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x2c,0x00,0x00,0x00,
+0x65,0x01,0x00,0x00,0x64,0x01,0x00,0x00,0x78,0x00,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x66,0x01,0x00,0x00,
+0x65,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,
+0x68,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x66,0x01,0x00,0x00,0x59,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x69,0x01,0x00,0x00,
+0x68,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,
+0x6a,0x01,0x00,0x00,0x68,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x5e,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x6a,0x01,0x00,0x00,
+0x69,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6c,0x01,0x00,0x00,0x41,0x00,0x00,0x00,0x84,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x46,0x00,0x00,0x00,0x6f,0x01,0x00,0x00,
+0x61,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x46,0x00,0x00,0x00,
+0x70,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x71,0x01,0x00,0x00,
+0x70,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,
+0x73,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x71,0x01,0x00,0x00,0x59,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x74,0x01,0x00,0x00,
+0x73,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,
+0x75,0x01,0x00,0x00,0x68,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x6c,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x75,0x01,0x00,0x00,
+0x74,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x97,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x97,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t dequant_q4_1_len = 5248;
+
+unsigned char dequant_q4_K_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xae,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
 0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x23,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,
+0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
 0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x33,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x49,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4b,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x30,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x45,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x47,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x48,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x48,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x48,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x49,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x4a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x4a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x06,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x06,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x06,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x08,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x08,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x33,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x14,0x00,0x02,0x00,0x11,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x1e,0x00,0x07,0x00,0x1e,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x1f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0x26,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
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+0x69,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
+0x72,0x00,0x00,0x00,0x51,0x01,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x0e,0x01,0x00,0x00,
+0x50,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x43,0x00,0x00,0x00,
+0x52,0x01,0x00,0x00,0x51,0x01,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x53,0x01,0x00,0x00,0x52,0x01,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x54,0x01,0x00,0x00,
+0x53,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0x55,0x01,0x00,0x00,0x54,0x01,0x00,0x00,0x8f,0x00,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x56,0x01,0x00,0x00,
+0x55,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x3e,0x00,0x00,0x00,
+0x58,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xb0,0x00,0x00,0x00,0x56,0x01,0x00,0x00,0xa4,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0x41,0x00,0x00,0x00,0x59,0x01,0x00,0x00,
+0x58,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x4e,0x00,0x00,0x00,
+0x5a,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x4f,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x5a,0x01,0x00,0x00,
+0x59,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5c,0x01,0x00,0x00,0x62,0x00,0x00,0x00,0xa7,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x43,0x00,0x00,0x00,0x5f,0x01,0x00,0x00,
+0x51,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x43,0x00,0x00,0x00,
+0x60,0x01,0x00,0x00,0x5f,0x01,0x00,0x00,0x98,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x61,0x01,0x00,0x00,
+0x60,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x3e,0x00,0x00,0x00,
+0x63,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xf7,0x00,0x00,0x00,0x61,0x01,0x00,0x00,0xa5,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0x41,0x00,0x00,0x00,0x64,0x01,0x00,0x00,
+0x63,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x4e,0x00,0x00,0x00,
+0x65,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x5c,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x65,0x01,0x00,0x00,
+0x64,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6d,0x01,0x00,0x00,0x62,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x01,0x00,0x00,
+0x69,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
+0x72,0x00,0x00,0x00,0x6f,0x01,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x0e,0x01,0x00,0x00,
+0x6e,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x43,0x00,0x00,0x00,
+0x70,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x71,0x01,0x00,0x00,0x70,0x01,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x72,0x01,0x00,0x00,
+0x71,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0x73,0x01,0x00,0x00,0x72,0x01,0x00,0x00,0x8f,0x00,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x74,0x01,0x00,0x00,
+0x73,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x3e,0x00,0x00,0x00,
+0x76,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xb0,0x00,0x00,0x00,0x74,0x01,0x00,0x00,0xa4,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0x41,0x00,0x00,0x00,0x77,0x01,0x00,0x00,
+0x76,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x4e,0x00,0x00,0x00,
+0x78,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x6d,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x78,0x01,0x00,0x00,
+0x77,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x7a,0x01,0x00,0x00,0x62,0x00,0x00,0x00,0xaa,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x43,0x00,0x00,0x00,0x7d,0x01,0x00,0x00,
+0x6f,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x43,0x00,0x00,0x00,
+0x7e,0x01,0x00,0x00,0x7d,0x01,0x00,0x00,0x98,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x7f,0x01,0x00,0x00,
+0x7e,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x3e,0x00,0x00,0x00,
+0x81,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xf7,0x00,0x00,0x00,0x7f,0x01,0x00,0x00,0xa5,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0x41,0x00,0x00,0x00,0x82,0x01,0x00,0x00,
+0x81,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x4e,0x00,0x00,0x00,
+0x83,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x7a,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x83,0x01,0x00,0x00,
+0x82,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8b,0x01,0x00,0x00,0x62,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x01,0x00,0x00,
+0x69,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
+0x72,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x0e,0x01,0x00,0x00,
+0x8c,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x43,0x00,0x00,0x00,
+0x8e,0x01,0x00,0x00,0x8d,0x01,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x90,0x01,0x00,0x00,
+0x8f,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0x91,0x01,0x00,0x00,0x90,0x01,0x00,0x00,0x8f,0x00,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x92,0x01,0x00,0x00,
+0x91,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x3e,0x00,0x00,0x00,
+0x94,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xb0,0x00,0x00,0x00,0x92,0x01,0x00,0x00,0xa4,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0x41,0x00,0x00,0x00,0x95,0x01,0x00,0x00,
+0x94,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x4e,0x00,0x00,0x00,
+0x96,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x8b,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x96,0x01,0x00,0x00,
+0x95,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x98,0x01,0x00,0x00,0x62,0x00,0x00,0x00,0xad,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x43,0x00,0x00,0x00,0x9b,0x01,0x00,0x00,
+0x8d,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x43,0x00,0x00,0x00,
+0x9c,0x01,0x00,0x00,0x9b,0x01,0x00,0x00,0x98,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x9d,0x01,0x00,0x00,
+0x9c,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x3e,0x00,0x00,0x00,
+0x9f,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xf7,0x00,0x00,0x00,0x9d,0x01,0x00,0x00,0xa5,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0x41,0x00,0x00,0x00,0xa0,0x01,0x00,0x00,
+0x9f,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x4e,0x00,0x00,0x00,
+0xa1,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x98,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0xa1,0x01,0x00,0x00,
+0xa0,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x32,0x01,0x00,0x00,0x3c,0x01,0x00,0x00,
+0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0a,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x11,0x00,0x00,0x00,0x45,0x01,0x00,0x00,0x36,0x01,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x39,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x3a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x45,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
+0x3a,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x3a,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x34,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x34,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t dequant_q4_K_len = 5916;
+
+unsigned char dequant_q5_0_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xb7,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x29,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x29,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x48,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4b,0x00,0x00,0x00,
 0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
 0x4c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
 0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x90,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
 0x48,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
 0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4e,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
@@ -3390,2770 +4238,1065 @@ unsigned char dequant_q4_K_data[] = {
 0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
 0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x08,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x09,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x3d,0x00,0x04,0x00,0x47,0x00,0x00,0x00,0x9e,0x01,0x00,0x00,
-0x90,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x47,0x00,0x00,0x00,
-0x9f,0x01,0x00,0x00,0x9e,0x01,0x00,0x00,0x63,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0xa0,0x01,0x00,0x00,
-0x9f,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,
-0xa2,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0xa0,0x01,0x00,0x00,0xa8,0x01,0x00,0x00,
-0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0xa3,0x01,0x00,0x00,
-0xa2,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x52,0x00,0x00,0x00,
-0xa4,0x01,0x00,0x00,0x0b,0x01,0x00,0x00,0x09,0x00,0x00,0x00,
-0x9b,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0xa4,0x01,0x00,0x00,
-0xa3,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x34,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,
-0x29,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0a,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x11,0x00,0x00,0x00,0x48,0x01,0x00,0x00,0x39,0x01,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,0x2f,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x3d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x48,0x01,0x00,0x00,0x37,0x01,0x00,0x00,
-0x3d,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x3d,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x37,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x37,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t dequant_q4_K_len = 5940;
-
-unsigned char dequant_q5_0_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x9b,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
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-0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
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-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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+0x06,0x00,0x00,0x00,0x68,0x01,0x00,0x00,0x40,0x00,0x00,0x00,
+0xb3,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x78,0x00,0x00,0x00,
+0x69,0x01,0x00,0x00,0x50,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
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+0x3d,0x00,0x04,0x00,0x49,0x00,0x00,0x00,0x6a,0x01,0x00,0x00,
+0x69,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x6b,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6c,0x01,0x00,0x00,0x41,0x00,0x00,0x00,
+0xb3,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6e,0x01,0x00,0x00,0x6b,0x01,0x00,0x00,0x86,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6f,0x01,0x00,0x00,
+0x64,0x00,0x00,0x00,0x68,0x01,0x00,0x00,0xc4,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x70,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,
+0x2d,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x71,0x01,0x00,0x00,0x70,0x01,0x00,0x00,0x4a,0x00,0x00,0x00,
+0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x72,0x01,0x00,0x00,
+0x6e,0x01,0x00,0x00,0x71,0x01,0x00,0x00,0x70,0x00,0x04,0x00,
+0x42,0x00,0x00,0x00,0x73,0x01,0x00,0x00,0x72,0x01,0x00,0x00,
+0x83,0x00,0x05,0x00,0x42,0x00,0x00,0x00,0x74,0x01,0x00,0x00,
+0x73,0x01,0x00,0x00,0x8f,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
+0x42,0x00,0x00,0x00,0x75,0x01,0x00,0x00,0x56,0x00,0x00,0x00,
+0x74,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
+0x76,0x01,0x00,0x00,0x75,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x53,0x00,0x00,0x00,0x77,0x01,0x00,0x00,0x7f,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x6c,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x77,0x01,0x00,0x00,0x76,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x79,0x01,0x00,0x00,0x41,0x00,0x00,0x00,
+0xb4,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x7a,0x01,0x00,0x00,0x6b,0x01,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7b,0x01,0x00,0x00,
+0x40,0x00,0x00,0x00,0xac,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x7c,0x01,0x00,0x00,0x64,0x00,0x00,0x00,
+0x7b,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x7d,0x01,0x00,0x00,0x7c,0x01,0x00,0x00,0x4a,0x00,0x00,0x00,
+0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x01,0x00,0x00,
+0x7a,0x01,0x00,0x00,0x7d,0x01,0x00,0x00,0x70,0x00,0x04,0x00,
+0x42,0x00,0x00,0x00,0x7f,0x01,0x00,0x00,0x7e,0x01,0x00,0x00,
+0x83,0x00,0x05,0x00,0x42,0x00,0x00,0x00,0x80,0x01,0x00,0x00,
+0x7f,0x01,0x00,0x00,0x8f,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
+0x42,0x00,0x00,0x00,0x81,0x01,0x00,0x00,0x56,0x00,0x00,0x00,
+0x80,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
+0x82,0x01,0x00,0x00,0x81,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x53,0x00,0x00,0x00,0x83,0x01,0x00,0x00,0x7f,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x79,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x83,0x01,0x00,0x00,0x82,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8b,0x01,0x00,0x00,0x40,0x00,0x00,0x00,
+0xb5,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x78,0x00,0x00,0x00,
+0x8c,0x01,0x00,0x00,0x50,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x8b,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x49,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,
+0x8c,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x8e,0x01,0x00,0x00,0x8d,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,0x41,0x00,0x00,0x00,
+0xb5,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x91,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x86,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x92,0x01,0x00,0x00,
+0x64,0x00,0x00,0x00,0x8b,0x01,0x00,0x00,0xc4,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x93,0x01,0x00,0x00,0x92,0x01,0x00,0x00,
+0x2d,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x94,0x01,0x00,0x00,0x93,0x01,0x00,0x00,0x4a,0x00,0x00,0x00,
+0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,
+0x91,0x01,0x00,0x00,0x94,0x01,0x00,0x00,0x70,0x00,0x04,0x00,
+0x42,0x00,0x00,0x00,0x96,0x01,0x00,0x00,0x95,0x01,0x00,0x00,
+0x83,0x00,0x05,0x00,0x42,0x00,0x00,0x00,0x97,0x01,0x00,0x00,
+0x96,0x01,0x00,0x00,0x8f,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
+0x42,0x00,0x00,0x00,0x98,0x01,0x00,0x00,0x56,0x00,0x00,0x00,
+0x97,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
+0x99,0x01,0x00,0x00,0x98,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x53,0x00,0x00,0x00,0x9a,0x01,0x00,0x00,0x7f,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x9a,0x01,0x00,0x00,0x99,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9c,0x01,0x00,0x00,0x41,0x00,0x00,0x00,
+0xb6,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9d,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x01,0x00,0x00,
+0x40,0x00,0x00,0x00,0xaf,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9f,0x01,0x00,0x00,0x64,0x00,0x00,0x00,
+0x9e,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa0,0x01,0x00,0x00,0x9f,0x01,0x00,0x00,0x4a,0x00,0x00,0x00,
+0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa1,0x01,0x00,0x00,
+0x9d,0x01,0x00,0x00,0xa0,0x01,0x00,0x00,0x70,0x00,0x04,0x00,
+0x42,0x00,0x00,0x00,0xa2,0x01,0x00,0x00,0xa1,0x01,0x00,0x00,
+0x83,0x00,0x05,0x00,0x42,0x00,0x00,0x00,0xa3,0x01,0x00,0x00,
+0xa2,0x01,0x00,0x00,0x8f,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
+0x42,0x00,0x00,0x00,0xa4,0x01,0x00,0x00,0x56,0x00,0x00,0x00,
+0xa3,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
+0xa5,0x01,0x00,0x00,0xa4,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x53,0x00,0x00,0x00,0xa6,0x01,0x00,0x00,0x7f,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x9c,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xa6,0x01,0x00,0x00,0xa5,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xac,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xac,0x00,0x00,0x00,
 0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
 };
-const uint64_t dequant_q5_0_len = 13952;
+const uint64_t dequant_q5_0_len = 6644;
 
 unsigned char dequant_q5_1_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x95,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0xb4,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
 0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
 0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x55,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x14,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x14,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x50,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x51,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x52,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x53,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x29,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x29,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x48,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x49,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x53,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x55,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x9a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x9b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x9b,0x00,0x00,0x00,
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+0x06,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,
+0x2d,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x90,0x01,0x00,0x00,0x8f,0x01,0x00,0x00,0x47,0x00,0x00,0x00,
+0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x91,0x01,0x00,0x00,
+0x8d,0x01,0x00,0x00,0x90,0x01,0x00,0x00,0x70,0x00,0x04,0x00,
+0x42,0x00,0x00,0x00,0x92,0x01,0x00,0x00,0x91,0x01,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,0x94,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x92,0x01,0x00,0x00,0x59,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x45,0x00,0x00,0x00,0x95,0x01,0x00,0x00,0x94,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,0x96,0x01,0x00,0x00,
+0x7a,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x8b,0x01,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x96,0x01,0x00,0x00,0x95,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x98,0x01,0x00,0x00,
+0x41,0x00,0x00,0x00,0xb3,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x99,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,
+0x2d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9a,0x01,0x00,0x00,0x40,0x00,0x00,0x00,0xac,0x01,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x01,0x00,0x00,
+0x5f,0x00,0x00,0x00,0x9a,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9c,0x01,0x00,0x00,0x9b,0x01,0x00,0x00,
+0x47,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9d,0x01,0x00,0x00,0x99,0x01,0x00,0x00,0x9c,0x01,0x00,0x00,
+0x70,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x9e,0x01,0x00,0x00,
+0x9d,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,
+0xa0,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x9e,0x01,0x00,0x00,0x59,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0xa1,0x01,0x00,0x00,
+0xa0,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,
+0xa2,0x01,0x00,0x00,0x7a,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x98,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0xa2,0x01,0x00,0x00,
+0xa1,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xa8,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa8,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
 };
-const uint64_t dequant_q5_1_len = 13548;
+const uint64_t dequant_q5_1_len = 6412;
 
 unsigned char dequant_q5_K_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xa0,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x9e,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
@@ -6161,1617 +5304,1273 @@ unsigned char dequant_q5_K_data[] = {
 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
 0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
-0x52,0x00,0x00,0x00,0x15,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
+0x16,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x4e,0x00,0x00,0x00,0x11,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
 0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x23,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,
+0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
 0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x33,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x49,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4b,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4e,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4e,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x50,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x50,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x52,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x12,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x13,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x13,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x13,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x15,0x01,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x89,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x14,0x00,0x02,0x00,
-0x11,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x15,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x1e,0x00,0x06,0x00,
-0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x24,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x24,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x26,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x39,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x42,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x45,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x46,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x47,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x49,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x4a,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x4b,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0x80,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x47,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,
-0x4e,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x4b,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x4f,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x50,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x51,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x52,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x69,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x47,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x80,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x82,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x98,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x9f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,
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-0x9d,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
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-0x54,0x00,0x00,0x00,0x4c,0x01,0x00,0x00,0x15,0x01,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x4e,0x01,0x00,0x00,0x67,0x00,0x00,0x00,
-0x69,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x47,0x00,0x00,0x00,
-0x53,0x01,0x00,0x00,0x1a,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x47,0x00,0x00,0x00,0x54,0x01,0x00,0x00,0x53,0x01,0x00,0x00,
-0x73,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x55,0x01,0x00,0x00,0x54,0x01,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x56,0x01,0x00,0x00,0x55,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x47,0x00,0x00,0x00,0x5a,0x01,0x00,0x00,
-0x21,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x47,0x00,0x00,0x00,
-0x5c,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,0x11,0x01,0x00,0x00,
-0x71,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x5d,0x01,0x00,0x00,
-0x5c,0x01,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x5e,0x01,0x00,0x00,0x5d,0x01,0x00,0x00,0xab,0x00,0x05,0x00,
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-0x69,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x47,0x00,0x00,0x00,
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-0x6f,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x80,0x01,0x00,0x00,
-0x7f,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,
-0x83,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xff,0x00,0x00,0x00,0x80,0x01,0x00,0x00,0x9f,0x01,0x00,0x00,
-0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x84,0x01,0x00,0x00,
-0x83,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x54,0x00,0x00,0x00,
-0x85,0x01,0x00,0x00,0x15,0x01,0x00,0x00,0x09,0x00,0x00,0x00,
-0x6a,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x85,0x01,0x00,0x00,
-0x84,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x87,0x01,0x00,0x00,0x92,0x01,0x00,0x00,
-0x29,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0a,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x11,0x00,0x00,0x00,0x99,0x01,0x00,0x00,0x8c,0x01,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,0x2f,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x90,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x99,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,
-0x90,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x90,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x8a,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8a,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t dequant_q5_K_len = 5988;
-
-unsigned char dequant_q6_K_data[] = {
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-0x10,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0xfd,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x6f,0x00,0x00,0x00,
-0xff,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
-0xff,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x74,0x00,0x00,0x00,
-0x01,0x01,0x00,0x00,0x7b,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0xe2,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x01,0x01,0x00,0x00,
-0x00,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x0e,0x01,0x00,0x00,
-0x29,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0a,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x11,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,0x08,0x01,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,0x2f,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x0c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x0f,0x01,0x00,0x00,0x06,0x01,0x00,0x00,
-0x0c,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x0c,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x06,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x06,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t dequant_q6_K_len = 4296;
-
-unsigned char dequant_q8_0_data[] = {
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+0x43,0x00,0x00,0x00,0x3e,0x01,0x00,0x00,0x3d,0x01,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x43,0x00,0x00,0x00,0x40,0x01,0x00,0x00,
+0x3e,0x01,0x00,0x00,0x06,0x01,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x41,0x01,0x00,0x00,0x40,0x01,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x42,0x01,0x00,0x00,
+0x41,0x01,0x00,0x00,0xab,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
+0x43,0x01,0x00,0x00,0x42,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0xa9,0x00,0x06,0x00,0x21,0x00,0x00,0x00,0x44,0x01,0x00,0x00,
+0x43,0x01,0x00,0x00,0x26,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x21,0x00,0x00,0x00,0x45,0x01,0x00,0x00,
+0x39,0x01,0x00,0x00,0x44,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,
+0x3e,0x00,0x00,0x00,0x46,0x01,0x00,0x00,0x45,0x01,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x3e,0x00,0x00,0x00,0x49,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,
+0x46,0x01,0x00,0x00,0x9b,0x01,0x00,0x00,0x73,0x00,0x04,0x00,
+0x41,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,0x49,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,0x4b,0x01,0x00,0x00,
+0x11,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x30,0x01,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x4b,0x01,0x00,0x00,0x4a,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4d,0x01,0x00,0x00,
+0x63,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x43,0x00,0x00,0x00,0x52,0x01,0x00,0x00,0x17,0x01,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x43,0x00,0x00,0x00,0x53,0x01,0x00,0x00,
+0x52,0x01,0x00,0x00,0x9c,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x54,0x01,0x00,0x00,0x53,0x01,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x55,0x01,0x00,0x00,
+0x54,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x43,0x00,0x00,0x00,
+0x59,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
+0x43,0x00,0x00,0x00,0x5b,0x01,0x00,0x00,0x59,0x01,0x00,0x00,
+0x0d,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x5c,0x01,0x00,0x00,0x5b,0x01,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0x5d,0x01,0x00,0x00,0x5c,0x01,0x00,0x00,
+0xab,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x5e,0x01,0x00,0x00,
+0x5d,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0xa9,0x00,0x06,0x00,
+0x21,0x00,0x00,0x00,0x5f,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,
+0x26,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x21,0x00,0x00,0x00,0x60,0x01,0x00,0x00,0x55,0x01,0x00,0x00,
+0x5f,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
+0x61,0x01,0x00,0x00,0x60,0x01,0x00,0x00,0x7f,0x00,0x04,0x00,
+0x3e,0x00,0x00,0x00,0x9d,0x01,0x00,0x00,0x00,0x01,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x3e,0x00,0x00,0x00,0x64,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
+0x61,0x01,0x00,0x00,0x9d,0x01,0x00,0x00,0x73,0x00,0x04,0x00,
+0x41,0x00,0x00,0x00,0x65,0x01,0x00,0x00,0x64,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,0x66,0x01,0x00,0x00,
+0x11,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x4d,0x01,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x66,0x01,0x00,0x00,0x65,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x01,0x00,0x00,
+0x63,0x00,0x00,0x00,0x68,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x43,0x00,0x00,0x00,0x6f,0x01,0x00,0x00,0x35,0x01,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x43,0x00,0x00,0x00,0x70,0x01,0x00,0x00,
+0x6f,0x01,0x00,0x00,0x9c,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x71,0x01,0x00,0x00,0x70,0x01,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x72,0x01,0x00,0x00,
+0x71,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x43,0x00,0x00,0x00,
+0x77,0x01,0x00,0x00,0x3d,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
+0x43,0x00,0x00,0x00,0x79,0x01,0x00,0x00,0x77,0x01,0x00,0x00,
+0x0d,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x7a,0x01,0x00,0x00,0x79,0x01,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0x7b,0x01,0x00,0x00,0x7a,0x01,0x00,0x00,
+0xab,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x7c,0x01,0x00,0x00,
+0x7b,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0xa9,0x00,0x06,0x00,
+0x21,0x00,0x00,0x00,0x7d,0x01,0x00,0x00,0x7c,0x01,0x00,0x00,
+0x26,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x21,0x00,0x00,0x00,0x7e,0x01,0x00,0x00,0x72,0x01,0x00,0x00,
+0x7d,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
+0x7f,0x01,0x00,0x00,0x7e,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,
+0x3e,0x00,0x00,0x00,0x82,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0x7f,0x01,0x00,0x00,
+0x9d,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x41,0x00,0x00,0x00,
+0x83,0x01,0x00,0x00,0x82,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x50,0x00,0x00,0x00,0x84,0x01,0x00,0x00,0x11,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x69,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x84,0x01,0x00,0x00,0x83,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x86,0x01,0x00,0x00,
+0x90,0x01,0x00,0x00,0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x11,0x00,0x00,0x00,0x97,0x01,0x00,0x00,
+0x8a,0x01,0x00,0x00,0x0a,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,
+0x2c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x8e,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x97,0x01,0x00,0x00,
+0x88,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8e,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x88,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x88,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t dequant_q5_K_len = 5980;
+
+unsigned char dequant_q6_K_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x0d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x5f,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x30,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x56,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x57,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x59,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x5b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x5b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x5b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x5b,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x5d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x5d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x5d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x5f,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x5f,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x71,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x72,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x72,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x72,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x74,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x74,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x02,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x14,0x00,0x02,0x00,0x11,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x1e,0x00,0x07,0x00,0x1e,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
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+0x70,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x5a,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x97,0x00,0x00,0x00,
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+0x4c,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,0x43,0x00,0x00,0x00,
+0x9f,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x7c,0x00,0x00,0x00,
+0xa1,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,
+0xa1,0x00,0x00,0x00,0x72,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0xa3,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x52,0x00,0x00,0x00,
+0x35,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x65,0x00,0x00,0x00,
+0xa7,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
+0xa7,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xa9,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,0xab,0x00,0x00,0x00,
+0xaa,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x53,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
+0x79,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xae,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0xae,0x00,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
+0xaf,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,
+0x21,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
+0x8e,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
+0x72,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0x72,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0xb4,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
+0x21,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,
+0x93,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0xb6,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x68,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
+0xb6,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x68,0x00,0x00,0x00,
+0xb8,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,
+0xb8,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x6d,0x00,0x00,0x00,
+0xba,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x9b,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xba,0x00,0x00,0x00,
+0xb9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,
+0x43,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
+0x7c,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
+0xc1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x58,0x00,0x00,0x00,
+0xc3,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0x72,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
+0x83,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x53,0x00,0x00,0x00,
+0xca,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,
+0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,
+0xca,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x53,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
+0x8e,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,
+0x21,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,
+0x8e,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0xd3,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0x72,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
+0xd3,0x00,0x00,0x00,0x72,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0xd5,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
+0x21,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
+0x93,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0xd7,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x68,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
+0xd7,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x68,0x00,0x00,0x00,
+0xd9,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
+0xd9,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x6d,0x00,0x00,0x00,
+0xdb,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xdb,0x00,0x00,0x00,
+0xda,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xde,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
+0x43,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
+0x7c,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
+0xe3,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x58,0x00,0x00,0x00,
+0xe5,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0x72,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
+0xa7,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x53,0x00,0x00,0x00,
+0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,
+0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
+0xec,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x53,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xf2,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
+0xf3,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,
+0x21,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
+0x8e,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0xf6,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,
+0x72,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
+0xf6,0x00,0x00,0x00,0x72,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0xf8,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
+0x21,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
+0x93,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0xfa,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x68,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
+0xfa,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x68,0x00,0x00,0x00,
+0xfc,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
+0xfc,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x6d,0x00,0x00,0x00,
+0xfe,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0xde,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xfe,0x00,0x00,0x00,
+0xfd,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x0b,0x01,0x00,0x00,
+0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0a,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x11,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,0x05,0x01,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x09,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x0c,0x01,0x00,0x00,0x03,0x01,0x00,0x00,
+0x09,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x09,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x03,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x03,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
 
 };
-const uint64_t dequant_q8_0_len = 8868;
+const uint64_t dequant_q6_K_len = 4272;
+
+unsigned char dequant_q8_0_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x4c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
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+0x09,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x4f,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x61,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x0a,0x01,0x00,0x00,0x09,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x12,0x01,0x00,0x00,0x41,0x00,0x00,0x00,
+0x48,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x13,0x01,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x01,0x00,0x00,
+0x41,0x00,0x08,0x00,0x6b,0x00,0x00,0x00,0x14,0x01,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x67,0x00,0x00,0x00,0x13,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x46,0x00,0x00,0x00,0x15,0x01,0x00,0x00,0x14,0x01,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x16,0x01,0x00,0x00,
+0x15,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x42,0x00,0x00,0x00,
+0x17,0x01,0x00,0x00,0x52,0x00,0x00,0x00,0x16,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x18,0x01,0x00,0x00,
+0x17,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x4f,0x00,0x00,0x00,
+0x19,0x01,0x00,0x00,0x61,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x12,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x19,0x01,0x00,0x00,
+0x18,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1b,0x01,0x00,0x00,0x41,0x00,0x00,0x00,0x49,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,
+0x40,0x00,0x00,0x00,0x49,0x01,0x00,0x00,0x41,0x00,0x08,0x00,
+0x6b,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
+0x1d,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x46,0x00,0x00,0x00,
+0x1f,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,
+0x42,0x00,0x00,0x00,0x20,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,
+0x85,0x00,0x05,0x00,0x42,0x00,0x00,0x00,0x21,0x01,0x00,0x00,
+0x52,0x00,0x00,0x00,0x20,0x01,0x00,0x00,0x73,0x00,0x04,0x00,
+0x45,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x21,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0x4f,0x00,0x00,0x00,0x23,0x01,0x00,0x00,
+0x61,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x1b,0x01,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x23,0x01,0x00,0x00,0x22,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2b,0x01,0x00,0x00,
+0x41,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,0x40,0x00,0x00,0x00,
+0x4a,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x6b,0x00,0x00,0x00,
+0x2d,0x01,0x00,0x00,0x4c,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x46,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,
+0x2d,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,0x42,0x00,0x00,0x00,
+0x2f,0x01,0x00,0x00,0x2e,0x01,0x00,0x00,0x85,0x00,0x05,0x00,
+0x42,0x00,0x00,0x00,0x30,0x01,0x00,0x00,0x52,0x00,0x00,0x00,
+0x2f,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
+0x31,0x01,0x00,0x00,0x30,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x4f,0x00,0x00,0x00,0x32,0x01,0x00,0x00,0x61,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x2b,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x32,0x01,0x00,0x00,0x31,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x34,0x01,0x00,0x00,0x41,0x00,0x00,0x00,
+0x4b,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x36,0x01,0x00,0x00,0x40,0x00,0x00,0x00,0x4b,0x01,0x00,0x00,
+0x41,0x00,0x08,0x00,0x6b,0x00,0x00,0x00,0x37,0x01,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x67,0x00,0x00,0x00,0x36,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x46,0x00,0x00,0x00,0x38,0x01,0x00,0x00,0x37,0x01,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x39,0x01,0x00,0x00,
+0x38,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x42,0x00,0x00,0x00,
+0x3a,0x01,0x00,0x00,0x52,0x00,0x00,0x00,0x39,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x3b,0x01,0x00,0x00,
+0x3a,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x4f,0x00,0x00,0x00,
+0x3c,0x01,0x00,0x00,0x61,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x34,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x3c,0x01,0x00,0x00,
+0x3b,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x88,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x88,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t dequant_q8_0_len = 4780;
 
 unsigned char diag_mask_inf_f32_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x4c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x53,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
 0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
 0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
 0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
+0x45,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
 0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
 0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
 0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
@@ -7781,23 +6580,23 @@ unsigned char diag_mask_inf_f32_data[] = {
 0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
 0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
 0x15,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2b,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x38,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x2b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x38,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3a,0x00,0x00,0x00,
 0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x30,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x30,0x00,0x00,0x00,
+0x42,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x43,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x43,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x30,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x32,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x32,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x43,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
 0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x49,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x4b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
 0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
 0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
 0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
@@ -7818,30 +6617,30 @@ unsigned char diag_mask_inf_f32_data[] = {
 0x18,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
 0x20,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
 0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x1d,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x29,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x2a,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x2f,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x30,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x31,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x34,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x43,0x00,0x00,0x00,0xff,0xff,0xff,0xff,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
-0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
+0x2e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x36,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x37,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x38,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x39,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x39,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x42,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x43,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x44,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x43,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x44,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x4a,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x2c,0x00,0x06,0x00,
+0x09,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x36,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x00,0x00,0x80,0xff,
 0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x4a,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x4c,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
-0x4b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x4b,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x4d,0x00,0x00,0x00,
 0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
 0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
 0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
@@ -7856,177 +6655,41 @@ unsigned char diag_mask_inf_f32_data[] = {
 0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
 0x1e,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
 0xf8,0x00,0x02,0x00,0x1f,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x20,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x20,0x00,0x00,0x00,
 0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
 0x13,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
 0x06,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x34,0x00,0x00,0x00,
-0x35,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x29,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1a,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1a,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x3c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
-0xac,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0xa9,0x00,0x06,0x00,
-0x06,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
-0x43,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x29,0x00,0x00,0x00,
-0x45,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x83,0x00,0x05,0x00,
-0x29,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
-0x45,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x34,0x00,0x00,0x00,
-0x47,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x47,0x00,0x00,0x00,
-0x46,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4a,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x4a,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x2f,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x2f,0x00,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x31,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0xac,0x00,0x05,0x00,
+0x1d,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x35,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x33,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x34,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x3e,0x00,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x3f,0x00,0x00,0x00,
+0x52,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x35,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x40,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x3e,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x36,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x3e,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x49,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x35,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x35,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4c,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x4c,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
 0x38,0x00,0x01,0x00,
 };
-const uint64_t diag_mask_inf_f32_len = 1480;
-
-unsigned char f32_to_f16_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x53,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x33,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x34,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x34,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x36,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x36,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x40,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x40,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x40,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x42,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x42,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x52,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x11,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
-0x23,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x32,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x33,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x34,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x35,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x35,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x3e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x3f,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x40,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x41,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x41,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x4a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x50,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2c,0x00,0x06,0x00,0x0a,0x00,0x00,0x00,0x52,0x00,0x00,0x00,
-0x50,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
-0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x05,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0e,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0xb1,0x00,0x05,0x00,
-0x23,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x29,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x27,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x28,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0xb1,0x00,0x05,0x00,0x23,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x29,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x29,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x23,0x00,0x00,0x00,
-0x2f,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x31,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x2f,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x30,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x3d,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x4a,0x00,0x00,0x00,
-0x4b,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0x49,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x32,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x4e,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x4f,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x31,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x31,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t f32_to_f16_len = 1596;
+const uint64_t diag_mask_inf_f32_len = 1504;
 
 unsigned char gelu_f32_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
@@ -8158,50 +6821,90 @@ const uint64_t gelu_f32_len = 1484;
 
 unsigned char get_rows_f16_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x7b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x87,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
 0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
-0x65,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
+0x7b,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
 0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
 0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
 0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
 0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
 0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1d,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x19,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x32,0x00,0x00,0x00,
 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x2b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x2b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x33,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x33,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x2b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
+0x33,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x35,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x35,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x71,0x00,0x00,0x00,
 0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x53,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x53,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x72,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x72,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x53,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x55,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x63,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x63,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x63,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x65,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x72,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x74,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x74,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x78,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x79,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x7b,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x84,0x00,0x00,0x00,
 0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
 0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
 0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
@@ -8212,163 +6915,250 @@ unsigned char get_rows_f16_data[] = {
 0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
 0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
 0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x1d,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x1e,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
-0x24,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x2a,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x2b,0x00,0x00,0x00,
-0x2a,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x30,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x51,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x52,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x53,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x54,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x58,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x62,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x63,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x64,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x64,0x00,0x00,0x00,0x65,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x77,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x2c,0x00,0x06,0x00,
-0x09,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x77,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x36,0x00,0x05,0x00,
-0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x21,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
-0x24,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x27,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x25,0x00,0x00,0x00,
-0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
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-0x38,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x1c,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
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+0x21,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
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+0x41,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x1c,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
+0x4a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x4f,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
+0x4f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
+0x56,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x5a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x59,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,
+0x5a,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,
 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x58,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x51,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
-0x5f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x68,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0x5b,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x58,0x00,0x00,0x00,
-0x6e,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x68,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x6e,0x00,0x00,0x00,
+0x57,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x66,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x65,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
+0x66,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x68,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
+0x63,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x6b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x6d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
 0x6d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x72,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
-0x60,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x58,0x00,0x00,0x00,
-0x76,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x72,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x76,0x00,0x00,0x00,
-0x75,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x79,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
+0x6f,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x77,0x00,0x00,0x00,
+0x6f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x7f,0x00,0x00,0x00,
+0x80,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
+0x7e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x70,0x00,0x00,0x00,
+0x81,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x7f,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x82,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x85,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x85,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
 };
-const uint64_t get_rows_f16_len = 1948;
+const uint64_t get_rows_f16_len = 2996;
 
 unsigned char get_rows_f16_f32_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x7a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x89,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
 0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
-0x65,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
+0x7b,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
 0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
 0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
 0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
 0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
 0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1d,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
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 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
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 0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
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+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x53,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x55,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x63,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x63,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x63,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x65,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,
+0x79,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x7b,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x86,0x00,0x00,0x00,
 0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
 0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
 0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
@@ -8379,572 +7169,253 @@ unsigned char get_rows_f16_f32_data[] = {
 0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
 0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
 0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x1d,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x1e,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
-0x24,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x2a,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x2b,0x00,0x00,0x00,
-0x2a,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
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-0x0c,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
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-0x0c,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
-0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0x77,0x00,0x00,0x00,
-0x76,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x78,0x00,0x00,0x00,
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-0x7c,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x21,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0xae,0x00,0x05,0x00,0x24,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x27,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x25,0x00,0x00,0x00,0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
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+0x1e,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x5b,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
 0x5b,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x72,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x6d,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
-0x65,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x75,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x78,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x78,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t get_rows_f16_f32_len = 1932;
-
-unsigned char get_rows_q4_0_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x98,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x2b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x2b,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x57,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x58,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x58,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x79,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x7a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7a,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x7a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x7c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7c,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x8e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x1c,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,
-0x1d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x1e,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x02,0x00,0x24,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x2a,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x2c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x30,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x49,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x52,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x53,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x55,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x1e,0x00,0x04,0x00,
-0x56,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x57,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x58,0x00,0x00,0x00,0x57,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x59,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x59,0x00,0x00,0x00,
-0x5a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x5c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x52,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x67,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
-0x74,0x00,0x00,0x00,0x00,0x00,0x00,0x41,0x1d,0x00,0x03,0x00,
-0x79,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x7a,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x7b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x8d,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x2c,0x00,0x06,0x00,
-0x09,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x2c,0x00,0x05,0x00,
-0x67,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
-0x74,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x8f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,
-0x0c,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x90,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0xae,0x00,0x05,0x00,0x24,0x00,0x00,0x00,
-0x25,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x27,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x25,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x26,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x8f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x27,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x30,0x00,0x00,0x00,
-0x31,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x33,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x4f,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0x41,0x00,0x07,0x00,0x5c,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x5a,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
-0x5e,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,
-0x41,0x00,0x08,0x00,0x63,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
-0x5a,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x53,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
-0x65,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x6c,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0x6c,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x70,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x71,0x00,0x00,0x00,
-0x70,0x00,0x00,0x00,0x50,0x00,0x05,0x00,0x67,0x00,0x00,0x00,
-0x72,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x71,0x00,0x00,0x00,
-0x83,0x00,0x05,0x00,0x67,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
-0x72,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,
-0x67,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
-0x5f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
-0x78,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x52,0x00,0x00,0x00,0x83,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x5c,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
-0x7c,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x84,0x00,0x00,0x00,0x83,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x1c,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
-0x8b,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x5c,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x8c,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x8f,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q4_0_len = 2372;
-
-unsigned char get_rows_q4_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x97,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
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-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,
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-0x47,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x2b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x2b,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x57,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x58,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x58,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x79,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
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-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7a,0x00,0x00,0x00,
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-0x47,0x00,0x04,0x00,0x7c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
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-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x8d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
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-0x21,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x2c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
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-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x49,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x52,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
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-0x58,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x59,0x00,0x00,0x00,
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-0x53,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x67,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
-0x74,0x00,0x00,0x00,0x00,0x00,0x00,0x41,0x1d,0x00,0x03,0x00,
-0x79,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x7a,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x7b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x83,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
-0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,
-0x8c,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x2c,0x00,0x05,0x00,0x67,0x00,0x00,0x00,0x96,0x00,0x00,0x00,
-0x74,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x36,0x00,0x05,0x00,
-0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x8e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x21,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
-0x24,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x27,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x25,0x00,0x00,0x00,
-0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8e,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x31,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x4a,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x41,0x00,0x07,0x00,0x5c,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x45,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x52,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,
-0x5e,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x63,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x45,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x65,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x66,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
-0x6b,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
-0x6d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
-0x6f,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
-0x71,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0x50,0x00,0x05,0x00,
-0x67,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0x71,0x00,0x00,0x00,0x83,0x00,0x05,0x00,0x67,0x00,0x00,0x00,
-0x76,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x96,0x00,0x00,0x00,
-0x8e,0x00,0x05,0x00,0x67,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
-0x76,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
-0x4a,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,
-0x82,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x83,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
-0x7c,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x84,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x1c,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x83,0x00,0x00,0x00,
-0x8b,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x88,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x8b,0x00,0x00,0x00,
-0x8a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8e,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8e,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x5d,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x65,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x67,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x67,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x69,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
+0x6e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x76,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x7f,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x77,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
+0x81,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x83,0x00,0x00,0x00,
+0x84,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
+0x76,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x84,0x00,0x00,0x00,
+0x82,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x87,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
 0x38,0x00,0x01,0x00,
 };
-const uint64_t get_rows_q4_0_f32_len = 2356;
+const uint64_t get_rows_f16_f32_len = 3028;
 
-unsigned char get_rows_q4_1_data[] = {
+unsigned char get_rows_f32_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x96,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x89,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x81,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
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+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x71,0x00,0x00,0x00,
 0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x7f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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+0x48,0x00,0x05,0x00,0x72,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x7f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x81,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x81,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x93,0x00,0x00,0x00,
+0x72,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x74,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x74,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x78,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x79,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x7b,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x86,0x00,0x00,0x00,
 0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
 0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
 0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
@@ -8955,33999 +7426,3791 @@ unsigned char get_rows_q4_1_data[] = {
 0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
 0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
 0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x1d,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x1e,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
-0x24,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x2a,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x2b,0x00,0x00,0x00,
-0x2a,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
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-0x0c,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x52,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
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-0x52,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
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-0x1e,0x00,0x03,0x00,0x58,0x00,0x00,0x00,0x57,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x59,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x59,0x00,0x00,0x00,
-0x5a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x5c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x52,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x68,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
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-0x41,0x00,0x08,0x00,0x68,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
-0x5a,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x53,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
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-0x6a,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x71,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
-0x71,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x75,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
-0x75,0x00,0x00,0x00,0x50,0x00,0x05,0x00,0x6c,0x00,0x00,0x00,
-0x77,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
-0x8e,0x00,0x05,0x00,0x6c,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
-0x77,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,0x50,0x00,0x05,0x00,
-0x6c,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x6c,0x00,0x00,0x00,
-0x7d,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
-0x4f,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x1c,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
-0x88,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x5c,0x00,0x00,0x00,0x89,0x00,0x00,0x00,0x81,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x89,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,
-0x8f,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x52,0x00,0x00,0x00,0x90,0x00,0x00,0x00,
-0x8f,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x5c,0x00,0x00,0x00,
-0x91,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x8d,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x91,0x00,0x00,0x00,
-0x90,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x94,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x94,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x3b,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x43,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x45,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x45,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
+0x35,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
+0x49,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x4b,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x4e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x56,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x57,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x56,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x5b,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x5b,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x65,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x67,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x67,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x69,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
+0x6e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x77,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x7f,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x18,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x70,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
+0x81,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x83,0x00,0x00,0x00,
+0x84,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
+0x77,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x84,0x00,0x00,0x00,
+0x82,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x87,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
 0x38,0x00,0x01,0x00,
 };
-const uint64_t get_rows_q4_1_len = 2440;
+const uint64_t get_rows_f32_len = 3028;
 
-unsigned char get_rows_q4_1_f32_data[] = {
+unsigned char get_rows_f32_f32_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x95,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x81,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x2b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x2b,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x56,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x57,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x58,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x58,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x58,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x5a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7e,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x7f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x7f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x81,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x81,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x92,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x1d,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x1e,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
-0x24,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x2a,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x2b,0x00,0x00,0x00,
-0x2a,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x30,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x52,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x1e,0x00,0x05,0x00,0x56,0x00,0x00,0x00,
-0x52,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x57,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x58,0x00,0x00,0x00,0x57,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x59,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x59,0x00,0x00,0x00,
-0x5a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x5c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x52,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x68,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x7e,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x7f,0x00,0x00,0x00,
-0x7e,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x80,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x80,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x88,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x91,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x2c,0x00,0x06,0x00,
-0x09,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x91,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x36,0x00,0x05,0x00,
-0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x93,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,0x94,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x94,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x21,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
-0x24,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x27,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x25,0x00,0x00,0x00,
-0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x93,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x31,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x4a,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x41,0x00,0x07,0x00,0x5c,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x45,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x52,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,
-0x5e,0x00,0x00,0x00,0x41,0x00,0x07,0x00,0x5c,0x00,0x00,0x00,
-0x62,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x45,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x52,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
-0x63,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x68,0x00,0x00,0x00,
-0x69,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x45,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,
-0x69,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x6b,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,
-0x70,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
-0x72,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,
-0x74,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
-0x76,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0x50,0x00,0x05,0x00,
-0x6c,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
-0x76,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,0x6c,0x00,0x00,0x00,
-0x7a,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,
-0x50,0x00,0x05,0x00,0x6c,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
-0x6c,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
-0x7c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x84,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,0x87,0x00,0x00,0x00,
-0x7d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x88,0x00,0x00,0x00,0x89,0x00,0x00,0x00,0x81,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x89,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,
-0x8f,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x88,0x00,0x00,0x00,0x90,0x00,0x00,0x00,
-0x81,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x90,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x93,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x93,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t get_rows_q4_1_f32_len = 2424;
-
-unsigned char get_rows_q5_0_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xc3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
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-0x44,0x00,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x4f,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0x41,0x00,0x07,0x00,0x5e,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,
-0x5c,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
-0x60,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
-0x41,0x00,0x08,0x00,0x64,0x00,0x00,0x00,0x65,0x00,0x00,0x00,
-0x5c,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x53,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x65,0x00,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
-0x66,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x69,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
-0x41,0x00,0x08,0x00,0x64,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,
-0x5c,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x53,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0x6c,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x6e,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
-0x6e,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
-0x75,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x77,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
-0x77,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x7c,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,
-0x6e,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,
-0x56,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
-0x84,0x00,0x00,0x00,0x85,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x4a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x55,0x00,0x00,0x00,
-0x86,0x00,0x00,0x00,0x85,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,
-0x87,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
-0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x92,0x00,0x00,0x00,
-0x8d,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0x92,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
-0x87,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
-0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x99,0x00,0x00,0x00,
-0x95,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0x99,0x00,0x00,0x00,
-0x50,0x00,0x05,0x00,0x88,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,
-0x93,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0x83,0x00,0x05,0x00,
-0x88,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xc2,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,0x88,0x00,0x00,0x00,
-0xa1,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
-0x4f,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x1c,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
-0xac,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x5e,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xad,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
-0x56,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,
-0xb3,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x52,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,
-0xb3,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x5e,0x00,0x00,0x00,
-0xb5,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0xb1,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xb5,0x00,0x00,0x00,
-0xb4,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb8,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb8,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q5_0_len = 2884;
-
-unsigned char get_rows_q5_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xc2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x2b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x2b,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x57,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x58,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x58,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x58,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x59,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x5a,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x5a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xa2,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xa3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xa3,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xa3,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xa5,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa5,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb6,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x1c,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,
-0x1d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x1e,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x02,0x00,0x24,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x2a,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
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-0x3e,0x00,0x03,0x00,0xb4,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb7,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb7,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t get_rows_q5_0_f32_len = 2868;
-
-unsigned char get_rows_q5_1_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xb6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x2b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x2b,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x56,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x57,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x58,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x58,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x58,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x5a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x9e,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x9f,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x9f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x9f,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa1,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xa1,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb3,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x1e,0x00,0x06,0x00,0x1d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x1d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x24,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x2a,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x30,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x52,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x55,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
-0x1e,0x00,0x06,0x00,0x56,0x00,0x00,0x00,0x52,0x00,0x00,0x00,
-0x52,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x57,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x58,0x00,0x00,0x00,0x57,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x59,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
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-0x5a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x5c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x52,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x69,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x6e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x80,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x84,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x9e,0x00,0x00,0x00,
-0x52,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x9f,0x00,0x00,0x00,
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-0xa0,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,
-0xb3,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xb4,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,
-0x0c,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb5,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0xae,0x00,0x05,0x00,0x24,0x00,0x00,0x00,
-0x25,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x27,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x25,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x26,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x27,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x30,0x00,0x00,0x00,
-0x31,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x33,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x4f,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0x41,0x00,0x07,0x00,0x5c,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x5a,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
-0x5e,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,
-0x41,0x00,0x07,0x00,0x5c,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
-0x5a,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
-0x63,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
-0x41,0x00,0x07,0x00,0x69,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,
-0x5a,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x6b,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,
-0x4a,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x6f,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
-0x6f,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
-0x6a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x77,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
-0x74,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x7a,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
-0x80,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,
-0x4a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
-0x82,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x83,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x89,0x00,0x00,0x00,
-0x83,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0x71,0x00,0x00,0x00,
-0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,
-0x89,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x91,0x00,0x00,0x00,
-0x83,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
-0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
-0x91,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
-0x50,0x00,0x05,0x00,0x84,0x00,0x00,0x00,0x97,0x00,0x00,0x00,
-0x8f,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,
-0x84,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0x97,0x00,0x00,0x00,
-0x5f,0x00,0x00,0x00,0x50,0x00,0x05,0x00,0x84,0x00,0x00,0x00,
-0x9c,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x84,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,
-0x9a,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
-0x4a,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,
-0xa7,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x52,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
-0xa7,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x5c,0x00,0x00,0x00,
-0xa9,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0xa4,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xa9,0x00,0x00,0x00,
-0xa8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xad,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,
-0x9d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x52,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x5c,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
-0xa1,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xb1,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb4,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t get_rows_q5_1_len = 2796;
-
-unsigned char get_rows_q5_1_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xb5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x2b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x2b,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x56,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x57,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x58,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x58,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x58,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x5a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x9e,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x9f,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x9f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x9f,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa1,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xa1,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb2,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x1e,0x00,0x06,0x00,0x1d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0xa8,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xb0,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xb3,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q5_1_f32_len = 2780;
-
-unsigned char get_rows_q8_0_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x8b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x2b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x2b,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x54,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x55,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x56,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x56,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x58,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x73,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x74,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x74,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x74,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x76,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x76,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x88,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x1c,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,
-0x1d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x1e,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x02,0x00,0x24,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x2a,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x2c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x30,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x51,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x53,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x1e,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x55,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x56,0x00,0x00,0x00,
-0x55,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x57,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x56,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x57,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x5e,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x63,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x52,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x73,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x74,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x75,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x74,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x75,0x00,0x00,0x00,
-0x76,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
-0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
-0x87,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x89,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,
-0x8a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x8a,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
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-0x0f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x21,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0xae,0x00,0x05,0x00,0x24,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x27,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x25,0x00,0x00,0x00,0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x89,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x31,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x33,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
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-0x45,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x41,0x00,0x07,0x00,
-0x5a,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x5b,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
-0x63,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x49,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
-0x65,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x72,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x65,0x00,0x00,0x00,
-0x6f,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
-0x66,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x6a,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x41,0x00,0x08,0x00,0x63,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x52,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,
-0x72,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0x6c,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
-0x6e,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x50,0x00,0x05,0x00,
-0x5e,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
-0x6e,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,0x5e,0x00,0x00,0x00,
-0x72,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x1c,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x51,0x00,0x00,0x00,
-0x7d,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x5a,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x7e,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,
-0x84,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x85,0x00,0x00,0x00,
-0x84,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x5a,0x00,0x00,0x00,
-0x86,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x82,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x86,0x00,0x00,0x00,
-0x85,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x89,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x89,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q8_0_len = 2296;
-
-unsigned char get_rows_q8_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x8a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
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-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
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-0x48,0x00,0x05,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x56,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
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-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
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-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
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-0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
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-0x0e,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
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-0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
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-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0xae,0x00,0x05,0x00,0x24,0x00,0x00,0x00,
-0x25,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x27,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x25,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x26,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x88,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x27,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x30,0x00,0x00,0x00,
-0x31,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
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-0x41,0x00,0x07,0x00,0x5a,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x51,0x00,0x00,0x00,
-0x5c,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x41,0x00,0x08,0x00,0x63,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
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-0x16,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x63,0x00,0x00,0x00,
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-0x3d,0x00,0x04,0x00,0x52,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
-0x6b,0x00,0x00,0x00,0x72,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x6d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0x50,0x00,0x05,0x00,0x5e,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,
-0x67,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,
-0x5e,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x79,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,
-0x72,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x7d,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x7e,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,
-0x84,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x7d,0x00,0x00,0x00,0x85,0x00,0x00,0x00,
-0x76,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x85,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x88,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x88,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t get_rows_q8_0_f32_len = 2280;
-
-unsigned char matmul_f16_aligned_l_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x5b,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
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-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0x80,0x01,0x00,0x00,0x8d,0x01,0x00,0x00,
-0xbb,0x02,0x00,0x00,0x04,0x03,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x47,0x00,0x04,0x00,0xf7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0xa3,0x02,0x00,0x00,0x2a,0x03,0x00,0x00,0x6c,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xca,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcc,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa8,0x02,0x00,0x00,0x55,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,
-0x8c,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0x59,0x00,0x00,0x00,
-0xaf,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xaf,0x02,0x00,0x00,0x9e,0x00,0x00,0x00,0xae,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,
-0x0f,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0x47,0x00,0x00,0x00,
-0xb5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0xbc,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbe,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,
-0xb6,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc1,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x2b,0x03,0x00,0x00,
-0x3e,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x28,0x03,0x00,0x00,
-0xc4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0xc7,0x02,0x00,0x00,0x2b,0x03,0x00,0x00,0xb5,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xc3,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xc7,0x02,0x00,0x00,
-0xc2,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xc2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x2c,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
-0xc2,0x02,0x00,0x00,0x26,0x03,0x00,0x00,0xcc,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,
-0x2c,0x03,0x00,0x00,0x60,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xcb,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xcf,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xca,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,
-0x2c,0x03,0x00,0x00,0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
-0xd3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd6,0x02,0x00,0x00,0x64,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,
-0xd4,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,0x2b,0x03,0x00,0x00,
-0x47,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdc,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xde,0x02,0x00,0x00,
-0x68,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,
-0xde,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x2e,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
-0xca,0x02,0x00,0x00,0x24,0x03,0x00,0x00,0xe4,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0xe7,0x02,0x00,0x00,
-0x2e,0x03,0x00,0x00,0xb2,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xe3,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xe7,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
-0xe3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe9,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x30,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0xe2,0x02,0x00,0x00,
-0x22,0x03,0x00,0x00,0xec,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0xef,0x02,0x00,0x00,0x30,0x03,0x00,0x00,
-0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xeb,0x02,0x00,0x00,
-0xec,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xef,0x02,0x00,0x00,0xea,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xea,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf2,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,
-0x30,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0xf5,0x02,0x00,0x00,0xf2,0x02,0x00,0x00,0x36,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xf7,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xf5,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,
-0xf7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf6,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,
-0xdf,0x02,0x00,0x00,0x2e,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0xfb,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
-0xc6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xfc,0x02,0x00,0x00,0xfb,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0xfd,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,
-0xfc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xf7,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf7,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0xb8,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,
-0xea,0x02,0x00,0x00,0xfd,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xfe,0x02,0x00,0x00,0xff,0x02,0x00,0x00,
-0x00,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0xff,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x08,0x03,0x00,0x00,
-0xdf,0x02,0x00,0x00,0x2e,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x0a,0x03,0x00,0x00,0x12,0x00,0x00,0x00,
-0x09,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0b,0x03,0x00,0x00,0x0a,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x03,0x00,0x00,0x08,0x03,0x00,0x00,
-0x0b,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0d,0x03,0x00,0x00,0xbf,0x02,0x00,0x00,0x0c,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0f,0x03,0x00,0x00,
-0x0d,0x03,0x00,0x00,0xd7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x11,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,
-0x30,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x13,0x03,0x00,0x00,0x2b,0x03,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x15,0x03,0x00,0x00,
-0x13,0x03,0x00,0x00,0x2e,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x17,0x03,0x00,0x00,0x15,0x03,0x00,0x00,
-0x16,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x19,0x03,0x00,0x00,0x2c,0x03,0x00,0x00,0x62,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1a,0x03,0x00,0x00,
-0x17,0x03,0x00,0x00,0x19,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,0x1a,0x03,0x00,0x00,
-0x30,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0xc3,0x00,0x00,0x00,
-0x1d,0x03,0x00,0x00,0xc0,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xba,0x00,0x00,0x00,0x1e,0x03,0x00,0x00,
-0x1d,0x03,0x00,0x00,0x41,0x00,0x06,0x00,0x1f,0x03,0x00,0x00,
-0x20,0x03,0x00,0x00,0x04,0x03,0x00,0x00,0x34,0x00,0x00,0x00,
-0x11,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x20,0x03,0x00,0x00,
-0x1e,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x00,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x00,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xec,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xec,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x22,0x03,0x00,0x00,
-0x30,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xeb,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x24,0x03,0x00,0x00,0x2e,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x26,0x03,0x00,0x00,0x2c,0x03,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x28,0x03,0x00,0x00,
-0x2b,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_aligned_l_len = 11936;
-
-unsigned char matmul_f16_aligned_l_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf3,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0xea,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0x4b,0x01,0x00,0x00,
-0x58,0x01,0x00,0x00,0x53,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x7a,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0x84,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x87,0x02,0x00,0x00,
-0xc8,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x83,0x02,0x00,0x00,0x84,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x87,0x02,0x00,0x00,0x82,0x02,0x00,0x00,
-0x83,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
-0x6f,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,
-0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x8f,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x8d,0x02,0x00,0x00,
-0x8e,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x92,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x93,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0x93,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
-0x92,0x02,0x00,0x00,0x94,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8f,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xb8,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x95,0x02,0x00,0x00,
-0x8e,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x98,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x96,0x02,0x00,0x00,
-0x97,0x02,0x00,0x00,0x98,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x97,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,
-0xa0,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,0x57,0x02,0x00,0x00,
-0xa4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa7,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,
-0xa7,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
-0xb2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xad,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,
-0xad,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb2,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
-0xb2,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc3,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xc0,0x00,0x00,0x00,
-0xb4,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
-0xb6,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0xb7,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0x34,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xb8,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x98,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x98,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xba,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x83,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xc4,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x63,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc0,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5b,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f16_aligned_l_fp32_len = 10512;
-
-unsigned char matmul_f16_aligned_m_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x5b,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0x80,0x01,0x00,0x00,0x8d,0x01,0x00,0x00,
-0xbb,0x02,0x00,0x00,0x04,0x03,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x9d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xaf,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb2,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xf7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xf8,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xf8,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xfa,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xfa,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x61,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x8a,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x8b,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x8b,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x8b,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x8b,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x8b,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x8d,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8d,0x01,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbb,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
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-0x8c,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0x59,0x00,0x00,0x00,
-0xaf,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xaf,0x02,0x00,0x00,0x9e,0x00,0x00,0x00,0xae,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,
-0x0f,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0x47,0x00,0x00,0x00,
-0xb5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0xbc,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbe,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,
-0xb6,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc1,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x2b,0x03,0x00,0x00,
-0x3e,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x28,0x03,0x00,0x00,
-0xc4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0xc7,0x02,0x00,0x00,0x2b,0x03,0x00,0x00,0xb5,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xc3,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xc7,0x02,0x00,0x00,
-0xc2,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xc2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x2c,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
-0xc2,0x02,0x00,0x00,0x26,0x03,0x00,0x00,0xcc,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,
-0x2c,0x03,0x00,0x00,0x60,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xcb,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xcf,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xca,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,
-0x2c,0x03,0x00,0x00,0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
-0xd3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd6,0x02,0x00,0x00,0x64,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,
-0xd4,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,0x2b,0x03,0x00,0x00,
-0x47,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdc,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xde,0x02,0x00,0x00,
-0x68,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,
-0xde,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x2e,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
-0xca,0x02,0x00,0x00,0x24,0x03,0x00,0x00,0xe4,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0xe7,0x02,0x00,0x00,
-0x2e,0x03,0x00,0x00,0xb2,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xe3,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xe7,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
-0xe3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe9,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x30,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0xe2,0x02,0x00,0x00,
-0x22,0x03,0x00,0x00,0xec,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0xef,0x02,0x00,0x00,0x30,0x03,0x00,0x00,
-0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xeb,0x02,0x00,0x00,
-0xec,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xef,0x02,0x00,0x00,0xea,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xea,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf2,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,
-0x30,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0xf5,0x02,0x00,0x00,0xf2,0x02,0x00,0x00,0x36,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xf7,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xf5,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,
-0xf7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf6,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,
-0xdf,0x02,0x00,0x00,0x2e,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0xfb,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
-0xc6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xfc,0x02,0x00,0x00,0xfb,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0xfd,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,
-0xfc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xf7,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf7,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0xb8,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,
-0xea,0x02,0x00,0x00,0xfd,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xfe,0x02,0x00,0x00,0xff,0x02,0x00,0x00,
-0x00,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0xff,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x08,0x03,0x00,0x00,
-0xdf,0x02,0x00,0x00,0x2e,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x0a,0x03,0x00,0x00,0x12,0x00,0x00,0x00,
-0x09,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0b,0x03,0x00,0x00,0x0a,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x03,0x00,0x00,0x08,0x03,0x00,0x00,
-0x0b,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0d,0x03,0x00,0x00,0xbf,0x02,0x00,0x00,0x0c,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0f,0x03,0x00,0x00,
-0x0d,0x03,0x00,0x00,0xd7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x11,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,
-0x30,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x13,0x03,0x00,0x00,0x2b,0x03,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x15,0x03,0x00,0x00,
-0x13,0x03,0x00,0x00,0x2e,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x17,0x03,0x00,0x00,0x15,0x03,0x00,0x00,
-0x16,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x19,0x03,0x00,0x00,0x2c,0x03,0x00,0x00,0x62,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1a,0x03,0x00,0x00,
-0x17,0x03,0x00,0x00,0x19,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,0x1a,0x03,0x00,0x00,
-0x30,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0xc3,0x00,0x00,0x00,
-0x1d,0x03,0x00,0x00,0xc0,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xba,0x00,0x00,0x00,0x1e,0x03,0x00,0x00,
-0x1d,0x03,0x00,0x00,0x41,0x00,0x06,0x00,0x1f,0x03,0x00,0x00,
-0x20,0x03,0x00,0x00,0x04,0x03,0x00,0x00,0x34,0x00,0x00,0x00,
-0x11,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x20,0x03,0x00,0x00,
-0x1e,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x00,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x00,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xec,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xec,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x22,0x03,0x00,0x00,
-0x30,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xeb,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x24,0x03,0x00,0x00,0x2e,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x26,0x03,0x00,0x00,0x2c,0x03,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x28,0x03,0x00,0x00,
-0x2b,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_aligned_m_len = 11936;
-
-unsigned char matmul_f16_aligned_m_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf3,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0xea,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0x4b,0x01,0x00,0x00,
-0x58,0x01,0x00,0x00,0x53,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0xfa,0x00,0x04,0x00,0x87,0x02,0x00,0x00,0x82,0x02,0x00,0x00,
-0x83,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
-0x6f,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,
-0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x8f,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x8d,0x02,0x00,0x00,
-0x8e,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x92,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x93,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0x93,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
-0x92,0x02,0x00,0x00,0x94,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8f,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xb8,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x95,0x02,0x00,0x00,
-0x8e,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x98,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x96,0x02,0x00,0x00,
-0x97,0x02,0x00,0x00,0x98,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x97,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,
-0xa0,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,0x57,0x02,0x00,0x00,
-0xa4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa7,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,
-0xa7,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
-0xb2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xad,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,
-0xad,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb2,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
-0xb2,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc3,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xc0,0x00,0x00,0x00,
-0xb4,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
-0xb6,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0xb7,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0x34,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xb8,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x98,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x98,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xba,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x83,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xc4,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x63,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc0,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5b,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f16_aligned_m_fp32_len = 10512;
-
-unsigned char matmul_f16_aligned_s_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x5b,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0x80,0x01,0x00,0x00,0x8d,0x01,0x00,0x00,
-0xbb,0x02,0x00,0x00,0x04,0x03,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x9d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xaf,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb2,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xf7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xf8,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xf8,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xfa,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xfa,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x61,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x8a,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x8b,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x8b,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x8b,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x8b,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x8b,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x8d,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8d,0x01,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbb,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x01,0x03,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x02,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x02,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x02,0x03,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x04,0x03,0x00,0x00,
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-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,
-0x0f,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0x47,0x00,0x00,0x00,
-0xb5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0xbc,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbe,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,
-0xb6,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc1,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x2b,0x03,0x00,0x00,
-0x3e,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x28,0x03,0x00,0x00,
-0xc4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0xc7,0x02,0x00,0x00,0x2b,0x03,0x00,0x00,0xb5,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xc3,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xc7,0x02,0x00,0x00,
-0xc2,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xc2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x2c,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
-0xc2,0x02,0x00,0x00,0x26,0x03,0x00,0x00,0xcc,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,
-0x2c,0x03,0x00,0x00,0x60,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xcb,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xcf,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xca,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,
-0x2c,0x03,0x00,0x00,0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
-0xd3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd6,0x02,0x00,0x00,0x64,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,
-0xd4,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,0x2b,0x03,0x00,0x00,
-0x47,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdc,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xde,0x02,0x00,0x00,
-0x68,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,
-0xde,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x2e,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
-0xca,0x02,0x00,0x00,0x24,0x03,0x00,0x00,0xe4,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0xe7,0x02,0x00,0x00,
-0x2e,0x03,0x00,0x00,0xb2,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xe3,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xe7,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
-0xe3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe9,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x30,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0xe2,0x02,0x00,0x00,
-0x22,0x03,0x00,0x00,0xec,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0xef,0x02,0x00,0x00,0x30,0x03,0x00,0x00,
-0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xeb,0x02,0x00,0x00,
-0xec,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xef,0x02,0x00,0x00,0xea,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xea,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf2,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,
-0x30,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0xf5,0x02,0x00,0x00,0xf2,0x02,0x00,0x00,0x36,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xf7,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xf5,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,
-0xf7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf6,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,
-0xdf,0x02,0x00,0x00,0x2e,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0xfb,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
-0xc6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xfc,0x02,0x00,0x00,0xfb,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0xfd,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,
-0xfc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xf7,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf7,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0xb8,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,
-0xea,0x02,0x00,0x00,0xfd,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xfe,0x02,0x00,0x00,0xff,0x02,0x00,0x00,
-0x00,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0xff,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x08,0x03,0x00,0x00,
-0xdf,0x02,0x00,0x00,0x2e,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x0a,0x03,0x00,0x00,0x12,0x00,0x00,0x00,
-0x09,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0b,0x03,0x00,0x00,0x0a,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x03,0x00,0x00,0x08,0x03,0x00,0x00,
-0x0b,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0d,0x03,0x00,0x00,0xbf,0x02,0x00,0x00,0x0c,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0f,0x03,0x00,0x00,
-0x0d,0x03,0x00,0x00,0xd7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x11,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,
-0x30,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x13,0x03,0x00,0x00,0x2b,0x03,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x15,0x03,0x00,0x00,
-0x13,0x03,0x00,0x00,0x2e,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x17,0x03,0x00,0x00,0x15,0x03,0x00,0x00,
-0x16,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x19,0x03,0x00,0x00,0x2c,0x03,0x00,0x00,0x62,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1a,0x03,0x00,0x00,
-0x17,0x03,0x00,0x00,0x19,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,0x1a,0x03,0x00,0x00,
-0x30,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0xc3,0x00,0x00,0x00,
-0x1d,0x03,0x00,0x00,0xc0,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xba,0x00,0x00,0x00,0x1e,0x03,0x00,0x00,
-0x1d,0x03,0x00,0x00,0x41,0x00,0x06,0x00,0x1f,0x03,0x00,0x00,
-0x20,0x03,0x00,0x00,0x04,0x03,0x00,0x00,0x34,0x00,0x00,0x00,
-0x11,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x20,0x03,0x00,0x00,
-0x1e,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x00,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x00,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xec,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xec,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x22,0x03,0x00,0x00,
-0x30,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xeb,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x24,0x03,0x00,0x00,0x2e,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x26,0x03,0x00,0x00,0x2c,0x03,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x28,0x03,0x00,0x00,
-0x2b,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_aligned_s_len = 11936;
-
-unsigned char matmul_f16_aligned_s_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf3,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0xea,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0x4b,0x01,0x00,0x00,
-0x58,0x01,0x00,0x00,0x53,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
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-0xb8,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,
-0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x8f,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x8d,0x02,0x00,0x00,
-0x8e,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x92,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x93,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0x93,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
-0x92,0x02,0x00,0x00,0x94,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8f,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xb8,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x95,0x02,0x00,0x00,
-0x8e,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x98,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x96,0x02,0x00,0x00,
-0x97,0x02,0x00,0x00,0x98,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x97,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,
-0xa0,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,0x57,0x02,0x00,0x00,
-0xa4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa7,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,
-0xa7,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
-0xb2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xad,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,
-0xad,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb2,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
-0xb2,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc3,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xc0,0x00,0x00,0x00,
-0xb4,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
-0xb6,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0xb7,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0x34,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xb8,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x98,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x98,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xba,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x83,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xc4,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x63,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc0,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5b,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f16_aligned_s_fp32_len = 10512;
-
-unsigned char matmul_f16_f32_aligned_l_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x65,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0x80,0x01,0x00,0x00,0x8f,0x01,0x00,0x00,
-0xc6,0x02,0x00,0x00,0x0f,0x03,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x9d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xaf,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb2,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xf7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xf8,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xf8,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xfa,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xfa,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x61,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x8c,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x8d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x8d,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x8d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x8d,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x8d,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x8f,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8f,0x01,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xc6,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0c,0x03,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x0d,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x0d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x0d,0x03,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0f,0x03,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0f,0x03,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
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-0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0x34,0x03,0x00,0x00,
-0x6c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xca,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcc,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0x55,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb4,0x02,0x00,0x00,0x8c,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,
-0x59,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0x9e,0x00,0x00,0x00,
-0xb9,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0xbf,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
-0xbf,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc1,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,
-0x47,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xc8,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
-0xc8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xca,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcc,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x35,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0x32,0x03,0x00,0x00,0xcf,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,0x35,0x03,0x00,0x00,
-0xb5,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xce,0x02,0x00,0x00,
-0xcf,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xd2,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,0xce,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd4,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x36,0x03,0x00,0x00,
-0x3e,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,0x30,0x03,0x00,0x00,
-0xd7,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0xda,0x02,0x00,0x00,0x36,0x03,0x00,0x00,0x60,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xd6,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xda,0x02,0x00,0x00,
-0xd5,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xde,0x02,0x00,0x00,0x36,0x03,0x00,0x00,0x61,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xde,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe1,0x02,0x00,0x00,0x64,0x00,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe2,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe6,0x02,0x00,0x00,
-0x35,0x03,0x00,0x00,0x52,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe7,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
-0xe6,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe9,0x02,0x00,0x00,0x68,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xea,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xec,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xec,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x38,0x03,0x00,0x00,
-0x3e,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,0x2e,0x03,0x00,0x00,
-0xef,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0xf2,0x02,0x00,0x00,0x38,0x03,0x00,0x00,0xb2,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xee,0x02,0x00,0x00,0xef,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf2,0x02,0x00,0x00,
-0xed,0x02,0x00,0x00,0xee,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xed,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x3a,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
-0xed,0x02,0x00,0x00,0x2c,0x03,0x00,0x00,0xf7,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,
-0x3a,0x03,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xf6,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xfa,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,
-0xf6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf5,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfd,0x02,0x00,0x00,
-0xe2,0x02,0x00,0x00,0x3a,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0xfd,0x02,0x00,0x00,
-0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x02,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x00,0x03,0x00,0x00,
-0x01,0x03,0x00,0x00,0x02,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x01,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x05,0x03,0x00,0x00,0xea,0x02,0x00,0x00,0x38,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x06,0x03,0x00,0x00,
-0x12,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x07,0x03,0x00,0x00,0x06,0x03,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x08,0x03,0x00,0x00,
-0x05,0x03,0x00,0x00,0x07,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x02,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x02,0x03,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xb8,0x00,0x00,0x00,0x09,0x03,0x00,0x00,
-0x00,0x03,0x00,0x00,0xf5,0x02,0x00,0x00,0x08,0x03,0x00,0x00,
-0x01,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,0x0b,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x09,0x03,0x00,0x00,
-0x0a,0x03,0x00,0x00,0x0b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x13,0x03,0x00,0x00,0xea,0x02,0x00,0x00,0x38,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x15,0x03,0x00,0x00,
-0x12,0x00,0x00,0x00,0x14,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x16,0x03,0x00,0x00,0x15,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x17,0x03,0x00,0x00,
-0x13,0x03,0x00,0x00,0x16,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x18,0x03,0x00,0x00,0xca,0x02,0x00,0x00,
-0x17,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1a,0x03,0x00,0x00,0x18,0x03,0x00,0x00,0xe2,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,
-0x1a,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x1e,0x03,0x00,0x00,0x35,0x03,0x00,0x00,
-0xb2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x20,0x03,0x00,0x00,0x1e,0x03,0x00,0x00,0x38,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x22,0x03,0x00,0x00,
-0x20,0x03,0x00,0x00,0x21,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x24,0x03,0x00,0x00,0x36,0x03,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x25,0x03,0x00,0x00,0x22,0x03,0x00,0x00,0x24,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x27,0x03,0x00,0x00,
-0x25,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc3,0x00,0x00,0x00,0x28,0x03,0x00,0x00,0xc0,0x00,0x00,0x00,
-0x27,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
-0x29,0x03,0x00,0x00,0x28,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
-0x91,0x01,0x00,0x00,0x2a,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,
-0x34,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x2a,0x03,0x00,0x00,0x29,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0b,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x2c,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xef,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xef,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2e,0x03,0x00,0x00,0x38,0x03,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xec,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xee,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd7,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x30,0x03,0x00,0x00,
-0x36,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd6,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcf,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x32,0x03,0x00,0x00,0x35,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xce,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f16_f32_aligned_l_len = 12096;
-
-unsigned char matmul_f16_f32_aligned_l_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf0,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0xea,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0x4b,0x01,0x00,0x00,
-0x59,0x01,0x00,0x00,0x51,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x81,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x85,0x02,0x00,0x00,0x80,0x02,0x00,0x00,
-0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,
-0x6d,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
-0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x8d,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x8b,0x02,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x90,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x91,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,0x91,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x93,0x02,0x00,0x00,
-0x90,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8d,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xb8,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
-0x8b,0x02,0x00,0x00,0x80,0x02,0x00,0x00,0x93,0x02,0x00,0x00,
-0x8c,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x96,0x02,0x00,0x00,
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-0x95,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x9e,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0x55,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa5,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,
-0xa5,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
-0xb2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xab,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xad,0x02,0x00,0x00,
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-0xb0,0x02,0x00,0x00,0xad,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,
-0xb0,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc3,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0xc0,0x00,0x00,0x00,
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-0xb4,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x5b,0x01,0x00,0x00,0xb5,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
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-0xb5,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x96,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x96,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x82,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb7,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x81,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x62,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x62,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,
-0xc1,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x61,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbd,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x59,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f16_f32_aligned_l_fp32_len = 10464;
-
-unsigned char matmul_f16_f32_aligned_m_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x65,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
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-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
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-0xfa,0x00,0x00,0x00,0x80,0x01,0x00,0x00,0x8f,0x01,0x00,0x00,
-0xc6,0x02,0x00,0x00,0x0f,0x03,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
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-0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x47,0x00,0x04,0x00,0xf7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xf8,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x0d,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x4e,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x02,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x04,0x02,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0xfa,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xcd,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcd,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0x34,0x03,0x00,0x00,
-0x6c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xca,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0x55,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb4,0x02,0x00,0x00,0x8c,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,
-0x59,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0x9e,0x00,0x00,0x00,
-0xb9,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0xbf,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
-0xbf,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc1,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,
-0x47,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xc8,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
-0xc8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xca,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcc,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x35,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0x32,0x03,0x00,0x00,0xcf,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,0x35,0x03,0x00,0x00,
-0xb5,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xce,0x02,0x00,0x00,
-0xcf,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xd2,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,0xce,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd4,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x36,0x03,0x00,0x00,
-0x3e,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,0x30,0x03,0x00,0x00,
-0xd7,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0xda,0x02,0x00,0x00,0x36,0x03,0x00,0x00,0x60,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xd6,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xda,0x02,0x00,0x00,
-0xd5,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xde,0x02,0x00,0x00,0x36,0x03,0x00,0x00,0x61,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xde,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe1,0x02,0x00,0x00,0x64,0x00,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe2,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe6,0x02,0x00,0x00,
-0x35,0x03,0x00,0x00,0x52,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe7,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
-0xe6,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe9,0x02,0x00,0x00,0x68,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xea,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xec,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xec,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x38,0x03,0x00,0x00,
-0x3e,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,0x2e,0x03,0x00,0x00,
-0xef,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0xf2,0x02,0x00,0x00,0x38,0x03,0x00,0x00,0xb2,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xee,0x02,0x00,0x00,0xef,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf2,0x02,0x00,0x00,
-0xed,0x02,0x00,0x00,0xee,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xed,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x3a,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
-0xed,0x02,0x00,0x00,0x2c,0x03,0x00,0x00,0xf7,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,
-0x3a,0x03,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xf6,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xfa,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,
-0xf6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf5,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfd,0x02,0x00,0x00,
-0xe2,0x02,0x00,0x00,0x3a,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0xfd,0x02,0x00,0x00,
-0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x02,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x00,0x03,0x00,0x00,
-0x01,0x03,0x00,0x00,0x02,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x01,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x05,0x03,0x00,0x00,0xea,0x02,0x00,0x00,0x38,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x06,0x03,0x00,0x00,
-0x12,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x07,0x03,0x00,0x00,0x06,0x03,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x08,0x03,0x00,0x00,
-0x05,0x03,0x00,0x00,0x07,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x02,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x02,0x03,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xb8,0x00,0x00,0x00,0x09,0x03,0x00,0x00,
-0x00,0x03,0x00,0x00,0xf5,0x02,0x00,0x00,0x08,0x03,0x00,0x00,
-0x01,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,0x0b,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x09,0x03,0x00,0x00,
-0x0a,0x03,0x00,0x00,0x0b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x13,0x03,0x00,0x00,0xea,0x02,0x00,0x00,0x38,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x15,0x03,0x00,0x00,
-0x12,0x00,0x00,0x00,0x14,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x16,0x03,0x00,0x00,0x15,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x17,0x03,0x00,0x00,
-0x13,0x03,0x00,0x00,0x16,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x18,0x03,0x00,0x00,0xca,0x02,0x00,0x00,
-0x17,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1a,0x03,0x00,0x00,0x18,0x03,0x00,0x00,0xe2,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,
-0x1a,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x1e,0x03,0x00,0x00,0x35,0x03,0x00,0x00,
-0xb2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x20,0x03,0x00,0x00,0x1e,0x03,0x00,0x00,0x38,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x22,0x03,0x00,0x00,
-0x20,0x03,0x00,0x00,0x21,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x24,0x03,0x00,0x00,0x36,0x03,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x25,0x03,0x00,0x00,0x22,0x03,0x00,0x00,0x24,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x27,0x03,0x00,0x00,
-0x25,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc3,0x00,0x00,0x00,0x28,0x03,0x00,0x00,0xc0,0x00,0x00,0x00,
-0x27,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
-0x29,0x03,0x00,0x00,0x28,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
-0x91,0x01,0x00,0x00,0x2a,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,
-0x34,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x2a,0x03,0x00,0x00,0x29,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0b,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x2c,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xef,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xef,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2e,0x03,0x00,0x00,0x38,0x03,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xec,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xee,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd7,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x30,0x03,0x00,0x00,
-0x36,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd6,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcf,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x32,0x03,0x00,0x00,0x35,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xce,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f16_f32_aligned_m_len = 12096;
-
-unsigned char matmul_f16_f32_aligned_m_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf0,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0xea,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0x4b,0x01,0x00,0x00,
-0x59,0x01,0x00,0x00,0x51,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0xf8,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x78,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,0x82,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x85,0x02,0x00,0x00,
-0xc5,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x81,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x85,0x02,0x00,0x00,0x80,0x02,0x00,0x00,
-0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,
-0x6d,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
-0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x8d,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x8b,0x02,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x90,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x91,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,0x91,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x93,0x02,0x00,0x00,
-0x90,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8d,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xb8,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
-0x8b,0x02,0x00,0x00,0x80,0x02,0x00,0x00,0x93,0x02,0x00,0x00,
-0x8c,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x96,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x94,0x02,0x00,0x00,
-0x95,0x02,0x00,0x00,0x96,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x95,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
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-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0x55,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa5,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,
-0xa5,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
-0xb2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xab,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xad,0x02,0x00,0x00,
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-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb0,0x02,0x00,0x00,0xad,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,
-0xb0,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc3,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0xc0,0x00,0x00,0x00,
-0xb2,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x5b,0x01,0x00,0x00,0xb5,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x34,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xb5,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x96,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x96,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x82,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb7,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x81,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x62,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x62,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,
-0xc1,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x61,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbd,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x59,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f16_f32_aligned_m_fp32_len = 10464;
-
-unsigned char matmul_f16_f32_aligned_s_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x65,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0x80,0x01,0x00,0x00,0x8f,0x01,0x00,0x00,
-0xc6,0x02,0x00,0x00,0x0f,0x03,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
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-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
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-0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
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-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x47,0x00,0x04,0x00,0x9d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb2,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xf7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xf8,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xfa,0x00,0x00,0x00,
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-0xfa,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x48,0x00,0x04,0x00,0x8d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x8d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x8d,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x8f,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8f,0x01,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x06,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,0x54,0x03,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x66,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x68,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x05,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xac,0x02,0x00,0x00,
-0x4e,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x02,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x04,0x02,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0xfa,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xcd,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcd,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0x34,0x03,0x00,0x00,
-0x6c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xca,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcc,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0x55,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb4,0x02,0x00,0x00,0x8c,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,
-0x59,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0x9e,0x00,0x00,0x00,
-0xb9,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0xbf,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
-0xbf,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc1,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,
-0x47,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xc8,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
-0xc8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xca,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcc,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x35,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0x32,0x03,0x00,0x00,0xcf,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,0x35,0x03,0x00,0x00,
-0xb5,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xce,0x02,0x00,0x00,
-0xcf,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xd2,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,0xce,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd4,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x36,0x03,0x00,0x00,
-0x3e,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,0x30,0x03,0x00,0x00,
-0xd7,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0xda,0x02,0x00,0x00,0x36,0x03,0x00,0x00,0x60,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xd6,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xda,0x02,0x00,0x00,
-0xd5,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xde,0x02,0x00,0x00,0x36,0x03,0x00,0x00,0x61,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xde,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe1,0x02,0x00,0x00,0x64,0x00,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe2,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe6,0x02,0x00,0x00,
-0x35,0x03,0x00,0x00,0x52,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe7,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
-0xe6,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe9,0x02,0x00,0x00,0x68,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xea,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xec,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xec,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x38,0x03,0x00,0x00,
-0x3e,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,0x2e,0x03,0x00,0x00,
-0xef,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0xf2,0x02,0x00,0x00,0x38,0x03,0x00,0x00,0xb2,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xee,0x02,0x00,0x00,0xef,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf2,0x02,0x00,0x00,
-0xed,0x02,0x00,0x00,0xee,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xed,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x3a,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
-0xed,0x02,0x00,0x00,0x2c,0x03,0x00,0x00,0xf7,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,
-0x3a,0x03,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xf6,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xfa,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,
-0xf6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf5,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfd,0x02,0x00,0x00,
-0xe2,0x02,0x00,0x00,0x3a,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0xfd,0x02,0x00,0x00,
-0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x02,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x00,0x03,0x00,0x00,
-0x01,0x03,0x00,0x00,0x02,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x01,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x05,0x03,0x00,0x00,0xea,0x02,0x00,0x00,0x38,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x06,0x03,0x00,0x00,
-0x12,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x07,0x03,0x00,0x00,0x06,0x03,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x08,0x03,0x00,0x00,
-0x05,0x03,0x00,0x00,0x07,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x02,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x02,0x03,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xb8,0x00,0x00,0x00,0x09,0x03,0x00,0x00,
-0x00,0x03,0x00,0x00,0xf5,0x02,0x00,0x00,0x08,0x03,0x00,0x00,
-0x01,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,0x0b,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x09,0x03,0x00,0x00,
-0x0a,0x03,0x00,0x00,0x0b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x13,0x03,0x00,0x00,0xea,0x02,0x00,0x00,0x38,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x15,0x03,0x00,0x00,
-0x12,0x00,0x00,0x00,0x14,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x16,0x03,0x00,0x00,0x15,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x17,0x03,0x00,0x00,
-0x13,0x03,0x00,0x00,0x16,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x18,0x03,0x00,0x00,0xca,0x02,0x00,0x00,
-0x17,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1a,0x03,0x00,0x00,0x18,0x03,0x00,0x00,0xe2,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,
-0x1a,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x1e,0x03,0x00,0x00,0x35,0x03,0x00,0x00,
-0xb2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x20,0x03,0x00,0x00,0x1e,0x03,0x00,0x00,0x38,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x22,0x03,0x00,0x00,
-0x20,0x03,0x00,0x00,0x21,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x24,0x03,0x00,0x00,0x36,0x03,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x25,0x03,0x00,0x00,0x22,0x03,0x00,0x00,0x24,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x27,0x03,0x00,0x00,
-0x25,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc3,0x00,0x00,0x00,0x28,0x03,0x00,0x00,0xc0,0x00,0x00,0x00,
-0x27,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
-0x29,0x03,0x00,0x00,0x28,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
-0x91,0x01,0x00,0x00,0x2a,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,
-0x34,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x2a,0x03,0x00,0x00,0x29,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0b,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x2c,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xef,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xef,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2e,0x03,0x00,0x00,0x38,0x03,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xec,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xee,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd7,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x30,0x03,0x00,0x00,
-0x36,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd6,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcf,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x32,0x03,0x00,0x00,0x35,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xce,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f16_f32_aligned_s_len = 12096;
-
-unsigned char matmul_f16_f32_aligned_s_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf0,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
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-0x7d,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0xb2,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x79,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x7d,0x02,0x00,0x00,
-0x78,0x02,0x00,0x00,0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x78,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x78,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,0x82,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x85,0x02,0x00,0x00,
-0xc5,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x81,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x85,0x02,0x00,0x00,0x80,0x02,0x00,0x00,
-0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,
-0x6d,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
-0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x8d,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x8b,0x02,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x90,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x91,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,0x91,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x93,0x02,0x00,0x00,
-0x90,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8d,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xb8,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
-0x8b,0x02,0x00,0x00,0x80,0x02,0x00,0x00,0x93,0x02,0x00,0x00,
-0x8c,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x96,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x94,0x02,0x00,0x00,
-0x95,0x02,0x00,0x00,0x96,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x95,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9e,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0x55,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa5,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,
-0xa5,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
-0xb2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xab,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xad,0x02,0x00,0x00,
-0xab,0x02,0x00,0x00,0xac,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb0,0x02,0x00,0x00,0xad,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,
-0xb0,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc3,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0xc0,0x00,0x00,0x00,
-0xb2,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x5b,0x01,0x00,0x00,0xb5,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x34,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xb5,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x96,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x96,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x82,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb7,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x81,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x62,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x62,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,
-0xc1,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x61,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbd,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x59,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f16_f32_aligned_s_fp32_len = 10464;
-
-unsigned char matmul_f16_f32_l_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xcd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,0x47,0x01,0x00,0x00,
-0x2e,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x9c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xae,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb1,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xf9,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xfa,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xfa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xfa,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xfc,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xfc,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x16,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x17,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x44,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x45,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x45,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x45,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x47,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x47,0x01,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2e,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x32,0x02,0x00,0x00,
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-0x37,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,
-0x3a,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0xb4,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x36,0x02,0x00,0x00,0x37,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x3a,0x02,0x00,0x00,
-0x35,0x02,0x00,0x00,0x36,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
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-0x3e,0x02,0x00,0x00,0x3f,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x51,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x54,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x54,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x3d,0x02,0x00,0x00,0x96,0x02,0x00,0x00,0x57,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,0x5a,0x02,0x00,0x00,
-0xa0,0x02,0x00,0x00,0xb1,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x56,0x02,0x00,0x00,0x57,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x5a,0x02,0x00,0x00,0x55,0x02,0x00,0x00,
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-0xb7,0x00,0x00,0x00,0x62,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,
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-0xfa,0x00,0x04,0x00,0x68,0x02,0x00,0x00,0x69,0x02,0x00,0x00,
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-0x52,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x6e,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
-0xc5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x6f,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb7,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
-0x6f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x6a,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x6a,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0xb7,0x00,0x00,0x00,0x71,0x02,0x00,0x00,0x68,0x02,0x00,0x00,
-0x5d,0x02,0x00,0x00,0x70,0x02,0x00,0x00,0x69,0x02,0x00,0x00,
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-0x7e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x80,0x02,0x00,0x00,0x32,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x82,0x02,0x00,0x00,
-0x80,0x02,0x00,0x00,0x4a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x84,0x02,0x00,0x00,0x82,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x86,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0xb1,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,
-0x86,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
-0x89,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,
-0x8a,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0x90,0x02,0x00,0x00,0xbf,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x91,0x02,0x00,0x00,
-0x90,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x52,0x01,0x00,0x00,
-0x92,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0x34,0x00,0x00,0x00,
-0x84,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x92,0x02,0x00,0x00,
-0x91,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x73,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x57,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x96,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x54,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x56,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x37,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x37,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x9d,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x34,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x36,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_f32_l_len = 10172;
-
-unsigned char matmul_f16_f32_l_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xc9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0xb7,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,0x64,0x02,0x00,0x00,
-0x59,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,0x65,0x02,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x6f,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x6d,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,
-0x6f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6e,0x02,0x00,0x00,
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-0x7c,0x02,0x00,0x00,0x2e,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x02,0x00,0x00,
-0x7c,0x02,0x00,0x00,0x46,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x80,0x02,0x00,0x00,0x7e,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x82,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0xb1,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x84,0x02,0x00,0x00,
-0x82,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,0x84,0x02,0x00,0x00,
-0x85,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x86,0x02,0x00,0x00,0x88,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0x8c,0x02,0x00,0x00,0xbf,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x52,0x01,0x00,0x00,
-0x8e,0x02,0x00,0x00,0x73,0x02,0x00,0x00,0x34,0x00,0x00,0x00,
-0x80,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x8e,0x02,0x00,0x00,
-0x8d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5b,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x90,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x58,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x53,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x92,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x50,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x52,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x3b,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x38,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x33,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x33,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x30,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x32,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_f32_l_fp32_len = 10100;
-
-unsigned char matmul_f16_f32_m_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xcd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
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-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,
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-0xfa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0xa0,0x02,0x00,0x00,0xb1,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
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-0x15,0x00,0x00,0x00,0x6e,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
-0xc5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x6f,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb7,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
-0x6f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x6a,0x02,0x00,0x00,
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-0x5d,0x02,0x00,0x00,0x70,0x02,0x00,0x00,0x69,0x02,0x00,0x00,
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-0xfa,0x00,0x04,0x00,0x71,0x02,0x00,0x00,0x72,0x02,0x00,0x00,
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-0x7c,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
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-0x80,0x02,0x00,0x00,0x4a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x84,0x02,0x00,0x00,0x82,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x86,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0xb1,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,
-0x86,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
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-0xa2,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0x90,0x02,0x00,0x00,0xbf,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x91,0x02,0x00,0x00,
-0x90,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x52,0x01,0x00,0x00,
-0x92,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0x34,0x00,0x00,0x00,
-0x84,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x92,0x02,0x00,0x00,
-0x91,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x73,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x57,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x96,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x54,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x56,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x37,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x37,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x9d,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x34,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x36,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_f32_m_len = 10172;
-
-unsigned char matmul_f16_f32_m_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xc9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,
-0x47,0x01,0x00,0x00,0x2a,0x02,0x00,0x00,0x73,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x3d,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x52,0x01,0x00,0x00,
-0x8e,0x02,0x00,0x00,0x73,0x02,0x00,0x00,0x34,0x00,0x00,0x00,
-0x80,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x8e,0x02,0x00,0x00,
-0x8d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5b,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x90,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x58,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x53,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x92,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x50,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x52,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x3b,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x38,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x33,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x33,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x30,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x32,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_f32_m_fp32_len = 10100;
-
-unsigned char matmul_f16_f32_s_data[] = {
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-0xf8,0x00,0x02,0x00,0x73,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x57,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x96,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x54,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x56,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x37,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x37,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x9d,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x34,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x36,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_f32_s_len = 10172;
-
-unsigned char matmul_f16_f32_s_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xc9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,
-0x47,0x01,0x00,0x00,0x2a,0x02,0x00,0x00,0x73,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
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-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
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-0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
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-0x47,0x00,0x04,0x00,0xb1,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf9,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xfa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xfa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xfa,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xfc,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xfc,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x16,0x01,0x00,0x00,
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-0xd7,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xd7,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0c,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd4,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xd6,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcf,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcf,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0e,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcc,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xce,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x6c,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x6c,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
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-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x69,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x6b,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x61,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcc,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x12,0x02,0x00,0x00,0x98,0x02,0x00,0x00,0x6c,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xc9,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcb,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x17,0x02,0x00,0x00,0x55,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x18,0x02,0x00,0x00,
-0x8b,0x00,0x00,0x00,0x17,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x1d,0x02,0x00,0x00,0x59,0x00,0x00,0x00,
-0xae,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1e,0x02,0x00,0x00,0x9d,0x00,0x00,0x00,0x1d,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x23,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0x22,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x24,0x02,0x00,0x00,0x23,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x25,0x02,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x24,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x29,0x02,0x00,0x00,0x47,0x00,0x00,0x00,
-0x24,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x2b,0x02,0x00,0x00,0x2a,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x2c,0x02,0x00,0x00,
-0x2b,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x2d,0x02,0x00,0x00,0x29,0x02,0x00,0x00,0x2c,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2e,0x02,0x00,0x00,
-0x25,0x02,0x00,0x00,0x2d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x30,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x30,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,
-0x3e,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
-0x33,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,
-0x36,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0xb4,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x32,0x02,0x00,0x00,0x33,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x36,0x02,0x00,0x00,
-0x31,0x02,0x00,0x00,0x32,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x31,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x38,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x38,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x31,0x02,0x00,0x00,0x94,0x02,0x00,0x00,0x3b,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,0x3e,0x02,0x00,0x00,
-0x9a,0x02,0x00,0x00,0x60,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x3a,0x02,0x00,0x00,0x3b,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x3e,0x02,0x00,0x00,0x39,0x02,0x00,0x00,
-0x3a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x39,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x42,0x02,0x00,0x00,
-0x9a,0x02,0x00,0x00,0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x43,0x02,0x00,0x00,0x18,0x02,0x00,0x00,
-0x42,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x45,0x02,0x00,0x00,0x64,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x46,0x02,0x00,0x00,
-0x43,0x02,0x00,0x00,0x45,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4a,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
-0xb8,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x4b,0x02,0x00,0x00,0x1e,0x02,0x00,0x00,0x4a,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4d,0x02,0x00,0x00,
-0x68,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4e,0x02,0x00,0x00,0x4b,0x02,0x00,0x00,
-0x4d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x50,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x50,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x39,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0x53,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,0x56,0x02,0x00,0x00,
-0x9c,0x02,0x00,0x00,0xb1,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x52,0x02,0x00,0x00,0x53,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x56,0x02,0x00,0x00,0x51,0x02,0x00,0x00,
-0x52,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x51,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x58,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x9e,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0x51,0x02,0x00,0x00,
-0x90,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb7,0x00,0x00,0x00,0x5e,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x5a,0x02,0x00,0x00,
-0x5b,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x5e,0x02,0x00,0x00,0x59,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x61,0x02,0x00,0x00,0x46,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,
-0x64,0x02,0x00,0x00,0x61,0x02,0x00,0x00,0x36,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x66,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x64,0x02,0x00,0x00,0x65,0x02,0x00,0x00,
-0x66,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x65,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x02,0x00,0x00,
-0x4e,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x6a,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
-0xc5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x6b,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb7,0x00,0x00,0x00,0x6c,0x02,0x00,0x00,0x69,0x02,0x00,0x00,
-0x6b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x66,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x66,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0xb7,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,0x64,0x02,0x00,0x00,
-0x59,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,0x65,0x02,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x6f,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x6d,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,
-0x6f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6e,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x77,0x02,0x00,0x00,
-0x4e,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x79,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
-0x78,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x7a,0x02,0x00,0x00,0x79,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
-0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x7c,0x02,0x00,0x00,0x2e,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x02,0x00,0x00,
-0x7c,0x02,0x00,0x00,0x46,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x80,0x02,0x00,0x00,0x7e,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x82,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0xb1,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x84,0x02,0x00,0x00,
-0x82,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,0x84,0x02,0x00,0x00,
-0x85,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x88,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x89,0x02,0x00,0x00,
-0x86,0x02,0x00,0x00,0x88,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0x8c,0x02,0x00,0x00,0xbf,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x52,0x01,0x00,0x00,
-0x8e,0x02,0x00,0x00,0x73,0x02,0x00,0x00,0x34,0x00,0x00,0x00,
-0x80,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x8e,0x02,0x00,0x00,
-0x8d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5b,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x90,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x58,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x53,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x92,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x50,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x52,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x3b,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x38,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x33,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x33,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x30,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x32,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_f32_s_fp32_len = 10100;
-
-unsigned char matmul_f16_l_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
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-0x66,0x02,0x00,0x00,0x67,0x02,0x00,0x00,0x68,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x67,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6b,0x02,0x00,0x00,0x50,0x02,0x00,0x00,
-0x9f,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x6c,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,
-0x6c,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,
-0x6e,0x02,0x00,0x00,0x6b,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x68,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x68,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xb7,0x00,0x00,0x00,
-0x6f,0x02,0x00,0x00,0x66,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,
-0x6e,0x02,0x00,0x00,0x67,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x71,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x6f,0x02,0x00,0x00,0x70,0x02,0x00,0x00,0x71,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x70,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x79,0x02,0x00,0x00,0x50,0x02,0x00,0x00,
-0x9f,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x7b,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,
-0x7b,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x7d,0x02,0x00,0x00,0x79,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x02,0x00,0x00,
-0x30,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x80,0x02,0x00,0x00,0x7e,0x02,0x00,0x00,
-0x48,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x82,0x02,0x00,0x00,0x80,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x84,0x02,0x00,0x00,
-0x9c,0x02,0x00,0x00,0xb1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,0x84,0x02,0x00,0x00,
-0x9f,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x88,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0x87,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
-0x9d,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
-0x8a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,
-0xbf,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xb9,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0x90,0x02,0x00,0x00,0x91,0x02,0x00,0x00,
-0x75,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0x82,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x91,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x71,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x71,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x55,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x55,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
-0x9f,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x52,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x54,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x3d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x3d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x97,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x3a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x3c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x35,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x35,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x32,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x34,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_l_len = 10156;
-
-unsigned char matmul_f16_l_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xca,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,
-0x47,0x01,0x00,0x00,0x2a,0x02,0x00,0x00,0x73,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
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-0x47,0x00,0x04,0x00,0xb1,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf9,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xfa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xfa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xfa,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x48,0x00,0x05,0x00,0x71,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x71,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x73,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
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-0x23,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x25,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,0x24,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x29,0x02,0x00,0x00,
-0x47,0x00,0x00,0x00,0x24,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0x2b,0x02,0x00,0x00,0x2a,0x02,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x2c,0x02,0x00,0x00,0x2b,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2d,0x02,0x00,0x00,0x29,0x02,0x00,0x00,
-0x2c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x2e,0x02,0x00,0x00,0x25,0x02,0x00,0x00,0x2d,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x30,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x30,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x9a,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,
-0x97,0x02,0x00,0x00,0x33,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb7,0x00,0x00,0x00,0x36,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0xb4,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x32,0x02,0x00,0x00,
-0x33,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x36,0x02,0x00,0x00,0x31,0x02,0x00,0x00,0x32,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x31,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x38,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x38,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x31,0x02,0x00,0x00,0x95,0x02,0x00,0x00,
-0x3b,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,
-0x3e,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0x60,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x3a,0x02,0x00,0x00,0x3b,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x3e,0x02,0x00,0x00,
-0x39,0x02,0x00,0x00,0x3a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x39,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x42,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0x61,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x43,0x02,0x00,0x00,
-0x18,0x02,0x00,0x00,0x42,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x45,0x02,0x00,0x00,0x64,0x00,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x46,0x02,0x00,0x00,0x43,0x02,0x00,0x00,0x45,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4a,0x02,0x00,0x00,
-0x9a,0x02,0x00,0x00,0xb8,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4b,0x02,0x00,0x00,0x1e,0x02,0x00,0x00,
-0x4a,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x4d,0x02,0x00,0x00,0x68,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4e,0x02,0x00,0x00,
-0x4b,0x02,0x00,0x00,0x4d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x50,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x50,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x39,0x02,0x00,0x00,0x93,0x02,0x00,0x00,
-0x53,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,
-0x56,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0xb1,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x52,0x02,0x00,0x00,0x53,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x56,0x02,0x00,0x00,
-0x51,0x02,0x00,0x00,0x52,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x51,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x58,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x51,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,0x5e,0x02,0x00,0x00,
-0x9f,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x5a,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x5e,0x02,0x00,0x00,0x59,0x02,0x00,0x00,
-0x5a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x59,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x61,0x02,0x00,0x00,
-0x46,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb7,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x61,0x02,0x00,0x00,
-0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x66,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x64,0x02,0x00,0x00,
-0x65,0x02,0x00,0x00,0x66,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x65,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x69,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x6a,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x6b,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,0x6c,0x02,0x00,0x00,
-0x69,0x02,0x00,0x00,0x6b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x66,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x66,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xb7,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,
-0x64,0x02,0x00,0x00,0x59,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,
-0x65,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x6f,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x6d,0x02,0x00,0x00,
-0x6e,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x77,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x79,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0x78,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,0x79,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,
-0x77,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,0x2e,0x02,0x00,0x00,
-0x7b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x7e,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,0x46,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x80,0x02,0x00,0x00,
-0x7e,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x82,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0xb1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x84,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,
-0x84,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x89,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,
-0x89,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,0xbf,0x00,0x00,0x00,
-0x8b,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x8e,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0x73,0x02,0x00,0x00,
-0x34,0x00,0x00,0x00,0x80,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x8f,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x6f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x91,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x53,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x50,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x52,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3b,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
-0x9b,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x38,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3a,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x33,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x33,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x97,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x30,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x32,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f16_l_fp32_len = 10116;
-
-unsigned char matmul_f16_m_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xcc,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,0x47,0x01,0x00,0x00,
-0x2c,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,0x84,0x02,0x00,0x00,
-0x9f,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x88,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0x87,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
-0x9d,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
-0x8a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,
-0xbf,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xb9,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0x90,0x02,0x00,0x00,0x91,0x02,0x00,0x00,
-0x75,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0x82,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x91,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x71,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x71,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x55,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x55,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
-0x9f,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x52,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x54,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x3d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x3d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x97,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x3a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x3c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x35,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x35,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x32,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x34,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_m_len = 10156;
-
-unsigned char matmul_f16_m_fp32_data[] = {
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-0xca,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
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-0x42,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0x61,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x43,0x02,0x00,0x00,
-0x18,0x02,0x00,0x00,0x42,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x45,0x02,0x00,0x00,0x64,0x00,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x46,0x02,0x00,0x00,0x43,0x02,0x00,0x00,0x45,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4a,0x02,0x00,0x00,
-0x9a,0x02,0x00,0x00,0xb8,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4b,0x02,0x00,0x00,0x1e,0x02,0x00,0x00,
-0x4a,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x4d,0x02,0x00,0x00,0x68,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4e,0x02,0x00,0x00,
-0x4b,0x02,0x00,0x00,0x4d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x50,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x50,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x39,0x02,0x00,0x00,0x93,0x02,0x00,0x00,
-0x53,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,
-0x56,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0xb1,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x52,0x02,0x00,0x00,0x53,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x56,0x02,0x00,0x00,
-0x51,0x02,0x00,0x00,0x52,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x51,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x58,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x51,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,0x5e,0x02,0x00,0x00,
-0x9f,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x5a,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x5e,0x02,0x00,0x00,0x59,0x02,0x00,0x00,
-0x5a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x59,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x61,0x02,0x00,0x00,
-0x46,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb7,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x61,0x02,0x00,0x00,
-0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x66,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x64,0x02,0x00,0x00,
-0x65,0x02,0x00,0x00,0x66,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x65,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x69,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x6a,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x6b,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,0x6c,0x02,0x00,0x00,
-0x69,0x02,0x00,0x00,0x6b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x66,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x66,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xb7,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,
-0x64,0x02,0x00,0x00,0x59,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,
-0x65,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x6f,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x6d,0x02,0x00,0x00,
-0x6e,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x77,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x79,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0x78,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,0x79,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,
-0x77,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,0x2e,0x02,0x00,0x00,
-0x7b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x7e,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,0x46,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x80,0x02,0x00,0x00,
-0x7e,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x82,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0xb1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x84,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,
-0x84,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x89,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,
-0x89,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,0xbf,0x00,0x00,0x00,
-0x8b,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x8e,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0x73,0x02,0x00,0x00,
-0x34,0x00,0x00,0x00,0x80,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x8f,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x6f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x91,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x53,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x50,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x52,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3b,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
-0x9b,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x38,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3a,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x33,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x33,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x97,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x30,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x32,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f16_m_fp32_len = 10116;
-
-unsigned char matmul_f16_s_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xcc,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,0x47,0x01,0x00,0x00,
-0x2c,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x9c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xae,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb1,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xf9,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xfa,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x3c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x35,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x35,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x32,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x34,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_s_len = 10156;
-
-unsigned char matmul_f16_s_fp32_data[] = {
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-0xfa,0x00,0x04,0x00,0x5e,0x02,0x00,0x00,0x59,0x02,0x00,0x00,
-0x5a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x59,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x61,0x02,0x00,0x00,
-0x46,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb7,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x61,0x02,0x00,0x00,
-0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x66,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x64,0x02,0x00,0x00,
-0x65,0x02,0x00,0x00,0x66,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x65,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x69,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x6a,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x6b,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,0x6c,0x02,0x00,0x00,
-0x69,0x02,0x00,0x00,0x6b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x66,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x66,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xb7,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,
-0x64,0x02,0x00,0x00,0x59,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,
-0x65,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x6f,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x6d,0x02,0x00,0x00,
-0x6e,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x77,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x79,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0x78,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,0x79,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,
-0x77,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,0x2e,0x02,0x00,0x00,
-0x7b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x7e,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,0x46,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x80,0x02,0x00,0x00,
-0x7e,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x82,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0xb1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x84,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,
-0x84,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x89,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,
-0x89,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,0xbf,0x00,0x00,0x00,
-0x8b,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x8e,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0x73,0x02,0x00,0x00,
-0x34,0x00,0x00,0x00,0x80,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x8f,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x6f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x91,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x53,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x50,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x52,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3b,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
-0x9b,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x38,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3a,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x33,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x33,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x97,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x30,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x32,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f16_s_fp32_len = 10116;
-
-unsigned char matmul_f32_aligned_l_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x6a,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x88,0x01,0x00,0x00,
-0x95,0x01,0x00,0x00,0xcb,0x02,0x00,0x00,0x14,0x03,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9d,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xaf,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb2,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf7,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xf8,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xf8,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xfa,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xfa,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x69,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6a,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x92,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x93,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x93,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x93,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x93,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x93,0x01,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x95,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x95,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xcb,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x03,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x12,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x03,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,0x59,0x03,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x6b,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x6d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x0a,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,
-0x53,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x07,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x09,0x02,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0xff,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xcd,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcd,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0x39,0x03,0x00,0x00,
-0x6c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xca,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcc,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,0x55,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb9,0x02,0x00,0x00,0x8c,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x59,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,0x9e,0x00,0x00,0x00,
-0xbe,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0xc4,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,
-0xc4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc6,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,
-0x47,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0xcc,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xcd,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xce,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
-0xcd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcf,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,0xce,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd1,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x3a,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0x37,0x03,0x00,0x00,0xd4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,0x3a,0x03,0x00,0x00,
-0xb5,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xd3,0x02,0x00,0x00,
-0xd4,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xd7,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd9,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x3b,0x03,0x00,0x00,
-0x3e,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,0x35,0x03,0x00,0x00,
-0xdc,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0xdf,0x02,0x00,0x00,0x3b,0x03,0x00,0x00,0x60,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xdb,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xdf,0x02,0x00,0x00,
-0xda,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xda,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe3,0x02,0x00,0x00,0x3b,0x03,0x00,0x00,0x61,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe6,0x02,0x00,0x00,0x64,0x00,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe7,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xeb,0x02,0x00,0x00,
-0x3a,0x03,0x00,0x00,0x57,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xec,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
-0xeb,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xee,0x02,0x00,0x00,0x68,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xef,0x02,0x00,0x00,
-0xec,0x02,0x00,0x00,0xee,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xf1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf1,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x3d,0x03,0x00,0x00,
-0x3e,0x00,0x00,0x00,0xda,0x02,0x00,0x00,0x33,0x03,0x00,0x00,
-0xf4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0xf7,0x02,0x00,0x00,0x3d,0x03,0x00,0x00,0xb2,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xf3,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf7,0x02,0x00,0x00,
-0xf2,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xf9,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf9,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x3f,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
-0xf2,0x02,0x00,0x00,0x31,0x03,0x00,0x00,0xfc,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0xff,0x02,0x00,0x00,
-0x3f,0x03,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xfb,0x02,0x00,0x00,0xfc,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xff,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,
-0xfb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xfa,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x02,0x03,0x00,0x00,
-0xe7,0x02,0x00,0x00,0x3f,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0x05,0x03,0x00,0x00,0x02,0x03,0x00,0x00,
-0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x07,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x05,0x03,0x00,0x00,
-0x06,0x03,0x00,0x00,0x07,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x06,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0a,0x03,0x00,0x00,0xef,0x02,0x00,0x00,0x3d,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x0b,0x03,0x00,0x00,
-0x12,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x03,0x00,0x00,0x0b,0x03,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x0d,0x03,0x00,0x00,
-0x0a,0x03,0x00,0x00,0x0c,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x07,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x07,0x03,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xb8,0x00,0x00,0x00,0x0e,0x03,0x00,0x00,
-0x05,0x03,0x00,0x00,0xfa,0x02,0x00,0x00,0x0d,0x03,0x00,0x00,
-0x06,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,0x10,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x0e,0x03,0x00,0x00,
-0x0f,0x03,0x00,0x00,0x10,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0f,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x18,0x03,0x00,0x00,0xef,0x02,0x00,0x00,0x3d,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x1a,0x03,0x00,0x00,
-0x12,0x00,0x00,0x00,0x19,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x1b,0x03,0x00,0x00,0x1a,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,
-0x18,0x03,0x00,0x00,0x1b,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x1d,0x03,0x00,0x00,0xcf,0x02,0x00,0x00,
-0x1c,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1f,0x03,0x00,0x00,0x1d,0x03,0x00,0x00,0xe7,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x21,0x03,0x00,0x00,
-0x1f,0x03,0x00,0x00,0x3f,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x23,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,
-0xb2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x25,0x03,0x00,0x00,0x23,0x03,0x00,0x00,0x3d,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x27,0x03,0x00,0x00,
-0x25,0x03,0x00,0x00,0x26,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x29,0x03,0x00,0x00,0x3b,0x03,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x2a,0x03,0x00,0x00,0x27,0x03,0x00,0x00,0x29,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2c,0x03,0x00,0x00,
-0x2a,0x03,0x00,0x00,0x3f,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc3,0x00,0x00,0x00,0x2d,0x03,0x00,0x00,0xc0,0x00,0x00,0x00,
-0x2c,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
-0x2e,0x03,0x00,0x00,0x2d,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
-0xfc,0x00,0x00,0x00,0x2f,0x03,0x00,0x00,0x14,0x03,0x00,0x00,
-0x34,0x00,0x00,0x00,0x21,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x2f,0x03,0x00,0x00,0x2e,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x10,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x10,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xfc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x31,0x03,0x00,0x00,0x3f,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x33,0x03,0x00,0x00,0x3d,0x03,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xf1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xdc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xdc,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x35,0x03,0x00,0x00,
-0x3b,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xdb,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x37,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd3,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f32_aligned_l_len = 12168;
-
-unsigned char matmul_f32_aligned_l_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xe9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
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-0x7b,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0x7e,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x7a,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x7e,0x02,0x00,0x00,
-0x79,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x79,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x81,0x02,0x00,0x00,0x66,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x84,0x02,0x00,0x00,
-0x81,0x02,0x00,0x00,0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x86,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x84,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0x86,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x85,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x89,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x8a,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,
-0x8a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x89,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x86,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xb8,0x00,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x84,0x02,0x00,0x00,0x79,0x02,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x8f,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x8d,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x97,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x99,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9b,0x02,0x00,0x00,0x97,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,
-0x4e,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0x66,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xb2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa6,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,
-0xba,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,
-0xa8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xab,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0xc3,0x00,0x00,0x00,0xac,0x02,0x00,0x00,
-0xc0,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xba,0x00,0x00,0x00,0xad,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0xfa,0x00,0x00,0x00,0xae,0x02,0x00,0x00,
-0x93,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xae,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x8f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x73,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x70,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x53,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x50,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x52,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f32_aligned_l_fp32_len = 10348;
-
-unsigned char matmul_f32_aligned_m_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x6a,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x88,0x01,0x00,0x00,
-0x95,0x01,0x00,0x00,0xcb,0x02,0x00,0x00,0x14,0x03,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9d,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xaf,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb2,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf7,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xf8,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xf8,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xfa,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xfa,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x69,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6a,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x92,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x93,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x93,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x93,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x93,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x93,0x01,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x95,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x95,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xcb,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x03,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x12,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
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-0x75,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x6e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x6e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,0x59,0x03,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x6b,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x6d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x0a,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,
-0x53,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x07,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x09,0x02,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0xff,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xcd,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcd,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0x39,0x03,0x00,0x00,
-0x6c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xca,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcc,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,0x55,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb9,0x02,0x00,0x00,0x8c,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x59,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,0x9e,0x00,0x00,0x00,
-0xbe,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0xc4,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,
-0xc4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc6,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,
-0x47,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0xcc,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xcd,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xce,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
-0xcd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcf,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,0xce,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd1,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x3a,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0x37,0x03,0x00,0x00,0xd4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,0x3a,0x03,0x00,0x00,
-0xb5,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xd3,0x02,0x00,0x00,
-0xd4,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xd7,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd9,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x3b,0x03,0x00,0x00,
-0x3e,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,0x35,0x03,0x00,0x00,
-0xdc,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0xdf,0x02,0x00,0x00,0x3b,0x03,0x00,0x00,0x60,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xdb,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xdf,0x02,0x00,0x00,
-0xda,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xda,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe3,0x02,0x00,0x00,0x3b,0x03,0x00,0x00,0x61,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe6,0x02,0x00,0x00,0x64,0x00,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe7,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xeb,0x02,0x00,0x00,
-0x3a,0x03,0x00,0x00,0x57,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xec,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
-0xeb,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xee,0x02,0x00,0x00,0x68,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xef,0x02,0x00,0x00,
-0xec,0x02,0x00,0x00,0xee,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xf1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf1,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x3d,0x03,0x00,0x00,
-0x3e,0x00,0x00,0x00,0xda,0x02,0x00,0x00,0x33,0x03,0x00,0x00,
-0xf4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0xf7,0x02,0x00,0x00,0x3d,0x03,0x00,0x00,0xb2,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xf3,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf7,0x02,0x00,0x00,
-0xf2,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xf9,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf9,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x3f,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
-0xf2,0x02,0x00,0x00,0x31,0x03,0x00,0x00,0xfc,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0xff,0x02,0x00,0x00,
-0x3f,0x03,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xfb,0x02,0x00,0x00,0xfc,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xff,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,
-0xfb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xfa,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x02,0x03,0x00,0x00,
-0xe7,0x02,0x00,0x00,0x3f,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0x05,0x03,0x00,0x00,0x02,0x03,0x00,0x00,
-0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x07,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x05,0x03,0x00,0x00,
-0x06,0x03,0x00,0x00,0x07,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x06,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0a,0x03,0x00,0x00,0xef,0x02,0x00,0x00,0x3d,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x0b,0x03,0x00,0x00,
-0x12,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x03,0x00,0x00,0x0b,0x03,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x0d,0x03,0x00,0x00,
-0x0a,0x03,0x00,0x00,0x0c,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x07,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x07,0x03,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xb8,0x00,0x00,0x00,0x0e,0x03,0x00,0x00,
-0x05,0x03,0x00,0x00,0xfa,0x02,0x00,0x00,0x0d,0x03,0x00,0x00,
-0x06,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,0x10,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x0e,0x03,0x00,0x00,
-0x0f,0x03,0x00,0x00,0x10,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0f,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x18,0x03,0x00,0x00,0xef,0x02,0x00,0x00,0x3d,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x1a,0x03,0x00,0x00,
-0x12,0x00,0x00,0x00,0x19,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x1b,0x03,0x00,0x00,0x1a,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,
-0x18,0x03,0x00,0x00,0x1b,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x1d,0x03,0x00,0x00,0xcf,0x02,0x00,0x00,
-0x1c,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1f,0x03,0x00,0x00,0x1d,0x03,0x00,0x00,0xe7,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x21,0x03,0x00,0x00,
-0x1f,0x03,0x00,0x00,0x3f,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x23,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,
-0xb2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x25,0x03,0x00,0x00,0x23,0x03,0x00,0x00,0x3d,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x27,0x03,0x00,0x00,
-0x25,0x03,0x00,0x00,0x26,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x29,0x03,0x00,0x00,0x3b,0x03,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x2a,0x03,0x00,0x00,0x27,0x03,0x00,0x00,0x29,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2c,0x03,0x00,0x00,
-0x2a,0x03,0x00,0x00,0x3f,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc3,0x00,0x00,0x00,0x2d,0x03,0x00,0x00,0xc0,0x00,0x00,0x00,
-0x2c,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
-0x2e,0x03,0x00,0x00,0x2d,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
-0xfc,0x00,0x00,0x00,0x2f,0x03,0x00,0x00,0x14,0x03,0x00,0x00,
-0x34,0x00,0x00,0x00,0x21,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x2f,0x03,0x00,0x00,0x2e,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x10,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x10,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xfc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x31,0x03,0x00,0x00,0x3f,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x33,0x03,0x00,0x00,0x3d,0x03,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xf1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xdc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xdc,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x35,0x03,0x00,0x00,
-0x3b,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xdb,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x37,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd3,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f32_aligned_m_len = 12168;
-
-unsigned char matmul_f32_aligned_m_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xe9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
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-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x71,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,
-0x7b,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0x7e,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x7a,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x7e,0x02,0x00,0x00,
-0x79,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x79,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x81,0x02,0x00,0x00,0x66,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x84,0x02,0x00,0x00,
-0x81,0x02,0x00,0x00,0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x86,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x84,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0x86,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x85,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x89,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x8a,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,
-0x8a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x89,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x86,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xb8,0x00,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x84,0x02,0x00,0x00,0x79,0x02,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x8f,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x8d,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x97,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x99,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9b,0x02,0x00,0x00,0x97,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,
-0x4e,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0x66,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xb2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa6,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,
-0xba,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,
-0xa8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xab,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0xc3,0x00,0x00,0x00,0xac,0x02,0x00,0x00,
-0xc0,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xba,0x00,0x00,0x00,0xad,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0xfa,0x00,0x00,0x00,0xae,0x02,0x00,0x00,
-0x93,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xae,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x8f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x73,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x70,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x53,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x50,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x52,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f32_aligned_m_fp32_len = 10348;
-
-unsigned char matmul_f32_aligned_s_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x6a,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x88,0x01,0x00,0x00,
-0x95,0x01,0x00,0x00,0xcb,0x02,0x00,0x00,0x14,0x03,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9d,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xaf,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb2,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf7,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xf8,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xf8,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xfa,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xfa,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x69,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6a,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x92,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x93,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x93,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x93,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x93,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x93,0x01,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x95,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x95,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xcb,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x03,0x00,0x00,
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-0x76,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xad,0x02,0x00,0x00,0x5d,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x73,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x75,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x6e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x6e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,0x59,0x03,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x6b,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x6d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x0a,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,
-0x53,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x07,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x09,0x02,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0xff,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xcd,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcd,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0x39,0x03,0x00,0x00,
-0x6c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xca,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcc,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,0x55,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb9,0x02,0x00,0x00,0x8c,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x59,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,0x9e,0x00,0x00,0x00,
-0xbe,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0xc4,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,
-0xc4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc6,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,
-0x47,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0xcc,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xcd,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xce,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
-0xcd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcf,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,0xce,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd1,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x3a,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0x37,0x03,0x00,0x00,0xd4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,0x3a,0x03,0x00,0x00,
-0xb5,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xd3,0x02,0x00,0x00,
-0xd4,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xd7,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd9,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x3b,0x03,0x00,0x00,
-0x3e,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,0x35,0x03,0x00,0x00,
-0xdc,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0xdf,0x02,0x00,0x00,0x3b,0x03,0x00,0x00,0x60,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xdb,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xdf,0x02,0x00,0x00,
-0xda,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xda,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe3,0x02,0x00,0x00,0x3b,0x03,0x00,0x00,0x61,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe6,0x02,0x00,0x00,0x64,0x00,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe7,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xeb,0x02,0x00,0x00,
-0x3a,0x03,0x00,0x00,0x57,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xec,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
-0xeb,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xee,0x02,0x00,0x00,0x68,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xef,0x02,0x00,0x00,
-0xec,0x02,0x00,0x00,0xee,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xf1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf1,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x3d,0x03,0x00,0x00,
-0x3e,0x00,0x00,0x00,0xda,0x02,0x00,0x00,0x33,0x03,0x00,0x00,
-0xf4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0xf7,0x02,0x00,0x00,0x3d,0x03,0x00,0x00,0xb2,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xf3,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf7,0x02,0x00,0x00,
-0xf2,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xf9,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf9,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x3f,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
-0xf2,0x02,0x00,0x00,0x31,0x03,0x00,0x00,0xfc,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0xff,0x02,0x00,0x00,
-0x3f,0x03,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xfb,0x02,0x00,0x00,0xfc,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xff,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,
-0xfb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xfa,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x02,0x03,0x00,0x00,
-0xe7,0x02,0x00,0x00,0x3f,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0x05,0x03,0x00,0x00,0x02,0x03,0x00,0x00,
-0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x07,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x05,0x03,0x00,0x00,
-0x06,0x03,0x00,0x00,0x07,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x06,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0a,0x03,0x00,0x00,0xef,0x02,0x00,0x00,0x3d,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x0b,0x03,0x00,0x00,
-0x12,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x03,0x00,0x00,0x0b,0x03,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x0d,0x03,0x00,0x00,
-0x0a,0x03,0x00,0x00,0x0c,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x07,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x07,0x03,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xb8,0x00,0x00,0x00,0x0e,0x03,0x00,0x00,
-0x05,0x03,0x00,0x00,0xfa,0x02,0x00,0x00,0x0d,0x03,0x00,0x00,
-0x06,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,0x10,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x0e,0x03,0x00,0x00,
-0x0f,0x03,0x00,0x00,0x10,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0f,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x18,0x03,0x00,0x00,0xef,0x02,0x00,0x00,0x3d,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x1a,0x03,0x00,0x00,
-0x12,0x00,0x00,0x00,0x19,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x1b,0x03,0x00,0x00,0x1a,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,
-0x18,0x03,0x00,0x00,0x1b,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x1d,0x03,0x00,0x00,0xcf,0x02,0x00,0x00,
-0x1c,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1f,0x03,0x00,0x00,0x1d,0x03,0x00,0x00,0xe7,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x21,0x03,0x00,0x00,
-0x1f,0x03,0x00,0x00,0x3f,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x23,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,
-0xb2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x25,0x03,0x00,0x00,0x23,0x03,0x00,0x00,0x3d,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x27,0x03,0x00,0x00,
-0x25,0x03,0x00,0x00,0x26,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x29,0x03,0x00,0x00,0x3b,0x03,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x2a,0x03,0x00,0x00,0x27,0x03,0x00,0x00,0x29,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2c,0x03,0x00,0x00,
-0x2a,0x03,0x00,0x00,0x3f,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc3,0x00,0x00,0x00,0x2d,0x03,0x00,0x00,0xc0,0x00,0x00,0x00,
-0x2c,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
-0x2e,0x03,0x00,0x00,0x2d,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
-0xfc,0x00,0x00,0x00,0x2f,0x03,0x00,0x00,0x14,0x03,0x00,0x00,
-0x34,0x00,0x00,0x00,0x21,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x2f,0x03,0x00,0x00,0x2e,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x10,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x10,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xfc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x31,0x03,0x00,0x00,0x3f,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x33,0x03,0x00,0x00,0x3d,0x03,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xf1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xdc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xdc,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x35,0x03,0x00,0x00,
-0x3b,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xdb,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x37,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd3,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f32_aligned_s_len = 12168;
-
-unsigned char matmul_f32_aligned_s_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xe9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x73,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x76,0x02,0x00,0x00,0x71,0x02,0x00,0x00,0x72,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x78,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x71,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,
-0x7b,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0x7e,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x7a,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x7e,0x02,0x00,0x00,
-0x79,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x79,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x81,0x02,0x00,0x00,0x66,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x84,0x02,0x00,0x00,
-0x81,0x02,0x00,0x00,0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x86,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x84,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0x86,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x85,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x89,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x8a,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,
-0x8a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x89,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x86,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xb8,0x00,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x84,0x02,0x00,0x00,0x79,0x02,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x8f,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x8d,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x97,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x99,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9b,0x02,0x00,0x00,0x97,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,
-0x4e,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0x66,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xb2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa6,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,
-0xba,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,
-0xa8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xab,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0xc3,0x00,0x00,0x00,0xac,0x02,0x00,0x00,
-0xc0,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xba,0x00,0x00,0x00,0xad,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0xfa,0x00,0x00,0x00,0xae,0x02,0x00,0x00,
-0x93,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xae,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x8f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x73,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x70,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x53,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x50,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x52,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f32_aligned_s_fp32_len = 10348;
-
-unsigned char matmul_f32_l_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xcd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0xf1,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x3d,0x01,0x00,0x00,
-0x48,0x01,0x00,0x00,0x2e,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9c,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xae,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb1,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf9,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xfa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xfa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xfa,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xfc,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xfc,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x17,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x18,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x45,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x46,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x46,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x46,0x01,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x48,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x48,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2e,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x74,0x02,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x75,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
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-0xf8,0x00,0x02,0x00,0xd0,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x6d,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x6d,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x14,0x02,0x00,0x00,
-0xb6,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x6a,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x6c,0x01,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x62,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xcc,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x16,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0x6c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc9,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcb,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x1b,0x02,0x00,0x00,0x55,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1c,0x02,0x00,0x00,0x8b,0x00,0x00,0x00,0x1b,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x21,0x02,0x00,0x00,
-0x59,0x00,0x00,0x00,0xae,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x22,0x02,0x00,0x00,0x9d,0x00,0x00,0x00,
-0x21,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x27,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0x26,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x28,0x02,0x00,0x00,
-0x27,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x29,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,0x28,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2d,0x02,0x00,0x00,
-0x47,0x00,0x00,0x00,0x28,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0x2f,0x02,0x00,0x00,0x2e,0x02,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x30,0x02,0x00,0x00,0x2f,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x31,0x02,0x00,0x00,0x2d,0x02,0x00,0x00,
-0x30,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x32,0x02,0x00,0x00,0x29,0x02,0x00,0x00,0x31,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x34,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x34,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x9d,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,
-0x9a,0x02,0x00,0x00,0x37,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb7,0x00,0x00,0x00,0x3a,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0xb4,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x36,0x02,0x00,0x00,
-0x37,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x3a,0x02,0x00,0x00,0x35,0x02,0x00,0x00,0x36,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x35,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x35,0x02,0x00,0x00,0x98,0x02,0x00,0x00,
-0x3f,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,
-0x42,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,0x60,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x3e,0x02,0x00,0x00,0x3f,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x42,0x02,0x00,0x00,
-0x3d,0x02,0x00,0x00,0x3e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x3d,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x46,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,0x61,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x47,0x02,0x00,0x00,
-0x1c,0x02,0x00,0x00,0x46,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x49,0x02,0x00,0x00,0x64,0x00,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x4a,0x02,0x00,0x00,0x47,0x02,0x00,0x00,0x49,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4e,0x02,0x00,0x00,
-0x9d,0x02,0x00,0x00,0xba,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4f,0x02,0x00,0x00,0x22,0x02,0x00,0x00,
-0x4e,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x51,0x02,0x00,0x00,0x68,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x52,0x02,0x00,0x00,
-0x4f,0x02,0x00,0x00,0x51,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x54,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x54,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x3d,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
-0x57,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,
-0x5a,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0xb1,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x56,0x02,0x00,0x00,0x57,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x5a,0x02,0x00,0x00,
-0x55,0x02,0x00,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x55,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x55,0x02,0x00,0x00,0x94,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,0x62,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x5e,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x62,0x02,0x00,0x00,0x5d,0x02,0x00,0x00,
-0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x65,0x02,0x00,0x00,
-0x4a,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb7,0x00,0x00,0x00,0x68,0x02,0x00,0x00,0x65,0x02,0x00,0x00,
-0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x6a,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x68,0x02,0x00,0x00,
-0x69,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x69,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x6d,0x02,0x00,0x00,0x52,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x6e,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,0x70,0x02,0x00,0x00,
-0x6d,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x6a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6a,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xb7,0x00,0x00,0x00,0x71,0x02,0x00,0x00,
-0x68,0x02,0x00,0x00,0x5d,0x02,0x00,0x00,0x70,0x02,0x00,0x00,
-0x69,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x73,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x71,0x02,0x00,0x00,
-0x72,0x02,0x00,0x00,0x73,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x72,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x7b,0x02,0x00,0x00,0x52,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x7d,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x7e,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7f,0x02,0x00,0x00,
-0x7b,0x02,0x00,0x00,0x7e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x80,0x02,0x00,0x00,0x32,0x02,0x00,0x00,
-0x7f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x82,0x02,0x00,0x00,0x80,0x02,0x00,0x00,0x4a,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x84,0x02,0x00,0x00,
-0x82,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0xb1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x88,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
-0x88,0x02,0x00,0x00,0x89,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,
-0x8d,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0x90,0x02,0x00,0x00,0xbf,0x00,0x00,0x00,
-0x8f,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
-0x91,0x02,0x00,0x00,0x90,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x07,0x01,0x00,0x00,0x92,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
-0x34,0x00,0x00,0x00,0x84,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x92,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x73,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x94,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x57,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x96,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x54,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3e,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x37,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x37,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9a,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x34,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x36,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f32_l_len = 10164;
-
-unsigned char matmul_f32_l_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xc6,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
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-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x77,0x02,0x00,0x00,
-0x76,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x78,0x02,0x00,0x00,0x74,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x79,0x02,0x00,0x00,
-0x2b,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,0x79,0x02,0x00,0x00,
-0x43,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x7d,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7f,0x02,0x00,0x00,
-0x96,0x02,0x00,0x00,0xb1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x81,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x83,0x02,0x00,0x00,0x81,0x02,0x00,0x00,0x82,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x85,0x02,0x00,0x00,
-0x97,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,0x83,0x02,0x00,0x00,
-0x85,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x88,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x89,0x02,0x00,0x00,
-0xbf,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xb9,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0x06,0x01,0x00,0x00,0x8b,0x02,0x00,0x00,
-0x70,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0x7d,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x8b,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x6c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x58,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x55,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x50,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x50,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x38,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x38,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x91,0x02,0x00,0x00,0x97,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x35,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x37,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x30,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x30,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x2d,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x2f,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f32_l_fp32_len = 10048;
-
-unsigned char matmul_f32_m_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xcd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0xf1,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x3d,0x01,0x00,0x00,
-0x48,0x01,0x00,0x00,0x2e,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9c,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xae,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb1,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf9,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xfa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xfa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xfa,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xfc,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xfc,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x17,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x18,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x45,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x46,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x46,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x46,0x01,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x48,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x48,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2e,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x74,0x02,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x75,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x75,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x75,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x77,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x77,0x02,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x1e,0x00,0x10,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x09,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x37,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
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-0xb7,0x00,0x00,0x00,0x3a,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
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-0x54,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x54,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x3d,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
-0x57,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,
-0x5a,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0xb1,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x56,0x02,0x00,0x00,0x57,0x02,0x00,0x00,
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-0x55,0x02,0x00,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x55,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,
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-0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,0x62,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x5e,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x62,0x02,0x00,0x00,0x5d,0x02,0x00,0x00,
-0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,
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-0x4a,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb7,0x00,0x00,0x00,0x68,0x02,0x00,0x00,0x65,0x02,0x00,0x00,
-0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x6a,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x68,0x02,0x00,0x00,
-0x69,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x69,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x6d,0x02,0x00,0x00,0x52,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x6e,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,0x70,0x02,0x00,0x00,
-0x6d,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x6a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6a,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xb7,0x00,0x00,0x00,0x71,0x02,0x00,0x00,
-0x68,0x02,0x00,0x00,0x5d,0x02,0x00,0x00,0x70,0x02,0x00,0x00,
-0x69,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x73,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x71,0x02,0x00,0x00,
-0x72,0x02,0x00,0x00,0x73,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x72,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x7b,0x02,0x00,0x00,0x52,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x7d,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x7e,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7f,0x02,0x00,0x00,
-0x7b,0x02,0x00,0x00,0x7e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x80,0x02,0x00,0x00,0x32,0x02,0x00,0x00,
-0x7f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x82,0x02,0x00,0x00,0x80,0x02,0x00,0x00,0x4a,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x84,0x02,0x00,0x00,
-0x82,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0xb1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x88,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
-0x88,0x02,0x00,0x00,0x89,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,
-0x8d,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0x90,0x02,0x00,0x00,0xbf,0x00,0x00,0x00,
-0x8f,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
-0x91,0x02,0x00,0x00,0x90,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x07,0x01,0x00,0x00,0x92,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
-0x34,0x00,0x00,0x00,0x84,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x92,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x73,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x94,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x57,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x96,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x54,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3e,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x37,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x37,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9a,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x34,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x36,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f32_m_len = 10164;
-
-unsigned char matmul_f32_m_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xc6,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
-0x3a,0x01,0x00,0x00,0x45,0x01,0x00,0x00,0x27,0x02,0x00,0x00,
-0x70,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x99,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x38,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x38,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x91,0x02,0x00,0x00,0x97,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x35,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x37,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x30,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x30,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x2d,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x2f,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f32_m_fp32_len = 10048;
-
-unsigned char matmul_f32_s_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xcd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0xf1,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x3d,0x01,0x00,0x00,
-0x48,0x01,0x00,0x00,0x2e,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
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-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
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-0xf6,0x00,0x04,0x00,0x56,0x02,0x00,0x00,0x57,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x5a,0x02,0x00,0x00,
-0x55,0x02,0x00,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x55,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x55,0x02,0x00,0x00,0x94,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,0x62,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x5e,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x62,0x02,0x00,0x00,0x5d,0x02,0x00,0x00,
-0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x65,0x02,0x00,0x00,
-0x4a,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb7,0x00,0x00,0x00,0x68,0x02,0x00,0x00,0x65,0x02,0x00,0x00,
-0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x6a,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x68,0x02,0x00,0x00,
-0x69,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x69,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x6d,0x02,0x00,0x00,0x52,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x6e,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,0x70,0x02,0x00,0x00,
-0x6d,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x6a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6a,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xb7,0x00,0x00,0x00,0x71,0x02,0x00,0x00,
-0x68,0x02,0x00,0x00,0x5d,0x02,0x00,0x00,0x70,0x02,0x00,0x00,
-0x69,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x73,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x71,0x02,0x00,0x00,
-0x72,0x02,0x00,0x00,0x73,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x72,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x7b,0x02,0x00,0x00,0x52,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x7d,0x02,0x00,0x00,
-0x12,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x7e,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7f,0x02,0x00,0x00,
-0x7b,0x02,0x00,0x00,0x7e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x80,0x02,0x00,0x00,0x32,0x02,0x00,0x00,
-0x7f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x82,0x02,0x00,0x00,0x80,0x02,0x00,0x00,0x4a,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x84,0x02,0x00,0x00,
-0x82,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0xb1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x88,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
-0x88,0x02,0x00,0x00,0x89,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,
-0x8d,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0x90,0x02,0x00,0x00,0xbf,0x00,0x00,0x00,
-0x8f,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
-0x91,0x02,0x00,0x00,0x90,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x07,0x01,0x00,0x00,0x92,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
-0x34,0x00,0x00,0x00,0x84,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x92,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x73,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x94,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x57,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x96,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x54,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3e,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x37,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x37,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9a,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x34,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x36,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f32_s_len = 10164;
-
-unsigned char matmul_f32_s_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xc6,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
-0x3a,0x01,0x00,0x00,0x45,0x01,0x00,0x00,0x27,0x02,0x00,0x00,
-0x70,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x9c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xae,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb1,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xf8,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xf9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xf9,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xf9,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xfb,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xfb,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x14,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x42,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x43,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x43,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x43,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x45,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x45,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x27,0x02,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6d,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x6e,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x95,0x02,0x00,0x00,0x6c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc9,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xcb,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x14,0x02,0x00,0x00,
-0x55,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x15,0x02,0x00,0x00,0x8b,0x00,0x00,0x00,
-0x14,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1a,0x02,0x00,0x00,0x59,0x00,0x00,0x00,0xae,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1b,0x02,0x00,0x00,
-0x9d,0x00,0x00,0x00,0x1a,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x20,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
-0x1f,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x21,0x02,0x00,0x00,0x20,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x22,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x21,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x26,0x02,0x00,0x00,0x47,0x00,0x00,0x00,0x21,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x28,0x02,0x00,0x00,
-0x27,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x29,0x02,0x00,0x00,0x28,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2a,0x02,0x00,0x00,
-0x26,0x02,0x00,0x00,0x29,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x02,0x00,0x00,0x22,0x02,0x00,0x00,
-0x2a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x2d,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x2d,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x96,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
-0xcb,0x00,0x00,0x00,0x93,0x02,0x00,0x00,0x30,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,0x33,0x02,0x00,0x00,
-0x96,0x02,0x00,0x00,0xb4,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x2f,0x02,0x00,0x00,0x30,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x33,0x02,0x00,0x00,0x2e,0x02,0x00,0x00,
-0x2f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x2e,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x35,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x35,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x97,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0x2e,0x02,0x00,0x00,
-0x91,0x02,0x00,0x00,0x38,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb7,0x00,0x00,0x00,0x3b,0x02,0x00,0x00,0x97,0x02,0x00,0x00,
-0x60,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x37,0x02,0x00,0x00,
-0x38,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x3b,0x02,0x00,0x00,0x36,0x02,0x00,0x00,0x37,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x36,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3f,0x02,0x00,0x00,0x97,0x02,0x00,0x00,
-0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x40,0x02,0x00,0x00,0x15,0x02,0x00,0x00,0x3f,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x42,0x02,0x00,0x00,
-0x64,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x43,0x02,0x00,0x00,0x40,0x02,0x00,0x00,
-0x42,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x47,0x02,0x00,0x00,0x96,0x02,0x00,0x00,0xb5,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x48,0x02,0x00,0x00,
-0x1b,0x02,0x00,0x00,0x47,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4a,0x02,0x00,0x00,0x68,0x00,0x00,0x00,
-0xb1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x4b,0x02,0x00,0x00,0x48,0x02,0x00,0x00,0x4a,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x4d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x4d,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x99,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0x36,0x02,0x00,0x00,
-0x8f,0x02,0x00,0x00,0x50,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb7,0x00,0x00,0x00,0x53,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
-0xb1,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x4f,0x02,0x00,0x00,
-0x50,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x53,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,0x4f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x4e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x55,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x55,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x4e,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
-0x58,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,
-0x5b,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x57,0x02,0x00,0x00,0x58,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x5b,0x02,0x00,0x00,
-0x56,0x02,0x00,0x00,0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x56,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5e,0x02,0x00,0x00,0x43,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,0x61,0x02,0x00,0x00,
-0x5e,0x02,0x00,0x00,0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x63,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x61,0x02,0x00,0x00,0x62,0x02,0x00,0x00,0x63,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x62,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x66,0x02,0x00,0x00,0x4b,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x67,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x68,0x02,0x00,0x00,
-0x67,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,
-0x69,0x02,0x00,0x00,0x66,0x02,0x00,0x00,0x68,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x63,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x63,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xb7,0x00,0x00,0x00,
-0x6a,0x02,0x00,0x00,0x61,0x02,0x00,0x00,0x56,0x02,0x00,0x00,
-0x69,0x02,0x00,0x00,0x62,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x6c,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x6a,0x02,0x00,0x00,0x6b,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x6b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x74,0x02,0x00,0x00,0x4b,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x76,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0x75,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x77,0x02,0x00,0x00,
-0x76,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x78,0x02,0x00,0x00,0x74,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x79,0x02,0x00,0x00,
-0x2b,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,0x79,0x02,0x00,0x00,
-0x43,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x7d,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7f,0x02,0x00,0x00,
-0x96,0x02,0x00,0x00,0xb1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x81,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x83,0x02,0x00,0x00,0x81,0x02,0x00,0x00,0x82,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x85,0x02,0x00,0x00,
-0x97,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,0x83,0x02,0x00,0x00,
-0x85,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x88,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x89,0x02,0x00,0x00,
-0xbf,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xb9,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0x06,0x01,0x00,0x00,0x8b,0x02,0x00,0x00,
-0x70,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0x7d,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x8b,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x6c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x58,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x55,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x50,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x50,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x38,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x38,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x91,0x02,0x00,0x00,0x97,0x02,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x35,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x37,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x30,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x30,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x2d,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x2f,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f32_s_fp32_len = 10048;
-
-unsigned char mul_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x3e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x86,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
 0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
 0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
 0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x73,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
 0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
 0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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+0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
+0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x2f,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
+0x2e,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x2e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x84,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x2f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
+0x3b,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x43,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x45,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x45,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
+0x35,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
+0x49,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x4b,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x4e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x56,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x57,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x56,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x5b,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x5b,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x65,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x67,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x67,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x69,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
+0x6e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x76,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x7e,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x18,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x7e,0x00,0x00,0x00,0x81,0x00,0x00,0x00,
+0x73,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x81,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x84,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x84,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t get_rows_f32_f32_len = 2976;
+
+unsigned char get_rows_q4_0_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x41,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x4f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x66,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x67,0x00,0x00,0x00,
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+0x06,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
+0x89,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x53,0x00,0x00,0x00,
+0x8d,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,
+0x8d,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8f,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x90,0x00,0x00,0x00,
+0x8a,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x53,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
+0x93,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x95,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
+0x95,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x53,0x00,0x00,0x00,
+0x99,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x98,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,
+0x99,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9b,0x00,0x00,0x00,0x56,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,
+0x96,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x53,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
+0x9e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xa0,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0xa0,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa2,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x90,0x00,0x00,0x00,
+0xa7,0x00,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xab,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
+0xab,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
+0xab,0x00,0x00,0x00,0x41,0x00,0x07,0x00,0x20,0x00,0x00,0x00,
+0xe0,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0xa8,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x12,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,
+0xe1,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x2a,0x00,0x00,0x00,
+0xe7,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0xa8,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,
+0xe7,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xe9,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
+0x2f,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
+0x33,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0xef,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x50,0x00,0x05,0x00,
+0x09,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
+0xef,0x00,0x00,0x00,0x83,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
+0xf2,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
+0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
+0xf2,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,
+0xb1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc2,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
+0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
+0xf4,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x12,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x20,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
+0xbd,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xc6,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
+0xc2,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x08,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x20,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xd0,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd3,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_q4_0_len = 3704;
+
+unsigned char get_rows_q4_0_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xf7,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x41,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x4f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x66,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x67,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x67,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
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+0x84,0x00,0x00,0x00,0x89,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x53,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
+0x8c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x8e,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x8e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x90,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x53,0x00,0x00,0x00,0x94,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x95,0x00,0x00,0x00,0x94,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x96,0x00,0x00,0x00,
+0x4b,0x00,0x00,0x00,0x95,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x53,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
+0x98,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x9a,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
+0x9a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9c,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x53,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,
+0xa1,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa7,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
+0x90,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
+0xa6,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xac,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
+0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0x41,0x00,0x07,0x00,
+0x20,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
+0xdf,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0xe1,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
+0x2a,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0xac,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0xe7,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
+0xe8,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
+0xe8,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
+0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0xeb,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x83,0x00,0x05,0x00,
+0x09,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0xf6,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
+0xf3,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
+0xa2,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
+0xac,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0xc4,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0xc5,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
+0xbd,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xc6,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
+0xc2,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x08,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xc5,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xcf,0x00,0x00,0x00,
+0xce,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xd2,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd2,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_q4_0_f32_len = 3688;
+
+unsigned char get_rows_q4_1_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x16,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x49,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x57,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x57,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x57,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x57,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x57,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x57,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x57,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x57,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x57,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x57,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x57,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6e,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x6f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x6f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x6f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x71,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x71,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
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+0x06,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
+0x96,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x98,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x97,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x5b,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x5b,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x59,0x00,0x00,0x00,
+0xa0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xa2,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,
+0xa2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa4,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x5b,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,
+0x64,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,
+0xa9,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xaf,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0xae,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
+0x98,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
+0xae,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb4,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,
+0x4f,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,0x41,0x00,0x07,0x00,
+0x20,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
+0xe9,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x41,0x00,0x07,0x00,
+0x20,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
+0xef,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0xf1,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
+0x32,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0xb4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0xf7,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
+0xf8,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
+0xf8,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
+0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0xff,0x00,0x00,0x00,
+0xfb,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,
+0x09,0x00,0x00,0x00,0x01,0x01,0x00,0x00,0xff,0x00,0x00,0x00,
+0xeb,0x00,0x00,0x00,0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
+0x03,0x01,0x00,0x00,0xf1,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x04,0x01,0x00,0x00,
+0x01,0x01,0x00,0x00,0x03,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
+0xb9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xca,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,
+0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
+0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x12,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x20,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
+0xc5,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xce,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
+0xca,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x08,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x04,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0xd7,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x20,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xd8,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xdb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xdb,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_q4_1_len = 3788;
+
+unsigned char get_rows_q4_1_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x16,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x49,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x57,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x57,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x57,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x57,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x57,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x57,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x57,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x57,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x57,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x57,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x57,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6e,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x6f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
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+0x06,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,
+0x90,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x92,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0x91,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x5b,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x97,0x00,0x00,0x00,
+0x64,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x92,0x00,0x00,0x00,
+0x97,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x5b,0x00,0x00,0x00,
+0x9c,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,
+0x9c,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9e,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x5b,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,
+0x5e,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
+0xa3,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x5b,0x00,0x00,0x00,
+0xa7,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
+0xa7,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa9,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
+0xa4,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
+0xae,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb0,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,
+0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,
+0x4f,0x00,0x00,0x00,0xae,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,
+0x4e,0x00,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb9,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,
+0x41,0x00,0x07,0x00,0x20,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0xe9,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
+0x41,0x00,0x07,0x00,0x20,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0xef,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0x41,0x00,0x08,0x00,0x32,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,
+0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
+0xf6,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf9,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
+0xf9,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xfc,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
+0xfc,0x00,0x00,0x00,0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
+0xfe,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
+0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
+0xfe,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x50,0x00,0x05,0x00,
+0x09,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0xf0,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
+0x03,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x02,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,
+0xaa,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xca,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,
+0xb4,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0xcd,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
+0xc5,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xce,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
+0xca,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x08,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x03,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xcd,0x00,0x00,0x00,
+0xd7,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0xd4,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xd7,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xda,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xda,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_q4_1_f32_len = 3772;
+
+unsigned char get_rows_q5_0_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x4d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
+0x7f,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x1b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x72,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x7d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x7d,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x7d,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x7d,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x7d,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x7d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x7d,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x7d,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
+0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x7d,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
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+0xa8,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0xa9,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
+0x97,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,0xab,0x00,0x00,0x00,
+0xaa,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xac,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x81,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
+0xaf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xb1,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
+0xb1,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x81,0x00,0x00,0x00,
+0xb5,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,
+0xb5,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb7,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x81,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
+0xba,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xbe,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x81,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
+0x7f,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
+0x79,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x81,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
+0xc6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xc8,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
+0xc8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xca,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x81,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
+0x7f,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
+0x8a,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd5,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
+0xbe,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
+0xd4,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xda,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,
+0x76,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x41,0x00,0x07,0x00,
+0x23,0x00,0x00,0x00,0x10,0x01,0x00,0x00,0x1d,0x00,0x00,0x00,
+0x1f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0x11,0x01,0x00,0x00,
+0x10,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0x12,0x01,0x00,0x00,0x11,0x01,0x00,0x00,0x41,0x00,0x08,0x00,
+0x2c,0x00,0x00,0x00,0x16,0x01,0x00,0x00,0x1d,0x00,0x00,0x00,
+0x1f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x17,0x01,0x00,0x00,0x16,0x01,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x18,0x01,0x00,0x00,0x17,0x01,0x00,0x00,
+0xc4,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x19,0x01,0x00,0x00,
+0x18,0x01,0x00,0x00,0x30,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
+0x2c,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0x1d,0x00,0x00,0x00,
+0x1f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0x1f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x1e,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,
+0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x20,0x01,0x00,0x00,
+0x19,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x23,0x01,0x00,0x00,0x20,0x01,0x00,0x00,
+0xda,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x24,0x01,0x00,0x00,0x23,0x01,0x00,0x00,0x3f,0x00,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x25,0x01,0x00,0x00,
+0x24,0x01,0x00,0x00,0x17,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x1e,0x00,0x00,0x00,0x26,0x01,0x00,0x00,0x25,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x29,0x01,0x00,0x00,
+0xda,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x2a,0x01,0x00,0x00,0x20,0x01,0x00,0x00,
+0x29,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x01,0x00,0x00,0x2a,0x01,0x00,0x00,0x17,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,
+0x2b,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x51,0x00,0x00,0x00,
+0x32,0x01,0x00,0x00,0x1d,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x33,0x01,0x00,0x00,
+0x32,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x34,0x01,0x00,0x00,0x33,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x36,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
+0x56,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x39,0x01,0x00,0x00,0x26,0x01,0x00,0x00,0xc5,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,0x36,0x01,0x00,0x00,
+0x39,0x01,0x00,0x00,0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0x3b,0x01,0x00,0x00,0x3a,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3d,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x40,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,0xc5,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x41,0x01,0x00,0x00,0x3d,0x01,0x00,0x00,
+0x40,0x01,0x00,0x00,0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0x42,0x01,0x00,0x00,0x41,0x01,0x00,0x00,0x50,0x00,0x05,0x00,
+0x09,0x00,0x00,0x00,0x43,0x01,0x00,0x00,0x3b,0x01,0x00,0x00,
+0x42,0x01,0x00,0x00,0x83,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
+0x45,0x01,0x00,0x00,0x43,0x01,0x00,0x00,0x4c,0x01,0x00,0x00,
+0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x47,0x01,0x00,0x00,
+0x45,0x01,0x00,0x00,0x12,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xdf,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
+0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
+0x47,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x12,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x23,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
+0xeb,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xf4,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x08,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x47,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0xfd,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x23,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
+0x1f,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xfe,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x01,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x01,0x01,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_q5_0_len = 4232;
+
+unsigned char get_rows_q5_0_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x4c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
+0x7f,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x1b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x72,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x7d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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+0x08,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x46,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xf3,0x00,0x00,0x00,
+0xfd,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
+0xfa,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xfd,0x00,0x00,0x00,
+0xfc,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x00,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_q5_0_f32_len = 4216;
+
+unsigned char get_rows_q5_1_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x43,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
+0x7b,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x16,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x79,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x79,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x79,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x79,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x79,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x79,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x79,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x79,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x79,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x79,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x90,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x91,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x91,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x91,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x93,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x93,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xe4,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xe5,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xe5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xe5,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe7,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xe7,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xfc,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,
+0x16,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x20,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1b,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x6b,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x6b,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
+0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x6e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x71,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x1e,0x00,0x1e,0x00,0x79,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x7a,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
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+0x13,0x00,0x00,0x00,0x2d,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,
+0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,
+0x2d,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x30,0x01,0x00,0x00,0x2e,0x01,0x00,0x00,0x51,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x33,0x01,0x00,0x00,
+0x20,0x01,0x00,0x00,0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x34,0x01,0x00,0x00,0x30,0x01,0x00,0x00,0x33,0x01,0x00,0x00,
+0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x35,0x01,0x00,0x00,
+0x34,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x37,0x01,0x00,0x00,0x2e,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,
+0x26,0x01,0x00,0x00,0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x3b,0x01,0x00,0x00,0x37,0x01,0x00,0x00,0x3a,0x01,0x00,0x00,
+0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,
+0x3b,0x01,0x00,0x00,0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
+0x3d,0x01,0x00,0x00,0x35,0x01,0x00,0x00,0x3c,0x01,0x00,0x00,
+0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x3f,0x01,0x00,0x00,
+0x3d,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,0x50,0x00,0x05,0x00,
+0x09,0x00,0x00,0x00,0x41,0x01,0x00,0x00,0x15,0x01,0x00,0x00,
+0x15,0x01,0x00,0x00,0x81,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
+0x42,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,0x41,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0xee,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0xee,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x20,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0xec,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xf0,0x00,0x00,0x00,
+0xef,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf6,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
+0x42,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x12,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x20,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
+0xe7,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xfa,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xfd,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xfd,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t get_rows_q5_1_len = 4128;
+
+unsigned char get_rows_q5_1_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x42,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
+0x7b,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x16,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x79,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x79,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x79,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x79,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x79,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x79,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x79,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x79,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x79,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x79,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x90,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x91,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x91,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x91,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x93,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x93,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xe4,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xe5,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xe5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xe5,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe7,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xe7,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xfb,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,
+0x16,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
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+0xd2,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,
+0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
+0x72,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
+0x71,0x00,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xdb,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
+0x41,0x00,0x07,0x00,0x20,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0x0d,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,0x73,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0x0e,0x01,0x00,0x00,0x0d,0x01,0x00,0x00,
+0x41,0x00,0x07,0x00,0x20,0x00,0x00,0x00,0x12,0x01,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0x13,0x01,0x00,0x00,0x12,0x01,0x00,0x00,0x73,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0x14,0x01,0x00,0x00,0x13,0x01,0x00,0x00,
+0x41,0x00,0x07,0x00,0x31,0x00,0x00,0x00,0x18,0x01,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x19,0x01,0x00,0x00,0x18,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x1c,0x01,0x00,0x00,0x19,0x01,0x00,0x00,
+0xd6,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1d,0x01,0x00,0x00,0x1c,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,
+0x1d,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x1b,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x22,0x01,0x00,0x00,
+0xd6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x23,0x01,0x00,0x00,0x19,0x01,0x00,0x00,
+0x22,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x24,0x01,0x00,0x00,0x23,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x25,0x01,0x00,0x00,
+0x24,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x4c,0x00,0x00,0x00,
+0x2b,0x01,0x00,0x00,0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0xd2,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,
+0x2b,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x2d,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,0x2d,0x01,0x00,0x00,
+0x51,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x32,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,0xc5,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x33,0x01,0x00,0x00,0x2f,0x01,0x00,0x00,
+0x32,0x01,0x00,0x00,0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0x34,0x01,0x00,0x00,0x33,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x36,0x01,0x00,0x00,0x2d,0x01,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x39,0x01,0x00,0x00,0x25,0x01,0x00,0x00,0xc5,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,0x36,0x01,0x00,0x00,
+0x39,0x01,0x00,0x00,0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0x3b,0x01,0x00,0x00,0x3a,0x01,0x00,0x00,0x50,0x00,0x05,0x00,
+0x09,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
+0x3b,0x01,0x00,0x00,0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
+0x3e,0x01,0x00,0x00,0x3c,0x01,0x00,0x00,0x0e,0x01,0x00,0x00,
+0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x40,0x01,0x00,0x00,
+0x14,0x01,0x00,0x00,0x14,0x01,0x00,0x00,0x81,0x00,0x05,0x00,
+0x09,0x00,0x00,0x00,0x41,0x01,0x00,0x00,0x3e,0x01,0x00,0x00,
+0x40,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xea,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
+0xea,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x08,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x41,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xef,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0xec,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xf0,0x00,0x00,0x00,
+0xee,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf6,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
+0x41,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0xef,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xf9,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xfc,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xfc,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_q5_1_f32_len = 4112;
+
+unsigned char get_rows_q8_0_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xf3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x4f,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x40,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
+0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x4d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x64,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x65,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x65,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
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+0x91,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x93,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
+0x93,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x51,0x00,0x00,0x00,
+0x97,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x96,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x98,0x00,0x00,0x00,
+0x97,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x99,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x98,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,
+0x94,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x51,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
+0x9c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,
+0x9e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa0,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,
+0x46,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,
+0xa4,0x00,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa8,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,
+0xa8,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xae,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
+0xa8,0x00,0x00,0x00,0x41,0x00,0x07,0x00,0x20,0x00,0x00,0x00,
+0xdc,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0xa5,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x12,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xde,0x00,0x00,0x00,
+0xdd,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x29,0x00,0x00,0x00,
+0xe3,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0xa5,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
+0xe3,0x00,0x00,0x00,0x72,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0xe5,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
+0xa9,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
+0x29,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0xeb,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0xed,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x72,0x00,0x04,0x00,
+0x1b,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0xee,0x00,0x00,0x00,0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0xde,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,
+0xae,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xbf,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,
+0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,
+0xf2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x12,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x20,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
+0xba,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xc3,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
+0xbf,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x08,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x20,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xcd,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd0,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_q8_0_len = 3644;
+
+unsigned char get_rows_q8_0_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xf2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x4f,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x40,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
+0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x4d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x64,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x65,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x65,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x67,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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+0x96,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x98,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x98,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9a,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x99,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x51,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,
+0x4f,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,
+0x5a,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,
+0x9f,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa4,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
+0x8e,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa9,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xae,0x00,0x00,0x00,
+0x46,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x41,0x00,0x07,0x00,
+0x20,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,
+0xdb,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0xdd,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
+0x29,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0xa9,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0xe3,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0x72,0x00,0x04,0x00,
+0x1b,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
+0xe4,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xea,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x41,0x00,0x08,0x00,0x29,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
+0x72,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
+0xec,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0x50,0x00,0x05,0x00,
+0x09,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
+0xee,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
+0xf1,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
+0xa0,0x00,0x00,0x00,0xae,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
+0xa9,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0xc1,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0xc2,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
+0xba,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xc3,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
+0xbf,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x08,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xc2,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0xc9,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xcc,0x00,0x00,0x00,
+0xcb,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xcf,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xcf,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_q8_0_f32_len = 3628;
+
+unsigned char matmul_f16_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xd8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
+0x06,0x01,0x00,0x00,0x46,0x01,0x00,0x00,0x51,0x01,0x00,0x00,
+0x38,0x02,0x00,0x00,0x81,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
 0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
 0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
 0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
 0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
 0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x20,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x04,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x04,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x06,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x20,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4e,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x4f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x01,0x00,0x00,
 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x20,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x22,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x22,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x24,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x25,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x25,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x25,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x27,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x27,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2c,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x2d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2f,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2f,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x4f,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x51,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x01,0x00,0x00,
 0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x38,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x7e,0x02,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x7f,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x7f,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x7f,0x02,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x81,0x02,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x81,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
 0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
 0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
 0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
@@ -42956,279 +11219,50010 @@ unsigned char mul_f32_data[] = {
 0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
 0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,
+0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x10,0x00,
 0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x1a,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x1f,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x20,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x24,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x25,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x26,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x29,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x2c,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x2d,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x2e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x31,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,
-0x3b,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x55,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x61,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x78,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
+0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,
+0x84,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
+0x84,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0xbd,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x61,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0xbe,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,
+0xbd,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0xbf,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
+0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0xc0,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0xc1,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0xc3,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
+0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
+0x84,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
+0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x1c,0x00,0x04,0x00,0xc7,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
+0xc6,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xc8,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0xc3,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0xcc,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0xf6,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
+0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
+0xf9,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0xfa,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0xf9,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0xfa,0x00,0x00,0x00,
+0xfb,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
+0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
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+0x06,0x00,0x00,0x00,0x89,0x02,0x00,0x00,0x85,0x02,0x00,0x00,
+0x88,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8a,0x02,0x00,0x00,0x3c,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
+0x8a,0x02,0x00,0x00,0x54,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,
+0xad,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x90,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,
+0x90,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0x92,0x02,0x00,0x00,
+0x93,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x96,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x97,0x02,0x00,0x00,
+0x94,0x02,0x00,0x00,0x96,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0x97,0x02,0x00,0x00,
+0xad,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,
+0x9a,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,0x99,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
+0x9a,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x9c,0x02,0x00,0x00,
+0x9d,0x02,0x00,0x00,0x81,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
+0x8e,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x9d,0x02,0x00,0x00,
+0x9b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x69,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x69,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
+0xad,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x66,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x68,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x61,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa1,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x60,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x49,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x49,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x46,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x48,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x41,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x41,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x3e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x40,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_f16_len = 10316;
+
+unsigned char matmul_f16_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xf5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
+0x07,0x01,0x00,0x00,0x49,0x01,0x00,0x00,0x51,0x01,0x00,0x00,
+0x55,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x05,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x05,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x07,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x21,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x22,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4e,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x4f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x4f,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x4f,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x51,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x55,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x9b,0x02,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x9c,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x9c,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x9c,0x02,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9e,0x02,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x9e,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x10,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x19,0x02,0x00,0x00,
+0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x4d,0x02,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x55,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x9b,0x02,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x9c,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0x20,0x00,0x04,0x00,
+0x9d,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x9d,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0xa3,0x02,0x00,0x00,0x05,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,0x84,0x00,0x00,0x00,
+0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0xb9,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
+0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x05,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0xc9,0x00,0x00,0x00,
+0xca,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0xab,0x01,0x00,0x00,0xac,0x01,0x00,0x00,0x07,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0xd9,0x01,0x00,0x00,0xda,0x01,0x00,0x00,
+0x07,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
 0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
 0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
 0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
 0x18,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
 0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0xae,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x1d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x1c,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x3c,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x1d,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x29,0x00,0x00,0x00,
-0x2a,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x31,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x33,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x33,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x29,0x00,0x00,0x00,
-0x35,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x29,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x38,0x00,0x00,0x00,
-0x37,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3c,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3c,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_f32_len = 1456;
+0x18,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x29,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
+0x29,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x31,0x00,0x00,0x00,
+0x25,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x31,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x39,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,
+0x39,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x43,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x41,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x0d,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x4b,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x0d,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x4f,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x56,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x65,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
+0x5e,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
+0x6e,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x74,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
+0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
+0x4f,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
+0x7e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x83,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
+0x83,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x85,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x89,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,
+0x48,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,
+0x84,0x00,0x00,0x00,0x0c,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x8f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
+0x89,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x92,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x94,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x95,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
+0x94,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x97,0x00,0x00,0x00,0x43,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x99,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0x99,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,
+0x97,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
+0x9b,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9e,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0x85,0x00,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,
+0x9e,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0xa2,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xa4,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0xa4,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa8,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
+0xa8,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
+0xac,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xaf,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x85,0x00,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
+0xaf,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xb2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xb2,0x00,0x00,0x00,
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+0xaf,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb4,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,
+0xb4,0x02,0x00,0x00,0xca,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcd,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,0xca,0x00,0x00,0x00,
+0xb6,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0xb8,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0xb9,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
+0x35,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xba,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x9a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x86,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xbc,0x02,0x00,0x00,0xca,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x83,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x85,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x66,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x66,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
+0xc6,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x63,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x65,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc2,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5d,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
 
-unsigned char mul_mat_vec_f16_f32_data[] = {
+};
+const uint64_t matmul_f16_aligned_len = 10812;
+
+unsigned char matmul_f16_aligned_fp32_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xb6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0xfb,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x49,0x01,0x00,0x00,
+0x50,0x01,0x00,0x00,0x39,0x02,0x00,0x00,0x82,0x02,0x00,0x00,
 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x28,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x28,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x28,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x28,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4e,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x4f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x62,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x63,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x63,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x65,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xa7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xa8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xa8,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xa8,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xaa,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xaa,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb2,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x17,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x1e,0x00,0x05,0x00,
-0x28,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x29,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x29,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
-0x30,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x35,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x4d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x4e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x4f,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x50,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x62,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x63,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x64,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x64,0x00,0x00,0x00,0x65,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x89,0x00,0x00,0x00,
-0x08,0x01,0x00,0x00,0x1d,0x00,0x03,0x00,0xa7,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xa8,0x00,0x00,0x00,
-0xa7,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xa9,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0xa9,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x2c,0x00,0x06,0x00,0x0a,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
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+0x7b,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x71,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd7,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd7,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x22,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0x6d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x27,0x02,0x00,0x00,
+0x56,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x28,0x02,0x00,0x00,0x97,0x00,0x00,0x00,
+0x27,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x2d,0x02,0x00,0x00,0x5a,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2e,0x02,0x00,0x00,
+0xa8,0x00,0x00,0x00,0x2d,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x32,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
+0x31,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x33,0x02,0x00,0x00,0x32,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x34,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x33,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x38,0x02,0x00,0x00,0x48,0x00,0x00,0x00,0x33,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x3a,0x02,0x00,0x00,
+0x39,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x3b,0x02,0x00,0x00,0x3a,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3c,0x02,0x00,0x00,
+0x38,0x02,0x00,0x00,0x3b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3d,0x02,0x00,0x00,0x34,0x02,0x00,0x00,
+0x3c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,0x42,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x45,0x02,0x00,0x00,
+0xa9,0x02,0x00,0x00,0xbf,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x41,0x02,0x00,0x00,0x42,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x45,0x02,0x00,0x00,0x40,0x02,0x00,0x00,
+0x41,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x40,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x47,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x47,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xaa,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x40,0x02,0x00,0x00,
+0xa4,0x02,0x00,0x00,0x4a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x4d,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
+0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x49,0x02,0x00,0x00,
+0x4a,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x4d,0x02,0x00,0x00,0x48,0x02,0x00,0x00,0x49,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x48,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x51,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x52,0x02,0x00,0x00,0x28,0x02,0x00,0x00,0x51,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x54,0x02,0x00,0x00,
+0x65,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x55,0x02,0x00,0x00,0x52,0x02,0x00,0x00,
+0x54,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x59,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xc8,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5a,0x02,0x00,0x00,
+0x2e,0x02,0x00,0x00,0x59,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,0x69,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5d,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,0x5c,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xac,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x48,0x02,0x00,0x00,
+0xa2,0x02,0x00,0x00,0x62,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x65,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
+0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x61,0x02,0x00,0x00,
+0x62,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x65,0x02,0x00,0x00,0x60,0x02,0x00,0x00,0x61,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x60,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x67,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x67,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x60,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
+0x6a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x6d,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x69,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x6d,0x02,0x00,0x00,
+0x68,0x02,0x00,0x00,0x69,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x68,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x70,0x02,0x00,0x00,0x55,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x73,0x02,0x00,0x00,
+0x70,0x02,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x75,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x73,0x02,0x00,0x00,0x74,0x02,0x00,0x00,0x75,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x74,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x78,0x02,0x00,0x00,0x5d,0x02,0x00,0x00,
+0xac,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x79,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,
+0x79,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x7b,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x75,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x75,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,
+0x7c,0x02,0x00,0x00,0x73,0x02,0x00,0x00,0x68,0x02,0x00,0x00,
+0x7b,0x02,0x00,0x00,0x74,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x7e,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x7c,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,0x7e,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,0x5d,0x02,0x00,0x00,
+0xac,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x88,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0x87,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x89,0x02,0x00,0x00,
+0x88,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8a,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,
+0x3d,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,
+0x55,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8f,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x91,0x02,0x00,0x00,
+0xa9,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,0x91,0x02,0x00,0x00,
+0xac,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x95,0x02,0x00,0x00,0x93,0x02,0x00,0x00,0x94,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x97,0x02,0x00,0x00,
+0xaa,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,0x95,0x02,0x00,0x00,
+0x97,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9a,0x02,0x00,0x00,0x98,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
+0xca,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc4,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
+0x41,0x00,0x06,0x00,0x9d,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
+0x82,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x9e,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x6a,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x6a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x67,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x69,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x62,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x62,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
+0xac,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x61,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x4a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x4a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa4,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x47,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x49,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x42,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x42,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x41,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_f16_aligned_fp32_len = 10288;
+
+unsigned char matmul_f16_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
+0x06,0x01,0x00,0x00,0x46,0x01,0x00,0x00,0x51,0x01,0x00,0x00,
+0x3a,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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+0x75,0x02,0x00,0x00,0x76,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x75,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x79,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,
+0x79,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x76,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x76,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0x7d,0x02,0x00,0x00,
+0x74,0x02,0x00,0x00,0x69,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,
+0x75,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x7f,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x7d,0x02,0x00,0x00,
+0x7e,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x87,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x89,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,
+0x87,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,0x3e,0x02,0x00,0x00,
+0x8b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8e,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,0x56,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x90,0x02,0x00,0x00,
+0x8e,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x94,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
+0x94,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x99,0x02,0x00,0x00,0x96,0x02,0x00,0x00,0x98,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
+0x99,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
+0x9b,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0x9d,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x5c,0x01,0x00,0x00,0x9e,0x02,0x00,0x00,0x83,0x02,0x00,0x00,
+0x35,0x00,0x00,0x00,0x90,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x9e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x6b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x6b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa0,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x68,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x6a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x63,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x63,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x60,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x62,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x4b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4b,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,
+0xaa,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x48,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4a,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x43,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x43,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa6,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x40,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x42,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_f16_f32_len = 10332;
+
+unsigned char matmul_f16_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xfd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
+0x07,0x01,0x00,0x00,0x49,0x01,0x00,0x00,0x51,0x01,0x00,0x00,
+0x5e,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x05,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x05,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x07,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x21,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x22,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4e,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x4f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x4f,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x4f,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x51,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x5e,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa4,0x02,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xa5,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xa5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xa5,0x02,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa7,0x02,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa7,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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+0x06,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,
+0xd2,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb6,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,
+0xb6,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,
+0xb9,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xbc,0x02,0x00,0x00,0xce,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,
+0xba,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
+0xd2,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
+0xc0,0x02,0x00,0x00,0xca,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,
+0xc0,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x53,0x01,0x00,0x00,
+0xc2,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
+0xb4,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xc2,0x02,0x00,0x00,
+0xc1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x8f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8f,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,
+0xd2,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x8c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8e,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x87,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x87,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc6,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x86,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,0xce,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x6c,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x6e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x67,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x67,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,
+0xcd,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x66,0x02,0x00,0x00,
 0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
 };
-const uint64_t mul_mat_vec_f16_f32_len = 2372;
+const uint64_t matmul_f16_f32_aligned_len = 10940;
+
+unsigned char matmul_f16_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xd5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0xfb,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x49,0x01,0x00,0x00,
+0x50,0x01,0x00,0x00,0x36,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x05,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x07,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x22,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4d,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x4e,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4e,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x4e,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x50,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x36,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7c,0x02,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x7d,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x7d,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x7d,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x7f,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x7f,0x02,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x00,0x10,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
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+0xbf,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x3e,0x02,0x00,0x00,
+0x3f,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x42,0x02,0x00,0x00,0x3d,0x02,0x00,0x00,0x3e,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x3d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x44,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x44,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x3d,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
+0x47,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x4a,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,0x61,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x46,0x02,0x00,0x00,0x47,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x4a,0x02,0x00,0x00,
+0x45,0x02,0x00,0x00,0x46,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x45,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x4e,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4f,0x02,0x00,0x00,
+0x25,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x51,0x02,0x00,0x00,0x65,0x00,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x52,0x02,0x00,0x00,0x4f,0x02,0x00,0x00,0x51,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x56,0x02,0x00,0x00,
+0xa5,0x02,0x00,0x00,0xc5,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x57,0x02,0x00,0x00,0x2b,0x02,0x00,0x00,
+0x56,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x59,0x02,0x00,0x00,0x69,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5a,0x02,0x00,0x00,
+0x57,0x02,0x00,0x00,0x59,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x45,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
+0x5f,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x62,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x5e,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x62,0x02,0x00,0x00,
+0x5d,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x5d,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0x67,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x6a,0x02,0x00,0x00,
+0xaa,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x66,0x02,0x00,0x00,0x67,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x6a,0x02,0x00,0x00,0x65,0x02,0x00,0x00,
+0x66,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x65,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,
+0x52,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
+0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x72,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x70,0x02,0x00,0x00,
+0x71,0x02,0x00,0x00,0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x71,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x75,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x76,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x77,0x02,0x00,0x00,0x76,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x78,0x02,0x00,0x00,
+0x75,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,0x79,0x02,0x00,0x00,
+0x70,0x02,0x00,0x00,0x65,0x02,0x00,0x00,0x78,0x02,0x00,0x00,
+0x71,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x7b,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x79,0x02,0x00,0x00,
+0x7a,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x83,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x85,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0x84,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,0x85,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x87,0x02,0x00,0x00,
+0x83,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x3a,0x02,0x00,0x00,
+0x87,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8a,0x02,0x00,0x00,0x88,0x02,0x00,0x00,0x52,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
+0x8a,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x90,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,
+0x90,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x95,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0x94,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x97,0x02,0x00,0x00,
+0x95,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcd,0x00,0x00,0x00,0x98,0x02,0x00,0x00,0xca,0x00,0x00,0x00,
+0x97,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0x99,0x02,0x00,0x00,0x98,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x52,0x01,0x00,0x00,0x9a,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
+0x35,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x9a,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x67,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x67,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9c,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x66,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x47,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x47,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
+0xa6,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x44,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x46,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x3f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa2,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x3e,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_f16_f32_aligned_fp32_len = 10224;
+
+unsigned char matmul_f16_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xd5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0xfa,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x46,0x01,0x00,0x00,
+0x51,0x01,0x00,0x00,0x36,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
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+0x95,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0x98,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
+0x97,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0x99,0x02,0x00,0x00,0x98,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x5c,0x01,0x00,0x00,0x9a,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
+0x35,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x9a,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x67,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x67,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9c,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x66,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x47,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x47,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
+0xa6,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x44,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x46,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x3f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa2,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x3e,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_f16_f32_fp32_len = 10260;
+
+unsigned char matmul_f16_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xd6,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0xfa,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x46,0x01,0x00,0x00,
+0x51,0x01,0x00,0x00,0x36,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x04,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x06,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x20,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x21,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4e,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x4f,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x4f,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x51,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x36,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7c,0x02,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x7d,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x7d,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x7d,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x7f,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x7f,0x02,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x00,0x10,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
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+0xa1,0x02,0x00,0x00,0x47,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x4a,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,
+0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x46,0x02,0x00,0x00,
+0x47,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x4a,0x02,0x00,0x00,0x45,0x02,0x00,0x00,0x46,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x45,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x4e,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x4f,0x02,0x00,0x00,0x25,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x51,0x02,0x00,0x00,
+0x65,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x52,0x02,0x00,0x00,0x4f,0x02,0x00,0x00,
+0x51,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x56,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,0xc5,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x57,0x02,0x00,0x00,
+0x2b,0x02,0x00,0x00,0x56,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x59,0x02,0x00,0x00,0x69,0x00,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5a,0x02,0x00,0x00,0x57,0x02,0x00,0x00,0x59,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5c,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xa9,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x45,0x02,0x00,0x00,
+0x9f,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x62,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
+0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x5e,0x02,0x00,0x00,
+0x5f,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x62,0x02,0x00,0x00,0x5d,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xab,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x5d,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
+0x67,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x6a,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x66,0x02,0x00,0x00,0x67,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x6a,0x02,0x00,0x00,
+0x65,0x02,0x00,0x00,0x66,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x65,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6d,0x02,0x00,0x00,0x52,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x70,0x02,0x00,0x00,
+0x6d,0x02,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x72,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x70,0x02,0x00,0x00,0x71,0x02,0x00,0x00,0x72,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x75,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,
+0xa9,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x76,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x77,0x02,0x00,0x00,
+0x76,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x78,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x72,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,
+0x79,0x02,0x00,0x00,0x70,0x02,0x00,0x00,0x65,0x02,0x00,0x00,
+0x78,0x02,0x00,0x00,0x71,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x7b,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x79,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x83,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,
+0xa9,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x85,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0x84,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,
+0x85,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x87,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0x86,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,
+0x3a,0x02,0x00,0x00,0x87,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
+0x52,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8c,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,
+0xa6,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x90,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,
+0xa9,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x92,0x02,0x00,0x00,0x90,0x02,0x00,0x00,0x91,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
+0xa7,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,0x92,0x02,0x00,0x00,
+0x94,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x97,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
+0xc9,0x00,0x00,0x00,0x97,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc3,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0x98,0x02,0x00,0x00,
+0x41,0x00,0x06,0x00,0x9a,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
+0x7f,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x9b,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x67,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x67,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x66,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
+0xa9,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x47,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x47,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa1,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x44,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x46,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x3e,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_f16_fp32_len = 10276;
+
+unsigned char matmul_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0xfb,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x47,0x01,0x00,0x00,
+0x52,0x01,0x00,0x00,0x3a,0x02,0x00,0x00,0x83,0x02,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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+0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,
+0xc9,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc3,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
+0x41,0x00,0x06,0x00,0x11,0x01,0x00,0x00,0x9e,0x02,0x00,0x00,
+0x83,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0x90,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x9e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x6b,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x6b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x68,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x6a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x63,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x63,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
+0xac,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x60,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x62,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x4b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x4b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa4,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x48,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x4a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x43,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x43,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x40,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x42,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_f32_len = 10324;
+
+unsigned char matmul_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xfd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0xfc,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x4a,0x01,0x00,0x00,
+0x52,0x01,0x00,0x00,0x5e,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x05,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x07,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x22,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x23,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x50,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x50,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x50,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x50,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x50,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x52,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x52,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x5e,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa4,0x02,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0xa5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xa5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0xa5,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa7,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa7,0x02,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x00,0x10,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
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+0x12,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x12,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3f,0x02,0x00,0x00,
+0xf2,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x11,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x0a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x0a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x41,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x07,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x09,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x02,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x02,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x43,0x02,0x00,0x00,0xec,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xff,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x01,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x9e,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x9e,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x45,0x02,0x00,0x00,
+0xe6,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x9b,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x9d,0x01,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x93,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xd7,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd7,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x47,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,
+0x6d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xd4,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x4c,0x02,0x00,0x00,0x56,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x4d,0x02,0x00,0x00,0x97,0x00,0x00,0x00,0x4c,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x52,0x02,0x00,0x00,
+0x5a,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x53,0x02,0x00,0x00,0xa8,0x00,0x00,0x00,
+0x52,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x57,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0x56,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x58,0x02,0x00,0x00,
+0x57,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x59,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,0x58,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5d,0x02,0x00,0x00,
+0x48,0x00,0x00,0x00,0x58,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x0d,0x00,0x00,0x00,0x5f,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x60,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x61,0x02,0x00,0x00,0x5d,0x02,0x00,0x00,
+0x60,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x62,0x02,0x00,0x00,0x59,0x02,0x00,0x00,0x61,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x64,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xcd,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
+0xca,0x02,0x00,0x00,0x67,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x6a,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,
+0xbf,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x66,0x02,0x00,0x00,
+0x67,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x6a,0x02,0x00,0x00,0x65,0x02,0x00,0x00,0x66,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x65,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x6c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6c,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xce,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x65,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,
+0x6f,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x72,0x02,0x00,0x00,0xce,0x02,0x00,0x00,0x61,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x6e,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x72,0x02,0x00,0x00,
+0x6d,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x6d,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x76,0x02,0x00,0x00,0xce,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x77,0x02,0x00,0x00,
+0x4d,0x02,0x00,0x00,0x76,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x79,0x02,0x00,0x00,0x65,0x00,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x7a,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0x79,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x02,0x00,0x00,
+0xcd,0x02,0x00,0x00,0xeb,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x7f,0x02,0x00,0x00,0x53,0x02,0x00,0x00,
+0x7e,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x81,0x02,0x00,0x00,0x69,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x82,0x02,0x00,0x00,
+0x7f,0x02,0x00,0x00,0x81,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x84,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x84,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
+0x87,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x8a,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x86,0x02,0x00,0x00,0x87,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x8a,0x02,0x00,0x00,
+0x85,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x85,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x8c,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8c,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x85,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x92,0x02,0x00,0x00,
+0xd2,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x8e,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x92,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
+0x8e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8d,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
+0x7a,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x98,0x02,0x00,0x00,0x95,0x02,0x00,0x00,
+0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x9a,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x98,0x02,0x00,0x00,
+0x99,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x99,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9d,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
+0x9d,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x9a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,
+0x98,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
+0x99,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xa3,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xa1,0x02,0x00,0x00,
+0xa2,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xab,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xad,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,
+0xab,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,0x62,0x02,0x00,0x00,
+0xaf,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb2,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
+0xb2,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb8,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,
+0xb8,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0xce,0x02,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xbd,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,
+0xbd,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcd,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,0xca,0x00,0x00,0x00,
+0xbf,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0xc1,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x12,0x01,0x00,0x00,0xc2,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,
+0x35,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xc2,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x8f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc4,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x8c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x87,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc6,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x84,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x86,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x6f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,
+0xce,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x6c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6e,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x67,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x67,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xca,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x66,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_f32_aligned_len = 10932;
+
+unsigned char matmul_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xd2,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0x06,0x01,0x00,0x00,
+0x47,0x01,0x00,0x00,0x4e,0x01,0x00,0x00,0x33,0x02,0x00,0x00,
+0x7c,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
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+0x6d,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,0x37,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x6f,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x6d,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,
+0x6f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6e,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x72,0x02,0x00,0x00,
+0x57,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x73,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x74,0x02,0x00,0x00,0x73,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x75,0x02,0x00,0x00,0x72,0x02,0x00,0x00,
+0x74,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0xc2,0x00,0x00,0x00,0x76,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
+0x62,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x78,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x76,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
+0x78,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x80,0x02,0x00,0x00,
+0x57,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x82,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
+0x81,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x83,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x84,0x02,0x00,0x00,0x80,0x02,0x00,0x00,
+0x83,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x85,0x02,0x00,0x00,0x37,0x02,0x00,0x00,0x84,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x87,0x02,0x00,0x00,
+0x85,0x02,0x00,0x00,0x4f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x89,0x02,0x00,0x00,0x87,0x02,0x00,0x00,
+0xa7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8b,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,
+0x8b,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
+0x8e,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x91,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,
+0x8f,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0x92,0x02,0x00,0x00,
+0xa7,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
+0x95,0x02,0x00,0x00,0xca,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
+0x95,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x11,0x01,0x00,0x00,
+0x97,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
+0x89,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x97,0x02,0x00,0x00,
+0x96,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,
+0xa7,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x63,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9b,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x44,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x44,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x41,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x43,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x3c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
+0xa2,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x39,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3b,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_f32_aligned_fp32_len = 10172;
+
+unsigned char matmul_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xd2,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x05,0x01,0x00,0x00,
+0x44,0x01,0x00,0x00,0x4f,0x01,0x00,0x00,0x33,0x02,0x00,0x00,
+0x7c,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x03,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1e,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1f,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x4d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x4d,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4f,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x33,0x02,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x79,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x7a,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7a,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x7a,0x02,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x7c,0x02,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7c,0x02,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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+0x21,0x02,0x00,0x00,0x56,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x22,0x02,0x00,0x00,
+0x96,0x00,0x00,0x00,0x21,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x27,0x02,0x00,0x00,0x5a,0x00,0x00,0x00,
+0xb8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x28,0x02,0x00,0x00,0xa7,0x00,0x00,0x00,0x27,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x2c,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0x2b,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x2d,0x02,0x00,0x00,0x2c,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2e,0x02,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x2d,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x32,0x02,0x00,0x00,0x48,0x00,0x00,0x00,
+0x2d,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
+0x34,0x02,0x00,0x00,0x33,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x35,0x02,0x00,0x00,
+0x34,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x36,0x02,0x00,0x00,0x32,0x02,0x00,0x00,0x35,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x37,0x02,0x00,0x00,
+0x2e,0x02,0x00,0x00,0x36,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x39,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x39,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
+0x3c,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x3f,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xbe,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x3b,0x02,0x00,0x00,0x3c,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x3f,0x02,0x00,0x00,
+0x3a,0x02,0x00,0x00,0x3b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x3a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x41,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x41,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x3a,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0x44,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x47,0x02,0x00,0x00,
+0xa3,0x02,0x00,0x00,0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x43,0x02,0x00,0x00,0x44,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x47,0x02,0x00,0x00,0x42,0x02,0x00,0x00,
+0x43,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x42,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4b,0x02,0x00,0x00,
+0xa3,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x4c,0x02,0x00,0x00,0x22,0x02,0x00,0x00,
+0x4b,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x4e,0x02,0x00,0x00,0x65,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4f,0x02,0x00,0x00,
+0x4c,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x53,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,
+0xc2,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x54,0x02,0x00,0x00,0x28,0x02,0x00,0x00,0x53,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x56,0x02,0x00,0x00,
+0x69,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x57,0x02,0x00,0x00,0x54,0x02,0x00,0x00,
+0x56,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x42,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0x5c,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x5f,0x02,0x00,0x00,
+0xa5,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x5b,0x02,0x00,0x00,0x5c,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x5f,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,
+0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x61,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xa7,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x5a,0x02,0x00,0x00,
+0x99,0x02,0x00,0x00,0x64,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x67,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,
+0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x63,0x02,0x00,0x00,
+0x64,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x67,0x02,0x00,0x00,0x62,0x02,0x00,0x00,0x63,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x62,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6a,0x02,0x00,0x00,0x4f,0x02,0x00,0x00,
+0xa7,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x6d,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,0x37,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x6f,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x6d,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,
+0x6f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6e,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x72,0x02,0x00,0x00,
+0x57,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x73,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x74,0x02,0x00,0x00,0x73,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x75,0x02,0x00,0x00,0x72,0x02,0x00,0x00,
+0x74,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0xc1,0x00,0x00,0x00,0x76,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
+0x62,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x78,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x76,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
+0x78,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x80,0x02,0x00,0x00,
+0x57,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x82,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
+0x81,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x83,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x84,0x02,0x00,0x00,0x80,0x02,0x00,0x00,
+0x83,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x85,0x02,0x00,0x00,0x37,0x02,0x00,0x00,0x84,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x87,0x02,0x00,0x00,
+0x85,0x02,0x00,0x00,0x4f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x89,0x02,0x00,0x00,0x87,0x02,0x00,0x00,
+0xa7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8b,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,
+0x8b,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
+0x8e,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x91,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,
+0x8f,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0x92,0x02,0x00,0x00,
+0xa7,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,
+0x95,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
+0x95,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x10,0x01,0x00,0x00,
+0x97,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
+0x89,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x97,0x02,0x00,0x00,
+0x96,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,
+0xa7,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x63,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9b,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x44,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x44,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x41,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x43,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x3c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
+0xa2,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x39,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3b,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_f32_fp32_len = 10208;
+
+unsigned char matmul_q2_k_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x4e,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,0x58,0x01,0x00,0x00,
+0x8b,0x01,0x00,0x00,0x96,0x01,0x00,0x00,0x80,0x02,0x00,0x00,
+0xc9,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
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+0xd5,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6e,0x02,0x00,0x00,0x56,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,
+0x96,0x00,0x00,0x00,0x6e,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x74,0x02,0x00,0x00,0x5a,0x00,0x00,0x00,
+0xb8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x75,0x02,0x00,0x00,0xa7,0x00,0x00,0x00,0x74,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x79,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0x78,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,0x79,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x7f,0x02,0x00,0x00,0x48,0x00,0x00,0x00,
+0x7a,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
+0x81,0x02,0x00,0x00,0x80,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x82,0x02,0x00,0x00,
+0x81,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x83,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,0x82,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x84,0x02,0x00,0x00,
+0x7b,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x86,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0xec,0x02,0x00,0x00,
+0x89,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x8c,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,0xbe,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x88,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x8c,0x02,0x00,0x00,
+0x87,0x02,0x00,0x00,0x88,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x87,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x8e,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8e,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x87,0x02,0x00,0x00,0xea,0x02,0x00,0x00,0x91,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
+0xf4,0x02,0x00,0x00,0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x90,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x94,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,
+0x90,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8f,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
+0xf4,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,
+0x98,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9b,0x02,0x00,0x00,0x65,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,
+0x99,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,
+0x0d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa1,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,
+0x69,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,
+0xa3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xf6,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x8f,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xac,0x02,0x00,0x00,
+0xf6,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xa8,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xac,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xae,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xae,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xf8,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,
+0xe6,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,
+0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xb0,0x02,0x00,0x00,
+0xb1,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xb4,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
+0xf8,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0xba,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,0x37,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xbc,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xba,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
+0xbc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbb,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,
+0xa4,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xc1,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
+0xc1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xbc,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xbc,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0xc1,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
+0xaf,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xc5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xc3,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,
+0xc5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
+0xa4,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
+0xce,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xd0,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,
+0xd0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd2,0x02,0x00,0x00,0x84,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,
+0xd2,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,
+0xf8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd8,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xda,0x02,0x00,0x00,
+0xd8,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,0xda,0x02,0x00,0x00,
+0xdb,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xde,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
+0xdc,0x02,0x00,0x00,0xde,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe1,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,
+0xf8,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,
+0xe2,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,0xe1,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,
+0xe2,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0xa1,0x01,0x00,0x00,
+0xe4,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
+0xd6,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xe4,0x02,0x00,0x00,
+0xe3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xb1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb1,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe6,0x02,0x00,0x00,
+0xf8,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xae,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb0,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe8,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x91,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x91,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xea,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8e,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x90,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x89,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x02,0x00,0x00,
+0xf3,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x88,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q2_k_f32_len = 11240;
+
+unsigned char matmul_q2_k_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x67,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,0x58,0x01,0x00,0x00,
+0x8d,0x01,0x00,0x00,0x95,0x01,0x00,0x00,0xa1,0x02,0x00,0x00,
+0xea,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
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+0xc4,0x00,0x00,0x00,0x04,0x03,0x00,0x00,0x03,0x03,0x00,0x00,
+0x41,0x00,0x06,0x00,0x97,0x01,0x00,0x00,0x05,0x03,0x00,0x00,
+0xea,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xf7,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x05,0x03,0x00,0x00,0x04,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xd2,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x07,0x03,0x00,0x00,0x19,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xcf,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xca,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xca,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x09,0x03,0x00,0x00,
+0x17,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xb2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x0b,0x03,0x00,0x00,0x15,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xaa,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xaa,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x0d,0x03,0x00,0x00,0x14,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q2_k_f32_aligned_len = 11800;
+
+unsigned char matmul_q2_k_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x3e,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x20,0x01,0x00,0x00,
+0x59,0x01,0x00,0x00,0x8c,0x01,0x00,0x00,0x93,0x01,0x00,0x00,
+0x78,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x18,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x19,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1c,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1c,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1d,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x1e,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1e,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x64,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x65,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x90,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x91,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x91,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x91,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x93,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x93,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x78,0x02,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbe,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xbf,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xbf,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xbf,0x02,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xc1,0x02,0x00,0x00,0x22,0x00,0x00,0x00,
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+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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+0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
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+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
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+0x89,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe2,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x88,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q2_k_f32_aligned_fp32_len = 11068;
+
+unsigned char matmul_q2_k_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x47,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,
+0x58,0x01,0x00,0x00,0x89,0x01,0x00,0x00,0x94,0x01,0x00,0x00,
+0x79,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
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+0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
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+0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
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+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x17,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x18,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1b,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1b,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1c,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x1d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x01,0x00,0x00,
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+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x63,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x64,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x91,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x92,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x92,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x92,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x94,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x94,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x79,0x02,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbf,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xc0,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xc0,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xc0,0x02,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xc2,0x02,0x00,0x00,0x22,0x00,0x00,0x00,
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+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
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+0xf8,0x00,0x02,0x00,0x2f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5a,0x02,0x00,0x00,0x11,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x2c,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x2e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x27,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x27,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,
+0x0f,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x24,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x26,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x1f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x1f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5e,0x02,0x00,0x00,0x0b,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x1c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x1e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xbc,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xbc,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x60,0x02,0x00,0x00,0x05,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb9,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xbb,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xb1,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xd6,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x62,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,0x6d,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xd3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xd5,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x67,0x02,0x00,0x00,0x56,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x68,0x02,0x00,0x00,
+0x96,0x00,0x00,0x00,0x67,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,0x5a,0x00,0x00,0x00,
+0xb8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6e,0x02,0x00,0x00,0xa7,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x72,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0x71,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x73,0x02,0x00,0x00,0x72,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x74,0x02,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x73,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x78,0x02,0x00,0x00,0x48,0x00,0x00,0x00,
+0x73,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
+0x7a,0x02,0x00,0x00,0x79,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,
+0x7a,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x7c,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7d,0x02,0x00,0x00,
+0x74,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xec,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,
+0x82,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x85,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0xbe,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x81,0x02,0x00,0x00,0x82,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x85,0x02,0x00,0x00,
+0x80,0x02,0x00,0x00,0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x80,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x87,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xed,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x80,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,
+0xed,0x02,0x00,0x00,0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x89,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x8d,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
+0x89,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x88,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x91,0x02,0x00,0x00,
+0xed,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,0x68,0x02,0x00,0x00,
+0x91,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x94,0x02,0x00,0x00,0x65,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
+0x92,0x02,0x00,0x00,0x94,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0xec,0x02,0x00,0x00,
+0x08,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9a,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,
+0x69,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
+0x9c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xef,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x88,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,
+0xef,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xa1,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xa5,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
+0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa7,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xf1,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
+0xdf,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0xad,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,
+0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xa9,0x02,0x00,0x00,
+0xaa,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xad,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,0x95,0x02,0x00,0x00,
+0xf1,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0xb3,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,0x37,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xb5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xb3,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,
+0xb5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb4,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,
+0x9d,0x02,0x00,0x00,0xef,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xba,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,
+0xba,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xb5,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb5,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0xc1,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xbe,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xbc,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
+0xbe,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc6,0x02,0x00,0x00,
+0x9d,0x02,0x00,0x00,0xef,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
+0xc7,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xc9,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
+0xc9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xcb,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
+0xcb,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,
+0xf1,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd1,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,
+0xd1,0x02,0x00,0x00,0xef,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,
+0xd4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd7,0x02,0x00,0x00,0xed,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,
+0xd5,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xda,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,
+0xf1,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,
+0xdb,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,0xda,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,
+0xdb,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x9f,0x01,0x00,0x00,
+0xdd,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
+0xcf,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xdd,0x02,0x00,0x00,
+0xdc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xbe,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xbe,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xaa,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xaa,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
+0xf1,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe1,0x02,0x00,0x00,0xef,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x8a,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,
+0xec,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q2_k_f32_fp32_len = 11120;
+
+unsigned char matmul_q3_k_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xa7,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x3a,0x01,0x00,0x00,0xaa,0x01,0x00,0x00,0x0b,0x02,0x00,0x00,
+0x16,0x02,0x00,0x00,0x00,0x03,0x00,0x00,0x49,0x03,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
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+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q3_k_f32_len = 13144;
+
+unsigned char matmul_q3_k_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xc5,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x3a,0x01,0x00,0x00,0xaa,0x01,0x00,0x00,0x0d,0x02,0x00,0x00,
+0x15,0x02,0x00,0x00,0x21,0x03,0x00,0x00,0x6a,0x03,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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+0x32,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x64,0x03,0x00,0x00,
+0x65,0x03,0x00,0x00,0x66,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x65,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6e,0x03,0x00,0x00,0x45,0x03,0x00,0x00,0x93,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x70,0x03,0x00,0x00,
+0x14,0x00,0x00,0x00,0x6f,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x71,0x03,0x00,0x00,0x70,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x72,0x03,0x00,0x00,
+0x6e,0x03,0x00,0x00,0x71,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x73,0x03,0x00,0x00,0x25,0x03,0x00,0x00,
+0x72,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x75,0x03,0x00,0x00,0x73,0x03,0x00,0x00,0x3d,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x77,0x03,0x00,0x00,
+0x75,0x03,0x00,0x00,0x95,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x79,0x03,0x00,0x00,0x90,0x03,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x7b,0x03,0x00,0x00,0x79,0x03,0x00,0x00,0x93,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7d,0x03,0x00,0x00,
+0x7b,0x03,0x00,0x00,0x7c,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x7f,0x03,0x00,0x00,0x91,0x03,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x80,0x03,0x00,0x00,0x7d,0x03,0x00,0x00,0x7f,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x82,0x03,0x00,0x00,
+0x80,0x03,0x00,0x00,0x95,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcd,0x00,0x00,0x00,0x83,0x03,0x00,0x00,0xca,0x00,0x00,0x00,
+0x82,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0x84,0x03,0x00,0x00,0x83,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
+0x17,0x02,0x00,0x00,0x85,0x03,0x00,0x00,0x6a,0x03,0x00,0x00,
+0x35,0x00,0x00,0x00,0x77,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x85,0x03,0x00,0x00,0x84,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x66,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x66,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x52,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x52,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x87,0x03,0x00,0x00,0x95,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x4f,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x51,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x4a,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x4a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x89,0x03,0x00,0x00,0x93,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x47,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x49,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x32,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x32,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8b,0x03,0x00,0x00,
+0x91,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x2f,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x31,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x2a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x2a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8d,0x03,0x00,0x00,0x90,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x27,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x29,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q3_k_f32_aligned_len = 13704;
+
+unsigned char matmul_q3_k_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x9c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,0xaa,0x01,0x00,0x00,
+0x0b,0x02,0x00,0x00,0x12,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,
+0x41,0x03,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x31,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x32,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x34,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x36,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x36,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x36,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x36,0x01,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x01,0x00,0x00,
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+0x38,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x38,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x38,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3a,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3a,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe3,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xe4,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0f,0x02,0x00,0x00,0x06,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x10,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x02,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x12,0x02,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x12,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xf8,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x03,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x3f,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x3f,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x3f,0x03,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x41,0x03,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x41,0x03,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x00,0x10,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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+0x49,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x4c,0x03,0x00,0x00,0x4a,0x03,0x00,0x00,0x14,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4e,0x03,0x00,0x00,
+0x4c,0x03,0x00,0x00,0x6c,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x50,0x03,0x00,0x00,0x67,0x03,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x52,0x03,0x00,0x00,0x50,0x03,0x00,0x00,0x6a,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x54,0x03,0x00,0x00,
+0x52,0x03,0x00,0x00,0x53,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x56,0x03,0x00,0x00,0x68,0x03,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x57,0x03,0x00,0x00,0x54,0x03,0x00,0x00,0x56,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x59,0x03,0x00,0x00,
+0x57,0x03,0x00,0x00,0x6c,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcd,0x00,0x00,0x00,0x5a,0x03,0x00,0x00,0xca,0x00,0x00,0x00,
+0x59,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0x5b,0x03,0x00,0x00,0x5a,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
+0x14,0x02,0x00,0x00,0x5c,0x03,0x00,0x00,0x41,0x03,0x00,0x00,
+0x35,0x00,0x00,0x00,0x4e,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x5c,0x03,0x00,0x00,0x5b,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x3d,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x3d,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x29,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x29,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5e,0x03,0x00,0x00,0x6c,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x26,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x28,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x21,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x21,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x60,0x03,0x00,0x00,0x6a,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x1e,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x20,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x09,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x09,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x62,0x03,0x00,0x00,
+0x68,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x06,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x08,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x01,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x01,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x64,0x03,0x00,0x00,0x67,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xfe,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x00,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q3_k_f32_aligned_fp32_len = 12972;
+
+unsigned char matmul_q3_k_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xa0,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,0xaa,0x01,0x00,0x00,
+0x09,0x02,0x00,0x00,0x14,0x02,0x00,0x00,0xf9,0x02,0x00,0x00,
+0x42,0x03,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x31,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x32,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x34,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x36,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x36,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x36,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x36,0x01,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x38,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x38,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x38,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3a,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3a,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe3,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xe4,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x11,0x02,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x12,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x02,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x14,0x02,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x14,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xf9,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3f,0x03,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x40,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x40,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x40,0x03,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x42,0x03,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x42,0x03,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x00,0x10,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
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+0x6b,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x55,0x03,0x00,0x00,0x53,0x03,0x00,0x00,0x54,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x57,0x03,0x00,0x00,
+0x69,0x03,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x58,0x03,0x00,0x00,0x55,0x03,0x00,0x00,
+0x57,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5a,0x03,0x00,0x00,0x58,0x03,0x00,0x00,0x6d,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0x5b,0x03,0x00,0x00,
+0xc9,0x00,0x00,0x00,0x5a,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc3,0x00,0x00,0x00,0x5c,0x03,0x00,0x00,0x5b,0x03,0x00,0x00,
+0x41,0x00,0x06,0x00,0x1f,0x02,0x00,0x00,0x5d,0x03,0x00,0x00,
+0x42,0x03,0x00,0x00,0x35,0x00,0x00,0x00,0x4f,0x03,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x5d,0x03,0x00,0x00,0x5c,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x3e,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x3e,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x2a,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x2a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5f,0x03,0x00,0x00,0x6d,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x27,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x29,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x22,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x22,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x61,0x03,0x00,0x00,
+0x6b,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x1f,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x21,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x0a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x0a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x63,0x03,0x00,0x00,0x69,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x07,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x09,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x02,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x02,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x65,0x03,0x00,0x00,0x68,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xff,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x01,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q3_k_f32_fp32_len = 13024;
+
+unsigned char matmul_q4_0_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x1b,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x27,0x01,0x00,0x00,
+0x5a,0x01,0x00,0x00,0x65,0x01,0x00,0x00,0x4f,0x02,0x00,0x00,
+0x98,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x03,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x05,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x07,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x34,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x35,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x62,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x63,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x63,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x63,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x65,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x65,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x95,0x02,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x96,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x96,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x96,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x98,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x98,0x02,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x00,0x10,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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+0x56,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3e,0x02,0x00,0x00,0x96,0x00,0x00,0x00,
+0x3d,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x43,0x02,0x00,0x00,0x5a,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x44,0x02,0x00,0x00,
+0xa7,0x00,0x00,0x00,0x43,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x48,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
+0x47,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x49,0x02,0x00,0x00,0x48,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x4a,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x49,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x4e,0x02,0x00,0x00,0x48,0x00,0x00,0x00,0x49,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x50,0x02,0x00,0x00,
+0x4f,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x51,0x02,0x00,0x00,0x50,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x52,0x02,0x00,0x00,
+0x4e,0x02,0x00,0x00,0x51,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x53,0x02,0x00,0x00,0x4a,0x02,0x00,0x00,
+0x52,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x55,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x55,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
+0xd5,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,0x58,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x5b,0x02,0x00,0x00,
+0xc0,0x02,0x00,0x00,0xbe,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x57,0x02,0x00,0x00,0x58,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x5b,0x02,0x00,0x00,0x56,0x02,0x00,0x00,
+0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x56,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5d,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xc1,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x56,0x02,0x00,0x00,
+0xb9,0x02,0x00,0x00,0x60,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x63,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
+0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x5f,0x02,0x00,0x00,
+0x60,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x63,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x67,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x68,0x02,0x00,0x00,0x3e,0x02,0x00,0x00,0x67,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6a,0x02,0x00,0x00,
+0x65,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6b,0x02,0x00,0x00,0x68,0x02,0x00,0x00,
+0x6a,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6f,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0xdc,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x70,0x02,0x00,0x00,
+0x44,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x72,0x02,0x00,0x00,0x69,0x00,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x73,0x02,0x00,0x00,0x70,0x02,0x00,0x00,0x72,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x75,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x75,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xc3,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x5e,0x02,0x00,0x00,
+0xb7,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
+0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x77,0x02,0x00,0x00,
+0x78,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x7b,0x02,0x00,0x00,0x76,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x76,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x76,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,
+0x80,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x83,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x7f,0x02,0x00,0x00,0x80,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x83,0x02,0x00,0x00,
+0x7e,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x86,0x02,0x00,0x00,0x6b,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x89,0x02,0x00,0x00,
+0x86,0x02,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x8b,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x89,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,0x73,0x02,0x00,0x00,
+0xc3,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x8f,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x90,0x02,0x00,0x00,
+0x8f,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x91,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,0x90,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x8b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8b,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,
+0x92,0x02,0x00,0x00,0x89,0x02,0x00,0x00,0x7e,0x02,0x00,0x00,
+0x91,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x94,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x92,0x02,0x00,0x00,0x93,0x02,0x00,0x00,0x94,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x93,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x73,0x02,0x00,0x00,
+0xc3,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x9e,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
+0x9e,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa0,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,
+0x53,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,
+0x6b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa5,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,
+0xc0,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,
+0xc3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xab,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xad,0x02,0x00,0x00,
+0xc1,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
+0xad,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb0,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,
+0xc9,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc3,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,
+0x41,0x00,0x06,0x00,0x70,0x01,0x00,0x00,0xb3,0x02,0x00,0x00,
+0x98,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xb3,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x94,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x94,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x80,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x78,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,
+0xc3,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x75,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x60,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x60,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb9,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x58,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x55,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q4_0_f32_len = 10564;
+
+unsigned char matmul_q4_0_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x36,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x28,0x01,0x00,0x00,
+0x5d,0x01,0x00,0x00,0x65,0x01,0x00,0x00,0x72,0x02,0x00,0x00,
+0xbb,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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+0x14,0x00,0x00,0x00,0x6a,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x6c,0x02,0x00,0x00,0x6b,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x6c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x71,0x02,0x00,0x00,0x48,0x00,0x00,0x00,
+0x6c,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
+0x73,0x02,0x00,0x00,0x72,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x74,0x02,0x00,0x00,
+0x73,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x75,0x02,0x00,0x00,0x71,0x02,0x00,0x00,0x74,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x76,0x02,0x00,0x00,
+0x6d,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x78,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0xde,0x02,0x00,0x00,
+0x7b,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x7e,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0xbf,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x7a,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x7e,0x02,0x00,0x00,
+0x79,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x79,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x80,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x79,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,0x83,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x86,0x02,0x00,0x00,
+0xe4,0x02,0x00,0x00,0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x82,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x86,0x02,0x00,0x00,0x81,0x02,0x00,0x00,
+0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
+0xe4,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,0x61,0x02,0x00,0x00,
+0x8a,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8d,0x02,0x00,0x00,0x65,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,
+0x8b,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
+0xff,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x93,0x02,0x00,0x00,0x67,0x02,0x00,0x00,0x92,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
+0x69,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x96,0x02,0x00,0x00,0x93,0x02,0x00,0x00,
+0x95,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x98,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x98,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xe6,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x81,0x02,0x00,0x00,0xda,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
+0xe6,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x9a,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x9e,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
+0x9a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x99,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa0,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xe8,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x99,0x02,0x00,0x00,
+0xd8,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,
+0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xa2,0x02,0x00,0x00,
+0xa3,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xa6,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,
+0xe8,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xac,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0x37,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xae,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xac,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
+0xae,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,
+0x96,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xb3,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,
+0xb3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xae,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xae,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0xc2,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
+0xa1,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xb7,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xb5,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,
+0xb7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb6,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,
+0x96,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
+0xc0,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xc2,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
+0xc2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc4,0x02,0x00,0x00,0x76,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc6,0x02,0x00,0x00,
+0xc4,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
+0xe8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xca,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcc,0x02,0x00,0x00,
+0xca,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xce,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,
+0xcd,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd0,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,
+0xce,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,
+0xe8,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
+0xd4,0x02,0x00,0x00,0xca,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,
+0xd4,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x67,0x01,0x00,0x00,
+0xd6,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
+0xc8,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xd6,0x02,0x00,0x00,
+0xd5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xb7,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,
+0xe8,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x9b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xda,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x98,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x9a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x83,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x80,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xde,0x02,0x00,0x00,
+0xe3,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x78,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q4_0_f32_aligned_len = 11156;
+
+unsigned char matmul_q4_0_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x0c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x08,0x01,0x00,0x00,
+0x28,0x01,0x00,0x00,0x5b,0x01,0x00,0x00,0x62,0x01,0x00,0x00,
+0x48,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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+0x88,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x8a,0x02,0x00,0x00,0x87,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x84,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,
+0x8b,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
+0x8a,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x8d,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x8b,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,
+0xbc,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x97,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
+0x97,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x99,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0x98,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
+0x4c,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
+0x64,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9e,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
+0xb9,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
+0xbc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa4,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,
+0xba,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,
+0xa6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa9,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,
+0xca,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc4,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
+0x41,0x00,0x06,0x00,0x64,0x01,0x00,0x00,0xac,0x02,0x00,0x00,
+0x91,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xac,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x8d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x71,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,
+0xbc,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x6e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x70,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x59,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb2,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x58,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x51,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x51,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4e,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x50,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q4_0_f32_aligned_fp32_len = 10408;
+
+unsigned char matmul_q4_0_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x14,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x07,0x01,0x00,0x00,
+0x27,0x01,0x00,0x00,0x58,0x01,0x00,0x00,0x63,0x01,0x00,0x00,
+0x48,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x04,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x05,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x32,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x33,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x61,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x61,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x61,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x63,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x63,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x48,0x02,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x8e,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x8f,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x8f,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x8f,0x02,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x91,0x02,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x91,0x02,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x1e,0x00,0x10,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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+0xd8,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xeb,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xed,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x8b,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8b,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x2f,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x88,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8a,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x80,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x31,0x02,0x00,0x00,
+0xb8,0x02,0x00,0x00,0x6d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd5,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x36,0x02,0x00,0x00,
+0x56,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x37,0x02,0x00,0x00,0x96,0x00,0x00,0x00,
+0x36,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x3c,0x02,0x00,0x00,0x5a,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3d,0x02,0x00,0x00,
+0xa7,0x00,0x00,0x00,0x3c,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x41,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
+0x40,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x42,0x02,0x00,0x00,0x41,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x43,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x42,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x47,0x02,0x00,0x00,0x48,0x00,0x00,0x00,0x42,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x49,0x02,0x00,0x00,
+0x48,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x4a,0x02,0x00,0x00,0x49,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4b,0x02,0x00,0x00,
+0x47,0x02,0x00,0x00,0x4a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x4c,0x02,0x00,0x00,0x43,0x02,0x00,0x00,
+0x4b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x4e,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x4e,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
+0xd5,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,0x51,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x54,0x02,0x00,0x00,
+0xb9,0x02,0x00,0x00,0xbe,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x50,0x02,0x00,0x00,0x51,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x54,0x02,0x00,0x00,0x4f,0x02,0x00,0x00,
+0x50,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x56,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xba,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x4f,0x02,0x00,0x00,
+0xb2,0x02,0x00,0x00,0x59,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
+0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x58,0x02,0x00,0x00,
+0x59,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x5c,0x02,0x00,0x00,0x57,0x02,0x00,0x00,0x58,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x60,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x61,0x02,0x00,0x00,0x37,0x02,0x00,0x00,0x60,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x63,0x02,0x00,0x00,
+0x65,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x61,0x02,0x00,0x00,
+0x63,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x68,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,0xd7,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x02,0x00,0x00,
+0x3d,0x02,0x00,0x00,0x68,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6b,0x02,0x00,0x00,0x69,0x00,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6c,0x02,0x00,0x00,0x69,0x02,0x00,0x00,0x6b,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x6e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x6e,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xbc,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x57,0x02,0x00,0x00,
+0xb0,0x02,0x00,0x00,0x71,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x74,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,
+0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x70,0x02,0x00,0x00,
+0x71,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x74,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,0x70,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x76,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x76,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
+0x79,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x7c,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x78,0x02,0x00,0x00,0x79,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x7c,0x02,0x00,0x00,
+0x77,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x77,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x7f,0x02,0x00,0x00,0x64,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x82,0x02,0x00,0x00,
+0x7f,0x02,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x84,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x82,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0x84,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x87,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,
+0xbc,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x88,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x89,0x02,0x00,0x00,
+0x88,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x8a,0x02,0x00,0x00,0x87,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x84,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,
+0x8b,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
+0x8a,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x8d,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x8b,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,
+0xbc,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x97,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
+0x97,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x99,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0x98,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
+0x4c,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
+0x64,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9e,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
+0xb9,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
+0xbc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa4,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,
+0xba,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,
+0xa6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa9,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,
+0xc9,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc3,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
+0x41,0x00,0x06,0x00,0x6e,0x01,0x00,0x00,0xac,0x02,0x00,0x00,
+0x91,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xac,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x8d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x71,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,
+0xbc,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x6e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x70,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x59,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb2,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x58,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x51,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x51,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4e,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x50,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q4_0_f32_fp32_len = 10444;
+
+unsigned char matmul_q4_1_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xf5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
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+0x43,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,0x65,0x00,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x70,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x74,0x02,0x00,0x00,
+0xc5,0x02,0x00,0x00,0xe1,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x75,0x02,0x00,0x00,0x49,0x02,0x00,0x00,
+0x74,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x77,0x02,0x00,0x00,0x69,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x78,0x02,0x00,0x00,
+0x75,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x63,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,
+0x7d,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x80,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x7c,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x80,0x02,0x00,0x00,
+0x7b,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x82,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x7b,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0x85,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x88,0x02,0x00,0x00,
+0xca,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x84,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x88,0x02,0x00,0x00,0x83,0x02,0x00,0x00,
+0x84,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,
+0x70,0x02,0x00,0x00,0xca,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,
+0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x90,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x8e,0x02,0x00,0x00,
+0x8f,0x02,0x00,0x00,0x90,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x93,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,0x94,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
+0x93,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x90,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x90,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0x97,0x02,0x00,0x00,
+0x8e,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
+0x8f,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x99,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x97,0x02,0x00,0x00,
+0x98,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x98,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa1,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,
+0xa1,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,0x58,0x02,0x00,0x00,
+0xa5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,0x70,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xca,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xae,0x02,0x00,0x00,0xac,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,
+0xae,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb3,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,
+0xb3,0x02,0x00,0x00,0xca,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
+0xb5,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0xb7,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x75,0x01,0x00,0x00,0xb8,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
+0x35,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xb8,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x99,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x99,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x85,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x85,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xba,0x02,0x00,0x00,0xca,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x84,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x65,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x65,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
+0xc6,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x62,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc0,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5c,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q4_1_f32_len = 10632;
+
+unsigned char matmul_q4_1_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x18,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x2d,0x01,0x00,0x00,
+0x62,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,0x77,0x02,0x00,0x00,
+0xc0,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x03,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x04,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,
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+0x65,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,0x90,0x02,0x00,0x00,
+0x92,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x97,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,0x04,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
+0x6c,0x02,0x00,0x00,0x97,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,0x69,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9b,0x02,0x00,0x00,0x98,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x9d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x9d,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xeb,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x86,0x02,0x00,0x00,
+0xdf,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,
+0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x9f,0x02,0x00,0x00,
+0xa0,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xa3,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xed,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xab,0x02,0x00,0x00,0xed,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xa7,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xab,0x02,0x00,0x00,
+0xa6,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xae,0x02,0x00,0x00,0x93,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,
+0xae,0x02,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xb3,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xb1,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
+0xeb,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0xb7,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,
+0xb7,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xb9,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xb3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb3,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,
+0xba,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,
+0xb9,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xbc,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xba,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xbb,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
+0xeb,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0xc6,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,
+0xc6,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc8,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,
+0x7b,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,
+0x93,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xcd,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,
+0xe8,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
+0xeb,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd3,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,
+0xe9,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,
+0xd5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd8,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,
+0xca,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc4,0x00,0x00,0x00,0xda,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,
+0x41,0x00,0x06,0x00,0x6c,0x01,0x00,0x00,0xdb,0x02,0x00,0x00,
+0xc0,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xdb,0x02,0x00,0x00,0xda,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xbc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xbc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
+0xeb,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x9d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x88,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x88,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe1,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x85,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x87,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x80,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q4_1_f32_aligned_len = 11224;
+
+unsigned char matmul_q4_1_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xee,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x08,0x01,0x00,0x00,
+0x2d,0x01,0x00,0x00,0x60,0x01,0x00,0x00,0x67,0x01,0x00,0x00,
+0x4d,0x02,0x00,0x00,0x96,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x04,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,
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+0xac,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,
+0xac,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcd,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,0xca,0x00,0x00,0x00,
+0xae,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0xb0,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x69,0x01,0x00,0x00,0xb1,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
+0x35,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xb1,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x92,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x92,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb3,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x76,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x75,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,
+0xbf,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x56,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb9,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x55,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q4_1_f32_aligned_fp32_len = 10476;
+
+unsigned char matmul_q4_1_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xee,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x07,0x01,0x00,0x00,
+0x2c,0x01,0x00,0x00,0x5d,0x01,0x00,0x00,0x68,0x01,0x00,0x00,
+0x4d,0x02,0x00,0x00,0x96,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x03,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x05,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x07,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x38,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x65,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x66,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x66,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x66,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x68,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x68,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4d,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x93,0x02,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x94,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x94,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x94,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x96,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x96,0x02,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x00,0x10,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
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+0x46,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x48,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,0x47,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4c,0x02,0x00,0x00,
+0x48,0x00,0x00,0x00,0x47,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x0d,0x00,0x00,0x00,0x4e,0x02,0x00,0x00,0x4d,0x02,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x4f,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x50,0x02,0x00,0x00,0x4c,0x02,0x00,0x00,
+0x4f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x51,0x02,0x00,0x00,0x48,0x02,0x00,0x00,0x50,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x53,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xbe,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
+0xb9,0x02,0x00,0x00,0x56,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x59,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
+0xbe,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x55,0x02,0x00,0x00,
+0x56,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x59,0x02,0x00,0x00,0x54,0x02,0x00,0x00,0x55,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x54,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5b,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x54,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,
+0x5e,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x61,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0x61,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x5d,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x61,0x02,0x00,0x00,
+0x5c,0x02,0x00,0x00,0x5d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x65,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x66,0x02,0x00,0x00,
+0x3c,0x02,0x00,0x00,0x65,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x68,0x02,0x00,0x00,0x65,0x00,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x69,0x02,0x00,0x00,0x66,0x02,0x00,0x00,0x68,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,
+0xbe,0x02,0x00,0x00,0xdc,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6e,0x02,0x00,0x00,0x42,0x02,0x00,0x00,
+0x6d,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x70,0x02,0x00,0x00,0x69,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x71,0x02,0x00,0x00,
+0x6e,0x02,0x00,0x00,0x70,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x73,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,
+0x76,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x79,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x75,0x02,0x00,0x00,0x76,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x79,0x02,0x00,0x00,
+0x74,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x74,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x74,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,0x7e,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x81,0x02,0x00,0x00,
+0xc3,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x7d,0x02,0x00,0x00,0x7e,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x81,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,
+0x7d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x84,0x02,0x00,0x00,
+0x69,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x87,0x02,0x00,0x00,0x84,0x02,0x00,0x00,
+0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x89,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x87,0x02,0x00,0x00,
+0x88,0x02,0x00,0x00,0x89,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x88,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8c,0x02,0x00,0x00,0x71,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,
+0x8c,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x89,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0x90,0x02,0x00,0x00,
+0x87,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,
+0x88,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x92,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x90,0x02,0x00,0x00,
+0x91,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x91,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9a,0x02,0x00,0x00,0x71,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
+0x9a,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x51,0x02,0x00,0x00,
+0x9e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa1,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,0x69,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,
+0xa1,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa7,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,
+0xa7,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xac,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,
+0xac,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
+0xae,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0xb0,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x73,0x01,0x00,0x00,0xb1,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
+0x35,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xb1,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x92,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x92,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb3,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x76,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x75,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,
+0xbf,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x56,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb9,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x55,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q4_1_f32_fp32_len = 10512;
+
+unsigned char matmul_q4_k_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x8d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x21,0x01,0x00,0x00,0x78,0x01,0x00,0x00,0xc6,0x01,0x00,0x00,
+0xd1,0x01,0x00,0x00,0xbb,0x02,0x00,0x00,0x04,0x03,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
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+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x11,0x03,0x00,0x00,
+0x0f,0x03,0x00,0x00,0x31,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x13,0x03,0x00,0x00,0x2c,0x03,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x15,0x03,0x00,0x00,0x13,0x03,0x00,0x00,0x2f,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x17,0x03,0x00,0x00,
+0x15,0x03,0x00,0x00,0x16,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x19,0x03,0x00,0x00,0x2d,0x03,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1a,0x03,0x00,0x00,0x17,0x03,0x00,0x00,0x19,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,
+0x1a,0x03,0x00,0x00,0x31,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0x1d,0x03,0x00,0x00,0xc9,0x00,0x00,0x00,
+0x1c,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0x1e,0x03,0x00,0x00,0x1d,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
+0xdc,0x01,0x00,0x00,0x1f,0x03,0x00,0x00,0x04,0x03,0x00,0x00,
+0x35,0x00,0x00,0x00,0x11,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x1f,0x03,0x00,0x00,0x1e,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x00,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x00,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xec,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xec,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x21,0x03,0x00,0x00,0x31,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xeb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xe4,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x23,0x03,0x00,0x00,0x2f,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xcc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x25,0x03,0x00,0x00,
+0x2d,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xc4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x27,0x03,0x00,0x00,0x2c,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xc1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xc3,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q4_k_f32_len = 12072;
+
+unsigned char matmul_q4_k_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xa7,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x22,0x01,0x00,0x00,0x79,0x01,0x00,0x00,0xc9,0x01,0x00,0x00,
+0xd1,0x01,0x00,0x00,0xdd,0x02,0x00,0x00,0x26,0x03,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1c,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1d,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1e,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1e,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1e,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1f,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x90,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x20,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x20,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x20,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x22,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x22,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa1,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa2,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xce,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xcf,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xcf,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xcf,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xcf,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xcf,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xd1,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd1,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xdd,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x23,0x03,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x24,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x24,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x24,0x03,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x26,0x03,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x26,0x03,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x10,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
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+0xd1,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0xd6,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,
+0xd6,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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+0xe1,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe3,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x4e,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
+0x49,0x03,0x00,0x00,0xe6,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xe9,0x02,0x00,0x00,0x4e,0x03,0x00,0x00,
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+0xe9,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xeb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xeb,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x4f,0x03,0x00,0x00,
+0x3f,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,0x47,0x03,0x00,0x00,
+0xee,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xf1,0x02,0x00,0x00,0x4f,0x03,0x00,0x00,0x61,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xed,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf1,0x02,0x00,0x00,
+0xec,0x02,0x00,0x00,0xed,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xec,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf5,0x02,0x00,0x00,0x4f,0x03,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf6,0x02,0x00,0x00,
+0xcc,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf8,0x02,0x00,0x00,0x65,0x00,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf9,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfd,0x02,0x00,0x00,
+0x4e,0x03,0x00,0x00,0x6a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,
+0xfd,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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+0xfe,0x02,0x00,0x00,0x00,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x03,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x03,0x03,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x51,0x03,0x00,0x00,
+0x3f,0x00,0x00,0x00,0xec,0x02,0x00,0x00,0x45,0x03,0x00,0x00,
+0x06,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x09,0x03,0x00,0x00,0x51,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x05,0x03,0x00,0x00,0x06,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x09,0x03,0x00,0x00,
+0x04,0x03,0x00,0x00,0x05,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x04,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x0b,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x0b,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x53,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x04,0x03,0x00,0x00,0x43,0x03,0x00,0x00,0x0e,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x11,0x03,0x00,0x00,
+0x53,0x03,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x0d,0x03,0x00,0x00,0x0e,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x11,0x03,0x00,0x00,0x0c,0x03,0x00,0x00,
+0x0d,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0c,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x14,0x03,0x00,0x00,
+0xf9,0x02,0x00,0x00,0x53,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x17,0x03,0x00,0x00,0x14,0x03,0x00,0x00,
+0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x19,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x17,0x03,0x00,0x00,
+0x18,0x03,0x00,0x00,0x19,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x18,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1c,0x03,0x00,0x00,0x01,0x03,0x00,0x00,0x51,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x1d,0x03,0x00,0x00,
+0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x1e,0x03,0x00,0x00,0x1d,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x1f,0x03,0x00,0x00,
+0x1c,0x03,0x00,0x00,0x1e,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x19,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x19,0x03,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,0x20,0x03,0x00,0x00,
+0x17,0x03,0x00,0x00,0x0c,0x03,0x00,0x00,0x1f,0x03,0x00,0x00,
+0x18,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,0x22,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x20,0x03,0x00,0x00,
+0x21,0x03,0x00,0x00,0x22,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x21,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x2a,0x03,0x00,0x00,0x01,0x03,0x00,0x00,0x51,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x2c,0x03,0x00,0x00,
+0x14,0x00,0x00,0x00,0x2b,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x2d,0x03,0x00,0x00,0x2c,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2e,0x03,0x00,0x00,
+0x2a,0x03,0x00,0x00,0x2d,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x2f,0x03,0x00,0x00,0xe1,0x02,0x00,0x00,
+0x2e,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x31,0x03,0x00,0x00,0x2f,0x03,0x00,0x00,0xf9,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x33,0x03,0x00,0x00,
+0x31,0x03,0x00,0x00,0x53,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x35,0x03,0x00,0x00,0x4e,0x03,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x37,0x03,0x00,0x00,0x35,0x03,0x00,0x00,0x51,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x39,0x03,0x00,0x00,
+0x37,0x03,0x00,0x00,0x38,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3b,0x03,0x00,0x00,0x4f,0x03,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x3c,0x03,0x00,0x00,0x39,0x03,0x00,0x00,0x3b,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3e,0x03,0x00,0x00,
+0x3c,0x03,0x00,0x00,0x53,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcd,0x00,0x00,0x00,0x3f,0x03,0x00,0x00,0xca,0x00,0x00,0x00,
+0x3e,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0x40,0x03,0x00,0x00,0x3f,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
+0xd3,0x01,0x00,0x00,0x41,0x03,0x00,0x00,0x26,0x03,0x00,0x00,
+0x35,0x00,0x00,0x00,0x33,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x41,0x03,0x00,0x00,0x40,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x22,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x22,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x0e,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x0e,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x43,0x03,0x00,0x00,0x53,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x0b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x0d,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x06,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x06,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x45,0x03,0x00,0x00,0x51,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x03,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x05,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xee,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xee,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x47,0x03,0x00,0x00,
+0x4f,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xeb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xed,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x49,0x03,0x00,0x00,0x4e,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe5,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q4_k_f32_aligned_len = 12648;
+
+unsigned char matmul_q4_k_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x7d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x78,0x01,0x00,0x00,
+0xc6,0x01,0x00,0x00,0xcd,0x01,0x00,0x00,0xb3,0x02,0x00,0x00,
+0xfc,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
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+0xc2,0x00,0x00,0x00,0xf6,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
+0xe2,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xf8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xf6,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,
+0xf8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf7,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x00,0x03,0x00,0x00,
+0xd7,0x02,0x00,0x00,0x27,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x02,0x03,0x00,0x00,0x14,0x00,0x00,0x00,
+0x01,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x03,0x03,0x00,0x00,0x02,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x04,0x03,0x00,0x00,0x00,0x03,0x00,0x00,
+0x03,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x05,0x03,0x00,0x00,0xb7,0x02,0x00,0x00,0x04,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x07,0x03,0x00,0x00,
+0x05,0x03,0x00,0x00,0xcf,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x09,0x03,0x00,0x00,0x07,0x03,0x00,0x00,
+0x29,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x0b,0x03,0x00,0x00,0x24,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0d,0x03,0x00,0x00,
+0x0b,0x03,0x00,0x00,0x27,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x0f,0x03,0x00,0x00,0x0d,0x03,0x00,0x00,
+0x0e,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x11,0x03,0x00,0x00,0x25,0x03,0x00,0x00,0x63,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x12,0x03,0x00,0x00,
+0x0f,0x03,0x00,0x00,0x11,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x14,0x03,0x00,0x00,0x12,0x03,0x00,0x00,
+0x29,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
+0x15,0x03,0x00,0x00,0xca,0x00,0x00,0x00,0x14,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x16,0x03,0x00,0x00,
+0x15,0x03,0x00,0x00,0x41,0x00,0x06,0x00,0xcf,0x01,0x00,0x00,
+0x17,0x03,0x00,0x00,0xfc,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
+0x09,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x17,0x03,0x00,0x00,
+0x16,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0xf8,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xf8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe4,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x19,0x03,0x00,0x00,
+0x29,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe3,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xdc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xdc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1b,0x03,0x00,0x00,0x27,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xd9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xdb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x1d,0x03,0x00,0x00,0x25,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc1,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xbc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbc,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1f,0x03,0x00,0x00,
+0x24,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xb9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbb,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q4_k_f32_aligned_fp32_len = 11900;
+
+unsigned char matmul_q4_k_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x86,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x21,0x01,0x00,0x00,0x78,0x01,0x00,0x00,
+0xc4,0x01,0x00,0x00,0xcf,0x01,0x00,0x00,0xb4,0x02,0x00,0x00,
+0xfd,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1b,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1c,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1d,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1d,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1e,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x1f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x1f,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x21,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x21,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9e,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x9f,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xcc,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xcd,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xcd,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xcd,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xcf,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xcf,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb4,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xfa,0x02,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0xfb,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xfb,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0xfb,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xfd,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xfd,0x02,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x00,0x10,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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+0xc1,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,0x25,0x03,0x00,0x00,
+0xbe,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xbc,0x02,0x00,0x00,
+0xbd,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xc0,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xbb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc2,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x26,0x03,0x00,0x00,
+0x3f,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,0x1e,0x03,0x00,0x00,
+0xc5,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0xc8,0x02,0x00,0x00,0x26,0x03,0x00,0x00,0x61,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xc4,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xc8,0x02,0x00,0x00,
+0xc3,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xc3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xcc,0x02,0x00,0x00,0x26,0x03,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
+0xa3,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0x65,0x00,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd0,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,
+0x25,0x03,0x00,0x00,0x43,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
+0xd4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd7,0x02,0x00,0x00,0x69,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,
+0xd5,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xda,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xda,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x28,0x03,0x00,0x00,
+0x3f,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,0x1c,0x03,0x00,0x00,
+0xdd,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0xe0,0x02,0x00,0x00,0x28,0x03,0x00,0x00,0xbb,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xdc,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xe0,0x02,0x00,0x00,
+0xdb,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xdb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x2a,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,
+0xdb,0x02,0x00,0x00,0x1a,0x03,0x00,0x00,0xe5,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xe8,0x02,0x00,0x00,
+0x2a,0x03,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xe4,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xe8,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
+0xe4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe3,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xeb,0x02,0x00,0x00,
+0xd0,0x02,0x00,0x00,0x2a,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0xee,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,
+0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xf0,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xee,0x02,0x00,0x00,
+0xef,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xef,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf3,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,0x28,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xf6,0x02,0x00,0x00,
+0xf3,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xf0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf0,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0xf7,0x02,0x00,0x00,
+0xee,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,
+0xef,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xf9,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf7,0x02,0x00,0x00,
+0xf8,0x02,0x00,0x00,0xf9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xf8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x01,0x03,0x00,0x00,0xd8,0x02,0x00,0x00,0x28,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x03,0x03,0x00,0x00,
+0x14,0x00,0x00,0x00,0x02,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x04,0x03,0x00,0x00,0x03,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x05,0x03,0x00,0x00,
+0x01,0x03,0x00,0x00,0x04,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x06,0x03,0x00,0x00,0xb8,0x02,0x00,0x00,
+0x05,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x08,0x03,0x00,0x00,0x06,0x03,0x00,0x00,0xd0,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0a,0x03,0x00,0x00,
+0x08,0x03,0x00,0x00,0x2a,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x0c,0x03,0x00,0x00,0x25,0x03,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x0e,0x03,0x00,0x00,0x0c,0x03,0x00,0x00,0x28,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x10,0x03,0x00,0x00,
+0x0e,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x12,0x03,0x00,0x00,0x26,0x03,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x13,0x03,0x00,0x00,0x10,0x03,0x00,0x00,0x12,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x15,0x03,0x00,0x00,
+0x13,0x03,0x00,0x00,0x2a,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0x16,0x03,0x00,0x00,0xc9,0x00,0x00,0x00,
+0x15,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0x17,0x03,0x00,0x00,0x16,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
+0xda,0x01,0x00,0x00,0x18,0x03,0x00,0x00,0xfd,0x02,0x00,0x00,
+0x35,0x00,0x00,0x00,0x0a,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x18,0x03,0x00,0x00,0x17,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xf9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf9,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1a,0x03,0x00,0x00,0x2a,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xdd,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xdd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,0x28,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xda,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xdc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1e,0x03,0x00,0x00,
+0x26,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xbd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x20,0x03,0x00,0x00,0x25,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xba,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xbc,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q4_k_f32_fp32_len = 11952;
+
+unsigned char matmul_q5_0_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x46,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0x50,0x01,0x00,0x00,
+0x83,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x78,0x02,0x00,0x00,
+0xc1,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
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+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x91,0x02,0x00,0x00,
+0x67,0x02,0x00,0x00,0x90,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,0x65,0x00,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x94,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0x93,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
+0xeb,0x02,0x00,0x00,0x05,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
+0x98,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9b,0x02,0x00,0x00,0x69,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,
+0x99,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x9e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9e,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xee,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x87,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,
+0xa1,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0xa4,0x02,0x00,0x00,0xee,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xa0,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xa4,0x02,0x00,0x00,
+0x9f,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x9f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xf0,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x9f,0x02,0x00,0x00,0xde,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xac,0x02,0x00,0x00,
+0xf0,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xa8,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xac,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,
+0x94,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
+0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xb4,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xb2,0x02,0x00,0x00,
+0xb3,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb7,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xba,0x02,0x00,0x00,
+0xb7,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xb4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb4,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,
+0xb2,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
+0xb3,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xbd,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xbb,0x02,0x00,0x00,
+0xbc,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xbc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc5,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0xc6,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,
+0xc5,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,
+0xc9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xcc,0x02,0x00,0x00,0xca,0x02,0x00,0x00,0x94,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xce,0x02,0x00,0x00,
+0xcc,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd2,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,
+0xd2,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,0xec,0x02,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd7,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,
+0xd7,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0xda,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
+0xd9,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0xdb,0x02,0x00,0x00,0xda,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x99,0x01,0x00,0x00,0xdc,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
+0x35,0x00,0x00,0x00,0xce,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xdc,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xbd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xde,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x9e,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x89,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe2,0x02,0x00,0x00,
+0xec,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x88,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x81,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe4,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x80,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q5_0_f32_len = 11076;
+
+unsigned char matmul_q5_0_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x61,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x51,0x01,0x00,0x00,
+0x86,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x9b,0x02,0x00,0x00,
+0xe4,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x03,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x06,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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+0x56,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,0x97,0x00,0x00,0x00,
+0x89,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8f,0x02,0x00,0x00,0x5a,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x90,0x02,0x00,0x00,
+0xa8,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
+0x93,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x95,0x02,0x00,0x00,0x94,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x96,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x95,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9a,0x02,0x00,0x00,0x48,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,
+0x9b,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
+0x9a,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
+0x9e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x0e,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0x07,0x03,0x00,0x00,0xa4,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,
+0x0e,0x03,0x00,0x00,0xbf,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xa3,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xa7,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,
+0xa3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa9,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x0f,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
+0x05,0x03,0x00,0x00,0xac,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,0x0f,0x03,0x00,0x00,
+0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xab,0x02,0x00,0x00,
+0xac,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xaf,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xaa,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0x0f,0x03,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb4,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,
+0x65,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,
+0xb6,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xbb,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,0x28,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,
+0x90,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,0x69,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xbf,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xc1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xc1,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x11,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,
+0x03,0x03,0x00,0x00,0xc4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,0x11,0x03,0x00,0x00,
+0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xc3,0x02,0x00,0x00,
+0xc4,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xc7,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x13,0x03,0x00,0x00,
+0x3f,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,0x01,0x03,0x00,0x00,
+0xcc,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xcf,0x02,0x00,0x00,0x13,0x03,0x00,0x00,0x63,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xcb,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xcf,0x02,0x00,0x00,
+0xca,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xca,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd2,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,0x13,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,
+0xd2,0x02,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xd7,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xd5,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xda,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
+0x11,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0xdb,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,
+0xdb,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xdd,0x02,0x00,0x00,0xda,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xd7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xd7,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,
+0xde,0x02,0x00,0x00,0xd5,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
+0xdd,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xe0,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xde,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xdf,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe8,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
+0x11,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0xea,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xe9,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xeb,0x02,0x00,0x00,
+0xea,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xec,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xed,0x02,0x00,0x00,
+0x9f,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xef,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
+0xb7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf1,0x02,0x00,0x00,0xef,0x02,0x00,0x00,0x13,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,
+0x0e,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,
+0x11,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf7,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf9,0x02,0x00,0x00,
+0x0f,0x03,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,
+0xf9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xfc,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,0x13,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0xfd,0x02,0x00,0x00,
+0xca,0x00,0x00,0x00,0xfc,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc4,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,0xfd,0x02,0x00,0x00,
+0x41,0x00,0x06,0x00,0x90,0x01,0x00,0x00,0xff,0x02,0x00,0x00,
+0xe4,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xf1,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xff,0x02,0x00,0x00,0xfe,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x01,0x03,0x00,0x00,0x13,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x03,0x03,0x00,0x00,
+0x11,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xac,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xac,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x05,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xab,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x07,0x03,0x00,0x00,0x0e,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q5_0_f32_aligned_len = 11668;
+
+unsigned char matmul_q5_0_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x37,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,
+0x51,0x01,0x00,0x00,0x84,0x01,0x00,0x00,0x8b,0x01,0x00,0x00,
+0x71,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
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+0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x78,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,
+0x82,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x85,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x61,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x81,0x02,0x00,0x00,0x82,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x85,0x02,0x00,0x00,
+0x80,0x02,0x00,0x00,0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x80,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x89,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
+0x60,0x02,0x00,0x00,0x89,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,0x65,0x00,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8d,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x91,0x02,0x00,0x00,
+0xe4,0x02,0x00,0x00,0x00,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,0x66,0x02,0x00,0x00,
+0x91,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x94,0x02,0x00,0x00,0x69,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
+0x92,0x02,0x00,0x00,0x94,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x97,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x97,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xe7,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x80,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,
+0x9a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x9d,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x99,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x9d,0x02,0x00,0x00,
+0x98,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x98,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xe9,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x98,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,
+0xe9,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xa1,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xa5,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
+0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,
+0x8d,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
+0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xad,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xab,0x02,0x00,0x00,
+0xac,0x02,0x00,0x00,0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xac,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb0,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,
+0xb0,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
+0xab,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,
+0xac,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xb6,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xb4,0x02,0x00,0x00,
+0xb5,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xbe,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,
+0xbe,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,0x75,0x02,0x00,0x00,
+0xc2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc5,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,
+0xc5,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xcb,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
+0xcb,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd0,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,
+0xd0,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcd,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,0xca,0x00,0x00,0x00,
+0xd2,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0xd4,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x8d,0x01,0x00,0x00,0xd5,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
+0x35,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xd5,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xb6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb6,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd7,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x97,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,
+0xe5,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xdd,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x79,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q5_0_f32_aligned_fp32_len = 10920;
+
+unsigned char matmul_q5_0_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x3f,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x09,0x01,0x00,0x00,
+0x50,0x01,0x00,0x00,0x81,0x01,0x00,0x00,0x8c,0x01,0x00,0x00,
+0x71,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
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+0x3f,0x00,0x00,0x00,0x80,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,
+0x9a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x9d,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x99,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x9d,0x02,0x00,0x00,
+0x98,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x98,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xe9,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x98,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,
+0xe9,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xa1,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xa5,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
+0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,
+0x8d,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
+0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xad,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xab,0x02,0x00,0x00,
+0xac,0x02,0x00,0x00,0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xac,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb0,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,
+0xb0,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
+0xab,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,
+0xac,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xb6,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xb4,0x02,0x00,0x00,
+0xb5,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xbe,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,
+0xbe,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,0x75,0x02,0x00,0x00,
+0xc2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc5,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,
+0xc5,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xcb,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
+0xcb,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd0,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,
+0xd0,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
+0xd2,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0xd4,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x97,0x01,0x00,0x00,0xd5,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
+0x35,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xd5,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xb6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb6,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd7,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x97,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,
+0xe5,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xdd,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x79,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q5_0_f32_fp32_len = 10956;
+
+unsigned char matmul_q5_1_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x16,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x4b,0x01,0x00,0x00,
+0x7e,0x01,0x00,0x00,0x89,0x01,0x00,0x00,0x73,0x02,0x00,0x00,
+0xbc,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x03,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x04,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x05,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x58,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x59,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
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+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,
+0xb2,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xaf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,
+0xad,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,
+0xae,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xb8,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xb6,0x02,0x00,0x00,
+0xb7,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc0,0x02,0x00,0x00,0x97,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,
+0xc0,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
+0xc4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc7,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,
+0xc7,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xcd,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,
+0xcd,0x02,0x00,0x00,0xce,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd2,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,
+0xd2,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
+0xd4,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0xd6,0x02,0x00,0x00,0xd5,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x94,0x01,0x00,0x00,0xd7,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,
+0x35,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xd7,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xb8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb8,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd9,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x99,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x84,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x84,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,
+0xe7,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xdf,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7b,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q5_1_f32_len = 10956;
+
+unsigned char matmul_q5_1_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x39,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x4c,0x01,0x00,0x00,
+0x81,0x01,0x00,0x00,0x89,0x01,0x00,0x00,0x96,0x02,0x00,0x00,
+0xdf,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x03,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x04,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x05,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x06,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x06,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x06,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x08,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x08,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x59,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x5a,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x86,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x87,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x87,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x87,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x87,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x87,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x89,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x89,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x96,0x02,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xdc,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xdd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xdd,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xdd,0x02,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xdf,0x02,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xdf,0x02,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
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+0xf9,0x00,0x02,0x00,0xbc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xbc,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,
+0xfe,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,0x0c,0x03,0x00,0x00,
+0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xbe,0x02,0x00,0x00,
+0xbf,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xc2,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x0e,0x03,0x00,0x00,
+0x3f,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,0xfc,0x02,0x00,0x00,
+0xc7,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xca,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,0x63,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xc6,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xca,0x02,0x00,0x00,
+0xc5,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xc5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xcd,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,
+0xcd,0x02,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xd2,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xd0,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
+0x0c,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0xd6,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,
+0xd6,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xd8,0x02,0x00,0x00,0xd5,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xd2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xd2,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,
+0xd9,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
+0xd8,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xdb,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xd9,0x02,0x00,0x00,0xda,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xda,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
+0x0c,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0xe5,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xe6,0x02,0x00,0x00,
+0xe5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe7,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe8,0x02,0x00,0x00,
+0x9a,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xea,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,
+0xb2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xec,0x02,0x00,0x00,0xea,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xee,0x02,0x00,0x00,
+0x09,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf0,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
+0x0c,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf2,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,
+0x0a,0x03,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,0xf2,0x02,0x00,0x00,
+0xf4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf7,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0xf8,0x02,0x00,0x00,
+0xca,0x00,0x00,0x00,0xf7,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc4,0x00,0x00,0x00,0xf9,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,
+0x41,0x00,0x06,0x00,0x8b,0x01,0x00,0x00,0xfa,0x02,0x00,0x00,
+0xdf,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xec,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xfa,0x02,0x00,0x00,0xf9,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xdb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xdb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc7,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xfc,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xbf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbf,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,
+0x0c,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xbc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbe,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x00,0x03,0x00,0x00,0x0a,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x02,0x03,0x00,0x00,0x09,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9e,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q5_1_f32_aligned_len = 11548;
+
+unsigned char matmul_q5_1_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x0f,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x08,0x01,0x00,0x00,
+0x4c,0x01,0x00,0x00,0x7f,0x01,0x00,0x00,0x86,0x01,0x00,0x00,
+0x6c,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x04,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x06,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x06,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x06,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x08,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x08,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,
+0xb9,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,0x70,0x02,0x00,0x00,
+0xbd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc0,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,
+0xc0,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc6,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,
+0xc6,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xcb,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
+0xcb,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcd,0x00,0x00,0x00,0xce,0x02,0x00,0x00,0xca,0x00,0x00,0x00,
+0xcd,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0xcf,0x02,0x00,0x00,0xce,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x88,0x01,0x00,0x00,0xd0,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,
+0x35,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xd0,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xb1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb1,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x9d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x9d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd2,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x9c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x95,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x95,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x92,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x94,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,
+0xe0,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x75,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x75,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd8,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x74,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q5_1_f32_aligned_fp32_len = 10800;
+
+unsigned char matmul_q5_1_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x0f,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x07,0x01,0x00,0x00,
+0x4b,0x01,0x00,0x00,0x7c,0x01,0x00,0x00,0x87,0x01,0x00,0x00,
+0x6c,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x03,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x05,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x05,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x07,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x56,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x57,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x84,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x85,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x85,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x85,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x87,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x87,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6c,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb2,0x02,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xb3,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xb3,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xb3,0x02,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb5,0x02,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb5,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x10,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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+0xf8,0x00,0x02,0x00,0x94,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,
+0xe0,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x75,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x75,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd8,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x74,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q5_1_f32_fp32_len = 10836;
+
+unsigned char matmul_q5_k_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xb2,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x2e,0x01,0x00,0x00,0x83,0x01,0x00,0x00,0xeb,0x01,0x00,0x00,
+0xf6,0x01,0x00,0x00,0xe0,0x02,0x00,0x00,0x29,0x03,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x27,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x28,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x29,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2a,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2a,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x01,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x2b,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0xb0,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x2c,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x2c,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2e,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x2e,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xc5,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc6,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xf3,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xf4,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xf4,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xf4,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xf6,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf6,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xe0,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x26,0x03,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x27,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x27,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x27,0x03,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x29,0x03,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x29,0x03,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x10,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x55,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x61,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
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+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,
+0x0f,0x00,0x00,0x00,0xda,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,0x48,0x00,0x00,0x00,
+0xda,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
+0xe1,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xe2,0x02,0x00,0x00,
+0xe1,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe3,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,
+0xdb,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe6,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x51,0x03,0x00,0x00,
+0x3f,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0x4c,0x03,0x00,0x00,
+0xe9,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0xec,0x02,0x00,0x00,0x51,0x03,0x00,0x00,0xbe,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xe8,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xec,0x02,0x00,0x00,
+0xe7,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xee,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xee,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x52,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,
+0xe7,0x02,0x00,0x00,0x4a,0x03,0x00,0x00,0xf1,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,
+0x52,0x03,0x00,0x00,0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xf0,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xf4,0x02,0x00,0x00,0xef,0x02,0x00,0x00,
+0xf0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xef,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf8,0x02,0x00,0x00,
+0x52,0x03,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf9,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
+0xf8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xfb,0x02,0x00,0x00,0x65,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfc,0x02,0x00,0x00,
+0xf9,0x02,0x00,0x00,0xfb,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x51,0x03,0x00,0x00,
+0x6d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x01,0x03,0x00,0x00,0xd5,0x02,0x00,0x00,0x00,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x03,0x03,0x00,0x00,
+0x69,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x04,0x03,0x00,0x00,0x01,0x03,0x00,0x00,
+0x03,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x06,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x06,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x54,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,
+0xef,0x02,0x00,0x00,0x48,0x03,0x00,0x00,0x09,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x0c,0x03,0x00,0x00,
+0x54,0x03,0x00,0x00,0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x08,0x03,0x00,0x00,0x09,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x0c,0x03,0x00,0x00,0x07,0x03,0x00,0x00,
+0x08,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x07,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x0e,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x0e,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x56,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,0x07,0x03,0x00,0x00,
+0x46,0x03,0x00,0x00,0x11,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x14,0x03,0x00,0x00,0x56,0x03,0x00,0x00,
+0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x10,0x03,0x00,0x00,
+0x11,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x14,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,0x10,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x0f,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x17,0x03,0x00,0x00,0xfc,0x02,0x00,0x00,
+0x56,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x1a,0x03,0x00,0x00,0x17,0x03,0x00,0x00,0x37,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x1c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x1a,0x03,0x00,0x00,0x1b,0x03,0x00,0x00,
+0x1c,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x1b,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1f,0x03,0x00,0x00,
+0x04,0x03,0x00,0x00,0x54,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x20,0x03,0x00,0x00,0x14,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x21,0x03,0x00,0x00,0x20,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x22,0x03,0x00,0x00,0x1f,0x03,0x00,0x00,
+0x21,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x1c,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x1c,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,
+0xc1,0x00,0x00,0x00,0x23,0x03,0x00,0x00,0x1a,0x03,0x00,0x00,
+0x0f,0x03,0x00,0x00,0x22,0x03,0x00,0x00,0x1b,0x03,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x25,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x23,0x03,0x00,0x00,0x24,0x03,0x00,0x00,
+0x25,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x24,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2d,0x03,0x00,0x00,
+0x04,0x03,0x00,0x00,0x54,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x2f,0x03,0x00,0x00,0x14,0x00,0x00,0x00,
+0x2e,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x30,0x03,0x00,0x00,0x2f,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x31,0x03,0x00,0x00,0x2d,0x03,0x00,0x00,
+0x30,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x32,0x03,0x00,0x00,0xe4,0x02,0x00,0x00,0x31,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x34,0x03,0x00,0x00,
+0x32,0x03,0x00,0x00,0xfc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x36,0x03,0x00,0x00,0x34,0x03,0x00,0x00,
+0x56,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x38,0x03,0x00,0x00,0x51,0x03,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3a,0x03,0x00,0x00,
+0x38,0x03,0x00,0x00,0x54,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3c,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,
+0x3b,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x3e,0x03,0x00,0x00,0x52,0x03,0x00,0x00,0x63,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3f,0x03,0x00,0x00,
+0x3c,0x03,0x00,0x00,0x3e,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x41,0x03,0x00,0x00,0x3f,0x03,0x00,0x00,
+0x56,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,
+0x42,0x03,0x00,0x00,0xc9,0x00,0x00,0x00,0x41,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0x43,0x03,0x00,0x00,
+0x42,0x03,0x00,0x00,0x41,0x00,0x06,0x00,0x01,0x02,0x00,0x00,
+0x44,0x03,0x00,0x00,0x29,0x03,0x00,0x00,0x35,0x00,0x00,0x00,
+0x36,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x44,0x03,0x00,0x00,
+0x43,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x25,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x25,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x11,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x11,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x46,0x03,0x00,0x00,
+0x56,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0e,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x10,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x09,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x09,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x48,0x03,0x00,0x00,0x54,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x06,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x08,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0xf1,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xf1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x4a,0x03,0x00,0x00,0x52,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xee,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xf0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4c,0x03,0x00,0x00,
+0x51,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe8,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q5_k_f32_len = 12596;
+
+unsigned char matmul_q5_k_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xcc,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x2f,0x01,0x00,0x00,0x84,0x01,0x00,0x00,0xee,0x01,0x00,0x00,
+0xf6,0x01,0x00,0x00,0x02,0x03,0x00,0x00,0x4b,0x03,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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+0x74,0x03,0x00,0x00,0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
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+0x8f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x23,0x03,0x00,0x00,0xf7,0x02,0x00,0x00,0x22,0x03,0x00,0x00,
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+0x69,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x26,0x03,0x00,0x00,0x23,0x03,0x00,0x00,
+0x25,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x28,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x28,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x76,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x11,0x03,0x00,0x00,0x6a,0x03,0x00,0x00,0x2b,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x2e,0x03,0x00,0x00,
+0x76,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x2a,0x03,0x00,0x00,0x2b,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x2e,0x03,0x00,0x00,0x29,0x03,0x00,0x00,
+0x2a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x29,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x30,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x30,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x78,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,0x29,0x03,0x00,0x00,
+0x68,0x03,0x00,0x00,0x33,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x36,0x03,0x00,0x00,0x78,0x03,0x00,0x00,
+0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x32,0x03,0x00,0x00,
+0x33,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x36,0x03,0x00,0x00,0x31,0x03,0x00,0x00,0x32,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x31,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x39,0x03,0x00,0x00,0x1e,0x03,0x00,0x00,
+0x78,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x3c,0x03,0x00,0x00,0x39,0x03,0x00,0x00,0x37,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x3e,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x3c,0x03,0x00,0x00,0x3d,0x03,0x00,0x00,
+0x3e,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x3d,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x41,0x03,0x00,0x00,
+0x26,0x03,0x00,0x00,0x76,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x42,0x03,0x00,0x00,0x14,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x43,0x03,0x00,0x00,0x42,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x44,0x03,0x00,0x00,0x41,0x03,0x00,0x00,
+0x43,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x3e,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x3e,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,
+0xc2,0x00,0x00,0x00,0x45,0x03,0x00,0x00,0x3c,0x03,0x00,0x00,
+0x31,0x03,0x00,0x00,0x44,0x03,0x00,0x00,0x3d,0x03,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x47,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x45,0x03,0x00,0x00,0x46,0x03,0x00,0x00,
+0x47,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x46,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4f,0x03,0x00,0x00,
+0x26,0x03,0x00,0x00,0x76,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x51,0x03,0x00,0x00,0x14,0x00,0x00,0x00,
+0x50,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x52,0x03,0x00,0x00,0x51,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x53,0x03,0x00,0x00,0x4f,0x03,0x00,0x00,
+0x52,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x54,0x03,0x00,0x00,0x06,0x03,0x00,0x00,0x53,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x56,0x03,0x00,0x00,
+0x54,0x03,0x00,0x00,0x1e,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x58,0x03,0x00,0x00,0x56,0x03,0x00,0x00,
+0x78,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5a,0x03,0x00,0x00,0x73,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5c,0x03,0x00,0x00,
+0x5a,0x03,0x00,0x00,0x76,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5e,0x03,0x00,0x00,0x5c,0x03,0x00,0x00,
+0x5d,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x60,0x03,0x00,0x00,0x74,0x03,0x00,0x00,0x63,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x61,0x03,0x00,0x00,
+0x5e,0x03,0x00,0x00,0x60,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x63,0x03,0x00,0x00,0x61,0x03,0x00,0x00,
+0x78,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
+0x64,0x03,0x00,0x00,0xca,0x00,0x00,0x00,0x63,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x65,0x03,0x00,0x00,
+0x64,0x03,0x00,0x00,0x41,0x00,0x06,0x00,0xf8,0x01,0x00,0x00,
+0x66,0x03,0x00,0x00,0x4b,0x03,0x00,0x00,0x35,0x00,0x00,0x00,
+0x58,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x66,0x03,0x00,0x00,
+0x65,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x47,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x47,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x33,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x33,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x68,0x03,0x00,0x00,
+0x78,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x30,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x32,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x2b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x2b,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6a,0x03,0x00,0x00,0x76,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x28,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x2a,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x13,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x13,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6c,0x03,0x00,0x00,0x74,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x10,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x12,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0b,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x03,0x00,0x00,
+0x73,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x08,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0a,0x03,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q5_k_f32_aligned_len = 13172;
+
+unsigned char matmul_q5_k_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xa2,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,0x83,0x01,0x00,0x00,
+0xeb,0x01,0x00,0x00,0xf2,0x01,0x00,0x00,0xd8,0x02,0x00,0x00,
+0x21,0x03,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x0c,0x03,0x00,0x00,
+0x07,0x03,0x00,0x00,0x08,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x07,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x0f,0x03,0x00,0x00,0xf4,0x02,0x00,0x00,0x4e,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x12,0x03,0x00,0x00,
+0x0f,0x03,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x14,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x12,0x03,0x00,0x00,0x13,0x03,0x00,0x00,0x14,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x13,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x17,0x03,0x00,0x00,0xfc,0x02,0x00,0x00,
+0x4c,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x18,0x03,0x00,0x00,0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x19,0x03,0x00,0x00,
+0x18,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x1a,0x03,0x00,0x00,0x17,0x03,0x00,0x00,0x19,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x14,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x14,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,
+0x1b,0x03,0x00,0x00,0x12,0x03,0x00,0x00,0x07,0x03,0x00,0x00,
+0x1a,0x03,0x00,0x00,0x13,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x1d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x1b,0x03,0x00,0x00,0x1c,0x03,0x00,0x00,0x1d,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x1c,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x25,0x03,0x00,0x00,0xfc,0x02,0x00,0x00,
+0x4c,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x27,0x03,0x00,0x00,0x14,0x00,0x00,0x00,0x26,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x28,0x03,0x00,0x00,
+0x27,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x29,0x03,0x00,0x00,0x25,0x03,0x00,0x00,0x28,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2a,0x03,0x00,0x00,
+0xdc,0x02,0x00,0x00,0x29,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x2c,0x03,0x00,0x00,0x2a,0x03,0x00,0x00,
+0xf4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x2e,0x03,0x00,0x00,0x2c,0x03,0x00,0x00,0x4e,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x30,0x03,0x00,0x00,
+0x49,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x32,0x03,0x00,0x00,0x30,0x03,0x00,0x00,
+0x4c,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x34,0x03,0x00,0x00,0x32,0x03,0x00,0x00,0x33,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x36,0x03,0x00,0x00,
+0x4a,0x03,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x37,0x03,0x00,0x00,0x34,0x03,0x00,0x00,
+0x36,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x39,0x03,0x00,0x00,0x37,0x03,0x00,0x00,0x4e,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0x3a,0x03,0x00,0x00,
+0xca,0x00,0x00,0x00,0x39,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc4,0x00,0x00,0x00,0x3b,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,
+0x41,0x00,0x06,0x00,0xf4,0x01,0x00,0x00,0x3c,0x03,0x00,0x00,
+0x21,0x03,0x00,0x00,0x35,0x00,0x00,0x00,0x2e,0x03,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x3c,0x03,0x00,0x00,0x3b,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x1d,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x1d,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x09,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x09,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3e,0x03,0x00,0x00,0x4e,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x06,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x08,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x01,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x01,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x40,0x03,0x00,0x00,
+0x4c,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xfe,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x00,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x42,0x03,0x00,0x00,0x4a,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x44,0x03,0x00,0x00,0x49,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xde,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe0,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q5_k_f32_aligned_fp32_len = 12424;
+
+unsigned char matmul_q5_k_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xab,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,0x83,0x01,0x00,0x00,
+0xe9,0x01,0x00,0x00,0xf4,0x01,0x00,0x00,0xd9,0x02,0x00,0x00,
+0x22,0x03,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x27,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x28,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x29,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2a,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2a,0x01,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2b,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x2c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x2c,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x2e,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x2e,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc3,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xc4,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xf1,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xf2,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xf2,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xf2,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf4,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xf4,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xd9,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
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+0x0a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0a,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3f,0x03,0x00,0x00,
+0x4f,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x07,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x09,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x02,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x02,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x41,0x03,0x00,0x00,0x4d,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xff,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x01,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0xea,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xea,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x43,0x03,0x00,0x00,0x4b,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe7,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x45,0x03,0x00,0x00,
+0x4a,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xdf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q5_k_f32_fp32_len = 12476;
+
+unsigned char matmul_q6_k_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x43,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x33,0x01,0x00,0x00,0x44,0x01,0x00,0x00,0xaf,0x01,0x00,0x00,
+0xba,0x01,0x00,0x00,0xa4,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2a,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x2b,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x2d,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2f,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2f,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2f,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0xc0,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2f,0x01,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x30,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0xd2,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x31,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x31,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x31,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x33,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x33,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x89,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8a,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb7,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xb8,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xb8,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xb8,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xba,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xba,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa4,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xea,0x02,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xeb,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xeb,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xeb,0x02,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xed,0x02,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xed,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x10,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x55,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x61,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
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+0xde,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xdf,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,
+0x16,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0xe4,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,
+0xe4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0xe6,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe0,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,
+0xe7,0x02,0x00,0x00,0xde,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,
+0xe6,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xe9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xe7,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf1,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,
+0x16,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0xf3,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xf2,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,
+0xf3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf5,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf6,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf8,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,
+0xc0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xfa,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,0x18,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfc,0x02,0x00,0x00,
+0x13,0x03,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,0xfc,0x02,0x00,0x00,
+0x16,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x00,0x03,0x00,0x00,0xfe,0x02,0x00,0x00,0xff,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x02,0x03,0x00,0x00,
+0x14,0x03,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x03,0x03,0x00,0x00,0x00,0x03,0x00,0x00,
+0x02,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x05,0x03,0x00,0x00,0x03,0x03,0x00,0x00,0x18,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0x06,0x03,0x00,0x00,
+0xc9,0x00,0x00,0x00,0x05,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc3,0x00,0x00,0x00,0x07,0x03,0x00,0x00,0x06,0x03,0x00,0x00,
+0x41,0x00,0x06,0x00,0xc5,0x01,0x00,0x00,0x08,0x03,0x00,0x00,
+0xed,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x08,0x03,0x00,0x00,0x07,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xd5,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x0a,0x03,0x00,0x00,0x18,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xd2,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xcd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xcd,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0c,0x03,0x00,0x00,
+0x16,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xca,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xb5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x0e,0x03,0x00,0x00,0x14,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xb2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xad,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x10,0x03,0x00,0x00,0x13,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xaa,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xac,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q6_k_f32_len = 11764;
+
+unsigned char matmul_q6_k_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x64,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x33,0x01,0x00,0x00,0x44,0x01,0x00,0x00,0xb1,0x01,0x00,0x00,
+0xb9,0x01,0x00,0x00,0xc5,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2a,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x2b,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x2d,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2f,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2f,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2f,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0xc0,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2f,0x01,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x30,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0xd2,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x31,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x31,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x31,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x33,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x33,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x89,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8a,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb6,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xb7,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xb7,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xb7,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xb7,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xb7,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb9,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xc5,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x03,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x0c,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x0c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x35,0x03,0x00,0x00,
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+0xe6,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xeb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xeb,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x37,0x03,0x00,0x00,
+0x3f,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0x2d,0x03,0x00,0x00,
+0xee,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xf1,0x02,0x00,0x00,0x37,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xed,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf1,0x02,0x00,0x00,
+0xec,0x02,0x00,0x00,0xed,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xec,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xf3,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xf3,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x39,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,
+0xec,0x02,0x00,0x00,0x2b,0x03,0x00,0x00,0xf6,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xf9,0x02,0x00,0x00,
+0x39,0x03,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xf5,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xf9,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,
+0xf5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfc,0x02,0x00,0x00,
+0xe1,0x02,0x00,0x00,0x39,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xff,0x02,0x00,0x00,0xfc,0x02,0x00,0x00,
+0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x01,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xff,0x02,0x00,0x00,
+0x00,0x03,0x00,0x00,0x01,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x00,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x04,0x03,0x00,0x00,0xe9,0x02,0x00,0x00,0x37,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x05,0x03,0x00,0x00,
+0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x06,0x03,0x00,0x00,0x05,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x07,0x03,0x00,0x00,
+0x04,0x03,0x00,0x00,0x06,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x01,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x01,0x03,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,0x08,0x03,0x00,0x00,
+0xff,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,0x07,0x03,0x00,0x00,
+0x00,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,0x0a,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x08,0x03,0x00,0x00,
+0x09,0x03,0x00,0x00,0x0a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x09,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x12,0x03,0x00,0x00,0xe9,0x02,0x00,0x00,0x37,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x14,0x03,0x00,0x00,
+0x14,0x00,0x00,0x00,0x13,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x15,0x03,0x00,0x00,0x14,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x16,0x03,0x00,0x00,
+0x12,0x03,0x00,0x00,0x15,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x17,0x03,0x00,0x00,0xc9,0x02,0x00,0x00,
+0x16,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x19,0x03,0x00,0x00,0x17,0x03,0x00,0x00,0xe1,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1b,0x03,0x00,0x00,
+0x19,0x03,0x00,0x00,0x39,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x1d,0x03,0x00,0x00,0x34,0x03,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1f,0x03,0x00,0x00,0x1d,0x03,0x00,0x00,0x37,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x21,0x03,0x00,0x00,
+0x1f,0x03,0x00,0x00,0x20,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x23,0x03,0x00,0x00,0x35,0x03,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x24,0x03,0x00,0x00,0x21,0x03,0x00,0x00,0x23,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x26,0x03,0x00,0x00,
+0x24,0x03,0x00,0x00,0x39,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcd,0x00,0x00,0x00,0x27,0x03,0x00,0x00,0xca,0x00,0x00,0x00,
+0x26,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0x28,0x03,0x00,0x00,0x27,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
+0xbb,0x01,0x00,0x00,0x29,0x03,0x00,0x00,0x0e,0x03,0x00,0x00,
+0x35,0x00,0x00,0x00,0x1b,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x29,0x03,0x00,0x00,0x28,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0a,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xf6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xf6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x03,0x00,0x00,0x39,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xf3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xf5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xee,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xee,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x2d,0x03,0x00,0x00,0x37,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xeb,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xed,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd6,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2f,0x03,0x00,0x00,
+0x35,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd5,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xce,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xce,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x31,0x03,0x00,0x00,0x34,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xcd,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q6_k_f32_aligned_len = 12324;
+
+unsigned char matmul_q6_k_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
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+0xfd,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0xff,0x02,0x00,0x00,0xfe,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0xb8,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0xe5,0x02,0x00,0x00,
+0x35,0x00,0x00,0x00,0xf2,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x00,0x03,0x00,0x00,0xff,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xcd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xcd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x02,0x03,0x00,0x00,0x10,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xca,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xcc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x04,0x03,0x00,0x00,0x0e,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc2,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x06,0x03,0x00,0x00,
+0x0c,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xaa,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xac,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x08,0x03,0x00,0x00,0x0b,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa4,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q6_k_f32_aligned_fp32_len = 11592;
+
+unsigned char matmul_q6_k_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x3c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x33,0x01,0x00,0x00,0x44,0x01,0x00,0x00,
+0xad,0x01,0x00,0x00,0xb8,0x01,0x00,0x00,0x9d,0x02,0x00,0x00,
+0xe6,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x2a,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x2b,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2d,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2f,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2f,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2f,0x01,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x30,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x31,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x31,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x31,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x33,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x33,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x87,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x88,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb5,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xb6,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xb6,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xb6,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb8,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x9d,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe3,0x02,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0xe4,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xe4,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0xe4,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xe6,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xe6,0x02,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x00,0x10,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
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+0xdd,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xde,0x02,0x00,0x00,
+0xdd,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0xdf,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,0xde,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xd9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xd9,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,
+0xe0,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,
+0xdf,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xe2,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xe0,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xea,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
+0x0f,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0xec,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xeb,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xed,0x02,0x00,0x00,
+0xec,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xee,0x02,0x00,0x00,0xea,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xef,0x02,0x00,0x00,
+0xa1,0x02,0x00,0x00,0xee,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf1,0x02,0x00,0x00,0xef,0x02,0x00,0x00,
+0xb9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf3,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,0x11,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,
+0x0c,0x03,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf7,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,
+0x0f,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf9,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfb,0x02,0x00,0x00,
+0x0d,0x03,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xfc,0x02,0x00,0x00,0xf9,0x02,0x00,0x00,
+0xfb,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xfe,0x02,0x00,0x00,0xfc,0x02,0x00,0x00,0x11,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0xff,0x02,0x00,0x00,
+0xc9,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc3,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0xff,0x02,0x00,0x00,
+0x41,0x00,0x06,0x00,0xc3,0x01,0x00,0x00,0x01,0x03,0x00,0x00,
+0xe6,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x01,0x03,0x00,0x00,0x00,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xce,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xce,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x03,0x03,0x00,0x00,0x11,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xcd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc6,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x05,0x03,0x00,0x00,
+0x0f,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xae,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xae,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x07,0x03,0x00,0x00,0x0d,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xab,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xad,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x09,0x03,0x00,0x00,0x0c,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q6_k_f32_fp32_len = 11644;
+
+unsigned char matmul_q8_0_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xf1,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x28,0x01,0x00,0x00,
+0x5b,0x01,0x00,0x00,0x66,0x01,0x00,0x00,0x50,0x02,0x00,0x00,
+0x99,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x05,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x06,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x06,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x08,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x08,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x08,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0a,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0a,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x35,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x36,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x63,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x64,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x64,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x64,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x66,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x66,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x50,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x96,0x02,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x97,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x97,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x97,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x99,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x99,0x02,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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+0xbc,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x58,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q8_0_f32_len = 10608;
+
+unsigned char matmul_q8_0_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x14,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,0x29,0x01,0x00,0x00,
+0x5e,0x01,0x00,0x00,0x66,0x01,0x00,0x00,0x73,0x02,0x00,0x00,
+0xbc,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
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+0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x06,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x07,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x07,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x08,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x09,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x09,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x09,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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+0x37,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x63,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x64,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x64,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x64,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x64,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x64,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x66,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x66,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x73,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x02,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0xba,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xba,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0xba,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbc,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbc,0x02,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
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+0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
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+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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+0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
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+0x3f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
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+0x68,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
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+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
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+0xf9,0x00,0x02,0x00,0x27,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x27,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x54,0x02,0x00,0x00,0x09,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x24,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x26,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x1f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x1f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x56,0x02,0x00,0x00,0x07,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x1c,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x1e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x17,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x17,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x58,0x02,0x00,0x00,
+0x03,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x14,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x16,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xb3,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb3,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5a,0x02,0x00,0x00,0xfd,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xb0,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb2,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0xa8,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd7,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd7,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,
+0xe3,0x02,0x00,0x00,0x6d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x61,0x02,0x00,0x00,
+0x56,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x62,0x02,0x00,0x00,0x97,0x00,0x00,0x00,
+0x61,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x67,0x02,0x00,0x00,0x5a,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x68,0x02,0x00,0x00,
+0xa8,0x00,0x00,0x00,0x67,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x6c,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
+0x6b,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x6d,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6e,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x6d,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x72,0x02,0x00,0x00,0x48,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x74,0x02,0x00,0x00,
+0x73,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x75,0x02,0x00,0x00,0x74,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x76,0x02,0x00,0x00,
+0x72,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x77,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,
+0x76,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x7f,0x02,0x00,0x00,
+0xe4,0x02,0x00,0x00,0xbf,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x7b,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x7f,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,
+0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x81,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xe5,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,
+0xdd,0x02,0x00,0x00,0x84,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x87,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,
+0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x83,0x02,0x00,0x00,
+0x84,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x87,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x83,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8c,0x02,0x00,0x00,0x62,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,
+0x65,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,
+0x8e,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x93,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0x00,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
+0x68,0x02,0x00,0x00,0x93,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x96,0x02,0x00,0x00,0x69,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x97,0x02,0x00,0x00,0x94,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x99,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xe7,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x82,0x02,0x00,0x00,
+0xdb,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
+0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x9b,0x02,0x00,0x00,
+0x9c,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x9f,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xe9,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,
+0xa4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xa7,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xa3,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xa7,0x02,0x00,0x00,
+0xa2,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xaa,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xad,0x02,0x00,0x00,
+0xaa,0x02,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xaf,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xad,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xae,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,0x97,0x02,0x00,0x00,
+0xe7,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0xb3,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
+0xb3,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xb5,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xaf,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,
+0xb6,0x02,0x00,0x00,0xad,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,
+0xb5,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xb8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xb6,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,0x97,0x02,0x00,0x00,
+0xe7,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0xc2,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,
+0xc2,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc4,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,
+0x77,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
+0x8f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc9,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,
+0xe4,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,
+0xe7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xcf,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,0xce,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,
+0xe5,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
+0xd1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd4,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,
+0xca,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc4,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,0xd5,0x02,0x00,0x00,
+0x41,0x00,0x06,0x00,0x68,0x01,0x00,0x00,0xd7,0x02,0x00,0x00,
+0xbc,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xd7,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xb8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x9c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,
+0xe7,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x99,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xdd,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x83,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q8_0_f32_aligned_len = 11200;
+
+unsigned char matmul_q8_0_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xea,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
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+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
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+0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,
+0x29,0x01,0x00,0x00,0x5c,0x01,0x00,0x00,0x63,0x01,0x00,0x00,
+0x49,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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+0xba,0x02,0x00,0x00,0xd8,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6a,0x02,0x00,0x00,0x3e,0x02,0x00,0x00,
+0x69,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6c,0x02,0x00,0x00,0x69,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,
+0x6a,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x6f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x58,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,
+0x72,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x75,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x71,0x02,0x00,0x00,0x72,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x75,0x02,0x00,0x00,
+0x70,0x02,0x00,0x00,0x71,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x70,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x77,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x70,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x7d,0x02,0x00,0x00,
+0xbf,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x79,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x7d,0x02,0x00,0x00,0x78,0x02,0x00,0x00,
+0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x80,0x02,0x00,0x00,
+0x65,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x83,0x02,0x00,0x00,0x80,0x02,0x00,0x00,
+0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x85,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x83,0x02,0x00,0x00,
+0x84,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x88,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x89,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,
+0x88,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x85,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x85,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
+0x83,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,
+0x84,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x8e,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x8c,0x02,0x00,0x00,
+0x8d,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x96,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
+0x14,0x00,0x00,0x00,0x97,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0x98,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
+0x96,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,0x4d,0x02,0x00,0x00,
+0x9a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9d,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0x65,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
+0x9d,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa3,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,
+0xa3,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
+0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcd,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xca,0x00,0x00,0x00,
+0xaa,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0xac,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x65,0x01,0x00,0x00,0xad,0x02,0x00,0x00,0x92,0x02,0x00,0x00,
+0x35,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xad,0x02,0x00,0x00,0xac,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x8e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8e,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xaf,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x79,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x72,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,
+0xbb,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x59,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x52,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x52,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb5,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x51,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q8_0_f32_aligned_fp32_len = 10452;
+
+unsigned char matmul_q8_0_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xea,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,
+0x28,0x01,0x00,0x00,0x59,0x01,0x00,0x00,0x64,0x01,0x00,0x00,
+0x49,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x06,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x06,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x07,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x08,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x08,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x08,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
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+0xa8,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
+0xaa,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0xac,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x6f,0x01,0x00,0x00,0xad,0x02,0x00,0x00,0x92,0x02,0x00,0x00,
+0x35,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xad,0x02,0x00,0x00,0xac,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x8e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8e,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xaf,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x79,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x72,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,
+0xbb,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x59,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x52,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x52,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb5,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x51,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q8_0_f32_fp32_len = 10488;
+
+unsigned char mul_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x67,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x17,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,
+0x4b,0x01,0x00,0x00,0x56,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x15,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2f,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3c,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x3d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3d,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x3d,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3f,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3f,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x48,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x49,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x49,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x49,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4b,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4b,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x53,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x54,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x54,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x54,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x56,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x56,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x61,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x1e,0x00,0x1e,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
+0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x18,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x18,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
+0x58,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x18,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
+0x69,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x18,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
+0xb6,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x18,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
+0xc4,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x18,0x00,0x00,0x00,0xca,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
+0xd7,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x18,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
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+0x06,0x00,0x00,0x00,0xfe,0x01,0x00,0x00,0xdd,0x01,0x00,0x00,
+0xfd,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xff,0x01,0x00,0x00,0xfa,0x01,0x00,0x00,0xfe,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x01,0x02,0x00,0x00,
+0x17,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x02,0x02,0x00,0x00,0x01,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x03,0x02,0x00,0x00,
+0xec,0x01,0x00,0x00,0x02,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x04,0x02,0x00,0x00,0xff,0x01,0x00,0x00,
+0x03,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x06,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x07,0x02,0x00,0x00,
+0x06,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x08,0x02,0x00,0x00,0xf6,0x01,0x00,0x00,0x07,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x09,0x02,0x00,0x00,
+0x04,0x02,0x00,0x00,0x08,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x50,0x01,0x00,0x00,0x51,0x01,0x00,0x00,0x4b,0x01,0x00,0x00,
+0x34,0x01,0x00,0x00,0x09,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x52,0x01,0x00,0x00,0x51,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x49,0x02,0x00,0x00,
+0x17,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x4a,0x02,0x00,0x00,0x49,0x02,0x00,0x00,
+0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4b,0x02,0x00,0x00,
+0xca,0x01,0x00,0x00,0x4a,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x4c,0x02,0x00,0x00,0x17,0x00,0x00,0x00,
+0xb1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x4d,0x02,0x00,0x00,0x4c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x4e,0x02,0x00,0x00,0x4b,0x02,0x00,0x00,
+0x4d,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x50,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x51,0x02,0x00,0x00,
+0x50,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x52,0x02,0x00,0x00,0xdd,0x01,0x00,0x00,0x51,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x53,0x02,0x00,0x00,
+0x17,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x54,0x02,0x00,0x00,0x53,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x55,0x02,0x00,0x00,
+0x52,0x02,0x00,0x00,0x54,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x56,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,
+0x55,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x58,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x59,0x02,0x00,0x00,
+0x58,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5a,0x02,0x00,0x00,0xec,0x01,0x00,0x00,0x59,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x5b,0x02,0x00,0x00,
+0x17,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5d,0x02,0x00,0x00,
+0x5a,0x02,0x00,0x00,0x5c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5e,0x02,0x00,0x00,0x56,0x02,0x00,0x00,
+0x5d,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x60,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x61,0x02,0x00,0x00,
+0x60,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x62,0x02,0x00,0x00,0xf6,0x01,0x00,0x00,0x61,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x63,0x02,0x00,0x00,
+0x17,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x63,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x65,0x02,0x00,0x00,
+0x62,0x02,0x00,0x00,0x64,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x66,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,
+0x65,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x01,0x00,0x00,
+0x5b,0x01,0x00,0x00,0x56,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
+0x66,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x5c,0x01,0x00,0x00,0x5b,0x01,0x00,0x00,0x85,0x00,0x05,0x00,
+0x14,0x00,0x00,0x00,0x5d,0x01,0x00,0x00,0x52,0x01,0x00,0x00,
+0x5c,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x01,0x00,0x00,
+0x5e,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
+0x47,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x5e,0x01,0x00,0x00,
+0x5d,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x62,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x62,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_f32_len = 4276;
+
+unsigned char mul_mat_vec_f16_f16_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x09,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0xab,0x00,0x00,0x00,
+0xeb,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x32,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x36,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x3b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x3b,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x3b,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x3b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa8,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xa9,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xa9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xa9,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xab,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xab,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xe8,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xe9,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xe9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xe9,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xeb,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xeb,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xf3,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf4,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x10,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x11,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x12,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x2a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2a,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,
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+0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,
+0xbe,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0xc0,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0xbf,0x00,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x08,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
+0xb3,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
+0xc3,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x75,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x07,0x01,0x00,0x00,
+0x85,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x77,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x79,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x85,0x00,0x00,0x00,0x85,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xcb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xcb,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x08,0x01,0x00,0x00,0xca,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
+0xe3,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0xac,0x00,0x05,0x00,
+0x80,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,0x08,0x01,0x00,0x00,
+0x2d,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xcd,0x00,0x00,0x00,
+0xce,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xd1,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xcc,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
+0x80,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x08,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,0xd6,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xd4,0x00,0x00,0x00,
+0xd5,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xd5,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xda,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x08,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x74,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
+0x71,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xde,0x00,0x00,0x00,
+0x75,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0xdf,0x00,0x00,0x00,0xde,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x75,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xd6,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x85,0x00,0x00,0x00,
+0x85,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xce,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xce,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
+0x08,0x01,0x00,0x00,0xe1,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xcb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xcd,0x00,0x00,0x00,
+0xaa,0x00,0x05,0x00,0x80,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xe7,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xe5,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe6,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x74,0x00,0x00,0x00,
+0xef,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
+0xef,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xf1,0x00,0x00,0x00,
+0xf2,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0xee,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xf2,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe7,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe7,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_f16_f16_f32_len = 3568;
+
+unsigned char mul_mat_vec_f16_f32_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x07,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0xab,0x00,0x00,0x00,
+0xea,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x32,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x36,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x3b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x3b,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x3b,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x3b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa8,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xa9,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xa9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xa9,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xab,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xab,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xe7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xe8,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xe8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xe8,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xea,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xea,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xf1,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf2,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x10,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x11,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x12,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x2a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2a,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x2b,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x1e,0x00,0x0d,0x00,0x3b,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x3c,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x3c,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
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+0x79,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
+0xac,0x00,0x05,0x00,0x80,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0x06,0x01,0x00,0x00,0x2d,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xcc,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xd0,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xcb,0x00,0x00,0x00,
+0xb0,0x00,0x05,0x00,0x80,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xd5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xd3,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd4,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x06,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x74,0x00,0x00,0x00,
+0xda,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
+0xda,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0xdd,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
+0x08,0x00,0x00,0x00,0xde,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
+0xdb,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x75,0x00,0x00,0x00,
+0xde,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xd5,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd5,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x85,0x00,0x00,0x00,0x85,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xcd,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xcd,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe2,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0xe0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xca,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xcc,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,0x80,0x00,0x00,0x00,
+0xe4,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xe6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xe4,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
+0xe6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe5,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
+0x6d,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x74,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x71,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0xef,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0xb1,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xf0,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe6,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_f16_f32_f32_len = 3536;
 
 unsigned char mul_mat_vec_nc_f16_f32_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
@@ -43705,5557 +61699,12470 @@ unsigned char mul_mat_vec_p021_f16_f32_data[] = {
 };
 const uint64_t mul_mat_vec_p021_f16_f32_len = 2768;
 
-unsigned char mul_mat_vec_q2_K_f32_data[] = {
+unsigned char mul_mat_vec_q2_K_f16_f32_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xc6,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x11,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
 0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,
 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x4b,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,
-0xb4,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x13,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x13,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x13,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x13,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x68,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
+0xc3,0x00,0x00,0x00,0xff,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x59,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x9c,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9e,0x00,0x00,0x00,
 0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x6d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x6d,0x00,0x00,0x00,
+0xa1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xa1,0x00,0x00,0x00,
 0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x6d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xa1,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
 0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6e,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x6f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x6f,0x00,0x00,0x00,
+0xa2,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xa3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xa3,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x6f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x71,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x71,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xa3,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa5,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa5,0x00,0x00,0x00,
 0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x8b,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x8c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x8c,0x00,0x00,0x00,
+0xc0,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xc1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x8c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x8e,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8e,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xc1,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
 0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb1,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xb2,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xb2,0x02,0x00,0x00,
+0xfc,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xfd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xfd,0x02,0x00,0x00,
 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xb2,0x02,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb4,0x02,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb4,0x02,0x00,0x00,
+0x47,0x00,0x03,0x00,0xfd,0x02,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xff,0x02,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xff,0x02,0x00,0x00,
 0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbc,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x07,0x03,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
 0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
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-0xf9,0x00,0x02,0x00,0x95,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x97,0x02,0x00,0x00,0xaa,0x00,0x05,0x00,0x5d,0x00,0x00,0x00,
-0xae,0x02,0x00,0x00,0x26,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xb0,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xae,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
-0xb0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,
-0x15,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,
-0xb6,0x02,0x00,0x00,0x11,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x52,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,0x4b,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x47,0x00,0x00,0x00,
-0xba,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x97,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,
-0x16,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xbb,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb0,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q2_K_f32_len = 7628;
-
-unsigned char mul_mat_vec_q3_K_f32_data[] = {
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-0x08,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
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-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
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-0x53,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x92,0x00,0x00,0x00,
-0xf9,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x13,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x13,0x00,0x00,0x00,
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-0x13,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x73,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x75,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x77,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x79,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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+0x66,0x02,0x00,0x00,0x50,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x9f,0x00,0x00,0x00,0x71,0x02,0x00,0x00,0x6c,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,0x72,0x02,0x00,0x00,
+0x71,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x9b,0x00,0x00,0x00,
+0x79,0x02,0x00,0x00,0x75,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x9b,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,0x79,0x02,0x00,0x00,
+0x33,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x7b,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x7d,0x02,0x00,0x00,
+0x7c,0x02,0x00,0x00,0xd8,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,
+0x7d,0x00,0x00,0x00,0x7e,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x7d,0x00,0x00,0x00,0x80,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x72,0x02,0x00,0x00,
+0x7e,0x02,0x00,0x00,0x68,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x9f,0x00,0x00,0x00,0x89,0x02,0x00,0x00,0x95,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
+0x89,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x9b,0x00,0x00,0x00,
+0x91,0x02,0x00,0x00,0x9e,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x9b,0x00,0x00,0x00,0x92,0x02,0x00,0x00,0x91,0x02,0x00,0x00,
+0x33,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x93,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0x93,0x02,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
+0x94,0x02,0x00,0x00,0xd8,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,
+0x7d,0x00,0x00,0x00,0x96,0x02,0x00,0x00,0x95,0x02,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x7d,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
+0x96,0x02,0x00,0x00,0x80,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x9f,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,0xbe,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
+0xa1,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x9b,0x00,0x00,0x00,
+0xa9,0x02,0x00,0x00,0xc7,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x9b,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
+0x33,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xab,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xad,0x02,0x00,0x00,
+0xac,0x02,0x00,0x00,0xd8,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,
+0x7d,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x7d,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
+0xae,0x02,0x00,0x00,0x98,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x9f,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,0xe7,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,0xba,0x02,0x00,0x00,
+0xb9,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x9b,0x00,0x00,0x00,
+0xc1,0x02,0x00,0x00,0xf0,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x9b,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
+0x33,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xc3,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,
+0xc4,0x02,0x00,0x00,0xd8,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,
+0x7d,0x00,0x00,0x00,0xc6,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x7d,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xba,0x02,0x00,0x00,
+0xc6,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,0x81,0x00,0x05,0x00,
+0x7d,0x00,0x00,0x00,0xca,0x02,0x00,0x00,0x0d,0x03,0x00,0x00,
+0xc8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0xcc,0x02,0x00,0x00,0x0b,0x03,0x00,0x00,0xdf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xb9,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xbb,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
+0xd6,0x02,0x00,0x00,0xb4,0x00,0x00,0x00,0x0d,0x03,0x00,0x00,
+0x7f,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,0x10,0x03,0x00,0x00,
+0xd6,0x02,0x00,0x00,0x0c,0x00,0x08,0x00,0x7d,0x00,0x00,0x00,
+0xd7,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xad,0x00,0x00,0x00,0x0c,0x03,0x00,0x00,0x10,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,
+0x88,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
+0xda,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x88,0x00,0x00,0x00,0xda,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x8e,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xdd,0x02,0x00,0x00,0x08,0x03,0x00,0x00,0x5c,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x8b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8d,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0xde,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe0,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x09,0x03,0x00,0x00,
+0x81,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0xf7,0x02,0x00,0x00,
+0xe3,0x02,0x00,0x00,0xac,0x00,0x05,0x00,0x92,0x00,0x00,0x00,
+0xe6,0x02,0x00,0x00,0x09,0x03,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xe2,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xe6,0x02,0x00,0x00,
+0xe1,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe1,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0x92,0x00,0x00,0x00,
+0xe9,0x02,0x00,0x00,0x5d,0x00,0x00,0x00,0x09,0x03,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xeb,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xe9,0x02,0x00,0x00,0xea,0x02,0x00,0x00,
+0xeb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xea,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xef,0x02,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x09,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0x87,0x00,0x00,0x00,0xf0,0x02,0x00,0x00,0x80,0x00,0x00,0x00,
+0xef,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,
+0xf1,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x87,0x00,0x00,0x00,0xf2,0x02,0x00,0x00,0x80,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,
+0xf3,0x02,0x00,0x00,0xf2,0x02,0x00,0x00,0x81,0x00,0x05,0x00,
+0x7d,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,
+0xf1,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xf2,0x02,0x00,0x00,
+0xf4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xeb,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xeb,0x02,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x5c,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0xde,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe3,0x02,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf7,0x02,0x00,0x00,0x09,0x03,0x00,0x00,0xdf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe2,0x02,0x00,0x00,0xaa,0x00,0x05,0x00,0x92,0x00,0x00,0x00,
+0xf9,0x02,0x00,0x00,0x5d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xfb,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xf9,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,
+0xfb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xfa,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x02,0x03,0x00,0x00,
+0x4a,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x87,0x00,0x00,0x00,0x03,0x03,0x00,0x00,0x80,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,
+0x04,0x03,0x00,0x00,0x03,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
+0x05,0x03,0x00,0x00,0x06,0x03,0x00,0x00,0xff,0x02,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x02,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x06,0x03,0x00,0x00,0x04,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xfb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xfb,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
 };
-const uint64_t mul_mat_vec_q3_K_f32_len = 9252;
+const uint64_t mul_mat_vec_q2_K_f16_f32_len = 8612;
 
-unsigned char mul_mat_vec_q4_0_f32_data[] = {
+unsigned char mul_mat_vec_q2_K_f32_f32_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x01,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
+0xc3,0x00,0x00,0x00,0xf0,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
 0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x28,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
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-0x38,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
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-0x8b,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x43,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x87,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x43,0x00,0x00,0x00,
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-0x49,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x43,0x00,0x00,0x00,
-0x41,0x00,0x07,0x00,0x56,0x00,0x00,0x00,0x57,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
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-0x63,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
-0x59,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
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-0x0c,0x00,0x08,0x00,0x17,0x00,0x00,0x00,0x89,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x77,0x00,0x00,0x00,
-0x85,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x1f,0x00,0x00,0x00,0x89,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x96,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x83,0x00,0x00,0x00,0x97,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x96,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x98,0x00,0x00,0x00,
-0x97,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x9b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x17,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,0x98,0x00,0x00,0x00,
-0x9b,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x1f,0x00,0x00,0x00,
-0x9c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9f,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x22,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x24,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0xa0,0x00,0x00,0x00,
-0xa0,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xa3,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
-0x95,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
-0xa6,0x00,0x00,0x00,0xad,0x00,0x05,0x00,0x30,0x00,0x00,0x00,
-0xa9,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xa5,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xa9,0x00,0x00,0x00,
-0xa4,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa4,0x00,0x00,0x00,0xb1,0x00,0x05,0x00,0x30,0x00,0x00,0x00,
-0xac,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xae,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xac,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
-0xae,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xad,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0xb2,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0xb4,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
-0xb6,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x1f,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xae,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xae,0x00,0x00,0x00,
-0xe0,0x00,0x04,0x00,0xa0,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,
-0xa1,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa6,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa6,0x00,0x00,0x00,0xc3,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa3,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa5,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,
-0x30,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xbe,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,
-0xbd,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xbd,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x2b,0x00,0x00,0x00,
-0xc3,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
-0xc3,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x83,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
-0xc2,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xc9,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xbe,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xbe,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_q4_0_f32_len = 3180;
-
-unsigned char mul_mat_vec_q4_1_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd4,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
-0x80,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x28,0x00,0x00,0x00,
+0xa1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xa1,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xa1,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa2,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xa3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xa3,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x28,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x28,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x28,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x50,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x50,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x50,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x51,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x52,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x52,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x52,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
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+0x5d,0x00,0x00,0x00,0xf9,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xdc,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xda,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xdb,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,0x5d,0x00,0x00,0x00,
+0xf9,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x87,0x00,0x00,0x00,
+0xe1,0x02,0x00,0x00,0x80,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,0xe2,0x02,0x00,0x00,
+0xe1,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x87,0x00,0x00,0x00,
+0xe3,0x02,0x00,0x00,0x80,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,
+0xe3,0x02,0x00,0x00,0x81,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
+0xe5,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xe3,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xdc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xdc,0x02,0x00,0x00,0xe0,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd4,0x02,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe8,0x02,0x00,0x00,
+0xf9,0x02,0x00,0x00,0xdf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd3,0x02,0x00,0x00,
+0xaa,0x00,0x05,0x00,0x92,0x00,0x00,0x00,0xea,0x02,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xec,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xea,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,0xec,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xeb,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,0x4a,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x87,0x00,0x00,0x00,
+0xf4,0x02,0x00,0x00,0x80,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,
+0xf4,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0xcb,0x00,0x00,0x00,
+0xf6,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0x4c,0x00,0x00,0x00,
+0xf3,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xf6,0x02,0x00,0x00,
+0xf5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xec,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xec,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_q2_K_f32_f32_len = 8356;
+
+unsigned char mul_mat_vec_q3_K_f16_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x4d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0x3f,0x03,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x59,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa8,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xaa,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xac,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xac,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xac,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xac,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xad,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xae,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xae,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xae,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb0,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb0,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xc4,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
 0x48,0x00,0x04,0x00,0xc5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xc5,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xc5,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
 0x47,0x00,0x03,0x00,0xc5,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
 0x47,0x00,0x04,0x00,0xc7,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc7,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3c,0x03,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x3d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3d,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x3d,0x03,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3f,0x03,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3f,0x03,0x00,0x00,
 0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xcf,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x03,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
 0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
 0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x17,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x1e,0x00,0x05,0x00,
-0x28,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x29,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x29,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
-0x30,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x35,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x4c,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x4d,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x1e,0x00,0x05,0x00,
-0x50,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0x4f,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x51,0x00,0x00,0x00,
-0x50,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x52,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x53,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
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+0x71,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x96,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x97,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
+0x97,0x02,0x00,0x00,0xb4,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0x98,0x02,0x00,0x00,
+0x33,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x9a,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
+0x72,0x00,0x04,0x00,0x73,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
+0x9a,0x02,0x00,0x00,0x72,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0x9c,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0x82,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
+0xed,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,0x85,0x00,0x00,0x00,
+0x9e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0x85,0x00,0x05,0x00,
+0x85,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x83,0x02,0x00,0x00,
+0x9e,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x6b,0x00,0x00,0x00,
+0xa9,0x02,0x00,0x00,0x14,0x02,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x6b,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
+0x33,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xab,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xad,0x02,0x00,0x00,
+0xac,0x02,0x00,0x00,0xb4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x6b,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,0x21,0x02,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,0xba,0x02,0x00,0x00,
+0xb7,0x02,0x00,0x00,0x94,0x01,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,
+0xbb,0x02,0x00,0x00,0xab,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,
+0xbd,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x4c,0x00,0x00,0x00,
+0xa9,0x00,0x06,0x00,0x1a,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
+0xbd,0x02,0x00,0x00,0x4c,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
+0x82,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,
+0xad,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0x6f,0x00,0x04,0x00,
+0x85,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x85,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
+0xc0,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,0xcd,0x00,0x00,0x00,
+0xc9,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0xb5,0x00,0x00,0x00,
+0xcb,0x02,0x00,0x00,0xc7,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0xca,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xab,0x00,0x00,0x00,
+0xcc,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,0x73,0x00,0x04,0x00,
+0x85,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,
+0x41,0x00,0x08,0x00,0xd5,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,
+0xb0,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,
+0xc2,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x6b,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,
+0xd2,0x02,0x00,0x00,0x71,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,
+0xd5,0x02,0x00,0x00,0xc7,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0xd7,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,0xdc,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x6b,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,
+0x47,0x02,0x00,0x00,0xc2,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,
+0xdf,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,0x71,0x01,0x00,0x00,
+0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,
+0xdf,0x02,0x00,0x00,0x7c,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0xe1,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,0xc7,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0xe2,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,
+0xb4,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0xe3,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,0x33,0x00,0x00,0x00,
+0xc5,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,
+0xd7,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0x72,0x00,0x04,0x00,
+0x73,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,
+0x72,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0xe6,0x02,0x00,0x00,
+0xe5,0x02,0x00,0x00,0x82,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0xe7,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,0xed,0x00,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x85,0x00,0x00,0x00,0xe8,0x02,0x00,0x00,
+0xe7,0x02,0x00,0x00,0x85,0x00,0x05,0x00,0x85,0x00,0x00,0x00,
+0xe9,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x6b,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,
+0x14,0x02,0x00,0x00,0xc2,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,
+0xf4,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,
+0xf4,0x02,0x00,0x00,0x7c,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0xf6,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,0xc7,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0xf7,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,
+0xb4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x6b,0x00,0x00,0x00,
+0x01,0x03,0x00,0x00,0x21,0x02,0x00,0x00,0xc7,0x00,0x05,0x00,
+0x6b,0x00,0x00,0x00,0x04,0x03,0x00,0x00,0x01,0x03,0x00,0x00,
+0xdc,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x05,0x03,0x00,0x00,0x04,0x03,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x06,0x03,0x00,0x00,0x05,0x03,0x00,0x00,
+0xab,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,0x07,0x03,0x00,0x00,
+0x06,0x03,0x00,0x00,0x4c,0x00,0x00,0x00,0xa9,0x00,0x06,0x00,
+0x1a,0x00,0x00,0x00,0x08,0x03,0x00,0x00,0x07,0x03,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x09,0x03,0x00,0x00,0xf7,0x02,0x00,0x00,
+0x08,0x03,0x00,0x00,0x6f,0x00,0x04,0x00,0x85,0x00,0x00,0x00,
+0x0a,0x03,0x00,0x00,0x09,0x03,0x00,0x00,0x0c,0x00,0x08,0x00,
+0x85,0x00,0x00,0x00,0x0c,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0xe9,0x02,0x00,0x00,0x0a,0x03,0x00,0x00,
+0xc2,0x02,0x00,0x00,0x81,0x00,0x05,0x00,0x85,0x00,0x00,0x00,
+0x0e,0x03,0x00,0x00,0x4c,0x03,0x00,0x00,0x0c,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x10,0x03,0x00,0x00,
+0x4b,0x03,0x00,0x00,0x6e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xbc,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xbe,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x85,0x00,0x00,0x00,0x19,0x03,0x00,0x00,
+0x90,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x85,0x00,0x00,0x00,
+0x1a,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xb8,0x00,0x00,0x00,0x4c,0x03,0x00,0x00,0x19,0x03,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x90,0x00,0x00,0x00,0x1a,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x99,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x99,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1d,0x03,0x00,0x00,0x48,0x03,0x00,0x00,0x5c,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x96,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x98,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x1e,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x20,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x20,0x03,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x49,0x03,0x00,0x00,
+0x89,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x37,0x03,0x00,0x00,
+0x23,0x03,0x00,0x00,0xac,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,
+0x26,0x03,0x00,0x00,0x49,0x03,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x22,0x03,0x00,0x00,0x23,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x26,0x03,0x00,0x00,
+0x21,0x03,0x00,0x00,0x22,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x21,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,
+0x29,0x03,0x00,0x00,0x5d,0x00,0x00,0x00,0x49,0x03,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x2b,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x29,0x03,0x00,0x00,0x2a,0x03,0x00,0x00,
+0x2b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x2a,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2f,0x03,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x49,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0x8f,0x00,0x00,0x00,0x30,0x03,0x00,0x00,0x88,0x00,0x00,0x00,
+0x2f,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x85,0x00,0x00,0x00,
+0x31,0x03,0x00,0x00,0x30,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0x8f,0x00,0x00,0x00,0x32,0x03,0x00,0x00,0x88,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x85,0x00,0x00,0x00,
+0x33,0x03,0x00,0x00,0x32,0x03,0x00,0x00,0x81,0x00,0x05,0x00,
+0x85,0x00,0x00,0x00,0x34,0x03,0x00,0x00,0x33,0x03,0x00,0x00,
+0x31,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x32,0x03,0x00,0x00,
+0x34,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x2b,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x2b,0x03,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x5c,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x1e,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x23,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x23,0x03,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x37,0x03,0x00,0x00,0x49,0x03,0x00,0x00,0x6e,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x20,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x22,0x03,0x00,0x00,0xaa,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,
+0x39,0x03,0x00,0x00,0x5d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x3b,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x39,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,
+0x3b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x3a,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x42,0x03,0x00,0x00,
+0x4a,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x8f,0x00,0x00,0x00,0x43,0x03,0x00,0x00,0x88,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x85,0x00,0x00,0x00,
+0x44,0x03,0x00,0x00,0x43,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
+0x45,0x03,0x00,0x00,0x46,0x03,0x00,0x00,0x3f,0x03,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x42,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x46,0x03,0x00,0x00,0x44,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x3b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x3b,0x03,0x00,0x00,
 0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
 };
-const uint64_t mul_mat_vec_q4_1_f32_len = 3248;
+const uint64_t mul_mat_vec_q3_K_f16_f32_len = 10028;
 
-unsigned char mul_mat_vec_q4_K_f32_data[] = {
+unsigned char mul_mat_vec_q3_K_f32_f32_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xa2,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x45,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
 0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,
 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0xef,0x01,0x00,0x00,
-0x94,0x03,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x13,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x13,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x13,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x13,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x74,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x76,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x77,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x77,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x77,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x78,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x90,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
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+0x59,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0x38,0x03,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
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+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
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 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
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 0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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 0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x92,0x03,0x00,0x00,
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 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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 0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
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-0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
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-0xc8,0x02,0x00,0x00,0x43,0x03,0x00,0x00,0x70,0x00,0x04,0x00,
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-0x4d,0x00,0x00,0x00,0x6b,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x66,0x03,0x00,0x00,
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-0x76,0x03,0x00,0x00,0xb1,0x00,0x05,0x00,0x63,0x00,0x00,0x00,
-0x7e,0x03,0x00,0x00,0x26,0x00,0x00,0x00,0x9e,0x03,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x80,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x7e,0x03,0x00,0x00,0x7f,0x03,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x84,0x03,0x00,0x00,
-0x26,0x00,0x00,0x00,0x9e,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0x58,0x00,0x00,0x00,0x85,0x03,0x00,0x00,0x51,0x00,0x00,0x00,
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-0x8c,0x03,0x00,0x00,0x9e,0x03,0x00,0x00,0x90,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x75,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x77,0x03,0x00,0x00,0xaa,0x00,0x05,0x00,0x63,0x00,0x00,0x00,
-0x8e,0x03,0x00,0x00,0x26,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x90,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x8e,0x03,0x00,0x00,0x8f,0x03,0x00,0x00,
-0x90,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x8f,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x95,0x03,0x00,0x00,
-0x15,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x96,0x03,0x00,0x00,0x95,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x98,0x03,0x00,0x00,
-0x96,0x03,0x00,0x00,0x11,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x58,0x00,0x00,0x00,0x99,0x03,0x00,0x00,0x51,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x9a,0x03,0x00,0x00,0x99,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
-0xf4,0x01,0x00,0x00,0x9b,0x03,0x00,0x00,0x94,0x03,0x00,0x00,
-0x16,0x00,0x00,0x00,0x98,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x9b,0x03,0x00,0x00,0x9a,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x90,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x90,0x03,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q4_K_f32_len = 9176;
-
-unsigned char mul_mat_vec_q5_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xfa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
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-0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
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-0xa5,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x47,0x00,0x04,0x00,0xa2,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x47,0x00,0x04,0x00,0xf1,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
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-0x0c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
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-0x1a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x43,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x87,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x43,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
-0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x43,0x00,0x00,0x00,0x41,0x00,0x07,0x00,
-0x59,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x57,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,
-0x5a,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x5c,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
-0x61,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x57,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
-0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x63,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
-0xc4,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
-0x61,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x57,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x69,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
-0xc5,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,
-0x66,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0x73,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
-0x73,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,
-0x79,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x81,0x00,0x00,0x00,
-0x82,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x83,0x00,0x00,0x00,
-0x82,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x84,0x00,0x00,0x00,0x83,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
-0x89,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x8d,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
-0x8d,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x8f,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
-0x72,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x95,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x91,0x00,0x00,0x00,
-0x95,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x97,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x50,0x00,0x05,0x00,
-0x85,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,
-0x97,0x00,0x00,0x00,0x83,0x00,0x05,0x00,0x85,0x00,0x00,0x00,
-0x9c,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
-0x8e,0x00,0x05,0x00,0x85,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
-0x9c,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x2b,0x00,0x00,0x00,
-0xa6,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,
-0xa6,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa9,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xab,0x00,0x00,0x00,
-0xa9,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0xad,0x00,0x00,0x00,0xae,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0xae,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x17,0x00,0x00,0x00,
-0xb3,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xa1,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x1f,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
-0x9e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0xab,0x00,0x00,0x00,
-0x65,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xad,0x00,0x00,0x00,
-0xbf,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0xbe,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0xc0,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x17,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
-0xc0,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x1f,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,
-0x35,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x22,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x24,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x4e,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xca,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xca,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xf7,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0xe1,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,0xad,0x00,0x05,0x00,
-0x30,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xcc,0x00,0x00,0x00,
-0xcd,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xd0,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcb,0x00,0x00,0x00,0xb1,0x00,0x05,0x00,
-0x30,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0xf7,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xd5,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xd3,0x00,0x00,0x00,
-0xd4,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd4,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd9,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0xde,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x1f,0x00,0x00,0x00,0xde,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd5,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xcd,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xcd,0x00,0x00,0x00,
-0xc3,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,
-0xf7,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xca,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xcc,0x00,0x00,0x00,
-0xaa,0x00,0x05,0x00,0x30,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xe5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xe3,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe4,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x2b,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
-0x35,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0xee,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
-0xee,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xad,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0xed,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xf0,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe5,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe5,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q5_0_f32_len = 3676;
+0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x1e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x32,0x03,0x00,0x00,
+0x33,0x03,0x00,0x00,0x34,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x33,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x3b,0x03,0x00,0x00,0x4a,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x8f,0x00,0x00,0x00,0x3c,0x03,0x00,0x00,
+0x88,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x85,0x00,0x00,0x00,0x3d,0x03,0x00,0x00,0x3c,0x03,0x00,0x00,
+0x41,0x00,0x06,0x00,0xcf,0x00,0x00,0x00,0x3e,0x03,0x00,0x00,
+0x38,0x03,0x00,0x00,0x4c,0x00,0x00,0x00,0x3b,0x03,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x3e,0x03,0x00,0x00,0x3d,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x34,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x34,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
 
-unsigned char mul_mat_vec_q5_1_f32_data[] = {
+};
+const uint64_t mul_mat_vec_q3_K_f32_f32_len = 9900;
+
+unsigned char mul_mat_vec_q4_0_f16_f32_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x2b,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
 0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
 0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,
 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
-0xa0,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x28,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0xff,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x28,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x28,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x28,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x50,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x50,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x50,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x50,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x52,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x52,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x41,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x47,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x51,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x51,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x51,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x51,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x83,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbd,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0xbe,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xbe,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
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-0x54,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9d,0x00,0x00,0x00,
+0xbe,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xc0,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xc0,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
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 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x9e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x9e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfd,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xfd,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x9e,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xa0,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xa0,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe3,0x00,0x00,0x00,
+0xfd,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xff,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xff,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,
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+0x08,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
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+0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
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+0x1c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x1e,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
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+0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x20,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x2a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,
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+0x08,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x00,0x00,0x00,0x41,
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+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x40,0x00,0x00,0x00,
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+0x40,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x43,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
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+0x3b,0x00,0x04,0x00,0x40,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
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+0xc5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
+0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,
+0x24,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x20,0x00,0x00,0x00,
+0xd2,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0xd1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0xd3,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,
+0x85,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
+0xcb,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,
+0x08,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,
+0xd5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0xd8,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
+0x08,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x8a,0x00,0x00,0x00,
+0xd9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xdc,0x00,0x00,0x00,0x27,0x01,0x00,0x00,0x9a,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x8c,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8e,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x9a,0x00,0x00,0x00,
+0x9a,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x28,0x01,0x00,0x00,
+0xdf,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
+0xe3,0x00,0x00,0x00,0xac,0x00,0x05,0x00,0x95,0x00,0x00,0x00,
+0xe6,0x00,0x00,0x00,0x28,0x01,0x00,0x00,0x42,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xe2,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xe6,0x00,0x00,0x00,
+0xe1,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe1,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0x95,0x00,0x00,0x00,
+0xe9,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x28,0x01,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xeb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xe9,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
+0xeb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xea,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0x49,0x00,0x00,0x00,0x28,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x89,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0xef,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0xf1,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
+0xf3,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x8a,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xeb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xeb,0x00,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x9a,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,
+0xdd,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe3,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe3,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x28,0x01,0x00,0x00,
+0x28,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe2,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,
+0x95,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
+0x42,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xfb,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf9,0x00,0x00,0x00,
+0xfa,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xfa,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x02,0x01,0x00,0x00,0x82,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x89,0x00,0x00,0x00,0x03,0x01,0x00,0x00,
+0x86,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x03,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0x05,0x01,0x00,0x00,0x06,0x01,0x00,0x00,
+0xff,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x02,0x01,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x06,0x01,0x00,0x00,0x04,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xfb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xfb,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t mul_mat_vec_q4_0_f16_f32_len = 3960;
+
+unsigned char mul_mat_vec_q4_0_f32_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x29,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x41,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x47,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x51,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x51,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x51,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x51,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x51,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x83,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbd,0x00,0x00,0x00,
 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xe4,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xe4,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0xbe,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xbe,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xe4,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xe6,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xe6,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xee,0x00,0x00,0x00,
+0xbe,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xc0,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xc0,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xfb,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0xfc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xfc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0xfc,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xfe,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xfe,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x06,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x1c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x1e,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
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+0x08,0x00,0x00,0x00,0x10,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,
+0x41,0x00,0x08,0x00,0x2a,0x00,0x00,0x00,0x15,0x01,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,
+0x28,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x16,0x01,0x00,0x00,0x15,0x01,0x00,0x00,
+0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x17,0x01,0x00,0x00,
+0x16,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x19,0x01,0x00,0x00,0x17,0x01,0x00,0x00,0x2f,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x1a,0x01,0x00,0x00,
+0x19,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1c,0x01,0x00,0x00,0x17,0x01,0x00,0x00,0x33,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,
+0x1c,0x01,0x00,0x00,0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
+0x1e,0x01,0x00,0x00,0x1a,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,
+0x83,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x20,0x01,0x00,0x00,
+0x1e,0x01,0x00,0x00,0x28,0x01,0x00,0x00,0x8e,0x00,0x05,0x00,
+0x09,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x20,0x01,0x00,0x00,
+0x10,0x01,0x00,0x00,0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
+0x7c,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
+0xaa,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xc6,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0xc5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0xc8,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x08,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0x22,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd1,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0xc6,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0xc0,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,
+0xd2,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0xd4,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x08,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
+0xc8,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
+0xd7,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x8a,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,0x25,0x01,0x00,0x00,
+0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8c,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8e,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x9a,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xdf,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xdf,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x26,0x01,0x00,0x00,0xde,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,
+0xf6,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0xac,0x00,0x05,0x00,
+0x95,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0x26,0x01,0x00,0x00,
+0x42,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xe1,0x00,0x00,0x00,
+0xe2,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xe5,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
+0x95,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
+0x26,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,0xea,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xe8,0x00,0x00,0x00,
+0xe9,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xee,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x26,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x89,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
+0x8a,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0xf3,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x8a,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xea,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xea,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x9a,0x00,0x00,0x00,
+0x9a,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe2,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,
+0x26,0x01,0x00,0x00,0x28,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xdf,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe1,0x00,0x00,0x00,
+0xaa,0x00,0x05,0x00,0x95,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
+0x49,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xfa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xf8,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xf9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x01,0x01,0x00,0x00,0x82,0x00,0x00,0x00,
+0x45,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x89,0x00,0x00,0x00,
+0x02,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x03,0x01,0x00,0x00,
+0x02,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0xc6,0x00,0x00,0x00,
+0x04,0x01,0x00,0x00,0xfe,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x01,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x04,0x01,0x00,0x00,
+0x03,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xfa,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xfa,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_q4_0_f32_f32_len = 3928;
+
+unsigned char mul_mat_vec_q4_1_f16_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x39,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
+0x4f,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,
+0x8e,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x07,0x01,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x16,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x49,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4f,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x59,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x59,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x59,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x59,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x8b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xc5,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xc6,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xc6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xc6,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc8,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xc8,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x05,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x05,0x01,0x00,0x00,
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-0xc5,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x89,0x00,0x00,0x00,
-0x85,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x89,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
-0xc5,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x91,0x00,0x00,0x00,
-0x8c,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x91,0x00,0x00,0x00,
-0x50,0x00,0x05,0x00,0x80,0x00,0x00,0x00,0x93,0x00,0x00,0x00,
-0x8a,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,
-0x80,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x93,0x00,0x00,0x00,
-0x59,0x00,0x00,0x00,0x50,0x00,0x05,0x00,0x80,0x00,0x00,0x00,
-0x98,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x80,0x00,0x00,0x00,0x99,0x00,0x00,0x00,
-0x96,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0x99,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x2b,0x00,0x00,0x00,
-0xa1,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,
-0xa1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa4,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,
-0xa4,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0xa8,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x17,0x00,0x00,0x00,
-0xae,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x9c,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x1f,0x00,0x00,0x00,0xae,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x99,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,
-0xb9,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xa8,0x00,0x00,0x00,
-0xbb,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0xba,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0xbc,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x17,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0xbc,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x1f,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
-0x35,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x22,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x24,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
-0xc4,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xc7,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xc7,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xf4,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0xde,0x00,0x00,0x00,0xca,0x00,0x00,0x00,0xad,0x00,0x05,0x00,
-0x30,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xc9,0x00,0x00,0x00,
-0xca,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xcd,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc8,0x00,0x00,0x00,0xb1,0x00,0x05,0x00,
-0x30,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0xf4,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xd2,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xd0,0x00,0x00,0x00,
-0xd1,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd6,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0xdb,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x1f,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd2,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
-0xc4,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xca,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xca,0x00,0x00,0x00,
-0xc3,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xde,0x00,0x00,0x00,
-0xf4,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc7,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xc9,0x00,0x00,0x00,
-0xaa,0x00,0x05,0x00,0x30,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xe2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xe0,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe1,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x2b,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
-0x35,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xe8,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xa8,0x00,0x00,0x00,
-0xed,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0xea,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xed,0x00,0x00,0x00,
-0xec,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe2,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe2,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q5_1_f32_len = 3604;
-
-unsigned char mul_mat_vec_q5_K_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x8d,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x5e,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0xfa,0x01,0x00,0x00,
-0x7f,0x04,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x13,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x13,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x13,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x13,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x80,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x81,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x83,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x84,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x84,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x84,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x84,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x85,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x86,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x86,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x88,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x88,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xf7,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xf8,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xf8,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xf8,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xfa,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xfa,0x01,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x7c,0x04,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x7d,0x04,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x04,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x7d,0x04,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x7f,0x04,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7f,0x04,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x87,0x04,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
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+0xd9,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0xdb,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
+0x85,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
+0xd3,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,
+0x08,0x00,0x00,0x00,0xde,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
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+0xde,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x92,0x00,0x00,0x00,
+0xe1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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+0xee,0x00,0x00,0x00,0x38,0x01,0x00,0x00,0x4a,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xea,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xee,0x00,0x00,0x00,
+0xe9,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe9,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,
+0xf1,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x38,0x01,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xf3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xf1,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
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+0x91,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,
+0xf7,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0xf9,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0x92,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
+0xfb,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x92,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xf3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf3,0x00,0x00,0x00,
+0xe0,0x00,0x04,0x00,0xa2,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,
+0xe5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xeb,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xeb,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0x38,0x01,0x00,0x00,
+0x28,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe8,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xea,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,
+0x9d,0x00,0x00,0x00,0x01,0x01,0x00,0x00,0x51,0x00,0x00,0x00,
+0x4a,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x03,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x01,0x01,0x00,0x00,
+0x02,0x01,0x00,0x00,0x03,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x02,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x0a,0x01,0x00,0x00,0x8a,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x91,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,
+0x8e,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,0x0b,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0x0d,0x01,0x00,0x00,0x0e,0x01,0x00,0x00,
+0x07,0x01,0x00,0x00,0x1c,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x0e,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x03,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x03,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
 
 };
-const uint64_t mul_mat_vec_q5_K_f32_len = 12048;
+const uint64_t mul_mat_vec_q4_1_f16_f32_len = 4044;
 
-unsigned char mul_mat_vec_q6_K_f32_data[] = {
+unsigned char mul_mat_vec_q4_1_f32_f32_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x97,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x57,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x94,0x00,0x00,0x00,
-0x9c,0x01,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x13,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x13,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x13,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x13,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x74,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x76,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x79,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x80,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7b,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7b,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x7c,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
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-0x5f,0x03,0x00,0x00,0x5e,0x03,0x00,0x00,0x82,0x00,0x05,0x00,
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-0x81,0x00,0x05,0x00,0x53,0x00,0x00,0x00,0x8a,0x03,0x00,0x00,
-0xea,0x02,0x00,0x00,0x89,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x53,0x00,0x00,0x00,0x75,0x01,0x00,0x00,0x5f,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x79,0x01,0x00,0x00,0xa6,0x01,0x00,0x00,
-0x25,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x62,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x64,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x7a,0x01,0x00,0x00,0x7a,0x01,0x00,0x00,0x7b,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7d,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7d,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
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-0xf8,0x00,0x02,0x00,0x7e,0x01,0x00,0x00,0xb1,0x00,0x05,0x00,
-0x69,0x00,0x00,0x00,0x86,0x01,0x00,0x00,0x26,0x00,0x00,0x00,
-0xa7,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,0x88,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x86,0x01,0x00,0x00,
-0x87,0x01,0x00,0x00,0x88,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x87,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8c,0x01,0x00,0x00,0x26,0x00,0x00,0x00,0xa7,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x5e,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,
-0x57,0x00,0x00,0x00,0x8c,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x53,0x00,0x00,0x00,0x8e,0x01,0x00,0x00,0x8d,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x5e,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,
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-0x53,0x00,0x00,0x00,0x90,0x01,0x00,0x00,0x8f,0x01,0x00,0x00,
-0x81,0x00,0x05,0x00,0x53,0x00,0x00,0x00,0x91,0x01,0x00,0x00,
-0x90,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x8f,0x01,0x00,0x00,0x91,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x88,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x88,0x01,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x7a,0x01,0x00,0x00,0x7a,0x01,0x00,0x00,
-0x7b,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x80,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x80,0x01,0x00,0x00,0xc3,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x94,0x01,0x00,0x00,0xa7,0x01,0x00,0x00,
-0x95,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7d,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7f,0x01,0x00,0x00,0xaa,0x00,0x05,0x00,
-0x69,0x00,0x00,0x00,0x96,0x01,0x00,0x00,0x26,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x98,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x96,0x01,0x00,0x00,
-0x97,0x01,0x00,0x00,0x98,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x97,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x9d,0x01,0x00,0x00,0x15,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x9e,0x01,0x00,0x00,
-0x9d,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x01,0x00,0x00,0x9e,0x01,0x00,0x00,0x11,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x5e,0x00,0x00,0x00,0xa1,0x01,0x00,0x00,
-0x57,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x53,0x00,0x00,0x00,0xa2,0x01,0x00,0x00,0xa1,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0x9d,0x00,0x00,0x00,0xa3,0x01,0x00,0x00,
-0x9c,0x01,0x00,0x00,0x16,0x00,0x00,0x00,0xa0,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xa3,0x01,0x00,0x00,0xa2,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x98,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x98,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_q6_K_f32_len = 10992;
-
-unsigned char mul_mat_vec_q8_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xc9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x37,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
 0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
 0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,
 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
-0x75,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
+0x4f,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,
+0x8e,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x06,0x01,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x16,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x49,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4f,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x59,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x59,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x59,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
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+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x35,0x01,0x00,0x00,
+0x4a,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
+0x95,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x5d,0x00,0x00,0x00,
+0x9a,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,
+0x9a,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9c,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,
+0xb0,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
+0x35,0x01,0x00,0x00,0x9c,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x96,0x00,0x00,0x00,0x95,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x9e,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
+0x96,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x95,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,
+0x35,0x01,0x00,0x00,0x8b,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa5,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
+0xa5,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xae,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
+0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
+0xa5,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
+0xa2,0x00,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb7,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x7e,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x16,0x01,0x00,0x00,0xbb,0x00,0x00,0x00,
+0xae,0x00,0x00,0x00,0x41,0x00,0x07,0x00,0x20,0x00,0x00,0x00,
+0x17,0x01,0x00,0x00,0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x16,0x01,0x00,0x00,0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x12,0x00,0x00,0x00,0x18,0x01,0x00,0x00,0x17,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x19,0x01,0x00,0x00,
+0x18,0x01,0x00,0x00,0x41,0x00,0x07,0x00,0x20,0x00,0x00,0x00,
+0x1d,0x01,0x00,0x00,0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x16,0x01,0x00,0x00,0x28,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x12,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,
+0x1e,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x32,0x00,0x00,0x00,
+0x24,0x01,0x00,0x00,0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x16,0x01,0x00,0x00,0x30,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x25,0x01,0x00,0x00,
+0x24,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x26,0x01,0x00,0x00,0x25,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x28,0x01,0x00,0x00,0x26,0x01,0x00,0x00,
+0x37,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0x29,0x01,0x00,0x00,0x28,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x01,0x00,0x00,0x26,0x01,0x00,0x00,
+0x3b,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0x2c,0x01,0x00,0x00,0x2b,0x01,0x00,0x00,0x50,0x00,0x05,0x00,
+0x09,0x00,0x00,0x00,0x2d,0x01,0x00,0x00,0x29,0x01,0x00,0x00,
+0x2c,0x01,0x00,0x00,0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
+0x2f,0x01,0x00,0x00,0x2d,0x01,0x00,0x00,0x19,0x01,0x00,0x00,
+0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x31,0x01,0x00,0x00,
+0x1f,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,0x81,0x00,0x05,0x00,
+0x09,0x00,0x00,0x00,0x32,0x01,0x00,0x00,0x2f,0x01,0x00,0x00,
+0x31,0x01,0x00,0x00,0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0xc4,0x00,0x00,0x00,0x32,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,
+0x84,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xce,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0xcd,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x08,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,0x32,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd9,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0xce,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
+0xc8,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
+0xda,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0xdc,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x08,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0x92,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
+0xdf,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x92,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,0x35,0x01,0x00,0x00,
+0xa2,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x94,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x96,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
+0xa2,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe7,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe7,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x36,0x01,0x00,0x00,0xe6,0x00,0x00,0x00,0x96,0x00,0x00,0x00,
+0xfe,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xac,0x00,0x05,0x00,
+0x9d,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0x36,0x01,0x00,0x00,
+0x4a,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xe9,0x00,0x00,0x00,
+0xea,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xed,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe8,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
+0x9d,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
+0x36,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,0xf2,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf0,0x00,0x00,0x00,
+0xf1,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xf1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf6,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x36,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x91,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
+0x8e,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
+0x92,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0xfb,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x92,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xf2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xf2,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0xa2,0x00,0x00,0x00,
+0xa2,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xea,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xea,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
+0x36,0x01,0x00,0x00,0x28,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe7,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe9,0x00,0x00,0x00,
+0xaa,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
+0x51,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x02,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x00,0x01,0x00,0x00,0x01,0x01,0x00,0x00,0x02,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x01,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0x8a,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x91,0x00,0x00,0x00,
+0x0a,0x01,0x00,0x00,0x8e,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,
+0x0a,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0xce,0x00,0x00,0x00,
+0x0c,0x01,0x00,0x00,0x06,0x01,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x09,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x0c,0x01,0x00,0x00,
+0x0b,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x02,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x02,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_q4_1_f32_f32_len = 4012;
+
+unsigned char mul_mat_vec_q4_K_f16_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xd4,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,
+0x23,0x02,0x00,0x00,0xc6,0x03,0x00,0x00,0x10,0x00,0x06,0x00,
 0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x28,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x28,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
 0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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+0x8e,0x03,0x00,0x00,0x7c,0x02,0x00,0x00,0x8d,0x03,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,0x8f,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x4d,0x02,0x00,0x00,
+0x89,0x03,0x00,0x00,0x8e,0x03,0x00,0x00,0x70,0x00,0x04,0x00,
+0x83,0x00,0x00,0x00,0x92,0x03,0x00,0x00,0x18,0x01,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,0x94,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,
+0x92,0x03,0x00,0x00,0x8f,0x03,0x00,0x00,0x70,0x00,0x04,0x00,
+0x83,0x00,0x00,0x00,0x97,0x03,0x00,0x00,0x34,0x01,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,0x99,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,
+0x97,0x03,0x00,0x00,0x94,0x03,0x00,0x00,0x85,0x00,0x05,0x00,
+0x83,0x00,0x00,0x00,0x9d,0x03,0x00,0x00,0xbd,0x00,0x00,0x00,
+0x81,0x03,0x00,0x00,0x7f,0x00,0x04,0x00,0x83,0x00,0x00,0x00,
+0xd3,0x03,0x00,0x00,0x9d,0x03,0x00,0x00,0x0c,0x00,0x08,0x00,
+0x83,0x00,0x00,0x00,0x9e,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,0x99,0x03,0x00,0x00,
+0xd3,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x83,0x00,0x00,0x00,
+0xa0,0x03,0x00,0x00,0x8e,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
+0x83,0x00,0x00,0x00,0xa1,0x03,0x00,0x00,0xa0,0x03,0x00,0x00,
+0x9e,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x8e,0x00,0x00,0x00,
+0xa1,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa4,0x03,0x00,0x00,0xcf,0x03,0x00,0x00,0x5c,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x91,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x93,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0xa5,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa7,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0xa7,0x03,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xd0,0x03,0x00,0x00,
+0x87,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0xbe,0x03,0x00,0x00,
+0xaa,0x03,0x00,0x00,0xac,0x00,0x05,0x00,0x98,0x00,0x00,0x00,
+0xad,0x03,0x00,0x00,0xd0,0x03,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xa9,0x03,0x00,0x00,0xaa,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xad,0x03,0x00,0x00,
+0xa8,0x03,0x00,0x00,0xa9,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa8,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0x98,0x00,0x00,0x00,
+0xb0,0x03,0x00,0x00,0x5d,0x00,0x00,0x00,0xd0,0x03,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xb2,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xb0,0x03,0x00,0x00,0xb1,0x03,0x00,0x00,
+0xb2,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0xb1,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb6,0x03,0x00,0x00,
+0x5d,0x00,0x00,0x00,0xd0,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0x8d,0x00,0x00,0x00,0xb7,0x03,0x00,0x00,0x86,0x00,0x00,0x00,
+0xb6,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x83,0x00,0x00,0x00,
+0xb8,0x03,0x00,0x00,0xb7,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0x8d,0x00,0x00,0x00,0xb9,0x03,0x00,0x00,0x86,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x83,0x00,0x00,0x00,
+0xba,0x03,0x00,0x00,0xb9,0x03,0x00,0x00,0x81,0x00,0x05,0x00,
+0x83,0x00,0x00,0x00,0xbb,0x03,0x00,0x00,0xba,0x03,0x00,0x00,
+0xb8,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0xb9,0x03,0x00,0x00,
+0xbb,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0xb2,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb2,0x03,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x5c,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0xa5,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xaa,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xaa,0x03,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xbe,0x03,0x00,0x00,0xd0,0x03,0x00,0x00,0xc3,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa7,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa9,0x03,0x00,0x00,0xaa,0x00,0x05,0x00,0x98,0x00,0x00,0x00,
+0xc0,0x03,0x00,0x00,0x5d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xc2,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xc0,0x03,0x00,0x00,0xc1,0x03,0x00,0x00,
+0xc2,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0xc1,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc9,0x03,0x00,0x00,
+0x4a,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x8d,0x00,0x00,0x00,0xca,0x03,0x00,0x00,0x86,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x83,0x00,0x00,0x00,
+0xcb,0x03,0x00,0x00,0xca,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
+0xcc,0x03,0x00,0x00,0xcd,0x03,0x00,0x00,0xc6,0x03,0x00,0x00,
+0x4c,0x00,0x00,0x00,0xc9,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xcd,0x03,0x00,0x00,0xcb,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc2,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0xc2,0x03,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_q4_K_f16_f32_len = 10400;
+
+unsigned char mul_mat_vec_q4_K_f32_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xb4,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,
+0x23,0x02,0x00,0x00,0xa7,0x03,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x59,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa9,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xaa,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xab,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xab,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xab,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xac,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x90,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xad,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xad,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xad,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xaf,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xaf,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x20,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x21,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x21,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x21,0x02,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x23,0x02,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x23,0x02,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa4,0x03,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xa5,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xa5,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xa5,0x03,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa7,0x03,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa7,0x03,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xae,0x03,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x1e,0x00,0x0d,0x00,0x17,0x00,0x00,0x00,
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+0x5a,0x02,0x00,0x00,0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,
+0xfe,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xfa,0x02,0x00,0x00,0xd5,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x83,0x00,0x00,0x00,0x04,0x03,0x00,0x00,
+0x83,0x02,0x00,0x00,0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,
+0x08,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x04,0x03,0x00,0x00,0xde,0x02,0x00,0x00,0xfe,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x83,0x00,0x00,0x00,0x0e,0x03,0x00,0x00,
+0xab,0x02,0x00,0x00,0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,
+0x12,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x0e,0x03,0x00,0x00,0xe8,0x02,0x00,0x00,0x08,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x83,0x00,0x00,0x00,0x18,0x03,0x00,0x00,
+0x3b,0x02,0x00,0x00,0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,
+0x1c,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x18,0x03,0x00,0x00,0xcc,0x02,0x00,0x00,0x12,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x83,0x00,0x00,0x00,0x22,0x03,0x00,0x00,
+0x65,0x02,0x00,0x00,0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,
+0x26,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x22,0x03,0x00,0x00,0xd5,0x02,0x00,0x00,0x1c,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x83,0x00,0x00,0x00,0x2c,0x03,0x00,0x00,
+0x8d,0x02,0x00,0x00,0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,
+0x30,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x2c,0x03,0x00,0x00,0xde,0x02,0x00,0x00,0x26,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x83,0x00,0x00,0x00,0x36,0x03,0x00,0x00,
+0xb5,0x02,0x00,0x00,0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,
+0x3a,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x36,0x03,0x00,0x00,0xe8,0x02,0x00,0x00,0x30,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x83,0x00,0x00,0x00,0x40,0x03,0x00,0x00,
+0x45,0x02,0x00,0x00,0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,
+0x44,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x40,0x03,0x00,0x00,0xcc,0x02,0x00,0x00,0x3a,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x83,0x00,0x00,0x00,0x4a,0x03,0x00,0x00,
+0x70,0x02,0x00,0x00,0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,
+0x4e,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x4a,0x03,0x00,0x00,0xd5,0x02,0x00,0x00,0x44,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x83,0x00,0x00,0x00,0x54,0x03,0x00,0x00,
+0x97,0x02,0x00,0x00,0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,
+0x58,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x54,0x03,0x00,0x00,0xde,0x02,0x00,0x00,0x4e,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x83,0x00,0x00,0x00,0x5e,0x03,0x00,0x00,
+0xbf,0x02,0x00,0x00,0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,
+0x62,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x5e,0x03,0x00,0x00,0xe8,0x02,0x00,0x00,0x58,0x03,0x00,0x00,
+0x70,0x00,0x04,0x00,0x83,0x00,0x00,0x00,0x6a,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x83,0x00,0x00,0x00,
+0x6e,0x03,0x00,0x00,0xdd,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
+0x83,0x00,0x00,0x00,0x6f,0x03,0x00,0x00,0x75,0x02,0x00,0x00,
+0x6e,0x03,0x00,0x00,0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,
+0x70,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x4a,0x02,0x00,0x00,0x6a,0x03,0x00,0x00,0x6f,0x03,0x00,0x00,
+0x70,0x00,0x04,0x00,0x83,0x00,0x00,0x00,0x73,0x03,0x00,0x00,
+0x18,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,
+0x75,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x9c,0x02,0x00,0x00,0x73,0x03,0x00,0x00,0x70,0x03,0x00,0x00,
+0x70,0x00,0x04,0x00,0x83,0x00,0x00,0x00,0x78,0x03,0x00,0x00,
+0x34,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,
+0x7a,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xc4,0x02,0x00,0x00,0x78,0x03,0x00,0x00,0x75,0x03,0x00,0x00,
+0x85,0x00,0x05,0x00,0x83,0x00,0x00,0x00,0x7e,0x03,0x00,0x00,
+0xbd,0x00,0x00,0x00,0x62,0x03,0x00,0x00,0x7f,0x00,0x04,0x00,
+0x83,0x00,0x00,0x00,0xb3,0x03,0x00,0x00,0x7e,0x03,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,0x7f,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,
+0x7a,0x03,0x00,0x00,0xb3,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x83,0x00,0x00,0x00,0x81,0x03,0x00,0x00,0x8e,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x83,0x00,0x00,0x00,0x82,0x03,0x00,0x00,
+0x81,0x03,0x00,0x00,0x7f,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x8e,0x00,0x00,0x00,0x82,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x85,0x03,0x00,0x00,0xaf,0x03,0x00,0x00,
+0x5c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x91,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x93,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x5c,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x86,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x88,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x88,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xb0,0x03,0x00,0x00,0x87,0x00,0x00,0x00,0x93,0x00,0x00,0x00,
+0x9f,0x03,0x00,0x00,0x8b,0x03,0x00,0x00,0xac,0x00,0x05,0x00,
+0x98,0x00,0x00,0x00,0x8e,0x03,0x00,0x00,0xb0,0x03,0x00,0x00,
+0x0c,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x8a,0x03,0x00,0x00,
+0x8b,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x8e,0x03,0x00,0x00,0x89,0x03,0x00,0x00,0x8a,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x89,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
+0x98,0x00,0x00,0x00,0x91,0x03,0x00,0x00,0x5d,0x00,0x00,0x00,
+0xb0,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,0x93,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x91,0x03,0x00,0x00,
+0x92,0x03,0x00,0x00,0x93,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x92,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x97,0x03,0x00,0x00,0x5d,0x00,0x00,0x00,0xb0,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x8d,0x00,0x00,0x00,0x98,0x03,0x00,0x00,
+0x86,0x00,0x00,0x00,0x97,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x83,0x00,0x00,0x00,0x99,0x03,0x00,0x00,0x98,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x8d,0x00,0x00,0x00,0x9a,0x03,0x00,0x00,
+0x86,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x83,0x00,0x00,0x00,0x9b,0x03,0x00,0x00,0x9a,0x03,0x00,0x00,
+0x81,0x00,0x05,0x00,0x83,0x00,0x00,0x00,0x9c,0x03,0x00,0x00,
+0x9b,0x03,0x00,0x00,0x99,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x9a,0x03,0x00,0x00,0x9c,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x93,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x93,0x03,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x86,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x8b,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8b,0x03,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9f,0x03,0x00,0x00,0xb0,0x03,0x00,0x00,
+0xc3,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x88,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8a,0x03,0x00,0x00,0xaa,0x00,0x05,0x00,
+0x98,0x00,0x00,0x00,0xa1,0x03,0x00,0x00,0x5d,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xa3,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xa1,0x03,0x00,0x00,
+0xa2,0x03,0x00,0x00,0xa3,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa2,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xaa,0x03,0x00,0x00,0x4a,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x8d,0x00,0x00,0x00,0xab,0x03,0x00,0x00,
+0x86,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x83,0x00,0x00,0x00,0xac,0x03,0x00,0x00,0xab,0x03,0x00,0x00,
+0x41,0x00,0x06,0x00,0x27,0x02,0x00,0x00,0xad,0x03,0x00,0x00,
+0xa7,0x03,0x00,0x00,0x4c,0x00,0x00,0x00,0xaa,0x03,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xad,0x03,0x00,0x00,0xac,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa3,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa3,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t mul_mat_vec_q4_K_f32_f32_len = 9888;
+
+unsigned char mul_mat_vec_q5_0_f16_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x80,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
+0x77,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
+0xb5,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x2d,0x01,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
 0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x1b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x72,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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+0x1d,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0x1f,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0x21,0x01,0x00,0x00,0xb9,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0x22,0x01,0x00,0x00,
+0x21,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xb9,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x19,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x19,0x01,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x0b,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x11,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x11,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x25,0x01,0x00,0x00,0x7d,0x01,0x00,0x00,
+0x2b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0e,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x10,0x01,0x00,0x00,0xaa,0x00,0x05,0x00,
+0xc4,0x00,0x00,0x00,0x27,0x01,0x00,0x00,0x79,0x00,0x00,0x00,
+0x58,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x29,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x27,0x01,0x00,0x00,
+0x28,0x01,0x00,0x00,0x29,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x28,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x30,0x01,0x00,0x00,0xb1,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x31,0x01,0x00,0x00,
+0xb5,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0x32,0x01,0x00,0x00,0x31,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0x33,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
+0x2d,0x01,0x00,0x00,0x1f,0x00,0x00,0x00,0x30,0x01,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x34,0x01,0x00,0x00,0x32,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x29,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x29,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t mul_mat_vec_q5_0_f16_f32_len = 4488;
+
+unsigned char mul_mat_vec_q5_0_f32_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x7e,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
+0x77,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
+0xb5,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x1b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x72,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x80,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x80,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x80,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x80,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x80,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x80,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x80,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x80,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x80,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x80,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x80,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x80,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb2,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xeb,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0xec,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xec,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0xec,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xee,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xee,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x29,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x2a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x2a,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x2c,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x2c,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x33,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x34,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x1c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x1c,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x17,0x00,0x00,0x00,0x1e,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x1a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x1b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
+0x1d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x1e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1e,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1e,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
+0x4f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x51,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x58,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
+0x00,0x00,0x80,0x41,0x17,0x00,0x04,0x00,0x70,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x71,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x71,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x73,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x71,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x71,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x1e,0x00,0x0d,0x00,0x80,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x81,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
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+0x64,0x01,0x00,0x00,0x62,0x01,0x00,0x00,0x56,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x67,0x01,0x00,0x00,
+0x54,0x01,0x00,0x00,0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x68,0x01,0x00,0x00,0x64,0x01,0x00,0x00,0x67,0x01,0x00,0x00,
+0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x69,0x01,0x00,0x00,
+0x68,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6b,0x01,0x00,0x00,0x62,0x01,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6e,0x01,0x00,0x00,
+0x5a,0x01,0x00,0x00,0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6f,0x01,0x00,0x00,0x6b,0x01,0x00,0x00,0x6e,0x01,0x00,0x00,
+0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x70,0x01,0x00,0x00,
+0x6f,0x01,0x00,0x00,0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
+0x71,0x01,0x00,0x00,0x69,0x01,0x00,0x00,0x70,0x01,0x00,0x00,
+0x83,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x73,0x01,0x00,0x00,
+0x71,0x01,0x00,0x00,0x7d,0x01,0x00,0x00,0x8e,0x00,0x05,0x00,
+0x09,0x00,0x00,0x00,0x75,0x01,0x00,0x00,0x73,0x01,0x00,0x00,
+0x40,0x01,0x00,0x00,0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0xea,0x00,0x00,0x00,0x75,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
+0xab,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
+0xd8,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xf4,0x00,0x00,0x00,
+0xf5,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
+0xf3,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0xf6,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x08,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0x75,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xff,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0xf4,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
+0xee,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0xff,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x01,0x01,0x00,0x00,
+0x00,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0x02,0x01,0x00,0x00,0xf9,0x00,0x00,0x00,0x01,0x01,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x08,0x00,0x00,0x00,0x03,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
+0xf6,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0xb9,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0x06,0x01,0x00,0x00,
+0x05,0x01,0x00,0x00,0x03,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xb9,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0x7a,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xbb,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xbd,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x0d,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x0d,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x7b,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,0xbd,0x00,0x00,0x00,
+0x24,0x01,0x00,0x00,0x10,0x01,0x00,0x00,0xac,0x00,0x05,0x00,
+0xc4,0x00,0x00,0x00,0x13,0x01,0x00,0x00,0x7b,0x01,0x00,0x00,
+0x58,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x0f,0x01,0x00,0x00,
+0x10,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x13,0x01,0x00,0x00,0x0e,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x0e,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc4,0x00,0x00,0x00,0x16,0x01,0x00,0x00,0x79,0x00,0x00,0x00,
+0x7b,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,0x18,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x16,0x01,0x00,0x00,
+0x17,0x01,0x00,0x00,0x18,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x17,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1c,0x01,0x00,0x00,0x79,0x00,0x00,0x00,0x7b,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,
+0xb5,0x00,0x00,0x00,0x1c,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x20,0x01,0x00,0x00,
+0xb9,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0x21,0x01,0x00,0x00,0x20,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xb9,0x00,0x00,0x00,0x21,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x18,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x18,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x10,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x10,0x01,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x24,0x01,0x00,0x00,
+0x7b,0x01,0x00,0x00,0x2b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0d,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x0f,0x01,0x00,0x00,
+0xaa,0x00,0x05,0x00,0xc4,0x00,0x00,0x00,0x26,0x01,0x00,0x00,
+0x79,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x28,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x26,0x01,0x00,0x00,0x27,0x01,0x00,0x00,0x28,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x27,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,0xb1,0x00,0x00,0x00,
+0x75,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
+0x30,0x01,0x00,0x00,0xb5,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x31,0x01,0x00,0x00,
+0x30,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0xf4,0x00,0x00,0x00,
+0x32,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,0x1f,0x00,0x00,0x00,
+0x2f,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x32,0x01,0x00,0x00,
+0x31,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x28,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x28,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_q5_0_f32_f32_len = 4456;
+
+unsigned char mul_mat_vec_q5_1_f16_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x79,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
+0x72,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,
+0xb0,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x29,0x01,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x16,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x52,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x52,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x54,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x72,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x73,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x73,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x73,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x75,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x75,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xba,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xba,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbc,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x72,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x76,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x7b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x7b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7b,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x7b,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x7b,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7b,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x7b,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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+0x41,0x00,0x05,0x00,0xb3,0x00,0x00,0x00,0x1a,0x01,0x00,0x00,
+0xb0,0x00,0x00,0x00,0x19,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0x1b,0x01,0x00,0x00,0x1a,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,
+0xb4,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0x1e,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,0x1b,0x01,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xb4,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x15,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x15,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0xc4,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0d,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x0d,0x01,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x21,0x01,0x00,0x00,
+0x78,0x01,0x00,0x00,0x28,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0a,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x0c,0x01,0x00,0x00,
+0xaa,0x00,0x05,0x00,0xbf,0x00,0x00,0x00,0x23,0x01,0x00,0x00,
+0x74,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x25,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x23,0x01,0x00,0x00,0x24,0x01,0x00,0x00,0x25,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x24,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,0xac,0x00,0x00,0x00,
+0x70,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0xb3,0x00,0x00,0x00,
+0x2d,0x01,0x00,0x00,0xb0,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,
+0x2d,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x2f,0x01,0x00,0x00,
+0x30,0x01,0x00,0x00,0x29,0x01,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x2c,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x30,0x01,0x00,0x00,
+0x2e,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x25,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x25,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_q5_1_f16_f32_len = 4384;
+
+unsigned char mul_mat_vec_q5_1_f32_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x77,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
+0x72,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,
+0xb0,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x28,0x01,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x16,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x72,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x76,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x7b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x7b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7b,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x7b,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x7b,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7b,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x7b,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x7b,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7b,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x7b,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x7b,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x7b,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xad,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xe7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xe8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xe8,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xe8,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xea,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xea,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x25,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x26,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x26,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x26,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x28,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x28,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x2f,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x30,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,
+0x16,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x20,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1b,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x6b,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x6b,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
+0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x6e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
+0x76,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x1e,0x00,0x0d,0x00,
+0x7b,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x7c,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x7b,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x7c,0x00,0x00,0x00,
+0x7d,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1b,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x7f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
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+0x06,0x00,0x00,0x00,0x68,0x01,0x00,0x00,0x54,0x01,0x00,0x00,
+0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x01,0x00,0x00,
+0x65,0x01,0x00,0x00,0x68,0x01,0x00,0x00,0x70,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0x6a,0x01,0x00,0x00,0x69,0x01,0x00,0x00,
+0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x6b,0x01,0x00,0x00,
+0x63,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,0x8e,0x00,0x05,0x00,
+0x09,0x00,0x00,0x00,0x6d,0x01,0x00,0x00,0x6b,0x01,0x00,0x00,
+0x3d,0x01,0x00,0x00,0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
+0x6f,0x01,0x00,0x00,0x43,0x01,0x00,0x00,0x43,0x01,0x00,0x00,
+0x81,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x70,0x01,0x00,0x00,
+0x6d,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,0x51,0x00,0x05,0x00,
+0x08,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0x70,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xed,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0xed,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0xf0,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
+0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,
+0x70,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xf0,0x00,0x00,0x00,
+0xfc,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0xfb,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0xfd,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
+0x08,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,
+0xfd,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x08,0x00,0x00,0x00,
+0xff,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xe6,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x01,0x01,0x00,0x00,
+0xb4,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0x02,0x01,0x00,0x00,0x01,0x01,0x00,0x00,0xff,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xb4,0x00,0x00,0x00,0x02,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x05,0x01,0x00,0x00,
+0x75,0x01,0x00,0x00,0xc4,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xb6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xb8,0x00,0x00,0x00,
+0xe0,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
+0x06,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x09,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x09,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x76,0x01,0x00,0x00,0x08,0x01,0x00,0x00,
+0xb8,0x00,0x00,0x00,0x20,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,
+0xac,0x00,0x05,0x00,0xbf,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,
+0x76,0x01,0x00,0x00,0x53,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x0b,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x0f,0x01,0x00,0x00,0x0a,0x01,0x00,0x00,
+0x0b,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x0a,0x01,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xbf,0x00,0x00,0x00,0x12,0x01,0x00,0x00,
+0x74,0x00,0x00,0x00,0x76,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x14,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x12,0x01,0x00,0x00,0x13,0x01,0x00,0x00,0x14,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x13,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x18,0x01,0x00,0x00,0x74,0x00,0x00,0x00,
+0x76,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0xb3,0x00,0x00,0x00,
+0x19,0x01,0x00,0x00,0xb0,0x00,0x00,0x00,0x18,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x1a,0x01,0x00,0x00,
+0x19,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0x1c,0x01,0x00,0x00,0xb4,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
+0x08,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0x1c,0x01,0x00,0x00,
+0x1a,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0xb4,0x00,0x00,0x00,
+0x1d,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x14,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x14,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,
+0xc4,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x06,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x0c,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x0c,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x20,0x01,0x00,0x00,0x76,0x01,0x00,0x00,0x28,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x09,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x0b,0x01,0x00,0x00,0xaa,0x00,0x05,0x00,0xbf,0x00,0x00,0x00,
+0x22,0x01,0x00,0x00,0x74,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x24,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x22,0x01,0x00,0x00,0x23,0x01,0x00,0x00,
+0x24,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x23,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2b,0x01,0x00,0x00,
+0xac,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0xb3,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,0xb0,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0x2d,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0xf0,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,0x28,0x01,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x2b,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x2e,0x01,0x00,0x00,0x2d,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x24,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x24,0x01,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_q5_1_f32_f32_len = 4352;
+
+unsigned char mul_mat_vec_q5_K_f16_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xc0,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x2e,0x02,0x00,0x00,0xb2,0x04,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x59,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb5,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb6,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb9,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xba,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xba,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xba,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x2b,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x2c,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2c,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x2c,0x02,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x2e,0x02,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2e,0x02,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xaf,0x04,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xb0,0x04,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xb0,0x04,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xb0,0x04,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb2,0x04,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb2,0x04,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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+0x73,0x00,0x04,0x00,0x90,0x00,0x00,0x00,0x1b,0x04,0x00,0x00,
+0x1a,0x04,0x00,0x00,0x81,0x00,0x05,0x00,0x90,0x00,0x00,0x00,
+0x1c,0x04,0x00,0x00,0x14,0x04,0x00,0x00,0x1b,0x04,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xb2,0x00,0x00,0x00,0x22,0x04,0x00,0x00,
+0xf3,0x02,0x00,0x00,0x73,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
+0x23,0x04,0x00,0x00,0x22,0x04,0x00,0x00,0x81,0x00,0x05,0x00,
+0x90,0x00,0x00,0x00,0x24,0x04,0x00,0x00,0x1c,0x04,0x00,0x00,
+0x23,0x04,0x00,0x00,0x70,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
+0x26,0x04,0x00,0x00,0x04,0x01,0x00,0x00,0x85,0x00,0x05,0x00,
+0x90,0x00,0x00,0x00,0x27,0x04,0x00,0x00,0x24,0x04,0x00,0x00,
+0x26,0x04,0x00,0x00,0x0c,0x00,0x08,0x00,0x90,0x00,0x00,0x00,
+0x28,0x04,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x02,0x04,0x00,0x00,0x04,0x04,0x00,0x00,0x27,0x04,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xb2,0x00,0x00,0x00,0x2d,0x04,0x00,0x00,
+0x0f,0x03,0x00,0x00,0x73,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
+0x2e,0x04,0x00,0x00,0x2d,0x04,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xb2,0x00,0x00,0x00,0x34,0x04,0x00,0x00,0x28,0x03,0x00,0x00,
+0x73,0x00,0x04,0x00,0x90,0x00,0x00,0x00,0x35,0x04,0x00,0x00,
+0x34,0x04,0x00,0x00,0x81,0x00,0x05,0x00,0x90,0x00,0x00,0x00,
+0x36,0x04,0x00,0x00,0x2e,0x04,0x00,0x00,0x35,0x04,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xb2,0x00,0x00,0x00,0x3c,0x04,0x00,0x00,
+0x43,0x03,0x00,0x00,0x73,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
+0x3d,0x04,0x00,0x00,0x3c,0x04,0x00,0x00,0x81,0x00,0x05,0x00,
+0x90,0x00,0x00,0x00,0x3e,0x04,0x00,0x00,0x36,0x04,0x00,0x00,
+0x3d,0x04,0x00,0x00,0x3d,0x00,0x04,0x00,0xb2,0x00,0x00,0x00,
+0x44,0x04,0x00,0x00,0x5e,0x03,0x00,0x00,0x73,0x00,0x04,0x00,
+0x90,0x00,0x00,0x00,0x45,0x04,0x00,0x00,0x44,0x04,0x00,0x00,
+0x81,0x00,0x05,0x00,0x90,0x00,0x00,0x00,0x46,0x04,0x00,0x00,
+0x3e,0x04,0x00,0x00,0x45,0x04,0x00,0x00,0x70,0x00,0x04,0x00,
+0x90,0x00,0x00,0x00,0x48,0x04,0x00,0x00,0x5a,0x01,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x90,0x00,0x00,0x00,0x4a,0x04,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x46,0x04,0x00,0x00,
+0x48,0x04,0x00,0x00,0x28,0x04,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xb2,0x00,0x00,0x00,0x50,0x04,0x00,0x00,0x7a,0x03,0x00,0x00,
+0x73,0x00,0x04,0x00,0x90,0x00,0x00,0x00,0x51,0x04,0x00,0x00,
+0x50,0x04,0x00,0x00,0x3d,0x00,0x04,0x00,0xb2,0x00,0x00,0x00,
+0x57,0x04,0x00,0x00,0x94,0x03,0x00,0x00,0x73,0x00,0x04,0x00,
+0x90,0x00,0x00,0x00,0x58,0x04,0x00,0x00,0x57,0x04,0x00,0x00,
+0x81,0x00,0x05,0x00,0x90,0x00,0x00,0x00,0x59,0x04,0x00,0x00,
+0x51,0x04,0x00,0x00,0x58,0x04,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xb2,0x00,0x00,0x00,0x5f,0x04,0x00,0x00,0xb0,0x03,0x00,0x00,
+0x73,0x00,0x04,0x00,0x90,0x00,0x00,0x00,0x60,0x04,0x00,0x00,
+0x5f,0x04,0x00,0x00,0x81,0x00,0x05,0x00,0x90,0x00,0x00,0x00,
+0x61,0x04,0x00,0x00,0x59,0x04,0x00,0x00,0x60,0x04,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xb2,0x00,0x00,0x00,0x67,0x04,0x00,0x00,
+0xcc,0x03,0x00,0x00,0x73,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
+0x68,0x04,0x00,0x00,0x67,0x04,0x00,0x00,0x81,0x00,0x05,0x00,
+0x90,0x00,0x00,0x00,0x69,0x04,0x00,0x00,0x61,0x04,0x00,0x00,
+0x68,0x04,0x00,0x00,0x70,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
+0x6b,0x04,0x00,0x00,0x76,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,
+0x90,0x00,0x00,0x00,0x6d,0x04,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x69,0x04,0x00,0x00,0x6b,0x04,0x00,0x00,
+0x4a,0x04,0x00,0x00,0x70,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
+0x75,0x04,0x00,0x00,0xd9,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
+0x90,0x00,0x00,0x00,0x79,0x04,0x00,0x00,0xe7,0x00,0x00,0x00,
+0x85,0x00,0x05,0x00,0x90,0x00,0x00,0x00,0x7a,0x04,0x00,0x00,
+0x0a,0x03,0x00,0x00,0x79,0x04,0x00,0x00,0x0c,0x00,0x08,0x00,
+0x90,0x00,0x00,0x00,0x7b,0x04,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x98,0x02,0x00,0x00,0x75,0x04,0x00,0x00,
+0x7a,0x04,0x00,0x00,0x70,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
+0x7e,0x04,0x00,0x00,0x22,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,
+0x90,0x00,0x00,0x00,0x80,0x04,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x74,0x03,0x00,0x00,0x7e,0x04,0x00,0x00,
+0x7b,0x04,0x00,0x00,0x70,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
+0x83,0x04,0x00,0x00,0x3e,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,
+0x90,0x00,0x00,0x00,0x85,0x04,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0xe3,0x03,0x00,0x00,0x83,0x04,0x00,0x00,
+0x80,0x04,0x00,0x00,0x85,0x00,0x05,0x00,0x90,0x00,0x00,0x00,
+0x89,0x04,0x00,0x00,0xca,0x00,0x00,0x00,0x6d,0x04,0x00,0x00,
+0x7f,0x00,0x04,0x00,0x90,0x00,0x00,0x00,0xbf,0x04,0x00,0x00,
+0x89,0x04,0x00,0x00,0x0c,0x00,0x08,0x00,0x90,0x00,0x00,0x00,
+0x8a,0x04,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xc3,0x00,0x00,0x00,0x85,0x04,0x00,0x00,0xbf,0x04,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x90,0x00,0x00,0x00,0x8c,0x04,0x00,0x00,
+0x9b,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x90,0x00,0x00,0x00,
+0x8d,0x04,0x00,0x00,0x8c,0x04,0x00,0x00,0x8a,0x04,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x9b,0x00,0x00,0x00,0x8d,0x04,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x90,0x04,0x00,0x00,
+0xbb,0x04,0x00,0x00,0x5c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x9e,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xa0,0x00,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x91,0x04,0x00,0x00,0xf9,0x00,0x02,0x00,0x93,0x04,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x93,0x04,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xbc,0x04,0x00,0x00,0x94,0x00,0x00,0x00,
+0xa0,0x00,0x00,0x00,0xaa,0x04,0x00,0x00,0x96,0x04,0x00,0x00,
+0xac,0x00,0x05,0x00,0xa5,0x00,0x00,0x00,0x99,0x04,0x00,0x00,
+0xbc,0x04,0x00,0x00,0x0c,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x95,0x04,0x00,0x00,0x96,0x04,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x99,0x04,0x00,0x00,0x94,0x04,0x00,0x00,
+0x95,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,0x94,0x04,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xa5,0x00,0x00,0x00,0x9c,0x04,0x00,0x00,
+0x5d,0x00,0x00,0x00,0xbc,0x04,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x9e,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x9c,0x04,0x00,0x00,0x9d,0x04,0x00,0x00,0x9e,0x04,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9d,0x04,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa2,0x04,0x00,0x00,0x5d,0x00,0x00,0x00,
+0xbc,0x04,0x00,0x00,0x41,0x00,0x05,0x00,0x9a,0x00,0x00,0x00,
+0xa3,0x04,0x00,0x00,0x93,0x00,0x00,0x00,0xa2,0x04,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x90,0x00,0x00,0x00,0xa4,0x04,0x00,0x00,
+0xa3,0x04,0x00,0x00,0x41,0x00,0x05,0x00,0x9a,0x00,0x00,0x00,
+0xa5,0x04,0x00,0x00,0x93,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x90,0x00,0x00,0x00,0xa6,0x04,0x00,0x00,
+0xa5,0x04,0x00,0x00,0x81,0x00,0x05,0x00,0x90,0x00,0x00,0x00,
+0xa7,0x04,0x00,0x00,0xa6,0x04,0x00,0x00,0xa4,0x04,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xa5,0x04,0x00,0x00,0xa7,0x04,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x9e,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x9e,0x04,0x00,0x00,0xe0,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x91,0x04,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x96,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,0x96,0x04,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaa,0x04,0x00,0x00,
+0xbc,0x04,0x00,0x00,0x86,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x93,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,0x95,0x04,0x00,0x00,
+0xaa,0x00,0x05,0x00,0xa5,0x00,0x00,0x00,0xac,0x04,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xae,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xac,0x04,0x00,0x00,0xad,0x04,0x00,0x00,0xae,0x04,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xad,0x04,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb5,0x04,0x00,0x00,0x4a,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x9a,0x00,0x00,0x00,
+0xb6,0x04,0x00,0x00,0x93,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x90,0x00,0x00,0x00,0xb7,0x04,0x00,0x00,
+0xb6,0x04,0x00,0x00,0x41,0x00,0x06,0x00,0xb8,0x04,0x00,0x00,
+0xb9,0x04,0x00,0x00,0xb2,0x04,0x00,0x00,0x4c,0x00,0x00,0x00,
+0xb5,0x04,0x00,0x00,0x3e,0x00,0x03,0x00,0xb9,0x04,0x00,0x00,
+0xb7,0x04,0x00,0x00,0xf9,0x00,0x02,0x00,0xae,0x04,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xae,0x04,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
 };
-const uint64_t mul_mat_vec_q8_0_f32_len = 3120;
+const uint64_t mul_mat_vec_q5_K_f16_f32_len = 13288;
+
+unsigned char mul_mat_vec_q5_K_f32_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xa0,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x2e,0x02,0x00,0x00,0x93,0x04,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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+0x0f,0x04,0x00,0x00,0x0c,0x00,0x08,0x00,0x90,0x00,0x00,0x00,
+0x11,0x04,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xef,0x03,0x00,0x00,0xf1,0x03,0x00,0x00,0x10,0x04,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x90,0x00,0x00,0x00,0x16,0x04,0x00,0x00,
+0x08,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
+0x1c,0x04,0x00,0x00,0x20,0x03,0x00,0x00,0x81,0x00,0x05,0x00,
+0x90,0x00,0x00,0x00,0x1d,0x04,0x00,0x00,0x16,0x04,0x00,0x00,
+0x1c,0x04,0x00,0x00,0x3d,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
+0x23,0x04,0x00,0x00,0x3a,0x03,0x00,0x00,0x81,0x00,0x05,0x00,
+0x90,0x00,0x00,0x00,0x24,0x04,0x00,0x00,0x1d,0x04,0x00,0x00,
+0x23,0x04,0x00,0x00,0x3d,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
+0x2a,0x04,0x00,0x00,0x54,0x03,0x00,0x00,0x81,0x00,0x05,0x00,
+0x90,0x00,0x00,0x00,0x2b,0x04,0x00,0x00,0x24,0x04,0x00,0x00,
+0x2a,0x04,0x00,0x00,0x70,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
+0x2d,0x04,0x00,0x00,0x5a,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,
+0x90,0x00,0x00,0x00,0x2f,0x04,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x2b,0x04,0x00,0x00,0x2d,0x04,0x00,0x00,
+0x11,0x04,0x00,0x00,0x3d,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
+0x35,0x04,0x00,0x00,0x6f,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x90,0x00,0x00,0x00,0x3b,0x04,0x00,0x00,0x88,0x03,0x00,0x00,
+0x81,0x00,0x05,0x00,0x90,0x00,0x00,0x00,0x3c,0x04,0x00,0x00,
+0x35,0x04,0x00,0x00,0x3b,0x04,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x90,0x00,0x00,0x00,0x42,0x04,0x00,0x00,0xa3,0x03,0x00,0x00,
+0x81,0x00,0x05,0x00,0x90,0x00,0x00,0x00,0x43,0x04,0x00,0x00,
+0x3c,0x04,0x00,0x00,0x42,0x04,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x90,0x00,0x00,0x00,0x49,0x04,0x00,0x00,0xbe,0x03,0x00,0x00,
+0x81,0x00,0x05,0x00,0x90,0x00,0x00,0x00,0x4a,0x04,0x00,0x00,
+0x43,0x04,0x00,0x00,0x49,0x04,0x00,0x00,0x70,0x00,0x04,0x00,
+0x90,0x00,0x00,0x00,0x4c,0x04,0x00,0x00,0x76,0x01,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x90,0x00,0x00,0x00,0x4e,0x04,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x4a,0x04,0x00,0x00,
+0x4c,0x04,0x00,0x00,0x2f,0x04,0x00,0x00,0x70,0x00,0x04,0x00,
+0x90,0x00,0x00,0x00,0x56,0x04,0x00,0x00,0xd9,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x90,0x00,0x00,0x00,0x5a,0x04,0x00,0x00,
+0xe7,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x90,0x00,0x00,0x00,
+0x5b,0x04,0x00,0x00,0x03,0x03,0x00,0x00,0x5a,0x04,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x90,0x00,0x00,0x00,0x5c,0x04,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
+0x56,0x04,0x00,0x00,0x5b,0x04,0x00,0x00,0x70,0x00,0x04,0x00,
+0x90,0x00,0x00,0x00,0x5f,0x04,0x00,0x00,0x22,0x01,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x90,0x00,0x00,0x00,0x61,0x04,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x69,0x03,0x00,0x00,
+0x5f,0x04,0x00,0x00,0x5c,0x04,0x00,0x00,0x70,0x00,0x04,0x00,
+0x90,0x00,0x00,0x00,0x64,0x04,0x00,0x00,0x3e,0x01,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x90,0x00,0x00,0x00,0x66,0x04,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xd4,0x03,0x00,0x00,
+0x64,0x04,0x00,0x00,0x61,0x04,0x00,0x00,0x85,0x00,0x05,0x00,
+0x90,0x00,0x00,0x00,0x6a,0x04,0x00,0x00,0xca,0x00,0x00,0x00,
+0x4e,0x04,0x00,0x00,0x7f,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
+0x9f,0x04,0x00,0x00,0x6a,0x04,0x00,0x00,0x0c,0x00,0x08,0x00,
+0x90,0x00,0x00,0x00,0x6b,0x04,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x66,0x04,0x00,0x00,
+0x9f,0x04,0x00,0x00,0x3d,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
+0x6d,0x04,0x00,0x00,0x9b,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
+0x90,0x00,0x00,0x00,0x6e,0x04,0x00,0x00,0x6d,0x04,0x00,0x00,
+0x6b,0x04,0x00,0x00,0x3e,0x00,0x03,0x00,0x9b,0x00,0x00,0x00,
+0x6e,0x04,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x71,0x04,0x00,0x00,0x9b,0x04,0x00,0x00,0x5c,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x9e,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa0,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x72,0x04,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x74,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,0x74,0x04,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x9c,0x04,0x00,0x00,
+0x94,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,0x8b,0x04,0x00,0x00,
+0x77,0x04,0x00,0x00,0xac,0x00,0x05,0x00,0xa5,0x00,0x00,0x00,
+0x7a,0x04,0x00,0x00,0x9c,0x04,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x76,0x04,0x00,0x00,0x77,0x04,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x7a,0x04,0x00,0x00,
+0x75,0x04,0x00,0x00,0x76,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x75,0x04,0x00,0x00,0xb0,0x00,0x05,0x00,0xa5,0x00,0x00,0x00,
+0x7d,0x04,0x00,0x00,0x5d,0x00,0x00,0x00,0x9c,0x04,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x7f,0x04,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x7d,0x04,0x00,0x00,0x7e,0x04,0x00,0x00,
+0x7f,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,0x7e,0x04,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x83,0x04,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x9c,0x04,0x00,0x00,0x41,0x00,0x05,0x00,
+0x9a,0x00,0x00,0x00,0x84,0x04,0x00,0x00,0x93,0x00,0x00,0x00,
+0x83,0x04,0x00,0x00,0x3d,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
+0x85,0x04,0x00,0x00,0x84,0x04,0x00,0x00,0x41,0x00,0x05,0x00,
+0x9a,0x00,0x00,0x00,0x86,0x04,0x00,0x00,0x93,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
+0x87,0x04,0x00,0x00,0x86,0x04,0x00,0x00,0x81,0x00,0x05,0x00,
+0x90,0x00,0x00,0x00,0x88,0x04,0x00,0x00,0x87,0x04,0x00,0x00,
+0x85,0x04,0x00,0x00,0x3e,0x00,0x03,0x00,0x86,0x04,0x00,0x00,
+0x88,0x04,0x00,0x00,0xf9,0x00,0x02,0x00,0x7f,0x04,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7f,0x04,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x5c,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x72,0x04,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x77,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x77,0x04,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8b,0x04,0x00,0x00,0x9c,0x04,0x00,0x00,0x86,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x74,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x76,0x04,0x00,0x00,0xaa,0x00,0x05,0x00,0xa5,0x00,0x00,0x00,
+0x8d,0x04,0x00,0x00,0x5d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x8f,0x04,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x8d,0x04,0x00,0x00,0x8e,0x04,0x00,0x00,
+0x8f,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,0x8e,0x04,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x96,0x04,0x00,0x00,
+0x4a,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x9a,0x00,0x00,0x00,0x97,0x04,0x00,0x00,0x93,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
+0x98,0x04,0x00,0x00,0x97,0x04,0x00,0x00,0x41,0x00,0x06,0x00,
+0x32,0x02,0x00,0x00,0x99,0x04,0x00,0x00,0x93,0x04,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x96,0x04,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x99,0x04,0x00,0x00,0x98,0x04,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x8f,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,0x8f,0x04,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_q5_K_f32_f32_len = 12776;
+
+unsigned char mul_mat_vec_q6_K_f16_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xee,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0xdb,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x59,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa8,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa9,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xab,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xad,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xad,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x80,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xad,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
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+0xf9,0x00,0x02,0x00,0xbc,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xbe,0x01,0x00,0x00,0xaa,0x00,0x05,0x00,0x9e,0x00,0x00,0x00,
+0xd5,0x01,0x00,0x00,0x5d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xd7,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xd5,0x01,0x00,0x00,0xd6,0x01,0x00,0x00,
+0xd7,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xd6,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xde,0x01,0x00,0x00,
+0x4a,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x93,0x00,0x00,0x00,0xdf,0x01,0x00,0x00,0x8c,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x89,0x00,0x00,0x00,
+0xe0,0x01,0x00,0x00,0xdf,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0xe1,0x01,0x00,0x00,0xe2,0x01,0x00,0x00,0xdb,0x01,0x00,0x00,
+0x4c,0x00,0x00,0x00,0xde,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xe2,0x01,0x00,0x00,0xe0,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd7,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xd7,0x01,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_q6_K_f16_f32_len = 11960;
+
+unsigned char mul_mat_vec_q6_K_f32_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xde,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0xd8,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x59,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa8,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa9,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xab,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xad,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xad,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x80,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xad,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xad,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xae,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xaf,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xaf,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xaf,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb1,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb1,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xc4,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xc5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xc5,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xc5,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xc7,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc7,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xd5,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xd6,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xd6,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xd6,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xd8,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd8,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xdf,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x1e,0x00,0x0d,0x00,0x17,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x18,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0x3b,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x59,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x73,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
+0x80,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x89,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x8a,0x00,0x00,0x00,
+0x89,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x8b,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x8b,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x8d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x89,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x93,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x89,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x9e,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0xa8,0x00,0x00,0x00,
+0xa7,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
+0xa9,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0xaa,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0xab,0x00,0x00,0x00,
+0xaa,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0xac,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,
+0xad,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,
+0xab,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0xae,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
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+0x00,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,0x89,0x00,0x00,0x00,
+0xcd,0x03,0x00,0x00,0xcc,0x03,0x00,0x00,0x0c,0x00,0x08,0x00,
+0x89,0x00,0x00,0x00,0xcf,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0xb5,0x03,0x00,0x00,0xcd,0x03,0x00,0x00,
+0xa8,0x03,0x00,0x00,0x81,0x00,0x05,0x00,0x89,0x00,0x00,0x00,
+0xd0,0x03,0x00,0x00,0x2c,0x03,0x00,0x00,0xcf,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x89,0x00,0x00,0x00,0xb2,0x01,0x00,0x00,
+0x94,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x89,0x00,0x00,0x00,
+0xb3,0x01,0x00,0x00,0xb2,0x01,0x00,0x00,0xd0,0x03,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x94,0x00,0x00,0x00,0xb3,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb6,0x01,0x00,0x00,
+0xe0,0x01,0x00,0x00,0x5c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x97,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x99,0x00,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0xb7,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xb9,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb9,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xe1,0x01,0x00,0x00,0x8d,0x00,0x00,0x00,
+0x99,0x00,0x00,0x00,0xd0,0x01,0x00,0x00,0xbc,0x01,0x00,0x00,
+0xac,0x00,0x05,0x00,0x9e,0x00,0x00,0x00,0xbf,0x01,0x00,0x00,
+0xe1,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xbb,0x01,0x00,0x00,0xbc,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xbf,0x01,0x00,0x00,0xba,0x01,0x00,0x00,
+0xbb,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xba,0x01,0x00,0x00,
+0xb0,0x00,0x05,0x00,0x9e,0x00,0x00,0x00,0xc2,0x01,0x00,0x00,
+0x5d,0x00,0x00,0x00,0xe1,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xc4,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xc2,0x01,0x00,0x00,0xc3,0x01,0x00,0x00,0xc4,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc3,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc8,0x01,0x00,0x00,0x5d,0x00,0x00,0x00,
+0xe1,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x93,0x00,0x00,0x00,
+0xc9,0x01,0x00,0x00,0x8c,0x00,0x00,0x00,0xc8,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x89,0x00,0x00,0x00,0xca,0x01,0x00,0x00,
+0xc9,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x93,0x00,0x00,0x00,
+0xcb,0x01,0x00,0x00,0x8c,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x89,0x00,0x00,0x00,0xcc,0x01,0x00,0x00,
+0xcb,0x01,0x00,0x00,0x81,0x00,0x05,0x00,0x89,0x00,0x00,0x00,
+0xcd,0x01,0x00,0x00,0xcc,0x01,0x00,0x00,0xca,0x01,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xcb,0x01,0x00,0x00,0xcd,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xc4,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xc4,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0xb7,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xbc,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xbc,0x01,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd0,0x01,0x00,0x00,
+0xe1,0x01,0x00,0x00,0xf1,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xb9,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xbb,0x01,0x00,0x00,
+0xaa,0x00,0x05,0x00,0x9e,0x00,0x00,0x00,0xd2,0x01,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xd4,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xd2,0x01,0x00,0x00,0xd3,0x01,0x00,0x00,0xd4,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd3,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdb,0x01,0x00,0x00,0x4a,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x93,0x00,0x00,0x00,
+0xdc,0x01,0x00,0x00,0x8c,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x89,0x00,0x00,0x00,0xdd,0x01,0x00,0x00,
+0xdc,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0xcf,0x00,0x00,0x00,
+0xde,0x01,0x00,0x00,0xd8,0x01,0x00,0x00,0x4c,0x00,0x00,0x00,
+0xdb,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0xde,0x01,0x00,0x00,
+0xdd,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xd4,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd4,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_q6_K_f32_f32_len = 11704;
+
+unsigned char mul_mat_vec_q8_0_f16_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x28,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x46,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
+0x85,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x40,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x46,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x4f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x82,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0xbc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xbc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0xbc,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbe,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbe,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xfa,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0xfb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xfb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0xfb,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xfd,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xfd,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x06,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x1c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x1e,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x20,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x29,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
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+0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x23,0x01,0x00,0x00,
+0x21,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,0x51,0x00,0x05,0x00,
+0x08,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x23,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc1,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
+0xc1,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x20,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x12,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
+0xc5,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0xc9,0x00,0x00,0x00,0x23,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xc3,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x20,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x12,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0xd1,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0xd3,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x08,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
+0xc6,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x89,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x89,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0x26,0x01,0x00,0x00,
+0x99,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8b,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8d,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x99,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xde,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xde,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x27,0x01,0x00,0x00,0xdd,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,
+0xf5,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,0xac,0x00,0x05,0x00,
+0x94,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0x27,0x01,0x00,0x00,
+0x41,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xe0,0x00,0x00,0x00,
+0xe1,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xe4,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xdf,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
+0x94,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x27,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,0xe9,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xe7,0x00,0x00,0x00,
+0xe8,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xed,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x27,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x88,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
+0x85,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
+0x89,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0xf2,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x89,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe9,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe9,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x99,0x00,0x00,0x00,
+0x99,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe1,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe1,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,
+0x27,0x01,0x00,0x00,0x27,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xde,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,
+0xaa,0x00,0x05,0x00,0x94,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
+0x48,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xf9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xf7,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xf8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x81,0x00,0x00,0x00,
+0x44,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x88,0x00,0x00,0x00,
+0x01,0x01,0x00,0x00,0x85,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x02,0x01,0x00,0x00,
+0x01,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x03,0x01,0x00,0x00,
+0x04,0x01,0x00,0x00,0xfd,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x04,0x01,0x00,0x00,
+0x02,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xf9,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xf9,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_q8_0_f16_f32_len = 3916;
+
+unsigned char mul_mat_vec_q8_0_f32_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x26,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x46,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
+0x85,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x40,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x46,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x4f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x82,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0xbc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xbc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0xbc,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbe,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbe,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf9,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0xfa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xfa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0xfa,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xfc,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xfc,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x04,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
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+0x12,0x01,0x00,0x00,0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x0a,0x01,0x00,0x00,0x27,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x13,0x01,0x00,0x00,
+0x12,0x01,0x00,0x00,0x72,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x14,0x01,0x00,0x00,0x13,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0x15,0x01,0x00,0x00,0x14,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1a,0x01,0x00,0x00,
+0xa8,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
+0x29,0x00,0x00,0x00,0x1b,0x01,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x27,0x00,0x00,0x00,
+0x1a,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x1c,0x01,0x00,0x00,0x1b,0x01,0x00,0x00,0x72,0x00,0x04,0x00,
+0x1b,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0x1c,0x01,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,
+0x1d,0x01,0x00,0x00,0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
+0x1f,0x01,0x00,0x00,0x15,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,
+0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x21,0x01,0x00,0x00,
+0x1f,0x01,0x00,0x00,0x0d,0x01,0x00,0x00,0x51,0x00,0x05,0x00,
+0x08,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x21,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc1,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
+0xc1,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0xc4,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x08,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
+0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
+0x21,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xc4,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0xd1,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
+0x08,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
+0xd1,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x08,0x00,0x00,0x00,
+0xd3,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xba,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
+0x89,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x89,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
+0x24,0x01,0x00,0x00,0x99,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x8b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x8d,0x00,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x99,0x00,0x00,0x00,0x99,0x00,0x00,0x00,
+0xda,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xdd,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xdd,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x25,0x01,0x00,0x00,0xdc,0x00,0x00,0x00,
+0x8d,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
+0xac,0x00,0x05,0x00,0x94,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
+0x25,0x01,0x00,0x00,0x41,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xdf,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xe3,0x00,0x00,0x00,0xde,0x00,0x00,0x00,
+0xdf,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xde,0x00,0x00,0x00,
+0xb0,0x00,0x05,0x00,0x94,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
+0x48,0x00,0x00,0x00,0x25,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xe8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xe6,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe7,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x25,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x88,0x00,0x00,0x00,
+0xed,0x00,0x00,0x00,0x85,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
+0xed,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0x89,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
+0x08,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
+0xee,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x89,0x00,0x00,0x00,
+0xf1,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe8,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe8,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x99,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe0,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf4,0x00,0x00,0x00,0x25,0x01,0x00,0x00,0x27,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xdd,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xdf,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,0x94,0x00,0x00,0x00,
+0xf6,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xf6,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
+0xf8,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf7,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xff,0x00,0x00,0x00,
+0x81,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x88,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x85,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
+0x01,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0xc4,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0xfc,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x02,0x01,0x00,0x00,0x01,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xf8,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf8,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_q8_0_f32_f32_len = 3884;
 
 unsigned char norm_f32_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xb5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0xb8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
 0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
 0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
 0x01,0x00,0x00,0x00,0x0f,0x00,0x0b,0x00,0x05,0x00,0x00,0x00,
 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
 0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x97,0x00,0x00,0x00,
+0x29,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,
 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
 0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
@@ -49276,15 +74183,15 @@ unsigned char norm_f32_data[] = {
 0x34,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
 0x36,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
 0x47,0x00,0x04,0x00,0x36,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x94,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x97,0x00,0x00,0x00,
 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x95,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x95,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x98,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x98,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x95,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x97,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x97,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xad,0x00,0x00,0x00,
+0x98,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x9a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x9a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb0,0x00,0x00,0x00,
 0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
 0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
 0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
@@ -49332,138 +74239,142 @@ unsigned char norm_f32_data[] = {
 0x08,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,
 0x56,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,
 0x2a,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
-0xac,0xc5,0x27,0x37,0x1d,0x00,0x03,0x00,0x94,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x95,0x00,0x00,0x00,
-0x94,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x96,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x95,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x96,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
-0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x05,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,
-0x1e,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x21,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x21,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xae,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
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-0x7b,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0x88,0x00,0x05,0x00,
-0x14,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
-0x77,0x00,0x00,0x00,0x7f,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0xb4,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x14,0x00,0x00,0x00,0x83,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
-0x87,0x00,0x00,0x00,0x83,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
-0x0c,0x00,0x06,0x00,0x14,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x87,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x8b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8b,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xb0,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x59,0x00,0x00,0x00,
-0xac,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x2f,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x8d,0x00,0x00,0x00,
-0x8c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x93,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8c,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9d,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
-0xa4,0x00,0x00,0x00,0x83,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
-0xa7,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,
-0xa7,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x3d,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,0x97,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xaa,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8b,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8d,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
+0x74,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
+0x88,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
+0x74,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x42,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
+0x88,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
+0x7b,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x7f,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x14,0x00,0x00,0x00,0x83,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
+0x78,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x87,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x89,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
+0x14,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x83,0x00,0x00,0x00,
+0x89,0x00,0x00,0x00,0x0c,0x00,0x06,0x00,0x14,0x00,0x00,0x00,
+0x8b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x8a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8e,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8e,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,
+0xb0,0x00,0x05,0x00,0x2f,0x00,0x00,0x00,0x96,0x00,0x00,0x00,
+0xb3,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x90,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x96,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,
+0x90,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x8f,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
+0xb3,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,
+0xa7,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0xa0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0xa8,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x83,0x00,0x05,0x00,
+0x14,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
+0x78,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
+0xac,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
+0x9a,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xad,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,
+0xb3,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x8e,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x90,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
 };
-const uint64_t norm_f32_len = 2572;
+const uint64_t norm_f32_len = 2624;
 
 unsigned char relu_f32_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
@@ -50951,112 +75862,284 @@ const uint64_t rope_neox_f32_len = 3792;
 
 unsigned char scale_f32_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x37,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x9c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
 0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
 0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
 0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
+0xe3,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
 0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x20,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x22,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x22,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x24,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x25,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x25,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x25,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x27,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x27,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x34,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x02,0x00,0x1a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x1f,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x20,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x24,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x25,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x26,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x29,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
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+0x17,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0xd8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xda,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
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+0x04,0x01,0x00,0x00,0x03,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0x72,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x06,0x01,0x00,0x00,0x05,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
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+0x06,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x08,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x09,0x01,0x00,0x00,
+0x08,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x0a,0x01,0x00,0x00,0x07,0x01,0x00,0x00,0x09,0x01,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,
+0xcb,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,0x0b,0x01,0x00,0x00,
+0x04,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x12,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,0x06,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x15,0x01,0x00,0x00,
+0x12,0x01,0x00,0x00,0x09,0x01,0x00,0x00,0x82,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x18,0x01,0x00,0x00,0xcb,0x00,0x00,0x00,
+0x15,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1d,0x01,0x00,0x00,0x06,0x01,0x00,0x00,0x09,0x01,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,
+0x18,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,
+0x06,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x25,0x01,0x00,0x00,0x22,0x01,0x00,0x00,0x09,0x01,0x00,0x00,
+0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2a,0x01,0x00,0x00,
+0x18,0x01,0x00,0x00,0x25,0x01,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x2d,0x01,0x00,0x00,0x2a,0x01,0x00,0x00,
+0x09,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x36,0x01,0x00,0x00,0x2d,0x01,0x00,0x00,0x09,0x01,0x00,0x00,
+0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x37,0x01,0x00,0x00,
+0x2a,0x01,0x00,0x00,0x36,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x39,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0xad,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3a,0x01,0x00,0x00,0x39,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3b,0x01,0x00,0x00,0x0b,0x01,0x00,0x00,
+0x3a,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x3d,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3e,0x01,0x00,0x00,
+0x3d,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x3f,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,0x3e,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x40,0x01,0x00,0x00,
+0x3b,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0xb8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x43,0x01,0x00,0x00,0x42,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x44,0x01,0x00,0x00,0x2d,0x01,0x00,0x00,
+0x43,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x45,0x01,0x00,0x00,0x40,0x01,0x00,0x00,0x44,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x47,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x48,0x01,0x00,0x00,0x47,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x49,0x01,0x00,0x00,
+0x37,0x01,0x00,0x00,0x48,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,0x45,0x01,0x00,0x00,
+0x49,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xdf,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x54,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x55,0x01,0x00,0x00,0x54,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x56,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x57,0x01,0x00,0x00,0x56,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x58,0x01,0x00,0x00,
+0x55,0x01,0x00,0x00,0x57,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x59,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0x1e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x5a,0x01,0x00,0x00,0x59,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5b,0x01,0x00,0x00,0x58,0x01,0x00,0x00,
+0x5a,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5c,0x01,0x00,0x00,0xcb,0x00,0x00,0x00,0x5b,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x60,0x01,0x00,0x00,
+0x5c,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x63,0x01,0x00,0x00,0x60,0x01,0x00,0x00,
+0x57,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x66,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,
+0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x01,0x00,0x00,
+0xcb,0x00,0x00,0x00,0x66,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6e,0x01,0x00,0x00,0x57,0x01,0x00,0x00,
+0x5a,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6f,0x01,0x00,0x00,0x69,0x01,0x00,0x00,0x6e,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x73,0x01,0x00,0x00,
+0x6f,0x01,0x00,0x00,0x57,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x76,0x01,0x00,0x00,0x73,0x01,0x00,0x00,
+0x5a,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x7b,0x01,0x00,0x00,0x69,0x01,0x00,0x00,0x76,0x01,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x01,0x00,0x00,
+0x7b,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x87,0x01,0x00,0x00,0x7e,0x01,0x00,0x00,
+0x5a,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x88,0x01,0x00,0x00,0x7b,0x01,0x00,0x00,0x87,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x8a,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x8b,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x01,0x00,0x00,
+0x5c,0x01,0x00,0x00,0x8b,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x8e,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0x5a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x8f,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x90,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,
+0x8f,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x91,0x01,0x00,0x00,0x8c,0x01,0x00,0x00,0x90,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x93,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x94,0x01,0x00,0x00,0x93,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,
+0x7e,0x01,0x00,0x00,0x94,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x96,0x01,0x00,0x00,0x91,0x01,0x00,0x00,
+0x95,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x98,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x99,0x01,0x00,0x00,
+0x98,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9a,0x01,0x00,0x00,0x88,0x01,0x00,0x00,0x99,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x01,0x00,0x00,
+0x96,0x01,0x00,0x00,0x9a,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0xe8,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0x9b,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x11,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0xec,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x11,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
+0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0xea,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0xe8,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xf0,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xf4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,
 0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
 };
-const uint64_t scale_f32_len = 1256;
+const uint64_t scale_f32_len = 3320;
 
 unsigned char silu_f32_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
@@ -51170,50 +76253,65 @@ const uint64_t silu_f32_len = 1264;
 
 unsigned char soft_max_f32_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x0e,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x5c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
 0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
 0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,
 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
 0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
-0xc2,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x5c,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,
+0xa3,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
 0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x3b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,
+0x72,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x73,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x73,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x3b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x73,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x75,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x75,0x00,0x00,0x00,
 0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x53,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x54,0x00,0x00,0x00,
+0x89,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x8a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x8a,0x00,0x00,0x00,
 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x54,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x56,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x56,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x8a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x8c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8c,0x00,0x00,0x00,
 0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbf,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xc0,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0xa0,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xa1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xa1,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xa1,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa3,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa3,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x06,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x07,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xc0,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xc2,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xc2,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xff,0x00,0x00,0x00,
+0x07,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x09,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x09,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x45,0x01,0x00,0x00,
 0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
 0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
 0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
@@ -51226,264 +76324,767 @@ unsigned char soft_max_f32_data[] = {
 0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
 0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
 0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x16,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,
+0x16,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1e,0x00,0x0a,0x00,
 0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x17,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x24,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
+0x27,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x4a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x59,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x1c,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x5b,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x5b,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x60,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1a,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x72,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x73,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x74,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x73,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x74,0x00,0x00,0x00,
+0x75,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x7c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x89,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x8a,0x00,0x00,0x00,
+0x89,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x8b,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x8b,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x98,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xa0,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xa1,0x00,0x00,0x00,
+0xa0,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xa2,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0xa2,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,
+0x08,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x06,0x01,0x00,0x00,0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x07,0x01,0x00,0x00,0x06,0x01,0x00,0x00,0x20,0x00,0x04,0x00,
+0x08,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x07,0x01,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x08,0x01,0x00,0x00,0x09,0x01,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,
+0x45,0x01,0x00,0x00,0x59,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
+0x4a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
+0x5b,0x01,0x00,0x00,0x00,0x00,0x80,0xff,0x36,0x00,0x05,0x00,
+0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
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+0xcd,0x00,0x00,0x00,0x49,0x01,0x00,0x00,0x1b,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xb3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb5,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x60,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x61,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd5,0x00,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,
+0x0f,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,0x0e,0x01,0x00,0x00,
+0xd8,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0x27,0x00,0x00,0x00,
+0xdd,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xd7,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xdd,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xd6,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe2,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
+0xe2,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x7c,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
+0x6a,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x24,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
+0xac,0x00,0x05,0x00,0x27,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
+0x1e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xf1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xee,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xf0,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf7,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0x7c,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
+0x8c,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
+0xf8,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xf1,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xfa,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xf1,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf1,0x00,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x16,0x00,0x00,0x00,0x4e,0x01,0x00,0x00,
+0xf9,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0xfa,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x16,0x00,0x00,0x00,
+0xfc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xe8,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x4e,0x01,0x00,0x00,
+0x83,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
+0xfc,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x0c,0x00,0x06,0x00,
+0x16,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x61,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x04,0x01,0x00,0x00,
+0x03,0x01,0x00,0x00,0xff,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x61,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x7c,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,0x09,0x01,0x00,0x00,
+0x6a,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x0c,0x01,0x00,0x00,0xff,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd8,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd8,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0e,0x01,0x00,0x00,
+0x4a,0x01,0x00,0x00,0x59,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd7,0x00,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0xaf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x10,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x10,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x1a,0x00,0x00,0x00,0x4b,0x01,0x00,0x00,0xb2,0x00,0x00,0x00,
+0xd7,0x00,0x00,0x00,0x29,0x01,0x00,0x00,0x13,0x01,0x00,0x00,
+0xad,0x00,0x05,0x00,0x27,0x00,0x00,0x00,0x16,0x01,0x00,0x00,
+0x4b,0x01,0x00,0x00,0x6a,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x12,0x01,0x00,0x00,0x13,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x16,0x01,0x00,0x00,0x11,0x01,0x00,0x00,
+0x12,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x11,0x01,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x19,0x01,0x00,0x00,
+0x4b,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,0x27,0x00,0x00,0x00,
+0x1a,0x01,0x00,0x00,0x0f,0x00,0x00,0x00,0x19,0x01,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x1c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x1a,0x01,0x00,0x00,0x1b,0x01,0x00,0x00,
+0x1c,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x1b,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x21,0x01,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x19,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x60,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x21,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
+0x23,0x01,0x00,0x00,0x22,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x25,0x01,0x00,0x00,0x61,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x26,0x01,0x00,0x00,
+0x25,0x01,0x00,0x00,0x23,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x61,0x00,0x00,0x00,0x26,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x1c,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x1c,0x01,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0xaf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x13,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x13,0x01,0x00,0x00,0xc3,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x29,0x01,0x00,0x00,0x4b,0x01,0x00,0x00,
+0x1b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x10,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x12,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x2f,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x2f,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x4c,0x01,0x00,0x00,0x0f,0x00,0x00,0x00,0x12,0x01,0x00,0x00,
+0x44,0x01,0x00,0x00,0x30,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,
+0x27,0x00,0x00,0x00,0x37,0x01,0x00,0x00,0x4c,0x01,0x00,0x00,
+0x6c,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x31,0x01,0x00,0x00,
+0x30,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x37,0x01,0x00,0x00,0x30,0x01,0x00,0x00,0x31,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x30,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3b,0x01,0x00,0x00,0x13,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x3d,0x01,0x00,0x00,0x3b,0x01,0x00,0x00,0x4c,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0x7c,0x00,0x00,0x00,0x3f,0x01,0x00,0x00,
+0x09,0x01,0x00,0x00,0x6a,0x00,0x00,0x00,0x3d,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x40,0x01,0x00,0x00,
+0x3f,0x01,0x00,0x00,0x88,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x41,0x01,0x00,0x00,0x40,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x3f,0x01,0x00,0x00,0x41,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x44,0x01,0x00,0x00,
+0x4c,0x01,0x00,0x00,0x59,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x2f,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x31,0x01,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t soft_max_f32_len = 4916;
+
+unsigned char soft_max_f32_f16_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x61,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x75,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,
+0x0e,0x01,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x17,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x72,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x73,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x73,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x73,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x75,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x75,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8a,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x8b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
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+0xae,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0x5b,0x01,0x00,0x00,
+0x0c,0x00,0x07,0x00,0x16,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x71,0x00,0x00,0x00,
+0xae,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x61,0x00,0x00,0x00,
+0xaf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x67,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x67,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x4d,0x01,0x00,0x00,
+0x59,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x64,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x66,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xb7,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb7,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x1a,0x00,0x00,0x00,
+0x4e,0x01,0x00,0x00,0xb6,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
+0xd1,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0xad,0x00,0x05,0x00,
+0x27,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x4e,0x01,0x00,0x00,
+0x6a,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
+0xba,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xbd,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb8,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x4e,0x01,0x00,0x00,
+0xb0,0x00,0x05,0x00,0x27,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xc3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xc1,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc2,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x60,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0xcb,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
+0xcd,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x0c,0x00,0x07,0x00,
+0x16,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x61,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xc3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xc3,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xba,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xba,0x00,0x00,0x00,
+0xc3,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,
+0x4e,0x01,0x00,0x00,0x1b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xb7,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xb9,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x60,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0xb3,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x61,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xd9,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd9,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x4f,0x01,0x00,0x00,0x0f,0x00,0x00,0x00,
+0xb9,0x00,0x00,0x00,0x13,0x01,0x00,0x00,0xdc,0x00,0x00,0x00,
+0xb0,0x00,0x05,0x00,0x27,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,
+0x4f,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xdb,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xe1,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
+0xdb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xda,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
+0x4f,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x7c,0x00,0x00,0x00,
+0xeb,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,
+0xe8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
+0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x24,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x7f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
+0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0xac,0x00,0x05,0x00,
+0x27,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xf5,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf2,0x00,0x00,0x00,
+0xf4,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xf4,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf9,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
+0xf9,0x00,0x00,0x00,0x4f,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x94,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,
+0x6a,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x89,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
+0xfd,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xf5,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xff,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xf5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf5,0x00,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x16,0x00,0x00,0x00,0x53,0x01,0x00,0x00,
+0xfe,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0xff,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x16,0x00,0x00,0x00,
+0x01,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xec,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x53,0x01,0x00,0x00,
+0x83,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x03,0x01,0x00,0x00,
+0x01,0x01,0x00,0x00,0xd4,0x00,0x00,0x00,0x0c,0x00,0x06,0x00,
+0x16,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x61,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x09,0x01,0x00,0x00,
+0x08,0x01,0x00,0x00,0x04,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x61,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x7c,0x00,0x00,0x00,0x11,0x01,0x00,0x00,0x0e,0x01,0x00,0x00,
+0x6a,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x11,0x01,0x00,0x00,0x04,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xdc,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xdc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x13,0x01,0x00,0x00,
+0x4f,0x01,0x00,0x00,0x59,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd9,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xdb,0x00,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0xb3,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x15,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x15,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x1a,0x00,0x00,0x00,0x50,0x01,0x00,0x00,0xb6,0x00,0x00,0x00,
+0xdb,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,0x18,0x01,0x00,0x00,
+0xad,0x00,0x05,0x00,0x27,0x00,0x00,0x00,0x1b,0x01,0x00,0x00,
+0x50,0x01,0x00,0x00,0x6a,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x17,0x01,0x00,0x00,0x18,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x1b,0x01,0x00,0x00,0x16,0x01,0x00,0x00,
+0x17,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x16,0x01,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,
+0x50,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,0x27,0x00,0x00,0x00,
+0x1f,0x01,0x00,0x00,0x0f,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x21,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x1f,0x01,0x00,0x00,0x20,0x01,0x00,0x00,
+0x21,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x20,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x26,0x01,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x60,0x00,0x00,0x00,0x27,0x01,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x26,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
+0x28,0x01,0x00,0x00,0x27,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x2a,0x01,0x00,0x00,0x61,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x2b,0x01,0x00,0x00,
+0x2a,0x01,0x00,0x00,0x28,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x61,0x00,0x00,0x00,0x2b,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x21,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x21,0x01,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0xb3,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x18,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x18,0x01,0x00,0x00,0xc3,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,0x50,0x01,0x00,0x00,
+0x1b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x15,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x17,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x31,0x01,0x00,0x00,0xd3,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x34,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x34,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x51,0x01,0x00,0x00,0x0f,0x00,0x00,0x00,0x17,0x01,0x00,0x00,
+0x49,0x01,0x00,0x00,0x35,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,
+0x27,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,0x51,0x01,0x00,0x00,
+0x6c,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x36,0x01,0x00,0x00,
+0x35,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x3c,0x01,0x00,0x00,0x35,0x01,0x00,0x00,0x36,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x35,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x40,0x01,0x00,0x00,0x13,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x42,0x01,0x00,0x00,0x40,0x01,0x00,0x00,0x51,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0x7c,0x00,0x00,0x00,0x44,0x01,0x00,0x00,
+0x0e,0x01,0x00,0x00,0x6a,0x00,0x00,0x00,0x42,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x45,0x01,0x00,0x00,
+0x44,0x01,0x00,0x00,0x88,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x46,0x01,0x00,0x00,0x45,0x01,0x00,0x00,0x31,0x01,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x44,0x01,0x00,0x00,0x46,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x49,0x01,0x00,0x00,
+0x51,0x01,0x00,0x00,0x59,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x34,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x36,0x01,0x00,0x00,
 0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
 };
-const uint64_t soft_max_f32_len = 3752;
+const uint64_t soft_max_f32_f16_len = 5000;
 
 unsigned char split_k_reduce_data[] = {
 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
@@ -51610,105 +77211,277 @@ const uint64_t split_k_reduce_len = 1416;
 
 unsigned char sqr_f32_data[] = {
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+0x66,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,
+0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x01,0x00,0x00,
+0xcb,0x00,0x00,0x00,0x66,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6e,0x01,0x00,0x00,0x57,0x01,0x00,0x00,
+0x5a,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6f,0x01,0x00,0x00,0x69,0x01,0x00,0x00,0x6e,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x73,0x01,0x00,0x00,
+0x6f,0x01,0x00,0x00,0x57,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x76,0x01,0x00,0x00,0x73,0x01,0x00,0x00,
+0x5a,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x7b,0x01,0x00,0x00,0x69,0x01,0x00,0x00,0x76,0x01,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x01,0x00,0x00,
+0x7b,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x87,0x01,0x00,0x00,0x7e,0x01,0x00,0x00,
+0x5a,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x88,0x01,0x00,0x00,0x7b,0x01,0x00,0x00,0x87,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x8a,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x8b,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x01,0x00,0x00,
+0x5c,0x01,0x00,0x00,0x8b,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x8e,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x8f,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x90,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,
+0x8f,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x91,0x01,0x00,0x00,0x8c,0x01,0x00,0x00,0x90,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x93,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x94,0x01,0x00,0x00,0x93,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,
+0x7e,0x01,0x00,0x00,0x94,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x96,0x01,0x00,0x00,0x91,0x01,0x00,0x00,
+0x95,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x98,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x99,0x01,0x00,0x00,
+0x98,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9a,0x01,0x00,0x00,0x88,0x01,0x00,0x00,0x99,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x01,0x00,0x00,
+0x96,0x01,0x00,0x00,0x9a,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
+0x9b,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
+0xef,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0xde,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
+0xe4,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xf0,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xf4,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
 
 };
-const uint64_t sqr_f32_len = 1188;
+const uint64_t sqr_f32_len = 3252;
 
diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp
index 4a30414df..b9449be03 100644
--- a/ggml-vulkan.cpp
+++ b/ggml-vulkan.cpp
@@ -9,7 +9,6 @@
 #include <algorithm>
 #include <cmath>
 #include <iostream>
-#include <iomanip>
 #include <limits>
 #include <tuple>
 #include <vector>
@@ -69,6 +68,33 @@ struct vk_queue {
     vk::PipelineStageFlags stage_flags;
 };
 
+struct vk_pipeline_struct {
+    std::string name;
+    vk::ShaderModule shader_module;
+    vk::DescriptorSetLayout dsl;
+    std::vector<vk::DescriptorPool> descriptor_pools;
+    std::vector<vk::DescriptorSet> descriptor_sets;
+    uint32_t descriptor_set_idx;
+    vk::PipelineLayout layout;
+    vk::Pipeline pipeline;
+    uint32_t push_constant_size;
+    uint32_t parameter_count;
+    std::array<uint32_t, 3> wg_denoms;
+    uint32_t align;
+};
+
+typedef std::shared_ptr<vk_pipeline_struct> vk_pipeline;
+typedef std::weak_ptr<vk_pipeline_struct> vk_pipeline_ref;
+
+static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline);
+
+struct vk_matmul_pipeline_struct {
+    vk_pipeline l, m, s;
+    vk_pipeline a_l, a_m, a_s;
+};
+
+typedef std::shared_ptr<vk_matmul_pipeline_struct> vk_matmul_pipeline;
+
 struct vk_device {
     vk::PhysicalDevice physical_device;
     vk::PhysicalDeviceProperties properties;
@@ -84,10 +110,69 @@ struct vk_device {
     uint32_t subgroup_size;
     bool uma;
 
+    bool initialized;
+    size_t idx;
+
+    vk_matmul_pipeline pipeline_matmul_f32;
+    vk_matmul_pipeline pipeline_matmul_f16;
+    vk_matmul_pipeline pipeline_matmul_f16_f32;
+    vk_pipeline pipeline_matmul_split_k_reduce;
+
+    vk_matmul_pipeline pipeline_dequant_mul_mat_mat[VK_NUM_TYPES];
+
+    vk_matmul_pipeline pipeline_matmul_id_f32;
+    vk_matmul_pipeline pipeline_matmul_id_f16;
+    vk_matmul_pipeline pipeline_matmul_id_f16_f32;
+
+    vk_matmul_pipeline pipeline_dequant_mul_mat_mat_id[VK_NUM_TYPES];
+
+    vk_pipeline pipeline_dequant[VK_NUM_TYPES];
+    vk_pipeline pipeline_dequant_mul_mat_vec_f32_f32[VK_NUM_TYPES];
+    vk_pipeline pipeline_dequant_mul_mat_vec_f16_f32[VK_NUM_TYPES];
+    vk_pipeline pipeline_dequant_mul_mat_vec_id_f32[VK_NUM_TYPES];
+
+    vk_pipeline pipeline_mul_mat_vec_p021_f16_f32;
+    vk_pipeline pipeline_mul_mat_vec_nc_f16_f32;
+    vk_pipeline pipeline_get_rows[VK_NUM_TYPES];
+    vk_pipeline pipeline_get_rows_f32[VK_NUM_TYPES];
+    vk_pipeline pipeline_mul_f32;
+    vk_pipeline pipeline_add_f32;
+    vk_pipeline pipeline_scale_f32;
+    vk_pipeline pipeline_sqr_f32;
+    vk_pipeline pipeline_clamp_f32;
+    vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16;
+    vk_pipeline pipeline_norm_f32;
+    vk_pipeline pipeline_rms_norm_f32;
+    vk_pipeline pipeline_gelu_f32;
+    vk_pipeline pipeline_silu_f32;
+    vk_pipeline pipeline_relu_f32;
+    vk_pipeline pipeline_diag_mask_inf_f32;
+    vk_pipeline pipeline_soft_max_f32, pipeline_soft_max_f32_f16;
+    vk_pipeline pipeline_rope_f32, pipeline_rope_f16;
+    vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16;
+    vk_pipeline pipeline_argsort_f32;
+
+    std::vector<vk_pipeline_ref> pipelines;
+
     ~vk_device() {
 #ifdef GGML_VULKAN_DEBUG
     std::cerr << "destroy device " << name << std::endl;
 #endif
+        device.destroyCommandPool(compute_queue.pool);
+        if (!single_queue) {
+            device.destroyCommandPool(transfer_queue.pool);
+        }
+
+        for (auto& pipeline : pipelines) {
+            if (pipeline.expired()) {
+                continue;
+            }
+
+            vk_pipeline pl = pipeline.lock();
+            ggml_vk_destroy_pipeline(device, pl);
+        }
+        pipelines.clear();
+
         device.destroy();
     }
 };
@@ -125,21 +210,6 @@ struct vk_subbuffer {
     uint64_t size;
 };
 
-struct vk_pipeline {
-    std::string name;
-    vk::ShaderModule shader_module;
-    vk::DescriptorSetLayout dsl;
-    std::vector<vk::DescriptorPool> descriptor_pools;
-    std::vector<vk::DescriptorSet> descriptor_sets;
-    uint32_t descriptor_set_idx;
-    vk::PipelineLayout layout;
-    vk::Pipeline pipeline;
-    uint32_t push_constant_size;
-    uint32_t parameter_count;
-    std::array<uint32_t, 3> wg_denoms;
-    uint32_t align;
-};
-
 struct vk_semaphore {
     vk::Semaphore s;
     uint64_t value;
@@ -153,6 +223,21 @@ struct vk_submission {
 
 typedef std::vector<vk_submission> vk_sequence;
 
+struct vk_mat_mat_push_constants {
+    uint32_t M; uint32_t N; uint32_t K;
+    uint32_t stride_a; uint32_t stride_b; uint32_t stride_d; uint32_t k_split;
+    uint32_t ne02; uint32_t ne12; uint32_t broadcast2; uint32_t broadcast3;
+    uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d;
+    uint32_t expert_stride_b; uint32_t expert_stride_d;
+    uint32_t idx; uint32_t nbi1; uint32_t n_as;
+};
+
+struct vk_mat_vec_push_constants {
+    uint32_t ncols; uint32_t stride_a; uint32_t stride_b; uint32_t stride_d;
+    uint32_t ne02; uint32_t ne12; uint32_t broadcast2; uint32_t broadcast3;
+    uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d;
+};
+
 struct vk_op_push_constants {
     uint32_t KX;
     uint32_t KY;
@@ -160,11 +245,21 @@ struct vk_op_push_constants {
     float param2;
 };
 
-struct vk_op_cpy_push_constants {
+struct vk_op_unary_push_constants {
     uint32_t ne;
-    uint32_t ne00; uint32_t ne01; uint32_t nb00; uint32_t nb01; uint32_t nb02;
-    uint32_t ne10; uint32_t ne11; uint32_t nb10; uint32_t nb11; uint32_t nb12;
+    uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
+    uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
     uint32_t d_offset;
+    float param1; float param2;
+};
+
+struct vk_op_binary_push_constants {
+    uint32_t ne;
+    uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
+    uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
+    uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23; uint32_t nb20; uint32_t nb21; uint32_t nb22; uint32_t nb23;
+    uint32_t d_offset;
+    float param1; float param2;
 };
 
 struct vk_op_diag_mask_push_constants {
@@ -196,6 +291,22 @@ struct vk_op_rope_neox_push_constants {
     float inv_ndims;
 };
 
+struct vk_op_soft_max_push_constants {
+    uint32_t KX;
+    uint32_t KY;
+    uint32_t KZ;
+    float scale;
+    float max_bias;
+    float m0;
+    float m1;
+    uint32_t n_head_log2;
+};
+
+struct vk_op_argsort_push_constants {
+    uint32_t ncols;
+    bool ascending;
+};
+
 // Allow pre-recording command buffers
 struct vk_staging_memcpy {
     vk_staging_memcpy(void * _dst, const void * _src, size_t _n) : dst(_dst), src(_src), n(_n) {}
@@ -236,7 +347,6 @@ struct ggml_tensor_extra_gpu {
 };
 
 struct ggml_vk_garbage_collector {
-    std::vector<vk_pipeline *> pipelines;
     std::vector<vk_semaphore> tl_semaphores;
     std::vector<vk_semaphore> semaphores;
     std::vector<vk::Event> events;
@@ -247,41 +357,13 @@ struct ggml_vk_garbage_collector {
 struct ggml_backend_vk_context {
     std::string name;
 
-    std::weak_ptr<vk_device> device;
-    vk_pipeline pipeline_matmul_f32_l, pipeline_matmul_f32_m, pipeline_matmul_f32_s;
-    vk_pipeline pipeline_matmul_f32_aligned_l, pipeline_matmul_f32_aligned_m, pipeline_matmul_f32_aligned_s;
-    vk_pipeline pipeline_matmul_f16_l, pipeline_matmul_f16_m, pipeline_matmul_f16_s;
-    vk_pipeline pipeline_matmul_f16_aligned_l, pipeline_matmul_f16_aligned_m, pipeline_matmul_f16_aligned_s;
-    vk_pipeline pipeline_matmul_f16_f32_l, pipeline_matmul_f16_f32_m, pipeline_matmul_f16_f32_s;
-    vk_pipeline pipeline_matmul_f16_f32_aligned_l, pipeline_matmul_f16_f32_aligned_m, pipeline_matmul_f16_f32_aligned_s;
-    vk_pipeline pipeline_matmul_split_k_reduce;
-    vk_pipeline pipeline_dequant[VK_NUM_TYPES];
-    vk_pipeline pipeline_dequant_mul_mat_vec_f32[VK_NUM_TYPES];
-    vk_pipeline pipeline_mul_mat_vec_p021_f16_f32;
-    vk_pipeline pipeline_mul_mat_vec_nc_f16_f32;
-    vk_pipeline pipeline_get_rows[VK_NUM_TYPES];
-    vk_pipeline pipeline_get_rows_f32[VK_NUM_TYPES];
-    vk_pipeline pipeline_mul_f32;
-    vk_pipeline pipeline_add_f32;
-    vk_pipeline pipeline_scale_f32;
-    vk_pipeline pipeline_sqr_f32;
-    vk_pipeline pipeline_clamp_f32;
-    vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16;
-    vk_pipeline pipeline_norm_f32;
-    vk_pipeline pipeline_rms_norm_f32;
-    vk_pipeline pipeline_gelu_f32;
-    vk_pipeline pipeline_silu_f32;
-    vk_pipeline pipeline_relu_f32;
-    vk_pipeline pipeline_diag_mask_inf_f32;
-    vk_pipeline pipeline_soft_max_f32;
-    vk_pipeline pipeline_rope_f32, pipeline_rope_f16;
-    vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16;
+    std::shared_ptr<vk_device> device;
 
     size_t semaphore_idx, event_idx;
     ggml_vk_garbage_collector gc;
     std::vector<std::tuple<void*, size_t, vk_buffer>> pinned_memory;
-    size_t prealloc_size_qx, prealloc_size_qy, prealloc_size_x, prealloc_size_y, prealloc_size_split_k;
-    vk_buffer prealloc_qx, prealloc_qy, prealloc_x, prealloc_y, prealloc_split_k;
+    size_t prealloc_size_x, prealloc_size_y, prealloc_size_split_k;
+    vk_buffer prealloc_x, prealloc_y, prealloc_split_k;
     vk::Fence fence;
     vk_buffer staging;
     size_t staging_size;
@@ -304,13 +386,31 @@ struct vk_instance {
 
     std::vector<size_t> device_indices;
 
-    std::shared_ptr<vk_device> devices[GGML_VK_MAX_DEVICES];
     ggml_backend_t backends[GGML_VK_MAX_DEVICES];
     ggml_backend_vk_context contexts[GGML_VK_MAX_DEVICES];
     ggml_backend_buffer_type buffer_types[GGML_VK_MAX_DEVICES];
     bool initialized[GGML_VK_MAX_DEVICES];
 };
 
+static std::shared_ptr<vk_device> ggml_vk_get_device(size_t idx) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_get_device(" << idx << ")" << std::endl;
+#endif
+    static std::weak_ptr<vk_device> devices[GGML_VK_MAX_DEVICES];
+
+    if (devices[idx].expired()) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "Initializing new vk_device" << std::endl;
+#endif
+        std::shared_ptr<vk_device> device = std::make_shared<vk_device>();
+        device->initialized = false;
+        devices[idx] = device;
+        return device;
+    }
+
+    return devices[idx].lock();
+}
+
 #ifdef GGML_VULKAN_CHECK_RESULTS
 static size_t vk_skip_checks;
 static size_t vk_output_tensor;
@@ -334,14 +434,15 @@ static void ggml_vk_create_pipeline(ggml_backend_vk_context * ctx, vk_pipeline&
     GGML_ASSERT(parameter_count > 0);
     GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT
 
-    pipeline.name = name;
-    pipeline.parameter_count = parameter_count;
-    pipeline.push_constant_size = push_constant_size;
-    pipeline.wg_denoms = wg_denoms;
-    pipeline.align = align;
+    pipeline = std::make_shared<vk_pipeline_struct>();
+    pipeline->name = name;
+    pipeline->parameter_count = parameter_count;
+    pipeline->push_constant_size = push_constant_size;
+    pipeline->wg_denoms = wg_denoms;
+    pipeline->align = align;
 
     vk::ShaderModuleCreateInfo shader_module_create_info({}, spv_size, reinterpret_cast<const uint32_t *>(spv_data));
-    pipeline.shader_module = ctx->device.lock()->device.createShaderModule(shader_module_create_info);
+    pipeline->shader_module = ctx->device->device.createShaderModule(shader_module_create_info);
 
     std::vector<vk::DescriptorSetLayoutBinding> dsl_binding;
     std::vector<vk::DescriptorBindingFlags> dsl_binding_flags;
@@ -355,49 +456,49 @@ static void ggml_vk_create_pipeline(ggml_backend_vk_context * ctx, vk_pipeline&
     vk::PushConstantRange pcr(
         vk::ShaderStageFlagBits::eCompute,
         0,
-        pipeline.push_constant_size
+        pipeline->push_constant_size
     );
 
     vk::DescriptorSetLayoutCreateInfo descriptor_set_layout_create_info(
         {},
         dsl_binding);
     descriptor_set_layout_create_info.setPNext(&dslbfci);
-    pipeline.dsl = ctx->device.lock()->device.createDescriptorSetLayout(descriptor_set_layout_create_info);
+    pipeline->dsl = ctx->device->device.createDescriptorSetLayout(descriptor_set_layout_create_info);
 
     // Check if device supports multiple descriptors per pool
-    if (ctx->device.lock()->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN) {
+    if (ctx->device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN) {
         const uint32_t alloc_count = 2;
 
         // Try allocating multiple sets from one pool
         // This fails on AMD for some reason, so add a fall back to allocating one pool per set
-        vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count);
+        vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count);
         vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, alloc_count, descriptor_pool_size);
-        vk::DescriptorPool pool = ctx->device.lock()->device.createDescriptorPool(descriptor_pool_create_info);
+        vk::DescriptorPool pool = ctx->device->device.createDescriptorPool(descriptor_pool_create_info);
 
         std::vector<vk::DescriptorSetLayout> layouts(alloc_count);
         for (uint32_t i = 0; i < alloc_count; i++) {
-            layouts[i] = pipeline.dsl;
+            layouts[i] = pipeline->dsl;
         }
         try {
             vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pool, alloc_count, layouts.data());
-            std::vector<vk::DescriptorSet> sets = ctx->device.lock()->device.allocateDescriptorSets(descriptor_set_alloc_info);
+            std::vector<vk::DescriptorSet> sets = ctx->device->device.allocateDescriptorSets(descriptor_set_alloc_info);
         } catch(vk::OutOfPoolMemoryError const&) {
-            ctx->device.lock()->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE;
+            ctx->device->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE;
         }
 
-        ctx->device.lock()->device.destroyDescriptorPool(pool);
+        ctx->device->device.destroyDescriptorPool(pool);
     }
 
-    if (ctx->device.lock()->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) {
-        vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count);
+    if (ctx->device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) {
+        vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count);
         vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 128, descriptor_pool_size);
-        pipeline.descriptor_pools.push_back(ctx->device.lock()->device.createDescriptorPool(descriptor_pool_create_info));
+        pipeline->descriptor_pools.push_back(ctx->device->device.createDescriptorPool(descriptor_pool_create_info));
     }
 
-    pipeline.descriptor_set_idx = 0;
+    pipeline->descriptor_set_idx = 0;
 
-    vk::PipelineLayoutCreateInfo pipeline_layout_create_info(vk::PipelineLayoutCreateFlags(), pipeline.dsl, pcr);
-    pipeline.layout = ctx->device.lock()->device.createPipelineLayout(pipeline_layout_create_info);
+    vk::PipelineLayoutCreateInfo pipeline_layout_create_info(vk::PipelineLayoutCreateFlags(), pipeline->dsl, pcr);
+    pipeline->layout = ctx->device->device.createPipelineLayout(pipeline_layout_create_info);
 
     std::vector<vk::SpecializationMapEntry> specialization_entries(specialization_constants.size());
 
@@ -417,72 +518,75 @@ static void ggml_vk_create_pipeline(ggml_backend_vk_context * ctx, vk_pipeline&
     vk::PipelineShaderStageCreateInfo pipeline_shader_create_info(
             vk::PipelineShaderStageCreateFlags(),
             vk::ShaderStageFlagBits::eCompute,
-            pipeline.shader_module,
+            pipeline->shader_module,
             entrypoint.c_str(),
             &specialization_info);
     vk::ComputePipelineCreateInfo compute_pipeline_create_info(
         vk::PipelineCreateFlags(),
         pipeline_shader_create_info,
-        pipeline.layout);
-    pipeline.pipeline = ctx->device.lock()->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value;
+        pipeline->layout);
+    pipeline->pipeline = ctx->device->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value;
 
-    ctx->gc.pipelines.push_back(&pipeline);
+    ctx->device->pipelines.push_back(pipeline);
 }
 
-static void ggml_vk_destroy_pipeline(ggml_backend_vk_context * ctx, vk_pipeline * pipeline) {
+static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_pipeline_destroy_pipeline(" << pipeline->name << ")" << std::endl;
+#endif
     for (auto& pool : pipeline->descriptor_pools) {
-        ctx->device.lock()->device.destroyDescriptorPool(pool);
+        device.destroyDescriptorPool(pool);
     }
     pipeline->descriptor_pools.clear();
     pipeline->descriptor_sets.clear();
     pipeline->descriptor_set_idx = 0;
 
-    ctx->device.lock()->device.destroyDescriptorSetLayout(pipeline->dsl);
+    device.destroyDescriptorSetLayout(pipeline->dsl);
 
-    ctx->device.lock()->device.destroyPipelineLayout(pipeline->layout);
+    device.destroyPipelineLayout(pipeline->layout);
 
-    ctx->device.lock()->device.destroyShaderModule(pipeline->shader_module);
+    device.destroyShaderModule(pipeline->shader_module);
 
-    ctx->device.lock()->device.destroyPipeline(pipeline->pipeline);
+    device.destroyPipeline(pipeline->pipeline);
 }
 
 static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx, vk_pipeline& pipeline, uint32_t n) {
 #ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_pipeline_allocate_descriptor_sets(" << pipeline.name << ", " << n << ")" << std::endl;
+    std::cerr << "ggml_pipeline_allocate_descriptor_sets(" << pipeline->name << ", " << n << ")" << std::endl;
 #endif
-    if (pipeline.descriptor_sets.size() >= pipeline.descriptor_set_idx + n) {
+    if (pipeline->descriptor_sets.size() >= pipeline->descriptor_set_idx + n) {
         // Enough descriptors are available
         return;
     }
 
-    if (ctx->device.lock()->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) {
-        const uint32_t alloc_count = pipeline.descriptor_set_idx + n - pipeline.descriptor_sets.size();
+    if (ctx->device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) {
+        const uint32_t alloc_count = pipeline->descriptor_set_idx + n - pipeline->descriptor_sets.size();
 
         std::vector<vk::DescriptorSetLayout> layouts(alloc_count);
         for (uint32_t i = 0; i < alloc_count; i++) {
-            layouts[i] = pipeline.dsl;
+            layouts[i] = pipeline->dsl;
         }
-        vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline.descriptor_pools[0], alloc_count, layouts.data());
-        std::vector<vk::DescriptorSet> sets = ctx->device.lock()->device.allocateDescriptorSets(descriptor_set_alloc_info);
-        pipeline.descriptor_sets.insert(pipeline.descriptor_sets.end(), sets.begin(), sets.end());
+        vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline->descriptor_pools[0], alloc_count, layouts.data());
+        std::vector<vk::DescriptorSet> sets = ctx->device->device.allocateDescriptorSets(descriptor_set_alloc_info);
+        pipeline->descriptor_sets.insert(pipeline->descriptor_sets.end(), sets.begin(), sets.end());
     } else {
-        for (uint32_t i = pipeline.descriptor_sets.size(); i < pipeline.descriptor_set_idx + n; i++) {
-            vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count);
+        for (uint32_t i = pipeline->descriptor_sets.size(); i < pipeline->descriptor_set_idx + n; i++) {
+            vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count);
             vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 1, descriptor_pool_size);
-            pipeline.descriptor_pools.push_back(ctx->device.lock()->device.createDescriptorPool(descriptor_pool_create_info));
+            pipeline->descriptor_pools.push_back(ctx->device->device.createDescriptorPool(descriptor_pool_create_info));
 
-            vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline.descriptor_pools[i], 1, &pipeline.dsl);
-            std::vector<vk::DescriptorSet> sets = ctx->device.lock()->device.allocateDescriptorSets(descriptor_set_alloc_info);
-            pipeline.descriptor_sets.push_back(sets[0]);
+            vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline->descriptor_pools[i], 1, &pipeline->dsl);
+            std::vector<vk::DescriptorSet> sets = ctx->device->device.allocateDescriptorSets(descriptor_set_alloc_info);
+            pipeline->descriptor_sets.push_back(sets[0]);
         }
     }
 }
 
 static void ggml_pipeline_cleanup(vk_pipeline& pipeline) {
 #ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_pipeline_cleanup(" << pipeline.name << ")" << std::endl;
+    std::cerr << "ggml_pipeline_cleanup(" << pipeline->name << ")" << std::endl;
 #endif
-    pipeline.descriptor_set_idx = 0;
+    pipeline->descriptor_set_idx = 0;
 }
 
 static vk::CommandBuffer ggml_vk_create_cmd_buffer(ggml_backend_vk_context * ctx, vk_queue& q) {
@@ -498,7 +602,7 @@ static vk::CommandBuffer ggml_vk_create_cmd_buffer(ggml_backend_vk_context * ctx
         q.pool,
         vk::CommandBufferLevel::ePrimary,
         1);
-    const std::vector<vk::CommandBuffer> cmd_buffers = ctx->device.lock()->device.allocateCommandBuffers(command_buffer_alloc_info);
+    const std::vector<vk::CommandBuffer> cmd_buffers = ctx->device->device.allocateCommandBuffers(command_buffer_alloc_info);
     auto buf = cmd_buffers.front();
 
     q.cmd_buffers.push_back(buf);
@@ -628,6 +732,12 @@ static uint32_t ggml_vk_find_queue_family_index(std::vector<vk::QueueFamilyPrope
         }
     }
 
+    // All commands that are allowed on a queue that supports transfer operations are also allowed on a queue that supports either graphics or compute operations.
+    // Thus, if the capabilities of a queue family include VK_QUEUE_GRAPHICS_BIT or VK_QUEUE_COMPUTE_BIT, then reporting the VK_QUEUE_TRANSFER_BIT capability separately for that queue family is optional.
+    if (compute_index >= 0) {
+        return compute_index;
+    }
+
     std::cerr << "ggml_vulkan: No suitable queue family index found." << std::endl;
 
     for(auto &q_family : queue_family_props) {
@@ -643,11 +753,11 @@ static void ggml_vk_create_queue(ggml_backend_vk_context * ctx, vk_queue& q, uin
     q.queue_family_index = queue_family_index;
 
     vk::CommandPoolCreateInfo command_pool_create_info_compute(vk::CommandPoolCreateFlags(VK_COMMAND_POOL_CREATE_TRANSIENT_BIT), queue_family_index);
-    q.pool = ctx->device.lock()->device.createCommandPool(command_pool_create_info_compute);
+    q.pool = ctx->device->device.createCommandPool(command_pool_create_info_compute);
 
     q.cmd_buffer_idx = 0;
 
-    q.queue = ctx->device.lock()->device.getQueue(queue_family_index, queue_index);
+    q.queue = ctx->device->device.getQueue(queue_family_index, queue_index);
 
     q.stage_flags = stage_flags;
 }
@@ -671,7 +781,7 @@ static vk_semaphore * ggml_vk_create_binary_semaphore(ggml_backend_vk_context *
     vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eBinary, 0 };
     vk::SemaphoreCreateInfo ci{};
     ci.setPNext(&tci);
-    vk::Semaphore semaphore = ctx->device.lock()->device.createSemaphore(ci);
+    vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci);
     ctx->gc.semaphores.push_back({ semaphore, 0 });
     return &ctx->gc.semaphores[ctx->gc.semaphores.size() - 1];
 }
@@ -684,7 +794,7 @@ static vk_semaphore * ggml_vk_create_timeline_semaphore(ggml_backend_vk_context
         vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 };
         vk::SemaphoreCreateInfo ci{};
         ci.setPNext(&tci);
-        vk::Semaphore semaphore = ctx->device.lock()->device.createSemaphore(ci);
+        vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci);
         ctx->gc.tl_semaphores.push_back({ semaphore, 0 });
     }
     return &ctx->gc.tl_semaphores[ctx->semaphore_idx++];
@@ -692,7 +802,7 @@ static vk_semaphore * ggml_vk_create_timeline_semaphore(ggml_backend_vk_context
 
 static vk::Event ggml_vk_create_event(ggml_backend_vk_context * ctx) {
     if (ctx->event_idx >= ctx->gc.events.size()) {
-        ctx->gc.events.push_back(ctx->device.lock()->device.createEvent({}));
+        ctx->gc.events.push_back(ctx->device->device.createEvent({}));
     }
     return ctx->gc.events[ctx->event_idx++];
 }
@@ -703,7 +813,7 @@ static void ggml_vk_queue_cleanup(ggml_backend_vk_context * ctx, vk_queue& q) {
 #endif
     // Requires command buffers to be done
 
-    ctx->device.lock()->device.resetCommandPool(q.pool);
+    ctx->device->device.resetCommandPool(q.pool);
     q.cmd_buffer_idx = 0;
 }
 
@@ -721,7 +831,7 @@ static uint32_t find_properties(const vk::PhysicalDeviceMemoryProperties* mem_pr
 
 static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0)) {
 #ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_create_buffer(" << size << ", " << to_string(req_flags) << ", " << to_string(fallback_flags) << ")" << std::endl;
+    std::cerr << "ggml_vk_create_buffer(device " << ctx->idx << ", " << size << ", " << to_string(req_flags) << ", " << to_string(fallback_flags) << ")" << std::endl;
 #endif
     vk_buffer buf = std::make_shared<vk_buffer_struct>();
 
@@ -740,11 +850,11 @@ static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t siz
         nullptr,
     };
 
-    buf->buffer = ctx->device.lock()->device.createBuffer(buffer_create_info);
+    buf->buffer = ctx->device->device.createBuffer(buffer_create_info);
 
-    vk::MemoryRequirements mem_req = ctx->device.lock()->device.getBufferMemoryRequirements(buf->buffer);
+    vk::MemoryRequirements mem_req = ctx->device->device.getBufferMemoryRequirements(buf->buffer);
 
-    vk::PhysicalDeviceMemoryProperties mem_props = ctx->device.lock()->physical_device.getMemoryProperties();
+    vk::PhysicalDeviceMemoryProperties mem_props = ctx->device->physical_device.getMemoryProperties();
 
     uint32_t memory_type_index = UINT32_MAX;
 
@@ -757,30 +867,30 @@ static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t siz
     }
 
     if (memory_type_index == UINT32_MAX) {
-        ctx->device.lock()->device.destroyBuffer(buf->buffer);
+        ctx->device->device.destroyBuffer(buf->buffer);
         buf->size = 0;
         throw vk::OutOfDeviceMemoryError("No suitable memory type found");
     }
 
     try {
-        buf->device_memory = ctx->device.lock()->device.allocateMemory({ mem_req.size, memory_type_index });
+        buf->device_memory = ctx->device->device.allocateMemory({ mem_req.size, memory_type_index });
     } catch (const vk::SystemError& e) {
         // Out of Host/Device memory, clean up buffer
-        ctx->device.lock()->device.destroyBuffer(buf->buffer);
+        ctx->device->device.destroyBuffer(buf->buffer);
         buf->size = 0;
         throw e;
     }
     buf->ptr = nullptr;
 
     if (buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
-        buf->ptr = ctx->device.lock()->device.mapMemory(buf->device_memory, 0, VK_WHOLE_SIZE);
+        buf->ptr = ctx->device->device.mapMemory(buf->device_memory, 0, VK_WHOLE_SIZE);
     }
 
-    ctx->device.lock()->device.bindBufferMemory(buf->buffer, buf->device_memory, 0);
+    ctx->device->device.bindBufferMemory(buf->buffer, buf->device_memory, 0);
 
     buf->ctx = ctx;
 
-    buf->device = ctx->device.lock();
+    buf->device = ctx->device;
 
 #ifdef GGML_VULKAN_DEBUG
     std::cerr << "Created buffer " << buf->buffer << std::endl;
@@ -802,7 +912,7 @@ static vk_buffer ggml_vk_create_buffer_check(ggml_backend_vk_context * ctx, size
 static vk_buffer ggml_vk_create_buffer_device(ggml_backend_vk_context * ctx, size_t size) {
     vk_buffer buf;
     try {
-        if (ctx->device.lock()->uma) {
+        if (ctx->device->uma) {
             // Fall back to host memory type
             buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
         } else {
@@ -883,10 +993,16 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) {
     std::cerr << "ggml_vk_load_shaders(" << ctx->name << ")" << std::endl;
 #endif
 
+    const std::shared_ptr<vk_device> device = ctx->device;
+
     // mulmat
-    std::initializer_list<uint32_t> warptile_l = { 128, 128, 128, 16, ctx->device.lock()->subgroup_size * 2, 64, 2, 4, 4, ctx->device.lock()->subgroup_size };
-    std::initializer_list<uint32_t> warptile_m = { 128,  64,  64, 16, ctx->device.lock()->subgroup_size, 32, 2, 4, 2, ctx->device.lock()->subgroup_size };
-    std::initializer_list<uint32_t> warptile_s = { ctx->device.lock()->subgroup_size,  32,  32, 16, 32, 32, 2, 2, 2, ctx->device.lock()->subgroup_size };
+    std::initializer_list<uint32_t> warptile_l = { 128, 128, 128, 16, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size };
+    std::initializer_list<uint32_t> warptile_m = { 128,  64,  64, 16, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size };
+    std::initializer_list<uint32_t> warptile_s = { device->subgroup_size,  32,  32, 16, 32, 32, 2, 2, 2, device->subgroup_size };
+
+    std::initializer_list<uint32_t> warptile_mmq_l = { 128, 128, 128, 32, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size };
+    std::initializer_list<uint32_t> warptile_mmq_m = { 128,  64,  64, 32, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size };
+    std::initializer_list<uint32_t> warptile_mmq_s = { device->subgroup_size,  32,  32, 32, 32, 32, 2, 2, 2, device->subgroup_size };
 
     std::array<uint32_t, 3> l_wg_denoms = {128, 128, 1 };
     std::array<uint32_t, 3> m_wg_denoms = { 64,  64, 1 };
@@ -896,126 +1012,505 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) {
     uint32_t m_align =  64;
     uint32_t s_align =  32;
 
-    if (ctx->device.lock()->fp16) {
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_l, "matmul_f32_l", matmul_f32_l_len, matmul_f32_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_m, "matmul_f32_m", matmul_f32_m_len, matmul_f32_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_s, "matmul_f32_s", matmul_f32_s_len, matmul_f32_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_l, "matmul_f32_aligned_l", matmul_f32_aligned_l_len, matmul_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_m, "matmul_f32_aligned_m", matmul_f32_aligned_m_len, matmul_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_s, "matmul_f32_aligned_s", matmul_f32_aligned_s_len, matmul_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align);
+    ctx->device->pipeline_matmul_f32 = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_matmul_f16_f32 = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_matmul_f16 = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K] = std::make_shared<vk_matmul_pipeline_struct>();
 
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_l, "matmul_f16_l", matmul_f16_l_len, matmul_f16_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_m, "matmul_f16_m", matmul_f16_m_len, matmul_f16_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_s, "matmul_f16_s", matmul_f16_s_len, matmul_f16_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_l, "matmul_f16_aligned_l", matmul_f16_aligned_l_len, matmul_f16_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_m, "matmul_f16_aligned_m", matmul_f16_aligned_m_len, matmul_f16_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_s, "matmul_f16_aligned_s", matmul_f16_aligned_s_len, matmul_f16_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align);
+    /*ctx->device->pipeline_matmul_id_f32 = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_matmul_id_f16_f32 = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_matmul_id_f16 = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K] = std::make_shared<vk_matmul_pipeline_struct>();*/
 
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_l, "matmul_f16_f32_l", matmul_f16_f32_l_len, matmul_f16_f32_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_m, "matmul_f16_f32_m", matmul_f16_f32_m_len, matmul_f16_f32_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_s, "matmul_f16_f32_s", matmul_f16_f32_s_len, matmul_f16_f32_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_len, matmul_f16_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_len, matmul_f16_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_len, matmul_f16_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align);
+    if (device->fp16) {
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_len, matmul_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_len, matmul_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_len, matmul_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_0_f32_l", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_0_f32_m", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_0_f32_s", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->l, "matmul_q2_k_f32_l", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->m, "matmul_q2_k_f32_m", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->s, "matmul_q2_k_f32_s", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_l, "matmul_q2_k_f32_aligned_l", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_m, "matmul_q2_k_f32_aligned_m", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_s, "matmul_q2_k_f32_aligned_s", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->l, "matmul_q3_k_f32_l", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->m, "matmul_q3_k_f32_m", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->s, "matmul_q3_k_f32_s", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_l, "matmul_q3_k_f32_aligned_l", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_m, "matmul_q3_k_f32_aligned_m", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_s, "matmul_q3_k_f32_aligned_s", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->l, "matmul_q4_k_f32_l", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->m, "matmul_q4_k_f32_m", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->s, "matmul_q4_k_f32_s", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_l, "matmul_q4_k_f32_aligned_l", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_m, "matmul_q4_k_f32_aligned_m", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_s, "matmul_q4_k_f32_aligned_s", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->l, "matmul_q5_k_f32_l", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->m, "matmul_q5_k_f32_m", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->s, "matmul_q5_k_f32_s", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_l, "matmul_q5_k_f32_aligned_l", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_m, "matmul_q5_k_f32_aligned_m", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_s, "matmul_q5_k_f32_aligned_s", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->l, "matmul_q6_k_f32_l", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->m, "matmul_q6_k_f32_m", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->s, "matmul_q6_k_f32_s", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_l, "matmul_q6_k_f32_aligned_l", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        /*ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->l, "matmul_id_f32_l", matmul_id_f32_len, matmul_id_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->m, "matmul_id_f32_m", matmul_id_f32_len, matmul_id_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->s, "matmul_id_f32_s", matmul_id_f32_len, matmul_id_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_l, "matmul_id_f32_aligned_l", matmul_id_f32_aligned_len, matmul_id_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_m, "matmul_id_f32_aligned_m", matmul_id_f32_aligned_len, matmul_id_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_s, "matmul_id_f32_aligned_s", matmul_id_f32_aligned_len, matmul_id_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->l, "matmul_id_f16_l", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->m, "matmul_id_f16_m", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->s, "matmul_id_f16_s", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_l, "matmul_id_f16_aligned_l", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_m, "matmul_id_f16_aligned_m", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_s, "matmul_id_f16_aligned_s", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->l, "matmul_id_f16_f32_l", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->m, "matmul_id_f16_f32_m", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->s, "matmul_id_f16_f32_s", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_l, "matmul_id_f16_f32_aligned_l", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_m, "matmul_id_f16_f32_aligned_m", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_s, "matmul_id_f16_f32_aligned_s", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->l, "matmul_id_q5_0_f32_l", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->m, "matmul_id_q5_0_f32_m", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->s, "matmul_id_q5_0_f32_s", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_l, "matmul_id_q5_0_f32_aligned_l", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_m, "matmul_id_q5_0_f32_aligned_m", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_s, "matmul_id_q5_0_f32_aligned_s", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->l, "matmul_id_q5_1_f32_l", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->m, "matmul_id_q5_1_f32_m", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->s, "matmul_id_q5_1_f32_s", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_l, "matmul_id_q5_1_f32_aligned_l", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_m, "matmul_id_q5_1_f32_aligned_m", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_s, "matmul_id_q5_1_f32_aligned_s", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->l, "matmul_id_q8_0_f32_l", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->m, "matmul_id_q8_0_f32_m", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->s, "matmul_id_q8_0_f32_s", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_l, "matmul_id_q8_0_f32_aligned_l", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_m, "matmul_id_q8_0_f32_aligned_m", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_s, "matmul_id_q8_0_f32_aligned_s", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->l, "matmul_id_q2_k_f32_l", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->m, "matmul_id_q2_k_f32_m", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->s, "matmul_id_q2_k_f32_s", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_l, "matmul_id_q2_k_f32_aligned_l", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_m, "matmul_id_q2_k_f32_aligned_m", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_s, "matmul_id_q2_k_f32_aligned_s", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->l, "matmul_id_q3_k_f32_l", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->m, "matmul_id_q3_k_f32_m", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->s, "matmul_id_q3_k_f32_s", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_l, "matmul_id_q3_k_f32_aligned_l", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_m, "matmul_id_q3_k_f32_aligned_m", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_s, "matmul_id_q3_k_f32_aligned_s", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->l, "matmul_id_q4_k_f32_l", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->m, "matmul_id_q4_k_f32_m", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->s, "matmul_id_q4_k_f32_s", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_l, "matmul_id_q4_k_f32_aligned_l", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_m, "matmul_id_q4_k_f32_aligned_m", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_s, "matmul_id_q4_k_f32_aligned_s", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->l, "matmul_id_q5_k_f32_l", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->m, "matmul_id_q5_k_f32_m", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->s, "matmul_id_q5_k_f32_s", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_l, "matmul_id_q5_k_f32_aligned_l", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_m, "matmul_id_q5_k_f32_aligned_m", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_s, "matmul_id_q5_k_f32_aligned_s", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->l, "matmul_id_q6_k_f32_l", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->m, "matmul_id_q6_k_f32_m", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->s, "matmul_id_q6_k_f32_s", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_l, "matmul_id_q6_k_f32_aligned_l", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_m, "matmul_id_q6_k_f32_aligned_m", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_s, "matmul_id_q6_k_f32_aligned_s", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);*/
     } else {
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_l, "matmul_f32_l", matmul_f32_l_fp32_len, matmul_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_m, "matmul_f32_m", matmul_f32_m_fp32_len, matmul_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_s, "matmul_f32_s", matmul_f32_s_fp32_len, matmul_f32_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_l, "matmul_f32_aligned_l", matmul_f32_aligned_l_fp32_len, matmul_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_m, "matmul_f32_aligned_m", matmul_f32_aligned_m_fp32_len, matmul_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_s, "matmul_f32_aligned_s", matmul_f32_aligned_s_fp32_len, matmul_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
 
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_l, "matmul_f16_l", matmul_f16_l_fp32_len, matmul_f16_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_m, "matmul_f16_m", matmul_f16_m_fp32_len, matmul_f16_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_s, "matmul_f16_s", matmul_f16_s_fp32_len, matmul_f16_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_l, "matmul_f16_aligned_l", matmul_f16_aligned_l_fp32_len, matmul_f16_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_m, "matmul_f16_aligned_m", matmul_f16_aligned_m_fp32_len, matmul_f16_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_s, "matmul_f16_aligned_s", matmul_f16_aligned_s_fp32_len, matmul_f16_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
 
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_l, "matmul_f16_f32_l", matmul_f16_f32_l_fp32_len, matmul_f16_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_m, "matmul_f16_f32_m", matmul_f16_f32_m_fp32_len, matmul_f16_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_s, "matmul_f16_f32_s", matmul_f16_f32_s_fp32_len, matmul_f16_f32_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_fp32_len, matmul_f16_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_fp32_len, matmul_f16_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_fp32_len, matmul_f16_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_1_f32_l", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_1_f32_m", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_1_f32_s", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_1_f32_aligned_l", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_1_f32_aligned_m", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_1_f32_aligned_s", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->l, "matmul_q2_k_f32_l", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->m, "matmul_q2_k_f32_m", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->s, "matmul_q2_k_f32_s", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_l, "matmul_q2_k_f32_aligned_l", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_m, "matmul_q2_k_f32_aligned_m", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_s, "matmul_q2_k_f32_aligned_s", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->l, "matmul_q3_k_f32_l", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->m, "matmul_q3_k_f32_m", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->s, "matmul_q3_k_f32_s", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_l, "matmul_q3_k_f32_aligned_l", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_m, "matmul_q3_k_f32_aligned_m", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_s, "matmul_q3_k_f32_aligned_s", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->l, "matmul_q4_k_f32_l", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->m, "matmul_q4_k_f32_m", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->s, "matmul_q4_k_f32_s", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_l, "matmul_q4_k_f32_aligned_l", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_m, "matmul_q4_k_f32_aligned_m", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_s, "matmul_q4_k_f32_aligned_s", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->l, "matmul_q5_k_f32_l", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->m, "matmul_q5_k_f32_m", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->s, "matmul_q5_k_f32_s", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_l, "matmul_q5_k_f32_aligned_l", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_m, "matmul_q5_k_f32_aligned_m", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_s, "matmul_q5_k_f32_aligned_s", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->l, "matmul_q6_k_f32_l", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->m, "matmul_q6_k_f32_m", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->s, "matmul_q6_k_f32_s", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_l, "matmul_q6_k_f32_aligned_l", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        /*ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->l, "matmul_id_f32_l", matmul_id_f32_fp32_len, matmul_id_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->m, "matmul_id_f32_m", matmul_id_f32_fp32_len, matmul_id_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->s, "matmul_id_f32_s", matmul_id_f32_fp32_len, matmul_id_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_l, "matmul_id_f32_aligned_l", matmul_id_f32_aligned_fp32_len, matmul_id_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_m, "matmul_id_f32_aligned_m", matmul_id_f32_aligned_fp32_len, matmul_id_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_s, "matmul_id_f32_aligned_s", matmul_id_f32_aligned_fp32_len, matmul_id_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->l, "matmul_id_f16_l", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->m, "matmul_id_f16_m", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->s, "matmul_id_f16_s", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_l, "matmul_id_f16_aligned_l", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_m, "matmul_id_f16_aligned_m", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_s, "matmul_id_f16_aligned_s", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->l, "matmul_id_f16_f32_l", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->m, "matmul_id_f16_f32_m", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->s, "matmul_id_f16_f32_s", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_l, "matmul_id_f16_f32_aligned_l", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_m, "matmul_id_f16_f32_aligned_m", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_s, "matmul_id_f16_f32_aligned_s", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->l, "matmul_id_q5_0_f32_l", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->m, "matmul_id_q5_0_f32_m", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->s, "matmul_id_q5_0_f32_s", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_l, "matmul_id_q5_0_f32_aligned_l", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_m, "matmul_id_q5_0_f32_aligned_m", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_s, "matmul_id_q5_0_f32_aligned_s", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->l, "matmul_id_q5_1_f32_l", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->m, "matmul_id_q5_1_f32_m", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->s, "matmul_id_q5_1_f32_s", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_l, "matmul_id_q5_1_f32_aligned_l", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_m, "matmul_id_q5_1_f32_aligned_m", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_s, "matmul_id_q5_1_f32_aligned_s", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->l, "matmul_id_q8_0_f32_l", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->m, "matmul_id_q8_0_f32_m", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->s, "matmul_id_q8_0_f32_s", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_l, "matmul_id_q8_0_f32_aligned_l", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_m, "matmul_id_q8_0_f32_aligned_m", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_s, "matmul_id_q8_0_f32_aligned_s", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->l, "matmul_id_q2_k_f32_l", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->m, "matmul_id_q2_k_f32_m", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->s, "matmul_id_q2_k_f32_s", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_l, "matmul_id_q2_k_f32_aligned_l", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_m, "matmul_id_q2_k_f32_aligned_m", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_s, "matmul_id_q2_k_f32_aligned_s", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->l, "matmul_id_q3_k_f32_l", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->m, "matmul_id_q3_k_f32_m", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->s, "matmul_id_q3_k_f32_s", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_l, "matmul_id_q3_k_f32_aligned_l", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_m, "matmul_id_q3_k_f32_aligned_m", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_s, "matmul_id_q3_k_f32_aligned_s", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->l, "matmul_id_q4_k_f32_l", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->m, "matmul_id_q4_k_f32_m", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->s, "matmul_id_q4_k_f32_s", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_l, "matmul_id_q4_k_f32_aligned_l", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_m, "matmul_id_q4_k_f32_aligned_m", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_s, "matmul_id_q4_k_f32_aligned_s", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->l, "matmul_id_q5_k_f32_l", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->m, "matmul_id_q5_k_f32_m", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->s, "matmul_id_q5_k_f32_s", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_l, "matmul_id_q5_k_f32_aligned_l", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_m, "matmul_id_q5_k_f32_aligned_m", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_s, "matmul_id_q5_k_f32_aligned_s", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->l, "matmul_id_q6_k_f32_l", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->m, "matmul_id_q6_k_f32_m", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->s, "matmul_id_q6_k_f32_s", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_l, "matmul_id_q6_k_f32_aligned_l", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_m, "matmul_id_q6_k_f32_aligned_m", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_s, "matmul_id_q6_k_f32_aligned_s", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);*/
     }
 
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32",  mul_mat_vec_f16_f32_len,  mul_mat_vec_f16_f32_data,  "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32", mul_mat_vec_q4_0_f32_len, mul_mat_vec_q4_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32", mul_mat_vec_q4_1_f32_len, mul_mat_vec_q4_1_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32", mul_mat_vec_q5_0_f32_len, mul_mat_vec_q5_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32", mul_mat_vec_q5_1_f32_len, mul_mat_vec_q5_1_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32", mul_mat_vec_q8_0_f32_len, mul_mat_vec_q8_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_K_f32", mul_mat_vec_q2_K_f32_len, mul_mat_vec_q2_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_K_f32", mul_mat_vec_q3_K_f32_len, mul_mat_vec_q3_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_K_f32", mul_mat_vec_q4_K_f32_len, mul_mat_vec_q4_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_K_f32", mul_mat_vec_q5_K_f32_len, mul_mat_vec_q5_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_K_f32", mul_mat_vec_q6_K_f32_len, mul_mat_vec_q6_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1);
+    // mul mat vec
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32_f32",  mul_mat_vec_f16_f32_f32_len,  mul_mat_vec_f16_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32_f32", mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32_f32", mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32_f32", mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32_f32", mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32_f32", mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_K_f32_f32", mul_mat_vec_q2_K_f32_f32_len, mul_mat_vec_q2_K_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_K_f32_f32", mul_mat_vec_q3_K_f32_f32_len, mul_mat_vec_q3_K_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_K_f32_f32", mul_mat_vec_q4_K_f32_f32_len, mul_mat_vec_q4_K_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_K_f32_f32", mul_mat_vec_q5_K_f32_f32_len, mul_mat_vec_q5_K_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_K_f32_f32", mul_mat_vec_q6_K_f32_f32_len, mul_mat_vec_q6_K_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f16_f32",  mul_mat_vec_f16_f16_f32_len,  mul_mat_vec_f16_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f16_f32", mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f16_f32", mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f16_f32", mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f16_f32", mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f16_f32", mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_K_f16_f32", mul_mat_vec_q2_K_f16_f32_len, mul_mat_vec_q2_K_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_K_f16_f32", mul_mat_vec_q3_K_f16_f32_len, mul_mat_vec_q3_K_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_K_f16_f32", mul_mat_vec_q4_K_f16_f32_len, mul_mat_vec_q4_K_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_K_f16_f32", mul_mat_vec_q5_K_f16_f32_len, mul_mat_vec_q5_K_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_K_f16_f32", mul_mat_vec_q6_K_f16_f32_len, mul_mat_vec_q6_K_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+
+    /*ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32",  mul_mat_vec_id_f16_f32_len,  mul_mat_vec_id_f16_f32_data,  "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_K_f32", mul_mat_vec_id_q2_K_f32_len, mul_mat_vec_id_q2_K_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_K_f32", mul_mat_vec_id_q3_K_f32_len, mul_mat_vec_id_q3_K_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_K_f32", mul_mat_vec_id_q4_K_f32_len, mul_mat_vec_id_q4_K_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_K_f32", mul_mat_vec_id_q5_K_f32_len, mul_mat_vec_id_q5_K_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_K_f32", mul_mat_vec_id_q6_K_f32_len, mul_mat_vec_id_q6_K_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);*/
 
     // dequant shaders
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16",   f32_to_f16_len,   f32_to_f16_data,   "main", 2, 4 * sizeof(int), {      64, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_F16 ], "dequant_f16",  dequant_f16_len,  dequant_f16_data,  "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q4_0], "dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q4_1], "dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q5_0], "dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q5_1], "dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q8_0], "dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q2_K], "dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q3_K], "dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q6_K], "dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16",   dequant_f32_len,  dequant_f32_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q4_0], "dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q4_1], "dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_0], "dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_1], "dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q8_0], "dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q2_K], "dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q3_K], "dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q6_K], "dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
 
     // get_rows
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_F16 ], "get_rows_f16",  get_rows_f16_len,  get_rows_f16_data,  "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q4_0], "get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q4_1], "get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q5_1], "get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q8_0], "get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_F32 ], "get_rows_f32",  get_rows_f32_len,  get_rows_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_F16 ], "get_rows_f16",  get_rows_f16_len,  get_rows_f16_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q4_0], "get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q4_1], "get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q5_1], "get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q8_0], "get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f16_f32",  get_rows_f16_f32_len,  get_rows_f16_f32_data,  "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q4_0], "get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q4_1], "get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q5_1], "get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q8_0], "get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f32_f32",  get_rows_f32_f32_len,  get_rows_f32_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_F16 ], "get_rows_f16_f32",  get_rows_f16_f32_len,  get_rows_f16_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q4_0], "get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q4_1], "get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q5_1], "get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q8_0], "get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, "mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 7 * sizeof(uint32_t), {1, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, "mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 7 * sizeof(uint32_t), {1, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_cpy_f32_f32, "cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_cpy_f32_f16, "cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_cpy_f16_f16, "cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_cpy_f32_f32, "cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_cpy_f32_f16, "cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_cpy_f16_f16, "cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_add_f32, "add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_add_f32, "add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_gelu_f32, "gelu_f32", gelu_f32_len, gelu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_silu_f32, "silu_f32", silu_f32_len, silu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_relu_f32, "relu_f32", relu_f32_len, relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_gelu_f32, "gelu_f32", gelu_f32_len, gelu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_silu_f32, "silu_f32", silu_f32_len, silu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_relu_f32, "relu_f32", relu_f32_len, relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_f32, "rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_f16, "rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f32, "rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f16, "rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_argsort_f32, "argsort_f32", argsort_f32_len, argsort_f32_data, "main", 2, sizeof(vk_op_argsort_push_constants), {1024, 1, 1}, {}, 1);
 }
 
 static void ggml_vk_print_gpu_info(size_t idx) {
@@ -1057,8 +1552,8 @@ static void ggml_vk_print_gpu_info(size_t idx) {
         }
     }
 
-    const char* GGML_VULKAN_DISABLE_F16 = getenv("GGML_VULKAN_DISABLE_F16");
-    bool force_disable_f16 = GGML_VULKAN_DISABLE_F16 != nullptr;
+    const char* GGML_VK_DISABLE_F16 = getenv("GGML_VK_DISABLE_F16");
+    bool force_disable_f16 = GGML_VK_DISABLE_F16 != nullptr;
 
     bool fp16 = !force_disable_f16 && fp16_storage && fp16_compute;
 
@@ -1091,7 +1586,10 @@ static void ggml_vk_print_gpu_info(size_t idx) {
     }
 }
 
-static void ggml_vk_instance_init() {
+static bool ggml_vk_instance_validation_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
+static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
+
+void ggml_vk_instance_init() {
     if (vk_instance_initialized) {
         return;
     }
@@ -1100,28 +1598,48 @@ static void ggml_vk_instance_init() {
 #endif
 
     vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, VK_API_VERSION };
-    const std::vector<const char*> layers = {
-#ifdef GGML_VULKAN_VALIDATE
-        "VK_LAYER_KHRONOS_validation",
-#endif
-    };
-    const std::vector<const char*> extensions = {
-#ifdef GGML_VULKAN_VALIDATE
-        "VK_EXT_validation_features",
-#endif
-    };
-    vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags(), &app_info, layers, extensions);
-#ifdef GGML_VULKAN_VALIDATE
-    const std::vector<vk::ValidationFeatureEnableEXT> features_enable = { vk::ValidationFeatureEnableEXT::eBestPractices };
-    vk::ValidationFeaturesEXT validation_features = {
-        features_enable,
-        {},
-    };
-    validation_features.setPNext(nullptr);
-    instance_create_info.setPNext(&validation_features);
 
-    std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl;
+    const std::vector<vk::ExtensionProperties> instance_extensions = vk::enumerateInstanceExtensionProperties();
+    const bool validation_ext = ggml_vk_instance_validation_ext_available(instance_extensions);
+#ifdef __APPLE__
+    const bool portability_enumeration_ext = ggml_vk_instance_portability_enumeration_ext_available(instance_extensions);
 #endif
+
+    std::vector<const char*> layers;
+
+    if (validation_ext) {
+        layers.push_back("VK_LAYER_KHRONOS_validation");
+    }
+    std::vector<const char*> extensions;
+    if (validation_ext) {
+        extensions.push_back("VK_EXT_validation_features");
+    }
+#ifdef __APPLE__
+    if (portability_enumeration_ext) {
+        extensions.push_back("VK_KHR_portability_enumeration");
+    }
+#endif
+    vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags{}, &app_info, layers, extensions);
+#ifdef __APPLE__
+    if (portability_enumeration_ext) {
+        instance_create_info.flags |= vk::InstanceCreateFlagBits::eEnumeratePortabilityKHR;
+    }
+#endif
+
+    std::vector<vk::ValidationFeatureEnableEXT> features_enable;
+    vk::ValidationFeaturesEXT validation_features;
+
+    if (validation_ext) {
+        features_enable = { vk::ValidationFeatureEnableEXT::eBestPractices };
+        validation_features = {
+            features_enable,
+            {},
+        };
+        validation_features.setPNext(nullptr);
+        instance_create_info.setPNext(&validation_features);
+
+        std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl;
+    }
     vk_instance.instance = vk::createInstance(instance_create_info);
 
     memset(vk_instance.initialized, 0, sizeof(bool) * GGML_VK_MAX_DEVICES);
@@ -1144,7 +1662,33 @@ static void ggml_vk_instance_init() {
             vk_instance.device_indices.push_back(tmp);
         }
     } else {
-        vk_instance.device_indices.push_back(0);
+        std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();
+
+        // Make sure at least one device exists
+        if (devices.empty()) {
+            std::cerr << "ggml_vulkan: Error: No devices found." << std::endl;
+            GGML_ASSERT(false);
+        }
+
+        // Default to using all dedicated GPUs
+        for (size_t i = 0; i < devices.size(); i++) {
+            vk::PhysicalDeviceProperties props = devices[i].getProperties();
+
+            if (props.deviceType == vk::PhysicalDeviceType::eDiscreteGpu) {
+                vk_instance.device_indices.push_back(i);
+            }
+        }
+
+        // If no dedicated GPUs found, fall back to GPU 0
+        if (vk_instance.device_indices.empty()) {
+            vk_instance.device_indices.push_back(0);
+        }
+    }
+
+    std::cerr << "ggml_vulkan: Found " << vk_instance.device_indices.size() << " Vulkan devices:" << std::endl;
+
+    for (size_t i = 0; i < vk_instance.device_indices.size(); i++) {
+        ggml_vk_print_gpu_info(i);
     }
 
     vk_instance_initialized = true;
@@ -1165,140 +1709,152 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) {
         throw std::runtime_error("Device not found");
     }
 
-    vk_instance.devices[idx] = std::make_shared<vk_device>();
-    ctx->device = vk_instance.devices[idx];
-    ctx->device.lock()->physical_device = devices[dev_num];
-    std::vector<vk::ExtensionProperties> ext_props = ctx->device.lock()->physical_device.enumerateDeviceExtensionProperties();
+    ctx->device = ggml_vk_get_device(idx);
+    if (!ctx->device->initialized) {
+        ctx->device->physical_device = devices[dev_num];
+        const std::vector<vk::ExtensionProperties> ext_props = ctx->device->physical_device.enumerateDeviceExtensionProperties();
 
-    bool maintenance4_support = false;
+        bool maintenance4_support = false;
 
-    // Check if maintenance4 is supported
-    for (auto properties : ext_props) {
-        if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) {
-            maintenance4_support = true;
+        // Check if maintenance4 is supported
+        for (const auto& properties : ext_props) {
+            if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) {
+                maintenance4_support = true;
+            }
         }
-    }
 
-    vk::PhysicalDeviceProperties2 props2;
-    vk::PhysicalDeviceMaintenance3Properties props3;
-    vk::PhysicalDeviceMaintenance4Properties props4;
-    vk::PhysicalDeviceSubgroupProperties subgroup_props;
-    props2.pNext = &props3;
-    props3.pNext = &subgroup_props;
-    if (maintenance4_support) {
-        subgroup_props.pNext = &props4;
-    }
-    ctx->device.lock()->physical_device.getProperties2(&props2);
-    ctx->device.lock()->properties = props2.properties;
-
-    if (maintenance4_support) {
-        ctx->device.lock()->max_memory_allocation_size = std::min(props3.maxMemoryAllocationSize, props4.maxBufferSize);
-    } else {
-        ctx->device.lock()->max_memory_allocation_size = props3.maxMemoryAllocationSize;
-    }
-
-    ctx->device.lock()->vendor_id = ctx->device.lock()->properties.vendorID;
-    ctx->device.lock()->subgroup_size = subgroup_props.subgroupSize;
-    ctx->device.lock()->uma = ctx->device.lock()->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu;
-
-    bool fp16_storage = false;
-    bool fp16_compute = false;
-
-    for (auto properties : ext_props) {
-        if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) {
-            fp16_storage = true;
-        } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) {
-            fp16_compute = true;
+        vk::PhysicalDeviceProperties2 props2;
+        vk::PhysicalDeviceMaintenance3Properties props3;
+        vk::PhysicalDeviceMaintenance4Properties props4;
+        vk::PhysicalDeviceSubgroupProperties subgroup_props;
+        props2.pNext = &props3;
+        props3.pNext = &subgroup_props;
+        if (maintenance4_support) {
+            subgroup_props.pNext = &props4;
         }
-    }
+        ctx->device->physical_device.getProperties2(&props2);
+        ctx->device->properties = props2.properties;
 
-    const char* GGML_VULKAN_DISABLE_F16 = getenv("GGML_VULKAN_DISABLE_F16");
-    bool force_disable_f16 = GGML_VULKAN_DISABLE_F16 != nullptr;
+        const char* GGML_VK_FORCE_MAX_ALLOCATION_SIZE = getenv("GGML_VK_FORCE_MAX_ALLOCATION_SIZE");
 
-    ctx->device.lock()->fp16 = !force_disable_f16 && fp16_storage && fp16_compute;
+        if (GGML_VK_FORCE_MAX_ALLOCATION_SIZE != nullptr) {
+            ctx->device->max_memory_allocation_size = std::stoi(GGML_VK_FORCE_MAX_ALLOCATION_SIZE);
+        } else if (maintenance4_support) {
+            ctx->device->max_memory_allocation_size = std::min(props3.maxMemoryAllocationSize, props4.maxBufferSize);
+        } else {
+            ctx->device->max_memory_allocation_size = props3.maxMemoryAllocationSize;
+        }
 
-    std::vector<vk::QueueFamilyProperties> queue_family_props = ctx->device.lock()->physical_device.getQueueFamilyProperties();
+        ctx->device->vendor_id = ctx->device->properties.vendorID;
+        ctx->device->subgroup_size = subgroup_props.subgroupSize;
+        ctx->device->uma = ctx->device->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu;
 
-    // Try to find a non-graphics compute queue and transfer-focused queues
-    const uint32_t compute_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eCompute, vk::QueueFlagBits::eGraphics, -1, 1);
-    const uint32_t transfer_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eTransfer, vk::QueueFlagBits::eCompute | vk::QueueFlagBits::eGraphics, compute_queue_family_index, 1);
+        bool fp16_storage = false;
+        bool fp16_compute = false;
 
-    const float priorities[] = { 1.0f, 1.0f };
-    ctx->device.lock()->single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1;
+        for (const auto& properties : ext_props) {
+            if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) {
+                fp16_storage = true;
+            } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) {
+                fp16_compute = true;
+            }
+        }
 
-    std::vector<vk::DeviceQueueCreateInfo> device_queue_create_infos;
-    if (compute_queue_family_index != transfer_queue_family_index) {
-        device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities});
-        device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), transfer_queue_family_index, 1, priorities + 1});
-    } else if(!ctx->device.lock()->single_queue) {
-        device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 2, priorities});
-    } else {
-        device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities});
-    }
-    vk::DeviceCreateInfo device_create_info;
-    std::vector<const char *> device_extensions;
-    vk::PhysicalDeviceFeatures device_features = ctx->device.lock()->physical_device.getFeatures();
+        const char* GGML_VK_DISABLE_F16 = getenv("GGML_VK_DISABLE_F16");
+        const bool force_disable_f16 = GGML_VK_DISABLE_F16 != nullptr;
 
-    VkPhysicalDeviceFeatures2 device_features2;
-    device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2;
-    device_features2.pNext = nullptr;
-    device_features2.features = (VkPhysicalDeviceFeatures)device_features;
+        ctx->device->fp16 = !force_disable_f16 && fp16_storage && fp16_compute;
 
-    VkPhysicalDeviceVulkan11Features vk11_features;
-    vk11_features.pNext = nullptr;
-    vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES;
-    device_features2.pNext = &vk11_features;
+        std::vector<vk::QueueFamilyProperties> queue_family_props = ctx->device->physical_device.getQueueFamilyProperties();
 
-    VkPhysicalDeviceVulkan12Features vk12_features;
-    vk12_features.pNext = nullptr;
-    vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES;
-    vk11_features.pNext = &vk12_features;
+        // Try to find a non-graphics compute queue and transfer-focused queues
+        const uint32_t compute_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eCompute, vk::QueueFlagBits::eGraphics, -1, 1);
+        const uint32_t transfer_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eTransfer, vk::QueueFlagBits::eCompute | vk::QueueFlagBits::eGraphics, compute_queue_family_index, 1);
 
-    vkGetPhysicalDeviceFeatures2(ctx->device.lock()->physical_device, &device_features2);
+        const float priorities[] = { 1.0f, 1.0f };
+        ctx->device->single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1;
 
-    ctx->device.lock()->fp16 = ctx->device.lock()->fp16 && vk12_features.shaderFloat16;
+        std::vector<vk::DeviceQueueCreateInfo> device_queue_create_infos;
+        if (compute_queue_family_index != transfer_queue_family_index) {
+            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities});
+            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), transfer_queue_family_index, 1, priorities + 1});
+        } else if(!ctx->device->single_queue) {
+            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 2, priorities});
+        } else {
+            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities});
+        }
+        vk::DeviceCreateInfo device_create_info;
+        std::vector<const char *> device_extensions;
+        vk::PhysicalDeviceFeatures device_features = ctx->device->physical_device.getFeatures();
 
-    if (!vk11_features.storageBuffer16BitAccess) {
-        std::cerr << "ggml_vulkan: device " << GGML_VK_NAME << idx << " does not support 16-bit storage." << std::endl;
-        throw std::runtime_error("Unsupported device");
-    }
+        VkPhysicalDeviceFeatures2 device_features2;
+        device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2;
+        device_features2.pNext = nullptr;
+        device_features2.features = (VkPhysicalDeviceFeatures)device_features;
 
-    device_extensions.push_back("VK_KHR_16bit_storage");
+        VkPhysicalDeviceVulkan11Features vk11_features;
+        vk11_features.pNext = nullptr;
+        vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES;
+        device_features2.pNext = &vk11_features;
+
+        VkPhysicalDeviceVulkan12Features vk12_features;
+        vk12_features.pNext = nullptr;
+        vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES;
+        vk11_features.pNext = &vk12_features;
+
+        vkGetPhysicalDeviceFeatures2(ctx->device->physical_device, &device_features2);
+
+        ctx->device->fp16 = ctx->device->fp16 && vk12_features.shaderFloat16;
+
+        if (!vk11_features.storageBuffer16BitAccess) {
+            std::cerr << "ggml_vulkan: device " << GGML_VK_NAME << idx << " does not support 16-bit storage." << std::endl;
+            throw std::runtime_error("Unsupported device");
+        }
+
+        device_extensions.push_back("VK_KHR_16bit_storage");
 
 #ifdef GGML_VULKAN_VALIDATE
-    device_extensions.push_back("VK_KHR_shader_non_semantic_info");
+        device_extensions.push_back("VK_KHR_shader_non_semantic_info");
 #endif
 
-    if (ctx->device.lock()->fp16) {
-        device_extensions.push_back("VK_KHR_shader_float16_int8");
-    }
-    ctx->device.lock()->name = ctx->device.lock()->properties.deviceName.data();
+        if (ctx->device->fp16) {
+            device_extensions.push_back("VK_KHR_shader_float16_int8");
+        }
+        ctx->device->name = ctx->device->properties.deviceName.data();
 
-    device_create_info = {
-        vk::DeviceCreateFlags(),
-        device_queue_create_infos,
-        {},
-        device_extensions
-    };
-    device_create_info.setPNext(&device_features2);
-    ctx->device.lock()->device = ctx->device.lock()->physical_device.createDevice(device_create_info);
+        device_create_info = {
+            vk::DeviceCreateFlags(),
+            device_queue_create_infos,
+            {},
+            device_extensions
+        };
+        device_create_info.setPNext(&device_features2);
+        ctx->device->device = ctx->device->physical_device.createDevice(device_create_info);
 
-    ctx->device.lock()->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN;
+        ctx->device->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN;
 
-    // Shaders
-    ggml_vk_load_shaders(ctx);
+        // Queues
+        ggml_vk_create_queue(ctx, ctx->device->compute_queue, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer });
 
-    // Queues
-    ggml_vk_create_queue(ctx, ctx->device.lock()->compute_queue, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer });
-    if (!ctx->device.lock()->single_queue) {
-        const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0;
-        ggml_vk_create_queue(ctx, ctx->device.lock()->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer });
-    } else {
-        // TODO: Use pointer or reference to avoid copy
-        ctx->device.lock()->transfer_queue = ctx->device.lock()->compute_queue;
+        // Shaders
+        ggml_vk_load_shaders(ctx);
+
+        if (!ctx->device->single_queue) {
+            const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0;
+            ggml_vk_create_queue(ctx, ctx->device->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer });
+        } else {
+            // TODO: Use pointer or reference to avoid copy
+            ctx->device->transfer_queue = ctx->device->compute_queue;
+        }
+
+        ctx->device->idx = dev_num;
+        ctx->device->initialized = true;
+    } else if (ctx->device->idx != dev_num) {
+        std::cerr << "ggml_vulkan: Device " << ctx->device->name << " already initialized with index " << ctx->device->idx << ", but trying to reinitialize with index " << dev_num << std::endl;
+        throw std::runtime_error("Device already initialized");
     }
 
-    ctx->fence = ctx->device.lock()->device.createFence({});
+    ctx->fence = ctx->device->device.createFence({});
 
     ctx->compute_ctx = nullptr;
     ctx->transfer_ctx = nullptr;
@@ -1316,7 +1872,7 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) {
 #endif
 }
 
-static vk_pipeline* ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type type) {
+static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type type) {
 #ifdef GGML_VULKAN_DEBUG
     std::cerr << "ggml_vk_get_to_fp16()" << std::endl;
 #endif
@@ -1337,14 +1893,86 @@ static vk_pipeline* ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type
             return nullptr;
     }
 
-    return &ctx->pipeline_dequant[type];
+    return ctx->device->pipeline_dequant[type];
 }
 
-static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type type) {
+static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_get_mul_mat_mat_pipeline()" << std::endl;
+#endif
+    if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
+        return ctx->device->pipeline_matmul_f32;
+    }
+    if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
+        return ctx->device->pipeline_matmul_f16_f32;
+    }
+    if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
+        return ctx->device->pipeline_matmul_f16;
+    }
+
+    GGML_ASSERT(src1_type == GGML_TYPE_F32);
+
+    switch (src0_type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+            break;
+        default:
+            return nullptr;
+    }
+
+    return ctx->device->pipeline_dequant_mul_mat_mat[src0_type];
+}
+
+static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_get_mul_mat_mat_id_pipeline()" << std::endl;
+#endif
+    if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
+        return ctx->device->pipeline_matmul_id_f32;
+    }
+    if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
+        return ctx->device->pipeline_matmul_id_f16_f32;
+    }
+    if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
+        return ctx->device->pipeline_matmul_id_f16;
+    }
+
+    GGML_ASSERT(src1_type == GGML_TYPE_F32);
+
+    switch (src0_type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+            break;
+        default:
+            return nullptr;
+    }
+
+    return ctx->device->pipeline_dequant_mul_mat_mat_id[src0_type];
+}
+
+static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type) {
 #ifdef GGML_VULKAN_DEBUG
     std::cerr << "ggml_vk_get_dequantize_mul_mat_vec()" << std::endl;
 #endif
-    switch (type) {
+    GGML_ASSERT(b_type == GGML_TYPE_F32 || b_type == GGML_TYPE_F16);
+
+    switch (a_type) {
         case GGML_TYPE_F16:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
@@ -1361,7 +1989,7 @@ static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context *
             return nullptr;
     }
 
-    return &ctx->pipeline_dequant_mul_mat_vec_f32[type];
+    return b_type == GGML_TYPE_F32 ? ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[a_type] : ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[a_type];
 }
 
 static vk_buffer ggml_vk_pool_malloc(ggml_backend_vk_context * ctx, size_t size) {
@@ -1440,8 +2068,8 @@ static void * ggml_vk_host_malloc(ggml_backend_vk_context * ctx, size_t size) {
     if(!(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) {
         fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory\n",
             size/1024.0/1024.0);
-        ctx->device.lock()->device.freeMemory(buf->device_memory);
-        ctx->device.lock()->device.destroyBuffer(buf->buffer);
+        ctx->device->device.freeMemory(buf->device_memory);
+        ctx->device->device.destroyBuffer(buf->buffer);
         return nullptr;
     }
 
@@ -1505,30 +2133,30 @@ static vk_submission ggml_vk_begin_submission(ggml_backend_vk_context * ctx, vk_
 }
 
 static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, std::vector<vk_subbuffer>&& buffers, size_t push_constant_size, const void* push_constants, std::array<uint32_t, 3> elements) {
-    const uint32_t wg0 = CEIL_DIV(elements[0], pipeline.wg_denoms[0]);
-    const uint32_t wg1 = CEIL_DIV(elements[1], pipeline.wg_denoms[1]);
-    const uint32_t wg2 = CEIL_DIV(elements[2], pipeline.wg_denoms[2]);
+    const uint32_t wg0 = CEIL_DIV(elements[0], pipeline->wg_denoms[0]);
+    const uint32_t wg1 = CEIL_DIV(elements[1], pipeline->wg_denoms[1]);
+    const uint32_t wg2 = CEIL_DIV(elements[2], pipeline->wg_denoms[2]);
 #ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_dispatch_pipeline(" << pipeline.name << ", (" << wg0 << "," << wg1 << "," << wg2 << "))" << std::endl;
+    std::cerr << "ggml_vk_dispatch_pipeline(" << pipeline->name << ", (" << wg0 << "," << wg1 << "," << wg2 << "))" << std::endl;
 #endif
     std::vector<vk::DescriptorBufferInfo> descriptor_buffer_infos;
     std::vector<vk::WriteDescriptorSet> write_descriptor_sets;
-    GGML_ASSERT(pipeline.descriptor_set_idx < pipeline.descriptor_sets.size());
-    GGML_ASSERT(buffers.size() == pipeline.parameter_count);
-    vk::DescriptorSet& descriptor_set = pipeline.descriptor_sets[pipeline.descriptor_set_idx++];
-    for (uint32_t i = 0; i < pipeline.parameter_count; i++) {
+    GGML_ASSERT(pipeline->descriptor_set_idx < pipeline->descriptor_sets.size());
+    GGML_ASSERT(buffers.size() == pipeline->parameter_count);
+    vk::DescriptorSet& descriptor_set = pipeline->descriptor_sets[pipeline->descriptor_set_idx++];
+    for (uint32_t i = 0; i < pipeline->parameter_count; i++) {
         descriptor_buffer_infos.push_back({buffers[i].buffer->buffer, buffers[i].offset, buffers[i].size});
     }
-    for (uint32_t i = 0; i < pipeline.parameter_count; i++) {
+    for (uint32_t i = 0; i < pipeline->parameter_count; i++) {
         write_descriptor_sets.push_back({descriptor_set, i, 0, 1, vk::DescriptorType::eStorageBuffer, nullptr, &descriptor_buffer_infos[i]});
     }
 
-    ctx->device.lock()->device.updateDescriptorSets(write_descriptor_sets, {});
+    ctx->device->device.updateDescriptorSets(write_descriptor_sets, {});
 
-    subctx->s->buffer.pushConstants(pipeline.layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size, push_constants);
-    subctx->s->buffer.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline.pipeline);
+    subctx->s->buffer.pushConstants(pipeline->layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size, push_constants);
+    subctx->s->buffer.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline->pipeline);
     subctx->s->buffer.bindDescriptorSets(vk::PipelineBindPoint::eCompute,
-                                pipeline.layout,
+                                pipeline->layout,
                                 0,
                                 { descriptor_set },
                                 {});
@@ -1567,6 +2195,9 @@ static void ggml_vk_ctx_begin(ggml_backend_vk_context * ctx, vk_context * subctx
 }
 
 static size_t ggml_vk_align_size(size_t width, size_t align) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_align_size(" << width << ", " << align << ")" << std::endl;
+#endif
     return CEIL_DIV(width, align) * align;
 }
 
@@ -1787,7 +2418,7 @@ static void ggml_vk_buffer_write_2d(ggml_backend_vk_context * ctx, vk_buffer& ds
             memcpy((uint8_t *)dst->ptr + offset + i * width, (const uint8_t *) src + i * spitch, width);
         }
     } else {
-        vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue);
+        vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
         ggml_vk_ctx_begin(ctx, subctx);
         ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, src, spitch, width, height, true);
         ggml_vk_ctx_end(subctx);
@@ -1797,8 +2428,8 @@ static void ggml_vk_buffer_write_2d(ggml_backend_vk_context * ctx, vk_buffer& ds
         }
 
         ggml_vk_submit(subctx, ctx->fence);
-        VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences");
-        ctx->device.lock()->device.resetFences({ ctx->fence });
+        VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences");
+        ctx->device->device.resetFences({ ctx->fence });
     }
 }
 
@@ -1883,14 +2514,14 @@ static void ggml_vk_buffer_read(ggml_backend_vk_context * ctx, vk_buffer& src, s
 
         memcpy(dst, (uint8_t *) src->ptr + offset, size);
     } else {
-        vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue);
+        vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
         ggml_vk_ctx_begin(ctx, subctx);
         ggml_vk_buffer_read_async(ctx, subctx, src, offset, dst, size, true);
         ggml_vk_ctx_end(subctx);
 
         ggml_vk_submit(subctx, ctx->fence);
-        VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_read waitForFences");
-        ctx->device.lock()->device.resetFences({ ctx->fence });
+        VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_read waitForFences");
+        ctx->device->device.resetFences({ ctx->fence });
 
         for (auto& cpy : subctx->out_memcpys) {
             memcpy(cpy.dst, cpy.src, cpy.n);
@@ -1918,15 +2549,13 @@ static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& sr
         // Copy within the device
         ggml_backend_vk_context * ctx = src->ctx;
 
-        VkBufferCopy bc{ src_offset, dst_offset, size };
-
-        vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue);
+        vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
         ggml_vk_ctx_begin(ctx, subctx);
         ggml_vk_buffer_copy_async(subctx, dst, dst_offset, src, src_offset, size);
         ggml_vk_ctx_end(subctx);
         ggml_vk_submit(subctx, ctx->fence);
-        VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences");
-        ctx->device.lock()->device.resetFences({ ctx->fence });
+        VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences");
+        ctx->device->device.resetFences({ ctx->fence });
     } else {
 #ifdef GGML_VULKAN_DEBUG
     std::cerr << "ggml_vk_buffer_copy(MULTI_DEVICE, " << size << ")" << std::endl;
@@ -1954,14 +2583,14 @@ static void ggml_vk_buffer_memset(ggml_backend_vk_context * ctx, vk_buffer& dst,
     // Make sure ctx owns the buffer
     GGML_ASSERT(dst->ctx == ctx);
 
-    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue);
+    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
     ggml_vk_ctx_begin(ctx, subctx);
     subctx->s->buffer.fillBuffer(dst->buffer, offset, size, c);
     ggml_vk_ctx_end(subctx);
 
     ggml_vk_submit(subctx, ctx->fence);
-    VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_memset waitForFences");
-    ctx->device.lock()->device.resetFences({ ctx->fence });
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_memset waitForFences");
+    ctx->device->device.resetFences({ ctx->fence });
 }
 
 static void ggml_vk_h2d_tensor_2d(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const ggml_tensor * src, uint64_t i3, uint64_t i2, uint64_t i1) {
@@ -2022,197 +2651,121 @@ static void ggml_vk_d2h_tensor_2d(ggml_backend_vk_context * ctx, vk_context * su
 
 static uint32_t ggml_vk_guess_split_k(int m, int n, int k) {
 #ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")";
+    std::cerr << "ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")" << std::endl;
 #endif
-    if (k > 128 && (m < 128 || n < 128) && m > 2 && n > 2) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << " = 4" << std::endl;
-#endif
-        return 4;
-    }
+    // if (k > 128 && (m < 128 || n < 128) && m > 2 && n > 2) {
+    //     return 4;
+    // }
 
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << " = 1" << std::endl;
-#endif
     return 1;
+
+    GGML_UNUSED(m); GGML_UNUSED(n); GGML_UNUSED(k);
 }
 
-static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, int m, int n) {
+static vk_pipeline ggml_vk_guess_matmul_pipeline_amd(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) {
+    if (m <= 32 || n <= 32) {
+        return aligned ? mmp->a_s : mmp->s;
+    }
+    return aligned ? mmp->a_m : mmp->m;
+
+    GGML_UNUSED(ctx);
+}
+
+static vk_pipeline ggml_vk_guess_matmul_pipeline_apple(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, bool aligned) {
+    return aligned ? mmp->a_m : mmp->m;
+
+    GGML_UNUSED(ctx);
+}
+
+static vk_pipeline ggml_vk_guess_matmul_pipeline_intel(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, bool aligned) {
+    return aligned ? mmp->a_s : mmp->s;
+
+    GGML_UNUSED(ctx);
+}
+
+static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_guess_matmul_pipeline(" << m << ", " << n << ", " << aligned << ")" << std::endl;
+#endif
+    switch (ctx->device->vendor_id) {
+    case VK_VENDOR_ID_AMD:
+        return ggml_vk_guess_matmul_pipeline_amd(ctx, mmp, m, n, aligned);
+    case VK_VENDOR_ID_APPLE:
+        return ggml_vk_guess_matmul_pipeline_apple(ctx, mmp, aligned);
+    case VK_VENDOR_ID_INTEL:
+        return ggml_vk_guess_matmul_pipeline_intel(ctx, mmp, aligned);
+    default:
+        break;
+    }
+
+    if (m <= 32 || n <= 32) {
+        return aligned ? mmp->a_s : mmp->s;
+    }
+    if (m <= 64 || n <= 64) {
+        return aligned ? mmp->a_m : mmp->m;
+    }
+    return aligned ? mmp->a_l : mmp->l;
+}
+
+static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n) {
 #ifdef GGML_VULKAN_DEBUG
     std::cerr << "ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")" << std::endl;
 #endif
-    if (m <= 32 || n <= 32) {
-        return ctx->pipeline_matmul_f32_aligned_s.align;
-    }
-    if (ctx->device.lock()->subgroup_size == 64 || m <= 64 || n <= 64) {
-        return ctx->pipeline_matmul_f32_aligned_m.align;
-    }
-    return ctx->pipeline_matmul_f32_aligned_l.align;
+    return ggml_vk_guess_matmul_pipeline(ctx, mmp, m, n, true)->align;
 }
 
-static vk_pipeline* ggml_vk_guess_matmul_pipeline_amd(ggml_backend_vk_context * ctx, bool bit16_x, bool bit16_y, int m, int n, bool aligned) {
-    if (bit16_x && bit16_y) {
-        if (m <= 32 || n <= 32) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << " S" << std::endl;
-#endif
-            return aligned ? &ctx->pipeline_matmul_f16_aligned_s : &ctx->pipeline_matmul_f16_s;
-        }
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << " M" << std::endl;
-#endif
-        return aligned ? &ctx->pipeline_matmul_f16_aligned_m : &ctx->pipeline_matmul_f16_m;
-    }
-    if (bit16_x && !bit16_y) {
-        if (m <= 32 || n <= 32) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << " S" << std::endl;
-#endif
-            return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_s : &ctx->pipeline_matmul_f16_f32_s;
-        }
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << " M" << std::endl;
-#endif
-        return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_m : &ctx->pipeline_matmul_f16_f32_m;
-    }
-    if (!bit16_x && bit16_y) {
-        GGML_ASSERT(false);
-    }
-
-    if (m <= 32 || n <= 32) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << " S" << std::endl;
-#endif
-        return aligned ? &ctx->pipeline_matmul_f32_aligned_s : &ctx->pipeline_matmul_f32_s;
-    }
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << " M" << std::endl;
-#endif
-    return aligned ? &ctx->pipeline_matmul_f32_aligned_m : &ctx->pipeline_matmul_f32_m;
-}
-
-static vk_pipeline* ggml_vk_guess_matmul_pipeline_apple(ggml_backend_vk_context * ctx, bool bit16_x, bool bit16_y, bool aligned) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << " M" << std::endl;
-#endif
-    if (bit16_x && bit16_y) {
-        return aligned ? &ctx->pipeline_matmul_f16_aligned_m : &ctx->pipeline_matmul_f16_m;
-    }
-    if (bit16_x && !bit16_y) {
-        return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_m : &ctx->pipeline_matmul_f16_f32_m;
-    }
-    if (!bit16_x && bit16_y) {
-        GGML_ASSERT(false);
-    }
-    return aligned ? &ctx->pipeline_matmul_f32_aligned_m : &ctx->pipeline_matmul_f32_m;
-}
-
-static vk_pipeline* ggml_vk_guess_matmul_pipeline_intel(ggml_backend_vk_context * ctx, bool bit16_x, bool bit16_y, bool aligned) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << " S" << std::endl;
-#endif
-    if (bit16_x && bit16_y) {
-        return aligned ? &ctx->pipeline_matmul_f16_aligned_s : &ctx->pipeline_matmul_f16_s;
-    }
-    if (bit16_x && !bit16_y) {
-        return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_s : &ctx->pipeline_matmul_f16_f32_s;
-    }
-    if (!bit16_x && bit16_y) {
-        GGML_ASSERT(false);
-    }
-    return aligned ? &ctx->pipeline_matmul_f32_aligned_s : &ctx->pipeline_matmul_f32_s;
-}
-
-static vk_pipeline* ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, bool bit16_x, bool bit16_y, int m, int n, bool aligned) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_guess_matmul_pipeline(" << bit16_x << ", " << bit16_y << ", " << m << ", " << n << ", " << aligned << ")";
-#endif
-    switch (ctx->device.lock()->vendor_id) {
-    case VK_VENDOR_ID_AMD:
-        return ggml_vk_guess_matmul_pipeline_amd(ctx, bit16_x, bit16_y, m, n, aligned);
-    case VK_VENDOR_ID_APPLE:
-        return ggml_vk_guess_matmul_pipeline_apple(ctx, bit16_x, bit16_y, aligned);
-    case VK_VENDOR_ID_INTEL:
-        return ggml_vk_guess_matmul_pipeline_intel(ctx, bit16_x, bit16_y, aligned);
-    }
-
-    if (bit16_x && bit16_y) {
-        if (m <= 32 || n <= 32) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << " S" << std::endl;
-#endif
-            return aligned ? &ctx->pipeline_matmul_f16_aligned_s : &ctx->pipeline_matmul_f16_s;
-        }
-        if (m <= 64 || n <= 64) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << " M" << std::endl;
-#endif
-            return aligned ? &ctx->pipeline_matmul_f16_aligned_m : &ctx->pipeline_matmul_f16_m;
-        }
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << " L" << std::endl;
-#endif
-        return aligned ? &ctx->pipeline_matmul_f16_aligned_l : &ctx->pipeline_matmul_f16_l;
-    }
-    if (bit16_x && !bit16_y) {
-        if (m <= 32 || n <= 32) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << " S" << std::endl;
-#endif
-            return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_s : &ctx->pipeline_matmul_f16_f32_s;
-        }
-        if (m <= 64 || n <= 64) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << " M" << std::endl;
-#endif
-            return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_m : &ctx->pipeline_matmul_f16_f32_m;
-        }
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << " L" << std::endl;
-#endif
-        return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_l : &ctx->pipeline_matmul_f16_f32_l;
-    }
-    if (!bit16_x && bit16_y) {
-        GGML_ASSERT(false);
-    }
-
-    if (m <= 32 || n <= 32) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << " S" << std::endl;
-#endif
-        return aligned ? &ctx->pipeline_matmul_f32_aligned_s : &ctx->pipeline_matmul_f32_s;
-    }
-    if (m <= 64 || n <= 64) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << " M" << std::endl;
-#endif
-        return aligned ? &ctx->pipeline_matmul_f32_aligned_m : &ctx->pipeline_matmul_f32_m;
-    }
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << " L" << std::endl;
-#endif
-    return aligned ? &ctx->pipeline_matmul_f32_aligned_l : &ctx->pipeline_matmul_f32_l;
-}
-
-static void ggml_vk_matmul(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d) {
+static void ggml_vk_matmul(
+        ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline,
+        vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer,
+        uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d,
+        uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3,
+        uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d,
+        uint32_t expert_stride_b, uint32_t expert_stride_d, uint32_t idx, uint32_t nbi1, uint32_t n_as) {
 #ifdef GGML_VULKAN_DEBUG
     std::cerr << "ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << split_k_buffer.buffer->buffer << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl;
 #endif
     ggml_vk_sync_buffers(subctx);
     if (split_k == 1) {
-        const std::array<uint32_t, 14> pc = { m, n, k, stride_a, stride_b, stride_d, k, ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d };
-        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d }, pc.size() * sizeof(uint32_t), pc.data(), { m, n, batch });
+        const vk_mat_mat_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, k, ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d, expert_stride_b, expert_stride_d, idx, nbi1, n_as };
+        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d }, sizeof(vk_mat_mat_push_constants), &pc, { m, n, batch });
         return;
     }
 
     GGML_ASSERT(batch_stride_d == m * n);
 
-    const std::array<uint32_t, 14> pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d };
+    const vk_mat_mat_push_constants pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d, expert_stride_b, expert_stride_d, idx, nbi1, n_as };
     // Make sure enough workgroups get assigned for split k to work
-    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, pc1.size() * sizeof(uint32_t), pc1.data(), { (CEIL_DIV(m, pipeline.wg_denoms[0]) * pipeline.wg_denoms[0]) * split_k, n, batch });
+    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, sizeof(vk_mat_mat_push_constants), &pc1, { (CEIL_DIV(m, pipeline->wg_denoms[0]) * pipeline->wg_denoms[0]) * split_k, n, batch });
     ggml_vk_sync_buffers(subctx);
     const std::array<uint32_t, 2> pc2 = { (uint32_t)(m * n * batch), split_k };
-    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 });
+    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 });
+}
+
+static void ggml_vk_matmul_id(
+        ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline,
+        vk_subbuffer&& ids, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& a, vk_subbuffer&& split_k_buffer,
+        uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d,
+        uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3,
+        uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d,
+        uint32_t expert_stride_b, uint32_t expert_stride_d, uint32_t idx, uint32_t nbi1, uint32_t n_as) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << split_k_buffer.buffer->buffer << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl;
+#endif
+    ggml_vk_sync_buffers(subctx);
+    if (split_k == 1) {
+        const vk_mat_mat_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, k, ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d, expert_stride_b, expert_stride_d, idx, nbi1, n_as };
+        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { ids, b, d, a }, sizeof(vk_mat_mat_push_constants), &pc, { m, n, batch });
+        return;
+    }
+
+    GGML_ASSERT(batch_stride_d == m * n);
+
+    const vk_mat_mat_push_constants pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d, expert_stride_b, expert_stride_d, idx, nbi1, n_as };
+    // Make sure enough workgroups get assigned for split k to work
+    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { ids, b, split_k_buffer, a }, sizeof(vk_mat_mat_push_constants), &pc1, { (CEIL_DIV(m, pipeline->wg_denoms[0]) * pipeline->wg_denoms[0]) * split_k, n, batch });
+    ggml_vk_sync_buffers(subctx);
+    const std::array<uint32_t, 2> pc2 = { (uint32_t)(m * n * batch), split_k };
+    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 });
 }
 
 static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) {
@@ -2222,41 +2775,39 @@ static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) {
         tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
 }
 
-static vk_pipeline * ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, ggml_type from, ggml_type to) {
+static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, ggml_type from, ggml_type to) {
     if (from == GGML_TYPE_F32 && to == GGML_TYPE_F32) {
-        return &ctx->pipeline_cpy_f32_f32;
+        return ctx->device->pipeline_cpy_f32_f32;
     }
     if (from == GGML_TYPE_F32 && to == GGML_TYPE_F16) {
-        return &ctx->pipeline_cpy_f32_f16;
+        return ctx->device->pipeline_cpy_f32_f16;
     }
     if (from == GGML_TYPE_F16 && to == GGML_TYPE_F16) {
-        return &ctx->pipeline_cpy_f16_f16;
+        return ctx->device->pipeline_cpy_f16_f16;
     }
 
     std::cerr << "Missing CPY op for types: " << ggml_type_name(from) << " " << ggml_type_name(to) << std::endl;
     GGML_ASSERT(false);
 }
 
-static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline * pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out, ggml_type buffer_type, bool aligned=true) {
+static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out) {
 #ifdef GGML_VULKAN_DEBUG
     std::cerr << "ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), ";
     std::cerr << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")" << std::endl;
 #endif
     const int tensor_type_size = ggml_type_size(tensor->type);
-    const int dst_type_size = ggml_type_size(buffer_type);
 
-    const uint32_t ne = tensor->ne[0] * tensor->ne[1] * tensor->ne[2];
+    const uint32_t ne = ggml_nelements(tensor);
 
-    const uint32_t nb2 = aligned ? ggml_vk_align_size(dst_type_size * tensor->ne[0] * tensor->ne[1], ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size : tensor->ne[0] * tensor->ne[1];
-
-    const vk_op_cpy_push_constants pc = {
+    const vk_op_unary_push_constants pc = {
         (uint32_t)ne,
-        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->nb[0] / tensor_type_size, (uint32_t)tensor->nb[1] / tensor_type_size, (uint32_t)tensor->nb[2] / tensor_type_size,
-        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1],                       1                   , (uint32_t)tensor->ne[0]                   , nb2,
+        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], (uint32_t)tensor->nb[0] / tensor_type_size, (uint32_t)tensor->nb[1] / tensor_type_size, (uint32_t)tensor->nb[2] / tensor_type_size, (uint32_t)tensor->nb[3] / tensor_type_size,
+        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3],                       1                   , (uint32_t)tensor->ne[0]                   , (uint32_t)(tensor->ne[0] * tensor->ne[1]) , (uint32_t)(tensor->ne[0] * tensor->ne[1] * tensor->ne[2]),
         0,
+        0.0f, 0.0f,
     };
     ggml_vk_sync_buffers(subctx);
-    ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { in, out }, sizeof(vk_op_cpy_push_constants), &pc, { ne, 1, 1 });
+    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, sizeof(vk_op_unary_push_constants), &pc, { ne, 1, 1 });
 }
 
 static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@@ -2296,23 +2847,27 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su
     bool src0_uma = false;
     bool src1_uma = false;
 
-    if (ctx->device.lock()->uma) {
+    if (ctx->device->uma) {
         ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset);
         ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset);
         src0_uma = d_Qx != nullptr;
         src1_uma = d_Qy != nullptr;
     }
 
-    const bool load_x = src0->backend != GGML_BACKEND_GPU && !src0_uma;
-    const bool load_y = src1->backend != GGML_BACKEND_GPU && !src1_uma;
+    const bool x_non_contig = !ggml_vk_dim01_contiguous(src0);
+    const bool y_non_contig = !ggml_vk_dim01_contiguous(src1);
 
-    const bool x_non_contig = !load_x && !ggml_vk_dim01_contiguous(src0);
-    const bool y_non_contig = !load_y && !ggml_vk_dim01_contiguous(src1);
+    const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig;
 
-    const bool f16_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig;
+    vk_matmul_pipeline mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, src0->type, y_non_contig ? GGML_TYPE_F16 : src1->type);
 
-    const bool qx_needs_dequant = src0->type != GGML_TYPE_F16 || x_non_contig;
-    const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig;
+    const bool qx_needs_dequant = mmp == nullptr || x_non_contig;
+    const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig;
+
+    if (mmp == nullptr) {
+        // Fall back to dequant + f16 mulmat
+        mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, GGML_TYPE_F16, y_f32_kernel ? GGML_TYPE_F32 : GGML_TYPE_F16);
+    }
 
     // Not implemented
     GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
@@ -2321,17 +2876,17 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su
     const int y_ne = ne11 * ne10;
     const int d_ne = ne11 * ne01;
 
-    const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, ne01, ne11));
-    const bool aligned = ne10 == kpad;
+    const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11));
+    const bool aligned = ne10 == kpad && ne01 > 8 && ne11 > 8;
 
     const uint32_t split_k = ggml_vk_guess_split_k(ne01, ne11, ne10);
 
-    vk_pipeline * pipeline = ggml_vk_guess_matmul_pipeline(ctx, true, !f16_f32_kernel, ne01, ne11, aligned);
+    vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned);
 
     const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type);
     const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
-    const uint64_t x_sz = sizeof(ggml_fp16_t) * x_ne;
-    const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne;
+    const uint64_t x_sz = !qx_needs_dequant ? qx_sz : sizeof(ggml_fp16_t) * x_ne;
+    const uint64_t y_sz = y_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne;
     const uint64_t d_sz = sizeof(float) * d_ne;
 
     vk_buffer d_D = extra->buffer_gpu.lock();
@@ -2342,16 +2897,12 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su
     uint64_t x_buf_offset = 0;
     vk_buffer d_Y;
     uint64_t y_buf_offset = 0;
-    if (load_x) {
-        d_Qx = ctx->prealloc_qx;
-    } else if (!src0_uma) {
+    if (!src0_uma) {
         d_Qx = extra_src0->buffer_gpu.lock();
         qx_buf_offset = extra_src0->offset;
         GGML_ASSERT(d_Qx != nullptr);
     }
-    if (load_y) {
-        d_Qy = ctx->prealloc_qy;
-    } else if (!src1_uma) {
+    if (!src1_uma) {
         d_Qy = extra_src1->buffer_gpu.lock();
         qy_buf_offset = extra_src1->offset;
         GGML_ASSERT(d_Qy != nullptr);
@@ -2362,7 +2913,7 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su
     } else {
         d_X = d_Qx;
         x_buf_offset = qx_buf_offset;
-        GGML_ASSERT(qx_sz == x_sz);  // NOLINT
+        GGML_ASSERT(qx_sz == x_sz);
     }
     if (qy_needs_dequant) {
         d_Y = ctx->prealloc_y;
@@ -2373,8 +2924,8 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su
         GGML_ASSERT(qy_sz == y_sz);
     }
 
-    vk_pipeline * to_fp16_vk_0 = nullptr;
-    vk_pipeline * to_fp16_vk_1 = nullptr;
+    vk_pipeline to_fp16_vk_0 = nullptr;
+    vk_pipeline to_fp16_vk_1 = nullptr;
 
     if (x_non_contig) {
         to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16);
@@ -2390,53 +2941,49 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su
     GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
 
     // Allocate descriptor sets
-    ggml_pipeline_allocate_descriptor_sets(ctx, *pipeline, ne12 * ne13);
+    ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, 1);
     if (qx_needs_dequant) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_0, x_non_contig ? 1 : ne12 * ne13);
+        ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_0, 1);
     }
     if (qy_needs_dequant) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13);
+        ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_1, 1);
     }
     if (split_k > 1) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_matmul_split_k_reduce, ne12 * ne13);
+        ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, 1);
     }
 
     if (x_non_contig) {
-        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }, dst->type, false);
-    } else if (load_x || qx_needs_dequant) {
-        if (load_x) {
-            // copy data to device
-            ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qx, 0, src0, 0, 0, ggml_nrows(src0));
-            ctx->staging_offset = qx_sz * ne02 * ne03;
-        }
-
-        if (qx_needs_dequant) {
-            const std::vector<int> pc = { (int)ne01, (int)ne10, (int)ne10, (int)ne10 };
-            ggml_vk_sync_buffers(subctx);
-            ggml_vk_dispatch_pipeline(ctx, subctx, *to_fp16_vk_0, { { d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1});
-        }
+        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
+    } else if (qx_needs_dequant) {
+        const std::vector<uint32_t> pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) };
+        ggml_vk_sync_buffers(subctx);
+        ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, { { d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1});
     }
     if (y_non_contig) {
-        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }, dst->type);
-    } else if (load_y) {
-        ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1));
+        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
     }
 
     uint32_t stride_batch_x = ne00*ne01;
     uint32_t stride_batch_y = ne10*ne11;
 
-    if (!ggml_vk_dim01_contiguous(src0) && !load_x && !qx_needs_dequant) {
+    if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) {
         stride_batch_x = src0->nb[0] / ggml_type_size(src0->type);
     }
 
-    if (!ggml_vk_dim01_contiguous(src1) && !load_y && !qy_needs_dequant) {
+    if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) {
         stride_batch_y = src1->nb[0] / ggml_type_size(src1->type);
     }
 
     // compute
-    ggml_vk_matmul(ctx, subctx, *pipeline, { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21);  // NOLINT
+    ggml_vk_matmul(
+        ctx, subctx, pipeline,
+        { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 },
+        { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k },
+        ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21,
+        0, 0, 0, 0, 1
+    );  // NOLINT
 
-    if (dst->backend == GGML_BACKEND_CPU) {
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
         // copy dst to host
         float * d = (float *) ((char *) dst->data);
         ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, sizeof(float) * d_ne * ne12 * ne13);
@@ -2464,8 +3011,10 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context
 
     GGML_ASSERT(ne11 == 1);
 
-    const uint64_t nb2  = dst->nb[2];
-    const uint64_t nb3  = dst->nb[3];
+    const uint64_t ne20 = dst->ne[0];
+    const uint64_t ne21 = dst->ne[1];
+    const uint64_t ne22 = dst->ne[2];
+    const uint64_t ne23 = dst->ne[3];
 
     const uint64_t r2 = ne12 / ne02;
     const uint64_t r3 = ne13 / ne03;
@@ -2482,31 +3031,31 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context
     bool src0_uma = false;
     bool src1_uma = false;
 
-    if (ctx->device.lock()->uma) {
+    if (ctx->device->uma) {
         ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset);
         ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset);
         src0_uma = d_Qx != nullptr;
         src1_uma = d_Qy != nullptr;
     }
 
-    const bool load_x = src0->backend != GGML_BACKEND_GPU && !src0_uma;
-    const bool load_y = src1->backend != GGML_BACKEND_GPU && !src1_uma;
-
-    const bool x_non_contig = !load_x && !ggml_vk_dim01_contiguous(src0);
-    const bool y_non_contig = !load_y && !ggml_vk_dim01_contiguous(src1);
+    const bool x_non_contig = !ggml_vk_dim01_contiguous(src0);
+    const bool y_non_contig = !ggml_vk_dim01_contiguous(src1);
 
     const bool f16_f32_kernel = src1->type == GGML_TYPE_F32;
 
     const bool qx_needs_dequant = x_non_contig;
     const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig;
 
+    // Not implemented
+    GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
+
     const uint64_t x_ne = ne01 * ne00;
     const uint64_t y_ne = ne11 * ne10;
     const uint64_t d_ne = ne11 * ne01;
 
-    const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment);
+    const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment);
     const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
-    const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) : qx_sz;
+    const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz;
     const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne;
     const uint64_t d_sz = sizeof(float) * d_ne;
 
@@ -2517,16 +3066,12 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context
     uint64_t x_buf_offset = 0;
     vk_buffer d_Y;
     uint64_t y_buf_offset = 0;
-    if (load_x) {
-        d_Qx = ctx->prealloc_qx;
-    } else if(!src1_uma) {
+    if(!src0_uma) {
         d_Qx = extra_src0->buffer_gpu.lock();
         qx_buf_offset = extra_src0->offset;
         GGML_ASSERT(d_Qx != nullptr);
     }
-    if (load_y) {
-        d_Qy = ctx->prealloc_qy;
-    } else if(!src1_uma) {
+    if(!src1_uma) {
         d_Qy = extra_src1->buffer_gpu.lock();
         qy_buf_offset = extra_src1->offset;
         GGML_ASSERT(d_Qy != nullptr);
@@ -2546,8 +3091,8 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context
         GGML_ASSERT(qy_sz == y_sz);
     }
 
-    vk_pipeline * to_fp16_vk_0 = nullptr;
-    vk_pipeline* to_fp16_vk_1 = nullptr;
+    vk_pipeline to_fp16_vk_0 = nullptr;
+    vk_pipeline to_fp16_vk_1 = nullptr;
     if (x_non_contig) {
         to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type);
     }
@@ -2556,71 +3101,48 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context
     } else {
         to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
     }
-    vk_pipeline* dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type);
+    vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type);
     GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
     GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
     GGML_ASSERT(dmmv != nullptr);
 
     // Allocate descriptor sets
     if (qx_needs_dequant) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_0, 1);
+        ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_0, 1);
     }
     if (qy_needs_dequant) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13);
+        ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13);
     }
-    ggml_pipeline_allocate_descriptor_sets(ctx, *dmmv, ne12 * ne13);
+    ggml_pipeline_allocate_descriptor_sets(ctx, dmmv, ne12 * ne13);
 
     if (x_non_contig) {
-        GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment));
-        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }, src0->type);
-    } else if (load_x) {
-        // copy data to device
-        ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qx, 0, src0, 0, 0, ggml_nrows(src0));
+        GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment));
+        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
     }
     if (y_non_contig) {
         GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne);
-        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }, src1->type);
-    } else if (load_y) {
-        ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1));
+        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
     }
 
-    for (uint64_t i13 = 0; i13 < ne13; i13++) {
-        const uint64_t i03 = i13 / r3;
-        for (uint64_t i12 = 0; i12 < ne12; i12++) {
-            const uint64_t i02 = i12 / r2;
+    uint32_t stride_batch_x = ne00*ne01;
+    uint32_t stride_batch_y = ne10*ne11;
 
-            const uint64_t it_idx0 = (i03 * ne02 + i02);
-            const uint64_t it_idx1 = (i13 * ne12 + i12);
-            const uint64_t x_offset = x_buf_offset + x_sz * it_idx0;
-            const uint64_t qy_offset = qy_buf_offset + qy_sz * it_idx1;
-            const uint64_t y_offset = y_buf_offset + y_sz * it_idx1;
-            const uint64_t d_offset = d_buf_offset + d_sz * it_idx1;
-
-            const uint64_t y_buffer_offset = (y_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment;
-            const uint64_t y_shader_offset = y_offset - y_buffer_offset;
-
-            const uint64_t d_buffer_offset = (d_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment;
-            const uint64_t d_shader_offset = d_offset - d_buffer_offset;
-
-            if (!y_non_contig && qy_needs_dequant) {
-                const std::vector<int> pc = { (int)ne11, (int)ne10, (int)ne10, (int)ne10 };
-                ggml_vk_sync_buffers(subctx);
-                ggml_vk_dispatch_pipeline(ctx, subctx, *to_fp16_vk_1, { { d_Qy, qy_offset, qy_sz }, { d_Y, y_offset, y_sz } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)y_ne, 1, 1});
-            }
-
-            // compute
-            const std::array<int, 3> pc = { (int)ne00, (int)(y_shader_offset / ggml_type_size(src1->type)), (int)(d_shader_offset / ggml_type_size(dst->type))};
-            ggml_vk_sync_buffers(subctx);
-            ggml_vk_dispatch_pipeline(ctx, subctx, *dmmv, { { d_X, x_offset, x_sz }, { d_Y, y_buffer_offset, y_sz + y_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 3 * sizeof(int), &pc, { (uint32_t)ne01, 1, 1});
-
-            if (dst->backend == GGML_BACKEND_CPU) {
-                // copy dst to host
-                float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
-                ggml_vk_sync_buffers(subctx);
-                ggml_vk_buffer_read_async(ctx, subctx, d_D, d_offset, d, sizeof(float) * d_ne);
-            }
-        }
+    if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) {
+        stride_batch_x = src0->nb[0] / ggml_type_size(src0->type);
     }
+
+    if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) {
+        stride_batch_y = src1->nb[0] / ggml_type_size(src1->type);
+    }
+
+    // compute
+    const vk_mat_vec_push_constants pc = {
+        (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01,
+        (uint32_t)ne02, (uint32_t)ne12, (uint32_t)r2, (uint32_t)r3,
+        stride_batch_x, stride_batch_y, (uint32_t)(ne20*ne21),
+    };
+    ggml_vk_sync_buffers(subctx);
+    ggml_vk_dispatch_pipeline(ctx, subctx, dmmv, { { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne22 * ne23} }, sizeof(vk_mat_vec_push_constants), &pc, { (uint32_t)ne01, (uint32_t)(ne12 * ne13), 1});
 }
 
 static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@@ -2630,7 +3152,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c
     std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ",  backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl;
 #endif
     GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
-    GGML_ASSERT(src0->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
     GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]);  // NOLINT
     GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]);  // NOLINT
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
@@ -2657,18 +3179,16 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c
 
     bool src1_uma = false;
 
-    if (ctx->device.lock()->uma) {
+    if (ctx->device->uma) {
         ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset);
         src1_uma = d_Qy != nullptr;
     }
 
-    const bool load_y = src1->backend != GGML_BACKEND_GPU && !src1_uma;
-
     const uint64_t x_ne = ne00 * ne01 * ne02;
     const uint64_t y_ne = ne10 * ne11 * ne12;
     const uint64_t d_ne = ne01 * ne11 * ne12;
 
-    const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment);
+    const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment);
     const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
     const uint64_t d_sz = sizeof(float) * d_ne;
 
@@ -2678,33 +3198,27 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c
     vk_buffer d_Qx = extra_src0->buffer_gpu.lock();
     const uint64_t qx_buf_offset = extra_src0->offset;
     GGML_ASSERT(d_Qx != nullptr);
-    if (load_y) {
-        d_Qy = ctx->prealloc_qy;
-    } else if (!src1_uma) {
+    if (!src1_uma) {
         d_Qy = extra_src1->buffer_gpu.lock();
         qy_buf_offset = extra_src1->offset;
         GGML_ASSERT(d_Qx != nullptr);
     }
 
     // Allocate descriptor sets
-    ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, 1);
+    ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, 1);
 
-    const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment;
+    const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
     const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset;
 
-    const uint64_t d_buffer_offset = (d_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment;
+    const uint64_t d_buffer_offset = (d_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
     const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset;
 
-    if (load_y) {
-        ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, qy_buf_offset, src1, 0, 0, ggml_nrows(src1));
-    }
-
     // compute
     const std::array<uint32_t, 6> pc = { (uint32_t)ne00, (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) };
     ggml_vk_sync_buffers(subctx);
-    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 });
+    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 });
 
-    if (dst->backend == GGML_BACKEND_CPU) {
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
         // copy dst to host
         float * d = (float *) dst->data;
         ggml_vk_sync_buffers(subctx);
@@ -2721,7 +3235,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
     GGML_ASSERT(!ggml_is_transposed(src0));
     GGML_ASSERT(!ggml_is_transposed(src1));
     GGML_ASSERT(!ggml_is_permuted(src0));
-    GGML_ASSERT(src0->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
 
@@ -2749,13 +3263,11 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
 
     bool src1_uma = false;
 
-    if (ctx->device.lock()->uma) {
+    if (ctx->device->uma) {
         ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset);
         src1_uma = d_Qy != nullptr;
     }
 
-    const bool load_y = src1->backend != GGML_BACKEND_GPU && !src1_uma;
-
     const uint64_t d_ne = ne01 * ne11 * ne12;
 
     const uint32_t row_stride_x = nb01 / sizeof(ggml_fp16_t);
@@ -2771,33 +3283,27 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
     vk_buffer d_Qx = extra_src0->buffer_gpu.lock();
     const uint64_t qx_buf_offset = extra_src0->offset;
     GGML_ASSERT(d_Qx != nullptr);
-    if (load_y) {
-        d_Qy = ctx->prealloc_qy;
-    } else {
+    if (!src1_uma) {
         d_Qy = extra_src1->buffer_gpu.lock();
         qy_buf_offset = extra_src1->offset;
         GGML_ASSERT(d_Qx != nullptr);
     }
 
     // Allocate descriptor sets
-    ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, 1);
+    ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, 1);
 
-    const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment;
+    const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
     const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset;
 
-    const uint64_t d_buffer_offset = (d_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment;
+    const uint64_t d_buffer_offset = (d_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
     const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset;
 
-    if (load_y) {
-        ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, qy_buf_offset, src1, 0, 0, ggml_nrows(src1));
-    }
-
     // compute
     const std::array<uint32_t, 7> pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, (uint32_t)(ne12 / ne02), (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) };
     ggml_vk_sync_buffers(subctx);
-    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 });
+    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 });
 
-    if (dst->backend == GGML_BACKEND_CPU) {
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
         // copy dst to host
         float * d = (float *) dst->data;
         ggml_vk_sync_buffers(subctx);
@@ -2815,10 +3321,10 @@ static bool ggml_vk_can_mul_mat(const ggml_tensor * src0, const ggml_tensor * sr
     return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
            (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16 || ggml_is_quantized(src1->type)) &&
            dst->type == GGML_TYPE_F32 &&
-           ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_GPU);
+           ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_TYPE_GPU);
 }
 
-static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
+static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
 #ifdef GGML_VULKAN_DEBUG
     std::cerr << "ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")" << std::endl;
 #endif
@@ -2833,6 +3339,348 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx,
     }
 }
 
+/*static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_mul_mat_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
+    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
+    std::cerr << "), (" << ids << ", name=" << ids->name << ", type=" << ids->type << ", backend=" << ids->backend << ", ne0=" << ids->ne[0] << ", ne1=" << ids->ne[1] << ", ne2=" << ids->ne[2] << ", ne3=" << ids->ne[3] << ", nb0=" << ids->nb[0] << ", nb1=" << ids->nb[1] << ", nb2=" << ids->nb[2] << ", nb3=" << ids->nb[3];
+    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl;
+#endif
+    GGML_ASSERT(src0->type == GGML_TYPE_I32);
+    GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);  // NOLINT
+
+    const uint64_t ne00 = src0->ne[0];
+    const uint64_t ne01 = src0->ne[1];
+    const uint64_t ne02 = src0->ne[2];
+    const uint64_t ne03 = src0->ne[3];
+
+    const uint64_t ne10 = src1->ne[0];
+    const uint64_t ne11 = src1->ne[1];
+    const uint64_t ne12 = src1->ne[2];
+    const uint64_t ne13 = src1->ne[3];
+
+    const uint32_t nb11 = src1->nb[1];
+
+    const uint64_t ne20 = dst->ne[0];
+    const uint64_t ne21 = dst->ne[1];
+
+    const uint64_t r2 = ne12 / ne02;
+    const uint64_t r3 = ne13 / ne03;
+
+    const uint32_t nbi1 = src0->nb[1];
+    const uint32_t idx = ((uint32_t *) dst->op_params)[0];
+    const uint64_t n_as = ne02;
+
+    GGML_ASSERT(n_as <= 8);
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
+    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
+    ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
+    ggml_tensor_extra_gpu * extra_ids = (ggml_tensor_extra_gpu *) ids->extra;
+
+    vk_buffer d_Qx;
+    size_t qx_buf_offset = 0;
+    vk_buffer d_Qy;
+    size_t qy_buf_offset = 0;
+
+    bool src0_uma = false;
+    bool src1_uma = false;
+
+    if (ctx->device->uma) {
+        ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset);
+        ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset);
+        src0_uma = d_Qx != nullptr;
+        src1_uma = d_Qy != nullptr;
+    }
+
+    const bool x_non_contig = !ggml_vk_dim01_contiguous(src0);
+    const bool y_non_contig = !ggml_vk_dim01_contiguous(src1);
+
+    const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig;
+
+    vk_matmul_pipeline mmp = ggml_vk_get_mul_mat_mat_id_pipeline(ctx, src0->type, y_non_contig ? GGML_TYPE_F16 : src1->type);
+
+    const bool qx_needs_dequant = mmp == nullptr || x_non_contig;
+    const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig;
+
+    if (mmp == nullptr) {
+        GGML_ASSERT(false);
+    }
+
+    // Not implemented
+    GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
+
+    const int x_ne = ne01 * ne00;
+    const int y_ne = ne11 * ne10;
+    const int d_ne = ne11 * ne01;
+
+    const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11));
+    const bool aligned = ne10 == kpad && ne01 > 8 && ne11 > 8;
+
+    const uint32_t split_k = ggml_vk_guess_split_k(ne01, ne11, ne10);
+
+    vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned);
+
+    const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type);
+    const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
+    const uint64_t x_sz = !qx_needs_dequant ? qx_sz : sizeof(ggml_fp16_t) * x_ne;
+    const uint64_t y_sz = y_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne;
+    const uint64_t d_sz = sizeof(float) * d_ne;
+
+    vk_buffer d_D = extra->buffer_gpu.lock();
+    const uint64_t d_buf_offset = extra->offset;
+    GGML_ASSERT(d_D != nullptr);
+    GGML_ASSERT(d_D->size >= d_buf_offset + d_sz * ne02 * ne03);
+    vk_buffer d_X;
+    uint64_t x_buf_offset = 0;
+    vk_buffer d_Y;
+    uint64_t y_buf_offset = 0;
+    if (!src0_uma) {
+        d_Qx = extra_src0->buffer_gpu.lock();
+        qx_buf_offset = extra_src0->offset;
+        GGML_ASSERT(d_Qx != nullptr);
+    }
+    if (!src1_uma) {
+        d_Qy = extra_src1->buffer_gpu.lock();
+        qy_buf_offset = extra_src1->offset;
+        GGML_ASSERT(d_Qy != nullptr);
+    }
+    if (qx_needs_dequant) {
+        d_X = ctx->prealloc_x;
+        GGML_ASSERT(d_X->size >= x_sz * ne02 * ne03);
+    } else {
+        d_X = d_Qx;
+        x_buf_offset = qx_buf_offset;
+        GGML_ASSERT(qx_sz == x_sz);
+    }
+    if (qy_needs_dequant) {
+        d_Y = ctx->prealloc_y;
+        GGML_ASSERT(d_Y->size >= y_sz * ne02 * ne03);
+    } else {
+        d_Y = d_Qy;
+        y_buf_offset = qy_buf_offset;
+        GGML_ASSERT(qy_sz == y_sz);
+    }
+
+    vk_pipeline to_fp16_vk_0 = nullptr;
+    vk_pipeline to_fp16_vk_1 = nullptr;
+
+    if (x_non_contig) {
+        to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16);
+    } else {
+        to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type);
+    }
+    if (y_non_contig) {
+        to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, GGML_TYPE_F16);
+    } else {
+        to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
+    }
+    GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
+    GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
+
+    // Allocate descriptor sets
+    ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, 1);
+    if (qx_needs_dequant) {
+        ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_0, 1);
+    }
+    if (qy_needs_dequant) {
+        ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_1, 1);
+    }
+    if (split_k > 1) {
+        ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, 1);
+    }
+
+    if (x_non_contig) {
+        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
+    } else if (qx_needs_dequant) {
+        const std::vector<uint32_t> pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) };
+        ggml_vk_sync_buffers(subctx);
+        ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, { { d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1});
+    }
+    if (y_non_contig) {
+        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
+    }
+
+    uint32_t stride_batch_x = ne00*ne01;
+    uint32_t stride_batch_y = ne10*ne11;
+
+    if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) {
+        stride_batch_x = src0->nb[0] / ggml_type_size(src0->type);
+    }
+
+    if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) {
+        stride_batch_y = src1->nb[0] / ggml_type_size(src1->type);
+    }
+
+    // compute
+    ggml_vk_matmul(
+        ctx, subctx, pipeline,
+        { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 },
+        { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k },
+        ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21,
+        nb11 / ggml_type_size(src1->type), ne20, idx, nbi1, n_as
+    );  // NOLINT
+}
+
+static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_mul_mat_vec_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ",  backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
+    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ",  backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
+    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ",  backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl;
+#endif
+    GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);  // NOLINT
+    GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);  // NOLINT
+
+    const uint64_t ne00 = src0->ne[0];
+    const uint64_t ne01 = src0->ne[1];
+    const uint64_t ne02 = src0->ne[2];
+    const uint64_t ne03 = src0->ne[3];
+
+    const uint64_t ne10 = src1->ne[0];
+    const uint64_t ne11 = src1->ne[1];
+    const uint64_t ne12 = src1->ne[2];
+    const uint64_t ne13 = src1->ne[3];
+
+    GGML_ASSERT(ne11 == 1);
+
+    const uint64_t ne20 = dst->ne[0];
+    const uint64_t ne21 = dst->ne[1];
+    const uint64_t ne22 = dst->ne[2];
+    const uint64_t ne23 = dst->ne[3];
+
+    const uint64_t nb22  = dst->nb[2];
+    const uint64_t nb23  = dst->nb[3];
+
+    const uint64_t r2 = ne12 / ne02;
+    const uint64_t r3 = ne13 / ne03;
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
+    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
+    ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
+
+    vk_buffer d_Qx;
+    size_t qx_buf_offset = 0;
+    vk_buffer d_Qy;
+    size_t qy_buf_offset = 0;
+
+    bool src0_uma = false;
+    bool src1_uma = false;
+
+    if (ctx->device->uma) {
+        ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset);
+        ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset);
+        src0_uma = d_Qx != nullptr;
+        src1_uma = d_Qy != nullptr;
+    }
+
+    const bool x_non_contig = !ggml_vk_dim01_contiguous(src0);
+    const bool y_non_contig = !ggml_vk_dim01_contiguous(src1);
+
+    const bool f16_f32_kernel = src1->type == GGML_TYPE_F32;
+
+    const bool qx_needs_dequant = x_non_contig;
+    const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig;
+
+    // Not implemented
+    GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
+
+    const uint64_t x_ne = ne01 * ne00;
+    const uint64_t y_ne = ne11 * ne10;
+    const uint64_t d_ne = ne11 * ne01;
+
+    const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment);
+    const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
+    const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz;
+    const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne;
+    const uint64_t d_sz = sizeof(float) * d_ne;
+
+    vk_buffer d_D = extra->buffer_gpu.lock();
+    const uint64_t d_buf_offset = extra->offset;
+    GGML_ASSERT(d_D != nullptr);
+    vk_buffer d_X;
+    uint64_t x_buf_offset = 0;
+    vk_buffer d_Y;
+    uint64_t y_buf_offset = 0;
+    if(!src0_uma) {
+        d_Qx = extra_src0->buffer_gpu.lock();
+        qx_buf_offset = extra_src0->offset;
+        GGML_ASSERT(d_Qx != nullptr);
+    }
+    if(!src1_uma) {
+        d_Qy = extra_src1->buffer_gpu.lock();
+        qy_buf_offset = extra_src1->offset;
+        GGML_ASSERT(d_Qy != nullptr);
+    }
+    if (qx_needs_dequant) {
+        d_X = ctx->prealloc_x;
+    } else {
+        d_X = d_Qx;
+        x_buf_offset = qx_buf_offset;
+        GGML_ASSERT(qx_sz == x_sz);
+    }
+    if (qy_needs_dequant) {
+        d_Y = ctx->prealloc_y;
+    } else {
+        d_Y = d_Qy;
+        y_buf_offset = qy_buf_offset;
+        GGML_ASSERT(qy_sz == y_sz);
+    }
+
+    vk_pipeline to_fp16_vk_0 = nullptr;
+    vk_pipeline to_fp16_vk_1 = nullptr;
+    if (x_non_contig) {
+        to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type);
+    }
+    if (y_non_contig) {
+        to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, src1->type);
+    } else {
+        to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
+    }
+    vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type);
+    GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
+    GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
+    GGML_ASSERT(dmmv != nullptr);
+
+    // Allocate descriptor sets
+    if (qx_needs_dequant) {
+        ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_0, 1);
+    }
+    if (qy_needs_dequant) {
+        ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13);
+    }
+    ggml_pipeline_allocate_descriptor_sets(ctx, dmmv, ne12 * ne13);
+
+    if (x_non_contig) {
+        GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment));
+        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
+    }
+    if (y_non_contig) {
+        GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne);
+        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
+    }
+
+    uint32_t stride_batch_x = ne00*ne01;
+    uint32_t stride_batch_y = ne10*ne11;
+
+    if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) {
+        stride_batch_x = src0->nb[0] / ggml_type_size(src0->type);
+    }
+
+    if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) {
+        stride_batch_y = src1->nb[0] / ggml_type_size(src1->type);
+    }
+
+    // compute
+    const vk_mat_vec_push_constants pc = {
+        (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01,
+        (uint32_t)ne02, (uint32_t)ne12, (uint32_t)r2, (uint32_t)r3,
+        stride_batch_x, stride_batch_y, (uint32_t)(ne20*ne21),
+        // 0, 0, 0, 0, 1
+    };
+    ggml_vk_sync_buffers(subctx);
+    ggml_vk_dispatch_pipeline(ctx, subctx, dmmv, { { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne22 * ne23} }, sizeof(vk_mat_vec_push_constants), &pc, { (uint32_t)ne01, (uint32_t)(ne12 * ne13), 1});
+}*/
+
 static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     // guaranteed to be an integer due to the check in ggml_can_repeat
     const uint64_t ne0 = dst->ne[0];
@@ -2863,8 +3711,8 @@ static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx
     // TODO: support for transposed / permuted tensors
     GGML_ASSERT(nb0  == sizeof(float));
     GGML_ASSERT(nb00 == sizeof(float));
-    GGML_ASSERT(src0->backend == GGML_BACKEND_GPU);
-    GGML_ASSERT(dst->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
+    GGML_ASSERT(dst->backend == GGML_BACKEND_TYPE_GPU);
 
     ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
     ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
@@ -2904,40 +3752,40 @@ static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx
 }
 
 
-static vk_pipeline* ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op) {
+static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op) {
     switch (op) {
     case GGML_OP_ADD:
         if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return &ctx->pipeline_add_f32;
+            return ctx->device->pipeline_add_f32;
         }
         return nullptr;
     case GGML_OP_GET_ROWS:
         GGML_ASSERT(src1->type == GGML_TYPE_I32);
         if (dst->type == GGML_TYPE_F16) {
-            return &ctx->pipeline_get_rows[src0->type];
+            return ctx->device->pipeline_get_rows[src0->type];
         }
         if (dst->type == GGML_TYPE_F32) {
-            return &ctx->pipeline_get_rows_f32[src0->type];
+            return ctx->device->pipeline_get_rows_f32[src0->type];
         }
         return nullptr;
     case GGML_OP_MUL:
         if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return &ctx->pipeline_mul_f32;
+            return ctx->device->pipeline_mul_f32;
         }
         return nullptr;
     case GGML_OP_SCALE:
         if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return &ctx->pipeline_scale_f32;
+            return ctx->device->pipeline_scale_f32;
         }
         return nullptr;
     case GGML_OP_SQR:
         if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return &ctx->pipeline_sqr_f32;
+            return ctx->device->pipeline_sqr_f32;
         }
         return nullptr;
     case GGML_OP_CLAMP:
         if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return &ctx->pipeline_clamp_f32;
+            return ctx->device->pipeline_clamp_f32;
         }
         return nullptr;
     case GGML_OP_CPY:
@@ -2946,29 +3794,29 @@ static vk_pipeline* ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
         return ggml_vk_get_cpy_pipeline(ctx, src0->type, dst->type);
     case GGML_OP_NORM:
         if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return &ctx->pipeline_norm_f32;
+            return ctx->device->pipeline_norm_f32;
         }
         return nullptr;
     case GGML_OP_RMS_NORM:
         if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return &ctx->pipeline_rms_norm_f32;
+            return ctx->device->pipeline_rms_norm_f32;
         }
         return nullptr;
     case GGML_OP_UNARY:
         switch (ggml_get_unary_op(dst)) {
             case GGML_UNARY_OP_SILU:
                 if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-                    return &ctx->pipeline_silu_f32;
+                    return ctx->device->pipeline_silu_f32;
                 }
                 break;
             case GGML_UNARY_OP_GELU:
                 if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-                    return &ctx->pipeline_gelu_f32;
+                    return ctx->device->pipeline_gelu_f32;
                 }
                 break;
             case GGML_UNARY_OP_RELU:
                 if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-                    return &ctx->pipeline_relu_f32;
+                    return ctx->device->pipeline_relu_f32;
                 }
                 break;
             default:
@@ -2977,12 +3825,17 @@ static vk_pipeline* ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
         return nullptr;
     case GGML_OP_DIAG_MASK_INF:
         if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return &ctx->pipeline_diag_mask_inf_f32;
+            return ctx->device->pipeline_diag_mask_inf_f32;
         }
         return nullptr;
     case GGML_OP_SOFT_MAX:
-        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return &ctx->pipeline_soft_max_f32;
+        GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);
+
+        if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_soft_max_f32;
+        }
+        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16 && src2->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_soft_max_f32_f16;
         }
         return nullptr;
     case GGML_OP_ROPE:
@@ -2997,21 +3850,26 @@ static vk_pipeline* ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
 
             if (is_neox) {
                 if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-                    return &ctx->pipeline_rope_neox_f32;
+                    return ctx->device->pipeline_rope_neox_f32;
                 }
                 if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
-                    return &ctx->pipeline_rope_neox_f16;
+                    return ctx->device->pipeline_rope_neox_f16;
                 }
             } else {
                 if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-                    return &ctx->pipeline_rope_f32;
+                    return ctx->device->pipeline_rope_f32;
                 }
                 if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
-                    return &ctx->pipeline_rope_f16;
+                    return ctx->device->pipeline_rope_f16;
                 }
             }
             return nullptr;
         }
+    case GGML_OP_ARGSORT:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_I32) {
+            return ctx->device->pipeline_argsort_f32;
+        }
+        return nullptr;
     default:
         return nullptr;
     }
@@ -3026,18 +3884,35 @@ static ggml_vk_func_t ggml_vk_op_get_func(ggml_op op) {
     }
 }
 
+static bool ggml_vk_op_supports_incontiguous(ggml_op op) {
+    switch (op) {
+    case GGML_OP_CPY:
+    case GGML_OP_GET_ROWS:
+    case GGML_OP_ADD:
+    case GGML_OP_MUL:
+    case GGML_OP_SCALE:
+    case GGML_OP_SQR:
+    case GGML_OP_CLAMP:
+        return true;
+    default:
+        return false;
+    }
+}
+
 template<typename PC>
-static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op, const PC&& pc) {
+static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op, const PC&& pc) {
 #ifdef GGML_VULKAN_DEBUG
     std::cerr << "ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
     if (src1 != nullptr) {
         std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
     }
+    if (src2 != nullptr) {
+        std::cerr << "), (" << src2 << ", name=" << src2->name << ", type=" << src2->type << ", backend=" << src2->backend << ", ne0=" << src2->ne[0] << ", ne1=" << src2->ne[1] << ", ne2=" << src2->ne[2] << ", ne3=" << src2->ne[3] << ", nb0=" << src2->nb[0] << ", nb1=" << src2->nb[1] << ", nb2=" << src2->nb[2] << ", nb3=" << src2->nb[3];
+    }
     std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "), " << ggml_op_name(op) << ")" << std::endl;
 #endif
-    GGML_ASSERT(!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type)));  // NOLINT
+    GGML_ASSERT(op == GGML_OP_GET_ROWS || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type))));  // NOLINT
     GGML_ASSERT(op == GGML_OP_CPY || ggml_vk_dim01_contiguous(src0));  // NOLINT
-    GGML_ASSERT(src1 == nullptr || ggml_vk_dim01_contiguous(src1));  // NOLINT
     GGML_ASSERT(dst->extra != nullptr);
     const uint64_t ne00 = src0->ne[0];
     const uint64_t ne01 = src0->ne[1];
@@ -3054,7 +3929,10 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c
     const uint64_t nb2  = dst->nb[2];
     const uint64_t nb3  = dst->nb[3];
 
-    vk_pipeline * pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, dst, op);
+    const bool use_src2 = src2 != nullptr;
+    const uint64_t ne2 = use_src2 ? src2->ne[0] * src2->ne[1] : 0;
+
+    vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, dst, op);
     ggml_vk_func_t op_func;
 
     if (pipeline == nullptr) {
@@ -3072,87 +3950,90 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c
         return;
     }
 
+    const bool op_supports_incontiguous = ggml_vk_op_supports_incontiguous(op);
+
     ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
     ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
     ggml_tensor_extra_gpu * extra_src1 = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
+    ggml_tensor_extra_gpu * extra_src2 = use_src2 ? (ggml_tensor_extra_gpu *) src2->extra : nullptr;
 
     vk_buffer d_X = nullptr;
     size_t x_buf_offset = 0;
     vk_buffer d_Y = nullptr;
     size_t y_buf_offset = 0;
+    vk_buffer d_Z = nullptr;
+    size_t z_buf_offset = 0;
 
     bool src0_uma = false;
     bool src1_uma = false;
+    bool src2_uma = false;
 
-    if (ctx->device.lock()->uma) {
+    if (ctx->device->uma) {
         ggml_vk_host_get(ctx, src0->data, d_X, x_buf_offset);
         src0_uma = d_X != nullptr;
         if (use_src1) {
             ggml_vk_host_get(ctx, src1->data, d_Y, y_buf_offset);
             src1_uma = d_Y != nullptr;
         }
+        if (use_src2) {
+            ggml_vk_host_get(ctx, src1->data, d_Z, z_buf_offset);
+            src2_uma = d_Z != nullptr;
+        }
     }
 
-    const bool transfer_src0 = src0->backend != GGML_BACKEND_GPU && !src0_uma;
-    const bool transfer_src1 = use_src1 && src1->backend != GGML_BACKEND_GPU && !src1_uma;
-
-    uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type) * ne0, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment);
-    uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) : 0;
+    uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type)/ggml_blck_size(src0->type) * ne0, ctx->device->properties.limits.minStorageBufferOffsetAlignment);
+    uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : 0;
+    uint64_t z_sz = use_src2 ? ggml_vk_align_size(ggml_type_size(src2->type) * ne2, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : 0;
     uint64_t d_sz = ggml_type_size(dst->type) * ne0;
 
     vk_buffer d_D = extra->buffer_gpu.lock();
 
     // Workaround for tiny tensor inputs on ROPE
-    if (use_src1 && src1->backend == GGML_BACKEND_GPU && y_sz > d_D->size) {
+    if (use_src1 && src1->backend == GGML_BACKEND_TYPE_GPU && y_sz > d_D->size) {
         y_sz = VK_WHOLE_SIZE;
     }
 
     GGML_ASSERT(d_D != nullptr);
-    uint64_t d_buf_offset = (extra->offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment;
+    uint64_t d_buf_offset = (extra->offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
     GGML_ASSERT(d_buf_offset == extra->offset || op == GGML_OP_CPY);  // NOLINT
-    if (transfer_src0) {
-        d_X = ctx->prealloc_qx;
-    } else if(!src0_uma) {
+    if(!src0_uma) {
         d_X = extra_src0->buffer_gpu.lock();
         x_buf_offset = extra_src0->offset;
         GGML_ASSERT(d_X != nullptr);
     }
-    if (transfer_src1) {
-        d_Y = ctx->prealloc_qy;
-    } else if (use_src1 && !src1_uma) {
+    if (use_src1 && !src1_uma) {
         d_Y = extra_src1->buffer_gpu.lock();
         y_buf_offset = extra_src1->offset;
         GGML_ASSERT(d_Y != nullptr);
     }
 
-    if (op == GGML_OP_CPY) {
-        GGML_ASSERT(!transfer_src0);
-        GGML_ASSERT(!transfer_src1);
+    if (use_src2 && !src2_uma) {
+        d_Z = extra_src2->buffer_gpu.lock();
+        z_buf_offset = extra_src2->offset;
+        GGML_ASSERT(d_Z != nullptr);
+    }
+
+    if (op_supports_incontiguous) {
         x_sz = ggml_nbytes(src0);
+        y_sz = use_src1 ? ggml_nbytes(src1) : 0;
         d_sz = ggml_nbytes(dst);
 
-        if (extra_src0->offset + x_sz >= d_X->size) {
+        if (x_buf_offset + x_sz >= d_X->size) {
             x_sz = VK_WHOLE_SIZE;
         }
-        if (extra->offset + d_sz >= d_D->size) {
+        if (use_src1 && y_buf_offset + y_sz >= d_Y->size) {
+            y_sz = VK_WHOLE_SIZE;
+        }
+        if (d_buf_offset + d_sz >= d_D->size) {
             d_sz = VK_WHOLE_SIZE;
         }
     }
 
     std::array<uint32_t, 3> elements;
 
-    // copy src0 to device
-    if (transfer_src0) {
-        ggml_vk_h2d_tensor_2d(ctx, subctx, d_X, 0, src0, 0, 0, ggml_nrows(src0));
-        ctx->staging_offset = x_sz * ne02 * ne03;
-    }
-    if (transfer_src1) {
-        ggml_vk_h2d_tensor_2d(ctx, subctx, d_Y, 0, src1, 0, 0, ggml_nrows(src1));
-    }
-
     // Single call if dimension 2 is contiguous
-    if (op == GGML_OP_CPY || (ggml_is_contiguous(src0) && (src1 == nullptr || ggml_is_contiguous(src1)))) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, *pipeline, 1);
+    if (op_supports_incontiguous || (ggml_is_contiguous(src0) && (src1 == nullptr || ggml_is_contiguous(src1)))) {
+        ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, 1);
 
         switch (dst->op) {
         case GGML_OP_NORM:
@@ -3164,16 +4045,19 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c
         case GGML_OP_ROPE:
             elements = { (uint32_t)ggml_nrows(src0), (uint32_t)ne00, 1 };
             break;
+        case GGML_OP_GET_ROWS:
+            elements = {  (uint32_t)ne00, (uint32_t)ne10, (uint32_t)(ne11 * ne12) };
+            break;
         default:
             elements = { (uint32_t)ggml_nelements(src0), 1, 1 };
             break;
         }
 
-        if (op != GGML_OP_CPY) {
+        if (!op_supports_incontiguous) {
             if (x_sz != VK_WHOLE_SIZE) {
                 x_sz *= ne02 * ne03;
             }
-            if (y_sz != VK_WHOLE_SIZE) {
+            if (use_src1 && y_sz != VK_WHOLE_SIZE) {
                 y_sz *= ne12 * ne13;
             }
             if (d_sz != VK_WHOLE_SIZE) {
@@ -3181,26 +4065,42 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c
             }
         }
 
-        if (!use_src1 && op == GGML_OP_SOFT_MAX) {
-            // Empty src1 is possible on soft_max, but the shader needs a buffer
+        if (op == GGML_OP_SOFT_MAX) {
+            // Empty src1 and src2 are possible on soft_max, but the shader needs buffers
+            vk_subbuffer subbuf_y;
+            if (use_src1) {
+                subbuf_y = { d_Y, y_buf_offset, y_sz };
+            } else {
+                subbuf_y = { d_X, 0, d_X->size };
+            }
+
+            vk_subbuffer subbuf_z;
+            if (use_src2) {
+                subbuf_z = { d_Z, z_buf_offset, z_sz };
+            } else {
+                subbuf_z = { d_X, 0, d_X->size };
+            }
+
             ggml_vk_sync_buffers(subctx);
-            ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { ctx->prealloc_y, 0, ctx->prealloc_y->size }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
+            ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, subbuf_y, subbuf_z, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
         } else if (use_src1) {
             ggml_vk_sync_buffers(subctx);
-            ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
+            ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
         } else {
             ggml_vk_sync_buffers(subctx);
-            ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
+            ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
         }
-        if (dst->backend == GGML_BACKEND_CPU && op == GGML_OP_CPY) {
+        if (dst->backend == GGML_BACKEND_TYPE_CPU && op == GGML_OP_CPY) {
             ggml_vk_d2h_tensor_2d(ctx, subctx, d_D, 0, dst);
-        } else if(dst->backend == GGML_BACKEND_CPU) {
+        } else if(dst->backend == GGML_BACKEND_TYPE_CPU) {
             // copy dst to host
             float * d = (float *) dst->data;
             ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, d_sz);
         }
     } else {
-        ggml_pipeline_allocate_descriptor_sets(ctx, *pipeline, ne02 * ne03);
+        GGML_ASSERT(op != GGML_OP_SOFT_MAX);
+
+        ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, ne02 * ne03);
 
         switch (dst->op) {
         case GGML_OP_NORM:
@@ -3212,6 +4112,9 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c
         case GGML_OP_ROPE:
             elements = { (uint32_t)ne01, (uint32_t)ne00, 1 };
             break;
+        case GGML_OP_GET_ROWS:
+            elements = {  (uint32_t)ne00, (uint32_t)ne10, (uint32_t)(ne11 * ne12) };
+            break;
         default:
             elements = { (uint32_t)ne0, 1, 1 };
             break;
@@ -3225,18 +4128,14 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c
                 const uint32_t y_offset = y_sz * it_idx1;
                 const uint32_t d_offset = d_sz * it_idx0;
 
-                if (!use_src1 && op == GGML_OP_SOFT_MAX) {
-                    // Empty src1 is possible on soft_max, but the shader needs a buffer
+                if (use_src1) {
                     ggml_vk_sync_buffers(subctx);
-                    ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { ctx->prealloc_y, 0, ctx->prealloc_y->size }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
-                } else if (use_src1) {
-                    ggml_vk_sync_buffers(subctx);
-                    ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_Y, y_buf_offset + y_offset, y_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements);
+                    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_Y, y_buf_offset + y_offset, y_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements);
                 } else {
                     ggml_vk_sync_buffers(subctx);
-                    ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements);
+                    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements);
                 }
-                if (dst->backend == GGML_BACKEND_CPU) {
+                if (dst->backend == GGML_BACKEND_TYPE_CPU) {
                     // copy dst to host
                     ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset + d_offset, (char *) dst->data + i02*nb2 + i03*nb3, d_sz);
                 }
@@ -3246,69 +4145,157 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c
 }
 
 static void ggml_vk_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, src1, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f });
+    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f });
 }
 
 static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, src1, dst, GGML_OP_GET_ROWS, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f });
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t src1_type_size = ggml_type_size(src1->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_GET_ROWS, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        0.0f, 0.0f,
+    });
 }
 
 static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, src1, dst, GGML_OP_ADD, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f });
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t src1_type_size = ggml_type_size(src1->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ADD, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        0.0f, 0.0f,
+    });
 }
 
 static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, src1, dst, GGML_OP_MUL, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f });
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t src1_type_size = ggml_type_size(src1->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_MUL, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        0.0f, 0.0f,
+    });
 }
 
 static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
     float * op_params = (float *)dst->op_params;
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, dst, GGML_OP_SCALE, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f });
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SCALE, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        op_params[0], 0.0f
+    });
 }
 
 static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, dst, GGML_OP_SQR, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f });
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SQR, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        0.0f, 0.0f,
+    });
 }
 
 static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
     float * op_params = (float *)dst->op_params;
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, dst, GGML_OP_CLAMP, { (uint32_t)ggml_nelements(src0), 0, op_params[0], op_params[1] });
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CLAMP, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        op_params[0], op_params[1],
+    });
 }
 
 static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
     ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
-    const int src0_type_size = ggml_type_size(src0->type);
-    const int dst_type_size = ggml_type_size(dst->type);
-    const uint32_t d_offset = (extra->offset % ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size;
-    ggml_vk_op_f32<vk_op_cpy_push_constants>(ctx, subctx, src0, nullptr, dst, GGML_OP_CPY, {
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+    const uint32_t d_offset = (extra->offset % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size;
+
+    ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CPY, {
         (uint32_t)ggml_nelements(src0),
-        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size,
-        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size,
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
         d_offset,
+        0.0f, 0.0f,
     });
 }
 
 static void ggml_vk_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], 0.0f, 0.0f });
+    float * op_params = (float *)dst->op_params;
+
+    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f });
 }
 
 static void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
     float * op_params = (float *)dst->op_params;
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f });
+    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f });
 }
 
 static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f });
+    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f });
 }
 
 static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
     int32_t * op_params = (int32_t *)dst->op_params;
-    ggml_vk_op_f32<vk_op_diag_mask_push_constants>(ctx, subctx, src0, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] });
+    ggml_vk_op_f32<vk_op_diag_mask_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] });
 }
 
-static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) {
     float * op_params = (float *)dst->op_params;
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, src1, dst, GGML_OP_SOFT_MAX, { (uint32_t)src0->ne[0], (uint32_t)(src1 != nullptr ? ggml_nrows(src1) : 0), op_params[0], 0.0f });
+
+    float scale = op_params[0];
+    float max_bias = op_params[1];
+
+    const uint32_t ncols =   (uint32_t)src0->ne[0];
+    const uint32_t nrows_x = (uint32_t)ggml_nrows(src0);
+    const uint32_t nrows_y = (uint32_t)src0->ne[1];
+
+    const uint32_t n_head_kv   = nrows_x/nrows_y;
+    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
+
+    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+#pragma message("TODO: src2 is no longer used in soft_max - should be removed and ALiBi calculation should be updated")
+#pragma message("ref:  https://github.com/ggerganov/llama.cpp/pull/7192")
+
+    ggml_vk_op_f32<vk_op_soft_max_push_constants>(ctx, subctx, src0, src1, src2, dst, GGML_OP_SOFT_MAX, {
+        ncols,
+        src1 != nullptr ? nrows_y : (uint32_t)0,
+        src2 != nullptr ? (uint32_t)1 : (uint32_t)0,
+        scale, max_bias,
+        m0, m1,
+        n_head_log2,
+    });
 }
 
 static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@@ -3334,20 +4321,15 @@ static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, con
     if (is_neox) {
         const float theta_scale = powf(freq_base, -2.0f/n_dims);
         const float inv_ndims = -1.0f / n_dims;
-        ggml_vk_op_f32<vk_op_rope_neox_push_constants>(ctx, subctx, src0, src1, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f, theta_scale, inv_ndims });
+        ggml_vk_op_f32<vk_op_rope_neox_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f}, theta_scale, inv_ndims });
     } else {
-        ggml_vk_op_f32<vk_op_rope_push_constants>(ctx, subctx, src0, src1, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f });
+        ggml_vk_op_f32<vk_op_rope_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f} });
     }
 }
 
-static void ggml_vk_nop(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
-    // If backend is CPU, data from src0 has to be copied off the device
-    if (dst->backend == GGML_BACKEND_CPU) {
-        ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
-        vk_buffer d_D = extra_src0->buffer_gpu.lock();
-        ggml_vk_sync_buffers(subctx);
-        ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, dst->data, d_D->size);
-    }
+static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
+    int32_t * op_params = (int32_t *)dst->op_params;
+    ggml_vk_op_f32<vk_op_argsort_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_ARGSORT, { (uint32_t)src0->ne[0], ((ggml_sort_order) op_params[0]) == GGML_SORT_ORDER_ASC });
 }
 
 #ifdef GGML_VULKAN_RUN_TESTS
@@ -3372,6 +4354,8 @@ static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0
                     val = *((const float *) data + i2*ne1*ne0 + idx1*ne0 + idx0);
                 } else if (type == GGML_TYPE_F16) {
                     val = ggml_fp16_to_fp32(*((const ggml_fp16_t *) data + i2*ne1*ne0 + idx1*ne0 + idx0));
+                } else {
+                    GGML_ASSERT(false);
                 }
                 fprintf(stderr, "% 7.2f ", val);
             } else {
@@ -3391,43 +4375,43 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
     const size_t y_ne = k * n * batch;
     const size_t d_ne = m * n * batch;
 
-    vk_pipeline * p;
+    vk_pipeline p;
     std::string shname;
     if (shader_size == 0) {
         if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-            p = &ctx->pipeline_matmul_f32_aligned_s;
+            p = ctx->device->pipeline_matmul_f32->a_s;
             shname = "F32_ALIGNED_S";
         } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-            p = &ctx->pipeline_matmul_f16_f32_aligned_s;
+            p = ctx->device->pipeline_matmul_f16_f32->a_s;
             shname = "F16_F32_ALIGNED_S";
         } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
-            p = &ctx->pipeline_matmul_f16_aligned_s;
+            p = ctx->device->pipeline_matmul_f16->a_s;
             shname = "F16_ALIGNED_S";
         } else {
             GGML_ASSERT(false);
         }
     } else if (shader_size == 1) {
         if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-            p = &ctx->pipeline_matmul_f32_aligned_m;
+            p = ctx->device->pipeline_matmul_f32->a_m;
             shname = "F32_ALIGNED_M";
         } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-            p = &ctx->pipeline_matmul_f16_f32_aligned_m;
+            p = ctx->device->pipeline_matmul_f16_f32->a_m;
             shname = "F16_F32_ALIGNED_M";
         } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
-            p = &ctx->pipeline_matmul_f16_aligned_m;
+            p = ctx->device->pipeline_matmul_f16->a_m;
             shname = "F16_ALIGNED_M";
         } else {
             GGML_ASSERT(false);
         }
     } else if (shader_size == 2) {
         if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-            p = &ctx->pipeline_matmul_f32_aligned_l;
+            p = ctx->device->pipeline_matmul_f32->a_l;
             shname = "F32_ALIGNED_L";
         } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-            p = &ctx->pipeline_matmul_f16_f32_aligned_l;
+            p = ctx->device->pipeline_matmul_f16_f32->a_l;
             shname = "F16_F32_ALIGNED_L";
         } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
-            p = &ctx->pipeline_matmul_f16_aligned_l;
+            p = ctx->device->pipeline_matmul_f16->a_l;
             shname = "F16_ALIGNED_L";
         } else {
             GGML_ASSERT(false);
@@ -3441,43 +4425,43 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
     if (k != kpad) {
         if (shader_size == 0) {
             if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-                p = &ctx->pipeline_matmul_f32_s;
+                p = ctx->device->pipeline_matmul_f32->s;
                 shname = "F32_S";
             } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-                p = &ctx->pipeline_matmul_f16_f32_s;
+                p = ctx->device->pipeline_matmul_f16_f32->s;
                 shname = "F16_F32_S";
             } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
-                p = &ctx->pipeline_matmul_f16_s;
+                p = ctx->device->pipeline_matmul_f16->s;
                 shname = "F16_S";
             }
         } else if (shader_size == 1) {
             if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-                p = &ctx->pipeline_matmul_f32_m;
+                p = ctx->device->pipeline_matmul_f32->m;
                 shname = "F32_M";
             } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-                p = &ctx->pipeline_matmul_f16_f32_m;
+                p = ctx->device->pipeline_matmul_f16_f32->m;
                 shname = "F16_F32_M";
             } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
-                p = &ctx->pipeline_matmul_f16_m;
+                p = ctx->device->pipeline_matmul_f16->m;
                 shname = "F16_M";
             }
         } else if (shader_size == 2) {
             if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-                p = &ctx->pipeline_matmul_f32_l;
+                p = ctx->device->pipeline_matmul_f32->l;
                 shname = "F32_L";
             } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-                p = &ctx->pipeline_matmul_f16_f32_l;
+                p = ctx->device->pipeline_matmul_f16_f32->l;
                 shname = "F16_F32_L";
             } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
-                p = &ctx->pipeline_matmul_f16_l;
+                p = ctx->device->pipeline_matmul_f16->l;
                 shname = "F16_L";
             }
         }
     }
 
-    ggml_pipeline_allocate_descriptor_sets(ctx, *p, num_it);
+    ggml_pipeline_allocate_descriptor_sets(ctx, p, num_it);
     if (split_k > 1) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_matmul_split_k_reduce, num_it);
+        ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, num_it);
 
         if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) {
             // Resize buffer
@@ -3507,9 +4491,11 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
     }
     for (size_t i = 0; i < y_ne; i++) {
         if (std::is_same<float, Y_TYPE>()) {
-            y[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
+            // y[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
+            y[i] = (i % k == i / k) ? 1.0f : 0.0f;
         } else if (std::is_same<ggml_fp16_t, Y_TYPE>()) {
-            y[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f);
+            // y[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f);
+            y[i] = ggml_fp32_to_fp16((i % k == i / k) ? 1.0f : 0.0f);
         } else {
             GGML_ASSERT(false);
         }
@@ -3518,17 +4504,20 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
     ggml_vk_buffer_write(ctx, d_X, 0, x, sizeof(X_TYPE) * k * m * batch);
     ggml_vk_buffer_write(ctx, d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch);
 
-    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue);
+    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
     for (size_t i = 0; i < num_it; i++) {
         ggml_vk_ctx_begin(ctx, subctx);
-        ggml_vk_matmul(ctx, subctx, *p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(ctx->prealloc_split_k), m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n);
+        ggml_vk_matmul(
+            ctx, subctx, p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(ctx->prealloc_split_k),
+            m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n, 0, 0, 0, 0, 1
+        );
         ggml_vk_ctx_end(subctx);
     }
 
     auto begin = std::chrono::high_resolution_clock::now();
     ggml_vk_submit(subctx, ctx->fence);
-    VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences");
-    ctx->device.lock()->device.resetFences({ ctx->fence });
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences");
+    ctx->device->device.resetFences({ ctx->fence });
 
     auto end = std::chrono::high_resolution_clock::now();
     double time = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
@@ -3607,6 +4596,8 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
         std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl;
         std::cerr << "Actual result: " << std::endl << std::endl;
         ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+        std::cerr << std::endl;
+        ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n + 15, first_err_b);
         std::cerr << "Expected result: " << std::endl << std::endl;
         ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
 
@@ -3632,15 +4623,15 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
 
     free(d_chk);
 
-    ggml_vk_queue_cleanup(ctx, ctx->device.lock()->transfer_queue);
-    ggml_vk_queue_cleanup(ctx, ctx->device.lock()->compute_queue);
+    ggml_vk_queue_cleanup(ctx, ctx->device->transfer_queue);
+    ggml_vk_queue_cleanup(ctx, ctx->device->compute_queue);
 
     ggml_vk_destroy_buffer(d_X);
     ggml_vk_destroy_buffer(d_Y);
     ggml_vk_destroy_buffer(d_D);
 
-    ggml_pipeline_cleanup(*p);
-    ggml_pipeline_cleanup(ctx->pipeline_matmul_split_k_reduce);
+    ggml_pipeline_cleanup(p);
+    ggml_pipeline_cleanup(ctx->device->pipeline_matmul_split_k_reduce);
 
     free(x);
     free(y);
@@ -3669,6 +4660,8 @@ static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, int i0, int i1
                     val = *(float *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]);
                 } else if (tensor->type == GGML_TYPE_F16) {
                     val = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]));
+                } else {
+                    GGML_ASSERT(false);
                 }
                 fprintf(stderr, "% 7.2f ", val);
             } else {
@@ -3713,7 +4706,7 @@ static void ggml_vk_test_h2d_nc(ggml_backend_vk_context * ctx, size_t ne0, size_
         data[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
     }
 
-    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue);
+    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
     ggml_vk_ctx_begin(ctx, subctx);
 
     vk_buffer buffer = ggml_vk_create_buffer_check(ctx, ggml_nbytes(tensor), vk::MemoryPropertyFlagBits::eDeviceLocal);
@@ -3722,8 +4715,8 @@ static void ggml_vk_test_h2d_nc(ggml_backend_vk_context * ctx, size_t ne0, size_
 
     ggml_vk_ctx_end(subctx);
     ggml_vk_submit(subctx, ctx->fence);
-    VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_h2d_nc waitForFences");
-    ctx->device.lock()->device.resetFences({ ctx->fence });
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_h2d_nc waitForFences");
+    ctx->device->device.resetFences({ ctx->fence });
 
     ggml_vk_buffer_read(ctx, buffer, 0, result_data, ggml_nbytes(tensor));
 
@@ -3795,7 +4788,7 @@ static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool
         x[i] = rand() / (float)RAND_MAX;
     }
 
-    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue);
+    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
     ggml_vk_ctx_begin(ctx, subctx);
 
     auto begin = std::chrono::high_resolution_clock::now();
@@ -3809,8 +4802,8 @@ static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool
 
     ggml_vk_ctx_end(subctx);
     ggml_vk_submit(subctx, ctx->fence);
-    VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences");
-    ctx->device.lock()->device.resetFences({ ctx->fence });
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences");
+    ctx->device->device.resetFences({ ctx->fence });
 
     auto end = std::chrono::high_resolution_clock::now();
 
@@ -3824,8 +4817,8 @@ static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool
 
     ggml_vk_ctx_end(subctx);
     ggml_vk_submit(subctx, ctx->fence);
-    VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences");
-    ctx->device.lock()->device.resetFences({ ctx->fence });
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences");
+    ctx->device->device.resetFences({ ctx->fence });
 
     for (auto& cpy : subctx->out_memcpys) {
         memcpy(cpy.dst, cpy.src, cpy.n);
@@ -3856,6 +4849,10 @@ static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool
     }
 }
 
+static void ggml_vk_quantize_data(const float * from, void * to, size_t ne, ggml_type quant) {
+    ggml_quantize_chunk(quant, from, to, 0, 1, ne, nullptr);
+}
+
 static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) {
 #ifdef GGML_VULKAN_DEBUG
     std::cerr << "ggml_vk_test_dequant(" << ne << ")" << std::endl;
@@ -3873,72 +4870,59 @@ static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_
         x[i] = rand() / (float)RAND_MAX;
     }
 
-    std::vector<int64_t> hist_cur(1 << 4, 0);
+    vk_pipeline p = ctx->device->pipeline_dequant[quant];
 
-    vk_pipeline& p = ctx->pipeline_dequant[quant];
-
-    switch(quant) {
-    case GGML_TYPE_Q4_0:
-        ggml_quantize_q4_0(x, qx, ne, ne, hist_cur.data());
-        break;
-    case GGML_TYPE_Q4_1:
-        ggml_quantize_q4_1(x, qx, ne, ne, hist_cur.data());
-        break;
-    case GGML_TYPE_Q5_0:
-        ggml_quantize_q5_0(x, qx, ne, ne, hist_cur.data());
-        break;
-    case GGML_TYPE_Q5_1:
-        ggml_quantize_q4_1(x, qx, ne, ne, hist_cur.data());
-        break;
-    case GGML_TYPE_Q8_0:
-        ggml_quantize_q8_0(x, qx, ne, ne, hist_cur.data());
-        break;
-    case GGML_TYPE_Q2_K:
-        ggml_quantize_q2_K(x, qx, ne, ne, hist_cur.data());
-        break;
-    case GGML_TYPE_Q3_K:
-        ggml_quantize_q3_K(x, qx, ne, ne, hist_cur.data());
-        break;
-    case GGML_TYPE_Q4_K:
-        ggml_quantize_q4_K(x, qx, ne, ne, hist_cur.data());
-        break;
-    case GGML_TYPE_Q5_K:
-        ggml_quantize_q5_K(x, qx, ne, ne, hist_cur.data());
-        break;
-    case GGML_TYPE_Q6_K:
-        ggml_quantize_q6_K(x, qx, ne, ne, hist_cur.data());
-        break;
-    default:
-        GGML_ASSERT(false);
-    }
+    ggml_vk_quantize_data(x, qx, ne, quant);
 
     ggml_pipeline_allocate_descriptor_sets(ctx, p, 1);
 
     ggml_vk_buffer_write(ctx, qx_buf, 0, qx, qx_sz);
 
-    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue);
+    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
     ggml_vk_ctx_begin(ctx, subctx);
-    const std::vector<int> pc = { 1, (int)ne, (int)ne, (int)ne };
+    const std::vector<uint32_t> pc = { 1, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne };
     ggml_vk_dispatch_pipeline(ctx, subctx, p, { { qx_buf, 0, qx_sz }, { x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1});
     ggml_vk_ctx_end(subctx);
 
     auto begin = std::chrono::high_resolution_clock::now();
 
     ggml_vk_submit(subctx, ctx->fence);
-    VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences");
-    ctx->device.lock()->device.resetFences({ ctx->fence });
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences");
+    ctx->device->device.resetFences({ ctx->fence });
 
     auto end = std::chrono::high_resolution_clock::now();
 
     double ms_dequant = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
     ggml_vk_buffer_read(ctx, x_buf, 0, x_chk, x_sz_f16);
 
+    int first_err = -1;
+
     double avg_err = 0.0;
     for (size_t i = 0; i < ne; i++) {
-        avg_err += std::fabs(x[i] - ggml_fp16_to_fp32(x_chk[i]));
+        double error = std::fabs(x[i] - ggml_fp16_to_fp32(x_chk[i]));
+        avg_err += error;
+
+        if (first_err < 0 && error > 0.05) {
+            first_err = i;
+        }
     }
 
-    std::cerr << "TEST DEQUANT " << ggml_type_name(quant) << " time=" << ms_dequant << "ms avg_err=" << avg_err / ne << std::endl;
+    avg_err /= ne;
+
+    std::cerr << "TEST DEQUANT " << ggml_type_name(quant) << " time=" << ms_dequant << "ms avg_err=" << avg_err << std::endl;
+
+    if (avg_err > 0.1) {
+        std::cerr << "first_error = " << first_err << std::endl;
+        std::cerr << "Actual result: " << std::endl << std::endl;
+        for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) {
+            std::cerr << ggml_fp16_to_fp32(x_chk[i]) << ", ";
+        }
+        std::cerr << std::endl << "Expected result: " << std::endl << std::endl;
+        for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) {
+            std::cerr << x[i] << ", ";
+        }
+        std::cerr << std::endl;
+    }
 
     ggml_vk_destroy_buffer(x_buf);
     ggml_vk_destroy_buffer(qx_buf);
@@ -3947,6 +4931,193 @@ static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_
     free(qx);
     free(x_chk);
 }
+
+static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, size_t split_k, size_t shader_size, ggml_type quant) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_test_dequant_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << ggml_type_name(quant) << ")" << std::endl;
+#endif
+    const size_t x_ne = m * k * batch;
+    const size_t y_ne = k * n * batch;
+    const size_t d_ne = m * n * batch;
+
+    vk_pipeline p;
+    std::string shname;
+    if (shader_size == 0) {
+        p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_s;
+        shname = std::string(ggml_type_name(quant)) + "_ALIGNED_S";
+    } else if (shader_size == 1) {
+        p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_m;
+        shname = std::string(ggml_type_name(quant)) + "_ALIGNED_M";
+    } else if (shader_size == 2) {
+        p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_l;
+        shname = std::string(ggml_type_name(quant)) + "_ALIGNED_L";
+    } else {
+        GGML_ASSERT(0);
+    }
+
+    const size_t kpad = ggml_vk_align_size(k, p->align);
+
+    if (k != kpad) {
+        if (shader_size == 0) {
+            p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->s;
+            shname = std::string(ggml_type_name(quant)) + "_S";
+        } else if (shader_size == 1) {
+            p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->m;
+            shname = std::string(ggml_type_name(quant)) + "_M";
+        } else if (shader_size == 2) {
+            p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->l;
+            shname = std::string(ggml_type_name(quant)) + "_L";
+        } else {
+            GGML_ASSERT(0);
+        }
+    }
+
+    const size_t x_sz = sizeof(float) * x_ne;
+    const size_t y_sz = sizeof(float) * y_ne;
+    const size_t qx_sz = x_ne * ggml_type_size(quant)/ggml_blck_size(quant);
+    const size_t d_sz = sizeof(float) * d_ne;
+    float * x = (float *) malloc(x_sz);
+    float * y = (float *) malloc(y_sz);
+    void * qx = malloc(qx_sz);
+    vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer y_buf = ggml_vk_create_buffer_check(ctx, y_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer d_buf = ggml_vk_create_buffer_check(ctx, d_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    float * d = (float *) malloc(d_sz);
+    float * d_chk = (float *) malloc(d_sz);
+
+    for (size_t i = 0; i < x_ne; i++) {
+        x[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
+    }
+
+    ggml_vk_quantize_data(x, qx, x_ne, quant);
+
+    for (size_t i = 0; i < y_ne; i++) {
+        // y[i] = rand() / (float)RAND_MAX;
+        y[i] = (i % k == i / k) ? 1.0f : 0.0f;
+    }
+
+    ggml_pipeline_allocate_descriptor_sets(ctx, p, num_it);
+    if (split_k > 1) {
+        ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, num_it);
+
+        if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) {
+            // Resize buffer
+            if (ctx->prealloc_split_k != nullptr) {
+                ggml_vk_destroy_buffer(ctx->prealloc_split_k);
+            }
+            ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal);
+        }
+    }
+
+    ggml_vk_buffer_write(ctx, qx_buf, 0, qx, qx_sz);
+    ggml_vk_buffer_write(ctx, y_buf, 0, y, y_sz);
+
+    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
+    for (size_t i = 0; i < num_it; i++) {
+        ggml_vk_ctx_begin(ctx, subctx);
+        ggml_vk_matmul(
+            ctx, subctx, p, ggml_vk_subbuffer(qx_buf), ggml_vk_subbuffer(y_buf), ggml_vk_subbuffer(d_buf), ggml_vk_subbuffer(ctx->prealloc_split_k),
+            m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n, 0, 0, 0, 0, 1
+        );
+        ggml_vk_ctx_end(subctx);
+    }
+
+    auto begin = std::chrono::high_resolution_clock::now();
+
+    ggml_vk_submit(subctx, ctx->fence);
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences");
+    ctx->device->device.resetFences({ ctx->fence });
+
+    auto end = std::chrono::high_resolution_clock::now();
+
+    double time_ms = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
+    ggml_vk_buffer_read(ctx, d_buf, 0, d, d_sz);
+
+    ggml_init_params iparams = {
+        /*.mem_size   =*/ 1024*1024*1024,
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ true,
+    };
+
+    ggml_context * ggml_ctx = ggml_init(iparams);
+
+    ggml_tensor * src0_ggml = ggml_new_tensor_3d(ggml_ctx, quant, k, m, batch);
+    ggml_tensor * src1_ggml = ggml_new_tensor_3d(ggml_ctx, GGML_TYPE_F32, k, n, batch);
+    ggml_tensor * tensor_ggml = ggml_mul_mat(ggml_ctx, src0_ggml, src1_ggml);
+
+    src0_ggml->data = qx;
+    src1_ggml->data = y;
+    tensor_ggml->data = d_chk;
+
+    ctx->disable = true;
+
+    ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx);
+    ggml_build_forward_expand(cgraph, tensor_ggml);
+
+    ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1);
+
+    ctx->disable = false;
+
+    ggml_free(ggml_ctx);
+
+    double avg_err = 0.0;
+    int first_err_n = -1;
+    int first_err_m = -1;
+    int first_err_b = -1;
+
+    for (size_t i = 0; i < m*n*batch; i++) {
+        double err = std::fabs(d[i] - d_chk[i]);
+        avg_err += err;
+
+        if ((err > 0.05f || std::isnan(err)) && first_err_n == -1) {
+            first_err_b = i / (m * n);
+            first_err_n = (i % (m * n)) / m;
+            first_err_m = (i % (m * n)) % m;
+        }
+    }
+
+    avg_err /= m * n;
+
+    std::cerr << "TEST MMQ " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time_ms / num_it << "ms avg_err=" << avg_err << std::endl;
+
+    if (avg_err > 0.01 || std::isnan(avg_err)) {
+        std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl;
+        std::cerr << "Actual result: " << std::endl << std::endl;
+        ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+        std::cerr << std::endl;
+        std::cerr << "Expected result: " << std::endl << std::endl;
+        ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+        if (split_k > 1) {
+            float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k);
+            ggml_vk_buffer_read(ctx, ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k);
+
+            std::cerr << "d_buf0: " << std::endl << std::endl;
+            ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+            std::cerr << "d_buf1: " << std::endl << std::endl;
+            ggml_vk_print_matrix_area(split_k_buf + d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+            std::cerr << "d_buf2: " << std::endl << std::endl;
+            ggml_vk_print_matrix_area(split_k_buf + 2 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+            std::cerr << "d_buf3: " << std::endl << std::endl;
+            ggml_vk_print_matrix_area(split_k_buf + 3 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+            free(split_k_buf);
+        }
+    }
+
+    ggml_vk_destroy_buffer(qx_buf);
+    ggml_vk_destroy_buffer(y_buf);
+    ggml_vk_destroy_buffer(d_buf);
+
+    free(x);
+    free(qx);
+    free(y);
+    free(d);
+    free(d_chk);
+}
 #endif
 
 static ggml_tensor_extra_gpu * ggml_vk_tensor_create_extra(ggml_tensor * tensor) {
@@ -3959,37 +5130,15 @@ static ggml_tensor_extra_gpu * ggml_vk_tensor_create_extra(ggml_tensor * tensor)
     return extra;
 }
 
-static ggml_tensor * ggml_vk_find_last_use(const ggml_tensor * node, ggml_cgraph * graph) {
-    GGML_ASSERT(node != nullptr);
-
-    for (int i = graph->n_nodes - 1; i >= 0; i--) {
-        for (int j = 0; j < GGML_MAX_SRC; j++) {
-            if (graph->nodes[i]->src[j] == node) {
-                return graph->nodes[i];
-            }
-        }
-    }
-
-    return nullptr;
-}
-
 static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggml_tensor * node){
 #ifdef GGML_VULKAN_DEBUG
     std::cerr << "ggml_vk_preallocate_buffers_graph(" << node << ")" << std::endl;
 #endif
-    const bool any_on_device = node->backend == GGML_BACKEND_GPU
-        || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_GPU || node->src[0]->backend == GGML_BACKEND_GPU_SPLIT))
-        || (node->src[1] != nullptr && (node->src[1]->backend == GGML_BACKEND_GPU));
-
-    if (ctx->disable || (!any_on_device && node->op != GGML_OP_MUL_MAT)) {
+    if (ctx->disable || node->backend != GGML_BACKEND_TYPE_GPU) {
         return;
     }
 
     ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra;
-    if (extra == nullptr) {
-        // Workaround for CPU backend BLAS matmul calls
-        extra = ggml_vk_tensor_create_extra(node);
-    }
 
     ggml_tensor * src0 = node->src[0];
     ggml_tensor * src1 = node->src[1];
@@ -4009,10 +5158,21 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm
     const int64_t ne22 = node->ne[2];
     const int64_t ne23 = node->ne[3];
 
-    const bool f16_f32_kernel = use_src1 && src1->type == GGML_TYPE_F32;
+    const ggml_type src0_type = (use_src0 && src0->type == GGML_TYPE_F32) ? src0->type : GGML_TYPE_F16;
+    const ggml_type src1_type = (use_src1 && src1->type == GGML_TYPE_F32) ? src1->type : GGML_TYPE_F16;
+
+    const bool x_non_contig = use_src0 && !ggml_vk_dim01_contiguous(src0);
+    const bool y_non_contig = use_src1 && !ggml_vk_dim01_contiguous(src1);
+
+    const bool y_f32_kernel = use_src1 && src1->type == GGML_TYPE_F32 && !y_non_contig;
+
+    bool mmp = (use_src0 && use_src1 && src1_type == GGML_TYPE_F32) ? ggml_vk_get_mul_mat_mat_pipeline(ctx, src0_type, y_non_contig ? GGML_TYPE_F16 : src1->type) != nullptr : false;
+
+    const bool qx_needs_dequant = use_src0 && (mmp || x_non_contig);
+    const bool qy_needs_dequant = use_src1 && ((src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig);
 
     int split_k;
-    if (node->op == GGML_OP_MUL_MAT) {
+    if (node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID) {
         split_k = ggml_vk_guess_split_k(ne01, ne11, ne10);
     } else {
         split_k = 1;
@@ -4021,11 +5181,9 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm
     const uint32_t y_ne = ne10 * ne11;
     const uint32_t d_ne = ne20 * ne21;
 
-    const uint64_t qx_sz = use_src0 ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0;
-    const uint64_t qy_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0;
-    const uint64_t x_sz = use_src0 ? ggml_vk_align_size(sizeof(ggml_fp16_t) * x_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0;
-    const uint64_t y_sz = use_src1 ? ggml_vk_align_size(f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0;
-    uint64_t d_sz = ggml_vk_align_size(ggml_type_size(node->type) * d_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne22 * ne23;
+    const uint64_t x_sz = (use_src0 && qx_needs_dequant) ? ggml_vk_align_size(sizeof(src0_type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0;
+    const uint64_t y_sz = (use_src1 && qy_needs_dequant) ? ggml_vk_align_size(sizeof(src1_type) * y_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0;
+    uint64_t d_sz = ggml_vk_align_size(ggml_type_size(node->type) * d_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne22 * ne23;
     const uint64_t split_k_size = split_k > 1 ? d_sz * 4 : 0;
 
     if (extra->buffer_gpu.expired()) {
@@ -4053,6 +5211,7 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm
     case GGML_OP_DIAG_MASK_INF:
     case GGML_OP_SOFT_MAX:
     case GGML_OP_ROPE:
+    case GGML_OP_ARGSORT:
         break;
     case GGML_OP_UNARY:
         switch (ggml_get_unary_op(node)) {
@@ -4065,12 +5224,7 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm
         }
         break;
     case GGML_OP_MUL_MAT:
-        if (ctx->prealloc_size_qx < qx_sz) {
-            ctx->prealloc_size_qx = qx_sz;
-        }
-        if (ctx->prealloc_size_qy < qy_sz) {
-            ctx->prealloc_size_qy = qy_sz;
-        }
+    case GGML_OP_MUL_MAT_ID:
         if (ctx->prealloc_size_x < x_sz) {
             ctx->prealloc_size_x = x_sz;
         }
@@ -4094,25 +5248,107 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) {
         return;
     }
 #ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_preallocate_buffers(qx_size: " << ctx->prealloc_size_qx << " qy_size: " << ctx->prealloc_size_qy << " x_size: " << ctx->prealloc_size_x << " y_size: " << ctx->prealloc_size_y << " split_k_size: " << ctx->prealloc_size_split_k << ")" << std::endl;
+    std::cerr << "ggml_vk_preallocate_buffers(x_size: " << ctx->prealloc_size_x << " y_size: " << ctx->prealloc_size_y << " split_k_size: " << ctx->prealloc_size_split_k << ")" << std::endl;
 #endif
 #if defined(GGML_VULKAN_RUN_TESTS)
     ctx->staging = ggml_vk_create_buffer_check(ctx, 100ul * 1024ul * 1024ul,
-        vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached
+        vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
         vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
     ggml_vk_test_transfer(ctx, 8192 * 1000, false);
     ggml_vk_test_transfer(ctx, 8192 * 1000, true);
 
-    ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q4_0);
-    ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q4_1);
-    ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q5_0);
-    ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q5_1);
-    ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q8_0);
-    ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q2_K);
-    ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q3_K);
-    ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q4_K);
-    ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q5_K);
-    ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q6_K);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_F32);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_0);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_1);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_0);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_1);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q8_0);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q2_K);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q3_K);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_K);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_K);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q6_K);
+
+    ggml_vk_test_matmul<ggml_fp16_t, ggml_fp16_t>(ctx, 512, 512, 100, 32, 100, 1, 2);
+
+    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 0);
+    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 1);
+    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 2);
+    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 0);
+    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 1);
+    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 2);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_0);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_1);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_0);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_1);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q8_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q8_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q8_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q8_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q8_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q8_0);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q2_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q2_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q2_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q2_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q2_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q2_K);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q3_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q3_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q3_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q3_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q3_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q3_K);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_K);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_K);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q6_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q6_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q6_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q6_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q6_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q6_K);
+
+    std::cerr << std::endl;
 
     const std::vector<size_t> vals {
         8, 8, 8,
@@ -4151,20 +5387,6 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) {
     GGML_ASSERT(false);
 #endif
 
-    if (ctx->prealloc_qx == nullptr || (ctx->prealloc_size_qx > 0 && ctx->prealloc_qx->size < ctx->prealloc_size_qx)) {
-        // Resize buffer
-        if (ctx->prealloc_qx != nullptr) {
-            ggml_vk_destroy_buffer(ctx->prealloc_qx);
-        }
-        ctx->prealloc_qx = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_qx);
-    }
-    if (ctx->prealloc_qy == nullptr || (ctx->prealloc_size_qy > 0 && ctx->prealloc_qy->size < ctx->prealloc_size_qy)) {
-        // Resize buffer
-        if (ctx->prealloc_qy != nullptr) {
-            ggml_vk_destroy_buffer(ctx->prealloc_qy);
-        }
-        ctx->prealloc_qy = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_qy);
-    }
     if (ctx->prealloc_x == nullptr || (ctx->prealloc_size_x > 0 && ctx->prealloc_x->size < ctx->prealloc_size_x)) {
         // Resize buffer
         if (ctx->prealloc_x != nullptr) {
@@ -4198,11 +5420,7 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) {
 }
 
 static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, bool last_node){
-    const bool any_on_device = node->backend == GGML_BACKEND_GPU
-        || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_GPU || node->src[0]->backend == GGML_BACKEND_GPU_SPLIT))
-        || (node->src[1] != nullptr && node->src[1]->backend == GGML_BACKEND_GPU);
-
-    if (ctx->disable || (!any_on_device && node->op != GGML_OP_MUL_MAT) || (node->op == GGML_OP_MUL_MAT && !any_on_device && !ggml_vk_can_mul_mat(node->src[0], node->src[1], node))) {
+    if (ctx->disable || node->backend != GGML_BACKEND_TYPE_GPU || ggml_is_empty(node)) {
         return;
     }
 
@@ -4214,6 +5432,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
 
     const ggml_tensor * src0 = node->src[0];
     const ggml_tensor * src1 = node->src[1];
+    const ggml_tensor * src2 = node->src[2];
 
     ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra;
 
@@ -4229,7 +5448,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
         }
         break;
     case GGML_OP_REPEAT:
-    // case GGML_OP_GET_ROWS:
+    case GGML_OP_GET_ROWS:
     case GGML_OP_ADD:
     case GGML_OP_MUL:
     case GGML_OP_SCALE:
@@ -4248,18 +5467,18 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
     case GGML_OP_SOFT_MAX:
     case GGML_OP_ROPE:
     case GGML_OP_MUL_MAT:
+    case GGML_OP_MUL_MAT_ID:
     case GGML_OP_NONE:
+    case GGML_OP_ARGSORT:
         break;
     default:
-        if (any_on_device) {
-            std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(node->op) << std::endl;
-            GGML_ASSERT(false);
-        }
+        std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(node->op) << std::endl;
+        GGML_ASSERT(false);
         return;
     }
 
     if (ctx->compute_ctx == nullptr) {
-        ctx->compute_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue);
+        ctx->compute_ctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
         ggml_vk_ctx_begin(ctx, ctx->compute_ctx);
     }
 
@@ -4303,8 +5522,6 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
     case GGML_OP_PERMUTE:
     case GGML_OP_TRANSPOSE:
     case GGML_OP_NONE:
-        ggml_vk_nop(ctx, ctx->compute_ctx, src0, node);
-
         break;
     case GGML_OP_NORM:
         ggml_vk_norm(ctx, ctx->compute_ctx, src0, node);
@@ -4330,16 +5547,25 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
 
         break;
     case GGML_OP_SOFT_MAX:
-        ggml_vk_soft_max(ctx, ctx->compute_ctx, src0, src1, node);
+        ggml_vk_soft_max(ctx, ctx->compute_ctx, src0, src1, src2, node);
 
         break;
     case GGML_OP_ROPE:
         ggml_vk_rope(ctx, ctx->compute_ctx, src0, src1, node);
 
+        break;
+    case GGML_OP_ARGSORT:
+        ggml_vk_argsort(ctx, ctx->compute_ctx, src0, node);
         break;
     case GGML_OP_MUL_MAT:
         ggml_vk_mul_mat(ctx, ctx->compute_ctx, src0, src1, node);
 
+        break;
+    case GGML_OP_MUL_MAT_ID:
+        //ggml_vk_mul_mat_id(ctx, ctx->compute_ctx, src0, src1, node);
+        std::cerr << "ggml_vulkan: GGML_OP_MUL_MAT_ID not implemented yet." << std::endl;
+        GGML_ASSERT(false);
+
         break;
     default:
         return;
@@ -4354,7 +5580,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
     last_node = true;
 #endif
 
-    if (node->backend == GGML_BACKEND_CPU || last_node) {
+    if (node->backend == GGML_BACKEND_TYPE_CPU || last_node) {
         ggml_vk_ctx_end(ctx->compute_ctx);
         ctx->compute_ctx->exit_tensor = node;
         ctx->compute_ctx = nullptr;
@@ -4362,11 +5588,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
 }
 
 static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor){
-    const bool any_on_device = tensor->backend == GGML_BACKEND_GPU
-        || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT))
-        || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU);
-
-    if (ctx->disable || (!any_on_device && tensor->op != GGML_OP_MUL_MAT)) {
+    if (ctx->disable) {
         return false;
     }
 
@@ -4392,6 +5614,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_
     case GGML_OP_PERMUTE:
     case GGML_OP_TRANSPOSE:
     case GGML_OP_NONE:
+    case GGML_OP_ARGSORT:
         extra = (ggml_tensor_extra_gpu *) tensor->extra;
 
         break;
@@ -4407,10 +5630,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_
         }
         break;
     case GGML_OP_MUL_MAT:
-        if (!any_on_device && !ggml_vk_can_mul_mat(tensor->src[0], tensor->src[1], tensor)) {
-            return false;
-        }
-
+    case GGML_OP_MUL_MAT_ID:
         extra = (ggml_tensor_extra_gpu *) tensor->extra;
 
         break;
@@ -4425,7 +5645,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_
     if (params->ith != 0) {
         return true;
     }
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return true;
     }
 
@@ -4452,8 +5672,8 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_
     }
 
     if (tensor == subctx.exit_tensor) {
-        VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences");
-        ctx->device.lock()->device.resetFences({ ctx->fence });
+        VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences");
+        ctx->device->device.resetFences({ ctx->fence });
 
         // Do staging buffer copies
         for (auto& cpy : subctx.out_memcpys) {
@@ -4481,20 +5701,25 @@ static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) {
     }
     ctx->gc.temp_buffers.clear();
 
-    for (auto * pipeline : ctx->gc.pipelines) {
-        ggml_pipeline_cleanup(*pipeline);
+    for (auto& pipeline : ctx->device->pipelines) {
+        if (pipeline.expired()) {
+            continue;
+        }
+
+        vk_pipeline pl = pipeline.lock();
+        ggml_pipeline_cleanup(pl);
     }
 
-    ggml_vk_queue_cleanup(ctx, ctx->device.lock()->compute_queue);
-    ggml_vk_queue_cleanup(ctx, ctx->device.lock()->transfer_queue);
+    ggml_vk_queue_cleanup(ctx, ctx->device->compute_queue);
+    ggml_vk_queue_cleanup(ctx, ctx->device->transfer_queue);
 
     for (size_t i = 0; i < ctx->gc.semaphores.size(); i++) {
-        ctx->device.lock()->device.destroySemaphore({ ctx->gc.semaphores[i].s });
+        ctx->device->device.destroySemaphore({ ctx->gc.semaphores[i].s });
     }
     ctx->gc.semaphores.clear();
 
     for (size_t i = 0; i < ctx->gc.tl_semaphores.size(); i++) {
-        ctx->device.lock()->device.destroySemaphore({ ctx->gc.tl_semaphores[i].s });
+        ctx->device->device.destroySemaphore({ ctx->gc.tl_semaphores[i].s });
     }
     ctx->gc.tl_semaphores.clear();
     ctx->semaphore_idx = 0;
@@ -4502,7 +5727,7 @@ static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) {
     ctx->event_idx = 0;
 
     for (auto& event : ctx->gc.events) {
-        ctx->device.lock()->device.resetEvent(event);
+        ctx->device->device.resetEvent(event);
     }
 
     ctx->staging_offset = 0;
@@ -4519,8 +5744,6 @@ static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) {
 #endif
     ggml_vk_graph_cleanup(ctx);
 
-    ggml_vk_destroy_buffer(ctx->prealloc_qx);
-    ggml_vk_destroy_buffer(ctx->prealloc_qy);
     ggml_vk_destroy_buffer(ctx->prealloc_x);
     ggml_vk_destroy_buffer(ctx->prealloc_y);
     ggml_vk_destroy_buffer(ctx->prealloc_split_k);
@@ -4531,29 +5754,17 @@ static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) {
         ggml_vk_destroy_buffer(buffer);
     }
 
-    ctx->prealloc_size_qx = 0;
-    ctx->prealloc_size_qy = 0;
     ctx->prealloc_size_x = 0;
     ctx->prealloc_size_y = 0;
     ctx->prealloc_size_split_k = 0;
     ctx->staging_size = 0;
 
     for (auto& event : ctx->gc.events) {
-        ctx->device.lock()->device.destroyEvent(event);
+        ctx->device->device.destroyEvent(event);
     }
     ctx->gc.events.clear();
 
-    for (auto* pipeline : ctx->gc.pipelines) {
-        ggml_vk_destroy_pipeline(ctx, pipeline);
-    }
-    ctx->gc.pipelines.clear();
-
-    ctx->device.lock()->device.destroyFence(ctx->fence);
-
-    ctx->device.lock()->device.destroyCommandPool(ctx->device.lock()->compute_queue.pool);
-    if (!ctx->device.lock()->single_queue) {
-        ctx->device.lock()->device.destroyCommandPool(ctx->device.lock()->transfer_queue.pool);
-    }
+    ctx->device->device.destroyFence(ctx->fence);
 }
 
 GGML_CALL static int ggml_vk_get_device_count() {
@@ -4573,80 +5784,6 @@ GGML_CALL static void ggml_vk_get_device_description(int device, char * descript
     snprintf(description, description_size, "%s", props.deviceName.data());
 }
 
-// CPU assist interface
-
-void ggml_vk_init_cpu_assist() {
-    ggml_vk_instance_init();
-
-    std::cerr << "ggml_vulkan: Found " << ggml_vk_get_device_count() << " Vulkan devices:" << std::endl;
-
-    for (int i = 0; i < ggml_vk_get_device_count(); i++) {
-        ggml_vk_print_gpu_info(i);
-    }
-    // Initialize the first backend to make sure CPU matrix multiplications can be offloaded.
-    ggml_backend_vk_init(0);
-}
-
-void ggml_vk_preallocate_buffers_graph_cpu_assist(ggml_tensor * node) {
-    ggml_backend_vk_context * ctx = &vk_instance.contexts[0];
-
-    if (!ctx->initialized) {
-        return;
-    }
-
-    ggml_vk_preallocate_buffers_graph(ctx, node);
-}
-
-void ggml_vk_preallocate_buffers_cpu_assist() {
-    ggml_backend_vk_context * ctx = &vk_instance.contexts[0];
-
-    if (!ctx->initialized) {
-        return;
-    }
-
-    ggml_vk_preallocate_buffers(ctx);
-}
-
-void ggml_vk_build_graph_cpu_assist(ggml_tensor * node, bool last_node) {
-    ggml_backend_vk_context * ctx = &vk_instance.contexts[0];
-
-    if (!ctx->initialized) {
-        return;
-    }
-
-    ggml_vk_build_graph(ctx, node, last_node);
-}
-
-bool ggml_vk_compute_forward_cpu_assist(ggml_compute_params * params, ggml_tensor * tensor){
-    ggml_backend_vk_context * ctx = &vk_instance.contexts[0];
-
-    if (!ctx->initialized) {
-        return false;
-    }
-
-    return ggml_vk_compute_forward(ctx, params, tensor);
-}
-
-void ggml_vk_graph_cleanup_cpu_assist() {
-    ggml_backend_vk_context * ctx = &vk_instance.contexts[0];
-
-    if (!ctx->initialized) {
-        return;
-    }
-
-    ggml_vk_graph_cleanup(ctx);
-}
-
-void ggml_vk_free_cpu_assist() {
-    ggml_backend_vk_context * ctx = &vk_instance.contexts[0];
-
-    if (!ctx->initialized || vk_instance.backends[0] == nullptr) {
-        return;
-    }
-
-    ggml_backend_vk_free(vk_instance.backends[0]);
-}
-
 // backend interface
 
 #define UNUSED GGML_UNUSED
@@ -4728,7 +5865,7 @@ GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t b
         extra->offset = (uint8_t *) tensor->data - (uint8_t *) vk_ptr_base;
     }
 
-    tensor->backend = GGML_BACKEND_GPU;
+    tensor->backend = GGML_BACKEND_TYPE_GPU;
     tensor->extra = extra;
 }
 
@@ -4736,7 +5873,7 @@ GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t bu
 #ifdef GGML_VULKAN_DEBUG
     std::cerr << "ggml_backend_vk_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl;
 #endif
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 
     ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
 
@@ -4751,7 +5888,7 @@ GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t bu
 #ifdef GGML_VULKAN_DEBUG
     std::cerr << "ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl;
 #endif
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 
     ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
 
@@ -4764,7 +5901,6 @@ GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t bu
 
 GGML_CALL static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
     if (ggml_backend_buffer_is_vk(src->buffer)) {
-        ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
         ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra;
         ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
 
@@ -4776,6 +5912,8 @@ GGML_CALL static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t bu
         return true;
     }
     return false;
+
+    UNUSED(buffer);
 }
 
 GGML_CALL static void ggml_backend_vk_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
@@ -4822,12 +5960,12 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(
 
 GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
     ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
-    return ctx->ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment;
+    return ctx->ctx->device->properties.limits.minStorageBufferOffsetAlignment;
 }
 
 GGML_CALL static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
     ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
-    return ctx->ctx->device.lock()->max_memory_allocation_size;
+    return ctx->ctx->device->max_memory_allocation_size;
 }
 
 GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
@@ -4857,16 +5995,16 @@ static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = {
     /* .is_host          = */ NULL,
 };
 
-GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t idx) {
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num) {
 #ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_backend_vk_buffer_type(" << idx << ")" << std::endl;
+    std::cerr << "ggml_backend_vk_buffer_type(" << dev_num << ")" << std::endl;
 #endif
 
-    GGML_ASSERT(idx < vk_instance.device_indices.size());
+    GGML_ASSERT(dev_num < vk_instance.device_indices.size());
 
-    ggml_backend_vk_init(idx);
+    ggml_backend_vk_init(dev_num);
 
-    return &vk_instance.buffer_types[idx];
+    return &vk_instance.buffer_types[dev_num];
 }
 
 // host buffer type
@@ -4913,7 +6051,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_bu
 }
 
 GGML_CALL static size_t ggml_backend_vk_host_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
-    return vk_instance.contexts[0].device.lock()->properties.limits.minMemoryMapAlignment;
+    return vk_instance.contexts[0].device->properties.limits.minMemoryMapAlignment;
 
     UNUSED(buft);
 }
@@ -4958,8 +6096,7 @@ GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend) {
 
     ggml_vk_cleanup(ctx);
 
-    // Release device
-    vk_instance.devices[ctx->idx].reset();
+    ctx->device.reset();
     ctx->initialized = false;
 
     vk_instance.initialized[idx] = false;
@@ -4982,13 +6119,13 @@ GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, g
 #endif
     ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
     GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type");
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 
     ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
 
     if (ctx->transfer_ctx == nullptr) {
         // Initialize new transfer context
-        ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue);
+        ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
         ggml_vk_ctx_begin(ctx, ctx->transfer_ctx);
     }
 
@@ -5003,13 +6140,13 @@ GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, c
 #endif
     ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
     GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type");
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 
     ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
 
     if (ctx->transfer_ctx == nullptr) {
         // Initialize new transfer context
-        ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue);
+        ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
         ggml_vk_ctx_begin(ctx, ctx->transfer_ctx);
     }
 
@@ -5029,14 +6166,14 @@ GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, c
 
         if (ctx->transfer_ctx == nullptr) {
             // Initialize new transfer context
-            ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue);
+            ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
             ggml_vk_ctx_begin(ctx, ctx->transfer_ctx);
         }
 
         vk_buffer src_buf = src_extra->buffer_gpu.lock();
         vk_buffer dst_buf = dst_extra->buffer_gpu.lock();
 
-        ggml_vk_buffer_copy_async(ctx->transfer_ctx, src_buf, src_extra->offset, dst_buf, dst_extra->offset, ggml_nbytes(src));
+        ggml_vk_buffer_copy_async(ctx->transfer_ctx, dst_buf, dst_extra->offset, src_buf, src_extra->offset, ggml_nbytes(src));
         return true;
     }
 
@@ -5059,8 +6196,8 @@ GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) {
     }
 
     ggml_vk_submit(ctx->transfer_ctx, ctx->fence);
-    VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences");
-    ctx->device.lock()->device.resetFences({ ctx->fence });
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences");
+    ctx->device->device.resetFences({ ctx->fence });
 
     for (auto& cpy : ctx->transfer_ctx->out_memcpys) {
         memcpy(cpy.dst, cpy.src, cpy.n);
@@ -5069,7 +6206,10 @@ GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) {
     ctx->transfer_ctx = nullptr;
 }
 
-GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_backend_vk_graph_compute(" << cgraph->n_nodes << " nodes)" << std::endl;
+#endif
     ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
 
     for (int i = 0; i < cgraph->n_nodes; i++) {
@@ -5080,7 +6220,7 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml
     int last_node = cgraph->n_nodes - 1;
 
     // If the last op in the cgraph isn't backend GPU, the command buffer doesn't get closed properly
-    while (last_node > 0 && cgraph->nodes[last_node]->backend != GGML_BACKEND_GPU) {
+    while (last_node > 0 && (cgraph->nodes[last_node]->backend != GGML_BACKEND_TYPE_GPU || ggml_is_empty(cgraph->nodes[last_node]))) {
         last_node -= 1;
     }
 
@@ -5089,12 +6229,12 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml
     }
 
     ggml_compute_params params = {};
-    params.type = GGML_TASK_COMPUTE;
+    params.type = GGML_TASK_TYPE_COMPUTE;
     params.ith = 0;
     for (int i = 0; i < cgraph->n_nodes; i++) {
         ggml_tensor * node = cgraph->nodes[i];
 
-        if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
+        if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
             continue;
         }
 
@@ -5112,12 +6252,14 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml
 
     ggml_vk_graph_cleanup(ctx);
 
-    return true;
+    return GGML_STATUS_SUCCESS;
 
     UNUSED(backend);
 }
 
 GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
+    // ggml_backend_vk_context * ctx = (ggml_backend_vk_context *) backend->context;
+
     switch (op->op) {
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(op)) {
@@ -5130,7 +6272,25 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const
             }
             break;
         case GGML_OP_MUL_MAT:
+        // case GGML_OP_MUL_MAT_ID:
             {
+                switch (op->src[0]->type) {
+                    case GGML_TYPE_F32:
+                    case GGML_TYPE_F16:
+                    case GGML_TYPE_Q4_0:
+                    case GGML_TYPE_Q4_1:
+                    case GGML_TYPE_Q5_0:
+                    case GGML_TYPE_Q5_1:
+                    case GGML_TYPE_Q8_0:
+                    case GGML_TYPE_Q2_K:
+                    case GGML_TYPE_Q3_K:
+                    case GGML_TYPE_Q4_K:
+                    case GGML_TYPE_Q5_K:
+                    case GGML_TYPE_Q6_K:
+                        break;
+                    default:
+                        return false;
+                }
                 struct ggml_tensor * a;
                 struct ggml_tensor * b;
                 if (op->op == GGML_OP_MUL_MAT) {
@@ -5145,25 +6305,26 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const
                 }
                 return true;
             } break;
-        // case GGML_OP_GET_ROWS:
-        //     {
-        //         switch (op->src[0]->type) {
-        //             case GGML_TYPE_F16:
-        //             case GGML_TYPE_F32:
-        //             case GGML_TYPE_Q4_0:
-        //             case GGML_TYPE_Q4_1:
-        //             case GGML_TYPE_Q5_0:
-        //             case GGML_TYPE_Q5_1:
-        //             case GGML_TYPE_Q8_0:
-        //                 return true;
-        //             default:
-        //                 return false;
-        //         }
-        //     } break;
+        case GGML_OP_GET_ROWS:
+            {
+                switch (op->src[0]->type) {
+                    case GGML_TYPE_F32:
+                    case GGML_TYPE_F16:
+                    case GGML_TYPE_Q4_0:
+                    case GGML_TYPE_Q4_1:
+                    case GGML_TYPE_Q5_0:
+                    case GGML_TYPE_Q5_1:
+                    case GGML_TYPE_Q8_0:
+                        return true;
+                    default:
+                        return false;
+                }
+            } break;
         case GGML_OP_CPY:
+        case GGML_OP_DUP:
             {
                 ggml_type src0_type = op->src[0]->type;
-                ggml_type src1_type = op->src[1]->type;
+                ggml_type src1_type = op->src[1] != nullptr ? op->src[1]->type : src0_type;
                 if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
                     return true;
                 }
@@ -5175,7 +6336,6 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const
                 }
                 return false;
             } break;
-        case GGML_OP_DUP:
         // case GGML_OP_REPEAT:
         //     {
         //         ggml_type src0_type = op->src[0]->type;
@@ -5203,6 +6363,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const
         case GGML_OP_CONT:
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_SOFT_MAX:
+        case GGML_OP_ARGSORT:
             return true;
         default:
             return false;
@@ -5211,6 +6372,20 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const
     UNUSED(backend);
 }
 
+GGML_CALL static bool ggml_backend_vk_offload_op(ggml_backend_t backend, const ggml_tensor * op) {
+    const ggml_tensor * dst = op;
+
+    const int min_batch_size = 32;
+
+    if (dst->ne[1] > min_batch_size && dst->op != GGML_OP_GET_ROWS) {
+        return true;
+    }
+
+    return false;
+
+    UNUSED(backend);
+}
+
 // TODO: enable async and synchronize
 static ggml_backend_i ggml_backend_vk_interface = {
     /* .get_name                = */ ggml_backend_vk_name,
@@ -5225,37 +6400,49 @@ static ggml_backend_i ggml_backend_vk_interface = {
     /* .graph_plan_compute      = */ NULL,
     /* .graph_compute           = */ ggml_backend_vk_graph_compute,
     /* .supports_op             = */ ggml_backend_vk_supports_op,
+    /* .offload_op              = */ ggml_backend_vk_offload_op,
+    /* .event_new               = */ NULL,
+    /* .event_free              = */ NULL,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+    /* .event_synchronize       = */ NULL,
 };
 
-GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t idx) {
-    if (vk_instance.initialized[idx]) {
-        return vk_instance.backends[idx];
+static ggml_guid_t ggml_backend_vk_guid() {
+    static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x40, 0x3c, 0xe1, 0x02, 0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b };
+    return &guid;
+}
+
+GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t dev_num) {
+    if (vk_instance.initialized[dev_num]) {
+        return vk_instance.backends[dev_num];
     }
 #ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_backend_vk_init(" << idx << ")" << std::endl;
+    std::cerr << "ggml_backend_vk_init(" << dev_num << ")" << std::endl;
 #endif
 
-    ggml_backend_vk_context * ctx = &vk_instance.contexts[idx];
-    ggml_vk_init(ctx, idx);
-    ctx->name = GGML_VK_NAME + std::to_string(idx);
-    vk_instance.buffer_types[idx] = {
+    ggml_backend_vk_context * ctx = &vk_instance.contexts[dev_num];
+    ggml_vk_init(ctx, dev_num);
+    ctx->name = GGML_VK_NAME + std::to_string(dev_num);
+    vk_instance.buffer_types[dev_num] = {
         /* .iface    = */ ggml_backend_vk_buffer_type_interface,
         /* .context  = */ new ggml_backend_vk_buffer_type_context{ ctx->name, ctx },
     };
-    vk_instance.initialized[idx] = true;
+    vk_instance.initialized[dev_num] = true;
 
     ggml_backend_t vk_backend = new ggml_backend {
+        /* .guid      = */ ggml_backend_vk_guid(),
         /* .interface = */ ggml_backend_vk_interface,
         /* .context   = */ &vk_instance.contexts[ctx->idx],
     };
 
-    vk_instance.backends[idx] = vk_backend;
+    vk_instance.backends[dev_num] = vk_backend;
 
     return vk_backend;
 }
 
 GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend) {
-    return backend && backend->iface.get_name == ggml_backend_vk_name;
+    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_vk_guid());
 }
 
 GGML_CALL int ggml_backend_vk_get_device_count() {
@@ -5293,14 +6480,52 @@ GGML_CALL static ggml_backend_t ggml_backend_reg_vk_init(const char * params, vo
 extern "C" GGML_CALL int ggml_backend_vk_reg_devices();
 
 GGML_CALL int ggml_backend_vk_reg_devices() {
-    for (auto idx : vk_instance.device_indices) {
+    ggml_vk_instance_init();
+
+    for (size_t i = 0; i < vk_instance.device_indices.size(); i++) {
         char name[128];
-        snprintf(name, sizeof(name), "%s%ld", GGML_VK_NAME, idx);
-        ggml_backend_register(name, ggml_backend_reg_vk_init, ggml_backend_vk_buffer_type(idx), (void *) (intptr_t) idx);
+        snprintf(name, sizeof(name), "%s%ld", GGML_VK_NAME, i);
+        ggml_backend_register(name, ggml_backend_reg_vk_init, ggml_backend_vk_buffer_type(i), (void *) (intptr_t) i);  // NOLINT
     }
     return vk_instance.device_indices.size();
 }
 
+// Extension availability
+static bool ggml_vk_instance_validation_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions) {
+#ifdef GGML_VULKAN_VALIDATE
+    bool portability_enumeration_ext = false;
+    // Check for portability enumeration extension for MoltenVK support
+    for (const auto& properties : instance_extensions) {
+        if (strcmp("VK_KHR_portability_enumeration", properties.extensionName) == 0) {
+            return true;
+        }
+    }
+    if (!portability_enumeration_ext) {
+        std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl;
+    }
+#endif
+    return false;
+
+    UNUSED(instance_extensions);
+}
+static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions) {
+#ifdef __APPLE__
+    bool portability_enumeration_ext = false;
+    // Check for portability enumeration extension for MoltenVK support
+    for (const auto& properties : instance_extensions) {
+        if (strcmp("VK_KHR_portability_enumeration", properties.extensionName) == 0) {
+            return true;
+        }
+    }
+    if (!portability_enumeration_ext) {
+        std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl;
+    }
+#endif
+    return false;
+
+    UNUSED(instance_extensions);
+}
+
 // checks
 
 #ifdef GGML_VULKAN_CHECK_RESULTS
@@ -5344,6 +6569,8 @@ static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * d
                     val = *(const float *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]);
                 } else if (tensor->type == GGML_TYPE_F16) {
                     val = ggml_fp16_to_fp32(*(const ggml_fp16_t *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]));
+                } else {
+                    GGML_ASSERT(false);
                 }
                 fprintf(stderr, "% 7.2f ", val);
             } else {
@@ -5357,13 +6584,14 @@ static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * d
 static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tensor * tensor, const char * name) {
     void * tensor_data = tensor->data;
 
-    if (tensor->backend == GGML_BACKEND_GPU) {
+    if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
         const size_t tensor_size = ggml_nbytes(tensor);
         tensor_data = malloc(tensor_size);
 
         ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
 
-        ggml_vk_buffer_read(ctx, extra->buffer_gpu, extra->offset, tensor_data, tensor_size);
+        vk_buffer buffer_gpu = extra->buffer_gpu.lock();
+        ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset, tensor_data, tensor_size);
     }
 
     std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl;
@@ -5383,14 +6611,14 @@ static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tenso
     std::vector<const ggml_tensor *> done;
     ggml_vk_print_graph_origin(tensor, done);
 
-    if (tensor->backend == GGML_BACKEND_GPU) {
+    if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
         free(tensor_data);
     }
 }
 
 static void ggml_vk_check_tensor(const std::string& name, const ggml_tensor * tensor) {
     return;
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_CPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_CPU);
     if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) {
         return;
     }
@@ -5428,7 +6656,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
     if (params->ith != 0) {
         return;
     }
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) {
         return;
     }
 
@@ -5437,8 +6665,13 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
         return;
     }
 
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_check_results_0(" << tensor->name << ")" << std::endl;
+#endif
+
     ggml_tensor * src0 = tensor->src[0];
     ggml_tensor * src1 = tensor->src[1];
+    ggml_tensor * src2 = tensor->src[2];
 
     struct ggml_init_params iparams = {
         /*.mem_size   =*/ 1024*1024*1024,
@@ -5450,13 +6683,16 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
 
     struct ggml_tensor * src0_clone = nullptr;
     struct ggml_tensor * src1_clone = nullptr;
+    struct ggml_tensor * src2_clone = nullptr;
     struct ggml_tensor * tensor_clone = nullptr;
 
     size_t src0_size;
     size_t src1_size;
+    size_t src2_size;
 
     void * src0_buffer;
     void * src1_buffer;
+    void * src2_buffer;
 
     if (src0 != nullptr) {
         src0_clone = ggml_dup_tensor(ggml_ctx, src0);
@@ -5465,17 +6701,18 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
 
         src0_buffer = malloc(src0_size);
         src0_clone->data = src0_buffer;
-        if (src0->backend == GGML_BACKEND_CPU) {
+        if (src0->backend == GGML_BACKEND_TYPE_CPU) {
             memcpy(src0_clone->data, src0->data, src0_size);
             memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS);
-        } else if (src0->backend == GGML_BACKEND_GPU) {
+        } else if (src0->backend == GGML_BACKEND_TYPE_GPU) {
             ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src0->extra;
+            vk_buffer buffer_gpu = extra->buffer_gpu.lock();
             uint64_t offset = extra->offset;
             if (!ggml_is_contiguous(src0) && ggml_vk_dim01_contiguous(src0)) {
                 for (int i3 = 0; i3 < src0->ne[3]; i3++) {
                     for (int i2 = 0; i2 < src0->ne[2]; i2++) {
                         const int idx = i3*src0->ne[2] + i2;
-                        ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]);
+                        ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]);
                     }
                 }
 
@@ -5485,10 +6722,10 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
                     src0_clone->nb[i] = src0_clone->nb[i - 1]*src0_clone->ne[i - 1];
                 }
             } else {
-                if (offset + src0_size >= extra->buffer_gpu->size) {
-                    src0_size = extra->buffer_gpu->size - offset;
+                if (offset + src0_size >= buffer_gpu->size) {
+                    src0_size = buffer_gpu->size - offset;
                 }
-                ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset, src0_clone->data, src0_size);
+                ggml_vk_buffer_read(ctx, buffer_gpu, offset, src0_clone->data, src0_size);
                 memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS);
             }
         } else {
@@ -5508,17 +6745,18 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
 
         src1_buffer = malloc(src1_size);
         src1_clone->data = src1_buffer;
-        if (src1->backend == GGML_BACKEND_CPU) {
+        if (src1->backend == GGML_BACKEND_TYPE_CPU) {
             memcpy(src1_clone->data, src1->data, src1_size);
             memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS);
-        } else if (src1->backend == GGML_BACKEND_GPU) {
+        } else if (src1->backend == GGML_BACKEND_TYPE_GPU) {
             ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src1->extra;
+            vk_buffer buffer_gpu = extra->buffer_gpu.lock();
             uint64_t offset = extra->offset;
             if (!ggml_is_contiguous(src1) && ggml_vk_dim01_contiguous(src1)) {
                 for (int i3 = 0; i3 < src1->ne[3]; i3++) {
                     for (int i2 = 0; i2 < src1->ne[2]; i2++) {
                         const int idx = i3*src1->ne[2] + i2;
-                        ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]);
+                        ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]);
                     }
                 }
 
@@ -5528,10 +6766,10 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
                     src1_clone->nb[i] = src1_clone->nb[i - 1]*src1_clone->ne[i - 1];
                 }
             } else {
-                if (offset + src1_size >= extra->buffer_gpu->size) {
-                    src1_size = extra->buffer_gpu->size - offset;
+                if (offset + src1_size >= buffer_gpu->size) {
+                    src1_size = buffer_gpu->size - offset;
                 }
-                ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset, src1_clone->data, src1_size);
+                ggml_vk_buffer_read(ctx, buffer_gpu, offset, src1_clone->data, src1_size);
                 memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS);
             }
         } else {
@@ -5560,6 +6798,66 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
 
         ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src1", src1_clone);
     }
+    if (src2 != nullptr) {
+        src2_clone = ggml_dup_tensor(ggml_ctx, src2);
+
+        src2_size = ggml_nbytes(src2);
+
+        src2_buffer = malloc(src2_size);
+        src2_clone->data = src2_buffer;
+        if (src2->backend == GGML_BACKEND_TYPE_CPU) {
+            memcpy(src2_clone->data, src2->data, src2_size);
+            memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS);
+        } else if (src2->backend == GGML_BACKEND_TYPE_GPU) {
+            ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src2->extra;
+            vk_buffer buf = extra->buffer_gpu.lock();
+            uint64_t offset = extra->offset;
+            if (!ggml_is_contiguous(src2) && ggml_vk_dim01_contiguous(src2)) {
+                for (int i3 = 0; i3 < src2->ne[3]; i3++) {
+                    for (int i2 = 0; i2 < src2->ne[2]; i2++) {
+                        const int idx = i3*src2->ne[2] + i2;
+                        ggml_vk_buffer_read(ctx, buf, offset + idx * src2->nb[2], ((char *)src2_clone->data + idx * src2_clone->nb[2]), src2->ne[1] * src2->nb[1]);
+                    }
+                }
+
+                src2_clone->nb[0] = src2->nb[0];
+                src2_clone->nb[1] = src2->nb[1];
+                for (int i = 2; i < GGML_MAX_DIMS; i++) {
+                    src2_clone->nb[i] = src2_clone->nb[i - 1]*src2_clone->ne[i - 1];
+                }
+            } else {
+                if (offset + src2_size >= buf->size) {
+                    src2_size = buf->size - offset;
+                }
+                ggml_vk_buffer_read(ctx, buf, offset, src2_clone->data, src2_size);
+                memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS);
+            }
+        } else {
+            GGML_ASSERT(false);
+        }
+
+        if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
+            ggml_vk_print_tensor(ctx, src2, "src2");
+            std::cerr << "TENSOR CHECK: " << ggml_op_name(src2_clone->op) << " (check " << check_counter << ")" << std::endl;
+            std::cerr << "src2_clone=" << tensor << " src2_clone->backend: " << src2_clone->backend << " src2_clone->type: " << ggml_type_name(src2_clone->type) << " ne0=" << src2_clone->ne[0] << " nb0=" << src2_clone->nb[0] << " ne1=" << src2_clone->ne[1] << " nb1=" << src2_clone->nb[1] << " ne2=" << src2_clone->ne[2] << " nb2=" << src2_clone->nb[2] << " ne3=" << src2_clone->ne[3] << " nb3=" << src2_clone->nb[3] << std::endl;
+            if (src2->src[0] != nullptr) {
+                std::cerr << "src2->src[0]=" << src2->src[0] << " op=" << ggml_op_name(src2->src[0]->op) << " type=" << ggml_type_name(src2->src[0]->type) << " backend=" << src2->src[0]->backend << " ne0=" << src2->src[0]->ne[0] << " nb0=" << src2->src[0]->nb[0] << " ne1=" << src2->src[0]->ne[1] << " nb1=" << src2->src[0]->nb[1] << " ne2=" << src2->src[0]->ne[2] << " nb2=" << src2->src[0]->nb[2] << " ne3=" << src2->src[0]->ne[3] << " nb3=" << src2->src[0]->nb[3] << std::endl;
+            }
+            if (src2->src[1] != nullptr) {
+                std::cerr << "src2->src[1]=" << src2->src[1] << " op=" << ggml_op_name(src2->src[1]->op) << " type=" << ggml_type_name(src2->src[1]->type) << " backend=" << src2->src[1]->backend << " ne0=" << src2->src[1]->ne[0] << " nb0=" << src2->src[1]->nb[0] << " ne1=" << src2->src[1]->ne[1] << " nb1=" << src2->src[1]->nb[1] << " ne2=" << src2->src[1]->ne[2] << " nb2=" << src2->src[1]->nb[2] << " ne3=" << src2->src[1]->ne[3] << " nb3=" << src2->src[1]->nb[3] << std::endl;
+            }
+            std::cerr << std::endl << "Result:" << std::endl;
+            ggml_vk_print_tensor_area(src2_clone, src2_clone->data, 5, 5, 0, 0);
+            std::cerr << std::endl;
+            std::cerr << std::endl << "Result:" << std::endl;
+            ggml_vk_print_tensor_area(src2_clone, src2_clone->data, 5, 5, 1, 0);
+            std::cerr << std::endl;
+            std::vector<const ggml_tensor *> done;
+            ggml_vk_print_graph_origin(src2_clone, done);
+        }
+
+        ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src2", src2_clone);
+    }
 
     if (tensor->op == GGML_OP_MUL_MAT) {
         tensor_clone = ggml_mul_mat(ggml_ctx, src0_clone, src1_clone);
@@ -5579,12 +6877,12 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
         tensor_clone = ggml_rms_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params);
     } else if (tensor->op == GGML_OP_SOFT_MAX) {
         if (src1 != nullptr) {
-            tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, *(float *)tensor->op_params);
+            tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, src2_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]);
         } else {
             tensor_clone = ggml_soft_max(ggml_ctx, src0_clone);
         }
     } else if (tensor->op == GGML_OP_DIAG_MASK_INF) {
-        tensor_clone = ggml_diag_mask_inf(ggml_ctx, src0_clone, *(float *)tensor->op_params);
+        tensor_clone = ggml_diag_mask_inf(ggml_ctx, src0_clone, *(int *)tensor->op_params);
     } else if (tensor->op == GGML_OP_ROPE) {
         const int n_dims      = ((int32_t *) tensor->op_params)[1];
         const int mode        = ((int32_t *) tensor->op_params)[2];
@@ -5630,6 +6928,10 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
         tensor_clone = ggml_permute(ggml_ctx, src0_clone, params[0], params[1], params[2], params[3]);
     } else if (tensor->op == GGML_OP_TRANSPOSE) {
         tensor_clone = ggml_transpose(ggml_ctx, src0_clone);
+    } else if (tensor->op == GGML_OP_GET_ROWS) {
+        tensor_clone = ggml_get_rows(ggml_ctx, src0_clone, src1_clone);
+    } else if (tensor->op == GGML_OP_ARGSORT) {
+        tensor_clone = ggml_argsort(ggml_ctx, src0_clone, (ggml_sort_order) *(int *)tensor->op_params);
     } else {
         std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl;
         GGML_ASSERT(false);
@@ -5662,6 +6964,9 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
     if (src1 != nullptr) {
         free(src1_buffer);
     }
+    if (src2 != nullptr) {
+        free(src2_buffer);
+    }
 
     ggml_free(ggml_ctx);
 }
@@ -5670,29 +6975,34 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_
     if (params->ith != 0) {
         return;
     }
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) {
         return;
     }
     if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) {
         return;
     }
 
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_check_results_1(" << tensor->name << ")" << std::endl;
+#endif
+
     ggml_tensor * src0 = tensor->src[0];
     ggml_tensor * src1 = tensor->src[1];
 
     void * tensor_data = tensor->data;
 
-    if (tensor->backend == GGML_BACKEND_GPU) {
+    if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
         size_t tensor_size = ggml_nbytes(tensor);
         tensor_data = malloc(tensor_size);
 
         ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
 
-        if (extra->offset + tensor_size >= extra->buffer_gpu->size) {
-            tensor_size = extra->buffer_gpu->size - (extra->offset);
+        vk_buffer buffer_gpu = extra->buffer_gpu.lock();
+        if (extra->offset + tensor_size >= buffer_gpu->size) {
+            tensor_size = buffer_gpu->size - (extra->offset);
         }
 
-        ggml_vk_buffer_read(ctx, extra->buffer_gpu, extra->offset, tensor_data, tensor_size);
+        ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset, tensor_data, tensor_size);
     }
 
     float first_error_result = -1.0f;
@@ -5815,14 +7125,8 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_
     comp_result = nullptr;
     comp_size = 0;
 
-    if (tensor->backend == GGML_BACKEND_GPU) {
+    if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
         free(tensor_data);
     }
 }
-
-void ggml_vk_check_results_1_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
-    ggml_backend_vk_context * ctx = &vk_instance.contexts[0];
-
-    ggml_vk_check_results_0(ctx, params, tensor);
-}
 #endif
diff --git a/ggml-vulkan.h b/ggml-vulkan.h
index 9645126b4..af661c2d7 100644
--- a/ggml-vulkan.h
+++ b/ggml-vulkan.h
@@ -10,17 +10,7 @@ extern "C" {
 #define GGML_VK_NAME "Vulkan"
 #define GGML_VK_MAX_DEVICES 16
 
-GGML_API void ggml_vk_init_cpu_assist(void);
-
-GGML_API void ggml_vk_preallocate_buffers_graph_cpu_assist(struct ggml_tensor * node);
-GGML_API void ggml_vk_preallocate_buffers_cpu_assist(void);
-GGML_API void ggml_vk_build_graph_cpu_assist(struct ggml_tensor * node, bool last_node);
-GGML_API bool ggml_vk_compute_forward_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor);
-#ifdef GGML_VULKAN_CHECK_RESULTS
-void ggml_vk_check_results_1_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor);
-#endif
-GGML_API void ggml_vk_graph_cleanup_cpu_assist(void);
-GGML_API void ggml_vk_free_cpu_assist(void);
+GGML_API void ggml_vk_instance_init(void);
 
 // backend API
 GGML_API GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t dev_num);
diff --git a/ggml.c b/ggml.c
index 4ee2c5e11..b96a82a41 100644
--- a/ggml.c
+++ b/ggml.c
@@ -3,6 +3,7 @@
 
 #include "ggml-impl.h"
 #include "ggml-quants.h"
+#include "ggml.h"
 
 #if defined(_MSC_VER) || defined(__MINGW32__)
 #include <malloc.h> // using malloc.h with MSC/MINGW
@@ -31,6 +32,14 @@
 #include <unistd.h>
 #endif
 
+#ifdef __ARM_FEATURE_MATMUL_INT8
+#undef GGML_USE_LLAMAFILE
+#endif
+
+#ifdef GGML_USE_LLAMAFILE
+#include "sgemm.h"
+#endif
+
 #if defined(_MSC_VER)
 // disable "possible loss of data" to avoid hundreds of casts
 // we should just be careful :)
@@ -43,6 +52,10 @@
 
 #if defined(_WIN32)
 
+#define WIN32_LEAN_AND_MEAN
+#ifndef NOMINMAX
+    #define NOMINMAX
+#endif
 #include <windows.h>
 
 typedef volatile LONG atomic_int;
@@ -280,14 +293,8 @@ inline static void * ggml_calloc(size_t num, size_t size) {
 #else
 #include <cblas.h>
 #endif
-#elif defined(GGML_USE_CUBLAS)
-#include "ggml-cuda.h"
 #elif defined(GGML_USE_CLBLAST)
 #include "ggml-opencl.h"
-#elif defined(GGML_USE_VULKAN)
-#include "ggml-vulkan.h"
-#elif defined(GGML_USE_SYCL)
-#include "ggml-sycl.h"
 #endif
 
 // floating point type used to accumulate sums
@@ -318,24 +325,45 @@ static ggml_fp16_t ggml_table_exp_f16[1 << 16];
 // precomputed f32 table for f16 (256 KB) (ggml-impl.h)
 float ggml_table_f32_f16[1 << 16];
 
-// note: do not use these inside ggml.c
-// these are meant to be used via the ggml.h API
+GGML_CALL const char * ggml_status_to_string(enum ggml_status status) {
+    switch (status) {
+        case GGML_STATUS_ALLOC_FAILED: return "GGML status: error (failed to allocate memory)";
+        case GGML_STATUS_FAILED:       return "GGML status: error (operation failed)";
+        case GGML_STATUS_SUCCESS:      return "GGML status: success";
+        case GGML_STATUS_ABORTED:      return "GGML status: warning (operation aborted)";
+    }
+
+    return "GGML status: unknown";
+}
+
 float ggml_fp16_to_fp32(ggml_fp16_t x) {
-    return (float) GGML_FP16_TO_FP32(x);
+#define ggml_fp16_to_fp32 do_not_use__ggml_fp16_to_fp32__in_ggml
+    return GGML_FP16_TO_FP32(x);
 }
 
 ggml_fp16_t ggml_fp32_to_fp16(float x) {
+#define ggml_fp32_to_fp16 do_not_use__ggml_fp32_to_fp16__in_ggml
     return GGML_FP32_TO_FP16(x);
 }
 
-void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n) {
-    for (int i = 0; i < n; i++) {
+float ggml_bf16_to_fp32(ggml_bf16_t x) {
+#define ggml_bf16_to_fp32 do_not_use__ggml_bf16_to_fp32__in_ggml
+    return GGML_BF16_TO_FP32(x);  // it just left shifts
+}
+
+ggml_bf16_t ggml_fp32_to_bf16(float x) {
+#define ggml_fp32_to_bf16 do_not_use__ggml_fp32_to_bf16__in_ggml
+    return GGML_FP32_TO_BF16(x);
+}
+
+void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int64_t n) {
+    for (int64_t i = 0; i < n; i++) {
         y[i] = GGML_FP16_TO_FP32(x[i]);
     }
 }
 
-void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n) {
-    int i = 0;
+void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int64_t n) {
+    int64_t i = 0;
 #if defined(__F16C__)
     for (; i + 7 < n; i += 8) {
         __m256 x_vec = _mm256_loadu_ps(x + i);
@@ -353,6 +381,53 @@ void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n) {
     }
 }
 
+void ggml_bf16_to_fp32_row(const ggml_bf16_t * x, float * y, int64_t n) {
+    int64_t i = 0;
+#if defined(__AVX512F__)
+    for (; i + 16 <= n; i += 16) {
+        _mm512_storeu_ps(y + i,
+                         _mm512_castsi512_ps(
+                             _mm512_slli_epi32(
+                                 _mm512_cvtepu16_epi32(
+                                     _mm256_loadu_si256(
+                                         (const __m256i *)(x + i))),
+                                 16)));
+    }
+#elif defined(__AVX2__)
+    for (; i + 8 <= n; i += 8) {
+        _mm256_storeu_ps(y + i,
+                         _mm256_castsi256_ps(
+                             _mm256_slli_epi32(
+                                 _mm256_cvtepu16_epi32(
+                                     _mm_loadu_si128(
+                                         (const __m128i *)(x + i))),
+                                 16)));
+    }
+#endif
+    for (; i < n; i++) {
+        y[i] = GGML_BF16_TO_FP32(x[i]);
+    }
+}
+
+void ggml_fp32_to_bf16_row(const float * x, ggml_bf16_t * y, int64_t n) {
+  int i = 0;
+#if defined(__AVX512BF16__)
+  for (; i + 32 <= n; i += 32) {
+        _mm512_storeu_ps(
+            (__m512 *)(y + i),
+            (__m512)_mm512_cvtne2ps_pbh(_mm512_loadu_ps(x + i + 16),
+                                        _mm512_loadu_ps(x + i)));
+  }
+#endif
+    for (; i < n; i++) {
+        y[i] = GGML_FP32_TO_BF16(x[i]);
+    }
+}
+
+bool ggml_guid_matches(ggml_guid_t guid_a, ggml_guid_t guid_b) {
+    return memcmp(guid_a, guid_b, sizeof(ggml_guid)) == 0;
+}
+
 //
 // timing
 //
@@ -415,6 +490,57 @@ int64_t ggml_cycles_per_ms(void) {
 #define ggml_perf_cycles_per_ms() 0
 #endif
 
+//
+// cross-platform UTF-8 file paths
+//
+
+#ifdef _WIN32
+static wchar_t * ggml_mbstowcs(const char * mbs) {
+    int wlen = MultiByteToWideChar(CP_UTF8, 0, mbs, -1, NULL, 0);
+    if (!wlen) {
+        errno = EINVAL;
+        return NULL;
+    }
+
+    wchar_t * wbuf = GGML_MALLOC(wlen * sizeof(wchar_t));
+    wlen = MultiByteToWideChar(CP_UTF8, 0, mbs, -1, wbuf, wlen);
+    if (!wlen) {
+        GGML_FREE(wbuf);
+        errno = EINVAL;
+        return NULL;
+    }
+
+    return wbuf;
+}
+#endif
+
+FILE * ggml_fopen(const char * fname, const char * mode) {
+#ifdef _WIN32
+    FILE * file = NULL;
+
+    // convert fname (UTF-8)
+    wchar_t * wfname = ggml_mbstowcs(fname);
+    if (wfname) {
+        // convert mode (ANSI)
+        wchar_t * wmode = GGML_MALLOC((strlen(mode) + 1) * sizeof(wchar_t));
+        wchar_t * wmode_p = wmode;
+        do {
+            *wmode_p++ = (wchar_t)*mode;
+        } while (*mode++);
+
+        // open file
+        file = _wfopen(wfname, wmode);
+
+        GGML_FREE(wfname);
+        GGML_FREE(wmode);
+    }
+
+    return file;
+#else
+    return fopen(fname, mode);
+#endif
+}
+
 //
 // cache line
 //
@@ -433,6 +559,7 @@ static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float);
 
 static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * restrict x, size_t bx, const float * restrict y, size_t by, int nrc);
 static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc);
+static void ggml_vec_dot_bf16(int n, float * restrict s, size_t bs, ggml_bf16_t * restrict x, size_t bx, ggml_bf16_t * restrict y, size_t by, int nrc);
 
 static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
     [GGML_TYPE_I8] = {
@@ -453,6 +580,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .type_size                = sizeof(int32_t),
         .is_quantized             = false,
     },
+    [GGML_TYPE_I64] = {
+        .type_name                = "i64",
+        .blck_size                = 1,
+        .type_size                = sizeof(int64_t),
+        .is_quantized             = false,
+    },
+    [GGML_TYPE_F64] = {
+        .type_name                = "f64",
+        .blck_size                = 1,
+        .type_size                = sizeof(double),
+        .is_quantized             = false,
+        .nrows                    = 1,
+    },
     [GGML_TYPE_F32] = {
         .type_name                = "f32",
         .blck_size                = 1,
@@ -676,6 +816,30 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
     },
+    [GGML_TYPE_IQ3_S] = {
+        .type_name                = "iq3_s",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_iq3_s),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_iq3_s,
+        .from_float               = quantize_row_iq3_s,
+        .from_float_reference     = (ggml_from_float_t)quantize_row_iq3_s_reference,
+        .vec_dot                  = ggml_vec_dot_iq3_s_q8_K,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+        .nrows                    = 1,
+    },
+    [GGML_TYPE_IQ2_S] = {
+        .type_name                = "iq2_s",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_iq2_s),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_iq2_s,
+        .from_float               = quantize_row_iq2_s,
+        .from_float_reference     = (ggml_from_float_t)quantize_row_iq2_s_reference,
+        .vec_dot                  = ggml_vec_dot_iq2_s_q8_K,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+        .nrows                    = 1,
+    },
     [GGML_TYPE_IQ1_S] = {
         .type_name                = "iq1_s",
         .blck_size                = QK_K,
@@ -688,12 +852,68 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
     },
+    [GGML_TYPE_IQ1_M] = {
+        .type_name                = "iq1_m",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_iq1_m),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_iq1_m,
+        .from_float               = NULL,
+        .from_float_reference     = NULL,
+        .vec_dot                  = ggml_vec_dot_iq1_m_q8_K,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+        .nrows                    = 1,
+    },
+    [GGML_TYPE_IQ4_NL] = {
+        .type_name                = "iq4_nl",
+        .blck_size                = QK4_NL,
+        .type_size                = sizeof(block_iq4_nl),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_iq4_nl,
+        .from_float               = quantize_row_iq4_nl,
+        .from_float_reference     = (ggml_from_float_t)quantize_row_iq4_nl_reference,
+        .vec_dot                  = ggml_vec_dot_iq4_nl_q8_0,
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+        .nrows                    = 1,
+    },
+    [GGML_TYPE_IQ4_XS] = {
+        .type_name                = "iq4_xs",
+#if QK_K == 64
+        .blck_size                = QK4_NL,
+#else
+        .blck_size                = QK_K,
+#endif
+        .type_size                = sizeof(block_iq4_xs),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_iq4_xs,
+        .from_float               = quantize_row_iq4_xs,
+        .from_float_reference     = (ggml_from_float_t)quantize_row_iq4_xs_reference,
+        .vec_dot                  = ggml_vec_dot_iq4_xs_q8_K,
+#if QK_K == 64
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+#else
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+#endif
+        .nrows                    = 1,
+    },
     [GGML_TYPE_Q8_K] = {
         .type_name                = "q8_K",
         .blck_size                = QK_K,
         .type_size                = sizeof(block_q8_K),
         .is_quantized             = true,
         .from_float               = quantize_row_q8_K,
+    },
+    [GGML_TYPE_BF16] = {
+        .type_name                = "bf16",
+        .blck_size                = 1,
+        .type_size                = sizeof(ggml_bf16_t),
+        .is_quantized             = false,
+        .to_float                 = (ggml_to_float_t) ggml_bf16_to_fp32_row,
+        .from_float               = (ggml_from_float_t) ggml_fp32_to_bf16_row,
+        .from_float_reference     = (ggml_from_float_t) ggml_fp32_to_bf16_row,
+        .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_bf16,
+        .vec_dot_type             = GGML_TYPE_BF16,
+        .nrows                    = 1,
     }
 };
 
@@ -707,18 +927,6 @@ ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) {
 // simd mappings
 //
 
-#if defined(__ARM_NEON)
-#if !defined(__aarch64__)
-
-// 64-bit compatibility
-
-inline static float vaddvq_f32(float32x4_t v) {
-    return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3);
-}
-
-#endif
-#endif
-
 // we define a common set of C macros which map to specific intrinsics based on the current architecture
 // we then implement the fundamental computation operations below using only these macros
 // adding support for new architectures requires to define the corresponding SIMD macros
@@ -784,7 +992,7 @@ inline static float vaddvq_f32(float32x4_t v) {
     #define GGML_F16x8              float16x8_t
     #define GGML_F16x8_ZERO         vdupq_n_f16(0.0f)
     #define GGML_F16x8_SET1(x)      vdupq_n_f16(x)
-    #define GGML_F16x8_LOAD         vld1q_f16
+    #define GGML_F16x8_LOAD(x)      vld1q_f16((const ggml_fp16_internal_t *)(x))
     #define GGML_F16x8_STORE        vst1q_f16
     #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c)
     #define GGML_F16x8_ADD          vaddq_f16
@@ -812,7 +1020,7 @@ inline static float vaddvq_f32(float32x4_t v) {
     #define GGML_F16_VEC_ZERO           GGML_F16x8_ZERO
     #define GGML_F16_VEC_SET1           GGML_F16x8_SET1
     #define GGML_F16_VEC_LOAD(p, i)     GGML_F16x8_LOAD(p)
-    #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE(p, r[i])
+    #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE((ggml_fp16_internal_t *)(p), r[i])
     #define GGML_F16_VEC_FMA            GGML_F16x8_FMA
     #define GGML_F16_VEC_ADD            GGML_F16x8_ADD
     #define GGML_F16_VEC_MUL            GGML_F16x8_MUL
@@ -827,7 +1035,7 @@ inline static float vaddvq_f32(float32x4_t v) {
     #define GGML_F32Cx4              float32x4_t
     #define GGML_F32Cx4_ZERO         vdupq_n_f32(0.0f)
     #define GGML_F32Cx4_SET1(x)      vdupq_n_f32(x)
-    #define GGML_F32Cx4_LOAD(x)      vcvt_f32_f16(vld1_f16(x))
+    #define GGML_F32Cx4_LOAD(x)      vcvt_f32_f16(vld1_f16((const ggml_fp16_internal_t *)(x)))
     #define GGML_F32Cx4_STORE(x, y)  vst1_f16(x, vcvt_f16_f32(y))
     #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c)
     #define GGML_F32Cx4_ADD          vaddq_f32
@@ -838,13 +1046,108 @@ inline static float vaddvq_f32(float32x4_t v) {
     #define GGML_F16_VEC_ZERO           GGML_F32Cx4_ZERO
     #define GGML_F16_VEC_SET1           GGML_F32Cx4_SET1
     #define GGML_F16_VEC_LOAD(p, i)     GGML_F32Cx4_LOAD(p)
-    #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i])
+    #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE((ggml_fp16_internal_t *)(p), r[i])
     #define GGML_F16_VEC_FMA            GGML_F32Cx4_FMA
     #define GGML_F16_VEC_ADD            GGML_F32Cx4_ADD
     #define GGML_F16_VEC_MUL            GGML_F32Cx4_MUL
     #define GGML_F16_VEC_REDUCE         GGML_F32Cx4_REDUCE
 #endif
 
+#elif defined(__AVX512F__)
+
+#define GGML_SIMD
+
+// F32 AVX512
+
+#define GGML_F32_STEP 64
+#define GGML_F32_EPR  16
+
+#define GGML_F32x16         __m512
+#define GGML_F32x16_ZERO    _mm512_setzero_ps()
+#define GGML_F32x16_SET1(x) _mm512_set1_ps(x)
+#define GGML_F32x16_LOAD    _mm512_loadu_ps
+#define GGML_F32x16_STORE   _mm512_storeu_ps
+// _mm512_fmadd_ps is defined in AVX512F so no guard is required
+#define GGML_F32x16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a)
+#define GGML_F32x16_ADD     _mm512_add_ps
+#define GGML_F32x16_MUL     _mm512_mul_ps
+#define GGML_F32x16_REDUCE(res, x)                                    \
+do {                                                                  \
+    int offset = GGML_F32_ARR >> 1;                                   \
+    for (int i = 0; i < offset; ++i) {                                \
+        x[i] = _mm512_add_ps(x[i], x[offset+i]);                      \
+    }                                                                 \
+    offset >>= 1;                                                     \
+    for (int i = 0; i < offset; ++i) {                                \
+        x[i] = _mm512_add_ps(x[i], x[offset+i]);                      \
+    }                                                                 \
+    offset >>= 1;                                                     \
+    for (int i = 0; i < offset; ++i) {                                \
+        x[i] = _mm512_add_ps(x[i], x[offset+i]);                      \
+    }                                                                 \
+    res = _mm512_reduce_add_ps(x[0]);                                 \
+} while (0)
+
+// TODO: is this optimal ?
+
+#define GGML_F32_VEC        GGML_F32x16
+#define GGML_F32_VEC_ZERO   GGML_F32x16_ZERO
+#define GGML_F32_VEC_SET1   GGML_F32x16_SET1
+#define GGML_F32_VEC_LOAD   GGML_F32x16_LOAD
+#define GGML_F32_VEC_STORE  GGML_F32x16_STORE
+#define GGML_F32_VEC_FMA    GGML_F32x16_FMA
+#define GGML_F32_VEC_ADD    GGML_F32x16_ADD
+#define GGML_F32_VEC_MUL    GGML_F32x16_MUL
+#define GGML_F32_VEC_REDUCE GGML_F32x16_REDUCE
+
+// F16 AVX512
+
+// F16 AVX
+
+#define GGML_F16_STEP 64
+#define GGML_F16_EPR  16
+
+// AVX512 has FP16 extension (AVX512_FP16) but I don't have it on my machine so I use FP32 instead
+
+#define GGML_F32Cx16             __m512
+#define GGML_F32Cx16_ZERO        _mm512_setzero_ps()
+#define GGML_F32Cx16_SET1(x)     _mm512_set1_ps(x)
+
+// unlike  _mm256_cvt intrinsics that require F16C, _mm512_cvt is defined in AVX512F
+// so F16C guard isn't required
+#define GGML_F32Cx16_LOAD(x)     _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(x)))
+#define GGML_F32Cx16_STORE(x, y) _mm256_storeu_si256((__m256i *)(x), _mm512_cvtps_ph(y, 0))
+
+#define GGML_F32Cx16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a)
+#define GGML_F32Cx16_ADD         _mm512_add_ps
+#define GGML_F32Cx16_MUL         _mm512_mul_ps
+#define GGML_F32Cx16_REDUCE(res, x)                               \
+do {                                                              \
+    int offset = GGML_F32_ARR >> 1;                               \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm512_add_ps(x[i], x[offset+i]);                  \
+    }                                                             \
+    offset >>= 1;                                                 \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm512_add_ps(x[i], x[offset+i]);                  \
+    }                                                             \
+    offset >>= 1;                                                 \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm512_add_ps(x[i], x[offset+i]);                  \
+    }                                                             \
+    res = _mm512_reduce_add_ps(x[0]);                             \
+} while (0)
+
+#define GGML_F16_VEC                GGML_F32Cx16
+#define GGML_F16_VEC_ZERO           GGML_F32Cx16_ZERO
+#define GGML_F16_VEC_SET1           GGML_F32Cx16_SET1
+#define GGML_F16_VEC_LOAD(p, i)     GGML_F32Cx16_LOAD(p)
+#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx16_STORE(p, r[i])
+#define GGML_F16_VEC_FMA            GGML_F32Cx16_FMA
+#define GGML_F16_VEC_ADD            GGML_F32Cx16_ADD
+#define GGML_F16_VEC_MUL            GGML_F32Cx16_MUL
+#define GGML_F16_VEC_REDUCE         GGML_F32Cx16_REDUCE
+
 #elif defined(__AVX__)
 
 #define GGML_SIMD
@@ -910,7 +1213,7 @@ do {                                                              \
 
 #if defined(__F16C__)
 // the  _mm256_cvt intrinsics require F16C
-#define GGML_F32Cx8_LOAD(x)     _mm256_cvtph_ps(_mm_loadu_si128((__m128i *)(x)))
+#define GGML_F32Cx8_LOAD(x)     _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)(x)))
 #define GGML_F32Cx8_STORE(x, y) _mm_storeu_si128((__m128i *)(x), _mm256_cvtps_ph(y, 0))
 #else
 static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) {
@@ -1246,6 +1549,8 @@ inline static void ggml_vec_set_i32(const int n, int32_t * x, const int32_t v) {
 
 inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
 
+inline static void ggml_vec_set_bf16(const int n, ggml_bf16_t * x, const ggml_bf16_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
+
 inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i] + y[i]; }
 inline static void ggml_vec_add1_f32(const int n, float * z, const float * x, const float   v) { for (int i = 0; i < n; ++i) z[i]  = x[i] + v;    }
 inline static void ggml_vec_acc_f32 (const int n, float * y, const float * x)                  { for (int i = 0; i < n; ++i) y[i] += x[i];        }
@@ -1264,7 +1569,7 @@ static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float *
    UNUSED(by);
    UNUSED(bs);
 
-#ifdef GGML_SIMD
+#if defined(GGML_SIMD)
     float sumf = 0.0f;
     const int np = (n & ~(GGML_F32_STEP - 1));
 
@@ -1300,6 +1605,70 @@ static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float *
     *s = sumf;
 }
 
+static void ggml_vec_dot_bf16(int n, float * restrict s, size_t bs, ggml_bf16_t * restrict x, size_t bx, ggml_bf16_t * restrict y, size_t by, int nrc) {
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+    int i = 0;
+    ggml_float sumf = 0;
+
+#if defined(__AVX512BF16__)
+    __m512 c1 = _mm512_setzero_ps();
+    __m512 c2 = _mm512_setzero_ps();
+    for (; i + 64 <= n; i += 64) {
+        c1 = _mm512_dpbf16_ps(c1, (__m512bh)_mm512_loadu_ps((const float *)(x + i)),
+                              (__m512bh)_mm512_loadu_ps((const float *)(y + i)));
+        c2 = _mm512_dpbf16_ps(c2, (__m512bh)_mm512_loadu_ps((const float *)(x + i + 32)),
+                              (__m512bh)_mm512_loadu_ps((const float *)(y + i + 32)));
+    }
+    sumf += (ggml_float)_mm512_reduce_add_ps(c1);
+    sumf += (ggml_float)_mm512_reduce_add_ps(c2);
+
+#elif defined(__AVX512F__)
+#define LOAD(p) _mm512_castsi512_ps(_mm512_slli_epi32(_mm512_cvtepu16_epi32(_mm256_loadu_si256((const __m256i *)(p))), 16))
+    __m512 c1 = _mm512_setzero_ps();
+    __m512 c2 = _mm512_setzero_ps();
+    for (; i + 32 <= n; i += 32) {
+        c1 = _mm512_add_ps(_mm512_mul_ps(LOAD(x + i), LOAD(y + i)), c1);
+        c2 = _mm512_add_ps(_mm512_mul_ps(LOAD(x + i + 16), LOAD(y + i + 16)), c2);
+    }
+    sumf += (ggml_float)_mm512_reduce_add_ps(c1);
+    sumf += (ggml_float)_mm512_reduce_add_ps(c2);
+
+#undef LOAD
+#elif defined(__AVX2__)
+#define LOAD(p) _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_cvtepu16_epi32(_mm_loadu_si128((const __m128i *)(p))), 16))
+    __m256 c1 = _mm256_setzero_ps();
+    __m256 c2 = _mm256_setzero_ps();
+    __m256 c3 = _mm256_setzero_ps();
+    __m256 c4 = _mm256_setzero_ps();
+    for (; i + 32 <= n; i += 32) {
+        c1 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i), LOAD(y + i)), c1);
+        c2 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 8), LOAD(y + i + 8)), c2);
+        c3 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 16), LOAD(y + i + 16)), c3);
+        c4 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 24), LOAD(y + i + 24)), c4);
+    }
+    __m128 g;
+    c1 = _mm256_add_ps(_mm256_add_ps(c1, c3),
+                       _mm256_add_ps(c2, c4));
+    g = _mm_add_ps(_mm256_extractf128_ps(c1, 1),
+                   _mm256_castps256_ps128(c1));
+    g = _mm_add_ps(g, _mm_movehl_ps(g, g));
+    g = _mm_add_ss(g, _mm_movehdup_ps(g));
+    sumf += (ggml_float)_mm_cvtss_f32(g);
+
+#undef LOAD
+#endif
+
+    for (; i < n; ++i) {
+        sumf += (ggml_float)(GGML_BF16_TO_FP32(x[i]) *
+                             GGML_BF16_TO_FP32(y[i]));
+    }
+    *s = sumf;
+}
+
 static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc) {
     assert(nrc == 1);
     UNUSED(nrc);
@@ -1428,6 +1797,37 @@ inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float
 #endif
 }
 
+inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, const ggml_fp16_t * restrict x, const float v) {
+#if defined(GGML_SIMD)
+    const int np = (n & ~(GGML_F16_STEP - 1));
+
+    GGML_F16_VEC vx = GGML_F16_VEC_SET1(v);
+
+    GGML_F16_VEC ax[GGML_F16_ARR];
+    GGML_F16_VEC ay[GGML_F16_ARR];
+
+    for (int i = 0; i < np; i += GGML_F16_STEP) {
+        for (int j = 0; j < GGML_F16_ARR; j++) {
+            ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j);
+            ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
+            ay[j] = GGML_F16_VEC_FMA(ay[j], ax[j], vx);
+
+            GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j);
+        }
+    }
+
+    // leftovers
+    for (int i = np; i < n; ++i) {
+        y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v);
+    }
+#else
+    // scalar
+    for (int i = 0; i < n; ++i) {
+        y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v);
+    }
+#endif
+}
+
 // xs and vs are byte strides of x and v
 inline static void ggml_vec_mad_f32_unroll(const int n, const int xs, const int vs, float * restrict y, const float * restrict xv, const float * restrict vv) {
 
@@ -1512,6 +1912,35 @@ inline static void ggml_vec_scale_f32(const int n, float * y, const float   v) {
 #endif
 }
 
+inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float v) {
+#if defined(GGML_SIMD)
+    const int np = (n & ~(GGML_F16_STEP - 1));
+
+    GGML_F16_VEC vx = GGML_F16_VEC_SET1(v);
+
+    GGML_F16_VEC ay[GGML_F16_ARR];
+
+    for (int i = 0; i < np; i += GGML_F16_STEP) {
+        for (int j = 0; j < GGML_F16_ARR; j++) {
+            ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
+            ay[j] = GGML_F16_VEC_MUL(ay[j], vx);
+
+            GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j);
+        }
+    }
+
+    // leftovers
+    for (int i = np; i < n; ++i) {
+        y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v);
+    }
+#else
+    // scalar
+    for (int i = 0; i < n; ++i) {
+        y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v);
+    }
+#endif
+}
+
 inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, 0, x, 0, x, 0, 1); *s = sqrtf(*s);   }
 inline static void ggml_vec_sqr_f32  (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i];   }
 inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); }
@@ -1523,6 +1952,7 @@ inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) {
 inline static void ggml_vec_elu_f32  (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expf(x[i])-1; }
 inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; }
 inline static void ggml_vec_leaky_relu_f32 (const int n, float * y, const float * x, const float ns) { for (int i = 0; i < n; ++i) y[i] = ((x[i] > 0.f) ? x[i] : 0.f) + ns * ((x[i] < 0.0f) ? x[i] : 0.f); }
+inline static void ggml_vec_sigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = 1.f / (1.f + expf(-x[i])); }
 // TODO: optimize performance
 inline static void ggml_vec_hardswish_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); }
 inline static void ggml_vec_hardsigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); }
@@ -1546,9 +1976,15 @@ inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp
 inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) {
     uint16_t t;
     for (int i = 0; i < n; ++i) {
-        ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]);
-        memcpy(&t, &fp16, sizeof(uint16_t));
-        y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_f16[t]);
+        if (x[i] <= -10.0f) {
+            y[i] = 0.0f;
+        } else if (x[i] >= 10.0f) {
+            y[i] = x[i];
+        } else {
+            ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]);
+            memcpy(&t, &fp16, sizeof(uint16_t));
+            y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_f16[t]);
+        }
     }
 }
 #else
@@ -1667,6 +2103,14 @@ inline static void ggml_vec_sum_f16_ggf(const int n, float * s, const ggml_fp16_
     *s = sum;
 }
 
+inline static void ggml_vec_sum_bf16_ggf(const int n, float * s, const ggml_bf16_t * x) {
+    float sum = 0.0f;
+    for (int i = 0; i < n; ++i) {
+        sum += GGML_BF16_TO_FP32(x[i]);
+    }
+    *s = sum;
+}
+
 inline static void ggml_vec_max_f32(const int n, float * s, const float * x) {
 #ifndef GGML_USE_ACCELERATE
     float max = -INFINITY;
@@ -1745,7 +2189,6 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "SOFT_MAX_BACK",
     "ROPE",
     "ROPE_BACK",
-    "ALIBI",
     "CLAMP",
     "CONV_TRANSPOSE_1D",
     "IM2COL",
@@ -1754,12 +2197,17 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "POOL_2D",
     "UPSCALE",
     "PAD",
+    "ARANGE",
+    "TIMESTEP_EMBEDDING",
     "ARGSORT",
     "LEAKY_RELU",
 
     "FLASH_ATTN",
+    "FLASH_ATTN_EXT",
     "FLASH_FF",
     "FLASH_ATTN_BACK",
+    "SSM_CONV",
+    "SSM_SCAN",
     "WIN_PART",
     "WIN_UNPART",
     "GET_REL_POS",
@@ -1782,7 +2230,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "CROSS_ENTROPY_LOSS_BACK",
 };
 
-static_assert(GGML_OP_COUNT == 72, "GGML_OP_COUNT != 72");
+static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76");
 
 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "none",
@@ -1831,7 +2279,6 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "soft_max_back(x)",
     "rope(x)",
     "rope_back(x)",
-    "alibi(x)",
     "clamp(x)",
     "conv_transpose_1d(x)",
     "im2col(x)",
@@ -1840,12 +2287,17 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "pool_2d(x)",
     "upscale(x)",
     "pad(x)",
+    "arange(start, stop, step)",
+    "timestep_embedding(timesteps, dim, max_period)",
     "argsort(x)",
     "leaky_relu(x)",
 
     "flash_attn(x)",
+    "flash_attn_ext(x)",
     "flash_ff(x)",
     "flash_attn_back(x)",
+    "ssm_conv(x)",
+    "ssm_scan(x)",
     "win_part(x)",
     "win_unpart(x)",
     "get_rel_pos(x)",
@@ -1868,7 +2320,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "cross_entropy_loss_back(x,y)",
 };
 
-static_assert(GGML_OP_COUNT == 72, "GGML_OP_COUNT != 72");
+static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76");
 
 static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
 
@@ -1881,6 +2333,7 @@ static const char * GGML_UNARY_OP_NAME[GGML_UNARY_OP_COUNT] = {
     "TANH",
     "ELU",
     "RELU",
+    "SIGMOID",
     "GELU",
     "GELU_QUICK",
     "SILU",
@@ -1888,7 +2341,7 @@ static const char * GGML_UNARY_OP_NAME[GGML_UNARY_OP_COUNT] = {
     "HARDSIGMOID",
 };
 
-static_assert(GGML_UNARY_OP_COUNT == 12, "GGML_UNARY_OP_COUNT != 12");
+static_assert(GGML_UNARY_OP_COUNT == 13, "GGML_UNARY_OP_COUNT != 13");
 
 
 static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
@@ -2067,11 +2520,14 @@ void ggml_numa_init(enum ggml_numa_strategy numa_flag) {
     // figure out which node we're on
     uint current_cpu;
     int getcpu_ret = 0;
-#if __GLIBC__ > 2 || (__GLIBC__ == 2 && __GLIBC_MINOR__ > 28)
+#if __GLIBC__ > 2 || (__GLIBC__ == 2 && __GLIBC_MINOR__ > 28) || defined(__COSMOPOLITAN__)
     getcpu_ret = getcpu(&current_cpu, &g_state.numa.current_node);
 #else
     // old glibc doesn't have a wrapper for this call. Fall back on direct syscall
-    getcpu_ret = syscall(SYS_getcpu,&current_cpu,&g_state.numa.current_node);
+#   if !defined(SYS_getcpu) && defined(SYS_get_cpu)
+#       define SYS_getcpu SYS_get_cpu // some older glibc versions use this name
+#   endif
+    getcpu_ret = syscall(SYS_getcpu, &current_cpu, &g_state.numa.current_node);
 #endif
 
     if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1 || getcpu_ret != 0) {
@@ -2275,6 +2731,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
     switch (ftype) {
         case GGML_FTYPE_ALL_F32:              wtype = GGML_TYPE_F32;   break;
         case GGML_FTYPE_MOSTLY_F16:           wtype = GGML_TYPE_F16;   break;
+        case GGML_FTYPE_MOSTLY_BF16:          wtype = GGML_TYPE_BF16;  break;
         case GGML_FTYPE_MOSTLY_Q4_0:          wtype = GGML_TYPE_Q4_0;  break;
         case GGML_FTYPE_MOSTLY_Q4_1:          wtype = GGML_TYPE_Q4_1;  break;
         case GGML_FTYPE_MOSTLY_Q5_0:          wtype = GGML_TYPE_Q5_0;  break;
@@ -2289,6 +2746,11 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
         case GGML_FTYPE_MOSTLY_IQ2_XS:        wtype = GGML_TYPE_IQ2_XS;   break;
         case GGML_FTYPE_MOSTLY_IQ3_XXS:       wtype = GGML_TYPE_IQ3_XXS;  break;
         case GGML_FTYPE_MOSTLY_IQ1_S:         wtype = GGML_TYPE_IQ1_S;    break;
+        case GGML_FTYPE_MOSTLY_IQ1_M:         wtype = GGML_TYPE_IQ1_M;    break;
+        case GGML_FTYPE_MOSTLY_IQ4_NL:        wtype = GGML_TYPE_IQ4_NL;   break;
+        case GGML_FTYPE_MOSTLY_IQ4_XS:        wtype = GGML_TYPE_IQ4_XS;   break;
+        case GGML_FTYPE_MOSTLY_IQ3_S:         wtype = GGML_TYPE_IQ3_S;    break;
+        case GGML_FTYPE_MOSTLY_IQ2_S:         wtype = GGML_TYPE_IQ2_S;    break;
         case GGML_FTYPE_UNKNOWN:              wtype = GGML_TYPE_COUNT; break;
         case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break;
     }
@@ -2340,6 +2802,16 @@ static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) {
         tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
 }
 
+GGML_CALL bool ggml_is_empty(const struct ggml_tensor * tensor) {
+    for (int i = 0; i < GGML_MAX_DIMS; ++i) {
+        if (tensor->ne[i] == 0) {
+            // empty if any dimension has no elements
+            return true;
+        }
+    }
+    return false;
+}
+
 bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
@@ -2354,7 +2826,7 @@ bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor
 static inline bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
-    return
+    return ggml_is_empty(t0) ? ggml_is_empty(t1) :
         (t1->ne[0]%t0->ne[0] == 0) &&
         (t1->ne[1]%t0->ne[1] == 0) &&
         (t1->ne[2]%t0->ne[2] == 0) &&
@@ -2401,15 +2873,16 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
         {
             const uint64_t t_start = ggml_time_us(); UNUSED(t_start);
 
-            ggml_fp16_t ii;
             for (int i = 0; i < (1 << 16); ++i) {
-                uint16_t ui = i;
-                memcpy(&ii, &ui, sizeof(ii));
-                const float f = ggml_table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(ii);
+                union {
+                    uint16_t u16;
+                    ggml_fp16_t fp16;
+                } u = {i};
+                float f = ggml_table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(u.fp16);
                 ggml_table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f));
                 ggml_table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f));
                 ggml_table_silu_f16[i] = GGML_FP32_TO_FP16(ggml_silu_f32(f));
-                ggml_table_exp_f16[i]  = GGML_FP32_TO_FP16(expf(f));
+                ggml_table_exp_f16[i] = GGML_FP32_TO_FP16(expf(f));
             }
 
             const uint64_t t_end = ggml_time_us(); UNUSED(t_end);
@@ -2438,14 +2911,8 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
             GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f);
         }
 
-#if defined(GGML_USE_CUBLAS)
-        ggml_init_cublas();
-#elif defined(GGML_USE_CLBLAST)
+#if defined(GGML_USE_CLBLAST)
         ggml_cl_init();
-#elif defined(GGML_USE_VULKAN)
-        ggml_vk_init_cpu_assist();
-#elif defined(GGML_USE_SYCL)
-        ggml_init_sycl();
 #endif
 
         ggml_setup_op_has_task_pass();
@@ -2665,7 +3132,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
         data_size *= ne[i];
     }
 
-    GGML_ASSERT(view_src == NULL || data_size + view_offs <= ggml_nbytes(view_src));
+    GGML_ASSERT(view_src == NULL || data_size == 0 || data_size + view_offs <= ggml_nbytes(view_src));
 
     void * data = view_src != NULL ? view_src->data : NULL;
     if (data != NULL) {
@@ -2693,7 +3160,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
         }
     }
 
-    struct ggml_object * const obj_new = ggml_new_object(ctx, GGML_OBJECT_TENSOR, GGML_TENSOR_SIZE + obj_alloc_size);
+    struct ggml_object * const obj_new = ggml_new_object(ctx, GGML_OBJECT_TYPE_TENSOR, GGML_TENSOR_SIZE + obj_alloc_size);
 
     // TODO: for recoverable errors, we would need to free the data allocated from the scratch buffer here
 
@@ -2701,7 +3168,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
 
     *result = (struct ggml_tensor) {
         /*.type         =*/ type,
-        /*.backend      =*/ GGML_BACKEND_CPU,
+        /*.backend      =*/ GGML_BACKEND_TYPE_CPU,
         /*.buffer       =*/ NULL,
         /*.ne           =*/ { 1, 1, 1, 1 },
         /*.nb           =*/ { 0, 0, 0, 0 },
@@ -2823,11 +3290,21 @@ static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_
     return ((const int32_t *)(tensor->op_params))[i];
 }
 
+static float ggml_get_op_params_f32(const struct ggml_tensor * tensor, uint32_t i) {
+    assert(i < GGML_MAX_OP_PARAMS / sizeof(float));
+    return ((const float *)(tensor->op_params))[i];
+}
+
 static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int32_t value) {
     assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t));
     ((int32_t *)(tensor->op_params))[i] = value;
 }
 
+static void ggml_set_op_params_f32(struct ggml_tensor * tensor, uint32_t i, float value) {
+    assert(i < GGML_MAX_OP_PARAMS / sizeof(float));
+    ((float *)(tensor->op_params))[i] = value;
+}
+
 struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) {
     memset(tensor->data, 0, ggml_nbytes(tensor));
     return tensor;
@@ -2869,6 +3346,13 @@ struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) {
                     ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value));
                 }
             } break;
+        case GGML_TYPE_BF16:
+            {
+                assert(tensor->nb[0] == sizeof(ggml_fp16_t));
+                for (int i = 0; i < n; i++) {
+                    ggml_vec_set_bf16(nc, (ggml_bf16_t *)(data + i*n1), GGML_FP32_TO_BF16(value));
+                }
+            } break;
         case GGML_TYPE_F32:
             {
                 assert(tensor->nb[0] == sizeof(float));
@@ -2921,6 +3405,13 @@ struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) {
                     ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value));
                 }
             } break;
+        case GGML_TYPE_BF16:
+            {
+                assert(tensor->nb[0] == sizeof(ggml_bf16_t));
+                for (int i = 0; i < n; i++) {
+                    ggml_vec_set_bf16(nc, (ggml_bf16_t *)(data + i*n1), GGML_FP32_TO_BF16(value));
+                }
+            } break;
         case GGML_TYPE_F32:
             {
                 assert(tensor->nb[0] == sizeof(float));
@@ -2988,6 +3479,11 @@ int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i) {
                 GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
                 return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]);
             }
+        case GGML_TYPE_BF16:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t));
+                return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]);
+            }
         case GGML_TYPE_F32:
             {
                 GGML_ASSERT(tensor->nb[0] == sizeof(float));
@@ -3030,6 +3526,11 @@ void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) {
                 GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
                 ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value);
             } break;
+        case GGML_TYPE_BF16:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t));
+                ((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value);
+            } break;
         case GGML_TYPE_F32:
             {
                 GGML_ASSERT(tensor->nb[0] == sizeof(float));
@@ -3053,6 +3554,8 @@ int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i
             return ((int32_t *) data)[0];
         case GGML_TYPE_F16:
             return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]);
+        case GGML_TYPE_BF16:
+            return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]);
         case GGML_TYPE_F32:
             return ((float *) data)[0];
         default:
@@ -3081,6 +3584,10 @@ void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2,
             {
                 ((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value);
             } break;
+        case GGML_TYPE_BF16:
+            {
+                ((ggml_bf16_t *)(data))[0] = GGML_FP32_TO_BF16(value);
+            } break;
         case GGML_TYPE_F32:
             {
                 ((float *)(data))[0] = value;
@@ -3119,6 +3626,11 @@ float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) {
                 GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
                 return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]);
             }
+        case GGML_TYPE_BF16:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t));
+                return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]);
+            }
         case GGML_TYPE_F32:
             {
                 GGML_ASSERT(tensor->nb[0] == sizeof(float));
@@ -3161,6 +3673,11 @@ void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) {
                 GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
                 ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value);
             } break;
+        case GGML_TYPE_BF16:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t));
+                ((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value);
+            } break;
         case GGML_TYPE_F32:
             {
                 GGML_ASSERT(tensor->nb[0] == sizeof(float));
@@ -3184,6 +3701,8 @@ float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2,
             return ((int32_t *) data)[0];
         case GGML_TYPE_F16:
             return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]);
+        case GGML_TYPE_BF16:
+            return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]);
         case GGML_TYPE_F32:
             return ((float *) data)[0];
         default:
@@ -3212,6 +3731,10 @@ void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2,
             {
                 ((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value);
             } break;
+        case GGML_TYPE_BF16:
+            {
+                ((ggml_bf16_t *)(data))[0] = GGML_FP32_TO_BF16(value);
+            } break;
         case GGML_TYPE_F32:
             {
                 ((float *)(data))[0] = value;
@@ -3274,7 +3797,7 @@ struct ggml_tensor * ggml_get_first_tensor(const struct ggml_context * ctx) {
     char * const mem_buffer = ctx->mem_buffer;
 
     while (obj != NULL) {
-        if (obj->type == GGML_OBJECT_TENSOR) {
+        if (obj->type == GGML_OBJECT_TYPE_TENSOR) {
             return (struct ggml_tensor *)(mem_buffer + obj->offs);
         }
 
@@ -3291,7 +3814,7 @@ struct ggml_tensor * ggml_get_next_tensor(const struct ggml_context * ctx, struc
     char * const mem_buffer = ctx->mem_buffer;
 
     while (obj != NULL) {
-        if (obj->type == GGML_OBJECT_TENSOR) {
+        if (obj->type == GGML_OBJECT_TYPE_TENSOR) {
             return (struct ggml_tensor *)(mem_buffer + obj->offs);
         }
 
@@ -3307,7 +3830,7 @@ struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * nam
     char * const mem_buffer = ctx->mem_buffer;
 
     while (obj != NULL) {
-        if (obj->type == GGML_OBJECT_TENSOR) {
+        if (obj->type == GGML_OBJECT_TYPE_TENSOR) {
             struct ggml_tensor * cur = (struct ggml_tensor *)(mem_buffer + obj->offs);
             if (strcmp(cur->name, name) == 0) {
                 return cur;
@@ -3406,7 +3929,11 @@ static struct ggml_tensor * ggml_add_cast_impl(
     // TODO: support less-strict constraint
     //       GGML_ASSERT(ggml_can_repeat(b, a));
     GGML_ASSERT(ggml_can_repeat_rows(b, a));
-    GGML_ASSERT(ggml_is_quantized(a->type) || a->type == GGML_TYPE_F16); // currently only supported for quantized input and f16
+
+    // currently only supported for quantized input and f16
+    GGML_ASSERT(ggml_is_quantized(a->type) ||
+                a->type == GGML_TYPE_F16 ||
+                a->type == GGML_TYPE_BF16);
 
     bool is_node = false;
 
@@ -4039,6 +4566,20 @@ struct ggml_tensor * ggml_leaky_relu(
     return result;
 }
 
+// ggml_sigmoid
+
+struct ggml_tensor * ggml_sigmoid(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary(ctx, a, GGML_UNARY_OP_SIGMOID);
+}
+
+struct ggml_tensor * ggml_sigmoid_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_SIGMOID);
+}
+
 // ggml_gelu
 
 struct ggml_tensor * ggml_gelu(
@@ -4289,6 +4830,8 @@ struct ggml_tensor * ggml_mul_mat(
 void ggml_mul_mat_set_prec(
         struct ggml_tensor * a,
         enum ggml_prec       prec) {
+    GGML_ASSERT(a->op == GGML_OP_MUL_MAT);
+
     const int32_t prec_i32 = (int32_t) prec;
 
     ggml_set_op_params_i32(a, 0, prec_i32);
@@ -4296,45 +4839,47 @@ void ggml_mul_mat_set_prec(
 
 // ggml_mul_mat_id
 
+/*
+    c = ggml_mul_mat_id(ctx, as, b, ids);
+
+    as  -> [cols, rows, n_expert]
+    ids -> [n_experts_used, n_tokens] (i32)
+    b   -> [cols, n_expert_used, n_tokens]
+    c   -> [cols, n_expert_used, n_tokens]
+
+    in b, n_experts_used can be broadcasted to match the n_expert_used of ids
+
+    c ~= as[:,:,i] @ b[:,i%r,t], i = ids[e,t] for all e,t in ids
+*/
 struct ggml_tensor * ggml_mul_mat_id(
         struct ggml_context * ctx,
-        struct ggml_tensor  * const as[],
-        int                   n_as,
-        struct ggml_tensor  * ids,
-        int                   id,
-        struct ggml_tensor  * b) {
-
+        struct ggml_tensor  * as,
+        struct ggml_tensor  * b,
+        struct ggml_tensor  * ids) {
+    GGML_ASSERT(!ggml_is_transposed(as));
     GGML_ASSERT(ids->type == GGML_TYPE_I32);
-    GGML_ASSERT(ids->ne[2] == 1 && ids->ne[3] == 1);
-    GGML_ASSERT(ids->ne[1] == b->ne[1]);
-    GGML_ASSERT(ids->ne[2] == b->ne[2] && ids->ne[3] == b->ne[3]);
-    GGML_ASSERT(n_as > 0 && n_as <= GGML_MAX_SRC - 2);
-    GGML_ASSERT(id >= 0 && id < ids->ne[0]);
+
+    GGML_ASSERT(as->ne[3] == 1); // as is 3d (one matrix per expert)
+    GGML_ASSERT(b->ne[3] == 1); // b is 3d
+    GGML_ASSERT(ids->ne[2] == 1 && ids->ne[3] == 1); // ids is 2d
+    GGML_ASSERT(ids->ne[1] == b->ne[2]); // must have an expert list per b row
+    GGML_ASSERT(as->ne[0] == b->ne[0]); // can_mul_mat
+    GGML_ASSERT(ids->ne[0] % b->ne[1] == 0); // can broadcast
 
     bool is_node = false;
 
-    if (as[0]->grad || b->grad) {
+    if (as->grad || b->grad) {
         is_node = true;
     }
 
-    const int64_t ne[4] = { as[0]->ne[1], b->ne[1], b->ne[2], b->ne[3] };
+    const int64_t ne[4] = { as->ne[1], ids->ne[0], b->ne[2], 1 };
     struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
 
-    ggml_set_op_params_i32(result, 0, id);
-    ggml_set_op_params_i32(result, 1, n_as);
-
     result->op   = GGML_OP_MUL_MAT_ID;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
-    result->src[0] = ids;
+    result->src[0] = as;
     result->src[1] = b;
-
-    for (int i = 0; i < n_as; i++) {
-        struct ggml_tensor * a = as[i];
-        GGML_ASSERT(ggml_are_same_shape(as[0], a));
-        GGML_ASSERT(ggml_can_mul_mat(a, b));
-        GGML_ASSERT(!ggml_is_transposed(a));
-        result->src[i + 2] = a;
-    }
+    result->src[2] = ids;
 
     return result;
 }
@@ -5118,26 +5663,21 @@ static struct ggml_tensor * ggml_soft_max_impl(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
         struct ggml_tensor  * mask,
-        struct ggml_tensor  * pos,
         float                 scale,
         float                 max_bias,
         bool                  inplace) {
     GGML_ASSERT(ggml_is_contiguous(a));
 
     if (mask) {
+        GGML_ASSERT(mask->type == GGML_TYPE_F16 || mask->type == GGML_TYPE_F32);
         GGML_ASSERT(ggml_is_contiguous(mask));
         GGML_ASSERT(ggml_is_matrix(mask));
-        GGML_ASSERT(ggml_can_repeat_rows(mask, a));
-    }
-
-    if (pos) {
-        GGML_ASSERT(ggml_is_vector(pos));
-        GGML_ASSERT(pos->type == GGML_TYPE_F32);
-        GGML_ASSERT(pos->ne[0] == a->ne[0]);
+        GGML_ASSERT(mask->ne[0] == a->ne[0]);
+        GGML_ASSERT(mask->ne[1] >= a->ne[1]);
     }
 
     if (max_bias > 0.0f) {
-        GGML_ASSERT(pos);
+        GGML_ASSERT(mask);
     }
 
     bool is_node = false;
@@ -5155,7 +5695,6 @@ static struct ggml_tensor * ggml_soft_max_impl(
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
     result->src[0] = a;
     result->src[1] = mask;
-    result->src[2] = pos;
 
     return result;
 }
@@ -5163,23 +5702,22 @@ static struct ggml_tensor * ggml_soft_max_impl(
 struct ggml_tensor * ggml_soft_max(
         struct ggml_context * ctx,
         struct ggml_tensor  * a) {
-    return ggml_soft_max_impl(ctx, a, NULL, NULL, 1.0f, 0.0f, false);
+    return ggml_soft_max_impl(ctx, a, NULL, 1.0f, 0.0f, false);
 }
 
 struct ggml_tensor * ggml_soft_max_inplace(
         struct ggml_context * ctx,
         struct ggml_tensor  * a) {
-    return ggml_soft_max_impl(ctx, a, NULL, NULL, 1.0f, 0.0f, true);
+    return ggml_soft_max_impl(ctx, a, NULL, 1.0f, 0.0f, true);
 }
 
 struct ggml_tensor * ggml_soft_max_ext(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
         struct ggml_tensor  * mask,
-        struct ggml_tensor  * pos,
         float                 scale,
         float                 max_bias) {
-    return ggml_soft_max_impl(ctx, a, mask, pos, scale, max_bias, false);
+    return ggml_soft_max_impl(ctx, a, mask, scale, max_bias, false);
 }
 
 // ggml_soft_max_back
@@ -5394,37 +5932,6 @@ struct ggml_tensor * ggml_rope_back(
     return result;
 }
 
-// ggml_alibi
-
-struct ggml_tensor * ggml_alibi(
-        struct ggml_context * ctx,
-        struct ggml_tensor  * a,
-        int                   n_past,
-        int                   n_head,
-        float                 bias_max) {
-    GGML_ASSERT(n_past >= 0);
-    bool is_node = false;
-
-    if (a->grad) {
-        GGML_ASSERT(false); // TODO: implement backward
-        is_node = true;
-    }
-
-    // TODO: when implement backward, fix this:
-    //struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
-    struct ggml_tensor * result = ggml_view_tensor(ctx, a);
-
-    int32_t op_params[3] = { n_past, n_head };
-    memcpy(op_params + 2, &bias_max, sizeof(float));
-    ggml_set_op_params(result, op_params, sizeof(op_params));
-
-    result->op   = GGML_OP_ALIBI;
-    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
-    result->src[0] = a;
-
-    return result;
-}
-
 // ggml_clamp
 
 struct ggml_tensor * ggml_clamp(
@@ -5629,7 +6136,9 @@ struct ggml_tensor * ggml_conv_2d(
                 ggml_reshape_2d(ctx, im2col, im2col->ne[0],  im2col->ne[3] * im2col->ne[2] * im2col->ne[1]), // [N, OH, OW, IC * KH * KW] => [N*OH*OW, IC * KH * KW]
                 ggml_reshape_2d(ctx, a, (a->ne[0] * a->ne[1] * a->ne[2]),  a->ne[3]));                       // [OC，IC, KH, KW] => [OC, IC * KH * KW]
 
-    result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], a->ne[3], im2col->ne[3]); // [N, OC, OH, OW]
+    result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], im2col->ne[3], a->ne[3]); // [OC, N, OH, OW]
+    result = ggml_cont(ctx, ggml_permute(ctx, result, 0, 1, 3, 2)); // [N, OC, OH, OW]
+
 
     return result;
 }
@@ -5712,11 +6221,13 @@ struct ggml_tensor * ggml_pool_1d(
         is_node = true;
     }
 
-    const int64_t ne[2] = {
+    const int64_t ne[4] = {
         ggml_calc_pool_output_size(a->ne[0], k0, s0, p0),
         a->ne[1],
+        a->ne[2],
+        a->ne[3],
     };
-    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
 
     int32_t params[] = { op, k0, s0, p0 };
     ggml_set_op_params(result, params, sizeof(params));
@@ -5822,6 +6333,55 @@ struct ggml_tensor * ggml_upscale(
     return ggml_upscale_impl(ctx, a, scale_factor);
 }
 
+struct ggml_tensor * ggml_arange(
+    struct ggml_context * ctx,
+    float start,
+    float stop,
+    float step) {
+
+    GGML_ASSERT(stop > start);
+
+    const int64_t steps = (int64_t) ceilf((stop - start) / step);
+
+    struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, steps);
+
+    result->op = GGML_OP_ARANGE;
+    ggml_set_op_params_f32(result, 0, start);
+    ggml_set_op_params_f32(result, 1, stop);
+    ggml_set_op_params_f32(result, 2, step);
+
+    return result;
+}
+
+struct ggml_tensor * ggml_timestep_embedding(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * timesteps,
+            int                   dim,
+            int                   max_period) {
+    bool is_node = false;
+
+    if (timesteps->grad) {
+        GGML_ASSERT(false); // TODO: implement backward
+        is_node = true;
+    }
+
+    int actual_dim = dim;
+    if (dim % 2 != 0) {
+        actual_dim = dim + 1;
+    }
+
+    struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, actual_dim, timesteps->ne[0]);
+
+    result->op = GGML_OP_TIMESTEP_EMBEDDING;
+    ggml_set_op_params_i32(result, 0, dim);
+    ggml_set_op_params_i32(result, 1, max_period);
+
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = timesteps;
+
+    return result;
+}
+
 // ggml_argsort
 
 struct ggml_tensor * ggml_argsort(
@@ -5849,7 +6409,7 @@ struct ggml_tensor * ggml_top_k(
         int                   k) {
     GGML_ASSERT(a->ne[0] >= k);
 
-    struct ggml_tensor * result = ggml_argsort(ctx, a, GGML_SORT_DESC);
+    struct ggml_tensor * result = ggml_argsort(ctx, a, GGML_SORT_ORDER_DESC);
 
     result = ggml_view_4d(ctx, result,
                 k, result->ne[1], result->ne[2], result->ne[3],
@@ -5891,6 +6451,65 @@ struct ggml_tensor * ggml_flash_attn(
     return result;
 }
 
+// ggml_flash_attn_ext
+
+struct ggml_tensor * ggml_flash_attn_ext(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * q,
+        struct ggml_tensor  * k,
+        struct ggml_tensor  * v,
+        struct ggml_tensor  * mask,
+        float                 scale,
+        float                 max_bias) {
+    GGML_ASSERT(ggml_can_mul_mat(k, q));
+    // TODO: check if vT can be multiplied by (k*qT)
+
+    if (mask) {
+        GGML_ASSERT(ggml_is_contiguous(mask));
+        GGML_ASSERT(mask->ne[2] == 1);
+        GGML_ASSERT(mask->ne[3] == 1);
+        GGML_ASSERT(mask->ne[1] >= GGML_PAD(q->ne[1], GGML_KQ_MASK_PAD) &&
+                "the Flash-Attention kernel requires the mask to be padded to GGML_KQ_MASK_PAD and at least n_queries big");
+        //GGML_ASSERT(ggml_can_repeat_rows(mask, qk));
+    }
+
+    if (max_bias > 0.0f) {
+        GGML_ASSERT(mask);
+    }
+
+    bool is_node = false;
+
+    if (q->grad || k->grad || v->grad) {
+        is_node = true;
+    }
+
+    // permute(0, 2, 1, 3)
+    int64_t ne[4] = { q->ne[0], q->ne[2], q->ne[1], q->ne[3] };
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
+
+    float params[] = { scale, max_bias };
+    ggml_set_op_params(result, params, sizeof(params));
+
+    result->op   = GGML_OP_FLASH_ATTN_EXT;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = q;
+    result->src[1] = k;
+    result->src[2] = v;
+    result->src[3] = mask;
+
+    return result;
+}
+
+void ggml_flash_attn_ext_set_prec(
+        struct ggml_tensor * a,
+        enum ggml_prec       prec) {
+    GGML_ASSERT(a->op == GGML_OP_FLASH_ATTN_EXT);
+
+    const int32_t prec_i32 = (int32_t) prec;
+
+    ggml_set_op_params_i32(a, 2, prec_i32); // scale is on first pos, max_bias on second
+}
+
 // ggml_flash_ff
 
 struct ggml_tensor * ggml_flash_ff(
@@ -6001,6 +6620,108 @@ struct ggml_tensor * ggml_flash_attn_back(
     return result;
 }
 
+// ggml_ssm_conv
+
+struct ggml_tensor * ggml_ssm_conv(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * s,
+        struct ggml_tensor  * x,
+        struct ggml_tensor  * c,
+        struct ggml_tensor  * sq) {
+    GGML_ASSERT(ggml_is_3d(s));
+    GGML_ASSERT(ggml_is_matrix(x));
+    GGML_ASSERT(ggml_is_matrix(c));
+    GGML_ASSERT(ggml_is_matrix(sq));
+    GGML_ASSERT(sq->type == GGML_TYPE_I32);
+
+    const int64_t d_conv   = c->ne[0];
+    const int64_t d_inner  = c->ne[1];
+    const int64_t n_tokens = x->ne[1];
+    const int64_t n_kv     = s->ne[2];
+
+    GGML_ASSERT( s->ne[0] == d_conv - 1);
+    GGML_ASSERT( s->ne[1] == d_inner);
+    GGML_ASSERT( x->ne[0] == d_inner);
+    GGML_ASSERT(sq->ne[0] == n_kv);
+    GGML_ASSERT(sq->ne[1] == n_tokens);
+
+    bool is_node = false;
+
+    if (s->grad || x->grad || c->grad || sq->grad) {
+        GGML_ASSERT(false); // TODO: implement
+        is_node = true;
+    }
+
+    // 2-in-1 concatenated x and conv_states, {d_inner, n_tokens} with {d_conv, d_inner, n_kv}
+    struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, (d_inner*n_tokens) + (d_conv*d_inner*n_kv));
+
+    result->op   = GGML_OP_SSM_CONV;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = s;
+    result->src[1] = x;
+    result->src[2] = c;
+    result->src[3] = sq;
+
+    return result;
+}
+
+// ggml_ssm_scan
+
+struct ggml_tensor * ggml_ssm_scan(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * s,
+        struct ggml_tensor  * x,
+        struct ggml_tensor  * dt,
+        struct ggml_tensor  * A,
+        struct ggml_tensor  * B,
+        struct ggml_tensor  * C,
+        struct ggml_tensor  * sq) {
+    GGML_ASSERT(ggml_is_contiguous(s));
+    GGML_ASSERT(ggml_is_contiguous(x));
+    GGML_ASSERT(ggml_is_contiguous(dt));
+    GGML_ASSERT(ggml_is_contiguous(A));
+    GGML_ASSERT(sq->type == GGML_TYPE_I32);
+    GGML_ASSERT(B->nb[0] == ggml_type_size(B->type));
+    GGML_ASSERT(C->nb[0] == ggml_type_size(C->type));
+    GGML_ASSERT(ggml_are_same_shape(x, dt));
+
+    {
+        const int64_t d_state  = s->ne[0];
+        const int64_t d_inner  = s->ne[1];
+        const int64_t n_tokens = x->ne[1];
+
+        GGML_ASSERT(x->ne[0] == d_inner);
+        GGML_ASSERT(A->ne[0] == d_state);
+        GGML_ASSERT(A->ne[1] == d_inner);
+        GGML_ASSERT(B->ne[0] == d_state);
+        GGML_ASSERT(B->ne[1] == n_tokens);
+        GGML_ASSERT(C->ne[0] == d_state);
+        GGML_ASSERT(C->ne[1] == n_tokens);
+    }
+
+    bool is_node = false;
+
+    if (s->grad || x->grad || dt->grad || A->grad || B->grad || C->grad || sq->grad) {
+        GGML_ASSERT(false); // TODO: implement
+        is_node = true;
+    }
+
+    // 2-in-1 concatenated y and ssm_states, {d_inner, n_tokens} with {d_state, d_inner, n_kv}
+    struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, ggml_nelements(x) + ggml_nelements(s));
+
+    result->op   = GGML_OP_SSM_SCAN;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = s;
+    result->src[1] = x;
+    result->src[2] = dt;
+    result->src[3] = A;
+    result->src[4] = B;
+    result->src[5] = C;
+    result->src[6] = sq;
+
+    return result;
+}
+
 // ggml_win_part
 
 struct ggml_tensor * ggml_win_part(
@@ -6635,13 +7356,15 @@ void ggml_set_param(
 
 static void ggml_compute_forward_dup_same_cont(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
     GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0));
     GGML_ASSERT(src0->type == dst->type);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -6663,15 +7386,17 @@ static void ggml_compute_forward_dup_same_cont(
             ((char *) src0->data + ie0*nb00),
             (ie1 - ie0) * ggml_type_size(src0->type));
     }
-
 }
+
 static void ggml_compute_forward_dup_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -6681,7 +7406,7 @@ static void ggml_compute_forward_dup_f16(
     const int nth = params->nth; // number of threads
 
     if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) {
-        ggml_compute_forward_dup_same_cont(params, src0, dst);
+        ggml_compute_forward_dup_same_cont(params, dst);
         return;
     }
 
@@ -6936,13 +7661,15 @@ static void ggml_compute_forward_dup_f16(
     }
 }
 
-static void ggml_compute_forward_dup_f32(
+static void ggml_compute_forward_dup_bf16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -6952,7 +7679,367 @@ static void ggml_compute_forward_dup_f32(
     const int nth = params->nth; // number of threads
 
     if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) {
-        ggml_compute_forward_dup_same_cont(params, src0, dst);
+        ggml_compute_forward_dup_same_cont(params, dst);
+        return;
+    }
+
+    // parallelize by rows
+    const int nr = ne01;
+    // number of rows per thread
+    const int dr = (nr + nth - 1) / nth;
+    // row range for this thread
+    const int ir0 = dr * ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    if (src0->type == dst->type &&
+        ne00 == ne0 &&
+        nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) {
+        // copy by rows
+        const size_t rs = ne00*nb00;
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                for (int64_t i01 = ir0; i01 < ir1; i01++) {
+                    memcpy(
+                        ((char *)  dst->data + i01*nb1  + i02*nb2  + i03*nb3),
+                        ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03),
+                        rs);
+                }
+            }
+        }
+        return;
+    }
+
+    // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy
+
+    if (ggml_is_contiguous(dst)) {
+        if (nb00 == sizeof(ggml_bf16_t)) {
+            if (dst->type == GGML_TYPE_BF16) {
+                size_t id = 0;
+                const size_t rs = ne00 * nb00;
+                char * dst_ptr = (char *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        id += rs * ir0;
+                        for (int i01 = ir0; i01 < ir1; i01++) {
+                            const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
+                            memcpy(dst_ptr + id, src0_ptr, rs);
+                            id += rs;
+                        }
+                        id += rs * (ne01 - ir1);
+                    }
+                }
+            } else if (dst->type == GGML_TYPE_F16) {
+                size_t id = 0;
+                ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        id += ne00 * ir0;
+                        for (int i01 = ir0; i01 < ir1; i01++) {
+                            const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
+                            for (int i00 = 0; i00 < ne00; i00++) {
+                                dst_ptr[id] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(src0_ptr[i00]));
+                                id++;
+                            }
+                        }
+                        id += ne00 * (ne01 - ir1);
+                    }
+                }
+            } else if (dst->type == GGML_TYPE_F32) {
+                size_t id = 0;
+                float * dst_ptr = (float *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        id += ne00 * ir0;
+                        for (int i01 = ir0; i01 < ir1; i01++) {
+                            const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
+                            for (int i00 = 0; i00 < ne00; i00++) {
+                                dst_ptr[id] = GGML_BF16_TO_FP32(src0_ptr[i00]);
+                                id++;
+                            }
+                        }
+                        id += ne00 * (ne01 - ir1);
+                    }
+                }
+            } else if (type_traits[dst->type].from_float) {
+                ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float;
+                float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith;
+
+                size_t id = 0;
+                size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type));
+                char * dst_ptr = (char *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        id += rs * ir0;
+                        for (int i01 = ir0; i01 < ir1; i01++) {
+                            const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
+
+                            for (int i00 = 0; i00 < ne00; i00++) {
+                                src0_f32[i00] = GGML_BF16_TO_FP32(src0_ptr[i00]);
+                            }
+
+                            quantize_row_q(src0_f32, dst_ptr + id, ne00);
+                            id += rs;
+                        }
+                        id += rs * (ne01 - ir1);
+                    }
+                }
+            } else {
+                GGML_ASSERT(false); // TODO: implement
+            }
+        } else {
+            //printf("%s: this is not optimal - fix me\n", __func__);
+
+            if (dst->type == GGML_TYPE_F32) {
+                size_t id = 0;
+                float * dst_ptr = (float *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        id += ne00 * ir0;
+                        for (int i01 = ir0; i01 < ir1; i01++) {
+                            for (int i00 = 0; i00 < ne00; i00++) {
+                                const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+
+                                dst_ptr[id] = GGML_BF16_TO_FP32(*src0_ptr);
+                                id++;
+                            }
+                        }
+                        id += ne00 * (ne01 - ir1);
+                    }
+                }
+            } else if (dst->type == GGML_TYPE_BF16) {
+                size_t id = 0;
+                ggml_bf16_t * dst_ptr = (ggml_bf16_t *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        id += ne00 * ir0;
+                        for (int i01 = ir0; i01 < ir1; i01++) {
+                            for (int i00 = 0; i00 < ne00; i00++) {
+                                const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+
+                                dst_ptr[id] = *src0_ptr;
+                                id++;
+                            }
+                        }
+                        id += ne00 * (ne01 - ir1);
+                    }
+                }
+            } else if (dst->type == GGML_TYPE_F16) {
+                size_t id = 0;
+                ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        id += ne00 * ir0;
+                        for (int i01 = ir0; i01 < ir1; i01++) {
+                            for (int i00 = 0; i00 < ne00; i00++) {
+                                const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+
+                                dst_ptr[id] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(*src0_ptr));
+                                id++;
+                            }
+                        }
+                        id += ne00 * (ne01 - ir1);
+                    }
+                }
+            } else {
+                GGML_ASSERT(false); // TODO: implement
+            }
+        }
+        return;
+    }
+
+    // dst counters
+    int64_t i10 = 0;
+    int64_t i11 = 0;
+    int64_t i12 = 0;
+    int64_t i13 = 0;
+
+    if (dst->type == GGML_TYPE_BF16) {
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                i10 += ne00 * ir0;
+                while (i10 >= ne0) {
+                    i10 -= ne0;
+                    if (++i11 == ne1) {
+                        i11 = 0;
+                        if (++i12 == ne2) {
+                            i12 = 0;
+                            if (++i13 == ne3) {
+                                i13 = 0;
+                            }
+                        }
+                    }
+                }
+                for (int64_t i01 = ir0; i01 < ir1; i01++) {
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+                              char * dst_ptr  = ((char *)  dst->data + i10*nb0  + i11*nb1  + i12*nb2  + i13*nb3);
+
+                        memcpy(dst_ptr, src0_ptr, sizeof(ggml_bf16_t));
+
+                        if (++i10 == ne00) {
+                            i10 = 0;
+                            if (++i11 == ne01) {
+                                i11 = 0;
+                                if (++i12 == ne02) {
+                                    i12 = 0;
+                                    if (++i13 == ne03) {
+                                        i13 = 0;
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+                i10 += ne00 * (ne01 - ir1);
+                while (i10 >= ne0) {
+                    i10 -= ne0;
+                    if (++i11 == ne1) {
+                        i11 = 0;
+                        if (++i12 == ne2) {
+                            i12 = 0;
+                            if (++i13 == ne3) {
+                                i13 = 0;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    } else if (dst->type == GGML_TYPE_F16) {
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                i10 += ne00 * ir0;
+                while (i10 >= ne0) {
+                    i10 -= ne0;
+                    if (++i11 == ne1) {
+                        i11 = 0;
+                        if (++i12 == ne2) {
+                            i12 = 0;
+                            if (++i13 == ne3) {
+                                i13 = 0;
+                            }
+                        }
+                    }
+                }
+                for (int64_t i01 = ir0; i01 < ir1; i01++) {
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+                              char * dst_ptr  = ((char *)  dst->data + i10*nb0  + i11*nb1  + i12*nb2  + i13*nb3);
+
+                        *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(*(const ggml_bf16_t *) src0_ptr));
+
+                        if (++i10 == ne0) {
+                            i10 = 0;
+                            if (++i11 == ne1) {
+                                i11 = 0;
+                                if (++i12 == ne2) {
+                                    i12 = 0;
+                                    if (++i13 == ne3) {
+                                        i13 = 0;
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+                i10 += ne00 * (ne01 - ir1);
+                while (i10 >= ne0) {
+                    i10 -= ne0;
+                    if (++i11 == ne1) {
+                        i11 = 0;
+                        if (++i12 == ne2) {
+                            i12 = 0;
+                            if (++i13 == ne3) {
+                                i13 = 0;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    } else if (dst->type == GGML_TYPE_F32) {
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                i10 += ne00 * ir0;
+                while (i10 >= ne0) {
+                    i10 -= ne0;
+                    if (++i11 == ne1) {
+                        i11 = 0;
+                        if (++i12 == ne2) {
+                            i12 = 0;
+                            if (++i13 == ne3) {
+                                i13 = 0;
+                            }
+                        }
+                    }
+                }
+                for (int64_t i01 = ir0; i01 < ir1; i01++) {
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+                              char * dst_ptr  = ((char *)  dst->data + i10*nb0  + i11*nb1  + i12*nb2  + i13*nb3);
+
+                        *(float *) dst_ptr = GGML_BF16_TO_FP32(*(const ggml_bf16_t *) src0_ptr);
+
+                        if (++i10 == ne0) {
+                            i10 = 0;
+                            if (++i11 == ne1) {
+                                i11 = 0;
+                                if (++i12 == ne2) {
+                                    i12 = 0;
+                                    if (++i13 == ne3) {
+                                        i13 = 0;
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+                i10 += ne00 * (ne01 - ir1);
+                while (i10 >= ne0) {
+                    i10 -= ne0;
+                    if (++i11 == ne1) {
+                        i11 = 0;
+                        if (++i12 == ne2) {
+                            i12 = 0;
+                            if (++i13 == ne3) {
+                                i13 = 0;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    } else {
+        GGML_ASSERT(false); // TODO: implement
+    }
+}
+
+static void ggml_compute_forward_dup_f32(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    const int ith = params->ith; // thread index
+    const int nth = params->nth; // number of threads
+
+    if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) {
+        ggml_compute_forward_dup_same_cont(params, dst);
         return;
     }
 
@@ -7061,6 +8148,24 @@ static void ggml_compute_forward_dup_f32(
                         id += ne00 * (ne01 - ir1);
                     }
                 }
+            } else if (dst->type == GGML_TYPE_BF16) {
+                size_t id = 0;
+                ggml_bf16_t * dst_ptr = (ggml_bf16_t *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        id += ne00 * ir0;
+                        for (int i01 = ir0; i01 < ir1; i01++) {
+                            for (int i00 = 0; i00 < ne00; i00++) {
+                                const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+
+                                dst_ptr[id] = GGML_FP32_TO_BF16(*src0_ptr);
+                                id++;
+                            }
+                        }
+                        id += ne00 * (ne01 - ir1);
+                    }
+                }
             } else {
                 GGML_ASSERT(false); // TODO: implement
             }
@@ -7180,6 +8285,58 @@ static void ggml_compute_forward_dup_f32(
                 }
             }
         }
+    } else if (dst->type == GGML_TYPE_BF16) {
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                i10 += ne00 * ir0;
+                while (i10 >= ne0) {
+                    i10 -= ne0;
+                    if (++i11 == ne1) {
+                        i11 = 0;
+                        if (++i12 == ne2) {
+                            i12 = 0;
+                            if (++i13 == ne3) {
+                                i13 = 0;
+                            }
+                        }
+                    }
+                }
+                for (int64_t i01 = ir0; i01 < ir1; i01++) {
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+                              char * dst_ptr  = ((char *)  dst->data + i10*nb0  + i11*nb1  + i12*nb2  + i13*nb3);
+
+                        *(ggml_bf16_t *) dst_ptr = GGML_FP32_TO_BF16(*(const float *) src0_ptr);
+
+                        if (++i10 == ne0) {
+                            i10 = 0;
+                            if (++i11 == ne1) {
+                                i11 = 0;
+                                if (++i12 == ne2) {
+                                    i12 = 0;
+                                    if (++i13 == ne3) {
+                                        i13 = 0;
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+                i10 += ne00 * (ne01 - ir1);
+                while (i10 >= ne0) {
+                    i10 -= ne0;
+                    if (++i11 == ne1) {
+                        i11 = 0;
+                        if (++i12 == ne2) {
+                            i12 = 0;
+                            if (++i13 == ne3) {
+                                i13 = 0;
+                            }
+                        }
+                    }
+                }
+            }
+        }
     } else {
         GGML_ASSERT(false); // TODO: implement
     }
@@ -7188,17 +8345,19 @@ static void ggml_compute_forward_dup_f32(
 // A simplified version of ggml_compute_forward_dup that doesn't do float upcasting, and just plain old memcpy.
 static void ggml_compute_forward_dup_bytes(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
     GGML_ASSERT(src0->type == dst->type);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
     if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) {
-        ggml_compute_forward_dup_same_cont(params, src0, dst);
+        ggml_compute_forward_dup_same_cont(params, dst);
         return;
     }
 
@@ -7337,21 +8496,27 @@ static void ggml_compute_forward_dup_bytes(
 
 static void ggml_compute_forward_dup(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     if (src0->type == dst->type) {
-        ggml_compute_forward_dup_bytes(params, src0, dst);
+        ggml_compute_forward_dup_bytes(params, dst);
         return;
     }
 
     switch (src0->type) {
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_dup_f16(params, src0, dst);
+                ggml_compute_forward_dup_f16(params, dst);
+            } break;
+        case GGML_TYPE_BF16:
+            {
+                ggml_compute_forward_dup_bf16(params, dst);
             } break;
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_dup_f32(params, src0, dst);
+                ggml_compute_forward_dup_f32(params, dst);
             } break;
         default:
             {
@@ -7364,12 +8529,14 @@ static void ggml_compute_forward_dup(
 
 static void ggml_compute_forward_add_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -7377,7 +8544,7 @@ static void ggml_compute_forward_add_f32(
     const int nth = params->nth;
 
 #ifdef GGML_USE_CLBLAST
-    if (src1->backend == GGML_BACKEND_GPU) {
+    if (src1->backend == GGML_BACKEND_TYPE_GPU) {
         // TODO: OpenCL kernel support full broadcast
         GGML_ASSERT(ggml_can_repeat_rows(src1, src0));
         if (ith == 0) {
@@ -7452,12 +8619,14 @@ static void ggml_compute_forward_add_f32(
 
 static void ggml_compute_forward_add_f16_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -7527,14 +8696,95 @@ static void ggml_compute_forward_add_f16_f32(
     }
 }
 
-static void ggml_compute_forward_add_f16_f16(
+static void ggml_compute_forward_add_bf16_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr  = ggml_nrows(src0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    GGML_ASSERT(src0->type == GGML_TYPE_BF16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    if (dst->type == GGML_TYPE_F32) {
+        GGML_ASSERT( nb0 == sizeof(float));
+    }
+    else {
+        GGML_ASSERT(dst->type  == GGML_TYPE_BF16);
+        GGML_ASSERT( nb0 == sizeof(ggml_bf16_t));
+    }
+
+    GGML_ASSERT(nb00 == sizeof(ggml_bf16_t));
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    if (nb10 == sizeof(float)) {
+        if (dst->type == GGML_TYPE_BF16) {
+            for (int ir = ir0; ir < ir1; ++ir) {
+                // src0, src1 and dst are same shape => same indices
+                const int i3 = ir/(ne2*ne1);
+                const int i2 = (ir - i3*ne2*ne1)/ne1;
+                const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+                ggml_bf16_t * dst_ptr  = (ggml_bf16_t *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1);
+                ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
+                float *       src1_ptr = (float *)       ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11);
+
+                for (int i = 0; i < ne0; i++) {
+                    dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + src1_ptr[i]);
+                }
+            }
+        } else {
+            for (int ir = ir0; ir < ir1; ++ir) {
+                // src0, src1 and dst are same shape => same indices
+                const int i3 = ir/(ne2*ne1);
+                const int i2 = (ir - i3*ne2*ne1)/ne1;
+                const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+                float *       dst_ptr  = (float *)       ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1);
+                ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
+                float *       src1_ptr = (float *)       ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11);
+
+                for (int i = 0; i < ne0; i++) {
+                    dst_ptr[i] = GGML_BF16_TO_FP32(src0_ptr[i]) + src1_ptr[i];
+                }
+            }
+        }
+    }
+    else {
+        // src1 is not contiguous
+        GGML_ASSERT(false);
+    }
+}
+
+static void ggml_compute_forward_add_f16_f16(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -7581,14 +8831,72 @@ static void ggml_compute_forward_add_f16_f16(
     }
 }
 
-static void ggml_compute_forward_add_q_f32(
+static void ggml_compute_forward_add_bf16_bf16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr  = ggml_nrows(src0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    GGML_ASSERT(src0->type == GGML_TYPE_BF16);
+    GGML_ASSERT(src1->type == GGML_TYPE_BF16);
+    GGML_ASSERT(dst->type  == GGML_TYPE_BF16);
+
+    GGML_ASSERT( nb0 == sizeof(ggml_bf16_t));
+    GGML_ASSERT(nb00 == sizeof(ggml_bf16_t));
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    if (nb10 == sizeof(ggml_bf16_t)) {
+        for (int ir = ir0; ir < ir1; ++ir) {
+            // src0, src1 and dst are same shape => same indices
+            const int i3 = ir/(ne2*ne1);
+            const int i2 = (ir - i3*ne2*ne1)/ne1;
+            const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+            ggml_bf16_t * dst_ptr  = (ggml_bf16_t *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1);
+            ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
+            ggml_bf16_t * src1_ptr = (ggml_bf16_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11);
+
+            for (int i = 0; i < ne0; i++) {
+                dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + GGML_BF16_TO_FP32(src1_ptr[i]));
+            }
+        }
+    }
+    else {
+        // src1 is not contiguous
+        GGML_ASSERT(false);
+    }
+}
+
+static void ggml_compute_forward_add_q_f32(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -7661,14 +8969,16 @@ static void ggml_compute_forward_add_q_f32(
 
 static void ggml_compute_forward_add(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
                 if (src1->type == GGML_TYPE_F32) {
-                    ggml_compute_forward_add_f32(params, src0, src1, dst);
+                    ggml_compute_forward_add_f32(params, dst);
                 }
                 else {
                     GGML_ASSERT(false);
@@ -7677,10 +8987,22 @@ static void ggml_compute_forward_add(
         case GGML_TYPE_F16:
             {
                 if (src1->type == GGML_TYPE_F16) {
-                    ggml_compute_forward_add_f16_f16(params, src0, src1, dst);
+                    ggml_compute_forward_add_f16_f16(params, dst);
                 }
                 else if (src1->type == GGML_TYPE_F32) {
-                    ggml_compute_forward_add_f16_f32(params, src0, src1, dst);
+                    ggml_compute_forward_add_f16_f32(params, dst);
+                }
+                else {
+                    GGML_ASSERT(false);
+                }
+            } break;
+        case GGML_TYPE_BF16:
+            {
+                if (src1->type == GGML_TYPE_BF16) {
+                    ggml_compute_forward_add_bf16_bf16(params, dst);
+                }
+                else if (src1->type == GGML_TYPE_F32) {
+                    ggml_compute_forward_add_bf16_f32(params, dst);
                 }
                 else {
                     GGML_ASSERT(false);
@@ -7700,8 +9022,13 @@ static void ggml_compute_forward_add(
         case GGML_TYPE_IQ2_XS:
         case GGML_TYPE_IQ3_XXS:
         case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ2_S:
             {
-                ggml_compute_forward_add_q_f32(params, src0, src1, dst);
+                ggml_compute_forward_add_q_f32(params, dst);
             } break;
         default:
             {
@@ -7714,13 +9041,15 @@ static void ggml_compute_forward_add(
 
 static void ggml_compute_forward_add1_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_is_scalar(src1));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -7766,13 +9095,15 @@ static void ggml_compute_forward_add1_f32(
 
 static void ggml_compute_forward_add1_f16_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_is_scalar(src1));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -7816,13 +9147,15 @@ static void ggml_compute_forward_add1_f16_f32(
 
 static void ggml_compute_forward_add1_f16_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_is_scalar(src1));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -7866,13 +9199,15 @@ static void ggml_compute_forward_add1_f16_f16(
 
 static void ggml_compute_forward_add1_q_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_is_scalar(src1));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -7931,23 +9266,141 @@ static void ggml_compute_forward_add1_q_f32(
     }
 }
 
+static void ggml_compute_forward_add1_bf16_f32(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+    GGML_ASSERT(ggml_is_scalar(src1));
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    // scalar to add
+    const float v = *(float *) src1->data;
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr  = ggml_nrows(src0);
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    GGML_ASSERT(src0->type == GGML_TYPE_BF16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type  == GGML_TYPE_BF16);
+
+    GGML_ASSERT( nb0 == sizeof(ggml_bf16_t));
+    GGML_ASSERT(nb00 == sizeof(ggml_bf16_t));
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int ir = ir0; ir < ir1; ++ir) {
+        // src0 and dst are same shape => same indices
+        const int i3 = ir/(ne2*ne1);
+        const int i2 = (ir - i3*ne2*ne1)/ne1;
+        const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+        ggml_bf16_t * dst_ptr  = (ggml_bf16_t *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1 );
+        ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
+        for (int i = 0; i < ne0; i++) {
+            dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + v);
+        }
+    }
+}
+
+static void ggml_compute_forward_add1_bf16_bf16(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+    GGML_ASSERT(ggml_is_scalar(src1));
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    // scalar to add
+    const float v = GGML_BF16_TO_FP32(*(ggml_bf16_t *) src1->data);
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr  = ggml_nrows(src0);
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    GGML_ASSERT(src0->type == GGML_TYPE_BF16);
+    GGML_ASSERT(src1->type == GGML_TYPE_BF16);
+    GGML_ASSERT(dst->type  == GGML_TYPE_BF16);
+
+    GGML_ASSERT( nb0 == sizeof(ggml_bf16_t));
+    GGML_ASSERT(nb00 == sizeof(ggml_bf16_t));
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int ir = ir0; ir < ir1; ++ir) {
+        // src0 and dst are same shape => same indices
+        const int i3 = ir/(ne2*ne1);
+        const int i2 = (ir - i3*ne2*ne1)/ne1;
+        const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+        ggml_bf16_t * dst_ptr  = (ggml_bf16_t *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1 );
+        ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
+        for (int i = 0; i < ne0; i++) {
+            dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + v);
+        }
+    }
+}
+
 static void ggml_compute_forward_add1(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_add1_f32(params, src0, src1, dst);
+                ggml_compute_forward_add1_f32(params, dst);
             } break;
         case GGML_TYPE_F16:
             {
                 if (src1->type == GGML_TYPE_F16) {
-                    ggml_compute_forward_add1_f16_f16(params, src0, src1, dst);
+                    ggml_compute_forward_add1_f16_f16(params, dst);
                 }
                 else if (src1->type == GGML_TYPE_F32) {
-                    ggml_compute_forward_add1_f16_f32(params, src0, src1, dst);
+                    ggml_compute_forward_add1_f16_f32(params, dst);
+                }
+                else {
+                    GGML_ASSERT(false);
+                }
+            } break;
+        case GGML_TYPE_BF16:
+            {
+                if (src1->type == GGML_TYPE_BF16) {
+                    ggml_compute_forward_add1_bf16_bf16(params, dst);
+                }
+                else if (src1->type == GGML_TYPE_F32) {
+                    ggml_compute_forward_add1_bf16_f32(params, dst);
                 }
                 else {
                     GGML_ASSERT(false);
@@ -7968,8 +9421,13 @@ static void ggml_compute_forward_add1(
         case GGML_TYPE_IQ2_XS:
         case GGML_TYPE_IQ3_XXS:
         case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ2_S:
             {
-                ggml_compute_forward_add1_q_f32(params, src0, src1, dst);
+                ggml_compute_forward_add1_q_f32(params, dst);
             } break;
         default:
             {
@@ -7982,9 +9440,11 @@ static void ggml_compute_forward_add1(
 
 static void ggml_compute_forward_acc_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0));
 
@@ -7996,7 +9456,7 @@ static void ggml_compute_forward_acc_f32(
     size_t offset  = ((int32_t *) dst->op_params)[3];
     bool   inplace = (bool) ((int32_t *) dst->op_params)[4];
 
-    if (!inplace && (params->type == GGML_TASK_INIT)) {
+    if (!inplace && (params->type == GGML_TASK_TYPE_INIT)) {
         if (params->ith != 0) {
             return;
         }
@@ -8008,7 +9468,7 @@ static void ggml_compute_forward_acc_f32(
             ggml_nbytes(dst));
     }
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8064,16 +9524,17 @@ static void ggml_compute_forward_acc_f32(
 
 static void ggml_compute_forward_acc(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
 
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_acc_f32(params, src0, src1, dst);
+                ggml_compute_forward_acc_f32(params, dst);
             } break;
         case GGML_TYPE_F16:
+        case GGML_TYPE_BF16:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:
@@ -8089,6 +9550,11 @@ static void ggml_compute_forward_acc(
         case GGML_TYPE_IQ2_XS:
         case GGML_TYPE_IQ3_XXS:
         case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ2_S:
         default:
             {
                 GGML_ASSERT(false);
@@ -8100,13 +9566,15 @@ static void ggml_compute_forward_acc(
 
 static void ggml_compute_forward_sub_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8160,13 +9628,14 @@ static void ggml_compute_forward_sub_f32(
 
 static void ggml_compute_forward_sub(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_sub_f32(params, src0, src1, dst);
+                ggml_compute_forward_sub_f32(params, dst);
             } break;
         default:
             {
@@ -8179,19 +9648,21 @@ static void ggml_compute_forward_sub(
 
 static void ggml_compute_forward_mul_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
     const int ith = params->ith;
     const int nth = params->nth;
 
 #if defined(GGML_USE_CLBLAST)
-    if (src1->backend == GGML_BACKEND_GPU) {
+    if (src1->backend == GGML_BACKEND_TYPE_GPU) {
         // TODO: OpenCL kernel support full broadcast
         GGML_ASSERT(ggml_can_repeat_rows(src1, src0));
         if (ith == 0) {
@@ -8262,15 +9733,17 @@ static void ggml_compute_forward_mul_f32(
 
 static void ggml_compute_forward_mul(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(src1->type == GGML_TYPE_F32 && "only f32 src1 supported for now");
 
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_mul_f32(params, src0, src1, dst);
+                ggml_compute_forward_mul_f32(params, dst);
             } break;
         default:
             {
@@ -8283,12 +9756,14 @@ static void ggml_compute_forward_mul(
 
 static void ggml_compute_forward_div_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8356,13 +9831,14 @@ static void ggml_compute_forward_div_f32(
 
 static void ggml_compute_forward_div(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_div_f32(params, src0, src1, dst);
+                ggml_compute_forward_div_f32(params, dst);
             } break;
         default:
             {
@@ -8375,12 +9851,14 @@ static void ggml_compute_forward_div(
 
 static void ggml_compute_forward_sqr_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8399,12 +9877,14 @@ static void ggml_compute_forward_sqr_f32(
 
 static void ggml_compute_forward_sqr(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_sqr_f32(params, src0, dst);
+                ggml_compute_forward_sqr_f32(params, dst);
             } break;
         default:
             {
@@ -8417,12 +9897,14 @@ static void ggml_compute_forward_sqr(
 
 static void ggml_compute_forward_sqrt_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8441,12 +9923,14 @@ static void ggml_compute_forward_sqrt_f32(
 
 static void ggml_compute_forward_sqrt(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_sqrt_f32(params, src0, dst);
+                ggml_compute_forward_sqrt_f32(params, dst);
             } break;
         default:
             {
@@ -8459,12 +9943,14 @@ static void ggml_compute_forward_sqrt(
 
 static void ggml_compute_forward_log_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(params->ith == 0);
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8483,12 +9969,14 @@ static void ggml_compute_forward_log_f32(
 
 static void ggml_compute_forward_log(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_log_f32(params, src0, dst);
+                ggml_compute_forward_log_f32(params, dst);
             } break;
         default:
             {
@@ -8501,12 +9989,14 @@ static void ggml_compute_forward_log(
 
 static void ggml_compute_forward_sum_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_is_scalar(dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8534,12 +10024,14 @@ static void ggml_compute_forward_sum_f32(
 
 static void ggml_compute_forward_sum_f16(
     const struct ggml_compute_params * params,
-    const struct ggml_tensor * src0,
           struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_is_scalar(dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8564,18 +10056,58 @@ static void ggml_compute_forward_sum_f16(
     ((ggml_fp16_t *) dst->data)[0] = GGML_FP32_TO_FP16(sum);
 }
 
+static void ggml_compute_forward_sum_bf16(
+    const struct ggml_compute_params * params,
+          struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    assert(params->ith == 0);
+    assert(ggml_is_scalar(dst));
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    assert(src0->nb[0] == sizeof(ggml_bf16_t));
+
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb)
+
+    float sum = 0;
+    float row_sum = 0;
+
+    for (int64_t i03 = 0; i03 < ne03; i03++) {
+        for (int64_t i02 = 0; i02 < ne02; i02++) {
+            for (int64_t i01 = 0; i01 < ne01; i01++) {
+                ggml_vec_sum_bf16_ggf(ne00,
+                    &row_sum,
+                    (ggml_bf16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03));
+                sum += row_sum;
+            }
+        }
+    }
+    ((ggml_bf16_t *) dst->data)[0] = GGML_FP32_TO_BF16(sum);
+}
+
 static void ggml_compute_forward_sum(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_sum_f32(params, src0, dst);
+                ggml_compute_forward_sum_f32(params, dst);
             } break;
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_sum_f16(params, src0, dst);
+                ggml_compute_forward_sum_f16(params, dst);
+            } break;
+        case GGML_TYPE_BF16:
+            {
+                ggml_compute_forward_sum_bf16(params, dst);
             } break;
         default:
             {
@@ -8588,11 +10120,13 @@ static void ggml_compute_forward_sum(
 
 static void ggml_compute_forward_sum_rows_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8621,12 +10155,14 @@ static void ggml_compute_forward_sum_rows_f32(
 
 static void ggml_compute_forward_sum_rows(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_sum_rows_f32(params, src0, dst);
+                ggml_compute_forward_sum_rows_f32(params, dst);
             } break;
         default:
             {
@@ -8639,11 +10175,13 @@ static void ggml_compute_forward_sum_rows(
 
 static void ggml_compute_forward_mean_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8676,12 +10214,14 @@ static void ggml_compute_forward_mean_f32(
 
 static void ggml_compute_forward_mean(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_mean_f32(params, src0, dst);
+                ggml_compute_forward_mean_f32(params, dst);
             } break;
         default:
             {
@@ -8694,11 +10234,13 @@ static void ggml_compute_forward_mean(
 
 static void ggml_compute_forward_argmax_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8722,12 +10264,14 @@ static void ggml_compute_forward_argmax_f32(
 
 static void ggml_compute_forward_argmax(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_argmax_f32(params, src0, dst);
+                ggml_compute_forward_argmax_f32(params, dst);
             } break;
         default:
             {
@@ -8740,12 +10284,14 @@ static void ggml_compute_forward_argmax(
 
 static void ggml_compute_forward_repeat_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(params->ith == 0);
     GGML_ASSERT(ggml_can_repeat(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8783,12 +10329,14 @@ static void ggml_compute_forward_repeat_f32(
 
 static void ggml_compute_forward_repeat_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(params->ith == 0);
     GGML_ASSERT(ggml_can_repeat(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8829,18 +10377,21 @@ static void ggml_compute_forward_repeat_f16(
 
 static void ggml_compute_forward_repeat(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F16:
+        case GGML_TYPE_BF16:
         case GGML_TYPE_I16:
             {
-                ggml_compute_forward_repeat_f16(params, src0, dst);
+                ggml_compute_forward_repeat_f16(params, dst);
             } break;
         case GGML_TYPE_F32:
         case GGML_TYPE_I32:
             {
-                ggml_compute_forward_repeat_f32(params, src0, dst);
+                ggml_compute_forward_repeat_f32(params, dst);
             } break;
         default:
             {
@@ -8853,12 +10404,14 @@ static void ggml_compute_forward_repeat(
 
 static void ggml_compute_forward_repeat_back_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(params->ith == 0);
     GGML_ASSERT(ggml_can_repeat(dst, src0));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8910,12 +10463,14 @@ static void ggml_compute_forward_repeat_back_f32(
 
 static void ggml_compute_forward_repeat_back(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_repeat_back_f32(params, src0, dst);
+                ggml_compute_forward_repeat_back_f32(params, dst);
             } break;
         default:
             {
@@ -8928,11 +10483,12 @@ static void ggml_compute_forward_repeat_back(
 
 static void ggml_compute_forward_concat_f32(
     const struct ggml_compute_params * params,
-    const struct ggml_tensor * src0,
-    const struct ggml_tensor * src1,
     struct ggml_tensor * dst) {
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8976,14 +10532,15 @@ static void ggml_compute_forward_concat_f32(
 
 static void ggml_compute_forward_concat(
     const struct ggml_compute_params* params,
-    const struct ggml_tensor* src0,
-    const struct ggml_tensor* src1,
     struct ggml_tensor* dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
         case GGML_TYPE_I32:
             {
-                ggml_compute_forward_concat_f32(params, src0, src1, dst);
+                ggml_compute_forward_concat_f32(params, dst);
             } break;
         default:
             {
@@ -8996,12 +10553,14 @@ static void ggml_compute_forward_concat(
 
 static void ggml_compute_forward_abs_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9020,12 +10579,14 @@ static void ggml_compute_forward_abs_f32(
 
 static void ggml_compute_forward_abs(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_abs_f32(params, src0, dst);
+                ggml_compute_forward_abs_f32(params, dst);
             } break;
         default:
             {
@@ -9038,12 +10599,14 @@ static void ggml_compute_forward_abs(
 
 static void ggml_compute_forward_sgn_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9062,12 +10625,14 @@ static void ggml_compute_forward_sgn_f32(
 
 static void ggml_compute_forward_sgn(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_sgn_f32(params, src0, dst);
+                ggml_compute_forward_sgn_f32(params, dst);
             } break;
         default:
             {
@@ -9080,12 +10645,14 @@ static void ggml_compute_forward_sgn(
 
 static void ggml_compute_forward_neg_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9104,12 +10671,14 @@ static void ggml_compute_forward_neg_f32(
 
 static void ggml_compute_forward_neg(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_neg_f32(params, src0, dst);
+                ggml_compute_forward_neg_f32(params, dst);
             } break;
         default:
             {
@@ -9122,12 +10691,14 @@ static void ggml_compute_forward_neg(
 
 static void ggml_compute_forward_step_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9146,12 +10717,14 @@ static void ggml_compute_forward_step_f32(
 
 static void ggml_compute_forward_step(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_step_f32(params, src0, dst);
+                ggml_compute_forward_step_f32(params, dst);
             } break;
         default:
             {
@@ -9164,12 +10737,14 @@ static void ggml_compute_forward_step(
 
 static void ggml_compute_forward_tanh_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9188,12 +10763,14 @@ static void ggml_compute_forward_tanh_f32(
 
 static void ggml_compute_forward_tanh(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_tanh_f32(params, src0, dst);
+                ggml_compute_forward_tanh_f32(params, dst);
             } break;
         default:
             {
@@ -9206,12 +10783,14 @@ static void ggml_compute_forward_tanh(
 
 static void ggml_compute_forward_elu_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9230,12 +10809,14 @@ static void ggml_compute_forward_elu_f32(
 
 static void ggml_compute_forward_elu(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_elu_f32(params, src0, dst);
+                ggml_compute_forward_elu_f32(params, dst);
             } break;
         default:
             {
@@ -9248,12 +10829,14 @@ static void ggml_compute_forward_elu(
 
 static void ggml_compute_forward_relu_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9272,12 +10855,60 @@ static void ggml_compute_forward_relu_f32(
 
 static void ggml_compute_forward_relu(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_relu_f32(params, src0, dst);
+                ggml_compute_forward_relu_f32(params, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_sigmoid
+
+static void ggml_compute_forward_sigmoid_f32(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    assert(params->ith == 0);
+    assert(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    assert(dst->nb[0]  == sizeof(float));
+    assert(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        ggml_vec_sigmoid_f32(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])));
+    }
+}
+
+static void ggml_compute_forward_sigmoid(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_sigmoid_f32(params, dst);
             } break;
         default:
             {
@@ -9290,13 +10921,15 @@ static void ggml_compute_forward_relu(
 
 static void ggml_compute_forward_gelu_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
     GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9331,12 +10964,14 @@ static void ggml_compute_forward_gelu_f32(
 
 static void ggml_compute_forward_gelu(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_gelu_f32(params, src0, dst);
+                ggml_compute_forward_gelu_f32(params, dst);
             } break;
         default:
             {
@@ -9349,13 +10984,15 @@ static void ggml_compute_forward_gelu(
 
 static void ggml_compute_forward_gelu_quick_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
     GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9390,12 +11027,14 @@ static void ggml_compute_forward_gelu_quick_f32(
 
 static void ggml_compute_forward_gelu_quick(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_gelu_quick_f32(params, src0, dst);
+                ggml_compute_forward_gelu_quick_f32(params, dst);
             } break;
         default:
             {
@@ -9408,13 +11047,15 @@ static void ggml_compute_forward_gelu_quick(
 
 static void ggml_compute_forward_silu_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
     GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9449,12 +11090,14 @@ static void ggml_compute_forward_silu_f32(
 
 static void ggml_compute_forward_silu(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_silu_f32(params, src0, dst);
+                ggml_compute_forward_silu_f32(params, dst);
             } break;
         default:
             {
@@ -9466,12 +11109,14 @@ static void ggml_compute_forward_silu(
 
 static void ggml_compute_forward_leaky_relu_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9493,12 +11138,14 @@ static void ggml_compute_forward_leaky_relu_f32(
 
 static void ggml_compute_forward_leaky_relu(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_leaky_relu_f32(params, src0, dst);
+                ggml_compute_forward_leaky_relu_f32(params, dst);
             } break;
         default:
             {
@@ -9511,16 +11158,18 @@ static void ggml_compute_forward_leaky_relu(
 
 static void ggml_compute_forward_silu_back_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * grad,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * grad = dst->src[1];
+
     GGML_ASSERT(ggml_is_contiguous_except_dim_1(grad));
     GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
     GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_are_same_shape(src0, grad));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9556,13 +11205,14 @@ static void ggml_compute_forward_silu_back_f32(
 
 static void ggml_compute_forward_silu_back(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * grad,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_silu_back_f32(params, src0, grad, dst);
+                ggml_compute_forward_silu_back_f32(params, dst);
             } break;
         default:
             {
@@ -9574,12 +11224,14 @@ static void ggml_compute_forward_silu_back(
 
 static void ggml_compute_forward_hardswish_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9597,12 +11249,14 @@ static void ggml_compute_forward_hardswish_f32(
 }
 static void ggml_compute_forward_hardswish(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_hardswish_f32(params, src0, dst);
+                ggml_compute_forward_hardswish_f32(params, dst);
             } break;
         default:
             {
@@ -9613,12 +11267,14 @@ static void ggml_compute_forward_hardswish(
 
 static void ggml_compute_forward_hardsigmoid_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9637,12 +11293,14 @@ static void ggml_compute_forward_hardsigmoid_f32(
 
 static void ggml_compute_forward_hardsigmoid(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_hardsigmoid_f32(params, src0, dst);
+                ggml_compute_forward_hardsigmoid_f32(params, dst);
             } break;
         default:
             {
@@ -9656,11 +11314,13 @@ static void ggml_compute_forward_hardsigmoid(
 
 static void ggml_compute_forward_norm_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9709,12 +11369,14 @@ static void ggml_compute_forward_norm_f32(
 
 static void ggml_compute_forward_norm(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_norm_f32(params, src0, dst);
+                ggml_compute_forward_norm_f32(params, dst);
             } break;
         default:
             {
@@ -9727,11 +11389,13 @@ static void ggml_compute_forward_norm(
 
 static void ggml_compute_forward_rms_norm_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9777,12 +11441,14 @@ static void ggml_compute_forward_rms_norm_f32(
 
 static void ggml_compute_forward_rms_norm(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_rms_norm_f32(params, src0, dst);
+                ggml_compute_forward_rms_norm_f32(params, dst);
             } break;
         default:
             {
@@ -9793,12 +11459,14 @@ static void ggml_compute_forward_rms_norm(
 
 static void ggml_compute_forward_rms_norm_back_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_are_same_shape(src0, src1));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9950,13 +11618,14 @@ static void ggml_compute_forward_rms_norm_back_f32(
 
 static void ggml_compute_forward_rms_norm_back(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_rms_norm_back_f32(params, src0, src1, dst);
+                ggml_compute_forward_rms_norm_back_f32(params, dst);
             } break;
         default:
             {
@@ -9969,11 +11638,13 @@ static void ggml_compute_forward_rms_norm_back(
 
 static void ggml_compute_forward_group_norm_f32(
     const struct ggml_compute_params * params,
-    const struct ggml_tensor * src0,
     struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9991,7 +11662,7 @@ static void ggml_compute_forward_group_norm_f32(
     int n_channels = src0->ne[2];
     int n_groups = dst->op_params[0];
     int n_channels_per_group = (n_channels + n_groups - 1) / n_groups;
-    for (int i = ith; i < n_groups; i+=nth) {
+    for (int i = ith; i < n_groups; i += nth) {
         int start = i * n_channels_per_group;
         int end = start + n_channels_per_group;
         if (end > n_channels) {
@@ -10005,28 +11676,32 @@ static void ggml_compute_forward_group_norm_f32(
                 for (int64_t i01 = 0; i01 < ne01; i01++) {
                     const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03);
 
+                    ggml_float sumr = 0.0;
                     for (int64_t i00 = 0; i00 < ne00; i00++) {
-                        sum += (ggml_float)x[i00];
+                        sumr += (ggml_float)x[i00];
                     }
+                    sum += sumr;
                 }
             }
-            float mean = sum / (ne00 * ne01 * step);
-            ggml_float sum2 = 0.0;
+            const float mean = sum / (ne00 * ne01 * step);
 
+            ggml_float sum2 = 0.0;
             for (int64_t i02 = start; i02 < end; i02++) {
                 for (int64_t i01 = 0; i01 < ne01; i01++) {
                     const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03);
 
                     float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3);
 
+                    ggml_float sumr = 0.0;
                     for (int64_t i00 = 0; i00 < ne00; i00++) {
                         float v = x[i00] - mean;
                         y[i00] = v;
-                        sum2 += (ggml_float)(v * v);
+                        sumr += (ggml_float)(v * v);
                     }
+                    sum2 += sumr;
                 }
             }
-            float variance = sum2 / (ne00 * ne01 * step);
+            const float variance = sum2 / (ne00 * ne01 * step);
             const float scale = 1.0f / sqrtf(variance + eps);
 
             for (int64_t i02 = start; i02 < end; i02++) {
@@ -10041,12 +11716,14 @@ static void ggml_compute_forward_group_norm_f32(
 
 static void ggml_compute_forward_group_norm(
     const struct ggml_compute_params * params,
-    const struct ggml_tensor * src0,
     struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_group_norm_f32(params, src0, dst);
+                ggml_compute_forward_group_norm_f32(params, dst);
             } break;
         default:
             {
@@ -10092,9 +11769,11 @@ static bool ggml_compute_forward_mul_mat_use_blas(struct ggml_tensor * dst) {
 
 static void ggml_compute_forward_mul_mat(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
@@ -10136,7 +11815,7 @@ static void ggml_compute_forward_mul_mat(
 
 #if defined(GGML_USE_CLBLAST)
     if (ggml_cl_can_mul_mat(src0, src1, dst)) {
-        if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
+        if (params->ith == 0 && params->type == GGML_TASK_TYPE_COMPUTE) {
             ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
         }
         return;
@@ -10149,7 +11828,7 @@ static void ggml_compute_forward_mul_mat(
         const size_t  desired_wsize = ne13*ne12*ne_plane*sizeof(float);
         UNUSED(desired_wsize);
 
-        if (params->type == GGML_TASK_INIT) {
+        if (params->type == GGML_TASK_TYPE_INIT) {
             if (type != GGML_TYPE_F32) {
                 assert(params->wsize >= desired_wsize);
                 // parallelize by src0 rows
@@ -10172,7 +11851,7 @@ static void ggml_compute_forward_mul_mat(
             return;
         }
 
-        if (params->type == GGML_TASK_FINALIZE) {
+        if (params->type == GGML_TASK_TYPE_FINALIZE) {
             return;
         }
 
@@ -10210,7 +11889,29 @@ static void ggml_compute_forward_mul_mat(
     }
 #endif
 
-    if (params->type == GGML_TASK_INIT) {
+#if GGML_USE_LLAMAFILE
+    if (src1_cont) {
+        for (int64_t i13 = 0; i13 < ne13; i13++)
+            for (int64_t i12 = 0; i12 < ne12; i12++)
+                if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type),
+                                     (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03,
+                                     nb01/ggml_type_size(src0->type),
+                                     (const char *)src1->data + i12*nb12 + i13*nb13,
+                                     nb11/ggml_type_size(src1->type),
+                                     (char *)dst->data + i12*nb2 + i13*nb3,
+                                     nb1/ggml_type_size(dst->type),
+                                     ith, nth,
+                                     params->type,
+                                     src0->type,
+                                     src1->type,
+                                     dst->type))
+                    goto UseGgmlGemm1;
+        return;
+    }
+UseGgmlGemm1:;
+#endif
+
+    if (params->type == GGML_TASK_TYPE_INIT) {
         if (ith != 0) {
             return;
         }
@@ -10234,13 +11935,35 @@ static void ggml_compute_forward_mul_mat(
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
     const void * wdata    = (src1->type == vec_dot_type) ? src1->data : params->wdata;
     const size_t row_size = ggml_row_size(vec_dot_type, ne10);
 
+#if GGML_USE_LLAMAFILE
+    if (src1->type != vec_dot_type) {
+        for (int64_t i13 = 0; i13 < ne13; i13++)
+            for (int64_t i12 = 0; i12 < ne12; i12++)
+                if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type),
+                                     (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03,
+                                     nb01/ggml_type_size(src0->type),
+                                     (const char *)wdata + (i12*ne11 + i13*ne12*ne11)*row_size,
+                                     row_size/ggml_type_size(vec_dot_type),
+                                     (char *)dst->data + i12*nb2 + i13*nb3,
+                                     nb1/ggml_type_size(dst->type),
+                                     ith, nth,
+                                     params->type,
+                                     src0->type,
+                                     vec_dot_type,
+                                     dst->type))
+                    goto UseGgmlGemm2;
+        return;
+    }
+UseGgmlGemm2:;
+#endif
+
     const int64_t nr0 = ne01;          // src0 rows
     const int64_t nr1 = ne1*ne12*ne13; // src1 rows
 
@@ -10339,11 +12062,11 @@ static void ggml_compute_forward_mul_mat(
 
 static void ggml_compute_forward_mul_mat_id(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * ids,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
 
-    const struct ggml_tensor * src0 = dst->src[2]; // only for GGML_TENSOR_BINARY_OP_LOCALS
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+    const struct ggml_tensor * ids = dst->src[2];
 
     GGML_TENSOR_BINARY_OP_LOCALS
 
@@ -10358,11 +12081,6 @@ static void ggml_compute_forward_mul_mat_id(
     enum ggml_type    const vec_dot_type          = type_traits[type].vec_dot_type;
     ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
 
-    GGML_ASSERT(ne0 == ne01);
-    GGML_ASSERT(ne1 == ne11);
-    GGML_ASSERT(ne2 == ne12);
-    GGML_ASSERT(ne3 == ne13);
-
     // we don't support permuted src0 or src1
     GGML_ASSERT(nb00 == ggml_type_size(type));
     GGML_ASSERT(nb10 == ggml_type_size(src1->type));
@@ -10373,24 +12091,23 @@ static void ggml_compute_forward_mul_mat_id(
     GGML_ASSERT(nb1 <= nb2);
     GGML_ASSERT(nb2 <= nb3);
 
-    // broadcast factors
-    const int64_t r2 = ne12/ne02;
-    const int64_t r3 = ne13/ne03;
-
     // row groups
-    const int id   = ggml_get_op_params_i32(dst, 0);
-    const int n_as = ggml_get_op_params_i32(dst, 1);
+    const int n_ids = ids->ne[0]; // n_expert_used
+    const int n_as  = ne02;       // n_expert
 
     char * wdata_src1_end = (src1->type == vec_dot_type) ?
             (char *) params->wdata :
             (char *) params->wdata + GGML_PAD(ggml_row_size(vec_dot_type, ggml_nelements(src1)), sizeof(int64_t));
 
+    struct mmid_row_mapping {
+        int32_t i1;
+        int32_t i2;
+    };
+
     int64_t * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as]
-    int64_t * matrix_rows       = matrix_row_counts + n_as;     // [n_as][ne11]
+    struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *)(matrix_row_counts + n_as); // [n_as][ne11]
 
-    #define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne11 + (i1)]
-
-   if (params->type == GGML_TASK_INIT) {
+   if (params->type == GGML_TASK_TYPE_INIT) {
         if (ith != 0) {
             return;
         }
@@ -10412,22 +12129,26 @@ static void ggml_compute_forward_mul_mat_id(
         }
 
         // initialize matrix_row_counts
-        GGML_ASSERT(wdata == wdata_src1_end);
         memset(matrix_row_counts, 0, n_as*sizeof(int64_t));
 
-        // group rows by src0 matrix
-        for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
-            const int32_t row_id = *(const int32_t *) ((const char *) ids->data + i01*ids->nb[1] + id*ids->nb[0]);
+#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne12 + (i1)]
 
-            GGML_ASSERT(row_id >= 0 && row_id < n_as);
-            MMID_MATRIX_ROW(row_id, matrix_row_counts[row_id]) = i01;
-            matrix_row_counts[row_id] += 1;
+        // group rows by src0 matrix
+        for (int64_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) {
+            for (int id = 0; id < n_ids; ++id) {
+                const int32_t i02 = *(const int32_t *) ((const char *) ids->data + iid1*ids->nb[1] + id*ids->nb[0]);
+
+                assert(i02 >= 0 && i02 < n_as);
+
+                MMID_MATRIX_ROW(i02, matrix_row_counts[i02]) = (struct mmid_row_mapping) {id, iid1};
+                matrix_row_counts[i02] += 1;
+            }
         }
 
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -10439,15 +12160,13 @@ static void ggml_compute_forward_mul_mat_id(
             continue;
         }
 
-        const struct ggml_tensor * src0_cur = dst->src[cur_a + 2];
+        const char * src0_cur = (const char *) src0->data + cur_a*nb02;
 
         const void * wdata    = (src1->type == vec_dot_type) ? src1->data : params->wdata;
         const size_t row_size = ggml_row_size(vec_dot_type, ne10);
 
-        const int64_t nr0 = ne01;           // src0 rows
-        const int64_t nr1 = cne1*ne12*ne13; // src1 rows
-
-        //printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1);
+        const int64_t nr0 = ne01; // src0 rows
+        const int64_t nr1 = cne1; // src1 rows
 
         // distribute the thread work across the inner or outer loop based on which one is larger
 
@@ -10466,16 +12185,11 @@ static void ggml_compute_forward_mul_mat_id(
         const int64_t ir110 = dr1*ith1;
         const int64_t ir111 = MIN(ir110 + dr1, nr1);
 
-        //printf("ir010 = %6lld, ir011 = %6lld, ir110 = %6lld, ir111 = %6lld\n", ir010, ir011, ir110, ir111);
-
         // threads with no work simply yield (not sure if it helps)
-        if (ir010 >= ir011 || ir110 >= ir111) {
-            sched_yield();
-            continue;
-        }
-
-        assert(ne12 % ne02 == 0);
-        assert(ne13 % ne03 == 0);
+        //if (ir010 >= ir011 || ir110 >= ir111) {
+        //    sched_yield();
+        //    continue;
+        //}
 
         // block-tiling attempt
         const int64_t blck_0 = 16;
@@ -10487,20 +12201,16 @@ static void ggml_compute_forward_mul_mat_id(
         for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) {
             for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) {
                 for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) {
-                    const int64_t  i13 = (ir1/(ne12*cne1)); // Note: currently, src1 is always a matrix
-                    const int64_t  i12 = (ir1 - i13*ne12*cne1)/cne1;
-                    const int64_t _i11 = (ir1 - i13*ne12*cne1 - i12*cne1);
-                    const int64_t  i11 = MMID_MATRIX_ROW(cur_a, _i11);
+                    const int64_t _i12 = ir1; // logical row index for this expert
 
-                    // broadcast src0 into src1
-                    const int64_t i03 = i13/r3;
-                    const int64_t i02 = i12/r2;
+                    struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, _i12);
+                    const int id       = row_mapping.i1; // selected expert index
 
-                    const int64_t i1 = i11;
-                    const int64_t i2 = i12;
-                    const int64_t i3 = i13;
+                    const int64_t  i11 = id % ne11;
+                    const int64_t  i12 = row_mapping.i2; // row index in src1
 
-                    const char * src0_row = (const char *) src0_cur->data + (0 + i02*nb02 + i03*nb03);
+                    const int64_t  i1 = id;  // selected expert index
+                    const int64_t  i2 = i12; // row
 
                     // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides
                     //       if it is, then we have either copied the data to params->wdata and made it contiguous or we are using
@@ -10508,34 +12218,37 @@ static void ggml_compute_forward_mul_mat_id(
                     // TODO: this is a bit of a hack, we should probably have a better way to handle this
                     const char * src1_col = (const char *) wdata +
                         (src1_cont || src1->type != vec_dot_type
-                        ? (i11      + i12*ne11 + i13*ne12*ne11)*row_size
-                        : (i11*nb11 + i12*nb12 + i13*nb13));
+                        ? (i11      + i12*ne11)*row_size
+                        : (i11*nb11 + i12*nb12));
 
-                    float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3));
+                    float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2));
 
                     //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
                     //    vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col);
                     //}
 
                     for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
-                        vec_dot(ne00, &tmp[ir0 - iir0], 0, src0_row + ir0*nb01, 0, src1_col, 0, 1);
+                        vec_dot(ne00, &tmp[ir0 - iir0], 0, src0_cur + ir0*nb01, 0, src1_col, 0, 1);
                     }
+
                     memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float));
                 }
             }
         }
     }
 
-    #undef MMID_MATRIX_ROW
+#undef MMID_MATRIX_ROW
 }
 
 // ggml_compute_forward_out_prod
 
 static void ggml_compute_forward_out_prod_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     // int64_t t0 = ggml_perf_time_us();
     // UNUSED(t0);
 
@@ -10563,7 +12276,6 @@ static void ggml_compute_forward_out_prod_f32(
     // nb01 >= nb00 - src0 is not transposed
     //   compute by src0 rows
 
-    // TODO: #if defined(GGML_USE_CUBLAS) ggml_cuda_out_prod
     // TODO: #if defined(GGML_USE_CLBLAST)
 
 #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
@@ -10573,7 +12285,7 @@ static void ggml_compute_forward_out_prod_f32(
         (ggml_is_contiguous(src1) || ggml_is_transposed(src1));
 #endif
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
 #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) // gemm beta will zero dst
         if (use_blas) {
             return;
@@ -10586,7 +12298,7 @@ static void ggml_compute_forward_out_prod_f32(
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -10725,9 +12437,11 @@ static void ggml_compute_forward_out_prod_f32(
 
 static void ggml_compute_forward_out_prod_q_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     // int64_t t0 = ggml_perf_time_us();
     // UNUSED(t0);
 
@@ -10761,10 +12475,9 @@ static void ggml_compute_forward_out_prod_q_f32(
     // nb01 >= nb00 - src0 is not transposed
     //   compute by src0 rows
 
-    // TODO: #if defined(GGML_USE_CUBLAS) ggml_cuda_out_prod
     // TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
         if (ith != 0) {
             return;
         }
@@ -10772,7 +12485,7 @@ static void ggml_compute_forward_out_prod_q_f32(
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -10838,9 +12551,10 @@ static void ggml_compute_forward_out_prod_q_f32(
 
 static void ggml_compute_forward_out_prod(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
@@ -10856,17 +12570,22 @@ static void ggml_compute_forward_out_prod(
         case GGML_TYPE_IQ2_XS:
         case GGML_TYPE_IQ3_XXS:
         case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ2_S:
             {
-                ggml_compute_forward_out_prod_q_f32(params, src0, src1, dst);
+                ggml_compute_forward_out_prod_q_f32(params, dst);
             } break;
         case GGML_TYPE_F16:
             {
                 GGML_ASSERT(false); // todo
-                // ggml_compute_forward_out_prod_f16_f32(params, src0, src1, dst);
+                // ggml_compute_forward_out_prod_f16_f32(params, dst);
             } break;
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_out_prod_f32(params, src0, src1, dst);
+                ggml_compute_forward_out_prod_f32(params, dst);
             } break;
         default:
             {
@@ -10879,13 +12598,15 @@ static void ggml_compute_forward_out_prod(
 
 static void ggml_compute_forward_scale_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_is_contiguous(src0));
     GGML_ASSERT(ggml_is_contiguous(dst));
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -10921,12 +12642,14 @@ static void ggml_compute_forward_scale_f32(
 
 static void ggml_compute_forward_scale(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_scale_f32(params, src0, dst);
+                ggml_compute_forward_scale_f32(params, dst);
             } break;
         default:
             {
@@ -10939,9 +12662,11 @@ static void ggml_compute_forward_scale(
 
 static void ggml_compute_forward_set_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0));
 
@@ -10953,7 +12678,7 @@ static void ggml_compute_forward_set_f32(
     size_t offset  = ((int32_t *) dst->op_params)[3];
     bool   inplace = (bool) ((int32_t *) dst->op_params)[4];
 
-    if (!inplace && (params->type == GGML_TASK_INIT)) {
+    if (!inplace && (params->type == GGML_TASK_TYPE_INIT)) {
         if (params->ith != 0) {
             return;
         }
@@ -10965,7 +12690,7 @@ static void ggml_compute_forward_set_f32(
             ggml_nbytes(dst));
     }
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -11012,16 +12737,17 @@ static void ggml_compute_forward_set_f32(
 
 static void ggml_compute_forward_set(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
 
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_set_f32(params, src0, src1, dst);
+                ggml_compute_forward_set_f32(params, dst);
             } break;
         case GGML_TYPE_F16:
+        case GGML_TYPE_BF16:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:
@@ -11037,6 +12763,11 @@ static void ggml_compute_forward_set(
         case GGML_TYPE_IQ2_XS:
         case GGML_TYPE_IQ3_XXS:
         case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ2_S:
         default:
             {
                 GGML_ASSERT(false);
@@ -11048,29 +12779,25 @@ static void ggml_compute_forward_set(
 
 static void ggml_compute_forward_cpy(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
-    ggml_compute_forward_dup(params, src0, dst);
+    ggml_compute_forward_dup(params, dst);
 }
 
 // ggml_compute_forward_cont
 
 static void ggml_compute_forward_cont(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
-    ggml_compute_forward_dup(params, src0, dst);
+    ggml_compute_forward_dup(params, dst);
 }
 
 // ggml_compute_forward_reshape
 
 static void ggml_compute_forward_reshape(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
     // NOP
     UNUSED(params);
-    UNUSED(src0);
     UNUSED(dst);
 }
 
@@ -11078,49 +12805,49 @@ static void ggml_compute_forward_reshape(
 
 static void ggml_compute_forward_view(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0) {
+        const struct ggml_tensor * dst) {
     // NOP
     UNUSED(params);
-    UNUSED(src0);
+    UNUSED(dst);
 }
 
 // ggml_compute_forward_permute
 
 static void ggml_compute_forward_permute(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0) {
+        const struct ggml_tensor * dst) {
     // NOP
     UNUSED(params);
-    UNUSED(src0);
+    UNUSED(dst);
 }
 
 // ggml_compute_forward_transpose
 
 static void ggml_compute_forward_transpose(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0) {
+        const struct ggml_tensor * dst) {
     // NOP
     UNUSED(params);
-    UNUSED(src0);
+    UNUSED(dst);
 }
 
 // ggml_compute_forward_get_rows
 
 static void ggml_compute_forward_get_rows_q(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
-    assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int64_t nc = ne00;
-    const int64_t nr = ggml_nelements(src1); GGML_UNUSED(nr);
+    const int64_t nr = ggml_nelements(src1);
 
     const enum ggml_type type = src0->type;
     ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;
@@ -11130,95 +12857,163 @@ static void ggml_compute_forward_get_rows_q(
     assert(nb00 == ggml_type_size(type));
     assert(ggml_nrows(dst) == nr);
 
-    // TODO: multi-thread
-    for (int64_t i12 = 0; i12 < ne12; ++i12) {
-        for (int64_t i11 = 0; i11 < ne11; ++i11) {
-            for (int64_t i10 = 0; i10 < ne10; ++i10) {
-                const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
+    const int ith = params->ith;
+    const int nth = params->nth;
 
-                dequantize_row_q(
-                        (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
-                             (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3), nc);
-            }
-        }
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int64_t i = ir0; i < ir1; ++i) {
+        const int64_t i12 = i/(ne11*ne10);
+        const int64_t i11 = (i - i12*ne11*ne10)/ne10;
+        const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10);
+        const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
+
+        dequantize_row_q(
+                (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
+                     (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3), nc);
     }
 }
 
 static void ggml_compute_forward_get_rows_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
-    assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int64_t nc = ne00;
-    const int64_t nr = ggml_nelements(src1); GGML_UNUSED(nr);
+    const int64_t nr = ggml_nelements(src1);
 
     assert(ne0  == nc);
     assert(ne02 == ne11);
     assert(nb00 == sizeof(ggml_fp16_t));
     assert(ggml_nrows(dst) == nr);
 
-    // TODO: multi-thread
-    for (int64_t i12 = 0; i12 < ne12; ++i12) {
-        for (int64_t i11 = 0; i11 < ne11; ++i11) {
-            for (int64_t i10 = 0; i10 < ne10; ++i10) {
-                const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
+    const int ith = params->ith;
+    const int nth = params->nth;
 
-                ggml_fp16_to_fp32_row(
-                        (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
-                             (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3), nc);
-            }
-        }
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int64_t i = ir0; i < ir1; ++i) {
+        const int64_t i12 = i/(ne11*ne10);
+        const int64_t i11 = (i - i12*ne11*ne10)/ne10;
+        const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10);
+        const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
+
+        ggml_fp16_to_fp32_row(
+                (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
+                     (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3), nc);
     }
 }
 
-static void ggml_compute_forward_get_rows_f32(
+static void ggml_compute_forward_get_rows_bf16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
-    assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int64_t nc = ne00;
-    const int64_t nr = ggml_nelements(src1); GGML_UNUSED(nr);
+    const int64_t nr = ggml_nelements(src1);
+
+    assert(ne0  == nc);
+    assert(ne02 == ne11);
+    assert(nb00 == sizeof(ggml_bf16_t));
+    assert(ggml_nrows(dst) == nr);
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int64_t i = ir0; i < ir1; ++i) {
+        const int64_t i12 = i/(ne11*ne10);
+        const int64_t i11 = (i - i12*ne11*ne10)/ne10;
+        const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10);
+        const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
+
+       ggml_bf16_to_fp32_row(
+                (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
+                     (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3), nc);
+    }
+}
+
+static void ggml_compute_forward_get_rows_f32(
+        const struct ggml_compute_params * params,
+              struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int64_t nc = ne00;
+    const int64_t nr = ggml_nelements(src1);
 
     assert(ne0  == nc);
     assert(ne02 == ne11);
     assert(nb00 == sizeof(float));
     assert(ggml_nrows(dst) == nr);
 
-    // TODO: multi-thread
-    for (int64_t i12 = 0; i12 < ne12; ++i12) {
-        for (int64_t i11 = 0; i11 < ne11; ++i11) {
-            for (int64_t i10 = 0; i10 < ne10; ++i10) {
-                const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
+    const int ith = params->ith;
+    const int nth = params->nth;
 
-                ggml_vec_cpy_f32(nc,
-                        (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3),
-                        (float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03));
-            }
-        }
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int64_t i = ir0; i < ir1; ++i) {
+        const int64_t i12 = i/(ne11*ne10);
+        const int64_t i11 = (i - i12*ne11*ne10)/ne10;
+        const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10);
+        const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
+
+        ggml_vec_cpy_f32(nc,
+                (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3),
+                (float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03));
     }
 }
 
 static void ggml_compute_forward_get_rows(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
@@ -11235,17 +13030,26 @@ static void ggml_compute_forward_get_rows(
         case GGML_TYPE_IQ2_XS:
         case GGML_TYPE_IQ3_XXS:
         case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ2_S:
             {
-                ggml_compute_forward_get_rows_q(params, src0, src1, dst);
+                ggml_compute_forward_get_rows_q(params, dst);
             } break;
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_get_rows_f16(params, src0, src1, dst);
+                ggml_compute_forward_get_rows_f16(params, dst);
+            } break;
+        case GGML_TYPE_BF16:
+            {
+                ggml_compute_forward_get_rows_bf16(params, dst);
             } break;
         case GGML_TYPE_F32:
         case GGML_TYPE_I32:
             {
-                ggml_compute_forward_get_rows_f32(params, src0, src1, dst);
+                ggml_compute_forward_get_rows_f32(params, dst);
             } break;
         default:
             {
@@ -11276,22 +13080,24 @@ static void ggml_compute_forward_get_rows(
 
 static void ggml_compute_forward_get_rows_back_f32_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(params->ith == 0);
     GGML_ASSERT(ggml_is_contiguous(dst));
 
     // ggml_compute_forward_dup_same_cont(params, opt0, dst);
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
         if (params->ith != 0) {
             return;
         }
         memset(dst->data, 0, ggml_nbytes(dst));
     }
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -11313,22 +13119,24 @@ static void ggml_compute_forward_get_rows_back_f32_f16(
 
 static void ggml_compute_forward_get_rows_back_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(params->ith == 0);
     GGML_ASSERT(ggml_is_contiguous(dst));
 
     // ggml_compute_forward_dup_same_cont(params, opt0, dst);
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
         if (params->ith != 0) {
             return;
         }
         memset(dst->data, 0, ggml_nbytes(dst));
     }
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -11350,17 +13158,18 @@ static void ggml_compute_forward_get_rows_back_f32(
 
 static void ggml_compute_forward_get_rows_back(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_get_rows_back_f32_f16(params, src0, src1, dst);
+                ggml_compute_forward_get_rows_back_f32_f16(params, dst);
             } break;
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_get_rows_back_f32(params, src0, src1, dst);
+                ggml_compute_forward_get_rows_back_f32(params, dst);
             } break;
         default:
             {
@@ -11391,11 +13200,13 @@ static void ggml_compute_forward_get_rows_back(
 
 static void ggml_compute_forward_diag_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -11431,12 +13242,14 @@ static void ggml_compute_forward_diag_f32(
 
 static void ggml_compute_forward_diag(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_diag_f32(params, src0, dst);
+                ggml_compute_forward_diag_f32(params, dst);
             } break;
         default:
             {
@@ -11449,10 +13262,11 @@ static void ggml_compute_forward_diag(
 
 static void ggml_compute_forward_diag_mask_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst,
         const float value) {
 
+    const struct ggml_tensor * src0 = dst->src[0];
+
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -11461,7 +13275,7 @@ static void ggml_compute_forward_diag_mask_f32(
 
     GGML_ASSERT(n_past >= 0);
 
-    if (!inplace && (params->type == GGML_TASK_INIT)) {
+    if (!inplace && (params->type == GGML_TASK_TYPE_INIT)) {
         if (ith != 0) {
             return;
         }
@@ -11475,7 +13289,7 @@ static void ggml_compute_forward_diag_mask_f32(
             ggml_nbytes(dst));
     }
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -11502,12 +13316,14 @@ static void ggml_compute_forward_diag_mask_f32(
 
 static void ggml_compute_forward_diag_mask_inf(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_diag_mask_f32(params, src0, dst, -INFINITY);
+                ggml_compute_forward_diag_mask_f32(params, dst, -INFINITY);
             } break;
         default:
             {
@@ -11518,12 +13334,14 @@ static void ggml_compute_forward_diag_mask_inf(
 
 static void ggml_compute_forward_diag_mask_zero(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_diag_mask_f32(params, src0, dst, 0);
+                ggml_compute_forward_diag_mask_f32(params, dst, 0);
             } break;
         default:
             {
@@ -11536,14 +13354,15 @@ static void ggml_compute_forward_diag_mask_zero(
 
 static void ggml_compute_forward_soft_max_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
-        const struct ggml_tensor * src2,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     assert(ggml_is_contiguous(dst));
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -11560,12 +13379,12 @@ static void ggml_compute_forward_soft_max_f32(
 
     GGML_TENSOR_UNARY_OP_LOCALS
 
-    const int64_t ne11 = src1 ? src1->ne[1] : 1;
+    //const int64_t ne11 = src1 ? src1->ne[1] : 1;
 
     // TODO: is this supposed to be ceil instead of floor?
     //       https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L370
-    const uint32_t n_head_kv   = ne02;
-    const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head_kv));
+    const uint32_t n_head      = ne02;
+    const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head));
 
     const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
     const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
@@ -11582,29 +13401,31 @@ static void ggml_compute_forward_soft_max_f32(
 
     float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith;
 
-    // when max_bias <= 0.0f, src2 is not used and we default it to src0 to avoid branching
-    float * pos = src2 ? (float *) src2->data : src0->data;
+    const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16);
 
     for (int i1 = ir0; i1 < ir1; i1++) {
+        // ALiBi
+        const uint32_t h = (i1/ne01)%ne02; // head
+        const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f;
+
         float * sp = (float *)((char *) src0->data + i1*src0->nb[1]);
         float * dp = (float *)((char *)  dst->data +  i1*dst->nb[1]);
 
         // broadcast the mask across rows
-        float * mp = src1 ? (float *)((char *) src1->data + (i1%ne11)*src1->nb[1]) : NULL;
+        ggml_fp16_t * mp_f16 = src1 ? (ggml_fp16_t *)((char *) src1->data) + (i1%ne01)*ne00 : NULL;
+        float       * mp_f32 = src1 ? (float       *)((char *) src1->data) + (i1%ne01)*ne00 : NULL;
 
         ggml_vec_cpy_f32  (nc, wp, sp);
         ggml_vec_scale_f32(nc, wp, scale);
-        if (mp) {
-            ggml_vec_acc_f32(nc, wp, mp);
-        }
-
-        // ALiBi bias
-        if (max_bias > 0.0f) {
-            const uint32_t h  = (i1/ne01)%ne02; // head
-            const float slope = h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1);
-
-            for (int i = 0; i < nc; i++) {
-                wp[i] = wp[i] + slope*pos[i];
+        if (mp_f32) {
+            if (use_f16) {
+                for (int i = 0; i < nc; ++i) {
+                    wp[i] += slope*GGML_FP16_TO_FP32(mp_f16[i]);
+                }
+            } else {
+                for (int i = 0; i < nc; ++i) {
+                    wp[i] += slope*mp_f32[i];
+                }
             }
         }
 
@@ -11650,14 +13471,14 @@ static void ggml_compute_forward_soft_max_f32(
 
 static void ggml_compute_forward_soft_max(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
-        const struct ggml_tensor * src2,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_soft_max_f32(params, src0, src1, src2, dst);
+                ggml_compute_forward_soft_max_f32(params, dst);
             } break;
         default:
             {
@@ -11670,16 +13491,18 @@ static void ggml_compute_forward_soft_max(
 
 static void ggml_compute_forward_soft_max_back_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_is_contiguous(src0));
     GGML_ASSERT(ggml_is_contiguous(src1));
     GGML_ASSERT(ggml_is_contiguous(dst));
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_are_same_shape(src1, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -11747,13 +13570,14 @@ static void ggml_compute_forward_soft_max_back_f32(
 
 static void ggml_compute_forward_soft_max_back(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_soft_max_back_f32(params, src0, src1, dst);
+                ggml_compute_forward_soft_max_back_f32(params, dst);
             } break;
         default:
             {
@@ -11762,173 +13586,17 @@ static void ggml_compute_forward_soft_max_back(
     }
 }
 
-// ggml_compute_forward_alibi
-
-static void ggml_compute_forward_alibi_f32(
-        const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        struct ggml_tensor * dst) {
-    assert(params->ith == 0);
-
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
-        return;
-    }
-
-    //const int n_past = ((int32_t *) dst->op_params)[0];
-    const int n_head = ((int32_t *) dst->op_params)[1];
-    float max_bias;
-    memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
-
-    const int64_t ne0 = src0->ne[0]; // all_seq_len = n_past + ne1
-    const int64_t ne1 = src0->ne[1]; // seq_len_without_past
-    const int64_t ne2 = src0->ne[2]; // n_head -> this is k
-    //const int64_t ne3 = src0->ne[3]; // 1 -> bsz
-
-    const int64_t n  = ggml_nrows(src0);
-    const int64_t ne2_ne3 = n/ne1; // ne2*ne3
-
-    const size_t nb0 = src0->nb[0];
-    const size_t nb1 = src0->nb[1];
-    const size_t nb2 = src0->nb[2];
-    //const int nb3 = src0->nb[3];
-
-    GGML_ASSERT(nb0 == sizeof(float));
-    GGML_ASSERT(n_head == ne2);
-
-    // add alibi to src0 (KQ_scaled)
-    const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
-
-    const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
-    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
-
-    for (int64_t k = 0; k < ne2_ne3; k++) {
-        // TODO: k*nb2 or k*nb3
-        float m_k;
-
-        if (k < n_heads_log2_floor) {
-            m_k = powf(m0, k + 1);
-        } else {
-            m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
-        }
-
-        for (int64_t i = 0; i < ne0; i++) {
-            for (int64_t j = 0; j < ne1; j++) {
-                float * const src = (float *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2);
-                float *      pdst = (float *)((char *)  dst->data + i*nb0 + j*nb1 + k*nb2);
-                pdst[0] = i * m_k + src[0];
-            }
-        }
-    }
-}
-
-static void ggml_compute_forward_alibi_f16(
-        const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        struct ggml_tensor * dst) {
-    assert(params->ith == 0);
-
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
-        return;
-    }
-
-    //const int n_past = ((int32_t *) dst->op_params)[0];
-    const int n_head = ((int32_t *) dst->op_params)[1];
-    float max_bias;
-    memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
-
-    const int ne0 = src0->ne[0]; // all_seq_len = n_past + ne1
-    const int ne1 = src0->ne[1]; // seq_len_without_past
-    const int ne2 = src0->ne[2]; // n_head -> this is k
-    //const int ne3 = src0->ne[3]; // 1 -> bsz
-
-    const int n  = ggml_nrows(src0);
-    const int ne2_ne3 = n/ne1; // ne2*ne3
-
-    const int nb0 = src0->nb[0];
-    const int nb1 = src0->nb[1];
-    const int nb2 = src0->nb[2];
-    //const int nb3 = src0->nb[3];
-
-    GGML_ASSERT(nb0 == sizeof(ggml_fp16_t));
-    //GGML_ASSERT(ne1 + n_past == ne0); (void) n_past;
-    GGML_ASSERT(n_head == ne2);
-
-    // add alibi to src0 (KQ_scaled)
-    const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
-
-    const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
-    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
-
-    for (int k = 0; k < ne2_ne3; k++) {
-        // TODO: k*nb2 or k*nb3
-        float m_k;
-
-        if (k < n_heads_log2_floor) {
-            m_k = powf(m0, k + 1);
-        } else {
-            m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
-        }
-
-        for (int i = 0; i < ne0; i++) {
-            for (int j = 0; j < ne1; j++) {
-                ggml_fp16_t * const src  = (ggml_fp16_t *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2);
-                float       *      pdst  =       (float *)((char *)  dst->data + i*nb0 + j*nb1 + k*nb2);
-
-                // we return F32
-                pdst[0] = i * m_k + GGML_FP16_TO_FP32(src[0]);
-            }
-        }
-    }
-}
-
-static void ggml_compute_forward_alibi(
-        const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        struct ggml_tensor * dst) {
-    switch (src0->type) {
-        case GGML_TYPE_F16:
-            {
-                ggml_compute_forward_alibi_f16(params, src0, dst);
-            } break;
-        case GGML_TYPE_F32:
-            {
-                ggml_compute_forward_alibi_f32(params, src0, dst);
-            } break;
-        case GGML_TYPE_Q4_0:
-        case GGML_TYPE_Q4_1:
-        case GGML_TYPE_Q5_0:
-        case GGML_TYPE_Q5_1:
-        case GGML_TYPE_Q8_0:
-        case GGML_TYPE_Q8_1:
-        case GGML_TYPE_Q2_K:
-        case GGML_TYPE_Q3_K:
-        case GGML_TYPE_Q4_K:
-        case GGML_TYPE_Q5_K:
-        case GGML_TYPE_Q6_K:
-        case GGML_TYPE_IQ2_XXS:
-        case GGML_TYPE_IQ2_XS:
-        case GGML_TYPE_IQ3_XXS:
-        case GGML_TYPE_IQ1_S:
-        case GGML_TYPE_Q8_K:
-        case GGML_TYPE_I8:
-        case GGML_TYPE_I16:
-        case GGML_TYPE_I32:
-        case GGML_TYPE_COUNT:
-            {
-                GGML_ASSERT(false);
-            } break;
-    }
-}
-
 // ggml_compute_forward_clamp
 
 static void ggml_compute_forward_clamp_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -11964,14 +13632,17 @@ static void ggml_compute_forward_clamp_f32(
 
 static void ggml_compute_forward_clamp(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_clamp_f32(params, src0, dst);
+                ggml_compute_forward_clamp_f32(params, dst);
             } break;
         case GGML_TYPE_F16:
+        case GGML_TYPE_BF16:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:
@@ -11987,10 +13658,17 @@ static void ggml_compute_forward_clamp(
         case GGML_TYPE_IQ2_XS:
         case GGML_TYPE_IQ3_XXS:
         case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ2_S:
         case GGML_TYPE_Q8_K:
         case GGML_TYPE_I8:
         case GGML_TYPE_I16:
         case GGML_TYPE_I32:
+        case GGML_TYPE_I64:
+        case GGML_TYPE_F64:
         case GGML_TYPE_COUNT:
             {
                 GGML_ASSERT(false);
@@ -12058,11 +13736,13 @@ GGML_CALL void ggml_rope_yarn_corr_dims(
 
 static void ggml_compute_forward_rope_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst,
         const bool forward) {
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -12234,11 +13914,13 @@ static void ggml_compute_forward_rope_f32(
 
 static void ggml_compute_forward_rope_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst,
         const bool forward) {
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -12399,17 +14081,18 @@ static void ggml_compute_forward_rope_f16(
 
 static void ggml_compute_forward_rope(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_rope_f16(params, src0, src1, dst, true);
+                ggml_compute_forward_rope_f16(params, dst, true);
             } break;
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_rope_f32(params, src0, src1, dst, true);
+                ggml_compute_forward_rope_f32(params, dst, true);
             } break;
         default:
             {
@@ -12422,17 +14105,18 @@ static void ggml_compute_forward_rope(
 
 static void ggml_compute_forward_rope_back(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_rope_f16(params, src0, src1, dst, false);
+                ggml_compute_forward_rope_f16(params, dst, false);
             } break;
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_rope_f32(params, src0, src1, dst, false);
+                ggml_compute_forward_rope_f32(params, dst, false);
             } break;
         default:
             {
@@ -12445,9 +14129,11 @@ static void ggml_compute_forward_rope_back(
 
 static void ggml_compute_forward_conv_transpose_1d_f16_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
@@ -12465,7 +14151,7 @@ static void ggml_compute_forward_conv_transpose_1d_f16_f32(
     GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
     GGML_ASSERT(nb10 == sizeof(float));
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
         if (ith != 0) {
             return;
         }
@@ -12505,7 +14191,7 @@ static void ggml_compute_forward_conv_transpose_1d_f16_f32(
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -12542,9 +14228,11 @@ static void ggml_compute_forward_conv_transpose_1d_f16_f32(
 
 static void ggml_compute_forward_conv_transpose_1d_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
@@ -12562,7 +14250,7 @@ static void ggml_compute_forward_conv_transpose_1d_f32(
     GGML_ASSERT(nb00 == sizeof(float));
     GGML_ASSERT(nb10 == sizeof(float));
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
         if (ith != 0) {
             return;
         }
@@ -12602,7 +14290,7 @@ static void ggml_compute_forward_conv_transpose_1d_f32(
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -12639,17 +14327,18 @@ static void ggml_compute_forward_conv_transpose_1d_f32(
 
 static void ggml_compute_forward_conv_transpose_1d(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_conv_transpose_1d_f16_f32(params, src0, src1, dst);
+                ggml_compute_forward_conv_transpose_1d_f16_f32(params, dst);
             } break;
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_conv_transpose_1d_f32(params, src0, src1, dst);
+                ggml_compute_forward_conv_transpose_1d_f32(params, dst);
             } break;
         default:
             {
@@ -12663,9 +14352,11 @@ static void ggml_compute_forward_conv_transpose_1d(
 // dst:  result [N, OH, OW, IC*KH*KW]
 static void ggml_compute_forward_im2col_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
@@ -12703,11 +14394,11 @@ static void ggml_compute_forward_im2col_f32(
     GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
     GGML_ASSERT(nb10 == sizeof(float));
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -12749,9 +14440,11 @@ static void ggml_compute_forward_im2col_f32(
 // dst:  result [N, OH, OW, IC*KH*KW]
 static void ggml_compute_forward_im2col_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F16);
@@ -12789,11 +14482,11 @@ static void ggml_compute_forward_im2col_f16(
     GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
     GGML_ASSERT(nb10 == sizeof(float));
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -12831,17 +14524,15 @@ static void ggml_compute_forward_im2col_f16(
 
 static void ggml_compute_forward_im2col(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
     switch (dst->type) {
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_im2col_f16(params, src0, src1, dst);
+                ggml_compute_forward_im2col_f16(params, dst);
             } break;
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_im2col_f32(params, src0, src1, dst);
+                ggml_compute_forward_im2col_f32(params, dst);
             } break;
         default:
             {
@@ -12855,9 +14546,11 @@ static void ggml_compute_forward_im2col(
 
 static void ggml_compute_forward_conv_transpose_2d(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
@@ -12875,7 +14568,7 @@ static void ggml_compute_forward_conv_transpose_2d(
     GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
     GGML_ASSERT(nb10 == sizeof(float));
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
         if (ith != 0) {
             return;
         }
@@ -12917,7 +14610,7 @@ static void ggml_compute_forward_conv_transpose_2d(
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -12961,13 +14654,15 @@ static void ggml_compute_forward_conv_transpose_2d(
 static void ggml_compute_forward_pool_1d_sk_p0(
         const struct ggml_compute_params * params,
         const enum ggml_op_pool op,
-        const struct ggml_tensor * src,
         const int k,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src = dst->src[0];
+
     assert(src->type == GGML_TYPE_F32);
     assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -13012,7 +14707,6 @@ static void ggml_compute_forward_pool_1d_sk_p0(
 
 static void ggml_compute_forward_pool_1d(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
               struct ggml_tensor * dst) {
 
     const int32_t * opts = (const int32_t *)dst->op_params;
@@ -13023,19 +14717,21 @@ static void ggml_compute_forward_pool_1d(
     GGML_ASSERT(p0 == 0); // padding not supported
     GGML_ASSERT(k0 == s0); // only s = k supported
 
-    ggml_compute_forward_pool_1d_sk_p0(params, op, src0, k0, dst);
+    ggml_compute_forward_pool_1d_sk_p0(params, op, k0, dst);
 }
 
 // ggml_compute_forward_pool_2d
 
 static void ggml_compute_forward_pool_2d(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src = dst->src[0];
+
     GGML_ASSERT(src->type == GGML_TYPE_F32);
     GGML_ASSERT(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -13104,10 +14800,11 @@ static void ggml_compute_forward_pool_2d(
 
 static void ggml_compute_forward_upscale_f32(
     const struct ggml_compute_params * params,
-    const struct ggml_tensor * src0,
     struct ggml_tensor * dst) {
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -13143,12 +14840,14 @@ static void ggml_compute_forward_upscale_f32(
 
 static void ggml_compute_forward_upscale(
     const struct ggml_compute_params * params,
-    const struct ggml_tensor * src0,
     struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_upscale_f32(params, src0, dst);
+                ggml_compute_forward_upscale_f32(params, dst);
             } break;
         default:
             {
@@ -13161,10 +14860,11 @@ static void ggml_compute_forward_upscale(
 
 static void ggml_compute_forward_pad_f32(
     const struct ggml_compute_params * params,
-    const struct ggml_tensor * src0,
           struct ggml_tensor * dst) {
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -13201,12 +14901,114 @@ static void ggml_compute_forward_pad_f32(
 
 static void ggml_compute_forward_pad(
     const struct ggml_compute_params * params,
-    const struct ggml_tensor * src0,
     struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_pad_f32(params, src0, dst);
+                ggml_compute_forward_pad_f32(params, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+
+// ggml_compute_forward_arange
+
+static void ggml_compute_forward_arange_f32(
+    const struct ggml_compute_params * params,
+    struct ggml_tensor * dst) {
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    GGML_ASSERT(dst->nb[0] == sizeof(float));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const float start = ggml_get_op_params_f32(dst, 0);
+    const float stop  = ggml_get_op_params_f32(dst, 1);
+    const float step  = ggml_get_op_params_f32(dst, 2);
+
+    const int64_t steps = (int64_t) ceilf((stop - start) / step);
+
+    GGML_ASSERT(ggml_nelements(dst) == steps);
+
+    for (int64_t i = ith; i < steps; i+= nth) {
+        float value = start + step * i;
+        ((float *)dst->data)[i] = value;
+    }
+}
+
+static void ggml_compute_forward_arange(
+    const struct ggml_compute_params * params,
+    struct ggml_tensor * dst) {
+    switch (dst->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_arange_f32(params, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+static void ggml_compute_forward_timestep_embedding_f32(
+    const struct ggml_compute_params * params,
+    struct ggml_tensor * dst) {
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    const int dim = ggml_get_op_params_i32(dst, 0);
+    const int max_period = ggml_get_op_params_i32(dst, 1);
+
+    int half = dim / 2;
+
+    for (int64_t i = 0; i < ne00; i++) {
+        float * embed_data = (float *)((char *)  dst->data +  i*nb1);
+        for (int64_t j = ith; j < half; j += nth) {
+            float timestep = ((float *)src0->data)[i];
+            float freq = (float)expf(-logf(max_period) * j / half);
+            float arg = timestep * freq;
+            embed_data[j] = cosf(arg);
+            embed_data[j + half] = sinf(arg);
+        }
+        if (dim % 2 != 0 && ith == 0) {
+            embed_data[dim] = 0.f;
+        }
+    }
+}
+
+static void ggml_compute_forward_timestep_embedding(
+    const struct ggml_compute_params * params,
+    struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_timestep_embedding_f32(params, dst);
             } break;
         default:
             {
@@ -13219,10 +15021,11 @@ static void ggml_compute_forward_pad(
 
 static void ggml_compute_forward_argsort_f32(
     const struct ggml_compute_params * params,
-    const struct ggml_tensor * src0,
     struct ggml_tensor * dst) {
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -13248,8 +15051,8 @@ static void ggml_compute_forward_argsort_f32(
         // C doesn't have a functional sort, so we do a bubble sort instead
         for (int64_t j = 0; j < ne0; j++) {
             for (int64_t k = j + 1; k < ne0; k++) {
-                if ((order == GGML_SORT_ASC && src_data[dst_data[j]] > src_data[dst_data[k]]) ||
-                    (order == GGML_SORT_DESC && src_data[dst_data[j]] < src_data[dst_data[k]])) {
+                if ((order == GGML_SORT_ORDER_ASC  && src_data[dst_data[j]] > src_data[dst_data[k]]) ||
+                    (order == GGML_SORT_ORDER_DESC && src_data[dst_data[j]] < src_data[dst_data[k]])) {
                     int32_t tmp = dst_data[j];
                     dst_data[j] = dst_data[k];
                     dst_data[k] = tmp;
@@ -13261,13 +15064,14 @@ static void ggml_compute_forward_argsort_f32(
 
 static void ggml_compute_forward_argsort(
     const struct ggml_compute_params * params,
-    const struct ggml_tensor * src0,
     struct ggml_tensor * dst) {
 
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_argsort_f32(params, src0, dst);
+                ggml_compute_forward_argsort_f32(params, dst);
             } break;
         default:
             {
@@ -13280,11 +15084,13 @@ static void ggml_compute_forward_argsort(
 
 static void ggml_compute_forward_flash_attn_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * q,
-        const struct ggml_tensor * k,
-        const struct ggml_tensor * v,
         const bool masked,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * q = dst->src[0];
+    const struct ggml_tensor * k = dst->src[1];
+    const struct ggml_tensor * v = dst->src[2];
+
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
@@ -13329,11 +15135,11 @@ static void ggml_compute_forward_flash_attn_f32(
     GGML_ASSERT(nb1 <= nb2);
     GGML_ASSERT(nb2 <= nb3);
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -13470,11 +15276,13 @@ static void ggml_compute_forward_flash_attn_f32(
 
 static void ggml_compute_forward_flash_attn_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * q,
-        const struct ggml_tensor * k,
-        const struct ggml_tensor * v,
         const bool masked,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * q = dst->src[0];
+    const struct ggml_tensor * k = dst->src[1];
+    const struct ggml_tensor * v = dst->src[2];
+
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
@@ -13519,11 +15327,11 @@ static void ggml_compute_forward_flash_attn_f16(
     GGML_ASSERT(nb1 <= nb2);
     GGML_ASSERT(nb2 <= nb3);
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -13696,19 +15504,223 @@ static void ggml_compute_forward_flash_attn_f16(
 
 static void ggml_compute_forward_flash_attn(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * q,
-        const struct ggml_tensor * k,
-        const struct ggml_tensor * v,
         const bool masked,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * q = dst->src[0];
+
     switch (q->type) {
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_flash_attn_f16(params, q, k, v, masked, dst);
+                ggml_compute_forward_flash_attn_f16(params, masked, dst);
             } break;
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_flash_attn_f32(params, q, k, v, masked, dst);
+                ggml_compute_forward_flash_attn_f32(params, masked, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_flash_attn_ext
+
+static void ggml_compute_forward_flash_attn_ext_f16(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * q,
+        const struct ggml_tensor * k,
+        const struct ggml_tensor * v,
+        const struct ggml_tensor * mask,
+        struct ggml_tensor * dst) {
+    int64_t t0 = ggml_perf_time_us();
+    UNUSED(t0);
+
+    GGML_TENSOR_LOCALS(int64_t, neq, q,   ne)
+    GGML_TENSOR_LOCALS(size_t,  nbq, q,   nb)
+    GGML_TENSOR_LOCALS(int64_t, nek, k,   ne)
+    GGML_TENSOR_LOCALS(size_t,  nbk, k,   nb)
+    GGML_TENSOR_LOCALS(int64_t, nev, v,   ne)
+    GGML_TENSOR_LOCALS(size_t,  nbv, v,   nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst, nb)
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int64_t D = neq0;
+    const int64_t N = neq1;
+
+    GGML_ASSERT(ne0 == D);
+    GGML_ASSERT(ne2 == N);
+
+    GGML_ASSERT(nbq0 == sizeof(float));
+    GGML_ASSERT(nbk0 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nbv0 == sizeof(ggml_fp16_t));
+
+    GGML_ASSERT(neq0 == D);
+    GGML_ASSERT(nek0 == D);
+    GGML_ASSERT(nev0 == D);
+
+    GGML_ASSERT(neq1 == N);
+    GGML_ASSERT(nev0 == D);
+
+    // dst cannot be transposed or permuted
+    GGML_ASSERT(nb0 == sizeof(float));
+    GGML_ASSERT(nb0 <= nb1);
+    GGML_ASSERT(nb1 <= nb2);
+    GGML_ASSERT(nb2 <= nb3);
+
+    // broadcast factors
+    const int64_t rk2 = neq2/nek2;
+    const int64_t rk3 = neq3/nek3;
+
+    const int64_t rv2 = neq2/nev2;
+    const int64_t rv3 = neq3/nev3;
+
+    if (params->type == GGML_TASK_TYPE_INIT) {
+        return;
+    }
+
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    // parallelize by q rows using ggml_vec_dot_f32
+
+    // total rows in q
+    const int nr = neq1*neq2*neq3;
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    float scale    = 1.0f;
+    float max_bias = 0.0f;
+
+    memcpy(&scale,    (float *) dst->op_params + 0, sizeof(float));
+    memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float));
+
+    const uint32_t n_head      = neq2;
+    const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head));
+
+    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+    // loop over n_batch and n_head
+    for (int ir = ir0; ir < ir1; ++ir) {
+        // q indices
+        const int iq3 = ir/(neq2*neq1);
+        const int iq2 = (ir - iq3*neq2*neq1)/neq1;
+        const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1);
+
+        const uint32_t h = iq2; // head
+        const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f;
+
+        float S = 0.0f;
+        float M = -INFINITY;
+
+        float       * V32 = (float       *) params->wdata + ith*(2*D + CACHE_LINE_SIZE_F32);
+        ggml_fp16_t * Q16 = (ggml_fp16_t *) (V32); // reuse memory
+        ggml_fp16_t * V16 = (ggml_fp16_t *) (V32 + D);
+
+        memset(V16, 0, D*sizeof(ggml_fp16_t));
+
+        const ggml_fp16_t * mp = mask ? (ggml_fp16_t *)((char *) mask->data + iq1*mask->nb[1]) : NULL;
+
+        // k indices
+        const int ik3 = iq3 / rk3;
+        const int ik2 = iq2 / rk2;
+
+        // v indices
+        const int iv3 = iq3 / rv3;
+        const int iv2 = iq2 / rv2;
+
+        // online softmax / attention
+        // loop over n_kv and n_head_kv
+        // ref: https://arxiv.org/pdf/2112.05682.pdf
+        for (int64_t ic = 0; ic < nek1; ++ic) {
+            const float mv = mp ? slope*GGML_FP16_TO_FP32(mp[ic]) : 0.0f;
+            if (mv == -INFINITY) {
+                continue;
+            }
+
+            float s;
+
+            // convert Q to F16 in V32
+            {
+                const float * pq = (const float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3));
+
+                for (int64_t d = 0; d < D; ++d) {
+                    Q16[d] = GGML_FP32_TO_FP16(pq[d]);
+                }
+            }
+
+            ggml_vec_dot_f16(D,
+                    &s, 0,
+                    (ggml_fp16_t *) ((char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3)), 0,
+                    Q16, 0, 1);
+
+            s = s*scale + mv;
+
+            const float Mold = M;
+
+            float ms = 1.0f;
+            float vs = 1.0f;
+
+            if (s > M) {
+                M = s;
+                ms = expf(Mold - M);
+
+                // V = V*expf(Mold - M)
+                ggml_vec_scale_f16(D, V16, ms);
+            } else {
+                vs = expf(s - M);
+            }
+
+            const ggml_fp16_t * v16 = (const ggml_fp16_t *) ((char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3));
+
+            // V += v*expf(s - M)
+            ggml_vec_mad_f16(D, V16, v16, vs);
+
+            S = S*ms + vs;
+        }
+
+        // V /= S
+        for (int64_t d = 0; d < D; ++d) {
+            V32[d] = GGML_FP16_TO_FP32(V16[d])/S;
+        }
+
+        // dst indices
+        const int i1 = iq1;
+        const int i2 = iq2;
+        const int i3 = iq3;
+
+        // original
+        //memcpy((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3), V, nev0*sizeof(float));
+
+        // permute(0, 2, 1, 3)
+        memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, V32, nb1);
+    }
+}
+
+static void ggml_compute_forward_flash_attn_ext(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * q,
+        const struct ggml_tensor * k,
+        const struct ggml_tensor * v,
+        const struct ggml_tensor * mask,
+        struct ggml_tensor * dst) {
+    switch (dst->op_params[2]) {
+        case GGML_PREC_DEFAULT:
+        case GGML_PREC_F32:
+            {
+                // uses F32 accumulators
+                ggml_compute_forward_flash_attn_ext_f16(params, q, k, v, mask, dst);
             } break;
         default:
             {
@@ -13721,12 +15733,14 @@ static void ggml_compute_forward_flash_attn(
 
 static void ggml_compute_forward_flash_ff_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * a,  // F16
-        const struct ggml_tensor * b0, // F16 fc_w
-        const struct ggml_tensor * b1, // F32 fc_b
-        const struct ggml_tensor * c0, // F16 proj_w
-        const struct ggml_tensor * c1, // F32 proj_b
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * a = dst->src[0];  // F16
+    const struct ggml_tensor * b0 = dst->src[1]; // F16 fc_w
+    const struct ggml_tensor * b1 = dst->src[2]; // F32 fc_b
+    const struct ggml_tensor * c0 = dst->src[3]; // F16 proj_w
+    const struct ggml_tensor * c1 = dst->src[4]; // F32 proj_b
+
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
@@ -13776,11 +15790,11 @@ static void ggml_compute_forward_flash_ff_f16(
     GGML_ASSERT(nb1 <= nb2);
     GGML_ASSERT(nb2 <= nb3);
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -13854,16 +15868,14 @@ static void ggml_compute_forward_flash_ff_f16(
 
 static void ggml_compute_forward_flash_ff(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * a,
-        const struct ggml_tensor * b0,
-        const struct ggml_tensor * b1,
-        const struct ggml_tensor * c0,
-        const struct ggml_tensor * c1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * b0 = dst->src[1];
+
     switch (b0->type) {
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_flash_ff_f16(params, a, b0, b1, c0, c1, dst);
+                ggml_compute_forward_flash_ff_f16(params, dst);
             } break;
         case GGML_TYPE_F32:
             {
@@ -13880,12 +15892,14 @@ static void ggml_compute_forward_flash_ff(
 
 static void ggml_compute_forward_flash_attn_back_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * q,
-        const struct ggml_tensor * k,
-        const struct ggml_tensor * v,
-        const struct ggml_tensor * d,
         const bool masked,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * q = dst->src[0];
+    const struct ggml_tensor * k = dst->src[1];
+    const struct ggml_tensor * v = dst->src[2];
+    const struct ggml_tensor * d = dst->src[3];
+
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
@@ -13935,14 +15949,14 @@ static void ggml_compute_forward_flash_attn_back_f32(
     GGML_ASSERT(nb1 <= nb2);
     GGML_ASSERT(nb2 <= nb3);
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
         if (ith == 0) {
             memset(dst->data, 0, nb0*ne0*ne1*ne2*ne3);
         }
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -14233,16 +16247,266 @@ static void ggml_compute_forward_flash_attn_back_f32(
 
 static void ggml_compute_forward_flash_attn_back(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * q,
-        const struct ggml_tensor * k,
-        const struct ggml_tensor * v,
-        const struct ggml_tensor * d,
         const bool masked,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * q = dst->src[0];
+
     switch (q->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_flash_attn_back_f32(params, q, k, v, d, masked, dst);
+                ggml_compute_forward_flash_attn_back_f32(params, masked, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_ssm_conv
+
+static void ggml_compute_forward_ssm_conv_f32(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    const struct ggml_tensor * src0 = dst->src[0]; // conv_state
+    const struct ggml_tensor * src1 = dst->src[1]; // x
+    const struct ggml_tensor * src2 = dst->src[2]; // conv1d.weight
+    const struct ggml_tensor * src3 = dst->src[3]; // state_seq
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nc   = src2->ne[0]; // d_conv
+    const int nr   = src0->ne[1]; // d_inner
+    const int n_t  = src1->ne[1]; // n_tokens
+    const int n_kv = src0->ne[2]; // max number of sequences in the batch
+
+    GGML_ASSERT((nr*n_t) + (nc*nr*n_kv) == ggml_nelements(dst));
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+    GGML_ASSERT(src1->nb[0] == sizeof(float));
+    GGML_ASSERT(src2->nb[0] == sizeof(float));
+    GGML_ASSERT(src3->nb[0] == sizeof(int32_t));
+    GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float));
+    // for use with the destination state offset between sequences
+    GGML_ASSERT(src2->nb[2] == src2->ne[1]*src2->ne[0]*sizeof(float));
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+    const int ir  = ir1 - ir0;
+
+    if (n_kv > 1) {
+        // multiple sequences means it's hard to know when it's the first time a state is read,
+        // so copy them all over to the destination, just to be sure.
+        for (int i3 = 0; i3 < n_kv; ++i3) {
+            float * s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]));
+            float * s  = (float *) ((char *)  dst->data + ir0*(src2->nb[1]) + i3*(src2->nb[2]) + nr*n_t*sizeof(float));
+            // can't use memcpy because of d_conv vs d_conv - 1
+            for (int i1 = 0; i1 < ir; ++i1) {
+                for (int i0 = 0; i0 < nc - 1; ++i0) {
+                    // copy s0 to last (d_conv - 1) columns of s
+                    s[1 + i0 + i1*nc] = s0[i0 + i1*(nc - 1)];
+                }
+            }
+        }
+    }
+
+    for (int i2 = 0; i2 < n_t; ++i2) {
+        int32_t * sq = (int32_t *) ((char *) src3->data +  i2*(src3->nb[1])); // {n_kv, n_tokens}
+        float *   x  = (float *)   ((char *)  dst->data + ir0*sizeof(float) + i2*(nr*sizeof(float))); // {d_inner, n_tokens}
+        float *   s  = (float *)   ((char *)  dst->data + ir0*(src2->nb[1]) + sq[0]*(src2->nb[2]) + nr*n_t*sizeof(float)); // {d_conv, d_inner, n_kv}
+        float *   s0; // {d_conv - 1, d_inner, n_kv}
+        float *   x0 = (float *)   ((char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens}
+        float *   c  = (float *)   ((char *) src2->data + ir0*(src2->nb[1])); // {d_conv, d_inner}
+        int ne0s0;
+
+        GGML_ASSERT(0 <= sq[0] && sq[0] < n_kv);
+
+        // avoid needing to copy the state for the first token
+        if (i2 == 0) {
+            s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2])); // {d_conv - 1, d_inner, n_kv}
+            ne0s0 = src0->ne[0];
+        } else {
+            // the source is the last (d_conv - 1) columns of the destination
+            s0 = s + 1;
+            ne0s0 = nc;
+        }
+
+        // d_inner
+        for (int i1 = 0; i1 < ir; ++i1) {
+            // shift state left
+            for (int i0 = 0; i0 < nc - 1; ++i0) {
+                s[i0 + i1*nc] = s0[i0 + i1*ne0s0];
+            }
+            // insert x on the last column
+            s[(nc - 1) + i1*nc] = x0[i1];
+        }
+
+        // handle copies when there are multiple output states
+        for (int i3 = 1; i3 < n_kv; ++i3) {
+            int32_t seq = sq[i3];
+            if (0 <= seq && seq < n_kv) {
+                float * s1 = s + (seq - sq[0])*nc*nr;
+                memcpy(s1, s, nc*ir*sizeof(float));
+            } else {
+                // stop at negative or too big seq_ids
+                break;
+            }
+        }
+
+        // it seems a little faster when this is separate from the state shift
+        for (int i1 = 0; i1 < ir; ++i1) {
+            // rowwise dot product
+            float sumf = 0.0f;
+            for (int i0 = 0; i0 < nc; ++i0) {
+                int i = i0 + i1*nc;
+                sumf += s[i] * c[i];
+            }
+            x[i1] = sumf;
+        }
+    }
+}
+
+static void ggml_compute_forward_ssm_conv(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+    switch (dst->src[0]->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_ssm_conv_f32(params, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_ssm_scan
+
+static void ggml_compute_forward_ssm_scan_f32(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    const struct ggml_tensor * src0 = dst->src[0]; // s
+    const struct ggml_tensor * src1 = dst->src[1]; // x
+    const struct ggml_tensor * src2 = dst->src[2]; // dt
+    const struct ggml_tensor * src3 = dst->src[3]; // A
+    const struct ggml_tensor * src4 = dst->src[4]; // B
+    const struct ggml_tensor * src5 = dst->src[5]; // C
+    const struct ggml_tensor * src6 = dst->src[6]; // sq
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int64_t nc   = src0->ne[0]; // d_state
+    const int64_t nr   = src0->ne[1]; // d_inner
+    const int64_t n_t  = src1->ne[1]; // number of tokens in the batch
+    const int64_t n_kv = src0->ne[2]; // max number of sequences in the batch
+
+    GGML_ASSERT(ggml_nelements(src1) + ggml_nelements(src0) == ggml_nelements(dst));
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+    GGML_ASSERT(src1->nb[0] == sizeof(float));
+    GGML_ASSERT(src2->nb[0] == sizeof(float));
+    GGML_ASSERT(src3->nb[0] == sizeof(float));
+    GGML_ASSERT(src4->nb[0] == sizeof(float));
+    GGML_ASSERT(src5->nb[0] == sizeof(float));
+    // required for the dot product between s and C, and when copying the states
+    GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float));
+    // required for per-sequence offsets for states
+    GGML_ASSERT(src0->nb[2] == src0->ne[0]*src0->ne[1]*sizeof(float));
+    // required to get correct offset for state destination (i.e. src1->nb[2])
+    GGML_ASSERT(src1->nb[2] == src1->ne[0]*src1->ne[1]*sizeof(float));
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+    const int ir  = ir1 - ir0;
+
+    if (n_kv > 1) {
+        // it's hard to know if the source states have already been copied
+        // when there are multiple, so copy them already.
+        for (int i3 = 0; i3 < n_kv; ++i3) {
+            float * s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]));
+            float * s  = (float *) ((char *)  dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[2]);
+            memcpy(s, s0, nc*ir*sizeof(float));
+        }
+    }
+
+    for (int i2 = 0; i2 < n_t; ++i2) {
+        int32_t * sq = (int32_t *) ((char *) src6->data +  i2*(src6->nb[1])); // {n_kv, n_tokens}
+        float *   y  = (float *)   ((char *)  dst->data + ir0*(src1->nb[0]) +    i2*(src1->nb[1])); // {d_inner, n_tokens}
+        float *   s  = (float *)   ((char *)  dst->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2]) + src1->nb[2]); // {d_state, d_inner, n_kv}
+        float *   s0;
+        float *   x  = (float *)   ((char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens}
+        float *   dt = (float *)   ((char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1])); // {d_inner, n_tokens}
+        float *   A  = (float *)   ((char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner}
+        float *   B  = (float *)   ((char *) src4->data +  i2*(src4->nb[1])); // {d_state, n_tokens}
+        float *   C  = (float *)   ((char *) src5->data +  i2*(src5->nb[1])); // {d_state, n_tokens}
+
+        GGML_ASSERT(0 <= sq[0] && sq[0] < n_kv);
+
+        // avoid needing to copy the state for the first token
+        if (i2 == 0) {
+            s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2])); // {d_state, d_inner, n_kv}
+        } else {
+            // otherwise the source is the same as the destination
+            s0 = s;
+        }
+
+        // d_inner
+        for (int i1 = 0; i1 < ir; ++i1) {
+            // ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78
+            float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1];
+            float x_dt = x[i1] * dt_soft_plus;
+            float sumf = 0.0f;
+            // d_state
+            for (int i0 = 0; i0 < nc; ++i0) {
+                int i = i0 + i1*nc;
+                // state = prev_state * dA + dB * x
+                float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt);
+                // y = rowwise_dotprod(state, C)
+                sumf += state * C[i0];
+                s[i] = state;
+            }
+            y[i1] = sumf;
+        }
+
+        // handle copies when there are multiple output states
+        for (int i3 = 1; i3 < n_kv; ++i3) {
+            int32_t seq = sq[i3];
+            if (0 <= seq && seq < n_kv) {
+                float * s1 = s + (seq - sq[0])*nc*nr;
+                memcpy(s1, s, nc*ir*sizeof(float));
+            } else {
+                // stop at negative or too big seq_ids
+                break;
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_ssm_scan(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+    switch (dst->src[0]->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_ssm_scan_f32(params, dst);
             } break;
         default:
             {
@@ -14255,9 +16519,11 @@ static void ggml_compute_forward_flash_attn_back(
 
 static void ggml_compute_forward_win_part_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -14299,12 +16565,14 @@ static void ggml_compute_forward_win_part_f32(
 
 static void ggml_compute_forward_win_part(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_win_part_f32(params, src0, dst);
+                ggml_compute_forward_win_part_f32(params, dst);
             } break;
         default:
             {
@@ -14317,9 +16585,11 @@ static void ggml_compute_forward_win_part(
 
 static void ggml_compute_forward_win_unpart_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -14359,12 +16629,14 @@ static void ggml_compute_forward_win_unpart_f32(
 
 static void ggml_compute_forward_win_unpart(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_win_unpart_f32(params, src0, dst);
+                ggml_compute_forward_win_unpart_f32(params, dst);
             } break;
         default:
             {
@@ -14377,58 +16649,62 @@ static void ggml_compute_forward_win_unpart(
 
 static void ggml_compute_forward_unary(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
     const enum ggml_unary_op op = ggml_get_unary_op(dst);
 
     switch (op) {
         case GGML_UNARY_OP_ABS:
             {
-                ggml_compute_forward_abs(params, src0, dst);
+                ggml_compute_forward_abs(params, dst);
             } break;
         case GGML_UNARY_OP_SGN:
             {
-                ggml_compute_forward_sgn(params, src0, dst);
+                ggml_compute_forward_sgn(params, dst);
             } break;
         case GGML_UNARY_OP_NEG:
             {
-                ggml_compute_forward_neg(params, src0, dst);
+                ggml_compute_forward_neg(params, dst);
             } break;
         case GGML_UNARY_OP_STEP:
             {
-                ggml_compute_forward_step(params, src0, dst);
+                ggml_compute_forward_step(params, dst);
             } break;
         case GGML_UNARY_OP_TANH:
             {
-                ggml_compute_forward_tanh(params, src0, dst);
+                ggml_compute_forward_tanh(params, dst);
             } break;
         case GGML_UNARY_OP_ELU:
             {
-                ggml_compute_forward_elu(params, src0, dst);
+                ggml_compute_forward_elu(params, dst);
             } break;
         case GGML_UNARY_OP_RELU:
             {
-                ggml_compute_forward_relu(params, src0, dst);
+                ggml_compute_forward_relu(params, dst);
+            } break;
+        case GGML_UNARY_OP_SIGMOID:
+            {
+                ggml_compute_forward_sigmoid(params, dst);
             } break;
         case GGML_UNARY_OP_GELU:
             {
-                ggml_compute_forward_gelu(params, src0, dst);
+                ggml_compute_forward_gelu(params, dst);
             } break;
         case GGML_UNARY_OP_GELU_QUICK:
             {
-                ggml_compute_forward_gelu_quick(params, src0, dst);
+                ggml_compute_forward_gelu_quick(params, dst);
             } break;
         case GGML_UNARY_OP_SILU:
             {
-                ggml_compute_forward_silu(params, src0, dst);
+                ggml_compute_forward_silu(params, dst);
             } break;
         case GGML_UNARY_OP_HARDSWISH:
             {
-                ggml_compute_forward_hardswish(params, src0, dst);
+                ggml_compute_forward_hardswish(params, dst);
             } break;
         case GGML_UNARY_OP_HARDSIGMOID:
             {
-                ggml_compute_forward_hardsigmoid(params, src0, dst);
+                ggml_compute_forward_hardsigmoid(params, dst);
             } break;
         default:
             {
@@ -14441,9 +16717,11 @@ static void ggml_compute_forward_unary(
 
 static void ggml_compute_forward_get_rel_pos_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -14468,12 +16746,15 @@ static void ggml_compute_forward_get_rel_pos_f16(
 
 static void ggml_compute_forward_get_rel_pos(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F16:
+        case GGML_TYPE_BF16:
             {
-                ggml_compute_forward_get_rel_pos_f16(params, src0, dst);
+                ggml_compute_forward_get_rel_pos_f16(params, dst);
             } break;
         default:
             {
@@ -14486,20 +16767,21 @@ static void ggml_compute_forward_get_rel_pos(
 
 static void ggml_compute_forward_add_rel_pos_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
-        const struct ggml_tensor * src2,
         struct ggml_tensor * dst) {
 
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+    const struct ggml_tensor * src2 = dst->src[2];
+
     const bool inplace = (bool) ((int32_t *) dst->op_params)[0];
-    if (!inplace && params->type == GGML_TASK_INIT) {
+    if (!inplace && params->type == GGML_TASK_TYPE_INIT) {
         if (params->ith != 0) {
             return;
         }
         memcpy((char *) dst->data, (char *) src0->data, ggml_nbytes(dst));
         return;
     }
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -14554,14 +16836,14 @@ static void ggml_compute_forward_add_rel_pos_f32(
 
 static void ggml_compute_forward_add_rel_pos(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
-        const struct ggml_tensor * src2,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_add_rel_pos_f32(params, src0, src1, src2, dst);
+                ggml_compute_forward_add_rel_pos_f32(params, dst);
             } break;
         default:
             {
@@ -14574,12 +16856,14 @@ static void ggml_compute_forward_add_rel_pos(
 
 static void ggml_compute_forward_map_unary_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst,
         const ggml_unary_op_f32_t fun) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -14598,13 +16882,15 @@ static void ggml_compute_forward_map_unary_f32(
 
 static void ggml_compute_forward_map_unary(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst,
         const ggml_unary_op_f32_t fun) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_map_unary_f32(params, src0, dst, fun);
+                ggml_compute_forward_map_unary_f32(params, dst, fun);
             } break;
         default:
             {
@@ -14617,14 +16903,16 @@ static void ggml_compute_forward_map_unary(
 
 static void ggml_compute_forward_map_binary_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst,
         const ggml_binary_op_f32_t fun) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -14645,14 +16933,15 @@ static void ggml_compute_forward_map_binary_f32(
 
 static void ggml_compute_forward_map_binary(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst,
         const ggml_binary_op_f32_t fun) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_map_binary_f32(params, src0, src1, dst, fun);
+                ggml_compute_forward_map_binary_f32(params, dst, fun);
             } break;
         default:
             {
@@ -14665,12 +16954,14 @@ static void ggml_compute_forward_map_binary(
 
 static void ggml_compute_forward_map_custom1_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * a,
         struct ggml_tensor * dst,
         const ggml_custom1_op_f32_t fun) {
+
+    const struct ggml_tensor * a = dst->src[0];
+
     assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -14681,13 +16972,15 @@ static void ggml_compute_forward_map_custom1_f32(
 
 static void ggml_compute_forward_map_custom2_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * a,
-        const struct ggml_tensor * b,
         struct ggml_tensor * dst,
         const ggml_custom2_op_f32_t fun) {
+
+    const struct ggml_tensor * a = dst->src[0];
+    const struct ggml_tensor * b = dst->src[1];
+
     assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -14698,14 +16991,16 @@ static void ggml_compute_forward_map_custom2_f32(
 
 static void ggml_compute_forward_map_custom3_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * a,
-        const struct ggml_tensor * b,
-        const struct ggml_tensor * c,
         struct ggml_tensor * dst,
         const ggml_custom3_op_f32_t fun) {
+
+    const struct ggml_tensor * a = dst->src[0];
+    const struct ggml_tensor * b = dst->src[1];
+    const struct ggml_tensor * c = dst->src[1];
+
     assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -14716,57 +17011,68 @@ static void ggml_compute_forward_map_custom3_f32(
 
 static void ggml_compute_forward_map_custom1(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * a,
               struct ggml_tensor * dst) {
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+
+    const struct ggml_tensor * a = dst->src[0];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
-    struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) dst->op_params;
+    struct ggml_map_custom1_op_params p;
+    memcpy(&p, dst->op_params, sizeof(p));
 
-    p->fun(dst, a, params->ith, params->nth, p->userdata);
+    p.fun(dst, a, params->ith, params->nth, p.userdata);
 }
 
 // ggml_compute_forward_map_custom2
 
 static void ggml_compute_forward_map_custom2(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * a,
-        const struct ggml_tensor * b,
               struct ggml_tensor * dst) {
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+
+    const struct ggml_tensor * a = dst->src[0];
+    const struct ggml_tensor * b = dst->src[1];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
-    struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) dst->op_params;
+    struct ggml_map_custom2_op_params p;
+    memcpy(&p, dst->op_params, sizeof(p));
 
-    p->fun(dst, a, b, params->ith, params->nth, p->userdata);
+    p.fun(dst, a, b, params->ith, params->nth, p.userdata);
 }
 
 // ggml_compute_forward_map_custom3
 
 static void ggml_compute_forward_map_custom3(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * a,
-        const struct ggml_tensor * b,
-        const struct ggml_tensor * c,
               struct ggml_tensor * dst) {
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+
+    const struct ggml_tensor * a = dst->src[0];
+    const struct ggml_tensor * b = dst->src[1];
+    const struct ggml_tensor * c = dst->src[2];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
-    struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) dst->op_params;
+    struct ggml_map_custom3_op_params p;
+    memcpy(&p, dst->op_params, sizeof(p));
 
-    p->fun(dst, a, b, c, params->ith, params->nth, p->userdata);
+    p.fun(dst, a, b, c, params->ith, params->nth, p.userdata);
 }
 
 // ggml_compute_forward_cross_entropy_loss
 
 static void ggml_compute_forward_cross_entropy_loss_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_is_contiguous(src0));
     GGML_ASSERT(ggml_is_contiguous(src1));
     GGML_ASSERT(ggml_is_scalar(dst));
@@ -14783,14 +17089,14 @@ static void ggml_compute_forward_cross_entropy_loss_f32(
 
     GGML_ASSERT(params->wsize >= sizeof(float) * (nth + nth * nc));
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
         if (ith == 0) {
             memset(sums, 0, sizeof(float) * (nth + nth * nc));
         }
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         if (ith == 0) {
             float * dp = (float *) dst->data;
             ggml_vec_sum_f32(nth, dp, sums);
@@ -14870,13 +17176,14 @@ static void ggml_compute_forward_cross_entropy_loss_f32(
 
 static void ggml_compute_forward_cross_entropy_loss(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_cross_entropy_loss_f32(params, src0, src1, dst);
+                ggml_compute_forward_cross_entropy_loss_f32(params, dst);
             } break;
         default:
             {
@@ -14889,10 +17196,12 @@ static void ggml_compute_forward_cross_entropy_loss(
 
 static void ggml_compute_forward_cross_entropy_loss_back_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
-        const struct ggml_tensor * opt0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+    const struct ggml_tensor * opt0 = dst->src[2];
+
     GGML_ASSERT(ggml_is_contiguous(dst));
     GGML_ASSERT(ggml_is_contiguous(src0));
     GGML_ASSERT(ggml_is_contiguous(src1));
@@ -14902,7 +17211,7 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32(
     const int64_t ith = params->ith;
     const int64_t nth = params->nth;
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -14979,14 +17288,14 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32(
 
 static void ggml_compute_forward_cross_entropy_loss_back(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
-        const struct ggml_tensor * opt0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_cross_entropy_loss_back_f32(params, src0, src1, opt0, dst);
+                ggml_compute_forward_cross_entropy_loss_back_f32(params, dst);
             } break;
         default:
             {
@@ -15000,346 +17309,335 @@ static void ggml_compute_forward_cross_entropy_loss_back(
 static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
     GGML_ASSERT(params);
 
-    if (tensor->op == GGML_OP_NONE) {
+    if (tensor->op == GGML_OP_NONE || ggml_is_empty(tensor)) {
         return;
     }
 
-#ifdef GGML_USE_CUBLAS
-    bool skip_cpu = ggml_cuda_compute_forward(params, tensor);
-    if (skip_cpu) {
-        return;
-    }
-    GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_CPU);
-    GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_CPU);
-#elif defined(GGML_USE_VULKAN)
-    const bool skip_cpu = ggml_vk_compute_forward_cpu_assist(params, tensor);
-#ifdef GGML_VULKAN_CHECK_RESULTS
-    if (skip_cpu) {
-        ggml_vk_check_results_1_cpu_assist(params, tensor);
-    }
-#endif
-    if (skip_cpu) {
-        return;
-    }
-    GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_CPU);
-    GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_CPU);
-#endif // GGML_USE_CUBLAS
-
-#ifdef GGML_USE_SYCL
-    bool skip_cpu = ggml_sycl_compute_forward(params, tensor);
-    if (skip_cpu) {
-        return;
-    }
-#endif // GGML_USE_SYCL
     switch (tensor->op) {
         case GGML_OP_DUP:
             {
-                ggml_compute_forward_dup(params, tensor->src[0], tensor);
+                ggml_compute_forward_dup(params, tensor);
             } break;
         case GGML_OP_ADD:
             {
-                ggml_compute_forward_add(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_add(params, tensor);
             } break;
         case GGML_OP_ADD1:
             {
-                ggml_compute_forward_add1(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_add1(params, tensor);
             } break;
         case GGML_OP_ACC:
             {
-                ggml_compute_forward_acc(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_acc(params, tensor);
             } break;
         case GGML_OP_SUB:
             {
-                ggml_compute_forward_sub(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_sub(params, tensor);
             } break;
         case GGML_OP_MUL:
             {
-                ggml_compute_forward_mul(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_mul(params, tensor);
             } break;
         case GGML_OP_DIV:
             {
-                ggml_compute_forward_div(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_div(params, tensor);
             } break;
         case GGML_OP_SQR:
             {
-                ggml_compute_forward_sqr(params, tensor->src[0], tensor);
+                ggml_compute_forward_sqr(params, tensor);
             } break;
         case GGML_OP_SQRT:
             {
-                ggml_compute_forward_sqrt(params, tensor->src[0], tensor);
+                ggml_compute_forward_sqrt(params, tensor);
             } break;
         case GGML_OP_LOG:
             {
-                ggml_compute_forward_log(params, tensor->src[0], tensor);
+                ggml_compute_forward_log(params, tensor);
             } break;
         case GGML_OP_SUM:
             {
-                ggml_compute_forward_sum(params, tensor->src[0], tensor);
+                ggml_compute_forward_sum(params, tensor);
             } break;
         case GGML_OP_SUM_ROWS:
             {
-                ggml_compute_forward_sum_rows(params, tensor->src[0], tensor);
+                ggml_compute_forward_sum_rows(params, tensor);
             } break;
         case GGML_OP_MEAN:
             {
-                ggml_compute_forward_mean(params, tensor->src[0], tensor);
+                ggml_compute_forward_mean(params, tensor);
             } break;
         case GGML_OP_ARGMAX:
             {
-                ggml_compute_forward_argmax(params, tensor->src[0], tensor);
+                ggml_compute_forward_argmax(params, tensor);
             } break;
         case GGML_OP_REPEAT:
             {
-                ggml_compute_forward_repeat(params, tensor->src[0], tensor);
+                ggml_compute_forward_repeat(params, tensor);
             } break;
         case GGML_OP_REPEAT_BACK:
             {
-                ggml_compute_forward_repeat_back(params, tensor->src[0], tensor);
+                ggml_compute_forward_repeat_back(params, tensor);
             } break;
         case GGML_OP_CONCAT:
             {
-                ggml_compute_forward_concat(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_concat(params, tensor);
             } break;
         case GGML_OP_SILU_BACK:
             {
-                ggml_compute_forward_silu_back(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_silu_back(params, tensor);
             } break;
         case GGML_OP_NORM:
             {
-                ggml_compute_forward_norm(params, tensor->src[0], tensor);
+                ggml_compute_forward_norm(params, tensor);
             } break;
         case GGML_OP_RMS_NORM:
             {
-                ggml_compute_forward_rms_norm(params, tensor->src[0], tensor);
+                ggml_compute_forward_rms_norm(params, tensor);
             } break;
         case GGML_OP_RMS_NORM_BACK:
             {
-                ggml_compute_forward_rms_norm_back(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_rms_norm_back(params, tensor);
             } break;
         case GGML_OP_GROUP_NORM:
             {
-                ggml_compute_forward_group_norm(params, tensor->src[0], tensor);
+                ggml_compute_forward_group_norm(params, tensor);
             } break;
         case GGML_OP_MUL_MAT:
             {
-                ggml_compute_forward_mul_mat(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_mul_mat(params, tensor);
             } break;
         case GGML_OP_MUL_MAT_ID:
             {
-                ggml_compute_forward_mul_mat_id(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_mul_mat_id(params, tensor);
             } break;
         case GGML_OP_OUT_PROD:
             {
-                ggml_compute_forward_out_prod(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_out_prod(params, tensor);
             } break;
         case GGML_OP_SCALE:
             {
-                ggml_compute_forward_scale(params, tensor->src[0], tensor);
+                ggml_compute_forward_scale(params, tensor);
             } break;
         case GGML_OP_SET:
             {
-                ggml_compute_forward_set(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_set(params, tensor);
             } break;
         case GGML_OP_CPY:
             {
-                ggml_compute_forward_cpy(params, tensor->src[0], tensor);
+                ggml_compute_forward_cpy(params, tensor);
             } break;
         case GGML_OP_CONT:
             {
-                ggml_compute_forward_cont(params, tensor->src[0], tensor);
+                ggml_compute_forward_cont(params, tensor);
             } break;
         case GGML_OP_RESHAPE:
             {
-                ggml_compute_forward_reshape(params, tensor->src[0], tensor);
+                ggml_compute_forward_reshape(params, tensor);
             } break;
         case GGML_OP_VIEW:
             {
-                ggml_compute_forward_view(params, tensor->src[0]);
+                ggml_compute_forward_view(params, tensor);
             } break;
         case GGML_OP_PERMUTE:
             {
-                ggml_compute_forward_permute(params, tensor->src[0]);
+                ggml_compute_forward_permute(params, tensor);
             } break;
         case GGML_OP_TRANSPOSE:
             {
-                ggml_compute_forward_transpose(params, tensor->src[0]);
+                ggml_compute_forward_transpose(params, tensor);
             } break;
         case GGML_OP_GET_ROWS:
             {
-                ggml_compute_forward_get_rows(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_get_rows(params, tensor);
             } break;
         case GGML_OP_GET_ROWS_BACK:
             {
-                ggml_compute_forward_get_rows_back(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_get_rows_back(params, tensor);
             } break;
         case GGML_OP_DIAG:
             {
-                ggml_compute_forward_diag(params, tensor->src[0], tensor);
+                ggml_compute_forward_diag(params, tensor);
             } break;
         case GGML_OP_DIAG_MASK_INF:
             {
-                ggml_compute_forward_diag_mask_inf(params, tensor->src[0], tensor);
+                ggml_compute_forward_diag_mask_inf(params, tensor);
             } break;
         case GGML_OP_DIAG_MASK_ZERO:
             {
-                ggml_compute_forward_diag_mask_zero(params, tensor->src[0], tensor);
+                ggml_compute_forward_diag_mask_zero(params, tensor);
             } break;
         case GGML_OP_SOFT_MAX:
             {
-                ggml_compute_forward_soft_max(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
+                ggml_compute_forward_soft_max(params, tensor);
             } break;
         case GGML_OP_SOFT_MAX_BACK:
             {
-                ggml_compute_forward_soft_max_back(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_soft_max_back(params, tensor);
             } break;
         case GGML_OP_ROPE:
             {
-                ggml_compute_forward_rope(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_rope(params, tensor);
             } break;
         case GGML_OP_ROPE_BACK:
             {
-                ggml_compute_forward_rope_back(params, tensor->src[0], tensor->src[1], tensor);
-            } break;
-        case GGML_OP_ALIBI:
-            {
-                ggml_compute_forward_alibi(params, tensor->src[0], tensor);
+                ggml_compute_forward_rope_back(params, tensor);
             } break;
         case GGML_OP_CLAMP:
             {
-                ggml_compute_forward_clamp(params, tensor->src[0], tensor);
+                ggml_compute_forward_clamp(params, tensor);
             } break;
         case GGML_OP_CONV_TRANSPOSE_1D:
             {
-                ggml_compute_forward_conv_transpose_1d(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_conv_transpose_1d(params, tensor);
             } break;
         case GGML_OP_IM2COL:
             {
-                ggml_compute_forward_im2col(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_im2col(params, tensor);
             } break;
         case GGML_OP_CONV_TRANSPOSE_2D:
             {
-                ggml_compute_forward_conv_transpose_2d(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_conv_transpose_2d(params, tensor);
             } break;
         case GGML_OP_POOL_1D:
             {
-                ggml_compute_forward_pool_1d(params, tensor->src[0], tensor);
+                ggml_compute_forward_pool_1d(params, tensor);
             } break;
         case GGML_OP_POOL_2D:
             {
-                ggml_compute_forward_pool_2d(params, tensor->src[0], tensor);
+                ggml_compute_forward_pool_2d(params, tensor);
             } break;
         case GGML_OP_UPSCALE:
             {
-                ggml_compute_forward_upscale(params, tensor->src[0], tensor);
+                ggml_compute_forward_upscale(params, tensor);
             } break;
         case GGML_OP_PAD:
             {
-                ggml_compute_forward_pad(params, tensor->src[0], tensor);
+                ggml_compute_forward_pad(params, tensor);
+            } break;
+        case GGML_OP_ARANGE:
+            {
+                ggml_compute_forward_arange(params, tensor);
+            } break;
+        case GGML_OP_TIMESTEP_EMBEDDING:
+            {
+                ggml_compute_forward_timestep_embedding(params, tensor);
             } break;
         case GGML_OP_ARGSORT:
             {
-                ggml_compute_forward_argsort(params, tensor->src[0], tensor);
+                ggml_compute_forward_argsort(params, tensor);
             } break;
         case GGML_OP_LEAKY_RELU:
             {
-                ggml_compute_forward_leaky_relu(params, tensor->src[0], tensor);
+                ggml_compute_forward_leaky_relu(params, tensor);
             } break;
         case GGML_OP_FLASH_ATTN:
             {
                 const int32_t t = ggml_get_op_params_i32(tensor, 0);
                 GGML_ASSERT(t == 0 || t == 1);
                 const bool masked = t != 0;
-                ggml_compute_forward_flash_attn(params, tensor->src[0], tensor->src[1], tensor->src[2], masked, tensor);
+                ggml_compute_forward_flash_attn(params, masked, tensor);
+            } break;
+        case GGML_OP_FLASH_ATTN_EXT:
+            {
+                ggml_compute_forward_flash_attn_ext(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor);
             } break;
         case GGML_OP_FLASH_FF:
             {
-                ggml_compute_forward_flash_ff(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor->src[4], tensor);
+                ggml_compute_forward_flash_ff(params, tensor);
             } break;
         case GGML_OP_FLASH_ATTN_BACK:
             {
                 int32_t t = ggml_get_op_params_i32(tensor, 0);
                 GGML_ASSERT(t == 0 || t == 1);
                 bool masked = t != 0;
-                ggml_compute_forward_flash_attn_back(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], masked, tensor);
+                ggml_compute_forward_flash_attn_back(params, masked, tensor);
+            } break;
+        case GGML_OP_SSM_CONV:
+            {
+                ggml_compute_forward_ssm_conv(params, tensor);
+            } break;
+        case GGML_OP_SSM_SCAN:
+            {
+                ggml_compute_forward_ssm_scan(params, tensor);
             } break;
         case GGML_OP_WIN_PART:
             {
-                ggml_compute_forward_win_part(params, tensor->src[0], tensor);
+                ggml_compute_forward_win_part(params, tensor);
             } break;
         case GGML_OP_WIN_UNPART:
             {
-                ggml_compute_forward_win_unpart(params, tensor->src[0], tensor);
+                ggml_compute_forward_win_unpart(params, tensor);
             } break;
         case GGML_OP_UNARY:
             {
-                ggml_compute_forward_unary(params, tensor->src[0], tensor);
+                ggml_compute_forward_unary(params, tensor);
             } break;
         case GGML_OP_GET_REL_POS:
             {
-                ggml_compute_forward_get_rel_pos(params, tensor->src[0], tensor);
+                ggml_compute_forward_get_rel_pos(params, tensor);
             } break;
         case GGML_OP_ADD_REL_POS:
             {
-                ggml_compute_forward_add_rel_pos(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
+                ggml_compute_forward_add_rel_pos(params, tensor);
             } break;
         case GGML_OP_MAP_UNARY:
             {
                 ggml_unary_op_f32_t fun;
                 memcpy(&fun, tensor->op_params, sizeof(fun));
-                ggml_compute_forward_map_unary(params, tensor->src[0], tensor, fun);
+                ggml_compute_forward_map_unary(params, tensor, fun);
             }
             break;
         case GGML_OP_MAP_BINARY:
             {
                 ggml_binary_op_f32_t fun;
                 memcpy(&fun, tensor->op_params, sizeof(fun));
-                ggml_compute_forward_map_binary(params, tensor->src[0], tensor->src[1], tensor, fun);
+                ggml_compute_forward_map_binary(params, tensor, fun);
             }
             break;
         case GGML_OP_MAP_CUSTOM1_F32:
             {
                 ggml_custom1_op_f32_t fun;
                 memcpy(&fun, tensor->op_params, sizeof(fun));
-                ggml_compute_forward_map_custom1_f32(params, tensor->src[0], tensor, fun);
+                ggml_compute_forward_map_custom1_f32(params, tensor, fun);
             }
             break;
         case GGML_OP_MAP_CUSTOM2_F32:
             {
                 ggml_custom2_op_f32_t fun;
                 memcpy(&fun, tensor->op_params, sizeof(fun));
-                ggml_compute_forward_map_custom2_f32(params, tensor->src[0], tensor->src[1], tensor, fun);
+                ggml_compute_forward_map_custom2_f32(params, tensor, fun);
             }
             break;
         case GGML_OP_MAP_CUSTOM3_F32:
             {
                 ggml_custom3_op_f32_t fun;
                 memcpy(&fun, tensor->op_params, sizeof(fun));
-                ggml_compute_forward_map_custom3_f32(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor, fun);
+                ggml_compute_forward_map_custom3_f32(params, tensor, fun);
             }
             break;
         case GGML_OP_MAP_CUSTOM1:
             {
-                ggml_compute_forward_map_custom1(params, tensor->src[0], tensor);
+                ggml_compute_forward_map_custom1(params, tensor);
             }
             break;
         case GGML_OP_MAP_CUSTOM2:
             {
-                ggml_compute_forward_map_custom2(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_map_custom2(params, tensor);
             }
             break;
         case GGML_OP_MAP_CUSTOM3:
             {
-                ggml_compute_forward_map_custom3(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
+                ggml_compute_forward_map_custom3(params, tensor);
             }
             break;
         case GGML_OP_CROSS_ENTROPY_LOSS:
             {
-                ggml_compute_forward_cross_entropy_loss(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_cross_entropy_loss(params, tensor);
             }
             break;
         case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
             {
-                ggml_compute_forward_cross_entropy_loss_back(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
+                ggml_compute_forward_cross_entropy_loss_back(params, tensor);
             }
             break;
         case GGML_OP_NONE:
@@ -16202,10 +18500,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                             zero_table);
                 }
             } break;
-        case GGML_OP_ALIBI:
-            {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
         case GGML_OP_CLAMP:
             {
                 GGML_ASSERT(false); // TODO: not implemented
@@ -16238,6 +18532,14 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             {
                 GGML_ASSERT(false); // TODO: not implemented
             } break;
+        case GGML_OP_ARANGE:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_TIMESTEP_EMBEDDING:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
         case GGML_OP_ARGSORT:
             {
                 GGML_ASSERT(false); // TODO: not implemented
@@ -16247,6 +18549,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                 GGML_ASSERT(false); // TODO: not implemented
             } break;
         case GGML_OP_FLASH_ATTN:
+        case GGML_OP_FLASH_ATTN_EXT:
             {
                 struct ggml_tensor * flash_grad = NULL;
                 if (src0->grad || src1->grad || tensor->src[2]->grad) {
@@ -16308,6 +18611,11 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             {
                 GGML_ASSERT(false); // not supported
             } break;
+        case GGML_OP_SSM_CONV:
+        case GGML_OP_SSM_SCAN:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
         case GGML_OP_WIN_PART:
         case GGML_OP_WIN_UNPART:
         case GGML_OP_UNARY:
@@ -16362,6 +18670,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                                         zero_table);
                             }
                         } break;
+                    case GGML_UNARY_OP_SIGMOID:
+                        {
+                            GGML_ASSERT(false); // TODO: not implemented
+                        } break;
                     case GGML_UNARY_OP_GELU:
                         {
                             GGML_ASSERT(false); // TODO: not implemented
@@ -16568,7 +18880,7 @@ size_t ggml_graph_overhead(void) {
 
 struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t size, bool grads) {
     const size_t obj_size = ggml_graph_nbytes(size, grads);
-    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_GRAPH, obj_size);
+    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_GRAPH, obj_size);
     struct ggml_cgraph * cgraph = (struct ggml_cgraph *) ((char *) ctx->mem_buffer + obj->offs);
 
     struct ggml_tensor ** data_start = (struct ggml_tensor **) (cgraph + 1);
@@ -16838,6 +19150,7 @@ struct ggml_compute_state {
     ggml_thread_t thrd;
     int ith;
     struct ggml_compute_state_shared * shared;
+    enum ggml_status ec;
 };
 
 static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const struct ggml_compute_state_shared * st) {
@@ -16849,9 +19162,15 @@ static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const
     node->perf_time_us += time_us_cur;
 }
 
-static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
+static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_threads) {
     int n_tasks = 0;
 
+    if (ggml_is_empty(node)) {
+        // no need to multi-thread a no-op
+        n_tasks = 1;
+        return n_tasks;
+    }
+
     switch (node->op) {
         case GGML_OP_CPY:
         case GGML_OP_DUP:
@@ -16884,6 +19203,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
                 case GGML_UNARY_OP_TANH:
                 case GGML_UNARY_OP_ELU:
                 case GGML_UNARY_OP_RELU:
+                case GGML_UNARY_OP_SIGMOID:
                 case GGML_UNARY_OP_HARDSWISH: // to opt for multiple threads
                 case GGML_UNARY_OP_HARDSIGMOID: // to opt for multiple threads
                     {
@@ -16930,6 +19250,12 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
             {
                 n_tasks = n_threads;
             } break;
+        case GGML_OP_GET_ROWS:
+            {
+                // FIXME: the cost of launching additional threads decreases performance with GPU offloading
+                //n_tasks = MIN(n_threads, ggml_nelements(node->src[1]));
+                n_tasks = MIN(n_cur_threads, ggml_nelements(node->src[1]));
+            } break;
         case GGML_OP_SCALE:
         case GGML_OP_SET:
         case GGML_OP_CONT:
@@ -16937,7 +19263,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
         case GGML_OP_TRANSPOSE:
-        case GGML_OP_GET_ROWS:
         case GGML_OP_GET_ROWS_BACK:
         case GGML_OP_DIAG:
             {
@@ -16952,10 +19277,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
             {
                 n_tasks = n_threads;
             } break;
-        case GGML_OP_ALIBI:
-            {
-                n_tasks = 1; //TODO
-            } break;
         case GGML_OP_CLAMP:
             {
                 n_tasks = 1; //TODO
@@ -16989,11 +19310,20 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
             {
                 n_tasks = n_threads;
             } break;
+        case GGML_OP_ARANGE:
+            {
+                n_tasks = n_threads;
+            } break;
+        case GGML_OP_TIMESTEP_EMBEDDING:
+            {
+                n_tasks = n_threads;
+            } break;
         case GGML_OP_ARGSORT:
             {
                 n_tasks = n_threads;
             } break;
         case GGML_OP_FLASH_ATTN:
+        case GGML_OP_FLASH_ATTN_EXT:
             {
                 n_tasks = n_threads;
             } break;
@@ -17005,6 +19335,11 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
             {
                 n_tasks = n_threads;
             } break;
+        case GGML_OP_SSM_CONV:
+        case GGML_OP_SSM_SCAN:
+            {
+                n_tasks = n_threads;
+            } break;
         case GGML_OP_WIN_PART:
         case GGML_OP_WIN_UNPART:
         case GGML_OP_GET_REL_POS:
@@ -17018,29 +19353,32 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
             } break;
         case GGML_OP_MAP_CUSTOM1:
             {
-                struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) node->op_params;
-                if (p->n_tasks == GGML_N_TASKS_MAX) {
+                struct ggml_map_custom1_op_params p;
+                memcpy(&p, node->op_params, sizeof(p));
+                if (p.n_tasks == GGML_N_TASKS_MAX) {
                     n_tasks = n_threads;
                 } else {
-                    n_tasks = MIN(p->n_tasks, n_threads);
+                    n_tasks = MIN(p.n_tasks, n_threads);
                 }
             } break;
         case GGML_OP_MAP_CUSTOM2:
             {
-                struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) node->op_params;
-                if (p->n_tasks == GGML_N_TASKS_MAX) {
+                struct ggml_map_custom2_op_params p;
+                memcpy(&p, node->op_params, sizeof(p));
+                if (p.n_tasks == GGML_N_TASKS_MAX) {
                     n_tasks = n_threads;
                 } else {
-                    n_tasks = MIN(p->n_tasks, n_threads);
+                    n_tasks = MIN(p.n_tasks, n_threads);
                 }
             } break;
         case GGML_OP_MAP_CUSTOM3:
             {
-                struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) node->op_params;
-                if (p->n_tasks == GGML_N_TASKS_MAX) {
+                struct ggml_map_custom3_op_params p;
+                memcpy(&p, node->op_params, sizeof(p));
+                if (p.n_tasks == GGML_N_TASKS_MAX) {
                     n_tasks = n_threads;
                 } else {
-                    n_tasks = MIN(p->n_tasks, n_threads);
+                    n_tasks = MIN(p.n_tasks, n_threads);
                 }
             } break;
         case GGML_OP_CROSS_ENTROPY_LOSS:
@@ -17115,19 +19453,20 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
     set_numa_thread_affinity(state->ith);
 
     int node_n     = -1;
-    int task_phase = GGML_TASK_FINALIZE;
+    int task_phase = GGML_TASK_TYPE_FINALIZE;
 
     while (true) {
         if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) {
             state->shared->node_n += 1;
-            return (thread_ret_t) GGML_EXIT_ABORTED;
+            state->ec = GGML_STATUS_ABORTED;
+            return 0;
         }
 
         if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
             // all other threads are finished and spinning
             // do finalize and init here so we don't have synchronize again
             struct ggml_compute_params params = {
-                /*.type  =*/ GGML_TASK_FINALIZE,
+                /*.type  =*/ GGML_TASK_TYPE_FINALIZE,
                 /*.ith   =*/ 0,
                 /*.nth   =*/ 0,
                 /*.wsize =*/ cplan->work_size,
@@ -17138,7 +19477,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
                 /* FINALIZE */
                 struct ggml_tensor * node = cgraph->nodes[node_n];
                 if (GGML_OP_HAS_FINALIZE[node->op]) {
-                    params.nth = ggml_get_n_tasks(node, n_threads);
+                    params.nth = ggml_get_n_tasks(node, n_threads, state->shared->n_threads);
                     ggml_compute_forward(&params, node);
                 }
                 ggml_graph_compute_perf_stats_node(node, state->shared);
@@ -17148,7 +19487,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
             while (++node_n < cgraph->n_nodes) {
                 GGML_PRINT_DEBUG_5("%s: %d/%d\n", __func__, node_n, cgraph->n_nodes);
                 struct ggml_tensor * node = cgraph->nodes[node_n];
-                const int n_tasks = ggml_get_n_tasks(node, n_threads);
+                const int n_tasks = ggml_get_n_tasks(node, n_threads, state->shared->n_threads);
 
                 state->shared->perf_node_start_cycles  = ggml_perf_cycles();
                 state->shared->perf_node_start_time_us = ggml_perf_time_us();
@@ -17158,17 +19497,17 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
                 if (n_tasks == 1) {
                     /* INIT */
                     if (GGML_OP_HAS_INIT[node->op]) {
-                        params.type = GGML_TASK_INIT;
+                        params.type = GGML_TASK_TYPE_INIT;
                         ggml_compute_forward(&params, node);
                     }
 
                     // TODO: maybe push node_n to the atomic but if other threads see n_tasks is 1,
                     // they do something more efficient than spinning (?)
-                    params.type = GGML_TASK_COMPUTE;
+                    params.type = GGML_TASK_TYPE_COMPUTE;
                     ggml_compute_forward(&params, node);
 
                     if (GGML_OP_HAS_FINALIZE[node->op]) {
-                        params.type = GGML_TASK_FINALIZE;
+                        params.type = GGML_TASK_TYPE_FINALIZE;
                         ggml_compute_forward(&params, node);
                     }
 
@@ -17182,7 +19521,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
                 }
             }
 
-            task_phase = GGML_TASK_INIT;
+            task_phase = GGML_TASK_TYPE_INIT;
             atomic_store(&state->shared->n_active,  n_threads);
             atomic_store(&state->shared->node_n,    node_n);
             atomic_store(&state->shared->node_task, task_phase);
@@ -17196,10 +19535,10 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
 
         /* INIT & COMPUTE */
         struct ggml_tensor * node = cgraph->nodes[node_n];
-        const int n_tasks = ggml_get_n_tasks(node, n_threads);
+        const int n_tasks = ggml_get_n_tasks(node, n_threads, state->shared->n_threads);
 
         struct ggml_compute_params params = {
-            /*.type  =*/ GGML_TASK_INIT,
+            /*.type  =*/ GGML_TASK_TYPE_INIT,
             /*.ith   =*/ state->ith,
             /*.nth   =*/ n_tasks,
             /*.wsize =*/ cplan->work_size,
@@ -17213,7 +19552,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
         }
 
         if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
-            task_phase = GGML_TASK_COMPUTE;
+            task_phase = GGML_TASK_TYPE_COMPUTE;
             atomic_store(&state->shared->n_active,  n_threads);
             atomic_store(&state->shared->node_task, task_phase);
         }
@@ -17228,12 +19567,12 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
         }
 
         if (state->ith < n_tasks) {
-            params.type = GGML_TASK_COMPUTE;
+            params.type = GGML_TASK_TYPE_COMPUTE;
             ggml_compute_forward(&params, node);
         }
 
         if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
-            task_phase = GGML_TASK_FINALIZE;
+            task_phase = GGML_TASK_TYPE_FINALIZE;
             atomic_store(&state->shared->n_active,  n_threads);
             atomic_store(&state->shared->node_task, task_phase);
         }
@@ -17242,7 +19581,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
         }
     }
 
-    return GGML_EXIT_SUCCESS;
+    return 0;
 }
 
 struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threads) {
@@ -17261,7 +19600,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
     for (int i = 0; i < cgraph->n_nodes; i++) {
         struct ggml_tensor * node = cgraph->nodes[i];
 
-        const int n_tasks = ggml_get_n_tasks(node, n_threads);
+        const int n_tasks = ggml_get_n_tasks(node, n_threads, 1);
 
         max_tasks = MAX(max_tasks, n_tasks);
 
@@ -17271,7 +19610,10 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
             case GGML_OP_CPY:
             case GGML_OP_DUP:
                 {
-                    if (ggml_is_quantized(node->type)) {
+                    if (ggml_is_quantized(node->type) ||
+                        // F16 -> BF16 and BF16 -> F16 copies go through intermediate F32
+                        (node->src[0]->type == GGML_TYPE_F16  && node->src[1] && node->src[1]->type == GGML_TYPE_BF16) ||
+                        (node->src[0]->type == GGML_TYPE_BF16 && node->src[1] && node->src[1]->type == GGML_TYPE_F16)) {
                         cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
                     }
                 } break;
@@ -17315,16 +19657,16 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
             case GGML_OP_MUL_MAT_ID:
                 {
                     cur = 0;
-                    const struct ggml_tensor * src0 = node->src[2];
+                    const struct ggml_tensor * src0 = node->src[0];
                     const struct ggml_tensor * src1 = node->src[1];
                     const enum ggml_type vec_dot_type = type_traits[src0->type].vec_dot_type;
                     if (src1->type != vec_dot_type) {
                         cur += ggml_row_size(vec_dot_type, ggml_nelements(src1));
                     }
-                    const int n_as = ggml_get_op_params_i32(node, 1);
+                    const int n_as = src0->ne[2];
                     cur += GGML_PAD(cur, sizeof(int64_t));       // align
                     cur += n_as * sizeof(int64_t);               // matrix_row_counts
-                    cur += n_as * src1->ne[1] * sizeof(int64_t); // matrix_rows
+                    cur += n_as * src1->ne[2] * sizeof(int64_t); // matrix_rows
                 } break;
             case GGML_OP_OUT_PROD:
                 {
@@ -17350,7 +19692,8 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
                     const int64_t ne10 = node->src[1]->ne[0];  // L
                     const int64_t ne11 = node->src[1]->ne[1];  // Cin
 
-                    if (node->src[0]->type == GGML_TYPE_F16 &&
+                    if ((node->src[0]->type == GGML_TYPE_F16 ||
+                         node->src[0]->type == GGML_TYPE_BF16) &&
                         node->src[1]->type == GGML_TYPE_F32) {
                         cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02;
                         cur += sizeof(ggml_fp16_t)*ne10*ne11;
@@ -17386,8 +19729,17 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
                     } else if (node->src[1]->type == GGML_TYPE_F16) {
                         cur  = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1)
                         cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2
+                    } else if (node->src[1]->type == GGML_TYPE_BF16) {
+                        cur  = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1)
+                        cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2
                     }
                 } break;
+            case GGML_OP_FLASH_ATTN_EXT:
+                {
+                    const int64_t ne00 = node->src[0]->ne[0]; // D
+
+                    cur = 2*sizeof(float)*ne00*n_tasks; // 2x head size
+                } break;
             case GGML_OP_FLASH_FF:
                 {
                     if (node->src[1]->type == GGML_TYPE_F32) {
@@ -17396,6 +19748,9 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
                     } else if (node->src[1]->type == GGML_TYPE_F16) {
                         cur  = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1)
                         cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2
+                    } else if (node->src[1]->type == GGML_TYPE_BF16) {
+                        cur  = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1)
+                        cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2
                     }
                 } break;
             case GGML_OP_FLASH_ATTN_BACK:
@@ -17409,6 +19764,9 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
                     } else if (node->src[1]->type == GGML_TYPE_F16) {
                         cur  = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1)
                         cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2
+                    } else if (node->src[1]->type == GGML_TYPE_BF16) {
+                        cur  = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1)
+                        cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2
                     }
                 } break;
 
@@ -17438,7 +19796,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
     return cplan;
 }
 
-int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
+enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
     {
         GGML_ASSERT(cplan);
         GGML_ASSERT(cplan->n_threads > 0);
@@ -17448,17 +19806,6 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
         }
     }
 
-#ifdef GGML_USE_VULKAN
-    for (int i = 0; i < cgraph->n_nodes; i++) {
-        ggml_vk_preallocate_buffers_graph_cpu_assist(cgraph->nodes[i]);
-    }
-    ggml_vk_preallocate_buffers_cpu_assist();
-
-    for (int i = 0; i < cgraph->n_nodes; i++) {
-        ggml_vk_build_graph_cpu_assist(cgraph->nodes[i], i == cgraph->n_nodes - 1);
-    }
-#endif
-
     const int n_threads = cplan->n_threads;
 
     struct ggml_compute_state_shared state_shared = {
@@ -17469,7 +19816,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
         /*.n_threads               =*/ n_threads,
         /*.n_active                =*/ n_threads,
         /*.node_n                  =*/ -1,
-        /*.node_task               =*/ GGML_TASK_FINALIZE,
+        /*.node_task               =*/ GGML_TASK_TYPE_FINALIZE,
         /*.abort_callback          =*/ NULL,
         /*.abort_callback_data     =*/ NULL,
     };
@@ -17482,6 +19829,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
                 .thrd   = 0,
                 .ith = j,
                 .shared = &state_shared,
+                .ec = GGML_STATUS_SUCCESS,
             };
 
             const int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]);
@@ -17492,12 +19840,14 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
 
     workers[0].ith = 0;
     workers[0].shared = &state_shared;
+    workers[0].ec = GGML_STATUS_SUCCESS;
 
     const int64_t perf_start_cycles  = ggml_perf_cycles();
     const int64_t perf_start_time_us = ggml_perf_time_us();
 
     // this is a work thread too
-    int compute_status = (size_t) ggml_graph_compute_thread(&workers[0]);
+    ggml_graph_compute_thread(&workers[0]);
+    enum ggml_status compute_status = workers[0].ec;
 
     // don't leave affinity set on the main thread
     clear_numa_thread_affinity();
@@ -17507,13 +19857,11 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
         for (int j = 1; j < n_threads; j++) {
             const int rc = ggml_thread_join(workers[j].thrd, NULL);
             GGML_ASSERT(rc == 0);
+            if (workers[j].ec != GGML_STATUS_SUCCESS)
+                compute_status = workers[j].ec;
         }
     }
 
-#ifdef GGML_USE_VULKAN
-    ggml_vk_graph_cleanup_cpu_assist();
-#endif
-
     // performance stats (graph)
     {
         int64_t perf_cycles_cur  = ggml_perf_cycles()  - perf_start_cycles;
@@ -17534,14 +19882,14 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
     return compute_status;
 }
 
-void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) {
+enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) {
     struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads);
 
-    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size);
+    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size);
 
     cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;
 
-    ggml_graph_compute(cgraph, &cplan);
+    return ggml_graph_compute(cgraph, &cplan);
 }
 
 struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name) {
@@ -17648,7 +19996,7 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) {
 
     // write binary data
     {
-        FILE * fout = fopen(fname, "wb");
+        FILE * fout = ggml_fopen(fname, "wb");
 
         if (!fout) {
             fprintf(stderr, "%s: failed to open %s\n", __func__, fname);
@@ -17786,7 +20134,7 @@ struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context *
 
     // read file into data
     {
-        FILE * fin = fopen(fname, "rb");
+        FILE * fin = ggml_fopen(fname, "rb");
         if (!fin) {
             fprintf(stderr, "%s: failed to open %s\n", __func__, fname);
             return result;
@@ -18122,7 +20470,7 @@ static void ggml_graph_dump_dot_leaf_edge(FILE * fp, struct ggml_tensor * node,
 void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename) {
     char color[16];
 
-    FILE * fp = fopen(filename, "w");
+    FILE * fp = ggml_fopen(filename, "w");
     GGML_ASSERT(fp);
 
     fprintf(fp, "digraph G {\n");
@@ -18195,7 +20543,9 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph
                 if (node->type == GGML_TYPE_I8 || node->type == GGML_TYPE_I16 || node->type == GGML_TYPE_I32) {
                     fprintf(fp, "%d", ggml_get_i32_1d(node, j));
                 }
-                else if (node->type == GGML_TYPE_F32 || node->type == GGML_TYPE_F16) {
+                else if (node->type == GGML_TYPE_F32 ||
+                         node->type == GGML_TYPE_F16 ||
+                         node->type == GGML_TYPE_BF16) {
                     fprintf(fp, "%.1e", (double)ggml_get_f32_1d(node, j));
                 }
                 else {
@@ -18345,7 +20695,7 @@ static enum ggml_opt_result ggml_opt_adam(
     float * pf = params.past > 0 ? opt->adam.pf->data : NULL; // past function values
 
     struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads);
-    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size);
+    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size);
     cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;
 
     bool cancel = false;
@@ -18357,7 +20707,7 @@ static enum ggml_opt_result ggml_opt_adam(
         if (callback) {
             callback(callback_data, accum_step, &sched, &cancel);
             if (cancel) {
-                return GGML_OPT_CANCEL;
+                return GGML_OPT_RESULT_CANCEL;
             }
         }
         // ggml_graph_reset  (gf);
@@ -18448,7 +20798,7 @@ static enum ggml_opt_result ggml_opt_adam(
             if (callback) {
                 callback(callback_data, accum_step, &sched, &cancel);
                 if (cancel) {
-                    return GGML_OPT_CANCEL;;
+                    return GGML_OPT_RESULT_CANCEL;;
                 }
             }
             // ggml_graph_reset  (gf);
@@ -18465,7 +20815,7 @@ static enum ggml_opt_result ggml_opt_adam(
         if (fabsf(fx - fx_prev[0])/fx < params.adam.eps_f) {
             GGML_PRINT_DEBUG("converged\n");
 
-            return GGML_OPT_OK;
+            return GGML_OPT_RESULT_OK;
         }
 
         // delta-based convergence test
@@ -18475,7 +20825,7 @@ static enum ggml_opt_result ggml_opt_adam(
                 const float rate = (pf[(iter0 + t)%params.past] - fx)/fx;
 
                 if (fabsf(rate) < params.delta) {
-                    return GGML_OPT_OK;
+                    return GGML_OPT_RESULT_OK;
                 }
             }
 
@@ -18491,7 +20841,7 @@ static enum ggml_opt_result ggml_opt_adam(
                 ++n_no_improvement[0];
 
                 if (n_no_improvement[0] >= params.max_no_improvement) {
-                    return GGML_OPT_OK;
+                    return GGML_OPT_RESULT_OK;
                 }
             }
         }
@@ -18509,7 +20859,7 @@ static enum ggml_opt_result ggml_opt_adam(
         }
     }
 
-    return GGML_OPT_DID_NOT_CONVERGE;
+    return GGML_OPT_RESULT_DID_NOT_CONVERGE;
 }
 
 //
@@ -18590,7 +20940,7 @@ static enum ggml_opt_result linesearch_backtracking(
                     float sched = 0;
                     callback(callback_data, accum_step, &sched, cancel);
                     if (*cancel) {
-                        return GGML_OPT_CANCEL;
+                        return GGML_OPT_RESULT_CANCEL;
                     }
                 }
                 // ggml_graph_reset  (gf);
@@ -18663,7 +21013,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
     if (params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE ||
         params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE) {
         if (params.lbfgs.wolfe <= params.lbfgs.ftol || 1.f <= params.lbfgs.wolfe) {
-            return GGML_OPT_INVALID_WOLFE;
+            return GGML_OPT_RESULT_INVALID_WOLFE;
         }
     }
 
@@ -18692,7 +21042,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
     }
 
     struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads);
-    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size);
+    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size);
     cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;
 
     float * x  = opt->lbfgs.x->data;  // current parameters
@@ -18733,7 +21083,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
                 float sched = 0;
                 callback(callback_data, accum_step, &sched, &cancel);
                 if (cancel) {
-                    return GGML_OPT_CANCEL;
+                    return GGML_OPT_RESULT_CANCEL;
                 }
             }
             // ggml_graph_reset  (gf);
@@ -18761,7 +21111,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
 
     // already optimized
     if (gnorm/xnorm <= params.lbfgs.eps) {
-        return GGML_OPT_OK;
+        return GGML_OPT_RESULT_OK;
     }
 
     if (opt->just_initialized) {
@@ -18806,7 +21156,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
         //       way to test and don't want to break something with so many changes lined up
         ls = linesearch_backtracking(&params, nx, x, &fx, g, d, step, xp, f, gb, &cplan, np, ps, &cancel, callback, callback_data);
         if (cancel) {
-            return GGML_OPT_CANCEL;
+            return GGML_OPT_RESULT_CANCEL;
         }
 
         if (ls < 0) {
@@ -18829,7 +21179,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
         }
         if (gnorm/xnorm <= params.lbfgs.eps) {
             // converged
-            return GGML_OPT_OK;
+            return GGML_OPT_RESULT_OK;
         }
 
         // delta-based convergence test
@@ -18839,7 +21189,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
                 const float rate = (pf[k[0]%params.past] - fx)/fx;
 
                 if (fabsf(rate) < params.delta) {
-                    return GGML_OPT_OK;
+                    return GGML_OPT_RESULT_OK;
                 }
             }
 
@@ -18855,14 +21205,14 @@ static enum ggml_opt_result ggml_opt_lbfgs(
                 n_no_improvement[0]++;
 
                 if (n_no_improvement[0] >= params.max_no_improvement) {
-                    return GGML_OPT_OK;
+                    return GGML_OPT_RESULT_OK;
                 }
             }
         }
 
         if (params.lbfgs.n_iter != 0 && params.lbfgs.n_iter < it + 1) {
             // reached the maximum number of iterations
-            return GGML_OPT_DID_NOT_CONVERGE;
+            return GGML_OPT_RESULT_DID_NOT_CONVERGE;
         }
 
         // update vectors s and y:
@@ -18918,17 +21268,17 @@ static enum ggml_opt_result ggml_opt_lbfgs(
 
     GGML_ASSERT(false && "lbfgs failed");
 
-    return GGML_OPT_DID_NOT_CONVERGE;
+    return GGML_OPT_RESULT_DID_NOT_CONVERGE;
 }
 
 struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) {
     struct ggml_opt_params result;
 
     switch (type) {
-        case GGML_OPT_ADAM:
+        case GGML_OPT_TYPE_ADAM:
             {
                 result = (struct ggml_opt_params) {
-                    .type       = GGML_OPT_ADAM,
+                    .type       = GGML_OPT_TYPE_ADAM,
                     .graph_size = GGML_DEFAULT_GRAPH_SIZE,
                     .n_threads  = 1, // FIXME: GGML_DEFAULT_N_THREADS ?
                     .past       = 0,
@@ -18956,10 +21306,10 @@ struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) {
                     },
                 };
             } break;
-        case GGML_OPT_LBFGS:
+        case GGML_OPT_TYPE_LBFGS:
             {
                 result = (struct ggml_opt_params) {
-                    .type       = GGML_OPT_LBFGS,
+                    .type       = GGML_OPT_TYPE_LBFGS,
                     .graph_size = GGML_DEFAULT_GRAPH_SIZE,
                     .n_threads  = 1,
                     .past       = 0,
@@ -19004,12 +21354,12 @@ GGML_API void ggml_opt_init(
     opt->just_initialized = true;
     if (opt->ctx == NULL) {
         struct ggml_init_params ctx_opt_params;
-        if (opt->params.type == GGML_OPT_ADAM) {
+        if (opt->params.type == GGML_OPT_TYPE_ADAM) {
             ctx_opt_params.mem_size = GGML_MEM_ALIGN*3 + ggml_tensor_overhead()*3 + ggml_type_size(GGML_TYPE_F32)*nx*3;
             if (opt->params.past > 0) {
                 ctx_opt_params.mem_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_size(GGML_TYPE_F32)*opt->params.past;
             }
-        } else if (opt->params.type == GGML_OPT_LBFGS) {
+        } else if (opt->params.type == GGML_OPT_TYPE_LBFGS) {
             ctx_opt_params.mem_size = GGML_MEM_ALIGN*9 + ggml_tensor_overhead()*9 + ggml_type_size(GGML_TYPE_F32)*(nx*5 + opt->params.lbfgs.m*2 + nx*opt->params.lbfgs.m*2);
             if (opt->params.past > 0) {
                 ctx_opt_params.mem_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_size(GGML_TYPE_F32)*opt->params.past;
@@ -19021,7 +21371,7 @@ GGML_API void ggml_opt_init(
         opt->ctx = ggml_init(ctx_opt_params);
     }
     switch (opt->params.type) {
-        case GGML_OPT_ADAM:
+        case GGML_OPT_TYPE_ADAM:
             {
                 opt->adam.g  = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx);
                 opt->adam.m  = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx);
@@ -19035,7 +21385,7 @@ GGML_API void ggml_opt_init(
                     ggml_set_zero(opt->adam.pf);
                 }
             } break;
-        case GGML_OPT_LBFGS:
+        case GGML_OPT_TYPE_LBFGS:
             {
                 opt->lbfgs.x  = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx);
                 opt->lbfgs.xp = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx);
@@ -19079,13 +21429,13 @@ enum ggml_opt_result ggml_opt(
 
         ctx = ggml_init(params_ctx);
         if (ctx == NULL) {
-            return GGML_OPT_NO_CONTEXT;
+            return GGML_OPT_RESULT_NO_CONTEXT;
         }
 
         free_ctx = true;
     }
 
-    enum ggml_opt_result result = GGML_OPT_OK;
+    enum ggml_opt_result result = GGML_OPT_RESULT_OK;
 
     struct ggml_opt_context * opt = (struct ggml_opt_context *) alloca(sizeof(struct ggml_opt_context));
 
@@ -19124,14 +21474,14 @@ enum ggml_opt_result ggml_opt_resume_g(
         void * callback_data) {
 
     // build forward + backward compute graphs
-    enum ggml_opt_result result = GGML_OPT_OK;
+    enum ggml_opt_result result = GGML_OPT_RESULT_OK;
 
     switch (opt->params.type) {
-        case GGML_OPT_ADAM:
+        case GGML_OPT_TYPE_ADAM:
             {
                 result = ggml_opt_adam(ctx, opt, opt->params, f, gf, gb, callback, callback_data);
             } break;
-        case GGML_OPT_LBFGS:
+        case GGML_OPT_TYPE_LBFGS:
             {
                 result = ggml_opt_lbfgs(ctx, opt, opt->params, f, gf, gb, callback, callback_data);
             } break;
@@ -19168,8 +21518,11 @@ void ggml_quantize_init(enum ggml_type type) {
     switch (type) {
         case GGML_TYPE_IQ2_XXS:
         case GGML_TYPE_IQ2_XS:
-        case GGML_TYPE_IQ1_S:   iq2xs_init_impl(type); break;
+        case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:   iq2xs_init_impl(type); break;
         case GGML_TYPE_IQ3_XXS: iq3xs_init_impl(256); break;
+        case GGML_TYPE_IQ3_S:   iq3xs_init_impl(512); break;
         default: // nothing
             break;
     }
@@ -19188,277 +21541,74 @@ void ggml_quantize_free(void) {
     ggml_critical_section_end();
 }
 
-size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist) {
-    assert(k % QK4_0 == 0);
-    const int nb = k / QK4_0;
-
-    for (int b = 0; b < n; b += k) {
-        block_q4_0 * restrict y = (block_q4_0 *) dst + b/QK4_0;
-
-        quantize_row_q4_0_reference(src + b, y, k);
-
-        for (int i = 0; i < nb; i++) {
-            for (int j = 0; j < QK4_0; j += 2) {
-                const uint8_t vi0 = y[i].qs[j/2] & 0x0F;
-                const uint8_t vi1 = y[i].qs[j/2] >> 4;
-
-                hist[vi0]++;
-                hist[vi1]++;
-            }
-        }
-    }
-
-    return (n/QK4_0*sizeof(block_q4_0));
-}
-
-size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist) {
-    assert(k % QK4_1 == 0);
-    const int nb = k / QK4_1;
-
-    for (int b = 0; b < n; b += k) {
-        block_q4_1 * restrict y = (block_q4_1 *) dst + b/QK4_1;
-
-        quantize_row_q4_1_reference(src + b, y, k);
-
-        for (int i = 0; i < nb; i++) {
-            for (int j = 0; j < QK4_1; j += 2) {
-                const uint8_t vi0 = y[i].qs[j/2] & 0x0F;
-                const uint8_t vi1 = y[i].qs[j/2] >> 4;
-
-                hist[vi0]++;
-                hist[vi1]++;
-            }
-        }
-    }
-
-    return (n/QK4_1*sizeof(block_q4_1));
-}
-
-size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist) {
-    assert(k % QK5_0 == 0);
-    const int nb = k / QK5_0;
-
-    for (int b = 0; b < n; b += k) {
-        block_q5_0 * restrict y = (block_q5_0 *)dst + b/QK5_0;
-
-        quantize_row_q5_0_reference(src + b, y, k);
-
-        for (int i = 0; i < nb; i++) {
-            uint32_t qh;
-            memcpy(&qh, &y[i].qh, sizeof(qh));
-
-            for (int j = 0; j < QK5_0; j += 2) {
-                const uint8_t vh0 = ((qh & (1u << (j/2 + 0 ))) >> (j/2 + 0 )) << 4;
-                const uint8_t vh1 = ((qh & (1u << (j/2 + 16))) >> (j/2 + 12));
-
-                // cast to 16 bins
-                const uint8_t vi0 = ((y[i].qs[j/2] & 0x0F) | vh0) / 2;
-                const uint8_t vi1 = ((y[i].qs[j/2] >>   4) | vh1) / 2;
-
-                hist[vi0]++;
-                hist[vi1]++;
-            }
-        }
-    }
-
-    return (n/QK5_0*sizeof(block_q5_0));
-}
-
-size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist) {
-    assert(k % QK5_1 == 0);
-    const int nb = k / QK5_1;
-
-    for (int b = 0; b < n; b += k) {
-        block_q5_1 * restrict y = (block_q5_1 *)dst + b/QK5_1;
-
-        quantize_row_q5_1_reference(src + b, y, k);
-
-        for (int i = 0; i < nb; i++) {
-            uint32_t qh;
-            memcpy(&qh, &y[i].qh, sizeof(qh));
-
-            for (int j = 0; j < QK5_1; j += 2) {
-                const uint8_t vh0 = ((qh & (1u << (j/2 + 0 ))) >> (j/2 + 0 )) << 4;
-                const uint8_t vh1 = ((qh & (1u << (j/2 + 16))) >> (j/2 + 12));
-
-                // cast to 16 bins
-                const uint8_t vi0 = ((y[i].qs[j/2] & 0x0F) | vh0) / 2;
-                const uint8_t vi1 = ((y[i].qs[j/2] >>   4) | vh1) / 2;
-
-                hist[vi0]++;
-                hist[vi1]++;
-            }
-        }
-    }
-
-    return (n/QK5_1*sizeof(block_q5_1));
-}
-
-size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist) {
-    assert(k % QK8_0 == 0);
-    const int nb = k / QK8_0;
-
-    for (int b = 0; b < n; b += k) {
-        block_q8_0 * restrict y = (block_q8_0 *)dst + b/QK8_0;
-
-        quantize_row_q8_0_reference(src + b, y, k);
-
-        for (int i = 0; i < nb; i++) {
-            for (int j = 0; j < QK8_0; ++j) {
-                const int8_t vi = y[i].qs[j];
-
-                hist[vi/16 + 8]++;
-            }
-        }
-    }
-
-    return (n/QK8_0*sizeof(block_q8_0));
-}
-
 bool ggml_quantize_requires_imatrix(enum ggml_type type) {
     return
         type == GGML_TYPE_IQ2_XXS ||
         type == GGML_TYPE_IQ2_XS  ||
-        type == GGML_TYPE_IQ1_S;
+        type == GGML_TYPE_IQ1_S;//   ||
+        //type == GGML_TYPE_IQ1_M;
 }
 
-size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start,
-        int nrows, int n_per_row, int64_t * hist, const float * imatrix) {
+size_t ggml_quantize_chunk(
+        enum ggml_type   type,
+           const float * src,
+                  void * dst,
+               int64_t   start,
+               int64_t   nrows,
+               int64_t   n_per_row,
+           const float * imatrix) {
+    const int64_t n = (int64_t) nrows * n_per_row;
+
+    if (ggml_quantize_requires_imatrix(type)) {
+        GGML_ASSERT(imatrix != NULL);
+    }
+
+    GGML_ASSERT(start % type_traits[type].blck_size == 0);
+    GGML_ASSERT(start % n_per_row == 0);
+
     ggml_quantize_init(type); // this is noop if already initialized
+
+    const size_t start_row = start / n_per_row;
+    const size_t row_size  = ggml_row_size(type, n_per_row);
+
     size_t result = 0;
-    int n = nrows * n_per_row;
+
     switch (type) {
-        case GGML_TYPE_Q4_0:
-            {
-                GGML_ASSERT(start % QK4_0 == 0);
-                GGML_ASSERT(start % n_per_row == 0);
-                size_t start_row = start / n_per_row;
-                size_t row_size = ggml_row_size(type, n_per_row);
-                result = quantize_q4_0(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
-                GGML_ASSERT(result == row_size * nrows);
-            } break;
-        case GGML_TYPE_Q4_1:
-            {
-                GGML_ASSERT(start % QK4_1 == 0);
-                GGML_ASSERT(start % n_per_row == 0);
-                size_t start_row = start / n_per_row;
-                size_t row_size = ggml_row_size(type, n_per_row);
-                result = quantize_q4_1(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
-                GGML_ASSERT(result == row_size * nrows);
-            } break;
-        case GGML_TYPE_Q5_0:
-            {
-                GGML_ASSERT(start % QK5_0 == 0);
-                GGML_ASSERT(start % n_per_row == 0);
-                size_t start_row = start / n_per_row;
-                size_t row_size = ggml_row_size(type, n_per_row);
-                result = quantize_q5_0(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
-                GGML_ASSERT(result == row_size * nrows);
-            } break;
-        case GGML_TYPE_Q5_1:
-            {
-                GGML_ASSERT(start % QK5_1 == 0);
-                GGML_ASSERT(start % n_per_row == 0);
-                size_t start_row = start / n_per_row;
-                size_t row_size = ggml_row_size(type, n_per_row);
-                result = quantize_q5_1(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
-                GGML_ASSERT(result == row_size * nrows);
-            } break;
-        case GGML_TYPE_Q8_0:
-            {
-                GGML_ASSERT(start % QK8_0 == 0);
-                block_q8_0 * block = (block_q8_0*)dst + start / QK8_0;
-                result = ggml_quantize_q8_0(src + start, block, n, n, hist);
-            } break;
-        case GGML_TYPE_Q2_K:
-            {
-                GGML_ASSERT(start % QK_K == 0);
-                GGML_ASSERT(start % n_per_row == 0);
-                size_t start_row = start / n_per_row;
-                size_t row_size = ggml_row_size(type, n_per_row);
-                result = quantize_q2_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
-                GGML_ASSERT(result == row_size * nrows);
-            } break;
-        case GGML_TYPE_Q3_K:
-            {
-                GGML_ASSERT(start % QK_K == 0);
-                GGML_ASSERT(start % n_per_row == 0);
-                size_t start_row = start / n_per_row;
-                size_t row_size = ggml_row_size(type, n_per_row);
-                result = quantize_q3_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
-                GGML_ASSERT(result == row_size * nrows);
-            } break;
-        case GGML_TYPE_Q4_K:
-            {
-                GGML_ASSERT(start % QK_K == 0);
-                GGML_ASSERT(start % n_per_row == 0);
-                size_t start_row = start / n_per_row;
-                size_t row_size = ggml_row_size(type, n_per_row);
-                result = quantize_q4_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
-                GGML_ASSERT(result == row_size * nrows);
-            } break;
-        case GGML_TYPE_Q5_K:
-            {
-                GGML_ASSERT(start % QK_K == 0);
-                GGML_ASSERT(start % n_per_row == 0);
-                size_t start_row = start / n_per_row;
-                size_t row_size = ggml_row_size(type, n_per_row);
-                result = quantize_q5_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
-                GGML_ASSERT(result == row_size * nrows);
-            } break;
-        case GGML_TYPE_Q6_K:
-            {
-                GGML_ASSERT(start % QK_K == 0);
-                GGML_ASSERT(start % n_per_row == 0);
-                size_t start_row = start / n_per_row;
-                size_t row_size = ggml_row_size(type, n_per_row);
-                result = quantize_q6_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
-                GGML_ASSERT(result == row_size * nrows);
-            } break;
-        case GGML_TYPE_IQ2_XXS:
-            {
-                GGML_ASSERT(start % QK_K == 0);
-                GGML_ASSERT(start % n_per_row == 0);
-                GGML_ASSERT(imatrix);
-                size_t start_row = start / n_per_row;
-                size_t row_size = ggml_row_size(type, n_per_row);
-                result = quantize_iq2_xxs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
-                GGML_ASSERT(result == row_size * nrows);
-            } break;
-        case GGML_TYPE_IQ2_XS:
-            {
-                GGML_ASSERT(start % QK_K == 0);
-                GGML_ASSERT(start % n_per_row == 0);
-                GGML_ASSERT(imatrix);
-                size_t start_row = start / n_per_row;
-                size_t row_size = ggml_row_size(type, n_per_row);
-                result = quantize_iq2_xs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
-                GGML_ASSERT(result == row_size * nrows);
-            } break;
-        case GGML_TYPE_IQ3_XXS:
-            {
-                GGML_ASSERT(start % QK_K == 0);
-                GGML_ASSERT(start % n_per_row == 0);
-                size_t start_row = start / n_per_row;
-                size_t row_size = ggml_row_size(type, n_per_row);
-                result = quantize_iq3_xxs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
-                GGML_ASSERT(result == row_size * nrows);
-            } break;
-        case GGML_TYPE_IQ1_S:
-            {
-                GGML_ASSERT(start % QK_K == 0);
-                GGML_ASSERT(start % n_per_row == 0);
-                size_t start_row = start / n_per_row;
-                size_t row_size = ggml_row_size(type, n_per_row);
-                result = quantize_iq1_s(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
-                GGML_ASSERT(result == row_size * nrows);
-            } break;
+        case GGML_TYPE_Q4_0:    result = quantize_q4_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q4_1:    result = quantize_q4_1(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q5_0:    result = quantize_q5_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q5_1:    result = quantize_q5_1(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q8_0:    result = quantize_q8_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q2_K:    result = quantize_q2_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q3_K:    result = quantize_q3_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q4_K:    result = quantize_q4_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q5_K:    result = quantize_q5_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q6_K:    result = quantize_q6_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_IQ2_XXS: result = quantize_iq2_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_IQ2_XS:  result = quantize_iq2_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_IQ3_XXS: result = quantize_iq3_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_IQ3_S:   result = quantize_iq3_s  (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_IQ2_S:   result = quantize_iq2_s  (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_IQ1_S:   result = quantize_iq1_s  (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_IQ1_M:   result = quantize_iq1_m  (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_IQ4_NL:  result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+#if QK_K == 64
+        case GGML_TYPE_IQ4_XS:  result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+#else
+        case GGML_TYPE_IQ4_XS:  result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+#endif
         case GGML_TYPE_F16:
             {
                 size_t elemsize = sizeof(ggml_fp16_t);
                 ggml_fp32_to_fp16_row(src + start, (ggml_fp16_t *)dst + start, n);
                 result = n * elemsize;
             } break;
+        case GGML_TYPE_BF16:
+            {
+                size_t elemsize = sizeof(ggml_bf16_t);
+                ggml_fp32_to_bf16_row(src + start, (ggml_bf16_t *)dst + start, n);
+                result = n * elemsize;
+            } break;
         case GGML_TYPE_F32:
             {
                 size_t elemsize = sizeof(float);
@@ -19468,6 +21618,9 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i
         default:
             assert(false);
     }
+
+    GGML_ASSERT(result == nrows * row_size);
+
     return result;
 }
 
@@ -19625,8 +21778,34 @@ static bool gguf_fread_str(FILE * file, struct gguf_str * p, size_t * offset) {
     return ok;
 }
 
+static void gguf_free_kv(struct gguf_kv * kv) {
+    if (kv->key.data) {
+        GGML_FREE(kv->key.data);
+    }
+
+    if (kv->type == GGUF_TYPE_STRING) {
+        if (kv->value.str.data) {
+            GGML_FREE(kv->value.str.data);
+        }
+    }
+
+    if (kv->type == GGUF_TYPE_ARRAY) {
+        if (kv->value.arr.data) {
+            if (kv->value.arr.type == GGUF_TYPE_STRING) {
+                for (uint64_t j = 0; j < kv->value.arr.n; ++j) {
+                    struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[j];
+                    if (str->data) {
+                        GGML_FREE(str->data);
+                    }
+                }
+            }
+            GGML_FREE(kv->value.arr.data);
+        }
+    }
+}
+
 struct gguf_context * gguf_init_empty(void) {
-    struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context));
+    struct gguf_context * ctx = GGML_CALLOC(1, sizeof(struct gguf_context));
 
     memcpy(ctx->header.magic, GGUF_MAGIC, sizeof(ctx->header.magic));
     ctx->header.version   = GGUF_VERSION;
@@ -19646,7 +21825,7 @@ struct gguf_context * gguf_init_empty(void) {
 }
 
 struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) {
-    FILE * file = fopen(fname, "rb");
+    FILE * file = ggml_fopen(fname, "rb");
     if (!file) {
         return NULL;
     }
@@ -19671,7 +21850,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
 
     bool ok = true;
 
-    struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context));
+    struct gguf_context * ctx = GGML_CALLOC(1, sizeof(struct gguf_context));
 
     // read the header
     {
@@ -19708,9 +21887,13 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
 
     // read the kv pairs
     {
-        ctx->kv = GGML_MALLOC(ctx->header.n_kv * sizeof(struct gguf_kv));
+        const uint64_t n_kv = ctx->header.n_kv;
 
-        for (uint64_t i = 0; i < ctx->header.n_kv; ++i) {
+        // header.n_kv will hold the actual value of pairs that were successfully read in the loop below
+        ctx->header.n_kv = 0;
+        ctx->kv = GGML_CALLOC(n_kv, sizeof(struct gguf_kv));
+
+        for (uint64_t i = 0; i < n_kv; ++i) {
             struct gguf_kv * kv = &ctx->kv[i];
 
             //fprintf(stderr, "%s: reading kv %d\n", __func__, i);
@@ -19759,7 +21942,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
                                         return NULL;
                                     }
 
-                                    kv->value.arr.data = GGML_MALLOC(kv->value.arr.n * gguf_type_size(kv->value.arr.type));
+                                    kv->value.arr.data = GGML_CALLOC(kv->value.arr.n, gguf_type_size(kv->value.arr.type));
 
                                     ok = ok && gguf_fread_el(file, kv->value.arr.data, kv->value.arr.n * gguf_type_size(kv->value.arr.type), &offset);
                                 } break;
@@ -19773,7 +21956,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
                                         return NULL;
                                     }
 
-                                    kv->value.arr.data = GGML_MALLOC(kv->value.arr.n * sizeof(struct gguf_str));
+                                    kv->value.arr.data = GGML_CALLOC(kv->value.arr.n, sizeof(struct gguf_str));
 
                                     for (uint64_t j = 0; j < kv->value.arr.n; ++j) {
                                         ok = ok && gguf_fread_str(file, &((struct gguf_str *) kv->value.arr.data)[j], &offset);
@@ -19789,6 +21972,8 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
             if (!ok) {
                 break;
             }
+
+            ctx->header.n_kv++;
         }
 
         if (!ok) {
@@ -19800,8 +21985,8 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
     }
 
     // read the tensor infos
-    {
-        ctx->infos = GGML_MALLOC(ctx->header.n_tensors * sizeof(struct gguf_tensor_info));
+    if (ctx->header.n_tensors > 0) {
+        ctx->infos = GGML_CALLOC(ctx->header.n_tensors, sizeof(struct gguf_tensor_info));
 
         for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) {
             struct gguf_tensor_info * info = &ctx->infos[i];
@@ -19822,8 +22007,17 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
             ok = ok && gguf_fread_el (file, &info->type,   sizeof(info->type),    &offset);
             ok = ok && gguf_fread_el (file, &info->offset, sizeof(info->offset),  &offset);
 
+            // TODO: return an error instead of crashing with GGML_ASSERT
             gguf_tensor_info_sanitize(info);
 
+            // make sure there is no duplicated tensor names
+            for (uint64_t j = 0; j < i; ++j) {
+                if (strcmp(info->name.data, ctx->infos[j].name.data) == 0) {
+                    fprintf(stderr, "%s: duplicated tensor name %s\n", __func__, info->name.data);
+                    ok = false;
+                }
+            }
+
             if (!ok) {
                 fprintf(stderr, "%s: failed to read tensor info\n", __func__);
                 fclose(file);
@@ -19937,12 +22131,12 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
 
             ok = ok && cur != NULL;
 
-            ggml_set_name(cur, ctx->infos[i].name.data);
-
             if (!ok) {
                 break;
             }
 
+            ggml_set_name(cur, ctx->infos[i].name.data);
+
             // point the data member to the appropriate location in the binary blob using the tensor infos
             if (!params.no_alloc) {
               //cur->data = (char *) data->data + ctx->infos[i].offset - ctx->offset; // offset from start of file
@@ -19974,31 +22168,7 @@ void gguf_free(struct gguf_context * ctx) {
     if (ctx->kv) {
         // free string memory - not great..
         for (uint64_t i = 0; i < ctx->header.n_kv; ++i) {
-            struct gguf_kv * kv = &ctx->kv[i];
-
-            if (kv->key.data) {
-                GGML_FREE(kv->key.data);
-            }
-
-            if (kv->type == GGUF_TYPE_STRING) {
-                if (kv->value.str.data) {
-                    GGML_FREE(kv->value.str.data);
-                }
-            }
-
-            if (kv->type == GGUF_TYPE_ARRAY) {
-                if (kv->value.arr.data) {
-                    if (kv->value.arr.type == GGUF_TYPE_STRING) {
-                        for (uint64_t j = 0; j < kv->value.arr.n; ++j) {
-                            struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[j];
-                            if (str->data) {
-                                GGML_FREE(str->data);
-                            }
-                        }
-                    }
-                    GGML_FREE(kv->value.arr.data);
-                }
-            }
+            gguf_free_kv(&ctx->kv[i]);
         }
 
         GGML_FREE(ctx->kv);
@@ -20016,7 +22186,7 @@ void gguf_free(struct gguf_context * ctx) {
         GGML_FREE(ctx->infos);
     }
 
-    GGML_ALIGNED_FREE(ctx);
+    GGML_FREE(ctx);
 }
 
 const char * gguf_type_name(enum gguf_type type) {
@@ -20223,6 +22393,19 @@ static int gguf_get_or_add_key(struct gguf_context * ctx, const char * key) {
     return n_kv;
 }
 
+void gguf_remove_key(struct gguf_context * ctx, const char * key) {
+    const int idx = gguf_find_key(ctx, key);
+    if (idx >= 0) {
+        const int n_kv = gguf_get_n_kv(ctx);
+        gguf_free_kv(&ctx->kv[idx]);
+        for (int i = idx; i < n_kv-1; ++i) {
+            ctx->kv[i] = ctx->kv[i+1];
+        }
+        ctx->kv = realloc(ctx->kv, (n_kv - 1) * sizeof(struct gguf_kv));
+        ctx->header.n_kv--;
+    }
+}
+
 void gguf_set_val_u8(struct gguf_context * ctx, const char * key, uint8_t val) {
     const int idx = gguf_get_or_add_key(ctx, key);
 
@@ -20314,7 +22497,7 @@ void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_ty
     ctx->kv[idx].type           = GGUF_TYPE_ARRAY;
     ctx->kv[idx].value.arr.type = type;
     ctx->kv[idx].value.arr.n    = n;
-    ctx->kv[idx].value.arr.data = GGML_MALLOC(n*gguf_type_size(type));
+    ctx->kv[idx].value.arr.data = GGML_CALLOC(n, gguf_type_size(type));
     memcpy(ctx->kv[idx].value.arr.data, data, n*gguf_type_size(type));
 }
 
@@ -20324,7 +22507,7 @@ void gguf_set_arr_str(struct gguf_context * ctx, const char * key, const char **
     ctx->kv[idx].type           = GGUF_TYPE_ARRAY;
     ctx->kv[idx].value.arr.type = GGUF_TYPE_STRING;
     ctx->kv[idx].value.arr.n    = n;
-    ctx->kv[idx].value.arr.data = GGML_MALLOC(n*sizeof(struct gguf_str));
+    ctx->kv[idx].value.arr.data = GGML_CALLOC(n, sizeof(struct gguf_str));
     for (int i = 0; i < n; i++) {
         struct gguf_str * str = &((struct gguf_str *)ctx->kv[idx].value.arr.data)[i];
         str->n    = strlen(data[i]);
@@ -20351,7 +22534,7 @@ void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src) {
             case GGUF_TYPE_ARRAY:
                 {
                     if (src->kv[i].value.arr.type == GGUF_TYPE_STRING) {
-                        const char ** data = GGML_MALLOC(src->kv[i].value.arr.n*sizeof(char *));
+                        const char ** data = GGML_CALLOC(src->kv[i].value.arr.n, sizeof(char *));
                         for (uint32_t j = 0; j < src->kv[i].value.arr.n; j++) {
                             data[j] = ((struct gguf_str *)src->kv[i].value.arr.data)[j].data;
                         }
@@ -20371,6 +22554,10 @@ void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src) {
 void gguf_add_tensor(
              struct gguf_context * ctx,
         const struct ggml_tensor * tensor) {
+    if (gguf_find_tensor(ctx, tensor->name) != -1) {
+        GGML_ASSERT(false && "duplicated tensor name");
+    }
+
     const int idx = ctx->header.n_tensors;
     ctx->infos = realloc(ctx->infos, (idx + 1)*sizeof(struct gguf_tensor_info));
 
@@ -20439,7 +22626,7 @@ struct gguf_buf {
 
 static struct gguf_buf gguf_buf_init(size_t size) {
     struct gguf_buf buf = {
-        /*buf.data   =*/ size == 0 ? NULL : GGML_MALLOC(size),
+        /*buf.data   =*/ size == 0 ? NULL : GGML_CALLOC(1, size),
         /*buf.size   =*/ size,
         /*buf.offset =*/ 0,
     };
@@ -20601,7 +22788,7 @@ static void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf *
 }
 
 void gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta) {
-    FILE * file = fopen(fname, "wb");
+    FILE * file = ggml_fopen(fname, "wb");
     if (!file) {
         GGML_ASSERT(false && "failed to open file for writing");
     }
@@ -20743,15 +22930,15 @@ int ggml_cpu_has_wasm_simd(void) {
 }
 
 int ggml_cpu_has_blas(void) {
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_VULKAN) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_SYCL)
+#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_SYCL)
     return 1;
 #else
     return 0;
 #endif
 }
 
-int ggml_cpu_has_cublas(void) {
-#if defined(GGML_USE_CUBLAS)
+int ggml_cpu_has_cuda(void) {
+#if defined(GGML_USE_CUDA)
     return 1;
 #else
     return 0;
@@ -20791,7 +22978,7 @@ int ggml_cpu_has_sycl(void) {
 }
 
 int ggml_cpu_has_gpublas(void) {
-    return ggml_cpu_has_cublas() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() ||
+    return ggml_cpu_has_cuda() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() ||
            ggml_cpu_has_sycl();
 }
 
diff --git a/ggml.h b/ggml.h
index 004d09c70..3fe95ed57 100644
--- a/ggml.h
+++ b/ggml.h
@@ -214,9 +214,10 @@
 #    define GGML_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
 #endif
 
-#include <stdint.h>
-#include <stddef.h>
 #include <stdbool.h>
+#include <stddef.h>
+#include <stdint.h>
+#include <stdio.h>
 
 #define GGML_FILE_MAGIC   0x67676d6c // "ggml"
 #define GGML_FILE_VERSION 1
@@ -315,49 +316,67 @@
 extern "C" {
 #endif
 
-#if defined(__ARM_NEON) && defined(__CUDACC__)
-    typedef half ggml_fp16_t;
-#elif defined(__ARM_NEON) && !defined(_MSC_VER)
-    typedef __fp16 ggml_fp16_t;
-#else
+    enum ggml_status {
+        GGML_STATUS_ALLOC_FAILED = -2,
+        GGML_STATUS_FAILED = -1,
+        GGML_STATUS_SUCCESS = 0,
+        GGML_STATUS_ABORTED = 1,
+    };
+
+    // get ggml_status name string
+    GGML_API GGML_CALL const char * ggml_status_to_string(enum ggml_status status);
+
+    // ieee 754-2008 half-precision float16
+    // todo: make this not an integral type
     typedef uint16_t ggml_fp16_t;
-#endif
+    GGML_API float       ggml_fp16_to_fp32(ggml_fp16_t);
+    GGML_API ggml_fp16_t ggml_fp32_to_fp16(float);
+    GGML_API void        ggml_fp16_to_fp32_row(const ggml_fp16_t *, float *, int64_t);
+    GGML_API void        ggml_fp32_to_fp16_row(const float *, ggml_fp16_t *, int64_t);
 
-    // convert FP16 <-> FP32
-    GGML_API float       ggml_fp16_to_fp32(ggml_fp16_t x);
-    GGML_API ggml_fp16_t ggml_fp32_to_fp16(float x);
-
-    GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n);
-    GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n);
+    // google brain half-precision bfloat16
+    typedef struct { uint16_t bits; } ggml_bf16_t;
+    GGML_API ggml_bf16_t ggml_fp32_to_bf16(float);
+    GGML_API float       ggml_bf16_to_fp32(ggml_bf16_t);  // consider just doing << 16
+    GGML_API void        ggml_bf16_to_fp32_row(const ggml_bf16_t *, float *, int64_t);
+    GGML_API void        ggml_fp32_to_bf16_row(const float *, ggml_bf16_t *, int64_t);
 
     struct ggml_object;
     struct ggml_context;
 
+    // NOTE: always add types at the end of the enum to keep backward compatibility
     enum ggml_type {
-        GGML_TYPE_F32  = 0,
-        GGML_TYPE_F16  = 1,
-        GGML_TYPE_Q4_0 = 2,
-        GGML_TYPE_Q4_1 = 3,
+        GGML_TYPE_F32     = 0,
+        GGML_TYPE_F16     = 1,
+        GGML_TYPE_Q4_0    = 2,
+        GGML_TYPE_Q4_1    = 3,
         // GGML_TYPE_Q4_2 = 4, support has been removed
-        // GGML_TYPE_Q4_3 (5) support has been removed
-        GGML_TYPE_Q5_0 = 6,
-        GGML_TYPE_Q5_1 = 7,
-        GGML_TYPE_Q8_0 = 8,
-        GGML_TYPE_Q8_1 = 9,
-        // k-quantizations
-        GGML_TYPE_Q2_K = 10,
-        GGML_TYPE_Q3_K = 11,
-        GGML_TYPE_Q4_K = 12,
-        GGML_TYPE_Q5_K = 13,
-        GGML_TYPE_Q6_K = 14,
-        GGML_TYPE_Q8_K = 15,
+        // GGML_TYPE_Q4_3 = 5, support has been removed
+        GGML_TYPE_Q5_0    = 6,
+        GGML_TYPE_Q5_1    = 7,
+        GGML_TYPE_Q8_0    = 8,
+        GGML_TYPE_Q8_1    = 9,
+        GGML_TYPE_Q2_K    = 10,
+        GGML_TYPE_Q3_K    = 11,
+        GGML_TYPE_Q4_K    = 12,
+        GGML_TYPE_Q5_K    = 13,
+        GGML_TYPE_Q6_K    = 14,
+        GGML_TYPE_Q8_K    = 15,
         GGML_TYPE_IQ2_XXS = 16,
         GGML_TYPE_IQ2_XS  = 17,
         GGML_TYPE_IQ3_XXS = 18,
         GGML_TYPE_IQ1_S   = 19,
-        GGML_TYPE_I8,
-        GGML_TYPE_I16,
-        GGML_TYPE_I32,
+        GGML_TYPE_IQ4_NL  = 20,
+        GGML_TYPE_IQ3_S   = 21,
+        GGML_TYPE_IQ2_S   = 22,
+        GGML_TYPE_IQ4_XS  = 23,
+        GGML_TYPE_I8      = 24,
+        GGML_TYPE_I16     = 25,
+        GGML_TYPE_I32     = 26,
+        GGML_TYPE_I64     = 27,
+        GGML_TYPE_F64     = 28,
+        GGML_TYPE_IQ1_M   = 29,
+        GGML_TYPE_BF16    = 30,
         GGML_TYPE_COUNT,
     };
 
@@ -368,31 +387,37 @@ extern "C" {
     };
 
     enum ggml_backend_type {
-        GGML_BACKEND_CPU = 0,
-        GGML_BACKEND_GPU = 10,
-        GGML_BACKEND_GPU_SPLIT = 20,
+        GGML_BACKEND_TYPE_CPU = 0,
+        GGML_BACKEND_TYPE_GPU = 10,
+        GGML_BACKEND_TYPE_GPU_SPLIT = 20,
     };
 
     // model file types
     enum ggml_ftype {
-        GGML_FTYPE_UNKNOWN     = -1,
-        GGML_FTYPE_ALL_F32     = 0,
-        GGML_FTYPE_MOSTLY_F16  = 1,  // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q4_0 = 2,  // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q4_1 = 3,  // except 1d tensors
+        GGML_FTYPE_UNKNOWN        = -1,
+        GGML_FTYPE_ALL_F32        = 0,
+        GGML_FTYPE_MOSTLY_F16     = 1,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_0    = 2,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_1    = 3,  // except 1d tensors
         GGML_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
-        GGML_FTYPE_MOSTLY_Q8_0 = 7,  // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q5_0 = 8,  // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q5_1 = 9,  // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q3_K = 11, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q4_K = 12, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q5_K = 13, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q6_K = 14, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q8_0    = 7,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q5_0    = 8,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q5_1    = 9,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q2_K    = 10, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q3_K    = 11, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_K    = 12, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q5_K    = 13, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q6_K    = 14, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ2_XXS = 15, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ2_XS  = 16, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ3_XXS = 17, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ1_S   = 18, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ4_NL  = 19, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ3_S   = 20, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ2_S   = 21, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ4_XS  = 22, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ1_M   = 23, // except 1d tensors
+        GGML_FTYPE_MOSTLY_BF16    = 24, // except 1d tensors
     };
 
     // available tensor operations:
@@ -443,7 +468,6 @@ extern "C" {
         GGML_OP_SOFT_MAX_BACK,
         GGML_OP_ROPE,
         GGML_OP_ROPE_BACK,
-        GGML_OP_ALIBI,
         GGML_OP_CLAMP,
         GGML_OP_CONV_TRANSPOSE_1D,
         GGML_OP_IM2COL,
@@ -452,12 +476,17 @@ extern "C" {
         GGML_OP_POOL_2D,
         GGML_OP_UPSCALE, // nearest interpolate
         GGML_OP_PAD,
+        GGML_OP_ARANGE,
+        GGML_OP_TIMESTEP_EMBEDDING,
         GGML_OP_ARGSORT,
         GGML_OP_LEAKY_RELU,
 
         GGML_OP_FLASH_ATTN,
+        GGML_OP_FLASH_ATTN_EXT,
         GGML_OP_FLASH_FF,
         GGML_OP_FLASH_ATTN_BACK,
+        GGML_OP_SSM_CONV,
+        GGML_OP_SSM_SCAN,
         GGML_OP_WIN_PART,
         GGML_OP_WIN_UNPART,
         GGML_OP_GET_REL_POS,
@@ -490,6 +519,7 @@ extern "C" {
         GGML_UNARY_OP_TANH,
         GGML_UNARY_OP_ELU,
         GGML_UNARY_OP_RELU,
+        GGML_UNARY_OP_SIGMOID,
         GGML_UNARY_OP_GELU,
         GGML_UNARY_OP_GELU_QUICK,
         GGML_UNARY_OP_SILU,
@@ -500,9 +530,9 @@ extern "C" {
     };
 
     enum ggml_object_type {
-        GGML_OBJECT_TENSOR,
-        GGML_OBJECT_GRAPH,
-        GGML_OBJECT_WORK_BUFFER
+        GGML_OBJECT_TYPE_TENSOR,
+        GGML_OBJECT_TYPE_GRAPH,
+        GGML_OBJECT_TYPE_WORK_BUFFER
     };
 
     enum ggml_log_level {
@@ -644,9 +674,9 @@ extern "C" {
     // NOTE: the INIT or FINALIZE pass is not scheduled unless explicitly enabled.
     // This behavior was changed since https://github.com/ggerganov/llama.cpp/pull/1995.
     enum ggml_task_type {
-        GGML_TASK_INIT = 0,
-        GGML_TASK_COMPUTE,
-        GGML_TASK_FINALIZE,
+        GGML_TASK_TYPE_INIT = 0,
+        GGML_TASK_TYPE_COMPUTE,
+        GGML_TASK_TYPE_FINALIZE,
     };
 
     struct ggml_compute_params {
@@ -670,6 +700,16 @@ extern "C" {
         GGML_NUMA_STRATEGY_COUNT
     };
 
+    //
+    // GUID
+    //
+
+    // GUID types
+    typedef uint8_t ggml_guid[16];
+    typedef ggml_guid * ggml_guid_t;
+
+    GGML_API bool ggml_guid_matches(ggml_guid_t guid_a, ggml_guid_t guid_b);
+
     // misc
 
     GGML_API void    ggml_time_init(void); // call this once at the beginning of the program
@@ -680,6 +720,9 @@ extern "C" {
 
     GGML_API void    ggml_print_backtrace(void);
 
+    // accepts a UTF-8 path, even on Windows
+    GGML_API FILE *  ggml_fopen(const char * fname, const char * mode);
+
     GGML_API void    ggml_numa_init(enum ggml_numa_strategy numa); // call once for better performance on NUMA systems
     GGML_API bool    ggml_is_numa(void); // true if init detected that system has >1 NUMA node
 
@@ -716,6 +759,7 @@ extern "C" {
     GGML_API GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor);
     GGML_API GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor);
     GGML_API GGML_CALL bool ggml_is_permuted  (const struct ggml_tensor * tensor);
+    GGML_API GGML_CALL bool ggml_is_empty     (const struct ggml_tensor * tensor);
     GGML_API           bool ggml_is_scalar    (const struct ggml_tensor * tensor);
     GGML_API           bool ggml_is_vector    (const struct ggml_tensor * tensor);
     GGML_API           bool ggml_is_matrix    (const struct ggml_tensor * tensor);
@@ -727,6 +771,8 @@ extern "C" {
     // use this to compute the memory overhead of a tensor
     GGML_API size_t ggml_tensor_overhead(void);
 
+    GGML_API bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbytes);
+
     // main
 
     GGML_API struct ggml_context * ggml_init(struct ggml_init_params params);
@@ -1028,6 +1074,14 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
 
+    GGML_API struct ggml_tensor * ggml_sigmoid(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_sigmoid_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
     GGML_API struct ggml_tensor * ggml_gelu(
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
@@ -1126,14 +1180,11 @@ extern "C" {
             enum ggml_prec       prec);
 
     // indirect matrix multiplication
-    //  ggml_mul_mat_id(ctx, as, ids, id, b) ~= ggml_mul_mat(as[ids[id]], b)
     GGML_API struct ggml_tensor * ggml_mul_mat_id(
             struct ggml_context * ctx,
-            struct ggml_tensor  * const as[],
-            int                   n_as,
-            struct ggml_tensor  * ids,
-            int                   id,
-            struct ggml_tensor  * b);
+            struct ggml_tensor  * as,
+            struct ggml_tensor  * b,
+            struct ggml_tensor  * ids);
 
     // A: m columns, n rows,
     // B: p columns, n rows,
@@ -1385,15 +1436,13 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
 
-    // fused soft_max(a*scale + mask + pos[i]*(ALiBi slope))
+    // fused soft_max(a*scale + mask*(ALiBi slope))
     // mask is optional
-    // pos is required when max_bias > 0.0f
     // max_bias = 0.0f for no ALiBi
     GGML_API struct ggml_tensor * ggml_soft_max_ext(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
             struct ggml_tensor  * mask,
-            struct ggml_tensor  * pos,
             float                 scale,
             float                 max_bias);
 
@@ -1495,16 +1544,6 @@ extern "C" {
             float                 xpos_base,
             bool                  xpos_down);
 
-    // alibi position embedding
-    // in-place, returns view(a)
-    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_alibi(
-            struct ggml_context * ctx,
-            struct ggml_tensor  * a,
-            int                   n_past,
-            int                   n_head,
-            float                 bias_max),
-        "use ggml_soft_max_ext instead (will be removed in Mar 2024)");
-
     // clamp
     // in-place, returns view(a)
     GGML_API struct ggml_tensor * ggml_clamp(
@@ -1649,10 +1688,19 @@ extern "C" {
             int                  p2,
             int                  p3);
 
+    // Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151
+    // timesteps: [N,]
+    // return: [N, dim]
+    GGML_API struct ggml_tensor * ggml_timestep_embedding(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * timesteps,
+            int                   dim,
+            int                   max_period);
+
     // sort rows
     enum ggml_sort_order {
-        GGML_SORT_ASC,
-        GGML_SORT_DESC,
+        GGML_SORT_ORDER_ASC,
+        GGML_SORT_ORDER_DESC,
     };
 
     GGML_API struct ggml_tensor * ggml_argsort(
@@ -1660,6 +1708,12 @@ extern "C" {
             struct ggml_tensor  * a,
             enum ggml_sort_order  order);
 
+    GGML_API struct ggml_tensor * ggml_arange(
+            struct ggml_context * ctx,
+            float                 start,
+            float                 stop,
+            float                 step);
+
     // top k elements per row
     GGML_API struct ggml_tensor * ggml_top_k(
             struct ggml_context * ctx,
@@ -1673,6 +1727,26 @@ extern "C" {
             struct ggml_tensor  * v,
             bool                  masked);
 
+#define GGML_KQ_MASK_PAD 32
+
+    // q:    [n_embd, n_batch,     n_head,    1]
+    // k:    [n_embd, n_kv,        n_head_kv, 1]
+    // v:    [n_embd, n_kv,        n_head_kv, 1] !! not transposed !!
+    // mask: [n_kv,   n_batch_pad, 1,         1] !! n_batch_pad = GGML_PAD(n_batch, GGML_KQ_MASK_PAD) !!
+    // res:  [n_embd, n_head,      n_batch,   1] !! permuted !!
+    GGML_API struct ggml_tensor * ggml_flash_attn_ext(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * q,
+            struct ggml_tensor  * k,
+            struct ggml_tensor  * v,
+            struct ggml_tensor  * mask,
+            float                 scale,
+            float                 max_bias);
+
+    GGML_API void ggml_flash_attn_ext_set_prec(
+            struct ggml_tensor * a,
+            enum ggml_prec       prec);
+
     GGML_API struct ggml_tensor * ggml_flash_attn_back(
            struct ggml_context * ctx,
            struct ggml_tensor  * q,
@@ -1689,6 +1763,23 @@ extern "C" {
             struct ggml_tensor  * c0,
             struct ggml_tensor  * c1);
 
+    GGML_API struct ggml_tensor * ggml_ssm_conv(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * s,
+            struct ggml_tensor  * x,
+            struct ggml_tensor  * c,
+            struct ggml_tensor  * sq);
+
+    GGML_API struct ggml_tensor * ggml_ssm_scan(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * s,
+            struct ggml_tensor  * x,
+            struct ggml_tensor  * dt,
+            struct ggml_tensor  * A,
+            struct ggml_tensor  * B,
+            struct ggml_tensor  * C,
+            struct ggml_tensor  * sq);
+
     // partition into non-overlapping windows with padding if needed
     // example:
     // a:   768   64   64    1
@@ -1911,12 +2002,11 @@ extern "C" {
 
     // ggml_graph_plan() has to be called before ggml_graph_compute()
     // when plan.work_size > 0, caller must allocate memory for plan.work_data
-    GGML_API struct ggml_cplan ggml_graph_plan   (const struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
-    GGML_API int               ggml_graph_compute(      struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
-
+    GGML_API struct ggml_cplan ggml_graph_plan            (const struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
+    GGML_API enum ggml_status  ggml_graph_compute         (      struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
     // same as ggml_graph_compute() but the work data is allocated as a part of the context
     // note: the drawback of this API is that you must have ensured that the context has enough memory for the work data
-    GGML_API void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads);
+    GGML_API enum ggml_status  ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads);
 
     GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name);
 
@@ -1945,8 +2035,8 @@ extern "C" {
 
     // optimization methods
     enum ggml_opt_type {
-        GGML_OPT_ADAM,
-        GGML_OPT_LBFGS,
+        GGML_OPT_TYPE_ADAM,
+        GGML_OPT_TYPE_LBFGS,
     };
 
     // linesearch methods
@@ -1960,12 +2050,12 @@ extern "C" {
 
     // optimization return values
     enum ggml_opt_result {
-        GGML_OPT_OK = 0,
-        GGML_OPT_DID_NOT_CONVERGE,
-        GGML_OPT_NO_CONTEXT,
-        GGML_OPT_INVALID_WOLFE,
-        GGML_OPT_FAIL,
-        GGML_OPT_CANCEL,
+        GGML_OPT_RESULT_OK = 0,
+        GGML_OPT_RESULT_DID_NOT_CONVERGE,
+        GGML_OPT_RESULT_NO_CONTEXT,
+        GGML_OPT_RESULT_INVALID_WOLFE,
+        GGML_OPT_RESULT_FAIL,
+        GGML_OPT_RESULT_CANCEL,
 
         GGML_LINESEARCH_FAIL = -128,
         GGML_LINESEARCH_MINIMUM_STEP,
@@ -2137,25 +2227,18 @@ extern "C" {
     GGML_API void ggml_quantize_init(enum ggml_type type);
     GGML_API void ggml_quantize_free(void);
 
-    // TODO: these would probably get removed in favor of the more general ggml_quantize_chunk
-    GGML_API size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist);
-    GGML_API size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist);
-    GGML_API size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist);
-    GGML_API size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist);
-    GGML_API size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist);
-
-    GGML_API size_t ggml_quantize_q2_K(const float * src, void * dst, int n, int k, int64_t * hist);
-    GGML_API size_t ggml_quantize_q3_K(const float * src, void * dst, int n, int k, int64_t * hist);
-    GGML_API size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist);
-    GGML_API size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist);
-    GGML_API size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist);
-
     // some quantization type cannot be used without an importance matrix
     GGML_API bool ggml_quantize_requires_imatrix(enum ggml_type type);
 
     // calls ggml_quantize_init internally (i.e. can allocate memory)
-    GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst,
-            int start, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+    GGML_API size_t ggml_quantize_chunk(
+            enum ggml_type   type,
+               const float * src,
+                      void * dst,
+                   int64_t   start,
+                   int64_t   nrows,
+                   int64_t   n_per_row,
+               const float * imatrix);
 
     //
     // gguf
@@ -2231,6 +2314,9 @@ extern "C" {
     GGML_API char *         gguf_get_tensor_name  (const struct gguf_context * ctx, int i);
     GGML_API enum ggml_type gguf_get_tensor_type  (const struct gguf_context * ctx, int i);
 
+    // removes key if it exists
+    GGML_API void gguf_remove_key(struct gguf_context * ctx, const char * key);
+
     // overrides existing values or adds a new one
     GGML_API void gguf_set_val_u8  (struct gguf_context * ctx, const char * key, uint8_t  val);
     GGML_API void gguf_set_val_i8  (struct gguf_context * ctx, const char * key, int8_t   val);
@@ -2298,7 +2384,7 @@ extern "C" {
     GGML_API int ggml_cpu_has_fp16_va    (void);
     GGML_API int ggml_cpu_has_wasm_simd  (void);
     GGML_API int ggml_cpu_has_blas       (void);
-    GGML_API int ggml_cpu_has_cublas     (void);
+    GGML_API int ggml_cpu_has_cuda       (void);
     GGML_API int ggml_cpu_has_clblast    (void);
     GGML_API int ggml_cpu_has_vulkan     (void);
     GGML_API int ggml_cpu_has_kompute    (void);
@@ -2319,8 +2405,8 @@ extern "C" {
 #else
 #define GGML_RESTRICT restrict
 #endif
-    typedef void (*ggml_to_float_t)  (const void  * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
-    typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void  * GGML_RESTRICT y, int k);
+    typedef void (*ggml_to_float_t)  (const void  * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+    typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void  * GGML_RESTRICT y, int64_t k);
     typedef void (*ggml_vec_dot_t)   (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx,
                                       const void * GGML_RESTRICT y, size_t by, int nrc);
 
diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py
index b2e86e182..162cf5c6e 100644
--- a/ggml_vk_generate_shaders.py
+++ b/ggml_vk_generate_shaders.py
@@ -1,11 +1,14 @@
 #!/usr/bin/env python
 
+import logging
 import argparse
 import asyncio
 import os
 import sys
 from tempfile import gettempdir, NamedTemporaryFile
 
+logger = logging.getLogger("ggml-vk-generate-shaders")
+
 shader_f32 = """
 #define FLOAT_TYPE float
 """
@@ -18,6 +21,12 @@ shader_int8_ext = """
 """
 
 # Type-specific defines
+shader_f32_defines = """
+#define QUANT_K 1
+#define QUANT_R 1
+
+#define A_TYPE float
+"""
 shader_f16_defines = """
 #define QUANT_K 1
 #define QUANT_R 1
@@ -64,6 +73,7 @@ struct block_q5_0
 #define A_TYPE block_q5_0
 """
 shader_q5_1_defines = """
+#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require
 #define QUANT_K 32
 #define QUANT_R 2
 
@@ -156,47 +166,55 @@ struct block_q6_K
 """
 
 # Dequant functions
-shader_f16_dequant_func = """
-#define DEQUANT_FUNC vec2 v = vec2(data_a[ib + 0], data_a[ib + 1]);
+shader_float_dequant_func = """
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]);
+}
 """
 
 shader_q4_0_dequant_func = """
-#define DEQUANT_FUNC const float d = float(data_a[ib].d); \
-const uint vui = uint(data_a[ib].qs[iqs]); \
-vec2 v = vec2(vui & 0xF, vui >> 4); \
-v = (v - 8.0f)*d;
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const float d = float(data_a[a_offset + ib].d);
+    const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
+    return (vec2(vui & 0xF, vui >> 4) - 8.0f) * d;
+}
 """
 
 shader_q4_1_dequant_func = """
-#define DEQUANT_FUNC const float d = float(data_a[ib].d); \
-const float m = float(data_a[ib].m); \
-const uint vui = uint(data_a[ib].qs[iqs]); \
-vec2 v = vec2(vui & 0xF, vui >> 4); \
-v = v*d + m;
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const float d = float(data_a[a_offset + ib].d);
+    const float m = float(data_a[a_offset + ib].m);
+    const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
+    return vec2(vui & 0xF, vui >> 4) * d + m;
+}
 """
 
 shader_q5_0_dequant_func = """
-#define DEQUANT_FUNC const float d = float(data_a[ib].d); \
-const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; \
-const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); \
-const uint vui = uint(data_a[ib].qs[iqs]); \
-vec2 v = vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); \
-v = (v - 16.0f) * d;
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const float d = float(data_a[a_offset + ib].d);
+    const uint uint_qh = uint(data_a[a_offset + ib].qh[1]) << 16 | data_a[a_offset + ib].qh[0];
+    const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
+    const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
+    return (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d;
+}
 """
 
 shader_q5_1_dequant_func = """
-#define DEQUANT_FUNC const float d = float(data_a[ib].d); \
-const float m = float(data_a[ib].m); \
-const ivec2 qh = ivec2(((data_a[ib].qh >> iqs) << 4) & 0x10, (data_a[ib].qh >> (iqs + 12)) & 0x10); \
-const uint vui = uint(data_a[ib].qs[iqs]); \
-vec2 v = vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); \
-v = v*d + m;
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const float d = float(data_a[a_offset + ib].d);
+    const float m = float(data_a[a_offset + ib].m);
+    const uint uint_qh = data_a[a_offset + ib].qh;
+    const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
+    const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
+    return vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m;
+}
 """
 
 shader_q8_0_dequant_func = """
-#define DEQUANT_FUNC const float d = float(data_a[ib].d); \
-vec2 v = vec2(int(data_a[ib].qs[iqs]), int(data_a[ib].qs[iqs + 1])); \
-v = v * d;
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const float d = float(data_a[a_offset + ib].d);
+    return vec2(int(data_a[a_offset + ib].qs[iqs]), int(data_a[a_offset + ib].qs[iqs + 1])) * d;
+}
 """
 
 # MULMAT
@@ -206,18 +224,34 @@ mulmat_head = """#version 450
 #extension GL_EXT_control_flow_attributes : enable
 #extension GL_EXT_shader_16bit_storage : require
 
-#ifndef LOAD_VEC
-#define LOAD_VEC 1
+#ifdef MUL_MAT_ID
+#extension GL_EXT_buffer_reference2 : require
+#extension GL_EXT_nonuniform_qualifier : require
+#extension GL_EXT_scalar_block_layout : require
+#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require
+
+#define EXPERT_COUNT 8
+#endif
+
+#ifndef LOAD_VEC_A
+#define LOAD_VEC_A 1
+#endif
+#ifndef LOAD_VEC_B
+#define LOAD_VEC_B 1
 #endif
 """
 
-mulmat_body = """
+mulmat_body1 = """
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
 layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
 layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
 
+#ifdef MUL_MAT_ID
+layout (binding = 3) readonly buffer IDS {int data_ids[];};
+#endif
+
 layout (push_constant) uniform parameter
 {
     uint M;
@@ -236,11 +270,26 @@ layout (push_constant) uniform parameter
     uint batch_stride_a;
     uint batch_stride_b;
     uint batch_stride_d;
+
+#ifdef MUL_MAT_ID
+    uint expert_stride_a;
+    uint expert_stride_b0;
+    uint expert_stride_b1;
+    uint expert_stride_d;
+
+    uint ids_stride;
+
+    uint n_as;
+    uint nei0;
+    uint nei1;
+    uint nbi1;
+    uint ne11;
+#endif
 } p;
 
 layout (constant_id = 1) const uint BM = 64;
 layout (constant_id = 2) const uint BN = 64;
-layout (constant_id = 3) const uint BK = 16;
+layout (constant_id = 3) const uint BK = 16;  // Assumed to be 32 if working with a quant
 layout (constant_id = 4) const uint WM = 32;
 layout (constant_id = 5) const uint WN = 32;
 layout (constant_id = 6) const uint WMITER = 2;
@@ -251,9 +300,20 @@ layout (constant_id = 9) const uint WARP = 32;
 shared FLOAT_TYPE buf_a[BM * (BK+1)];
 shared FLOAT_TYPE buf_b[BN * (BK+1)];
 
+#ifdef MUL_MAT_ID
+shared u8vec2 rowids[2048];
+#endif
+
 void main() {
-    const uint i13 = gl_GlobalInvocationID.z / p.ne12;
-    const uint i12 = gl_GlobalInvocationID.z % p.ne12;
+#ifdef MUL_MAT_ID
+    const uint batch_idx = gl_GlobalInvocationID.z / p.n_as;
+    const uint expert_idx = gl_GlobalInvocationID.z % p.n_as;
+#else
+    const uint batch_idx = gl_GlobalInvocationID.z;
+#endif
+
+    const uint i13 = batch_idx / p.ne12;
+    const uint i12 = batch_idx % p.ne12;
 
     const uint i03 = i13 / p.broadcast3;
     const uint i02 = i12 / p.broadcast2;
@@ -277,16 +337,43 @@ void main() {
     const uint tiwr = tiw % (WSUBM / TM);
     const uint tiwc = tiw / (WSUBM / TM);
 
-    const uint loadr = gl_LocalInvocationID.x % (BK / LOAD_VEC);
-    const uint loadc = gl_LocalInvocationID.x / (BK / LOAD_VEC);
+    const uint loadr_a = gl_LocalInvocationID.x % (BK / LOAD_VEC_A);
+    const uint loadc_a = gl_LocalInvocationID.x / (BK / LOAD_VEC_A);
+    const uint loadr_b = gl_LocalInvocationID.x % (BK / LOAD_VEC_B);
+    const uint loadc_b = gl_LocalInvocationID.x / (BK / LOAD_VEC_B);
 
-    const uint loadstride = gl_WorkGroupSize.x * LOAD_VEC / BK;
+    const uint loadstride_a = gl_WorkGroupSize.x * LOAD_VEC_A / BK;
+    const uint loadstride_b = gl_WorkGroupSize.x * LOAD_VEC_B / BK;
+
+#ifdef MUL_MAT_ID
+    uint _ne1 = 0;
+    for (uint ii1 = 0; ii1 < p.nei1; ii1++) {
+        for (uint ii0 = 0; ii0 < p.nei0; ii0++) {
+            if (data_ids[ii1*p.nbi1 + ii0] == expert_idx) {
+                rowids[_ne1] = u8vec2(ii0, ii1);
+                _ne1++;
+            }
+        }
+    }
+
+    const u8vec2 id = rowids[ir * BN + ic];
+#endif
 
     const uint start_k = ik * p.k_split;
     const uint end_k = min(p.K, (ik + 1) * p.k_split);
 
-    uint pos_a = (batch_idx_a * p.batch_stride_a + ir * BM * p.stride_a + start_k) / LOAD_VEC;
-    uint pos_b = (gl_GlobalInvocationID.z * p.batch_stride_b + ic * BN * p.stride_b + start_k) / LOAD_VEC;
+    uint pos_a = (
+#ifdef MUL_MAT_ID
+        expert_idx * p.expert_stride_a +
+#endif
+        batch_idx_a * p.batch_stride_a + ir * BM * p.stride_a + start_k) / LOAD_VEC_A;
+    uint pos_b = (
+#ifdef MUL_MAT_ID
+        id.y * p.expert_stride_b1 +
+        (id.x % p.ne11) * p.expert_stride_b0 +
+#endif
+        batch_idx * p.batch_stride_b +
+        ic * BN * p.stride_b + start_k) / LOAD_VEC_B;
 
     float sums[WMITER * TM * WNITER * TN];
     FLOAT_TYPE cache_a[WMITER * TM];
@@ -297,61 +384,272 @@ void main() {
     }
 
     [[unroll]] for (uint block = start_k; block < end_k; block += BK) {
-        [[unroll]] for (uint l = 0; l < BM; l += loadstride) {
-#if LOAD_VEC == 8
-            const uint idx = pos_a + (loadc + l) * p.stride_a / LOAD_VEC + loadr;
-            buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 0] = FLOAT_TYPE(data_a[idx][0].x);
-            buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 1] = FLOAT_TYPE(data_a[idx][0].y);
-            buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 2] = FLOAT_TYPE(data_a[idx][0].z);
-            buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 3] = FLOAT_TYPE(data_a[idx][0].w);
-            buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 4] = FLOAT_TYPE(data_a[idx][1].x);
-            buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 5] = FLOAT_TYPE(data_a[idx][1].y);
-            buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 6] = FLOAT_TYPE(data_a[idx][1].z);
-            buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 7] = FLOAT_TYPE(data_a[idx][1].w);
-#elif LOAD_VEC == 4
-            const uint idx = pos_a + (loadc + l) * p.stride_a / LOAD_VEC + loadr;
-            buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 0] = FLOAT_TYPE(data_a[idx].x);
-            buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 1] = FLOAT_TYPE(data_a[idx].y);
-            buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 2] = FLOAT_TYPE(data_a[idx].z);
-            buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 3] = FLOAT_TYPE(data_a[idx].w);
+        [[unroll]] for (uint l = 0; l < BM; l += loadstride_a) {"""
+
+mulmat_load_scalar = """
+#if LOAD_VEC_A == 8
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+            buf_a[buf_idx    ] = FLOAT_TYPE(data_a[idx][0].x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(data_a[idx][0].y);
+            buf_a[buf_idx + 2] = FLOAT_TYPE(data_a[idx][0].z);
+            buf_a[buf_idx + 3] = FLOAT_TYPE(data_a[idx][0].w);
+            buf_a[buf_idx + 4] = FLOAT_TYPE(data_a[idx][1].x);
+            buf_a[buf_idx + 5] = FLOAT_TYPE(data_a[idx][1].y);
+            buf_a[buf_idx + 6] = FLOAT_TYPE(data_a[idx][1].z);
+            buf_a[buf_idx + 7] = FLOAT_TYPE(data_a[idx][1].w);
+#elif LOAD_VEC_A == 4
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+            buf_a[buf_idx    ] = FLOAT_TYPE(data_a[idx].x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(data_a[idx].y);
+            buf_a[buf_idx + 2] = FLOAT_TYPE(data_a[idx].z);
+            buf_a[buf_idx + 3] = FLOAT_TYPE(data_a[idx].w);
 #else
-            if (ir * BM + loadc + l < p.M && block + loadr < end_k) {
-                buf_a[(loadc + l) * (BK+1) + loadr] = FLOAT_TYPE(data_a[pos_a + (loadc + l) * p.stride_a + loadr]);
+            if (ir * BM + loadc_a + l < p.M && block + loadr_a < end_k) {
+                buf_a[(loadc_a + l) * (BK+1) + loadr_a] = FLOAT_TYPE(data_a[pos_a + (loadc_a + l) * p.stride_a + loadr_a]);
             } else {
-                buf_a[(loadc + l) * (BK+1) + loadr] = FLOAT_TYPE(0.0f);
+                buf_a[(loadc_a + l) * (BK+1) + loadr_a] = FLOAT_TYPE(0.0f);
             }
 #endif
-        }
-        [[unroll]] for (uint l = 0; l < BN; l += loadstride) {
-#if LOAD_VEC == 8
-            const uint idx = pos_b + (loadc + l) * p.stride_b / LOAD_VEC + loadr;
-            buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 0] = FLOAT_TYPE(data_b[idx][0].x);
-            buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 1] = FLOAT_TYPE(data_b[idx][0].y);
-            buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 2] = FLOAT_TYPE(data_b[idx][0].z);
-            buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 3] = FLOAT_TYPE(data_b[idx][0].w);
-            buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 4] = FLOAT_TYPE(data_b[idx][1].x);
-            buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 5] = FLOAT_TYPE(data_b[idx][1].y);
-            buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 6] = FLOAT_TYPE(data_b[idx][1].z);
-            buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 7] = FLOAT_TYPE(data_b[idx][1].w);
-#elif LOAD_VEC == 4
-            const uint idx = pos_b + (loadc + l) * p.stride_b / LOAD_VEC + loadr;
-            buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 0] = FLOAT_TYPE(data_b[idx].x);
-            buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 1] = FLOAT_TYPE(data_b[idx].y);
-            buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 2] = FLOAT_TYPE(data_b[idx].z);
-            buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 3] = FLOAT_TYPE(data_b[idx].w);
-#else
-            if (ic * BN + loadc + l < p.N && block + loadr < end_k) {
-                buf_b[(loadc + l) * (BK+1) + loadr] = FLOAT_TYPE(data_b[pos_b + (loadc + l) * p.stride_b + loadr]);
+"""
+
+mulmat_load_q4_0 = """
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
+
+            const uint ib = idx / 16;
+            const uint iqs = idx & 0xF;
+
+            const float d = float(data_a[ib].d);
+            const uint vui = uint(data_a[ib].qs[iqs]);
+            const vec2 v = (vec2(vui & 0xF, vui >> 4) - 8.0f) * d;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);"""
+
+mulmat_load_q4_1 = """
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
+
+            const uint ib = idx / 16;
+            const uint iqs = idx & 0xF;
+
+            const float d = float(data_a[ib].d);
+            const float m = float(data_a[ib].m);
+            const uint vui = uint(data_a[ib].qs[iqs]);
+            const vec2 v = vec2(vui & 0xF, vui >> 4) * d + m;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);"""
+
+mulmat_load_q5_0 = """
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
+
+            const uint ib = idx / 16;
+            const uint iqs = idx & 0xF;
+
+            const float d = float(data_a[ib].d);
+            const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0];
+            const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
+            const uint vui = uint(data_a[ib].qs[iqs]);
+            const vec2 v = (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);"""
+
+mulmat_load_q5_1 = """
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
+
+            const uint ib = idx / 16;
+            const uint iqs = idx & 0xF;
+
+            const float d = float(data_a[ib].d);
+            const float m = float(data_a[ib].m);
+            const uint uint_qh = data_a[ib].qh;
+            const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
+            const uint vui = uint(data_a[ib].qs[iqs]);
+            const vec2 v = vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);"""
+
+mulmat_load_q8_0 = """
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 16;
+            const uint iqs = (idx & 0xF) * 2;
+
+            const float d = float(data_a[ib].d);
+            const vec2 v = vec2(int(data_a[ib].qs[iqs]), int(data_a[ib].qs[iqs + 1])) * d;
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);"""
+
+
+mulmat_load_q2_K = """
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                         // 2 values per idx
+            const uint iqs = idx % 128;                        // 0..127
+
+            const uint qsi = (iqs / 64) * 32 + (iqs % 16) * 2; // 0,2,4..30
+            const uint scalesi = iqs / 8;                      // 0..15
+            const uint qsshift = ((iqs % 64) / 16) * 2;        // 0,2,4,6
+
+            const uvec2 qs = uvec2(data_a[ib].qs[qsi], data_a[ib].qs[qsi + 1]);
+            const uint scales = data_a[ib].scales[scalesi];
+            const vec2 d = vec2(data_a[ib].d);
+
+            const vec2 v = d.x * float(scales & 0xF) * vec2((qs >> qsshift) & 3) - d.y * float(scales >> 4);
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);"""
+
+mulmat_load_q3_K = """
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                   // 2 values per idx
+            const uint iqs = idx % 128;                  // 0..127
+
+            const uint n = iqs / 64;                     // 0,1
+            const uint qsi = n * 32 + (iqs % 16) * 2;    // 0,2,4..62
+            const uint hmi =          (iqs % 16) * 2;    // 0,2,4..30
+            const uint j = (iqs % 64) / 4;               // 0..3
+            const uint is = iqs / 8;                     // 0..15
+            const uint halfsplit = ((iqs % 64) / 16);    // 0,1,2,3
+            const uint qsshift = halfsplit * 2;          // 0,2,4,6
+            const uint m = 1 << (4 * n + halfsplit);     // 1,2,4,8,16,32,64,128
+
+            const int8_t us = int8_t(is <  4 ? (data_a[ib].scales[is-0] & 0xF) | (((data_a[ib].scales[is+8] >> 0) & 3) << 4) :
+                                    is <  8 ? (data_a[ib].scales[is-0] & 0xF) | (((data_a[ib].scales[is+4] >> 2) & 3) << 4) :
+                                    is < 12 ? (data_a[ib].scales[is-8] >>  4) | (((data_a[ib].scales[is+0] >> 4) & 3) << 4) :
+                                            (data_a[ib].scales[is-8] >>  4) | (((data_a[ib].scales[is-4] >> 6) & 3) << 4));
+            const float dl = float(data_a[ib].d) * float(us - 32);
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(dl * float(int8_t((data_a[ib].qs[qsi    ] >> qsshift) & 3) - (((data_a[ib].hmask[hmi    ] & m) != 0) ? 0 : 4)));
+            buf_a[buf_idx + 1] = FLOAT_TYPE(dl * float(int8_t((data_a[ib].qs[qsi + 1] >> qsshift) & 3) - (((data_a[ib].hmask[hmi + 1] & m) != 0) ? 0 : 4)));"""
+
+mulmat_load_q4_K = """
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                 // 2 values per idx
+            const uint iqs = idx % 128;                // 0..127
+
+            const uint n = iqs / 32;                   // 0,1,2,3
+            const uint b = (iqs % 32) / 16;            // 0,1
+            const uint is = 2 * n + b;                 // 0..7
+            const uint qsi = n * 32 + (iqs % 16) * 2;  // 0,2,4..126
+
+            const vec2 loadd = vec2(data_a[ib].d);
+
+            uint8_t sc;
+            uint8_t mbyte;
+            if (is < 4) {
+                sc    = uint8_t(data_a[ib].scales[is    ] & 63);
+                mbyte = uint8_t(data_a[ib].scales[is + 4] & 63);
             } else {
-                buf_b[(loadc + l) * (BK+1) + loadr] = FLOAT_TYPE(0.0f);
+                sc    = uint8_t((data_a[ib].scales[is + 4] & 0xF) | ((data_a[ib].scales[is - 4] >> 6) << 4));
+                mbyte = uint8_t((data_a[ib].scales[is + 4] >>  4) | ((data_a[ib].scales[is    ] >> 6) << 4));
+            }
+            const float d = loadd.x * sc;
+            const float m = loadd.y * mbyte;
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(d * float((data_a[ib].qs[qsi    ] >> (b * 4)) & 0xF) - m);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(d * float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF) - m);"""
+
+mulmat_load_q5_K = """
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                 // 2 values per idx
+            const uint iqs = idx % 128;                // 0..127
+
+            const uint n = iqs / 32;                   // 0,1,2,3
+            const uint b = (iqs % 32) / 16;            // 0,1
+            const uint is = 2 * n + b;                 // 0..7
+            const uint qsi = n * 32 + (iqs % 16) * 2;  // 0,2,4..126
+            const uint qhi = (iqs % 16) * 2;           // 0,2,4..30
+
+            const uint8_t hm = uint8_t(1 << (iqs / 16));
+
+            const vec2 loadd = vec2(data_a[ib].d);
+
+            uint8_t sc;
+            uint8_t mbyte;
+            if (is < 4) {
+                sc    = uint8_t(data_a[ib].scales[is    ] & 63);
+                mbyte = uint8_t(data_a[ib].scales[is + 4] & 63);
+            } else {
+                sc    = uint8_t((data_a[ib].scales[is + 4] & 0xF) | ((data_a[ib].scales[is - 4] >> 6) << 4));
+                mbyte = uint8_t((data_a[ib].scales[is + 4] >>  4) | ((data_a[ib].scales[is    ] >> 6) << 4));
+            }
+            const float d = loadd.x * sc;
+            const float m = loadd.y * mbyte;
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(d * (float((data_a[ib].qs[qsi    ] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi    ] & hm) != 0 ? 16 : 0)) - m);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(d * (float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi + 1] & hm) != 0 ? 16 : 0)) - m);"""
+
+mulmat_load_q6_K = """
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                  // 2 values per idx
+            const uint iqs = idx % 128;                 // 0..127
+
+            const uint n = iqs / 64;                    // 0,1
+            const uint b = (iqs % 64) / 32;             // 0,1
+            const uint is_b = (iqs % 16) / 8;           // 0,1
+            const uint qhshift = ((iqs % 64) / 16) * 2; // 0,2,4,6
+            const uint is = 8 * n + qhshift + is_b;     // 0..15
+            const uint qsi = n * 64 + (iqs % 32) * 2;   // 0,2,4..126
+            const uint qhi = n * 32 + (iqs % 16) * 2;   // 0,2,4..62
+
+            const float dscale = float(data_a[ib].d) * float(data_a[ib].scales[is]);
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi    ] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi    ] >> qhshift) & 3) << 4)) - 32));
+            buf_a[buf_idx + 1] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi + 1] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi + 1] >> qhshift) & 3) << 4)) - 32));"""
+
+mulmat_body2 = """
+        }
+        [[unroll]] for (uint l = 0; l < BN; l += loadstride_b) {
+#if LOAD_VEC_B == 8
+            const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b;
+            const uint buf_idx = (loadc_b + l) * (BK+1) + loadr_b * LOAD_VEC_B;
+            buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx][0].x);
+            buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx][0].y);
+            buf_b[buf_idx + 2] = FLOAT_TYPE(data_b[idx][0].z);
+            buf_b[buf_idx + 3] = FLOAT_TYPE(data_b[idx][0].w);
+            buf_b[buf_idx + 4] = FLOAT_TYPE(data_b[idx][1].x);
+            buf_b[buf_idx + 5] = FLOAT_TYPE(data_b[idx][1].y);
+            buf_b[buf_idx + 6] = FLOAT_TYPE(data_b[idx][1].z);
+            buf_b[buf_idx + 7] = FLOAT_TYPE(data_b[idx][1].w);
+#elif LOAD_VEC_B == 4
+            const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b;
+            const uint buf_idx = (loadc_b + l) * (BK+1) + loadr_b * LOAD_VEC_B;
+            buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx].x);
+            buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx].y);
+            buf_b[buf_idx + 2] = FLOAT_TYPE(data_b[idx].z);
+            buf_b[buf_idx + 3] = FLOAT_TYPE(data_b[idx].w);
+#else
+            if (ic * BN + loadc_b + l < p.N && block + loadr_b < end_k) {
+                buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(data_b[pos_b + (loadc_b + l) * p.stride_b + loadr_b]);
+            } else {
+                buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(0.0f);
             }
 #endif
         }
 
         barrier();
 
-        pos_a += BK / LOAD_VEC;
-        pos_b += BK / LOAD_VEC;
+        pos_a += BK / LOAD_VEC_A;
+        pos_b += BK / LOAD_VEC_B;
 
         for (uint i = 0; i < BK; i++) {
             // Load from shared into cache
@@ -383,7 +681,11 @@ void main() {
     const uint dr = ir * BM + warp_r * WM;
     const uint dc = ic * BN + warp_c * WN;
 
-    const uint offsets = gl_GlobalInvocationID.z * p.batch_stride_d + ik * p.batch_stride_d * gl_NumWorkGroups.z;
+    const uint offsets =
+#ifdef MUL_MAT_ID
+            expert_idx * p.expert_stride_d +
+#endif
+            batch_idx * p.batch_stride_d + ik * p.batch_stride_d * gl_NumWorkGroups.z;
 
     [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) {
         [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) {
@@ -438,45 +740,191 @@ dequant_head = """#version 450
 
 #extension GL_EXT_control_flow_attributes : require
 #extension GL_EXT_shader_16bit_storage : require
-"""
-
-dequant_body = """
-layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
 
 layout (push_constant) uniform parameter
 {
-    int M;
-    int K;
-    int stride_a;
-    int stride_b;
+    uint M;
+    uint K;
+    uint stride_a;
+    uint stride_b;
+    uint nel;
 } p;
+"""
+
+dequant_f32_body = """
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {float data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
 
 void main() {
-    const int i = int(gl_GlobalInvocationID.x);
+    const uint i = gl_GlobalInvocationID.x * 16;
 
-    // Transposed
-    const int row = i % (p.K / QUANT_K);
-    const int col = i / (p.K / QUANT_K);
-
-    if (row * QUANT_K >= p.K || col >= p.M) {
+    if (i >= p.nel) {
         return;
     }
 
-    const int stride_a = p.stride_a / QUANT_K;
+    [[unroll]] for (uint l = 0; l < 16; l++) {
+        data_b[i + l] = D_TYPE(data_a[i + l]);
+    }
+}
+"""
 
-    const int ib = col * stride_a + row;
+dequant_q4_0_body = """
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
 
-    const int y_offset = QUANT_R == 1 ? 1 : QUANT_K/2;
-    const int step = QUANT_R == 1 ? 2 : 1;
+layout (binding = 0) readonly buffer A {block_q4_0 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
 
-    [[unroll]] for (int iqs = 0; iqs < QUANT_K/QUANT_R; iqs += step) {
-        DEQUANT_FUNC
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
 
-        data_b[col * p.stride_b + row*QUANT_K + iqs + 0       ] = D_TYPE(v.x);
-        data_b[col * p.stride_b + row*QUANT_K + iqs + y_offset] = D_TYPE(v.y);
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint b_idx = 1024*i + 32*ir + 8*il;
+
+    const float d = float(data_a[ib].d);
+    const float dm = -8.0f * d;
+
+    const uint q_idx = 8*il;
+
+    [[unroll]] for (uint l = 0; l < 8; ++l) {
+        data_b[b_idx + l +  0] = D_TYPE(d * (data_a[ib].qs[q_idx + l] & 0xF) + dm);
+        data_b[b_idx + l + 16] = D_TYPE(d * (data_a[ib].qs[q_idx + l] >>  4) + dm);
+    }
+}
+"""
+
+dequant_q4_1_body = """
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_q4_1 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint b_idx = 1024*i + 32*ir + 8*il;
+
+    const float d = float(data_a[ib].d);
+    const float m = float(data_a[ib].m);
+
+    const uint q_idx = 8*il;
+
+    [[unroll]] for (uint l = 0; l < 8; ++l) {
+        data_b[b_idx + l +  0] = D_TYPE(d * (data_a[ib].qs[q_idx + l] & 0xF) + m);
+        data_b[b_idx + l + 16] = D_TYPE(d * (data_a[ib].qs[q_idx + l] >>  4) + m);
+    }
+}
+"""
+
+dequant_q5_0_body = """
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_q5_0 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint b_idx = 1024*i + 32*ir + 8*il;
+
+    const float d = float(data_a[ib].d);
+    const uint qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0];
+
+    const uint q_idx = 8*il;
+
+    [[unroll]] for (uint l = 0; l < 8; ++l) {
+        const uint iqs = q_idx + l;
+        const uint vui = uint(data_a[ib].qs[iqs]);
+        data_b[b_idx + l +  0] = D_TYPE(d * (((vui & 0xF) | (((qh >> iqs) << 4) & 0x10)) - 16.0f));
+        data_b[b_idx + l + 16] = D_TYPE(d * (((vui >>  4) | ((qh >> (iqs + 12)) & 0x10)) - 16.0f));
+    }
+}
+"""
+
+dequant_q5_1_body = """
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_q5_1 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint b_idx = 1024*i + 32*ir + 8*il;
+
+    const float d = float(data_a[ib].d);
+    const float m = float(data_a[ib].m);
+    const uint qh = data_a[ib].qh;
+
+    const uint q_idx = 8*il;
+
+    [[unroll]] for (uint l = 0; l < 8; ++l) {
+        const uint iqs = q_idx + l;
+        const uint vui = uint(data_a[ib].qs[iqs]);
+        data_b[b_idx + l +  0] = D_TYPE(d * (((vui & 0xF) | (((qh >> iqs) << 4) & 0x10))) + m);
+        data_b[b_idx + l + 16] = D_TYPE(d * (((vui >>  4) | ((qh >> (iqs + 12)) & 0x10))) + m);
+    }
+}
+"""
+
+dequant_q8_0_body = """
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_q8_0 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint b_idx = 1024*i + 32*ir + 16*il;
+
+    const float d = float(data_a[ib].d);
+
+    const uint q_idx = 16*il;
+
+    [[unroll]] for (uint l = 0; l < 16; l += 2) {
+        data_b[b_idx + l    ] = D_TYPE(d * data_a[ib].qs[q_idx + l    ]);
+        data_b[b_idx + l + 1] = D_TYPE(d * data_a[ib].qs[q_idx + l + 1]);
     }
 }
 """
@@ -488,29 +936,21 @@ layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
 layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
 
-layout (push_constant) uniform parameter
-{
-    int M;
-    int K;
-    int stride_a;
-    int stride_b;
-} p;
-
 void main() {
-    [[unroll]] for (int wgy = 0; wgy < 256; wgy++) {
-        const int i = int(gl_WorkGroupID.x * 256 + wgy);
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = gl_WorkGroupID.x * 256 + wgy;
         if (i >= p.M * p.K / QUANT_K) {
             return;
         }
 
-        const int tid = int(gl_LocalInvocationID.x);
-        const int ip = tid / 32;
-        const int il = tid - 32 * ip;
-        const int is = 8 * ip + il / 16;
+        const uint tid = gl_LocalInvocationID.x;
+        const uint ip = tid / 32;
+        const uint il = tid - 32 * ip;
+        const uint is = 8 * ip + il / 16;
 
-        const int y_idx = i * QUANT_K + 128 * ip + il;
+        const uint y_idx = i * QUANT_K + 128 * ip + il;
 
-        const int ql_idx = 32 * ip + il;
+        const uint ql_idx = 32 * ip + il;
         const uint8_t qs = data_a[i].qs[32 * ip + il];
 
         FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x);
@@ -528,31 +968,23 @@ layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
 layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
 
-layout (push_constant) uniform parameter
-{
-    int M;
-    int K;
-    int stride_a;
-    int stride_b;
-} p;
-
 void main() {
-    [[unroll]] for (int wgy = 0; wgy < 256; wgy++) {
-        const int i = int(gl_WorkGroupID.x * 256 + wgy);
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = uint(gl_WorkGroupID.x * 256 + wgy);
         if (i >= p.M * p.K / QUANT_K) {
             return;
         }
 
-        const int r = int(gl_LocalInvocationID.x) / 4;
-        const int tid = r / 2;
-        const int is0 = r % 2;
-        const int l0 = 16 * is0 + 4 * (int(gl_LocalInvocationID.x) % 4);
-        const int n = tid / 4;
-        const int j = tid - 4*n;
+        const uint r = gl_LocalInvocationID.x / 4;
+        const uint tid = r / 2;
+        const uint is0 = r % 2;
+        const uint l0 = 16 * is0 + 4 * (gl_LocalInvocationID.x % 4);
+        const uint n = tid / 4;
+        const uint j = tid - 4*n;
 
         const uint8_t m = uint8_t(1 << (4*n + j));
-        const int is = 8*n + 2*j + is0;
-        const int shift = 2*j;
+        const uint is = 8*n + 2*j + is0;
+        const uint shift = 2*j;
 
         const int8_t us = int8_t(is <  4 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+8] >> 0) & 3) << 4) :
                                  is <  8 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+4] >> 2) & 3) << 4) :
@@ -561,10 +993,10 @@ void main() {
         const FLOAT_TYPE d_all = FLOAT_TYPE(data_a[i].d);
         const FLOAT_TYPE dl    = d_all * FLOAT_TYPE(us - 32);
 
-        const int y_idx = i * QUANT_K + 128 * n + 32 * j;
-        const int qs_idx = 32*n;
+        const uint y_idx = i * QUANT_K + 128 * n + 32 * j;
+        const uint qs_idx = 32*n;
 
-        for (int l = l0; l < l0 + 4; ++l) {
+        for (uint l = l0; l < l0 + 4; ++l) {
             data_b[y_idx + l] = D_TYPE(dl * FLOAT_TYPE(int8_t((data_a[i].qs[qs_idx + l] >> shift) & 3) - (((data_a[i].hmask[l] & m) != 0) ? 0 : 4)));
         }
     }
@@ -576,32 +1008,24 @@ layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
 layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
 
-layout (push_constant) uniform parameter
-{
-    int M;
-    int K;
-    int stride_a;
-    int stride_b;
-} p;
-
 void main() {
-    [[unroll]] for (int wgy = 0; wgy < 256; wgy++) {
-        const int i = int(gl_WorkGroupID.x * 256 + wgy);
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = gl_WorkGroupID.x * 256 + wgy;
         if (i >= p.M * p.K / QUANT_K) {
             return;
         }
 
-        const int tid = int(gl_LocalInvocationID.x);
-        const int il = tid / 8;
-        const int ir = tid % 8;
-        const int is = 2 * il;
-        const int n = 4;
+        const uint tid = gl_LocalInvocationID.x;
+        const uint il = tid / 8;
+        const uint ir = tid % 8;
+        const uint is = 2 * il;
+        const uint n = 4;
 
         const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x);
         const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y);
 
-        const int y_idx = i * QUANT_K + 64 * il + n * ir;
-        const int qs_idx = 32*il + n * ir;
+        const uint y_idx = i * QUANT_K + 64 * il + n * ir;
+        const uint qs_idx = 32*il + n * ir;
 
         uint8_t sc;
         uint8_t m;
@@ -625,7 +1049,7 @@ void main() {
         const FLOAT_TYPE d2 = dall * sc;
         const FLOAT_TYPE m2 = dmin * m;
 
-        [[unroll]] for (int l = 0; l < n; ++l) {
+        [[unroll]] for (uint l = 0; l < n; ++l) {
             data_b[y_idx + l     ] = D_TYPE(d1 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] & 0xF) - m1);
             data_b[y_idx + l + 32] = D_TYPE(d2 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] >>  4) - m2);
         }
@@ -638,32 +1062,24 @@ layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
 layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
 
-layout (push_constant) uniform parameter
-{
-    int M;
-    int K;
-    int stride_a;
-    int stride_b;
-} p;
-
 void main() {
-    [[unroll]] for (int wgy = 0; wgy < 256; wgy++) {
-        const int i = int(gl_WorkGroupID.x * 256 + wgy);
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = gl_WorkGroupID.x * 256 + wgy;
         if (i >= p.M * p.K / QUANT_K) {
             return;
         }
 
-        const int tid = int(gl_LocalInvocationID.x);
-        const int il = tid / 16;
-        const int ir = tid % 16;
-        const int is = 2 * il;
+        const uint tid = gl_LocalInvocationID.x;
+        const uint il = tid / 16;
+        const uint ir = tid % 16;
+        const uint is = 2 * il;
 
         const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x);
         const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y);
 
-        const int y_idx = i * QUANT_K + 64 * il + 2 * ir;
-        const int qs_idx = 32*il + 2 * ir;
-        const int qh_idx = 2 * ir;
+        const uint y_idx = i * QUANT_K + 64 * il + 2 * ir;
+        const uint qs_idx = 32*il + 2 * ir;
+        const uint qh_idx = 2 * ir;
 
         uint8_t sc;
         uint8_t m;
@@ -702,28 +1118,20 @@ layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
 layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
 
-layout (push_constant) uniform parameter
-{
-    int M;
-    int K;
-    int stride_a;
-    int stride_b;
-} p;
-
 void main() {
-    [[unroll]] for (int wgy = 0; wgy < 256; wgy++) {
-        const int i = int(gl_WorkGroupID.x * 256 + wgy);
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = gl_WorkGroupID.x * 256 + wgy;
         if (i >= p.M * p.K / QUANT_K) {
             return;
         }
-        const int tid = int(gl_LocalInvocationID.x);
-        const int ip = tid / 32;
-        const int il = tid - 32 * ip;
-        const int is = 8 * ip + il / 16;
+        const uint tid = gl_LocalInvocationID.x;
+        const uint ip = tid / 32;
+        const uint il = tid - 32 * ip;
+        const uint is = 8 * ip + il / 16;
 
-        const int y_idx = i * QUANT_K + 128 * ip + il;
+        const uint y_idx = i * QUANT_K + 128 * ip + il;
 
-        const int ql_idx = 64 * ip + il;
+        const uint ql_idx = 64 * ip + il;
         const uint8_t qh = data_a[i].qh[32 * ip + il];
 
         const FLOAT_TYPE d = FLOAT_TYPE(data_a[i].d);
@@ -742,56 +1150,125 @@ mul_mat_vec_head = """#version 450
 #extension GL_EXT_control_flow_attributes : enable
 #extension GL_EXT_shader_16bit_storage : require
 #extension GL_EXT_shader_8bit_storage : require
+
+#ifdef MUL_MAT_ID
+#define EXPERT_COUNT 8
+#endif
 """
 
-mul_mat_vec_body = """
-layout(local_size_x = QUANT_K, local_size_y = 1, local_size_z = 1) in;
 
+mul_mat_vec_layout = """
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
 layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
-layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
+layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
+#ifdef MUL_MAT_ID
+layout (binding = 3) readonly buffer IDS {int data_ids[];};
+#endif
 
 layout (push_constant) uniform parameter
 {
-    int ncols;
-    int b_offset;
-    int d_offset;
-} p;
+    uint ncols;
+    uint stride_a;
+    uint stride_b;
+    uint stride_d;
 
-shared FLOAT_TYPE tmp[QUANT_K];
+    uint ne02;
+    uint ne12;
+    uint broadcast2;
+    uint broadcast3;
+
+    uint batch_stride_a;
+    uint batch_stride_b;
+    uint batch_stride_d;
+
+#ifdef MUL_MAT_ID
+    uint expert_stride_a;
+    uint expert_stride_b0;
+    uint expert_stride_b1;
+    uint expert_stride_d0;
+    uint expert_stride_d1;
+
+    uint ne11;
+    uint nei0;
+    uint nbi1;
+    uint n_as;
+#endif
+} p;
+"""
+
+mul_mat_vec_body = """
+layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
+
+layout (constant_id = 0) const uint BLOCK_SIZE = 32;
+
+shared FLOAT_TYPE tmp[BLOCK_SIZE];
 
 void main() {
-    const int block_size = int(gl_WorkGroupSize.x);
-    const int row = int(gl_WorkGroupID.x);
-    const int tid = int(gl_LocalInvocationID.x);
+    const uint row = gl_WorkGroupID.x;
+    const uint tid = gl_LocalInvocationID.x;
+    const uint batch_idx = gl_GlobalInvocationID.y;
+#ifdef MUL_MAT_ID
+    const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0;
+    const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0;
+#endif
 
-    const int y_offset = QUANT_R == 1 ? 1 : QUANT_K/2;
+    const uint i13 = batch_idx / p.ne12;
+    const uint i12 = batch_idx % p.ne12;
+
+    const uint i03 = i13 / p.broadcast3;
+    const uint i02 = i12 / p.broadcast2;
+
+    const uint batch_idx_a = i03 * p.ne02 + i02;
+
+#ifdef MUL_MAT_ID
+    const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0];
+#endif
+
+    const uint a_offset =
+#ifdef MUL_MAT_ID
+            expert_id * p.expert_stride_a +
+#endif
+            batch_idx_a * p.batch_stride_a;
+    const uint b_offset =
+#ifdef MUL_MAT_ID
+            (expert_idx0 % p.ne11) * p.expert_stride_b0 +
+            expert_idx1 * p.expert_stride_b1 +
+#endif
+            batch_idx * p.batch_stride_b;
+    const uint d_offset =
+#ifdef MUL_MAT_ID
+            expert_idx0 * p.expert_stride_b0 +
+            expert_idx1 * p.expert_stride_b1 +
+#endif
+            batch_idx * p.batch_stride_d;
+
+    const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2;
 
     tmp[tid] = FLOAT_TYPE(0.0f);
 
-    [[unroll]] for (int i = 0; i < p.ncols/block_size; i += 2) {
-        const int col = i*block_size + 2*tid;
-        const int ib = (row*p.ncols + col)/QUANT_K; // block index
-        const int iqs = (col%QUANT_K)/QUANT_R; // quant index
-        const int iybs = col - col%QUANT_K; // y block start index
+    [[unroll]] for (uint i = 0; i < p.ncols/BLOCK_SIZE; i += 2) {
+        const uint col = i*BLOCK_SIZE + 2*tid;
+        const uint ib = (row*p.ncols + col)/QUANT_K; // block index
+        const uint iqs = (col%QUANT_K)/QUANT_R; // quant index
+        const uint iybs = col - col%QUANT_K; // y block start index
 
-        DEQUANT_FUNC
+        vec2 v = dequantize(ib, iqs, a_offset / QUANT_K);
 
         // matrix multiplication
-        tmp[tid] += FLOAT_TYPE(v.x) * FLOAT_TYPE(data_b[p.b_offset + iybs + iqs + 0]);
-        tmp[tid] += FLOAT_TYPE(v.y) * FLOAT_TYPE(data_b[p.b_offset + iybs + iqs + y_offset]);
+        tmp[tid] += FLOAT_TYPE(v.x) * FLOAT_TYPE(data_b[b_offset + iybs + iqs]) +
+                    FLOAT_TYPE(v.y) * FLOAT_TYPE(data_b[b_offset + iybs + iqs + y_offset]);
     }
 
     // sum up partial sums and write back result
     barrier();
-    [[unroll]] for (int s = block_size/2; s > 0; s >>= 1) {
+    [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) {
         if (tid < s) {
             tmp[tid] += tmp[tid + s];
         }
         barrier();
     }
     if (tid == 0) {
-        dst[p.d_offset + row] = D_TYPE(tmp[0]);
+        data_d[d_offset + row] = D_TYPE(tmp[0]);
     }
 }
 """
@@ -800,42 +1277,66 @@ void main() {
 mul_mat_vec_q2_K_body = """
 layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
 
-layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
-layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
-
-layout (push_constant) uniform parameter
-{
-    int ncols;
-    int b_offset;
-    int d_offset;
-} p;
-
 shared FLOAT_TYPE tmp[32];
 
 void main() {
-    const int row = int(gl_WorkGroupID.x);
+    const uint row = gl_WorkGroupID.x;
+    const uint batch_idx = gl_GlobalInvocationID.y;
+#ifdef MUL_MAT_ID
+    const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0;
+    const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0;
+#endif
 
-    const int num_blocks_per_row = p.ncols / QUANT_K;
-    const int ib0 = row*num_blocks_per_row;
+    const uint i13 = batch_idx / p.ne12;
+    const uint i12 = batch_idx % p.ne12;
 
-    const int tid = int(gl_LocalInvocationID.x)/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
-    const int ix  = int(gl_LocalInvocationID.x)%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+    const uint i03 = i13 / p.broadcast3;
+    const uint i02 = i12 / p.broadcast2;
 
-    const int step = 16/K_QUANTS_PER_ITERATION;            // 16 or 8
+    const uint batch_idx_a = i03 * p.ne02 + i02;
 
-    const int v_im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
-    const int v_in = tid - step*v_im;                      // 0...15 or 0...7
+#ifdef MUL_MAT_ID
+    const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0];
+#endif
 
-    const int l0 = K_QUANTS_PER_ITERATION*v_in;            // 0...15
-    const int q_offset = 32*v_im + l0;
-    const int s_offset = 8*v_im;
-    const int y_offset = 128*v_im + l0;
+    const uint a_offset =
+#ifdef MUL_MAT_ID
+            expert_id * p.expert_stride_a +
+#endif
+            batch_idx_a * p.batch_stride_a;
+    const uint b_offset =
+#ifdef MUL_MAT_ID
+            (expert_idx0 % p.ne11) * p.expert_stride_b0 +
+            expert_idx1 * p.expert_stride_b1 +
+#endif
+            batch_idx * p.batch_stride_b;
+    const uint d_offset =
+#ifdef MUL_MAT_ID
+            expert_idx0 * p.expert_stride_b0 +
+            expert_idx1 * p.expert_stride_b1 +
+#endif
+            batch_idx * p.batch_stride_d;
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
+
+    const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const uint ix  = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const uint step = 16/K_QUANTS_PER_ITERATION;            // 16 or 8
+
+    const uint v_im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const uint v_in = tid - step*v_im;                      // 0...15 or 0...7
+
+    const uint l0 = K_QUANTS_PER_ITERATION*v_in;            // 0...15
+    const uint q_offset = 32*v_im + l0;
+    const uint s_offset = 8*v_im;
+    const uint y_offset = 128*v_im + l0;
 
     tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
 
-    [[unroll]] for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-        const int y_idx = i * QUANT_K + y_offset;
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+        const uint y_idx = i * QUANT_K + y_offset;
 
         const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x);
         const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y);
@@ -843,154 +1344,202 @@ void main() {
         FLOAT_TYPE sum1 = FLOAT_TYPE(0.0);
         FLOAT_TYPE sum2 = FLOAT_TYPE(0.0);
         for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
-            sum1 += FLOAT_TYPE(data_b[p.b_offset + y_idx + l +  0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 0) & 3)
-                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 0) & 3)
-                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 2) & 3)
-                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 2) & 3)
-                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 4) & 3)
-                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 4) & 3)
-                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 6) & 3)
-                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 6) & 3);
-            sum2 += FLOAT_TYPE(data_b[p.b_offset + y_idx + l +  0]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 0] >> 4) & 0xF)
-                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 16]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 1] >> 4) & 0xF)
-                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 32]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 2] >> 4) & 0xF)
-                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 48]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 3] >> 4) & 0xF)
-                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 64]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 4] >> 4) & 0xF)
-                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 80]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 5] >> 4) & 0xF)
-                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 96]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 6] >> 4) & 0xF)
-                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l +112]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 7] >> 4) & 0xF);
+            sum1 += FLOAT_TYPE(data_b[b_offset + y_idx + l +  0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 0) & 3)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 0) & 3)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 2) & 3)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 2) & 3)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 4) & 3)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 4) & 3)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 6) & 3)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 6) & 3);
+            sum2 += FLOAT_TYPE(data_b[b_offset + y_idx + l +  0]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 0] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 1] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 2] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 3] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 4] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 5] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 6] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 7] >> 4) & 0xF);
         }
         tmp[16 * ix + tid] += dall * sum1 - dmin * sum2;
     }
 
     // sum up partial sums and write back result
     barrier();
-    [[unroll]] for (int s = 16; s > 0; s >>= 1) {
+    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
         if (tid < s) {
             tmp[tid] += tmp[tid + s];
         }
         barrier();
     }
     if (tid == 0) {
-        dst[p.d_offset + row] = D_TYPE(tmp[0]);
+        data_d[d_offset + row] = D_TYPE(tmp[0]);
     }
 }
 """
 mul_mat_vec_q3_K_body = """
 layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
 
-layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
-layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
-
-layout (push_constant) uniform parameter
-{
-    int ncols;
-    int b_offset;
-    int d_offset;
-} p;
-
 shared FLOAT_TYPE tmp[32];
 
 void main() {
-    const int row = int(gl_WorkGroupID.x);
+    const uint row = gl_WorkGroupID.x;
+    const uint batch_idx = gl_GlobalInvocationID.y;
+#ifdef MUL_MAT_ID
+    const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0;
+    const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0;
+#endif
 
-    const int num_blocks_per_row = p.ncols / QUANT_K;
-    const int ib0 = row*num_blocks_per_row;
+    const uint i13 = batch_idx / p.ne12;
+    const uint i12 = batch_idx % p.ne12;
 
-    const int tid = int(gl_LocalInvocationID.x)/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
-    const int ix  = int(gl_LocalInvocationID.x)%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+    const uint i03 = i13 / p.broadcast3;
+    const uint i02 = i12 / p.broadcast2;
 
-    const int step = 16/K_QUANTS_PER_ITERATION;            // 16 or 8
+    const uint batch_idx_a = i03 * p.ne02 + i02;
 
-    const int v_im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
-    const int v_in = tid - step*v_im;                      // 0...15 or 0...7
+#ifdef MUL_MAT_ID
+    const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0];
+#endif
+
+    const uint a_offset =
+#ifdef MUL_MAT_ID
+            expert_id * p.expert_stride_a +
+#endif
+            batch_idx_a * p.batch_stride_a;
+    const uint b_offset =
+#ifdef MUL_MAT_ID
+            (expert_idx0 % p.ne11) * p.expert_stride_b0 +
+            expert_idx1 * p.expert_stride_b1 +
+#endif
+            batch_idx * p.batch_stride_b;
+    const uint d_offset =
+#ifdef MUL_MAT_ID
+            expert_idx0 * p.expert_stride_b0 +
+            expert_idx1 * p.expert_stride_b1 +
+#endif
+            batch_idx * p.batch_stride_d;
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
+
+    const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const uint ix  = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const uint step = 16/K_QUANTS_PER_ITERATION;            // 16 or 8
+
+    const uint v_im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const uint v_in = tid - step*v_im;                      // 0...15 or 0...7
 
     const uint8_t m = uint8_t(1 << (4 * v_im));
 
-    const int l0 = K_QUANTS_PER_ITERATION*v_in;            // 0...15
-    const int q_offset = 32*v_im + l0;
-    const int y_offset = 128*v_im + l0;
+    const uint l0 = K_QUANTS_PER_ITERATION*v_in;            // 0...15
+    const uint q_offset = 32*v_im + l0;
+    const uint y_offset = 128*v_im + l0;
 
     tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
 
     const uint s_shift = 4 * v_im;
 
-    [[unroll]] for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-        const int y_idx = i * QUANT_K + y_offset;
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+        const uint y_idx = i * QUANT_K + y_offset;
 
         const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
 
         FLOAT_TYPE sum = FLOAT_TYPE(0.0);
         for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
-            sum += FLOAT_TYPE(data_b[p.b_offset + y_idx + l +  0]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[0] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 0)) != 0) ? 0 : 4))
-                 + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 32]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[2] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 1)) != 0) ? 0 : 4))
-                 + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 64]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[4] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 2)) != 0) ? 0 : 4))
-                 + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 96]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[6] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 3)) != 0) ? 0 : 4))
-                 + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 16]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[1] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4))
-                 + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 48]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[3] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4))
-                 + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 80]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[5] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4))
-                 + FLOAT_TYPE(data_b[p.b_offset + y_idx + l +112]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[7] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4));
+            sum += FLOAT_TYPE(data_b[b_offset + y_idx + l +  0]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[0] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 0)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[2] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 1)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[4] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 2)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[6] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 3)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[1] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[3] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[5] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[7] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4));
         }
         tmp[16 * ix + tid] += d * sum;
     }
 
     // sum up partial sums and write back result
     barrier();
-    [[unroll]] for (int s = 16; s > 0; s >>= 1) {
+    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
         if (tid < s) {
             tmp[tid] += tmp[tid + s];
         }
         barrier();
     }
     if (tid == 0) {
-        dst[p.d_offset + row] = D_TYPE(tmp[0]);
+        data_d[d_offset + row] = D_TYPE(tmp[0]);
     }
 }
 """
 mul_mat_vec_q4_K_body = """
 layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
 
-layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
-layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
-
-layout (push_constant) uniform parameter
-{
-    int ncols;
-    int b_offset;
-    int d_offset;
-} p;
-
 shared FLOAT_TYPE tmp[32];
 
 void main() {
-    const int row = int(gl_WorkGroupID.x);
+    const uint row = gl_WorkGroupID.x;
+    const uint batch_idx = gl_GlobalInvocationID.y;
+#ifdef MUL_MAT_ID
+    const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0;
+    const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0;
+#endif
 
-    const int num_blocks_per_row = p.ncols / QUANT_K;
-    const int ib0 = row*num_blocks_per_row;
+    const uint i13 = batch_idx / p.ne12;
+    const uint i12 = batch_idx % p.ne12;
 
-    const int tid = int(gl_LocalInvocationID.x)/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
-    const int ix  = int(gl_LocalInvocationID.x)%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+    const uint i03 = i13 / p.broadcast3;
+    const uint i02 = i12 / p.broadcast2;
 
-    const int step = 8/K_QUANTS_PER_ITERATION;             // 8 or 4
+    const uint batch_idx_a = i03 * p.ne02 + i02;
 
-    const int il = tid/step;                               // 0...3
-    const int ir = tid - step*il;                          // 0...7 or 0...3
-    const int n =  2 * K_QUANTS_PER_ITERATION;             // 2 or 4
+#ifdef MUL_MAT_ID
+    const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0];
+#endif
 
-    const int v_im = il / 2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
-    const int v_in = il % 2;
+    const uint a_offset =
+#ifdef MUL_MAT_ID
+            expert_id * p.expert_stride_a +
+#endif
+            batch_idx_a * p.batch_stride_a;
+    const uint b_offset =
+#ifdef MUL_MAT_ID
+            (expert_idx0 % p.ne11) * p.expert_stride_b0 +
+            expert_idx1 * p.expert_stride_b1 +
+#endif
+            batch_idx * p.batch_stride_b;
+    const uint d_offset =
+#ifdef MUL_MAT_ID
+            expert_idx0 * p.expert_stride_b0 +
+            expert_idx1 * p.expert_stride_b1 +
+#endif
+            batch_idx * p.batch_stride_d;
 
-    const int l0 = n * (2 * ir + v_in);            // 0...15
-    const int q_offset = 32*v_im + l0;
-    const int y_offset = 64*v_im + l0;
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
+
+    const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const uint ix  = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const uint step = 8/K_QUANTS_PER_ITERATION;             // 8 or 4
+
+    const uint il = tid/step;                               // 0...3
+    const uint ir = tid - step*il;                          // 0...7 or 0...3
+    const uint n =  2 * K_QUANTS_PER_ITERATION;             // 2 or 4
+
+    const uint v_im = il / 2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const uint v_in = il % 2;
+
+    const uint l0 = n * (2 * ir + v_in);            // 0...15
+    const uint q_offset = 32*v_im + l0;
+    const uint y_offset = 64*v_im + l0;
 
     tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
 
-    [[unroll]] for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-        const int y1_idx = i * QUANT_K + y_offset;
-        const int y2_idx = y1_idx + 128;
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+        const uint y1_idx = i * QUANT_K + y_offset;
+        const uint y2_idx = y1_idx + 128;
 
         const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x);
         const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y);
@@ -1022,15 +1571,15 @@ void main() {
         const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 66]  >> 4);
         const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 67]  >> 4);
 
-        const FLOAT_TYPE sx = FLOAT_TYPE(data_b[p.b_offset + y1_idx] * q4_0 + data_b[p.b_offset + y1_idx + 1] * q4_1 + data_b[p.b_offset + y1_idx + 2] * q4_2 + data_b[p.b_offset + y1_idx + 3] * q4_3);
-        const FLOAT_TYPE sy = FLOAT_TYPE(data_b[p.b_offset + y1_idx + 32] * q4_4 + data_b[p.b_offset + y1_idx + 33] * q4_5 + data_b[p.b_offset + y1_idx + 34] * q4_6 + data_b[p.b_offset + y1_idx + 35] * q4_7);
-        const FLOAT_TYPE sz = FLOAT_TYPE(data_b[p.b_offset + y2_idx] * q4_8 + data_b[p.b_offset + y2_idx + 1] * q4_9 + data_b[p.b_offset + y2_idx + 2] * q4_10 + data_b[p.b_offset + y2_idx + 3] * q4_11);
-        const FLOAT_TYPE sw = FLOAT_TYPE(data_b[p.b_offset + y2_idx + 32] * q4_12 + data_b[p.b_offset + y2_idx + 33] * q4_13 + data_b[p.b_offset + y2_idx + 34] * q4_14 + data_b[p.b_offset + y2_idx + 35] * q4_15);
+        const FLOAT_TYPE sx = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx]) * q4_0 + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * q4_1 + FLOAT_TYPE(data_b[b_offset + y1_idx + 2]) * q4_2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 3]) * q4_3);
+        const FLOAT_TYPE sy = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * q4_4 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * q4_5 + FLOAT_TYPE(data_b[b_offset + y1_idx + 34]) * q4_6 + FLOAT_TYPE(data_b[b_offset + y1_idx + 35]) * q4_7);
+        const FLOAT_TYPE sz = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx]) * q4_8 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * q4_9 + FLOAT_TYPE(data_b[b_offset + y2_idx + 2]) * q4_10 + FLOAT_TYPE(data_b[b_offset + y2_idx + 3]) * q4_11);
+        const FLOAT_TYPE sw = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * q4_12 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * q4_13 + FLOAT_TYPE(data_b[b_offset + y2_idx + 34]) * q4_14 + FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * q4_15);
         const FLOAT_TYPE smin = FLOAT_TYPE(
-            data_b[p.b_offset + y1_idx    ] * sc2 + data_b[p.b_offset + y1_idx + 32] * sc3 + data_b[p.b_offset + y2_idx    ] * sc6 + data_b[p.b_offset + y2_idx + 32] * sc7
-          + data_b[p.b_offset + y1_idx + 1] * sc2 + data_b[p.b_offset + y1_idx + 33] * sc3 + data_b[p.b_offset + y2_idx + 1] * sc6 + data_b[p.b_offset + y2_idx + 33] * sc7
-          + data_b[p.b_offset + y1_idx + 2] * sc2 + data_b[p.b_offset + y1_idx + 34] * sc3 + data_b[p.b_offset + y2_idx + 2] * sc6 + data_b[p.b_offset + y2_idx + 34] * sc7
-          + data_b[p.b_offset + y1_idx + 3] * sc2 + data_b[p.b_offset + y1_idx + 35] * sc3 + data_b[p.b_offset + y2_idx + 3] * sc6 + data_b[p.b_offset + y2_idx + 35] * sc7
+            FLOAT_TYPE(data_b[b_offset + y1_idx    ]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx    ]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * sc7
+          + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * sc7
+          + FLOAT_TYPE(data_b[b_offset + y1_idx + 2]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 34]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 2]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 34]) * sc7
+          + FLOAT_TYPE(data_b[b_offset + y1_idx + 3]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 35]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 3]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * sc7
         );
         tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * sc0 + sy * sc1 + sz * sc4 + sw * sc5) - dmin * smin);
 #else
@@ -1043,13 +1592,13 @@ void main() {
         const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 64]  >> 4);
         const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 65]  >> 4);
 
-        const FLOAT_TYPE sx = FLOAT_TYPE(data_b[p.b_offset + y1_idx     ] * q4_0  + data_b[p.b_offset + y1_idx +  1] * q4_1);
-        const FLOAT_TYPE sy = FLOAT_TYPE(data_b[p.b_offset + y1_idx + 32] * q4_2  + data_b[p.b_offset + y1_idx + 33] * q4_3);
-        const FLOAT_TYPE sz = FLOAT_TYPE(data_b[p.b_offset + y2_idx     ] * q4_4  + data_b[p.b_offset + y2_idx +  1] * q4_5);
-        const FLOAT_TYPE sw = FLOAT_TYPE(data_b[p.b_offset + y2_idx + 32] * q4_6 + data_b[p.b_offset + y2_idx + 33] * q4_7);
+        const FLOAT_TYPE sx = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx     ]) * q4_0  + FLOAT_TYPE(data_b[b_offset + y1_idx +  1]) * q4_1);
+        const FLOAT_TYPE sy = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * q4_2  + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * q4_3);
+        const FLOAT_TYPE sz = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx     ]) * q4_4  + FLOAT_TYPE(data_b[b_offset + y2_idx +  1]) * q4_5);
+        const FLOAT_TYPE sw = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * q4_6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * q4_7);
         const FLOAT_TYPE smin = FLOAT_TYPE(
-            data_b[p.b_offset + y1_idx] * sc2 + data_b[p.b_offset + y1_idx + 32] * sc3 + data_b[p.b_offset + y2_idx] * sc6 + data_b[p.b_offset + y2_idx + 32] * sc7
-          + data_b[p.b_offset + y1_idx + 1] * sc2 + data_b[p.b_offset + y1_idx + 33] * sc3 + data_b[p.b_offset + y2_idx + 1] * sc6 + data_b[p.b_offset + y2_idx + 33] * sc7
+            FLOAT_TYPE(data_b[b_offset + y1_idx]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * sc7
+          + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * sc7
         );
 
         tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * FLOAT_TYPE(data_a[ib0 + i].scales[v_im] & 0x3f) + sy * FLOAT_TYPE(data_a[ib0 + i].scales[v_im + 1] & 0x3f) + sz * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 4] & 0x0f) | ((data_a[ib0 + i].scales[v_im] & 0xc0) >> 2)) + sw * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 5] & 0x0f) | ((data_a[ib0 + i].scales[v_im + 1] & 0xc0) >> 2))) - dmin * smin);
@@ -1058,60 +1607,84 @@ void main() {
 
     // sum up partial sums and write back result
     barrier();
-    [[unroll]] for (int s = 16; s > 0; s >>= 1) {
+    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
         if (tid < s) {
             tmp[tid] += tmp[tid + s];
         }
         barrier();
     }
     if (tid == 0) {
-        dst[p.d_offset + row] = D_TYPE(tmp[0]);
+        data_d[d_offset + row] = D_TYPE(tmp[0]);
     }
 }
 """
 mul_mat_vec_q5_K_body = """
 layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
 
-layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
-layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
-
-layout (push_constant) uniform parameter
-{
-    int ncols;
-    int b_offset;
-    int d_offset;
-} p;
-
 shared FLOAT_TYPE tmp[32];
 
 void main() {
-    const int row = int(gl_WorkGroupID.x);
+    const uint row = gl_WorkGroupID.x;
+    const uint batch_idx = gl_GlobalInvocationID.y;
+#ifdef MUL_MAT_ID
+    const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0;
+    const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0;
+#endif
 
-    const int num_blocks_per_row = p.ncols / QUANT_K;
-    const int ib0 = row*num_blocks_per_row;
+    const uint i13 = batch_idx / p.ne12;
+    const uint i12 = batch_idx % p.ne12;
 
-    const int tid = int(gl_LocalInvocationID.x)/2;  // 0...31 or 0...16
-    const int ix  = int(gl_LocalInvocationID.x)%2;  // 0 or 0, 1
+    const uint i03 = i13 / p.broadcast3;
+    const uint i02 = i12 / p.broadcast2;
 
-    const int il = tid/4;                           // 0...3
-    const int ir = tid - 4*il;                      // 0...7 or 0...3
+    const uint batch_idx_a = i03 * p.ne02 + i02;
 
-    const int v_im = il / 2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
-    const int v_in = il % 2;
+#ifdef MUL_MAT_ID
+    const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0];
+#endif
 
-    const int l0 = 4*ir + 2*v_in;                   // 0...15
-    const int q_offset = 32*v_im + l0;
-    const int y_offset = 64*v_im + l0;
+    const uint a_offset =
+#ifdef MUL_MAT_ID
+            expert_id * p.expert_stride_a +
+#endif
+            batch_idx_a * p.batch_stride_a;
+    const uint b_offset =
+#ifdef MUL_MAT_ID
+            (expert_idx0 % p.ne11) * p.expert_stride_b0 +
+            expert_idx1 * p.expert_stride_b1 +
+#endif
+            batch_idx * p.batch_stride_b;
+    const uint d_offset =
+#ifdef MUL_MAT_ID
+            expert_idx0 * p.expert_stride_b0 +
+            expert_idx1 * p.expert_stride_b1 +
+#endif
+            batch_idx * p.batch_stride_d;
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
+
+    const uint tid = gl_LocalInvocationID.x/2;  // 0...31 or 0...16
+    const uint ix  = gl_LocalInvocationID.x%2;  // 0 or 0, 1
+
+    const uint il = tid/4;                           // 0...3
+    const uint ir = tid - 4*il;                      // 0...7 or 0...3
+
+    const uint v_im = il / 2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const uint v_in = il % 2;
+
+    const uint l0 = 4*ir + 2*v_in;                   // 0...15
+    const uint q_offset = 32*v_im + l0;
+    const uint y_offset = 64*v_im + l0;
 
     const uint8_t hm1 = uint8_t(1 << (2*v_im));
     const uint8_t hm2 = uint8_t(hm1 << 4);
 
     tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
 
-    [[unroll]] for (int i = ix; i < num_blocks_per_row; i += 2) {
-        const int y1_idx = i * QUANT_K + y_offset;
-        const int y2_idx = y1_idx + 128;
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += 2) {
+        const uint y1_idx = i * QUANT_K + y_offset;
+        const uint y2_idx = y1_idx + 128;
 
         const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x);
         const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y);
@@ -1143,116 +1716,140 @@ void main() {
         const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 81]  >> 4);
 
         const FLOAT_TYPE sx = FLOAT_TYPE(
-            data_b[p.b_offset + y1_idx     ] * (q4_0 + (((data_a[ib0 + i].qh[l0     ] & hm1) != 0) ? 16 : 0))
-          + data_b[p.b_offset + y1_idx +  1] * (q4_1 + (((data_a[ib0 + i].qh[l0 +  1] & hm1) != 0) ? 16 : 0))
-          + data_b[p.b_offset + y1_idx + 16] * (q4_2 + (((data_a[ib0 + i].qh[l0 + 16] & hm1) != 0) ? 16 : 0))
-          + data_b[p.b_offset + y1_idx + 17] * (q4_3 + (((data_a[ib0 + i].qh[l0 + 17] & hm1) != 0) ? 16 : 0))
+            FLOAT_TYPE(data_b[b_offset + y1_idx     ]) * (q4_0 + (((data_a[ib0 + i].qh[l0     ] & hm1) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y1_idx +  1]) * (q4_1 + (((data_a[ib0 + i].qh[l0 +  1] & hm1) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y1_idx + 16]) * (q4_2 + (((data_a[ib0 + i].qh[l0 + 16] & hm1) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y1_idx + 17]) * (q4_3 + (((data_a[ib0 + i].qh[l0 + 17] & hm1) != 0) ? 16 : 0))
         );
         const FLOAT_TYPE sy = FLOAT_TYPE(
-            data_b[p.b_offset + y1_idx + 32] * (q4_4 + (((data_a[ib0 + i].qh[l0     ] & (hm1 << 1)) != 0) ? 16 : 0))
-          + data_b[p.b_offset + y1_idx + 33] * (q4_5 + (((data_a[ib0 + i].qh[l0 +  1] & (hm1 << 1)) != 0) ? 16 : 0))
-          + data_b[p.b_offset + y1_idx + 48] * (q4_6 + (((data_a[ib0 + i].qh[l0 + 16] & (hm1 << 1)) != 0) ? 16 : 0))
-          + data_b[p.b_offset + y1_idx + 49] * (q4_7 + (((data_a[ib0 + i].qh[l0 + 17] & (hm1 << 1)) != 0) ? 16 : 0))
+            FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * (q4_4 + (((data_a[ib0 + i].qh[l0     ] & (hm1 << 1)) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * (q4_5 + (((data_a[ib0 + i].qh[l0 +  1] & (hm1 << 1)) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y1_idx + 48]) * (q4_6 + (((data_a[ib0 + i].qh[l0 + 16] & (hm1 << 1)) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y1_idx + 49]) * (q4_7 + (((data_a[ib0 + i].qh[l0 + 17] & (hm1 << 1)) != 0) ? 16 : 0))
         );
         const FLOAT_TYPE sz = FLOAT_TYPE(
-            data_b[p.b_offset + y2_idx     ] * (q4_8  + (((data_a[ib0 + i].qh[l0     ] & hm2) != 0) ? 16 : 0))
-          + data_b[p.b_offset + y2_idx +  1] * (q4_9  + (((data_a[ib0 + i].qh[l0 +  1] & hm2) != 0) ? 16 : 0))
-          + data_b[p.b_offset + y2_idx + 16] * (q4_10 + (((data_a[ib0 + i].qh[l0 + 16] & hm2) != 0) ? 16 : 0))
-          + data_b[p.b_offset + y2_idx + 17] * (q4_11 + (((data_a[ib0 + i].qh[l0 + 17] & hm2) != 0) ? 16 : 0))
+            FLOAT_TYPE(data_b[b_offset + y2_idx     ]) * (q4_8  + (((data_a[ib0 + i].qh[l0     ] & hm2) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y2_idx +  1]) * (q4_9  + (((data_a[ib0 + i].qh[l0 +  1] & hm2) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y2_idx + 16]) * (q4_10 + (((data_a[ib0 + i].qh[l0 + 16] & hm2) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y2_idx + 17]) * (q4_11 + (((data_a[ib0 + i].qh[l0 + 17] & hm2) != 0) ? 16 : 0))
         );
         const FLOAT_TYPE sw = FLOAT_TYPE(
-            data_b[p.b_offset + y2_idx + 32] * (q4_12 + (((data_a[ib0 + i].qh[l0     ] & (hm2 << 1)) != 0) ? 16 : 0))
-          + data_b[p.b_offset + y2_idx + 33] * (q4_13 + (((data_a[ib0 + i].qh[l0 +  1] & (hm2 << 1)) != 0) ? 16 : 0))
-          + data_b[p.b_offset + y2_idx + 48] * (q4_14 + (((data_a[ib0 + i].qh[l0 + 16] & (hm2 << 1)) != 0) ? 16 : 0))
-          + data_b[p.b_offset + y2_idx + 49] * (q4_15 + (((data_a[ib0 + i].qh[l0 + 17] & (hm2 << 1)) != 0) ? 16 : 0))
+            FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * (q4_12 + (((data_a[ib0 + i].qh[l0     ] & (hm2 << 1)) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * (q4_13 + (((data_a[ib0 + i].qh[l0 +  1] & (hm2 << 1)) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y2_idx + 48]) * (q4_14 + (((data_a[ib0 + i].qh[l0 + 16] & (hm2 << 1)) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y2_idx + 49]) * (q4_15 + (((data_a[ib0 + i].qh[l0 + 17] & (hm2 << 1)) != 0) ? 16 : 0))
         );
         const FLOAT_TYPE smin = FLOAT_TYPE(
-            (data_b[p.b_offset + y1_idx] + data_b[p.b_offset + y1_idx + 1] + data_b[p.b_offset + y1_idx + 16] + data_b[p.b_offset + y1_idx + 17]) * sc2 + (data_b[p.b_offset + y1_idx + 32] + data_b[p.b_offset + y1_idx + 33] + data_b[p.b_offset + y1_idx + 48] + data_b[p.b_offset + y1_idx + 49]) * sc3
-          + (data_b[p.b_offset + y2_idx] + data_b[p.b_offset + y2_idx + 1] + data_b[p.b_offset + y2_idx + 16] + data_b[p.b_offset + y2_idx + 17]) * sc6 + (data_b[p.b_offset + y2_idx + 32] + data_b[p.b_offset + y2_idx + 33] + data_b[p.b_offset + y2_idx + 48] + data_b[p.b_offset + y2_idx + 49]) * sc7
+            (FLOAT_TYPE(data_b[b_offset + y1_idx]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 17])) * sc2 + (FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 49])) * sc3
+          + (FLOAT_TYPE(data_b[b_offset + y2_idx]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 17])) * sc6 + (FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 49])) * sc7
         );
         tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * sc0 + sy * sc1 + sz * sc4 + sw * sc5) - dmin * smin);
     }
 
     // sum up partial sums and write back result
     barrier();
-    [[unroll]] for (int s = 16; s > 0; s >>= 1) {
+    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
         if (tid < s) {
             tmp[tid] += tmp[tid + s];
         }
         barrier();
     }
     if (tid == 0) {
-        dst[p.d_offset + row] = D_TYPE(tmp[0]);
+        data_d[d_offset + row] = D_TYPE(tmp[0]);
     }
 }
 """
 mul_mat_vec_q6_K_body = """
 layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
 
-layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
-layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
-
-layout (push_constant) uniform parameter
-{
-    int ncols;
-    int b_offset;
-    int d_offset;
-} p;
-
 shared FLOAT_TYPE tmp[32];
 
 void main() {
-    const int row = int(gl_WorkGroupID.x);
-
-    const int num_blocks_per_row = p.ncols / QUANT_K;
-    const int ib0 = row*num_blocks_per_row;
-
-    const int tid = int(gl_LocalInvocationID.x)/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
-    const int ix  = int(gl_LocalInvocationID.x)%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
-
-    const int step = 16/K_QUANTS_PER_ITERATION;            // 16 or 8
-
-    const int v_im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
-    const int v_in = tid - step*v_im;                      // 0...15 or 0...7
-
-#if K_QUANTS_PER_ITERATION == 1
-    const int l0 = K_QUANTS_PER_ITERATION*v_in;            // 0...15
-    const int is = 0;
-#else
-    const int l0 = 4 * v_in;                               // 0, 4, 8, ..., 28
-    const int is = v_in / 4;
+    const uint row = gl_WorkGroupID.x;
+    const uint batch_idx = gl_GlobalInvocationID.y;
+#ifdef MUL_MAT_ID
+    const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0;
+    const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0;
 #endif
 
-    const int ql_offset = 64*v_im + l0;
-    const int qh_offset = 32*v_im + l0;
-    const int s_offset  =  8*v_im + is;
-    const int y_offset = 128*v_im + l0;
+    const uint i13 = batch_idx / p.ne12;
+    const uint i12 = batch_idx % p.ne12;
+
+    const uint i03 = i13 / p.broadcast3;
+    const uint i02 = i12 / p.broadcast2;
+
+    const uint batch_idx_a = i03 * p.ne02 + i02;
+
+#ifdef MUL_MAT_ID
+    const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0];
+#endif
+
+    const uint a_offset =
+#ifdef MUL_MAT_ID
+            expert_id * p.expert_stride_a +
+#endif
+            batch_idx_a * p.batch_stride_a;
+    const uint b_offset =
+#ifdef MUL_MAT_ID
+            (expert_idx0 % p.ne11) * p.expert_stride_b0 +
+            expert_idx1 * p.expert_stride_b1 +
+#endif
+            batch_idx * p.batch_stride_b;
+    const uint d_offset =
+#ifdef MUL_MAT_ID
+            expert_idx0 * p.expert_stride_b0 +
+            expert_idx1 * p.expert_stride_b1 +
+#endif
+            batch_idx * p.batch_stride_d;
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
+
+    const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const uint ix  = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const uint step = 16/K_QUANTS_PER_ITERATION;            // 16 or 8
+
+    const uint v_im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const uint v_in = tid - step*v_im;                      // 0...15 or 0...7
+
+#if K_QUANTS_PER_ITERATION == 1
+    const uint l0 = v_in;                                   // 0...15
+    const uint is = 0;
+#else
+    const uint l0 = 4 * v_in;                               // 0, 4, 8, ..., 28
+    const uint is = v_in / 4;
+#endif
+
+    const uint ql_offset = 64*v_im + l0;
+    const uint qh_offset = 32*v_im + l0;
+    const uint s_offset  =  8*v_im + is;
+    const uint y_offset = 128*v_im + l0;
 
     tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
 
-    [[unroll]] for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-        const int y_idx    = i * QUANT_K + y_offset;
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+        const uint y_idx   = i * QUANT_K + y_offset;
 
         const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
 
 #if K_QUANTS_PER_ITERATION == 1
-        FLOAT_TYPE sum = FLOAT_TYPE(data_b[p.b_offset + y_idx +  0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset +  0] & 0xF) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0x03) << 4)) - 32)
-                       + FLOAT_TYPE(data_b[p.b_offset + y_idx + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x03) << 4)) - 32)
-                       + FLOAT_TYPE(data_b[p.b_offset + y_idx + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] & 0xF) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0x0c) << 2)) - 32)
-                       + FLOAT_TYPE(data_b[p.b_offset + y_idx + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x0c) << 2)) - 32)
-                       + FLOAT_TYPE(data_b[p.b_offset + y_idx + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset +  0]  >> 4) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0x30) >> 0)) - 32)
-                       + FLOAT_TYPE(data_b[p.b_offset + y_idx + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16]  >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x30) >> 0)) - 32)
-                       + FLOAT_TYPE(data_b[p.b_offset + y_idx + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32]  >> 4) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0xc0) >> 2)) - 32)
-                       + FLOAT_TYPE(data_b[p.b_offset + y_idx +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48]  >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0xc0) >> 2)) - 32);
+        FLOAT_TYPE sum = FLOAT_TYPE(data_b[b_offset + y_idx +  0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset +  0] & 0xF) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0x03) << 4)) - 32)
+                       + FLOAT_TYPE(data_b[b_offset + y_idx + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x03) << 4)) - 32)
+                       + FLOAT_TYPE(data_b[b_offset + y_idx + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] & 0xF) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0x0c) << 2)) - 32)
+                       + FLOAT_TYPE(data_b[b_offset + y_idx + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x0c) << 2)) - 32)
+                       + FLOAT_TYPE(data_b[b_offset + y_idx + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset +  0]  >> 4) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0x30) >> 0)) - 32)
+                       + FLOAT_TYPE(data_b[b_offset + y_idx + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16]  >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x30) >> 0)) - 32)
+                       + FLOAT_TYPE(data_b[b_offset + y_idx + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32]  >> 4) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0xc0) >> 2)) - 32)
+                       + FLOAT_TYPE(data_b[b_offset + y_idx +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48]  >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0xc0) >> 2)) - 32);
         tmp[16 * ix + tid] += sum;
 #else
         FLOAT_TYPE sum = FLOAT_TYPE(0.0);
         [[unroll]] for (int l = 0; l < 4; ++l) {
-            sum += FLOAT_TYPE(data_b[p.b_offset + y_idx + l+ 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 0) & 3) << 4)) - 32)
-                 + FLOAT_TYPE(data_b[p.b_offset + y_idx + l+32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 2) & 3) << 4)) - 32)
-                 + FLOAT_TYPE(data_b[p.b_offset + y_idx + l+64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0]  >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 4) & 3) << 4)) - 32)
-                 + FLOAT_TYPE(data_b[p.b_offset + y_idx + l+96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32]  >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 6) & 3) << 4)) - 32);
+            sum += FLOAT_TYPE(data_b[b_offset + y_idx + l+ 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 0) & 3) << 4)) - 32)
+                 + FLOAT_TYPE(data_b[b_offset + y_idx + l+32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 2) & 3) << 4)) - 32)
+                 + FLOAT_TYPE(data_b[b_offset + y_idx + l+64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0]  >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 4) & 3) << 4)) - 32)
+                 + FLOAT_TYPE(data_b[b_offset + y_idx + l+96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32]  >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 6) & 3) << 4)) - 32);
         }
         tmp[16 * ix + tid] += sum;
 #endif
@@ -1260,14 +1857,14 @@ void main() {
 
     // sum up partial sums and write back result
     barrier();
-    [[unroll]] for (int s = 16; s > 0; s >>= 1) {
+    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
         if (tid < s) {
             tmp[tid] += tmp[tid + s];
        }
         barrier();
     }
     if (tid == 0) {
-        dst[p.d_offset + row] = D_TYPE(tmp[0]);
+        data_d[d_offset + row] = D_TYPE(tmp[0]);
     }
 }
 """
@@ -1421,34 +2018,6 @@ void main() {
 }
 """
 
-# F16 to F32
-f32_to_f16_src = """#version 450
-
-#extension GL_EXT_shader_16bit_storage : require
-
-layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {float data_a[];};
-layout (binding = 1) writeonly buffer D {float16_t data_b[];};
-
-layout (push_constant) uniform parameter
-{
-    int M;
-    int K;
-    int stride_a;
-    int stride_b;
-} p;
-
-void main() {
-    const int row = int(gl_GlobalInvocationID.x % p.K);
-    const int col = int(gl_GlobalInvocationID.x / p.K);
-
-    if (row < p.K && col < p.M) {
-        data_b[col * p.stride_b + row] = float16_t(data_a[col * p.stride_a + row]);
-    }
-}
-"""
-
 generic_head = """
 #version 450
 
@@ -1463,173 +2032,210 @@ layout (push_constant) uniform parameter
 } p;
 """
 
-# MUL F32
-mul_body = """layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer Y {B_TYPE data_b[];};
-layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint idx = gl_GlobalInvocationID.x;
-
-    if (idx >= p.KX) {
-        return;
-    }
-
-    data_d[idx] = D_TYPE(FLOAT_TYPE(data_a[idx]) * FLOAT_TYPE(data_b[idx % p.KY]));
-}
-"""
-
-# ADD
-add_body = """
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer Y {B_TYPE data_b[];};
-layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint idx = gl_GlobalInvocationID.x;
-
-    if (idx >= p.KX) {
-        return;
-    }
-
-    data_d[idx] = D_TYPE(FLOAT_TYPE(data_a[idx]) + FLOAT_TYPE(data_b[idx % p.KY]));
-}
-"""
-
-# SCALE
-scale_body = """layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint idx = gl_GlobalInvocationID.x;
-
-    if (idx >= p.KX) {
-        return;
-    }
-
-    data_d[idx] = D_TYPE(FLOAT_TYPE(data_a[idx]) * FLOAT_TYPE(p.param1));
-}
-"""
-
-# SQR
-sqr_body = """layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint idx = gl_GlobalInvocationID.x;
-
-    if (idx >= p.KX) {
-        return;
-    }
-
-    const FLOAT_TYPE val = FLOAT_TYPE(data_a[idx]);
-    data_d[idx] = D_TYPE(val * val);
-}
-"""
-
-# CLAMP
-clamp_body = """layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint idx = gl_GlobalInvocationID.x;
-
-    if (idx >= p.KX) {
-        return;
-    }
-
-    const FLOAT_TYPE val = FLOAT_TYPE(data_a[idx]);
-    data_d[idx] = D_TYPE(val < p.param1 ? p.param1 : (val > p.param2 ? p.param2 : val));
-}
-"""
-
-# CPY
-cpy_src = """#version 450
+generic_unary_op_head = """#version 450
 
 #extension GL_EXT_shader_16bit_storage : require
 
 layout (push_constant) uniform parameter
 {
     uint ne;
-    uint ne00; uint ne01; uint nb00; uint nb01; uint nb02;
-    uint ne10; uint ne11; uint nb10; uint nb11; uint nb12;
+    uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03;
+    uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13;
     uint d_offset;
-} p;
+    float param1; float param2;
+} p;"""
 
+generic_unary_op_layout = """
 layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
 
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};"""
 
+generic_unary_op_funcs = """
+uint src0_idx(uint idx) {
+    const uint i03 = idx / (p.ne02*p.ne01*p.ne00);
+    const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
+    const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00);
+    const uint i02_offset = i02*p.ne01*p.ne00;
+    const uint i01 = (idx - i03_offset - i02_offset) / p.ne00;
+    const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
+    return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00;
+}
+
+uint dst_idx(uint idx) {
+    const uint i13 = idx / (p.ne12*p.ne11*p.ne10);
+    const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10;
+    const uint i12 = (idx - i13_offset) / (p.ne11*p.ne10);
+    const uint i12_offset = i12*p.ne11*p.ne10;
+    const uint i11 = (idx - i13_offset - i12_offset) / p.ne10;
+    const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10;
+    return i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + i10*p.nb10;
+}"""
+
+generic_unary_op_main = """
 void main() {
     if (gl_GlobalInvocationID.x >= p.ne) {
         return;
     }
-
-    const uint i02 = gl_GlobalInvocationID.x / (p.ne00*p.ne01);
-    const uint i01 = (gl_GlobalInvocationID.x - i02*p.ne01*p.ne00) / p.ne00;
-    const uint i00 = gl_GlobalInvocationID.x - i02*p.ne01*p.ne00 - i01*p.ne00;
-    const uint a_idx = i00*p.nb00 + i01*p.nb01 + i02*p.nb02;
-
-    const uint i12 = gl_GlobalInvocationID.x / (p.ne10*p.ne11);
-    const uint i11 = (gl_GlobalInvocationID.x - i12*p.ne11*p.ne10) / p.ne10;
-    const uint i10 = gl_GlobalInvocationID.x - i12*p.ne11*p.ne10 - i11*p.ne10;
-    const uint d_idx = i10*p.nb10 + i11*p.nb11 + i12*p.nb12;
 """
+
+generic_unary_op_combined = f"{generic_unary_op_head}\n{generic_unary_op_layout}\n{generic_unary_op_funcs}\n{generic_unary_op_main}"
+
+generic_binary_op_head = """#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint ne;
+    uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03;
+    uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13;
+    uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23;
+    uint d_offset;
+    float param1; float param2;
+} p;"""
+
+generic_binary_op_layout = """
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};"""
+
+generic_binary_op_funcs = """
+uint src0_idx(uint idx) {
+    const uint i03 = idx / (p.ne02*p.ne01*p.ne00);
+    const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
+    const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00);
+    const uint i02_offset = i02*p.ne01*p.ne00;
+    const uint i01 = (idx - i03_offset - i02_offset) / p.ne00;
+    const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
+    return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00;
+}
+
+uint src1_idx(uint idx) {
+    const uint i03 = idx / (p.ne02*p.ne01*p.ne00);
+    const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
+    const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00);
+    const uint i02_offset = i02*p.ne01*p.ne00;
+    const uint i01 = (idx - i03_offset - i02_offset) / p.ne00;
+    const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
+
+    return (i03 % p.ne13)*p.nb13 + (i02 % p.ne12)*p.nb12 + (i01 % p.ne11)*p.nb11 + (i00 % p.ne10)*p.nb10;
+}
+
+uint dst_idx(uint idx) {
+    const uint i23 = idx / (p.ne22*p.ne21*p.ne20);
+    const uint i23_offset = i23 * p.ne22*p.ne21*p.ne20;
+    const uint i22 = (idx - i23_offset) / (p.ne21*p.ne20);
+    const uint i22_offset = i22*p.ne21*p.ne20;
+    const uint i21 = (idx - i23_offset - i22_offset) / p.ne20;
+    const uint i20 = idx - i23_offset - i22_offset - i21*p.ne20;
+    return i23*p.nb23 + i22*p.nb22 + i21*p.nb21 + i20*p.nb20;
+}"""
+
+generic_binary_op_main = """
+void main() {
+    if (gl_GlobalInvocationID.x >= p.ne) {
+        return;
+    }
+"""
+
+generic_binary_op_combined = f"{generic_binary_op_head}\n{generic_binary_op_layout}\n{generic_binary_op_funcs}\n{generic_binary_op_main}"
+
+# MUL F32
+mul_body = """
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) * FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)]));
+}
+"""
+
+# ADD
+add_body = """
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) + FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)]));
+}
+"""
+
+# SCALE
+scale_body = """
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) * FLOAT_TYPE(p.param1));
+}
+"""
+
+# SQR
+sqr_body = """
+    const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]);
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(val * val);
+}
+"""
+
+# CLAMP
+clamp_body = """
+    const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]);
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(val < p.param1 ? p.param1 : (val > p.param2 ? p.param2 : val));
+}
+"""
+
+# CPY
 cpy_end = """
-    data_d[p.d_offset + d_idx] = D_TYPE(data_a[a_idx]);
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]);
 }
 """
 # Causes an optimization error otherwise
 cpy_f16_f16_end = """
-    data_d[p.d_offset + d_idx] = data_a[a_idx];
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = data_a[src0_idx(gl_GlobalInvocationID.x)];
 }
 """
 
 # GET_ROWS
-get_rows_body = """
-#extension GL_EXT_control_flow_attributes : enable
-#extension GL_EXT_shader_8bit_storage : require
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer Y {int data_b[];};
-layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
-
+get_rows_float_body = """
 void main() {
-    const uint col = int(gl_GlobalInvocationID.x) * 2;
-    const uint row = int(gl_GlobalInvocationID.y);
+    const uint i00 = gl_GlobalInvocationID.x;
+    const uint i10 = gl_GlobalInvocationID.y;
+    const uint i11 = (gl_GlobalInvocationID.z)/p.ne12;
+    const uint i12 = (gl_GlobalInvocationID.z)%p.ne12;
 
-    if (col >= p.KY) {
+    if (i00 >= p.ne00) {
         return;
     }
 
-    const uint r = uint(data_b[row]);
+    const uint i01 = data_b[i10*p.nb10 + i11*p.nb11 + i12*p.nb12];
 
-    // copy data_a[r*p.KY + col] to dst[row*p.KX + col]
-    const uint xi = r*p.KY + col;
-    const uint di = row*p.KY + col;
+    const uint a_offset = i01*p.nb01 + i11*p.nb02 + i12*p.nb03;
+    const uint d_offset = i10*p.nb21 + i11*p.nb22 + i12*p.nb23;
 
-    const uint ib = xi/QUANT_K; // block index
-    const uint iqs = (xi%QUANT_K)/QUANT_R; // quant index
-    const uint iybs = di - di%QUANT_K; // y block start index
+#ifndef OPTIMIZATION_ERROR_WORKAROUND
+    data_d[d_offset + i00] = D_TYPE(data_a[a_offset + i00]);
+#else
+    data_d[d_offset + i00] = data_a[a_offset + i00];
+#endif
+}
+"""
+
+get_rows_body = """
+void main() {
+    const uint i00 = (gl_GlobalInvocationID.x)*2;
+    const uint i10 = gl_GlobalInvocationID.y;
+    const uint i11 = (gl_GlobalInvocationID.z)/p.ne12;
+    const uint i12 = (gl_GlobalInvocationID.z)%p.ne12;
+
+    if (i00 >= p.ne00) {
+        return;
+    }
+
+    const uint i01 = data_b[i10*p.nb10 + i11*p.nb11 + i12*p.nb12];
+
+    const uint a_offset = i01*p.nb01 + i11*p.nb02 + i12*p.nb03;
+    const uint d_offset = i10*p.nb21 + i11*p.nb22 + i12*p.nb23;
+
+    const uint ib = a_offset + i00/QUANT_K; // block index
+    const uint iqs = (i00%QUANT_K)/QUANT_R; // quant index
+    const uint iybs = i00 - i00%QUANT_K; // dst block start index
     const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2;
 
-    DEQUANT_FUNC
+    vec2 v = dequantize(ib, iqs, 0);
 
-    dst[iybs + iqs + 0]        = D_TYPE(v.x);
-    dst[iybs + iqs + y_offset] = D_TYPE(v.y);
+    data_d[d_offset + iybs + iqs           ] = D_TYPE(v.x);
+    data_d[d_offset + iybs + iqs + y_offset] = D_TYPE(v.y);
 }
 """
 
@@ -1725,7 +2331,11 @@ void main() {
     }
 
     const uint i = row*p.ncols + col;
-    data_d[i] = D_TYPE(data_a[i] - float(uint(col > p.n_past + row % p.rows_per_channel) * 0xFFFFFFFF));
+    if (col > p.n_past + row % p.rows_per_channel) {
+        data_d[i] = D_TYPE(uintBitsToFloat(0xFF800000));
+    } else {
+        data_d[i] = D_TYPE(data_a[i]);
+    }
 }
 """
 
@@ -1745,8 +2355,6 @@ void main() {
     const uint row = gl_WorkGroupID.x;
     const uint tid = gl_LocalInvocationID.x;
 
-    const float eps = 1e-5f;
-
     sum[tid] = vec2(0.0f, 0.0f);
 
     [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
@@ -1766,7 +2374,7 @@ void main() {
 
     const float mean = sum[0].x / p.KX;
     const float var = sum[0].y / p.KX - mean * mean;
-    const float inv_std = inversesqrt(var + 1e-5f);
+    const float inv_std = inversesqrt(var + p.param1);
 
     [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
         data_d[row*p.KX + col] = D_TYPE((float(data_a[row*p.KX + col]) - mean) * inv_std);
@@ -1815,6 +2423,24 @@ void main() {
 """
 
 # SOFT_MAX
+soft_max_head = """
+#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint KX;
+    uint KY;
+    uint KZ;
+    float scale;
+    float max_bias;
+    float m0;
+    float m1;
+    uint n_head_log2;
+} p;
+"""
+
 soft_max_body = """
 #extension GL_EXT_control_flow_attributes : enable
 #define BLOCK_SIZE 512
@@ -1823,7 +2449,8 @@ layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
 
 layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
 layout (binding = 1) readonly buffer Y {B_TYPE data_b[];};
-layout (binding = 2) buffer D {D_TYPE data_d[];};
+layout (binding = 2) readonly buffer Z {C_TYPE data_c[];};
+layout (binding = 3) buffer D {D_TYPE data_d[];};
 
 shared FLOAT_TYPE vals[BLOCK_SIZE];
 
@@ -1832,11 +2459,23 @@ void main() {
     const uint rowx = gl_WorkGroupID.x;
     const uint rowy = rowx % p.KY;
 
+    float slope = 0.0f;
+
+    // ALiBi
+    if (p.max_bias > 0.0f) {
+        const uint h = rowx/p.KY; // head index
+
+        const float base = h < p.n_head_log2 ? p.m0 : p.m1;
+        const uint   exp  = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1;
+
+        slope = pow(base, exp);
+    }
+
     // Find max
     vals[tid] = uintBitsToFloat(0xFF800000);
 
     [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
-        vals[tid] = max(vals[tid], FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.param1 + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)));
+        vals[tid] = max(vals[tid], FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.scale + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) + (p.KZ > 0 ? slope * FLOAT_TYPE(data_c[col]) : 0.0f));
     }
 
     barrier();
@@ -1855,7 +2494,7 @@ void main() {
 
     [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
         const uint i = rowx * p.KX + col;
-        const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.param1 + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val);
+        const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val);
         vals[tid] += val;
         data_d[i] = D_TYPE(val);
     }
@@ -2028,6 +2667,65 @@ void main() {
 }
 """
 
+argsort_src = """
+#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout(local_size_x = 1024, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1)          buffer D {int data_d[];};
+
+layout (push_constant) uniform parameter {
+    uint ncols;
+    bool ascending;
+} p;
+
+void swap(uint idx0, uint idx1) {
+    int tmp = data_d[idx0];
+    data_d[idx0] = data_d[idx1];
+    data_d[idx1] = tmp;
+}
+
+void main() {
+    // bitonic sort
+    const int col = int(gl_LocalInvocationID.x);
+    const uint row = gl_WorkGroupID.y;
+
+    if (col >= p.ncols) {
+        return;
+    }
+
+    const uint a_idx = row * p.ncols;
+    const uint d_idx = row * p.ncols;
+
+    // initialize indices
+    if (col < p.ncols) {
+        data_d[col] = col;
+    }
+    barrier();
+
+    for (uint k = 2; k <= p.ncols; k *= 2) {
+        for (uint j = k / 2; j > 0; j /= 2) {
+            const uint ixj = col ^ j;
+            if (ixj > col) {
+                if ((col & k) == 0) {
+                    if (p.ascending ? data_a[a_idx + data_d[d_idx + col]] > data_a[a_idx + data_d[d_idx + ixj]] : data_a[a_idx + data_d[d_idx + col]] < data_a[a_idx + data_d[d_idx + ixj]]) {
+                        swap(d_idx + col, d_idx + ixj);
+                    }
+                } else {
+                    if (p.ascending ? data_a[a_idx + data_d[d_idx + col]] < data_a[a_idx + data_d[d_idx + ixj]] : data_a[a_idx + data_d[d_idx + col]] > data_a[a_idx + data_d[d_idx + ixj]]) {
+                        swap(d_idx + col, d_idx + ixj);
+                    }
+                }
+            }
+            barrier();
+        }
+    }
+}
+"""
+
 GLSLC = "glslc"
 
 VK_NUM_TYPES = 16
@@ -2103,7 +2801,7 @@ async def string_to_spv(name, code, defines, fp16=True):
 
         stdout, stderr = await proc.communicate()
 
-        print(" ".join(cmd))
+        logger.info(" ".join(cmd))
 
         if proc.returncode:
             raise RuntimeError(f"{name=} {f.name=} {stdout=} {stderr=}")
@@ -2112,7 +2810,7 @@ async def string_to_spv(name, code, defines, fp16=True):
 
         cmd.extend([f"-D{key}={value}" for key, value in defines.items()])
         code_with_lines = "\n".join([f"{i + 1}: {line}" for i, line in enumerate(preprocessed_code.splitlines())])
-        print(f"ERROR compiling {name}\n\n{code_with_lines}\n\n{error}")
+        logger.error(f"cannot compile {name}\n\n{code_with_lines}\n\n{error}")
         f.close()
         os.remove(f.name)
         sys.exit(proc.returncode)
@@ -2125,10 +2823,12 @@ async def string_to_spv(name, code, defines, fp16=True):
 
 
 async def main():
-    print("ggml_vulkan: Generating and compiling shaders to SPIR-V")
+    logger.info("ggml_vulkan: Generating and compiling shaders to SPIR-V")
 
     tasks = []
 
+    stream = []
+
     for fp16 in (False, True):
         # mulmat
         if fp16:
@@ -2142,28 +2842,128 @@ async def main():
             vec_type_f16 = "f16vec4"
             vec_type = "vec4"
 
-        stream = []
-        stream.extend((mulmat_head, shader_float_type, mulmat_body))
-        tasks.append(string_to_spv("matmul_f32_l", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_f32_m", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_f32_s", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_f32_aligned_l", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_f32_aligned_m", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_f32_aligned_s", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+        stream.clear()
+        stream.extend((mulmat_head, shader_float_type, mulmat_body1, mulmat_load_scalar, mulmat_body2))
+        tasks.append(string_to_spv("matmul_f32", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_f32_aligned", "".join(stream), {"LOAD_VEC_A": 1, "LOAD_VEC_B": load_vec, "A_TYPE": "float", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
 
-        tasks.append(string_to_spv("matmul_f16_l", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_f16_m", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_f16_s", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_f16_aligned_l", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_f16_aligned_m", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_f16_aligned_s", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_f16", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_f16_aligned", "".join(stream), {"LOAD_VEC_A": 1, "LOAD_VEC_B": load_vec, "A_TYPE": "float16_t", "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16))
 
-        tasks.append(string_to_spv("matmul_f16_f32_l", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_f16_f32_m", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_f16_f32_s", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_f16_f32_aligned_l", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_f16_f32_aligned_m", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_f16_f32_aligned_s", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_f16_f32", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_f16_f32_aligned", "".join(stream), {"LOAD_VEC_A": 1, "LOAD_VEC_B": load_vec, "A_TYPE": "float16_t", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_0_defines, mulmat_body1, mulmat_load_q4_0, mulmat_body2))
+        tasks.append(string_to_spv("matmul_q4_0_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q4_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_q4_0_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_1_defines, mulmat_body1, mulmat_load_q4_1, mulmat_body2))
+        tasks.append(string_to_spv("matmul_q4_1_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q4_1", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_q4_1_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_1", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_0_defines, mulmat_body1, mulmat_load_q5_0, mulmat_body2))
+        tasks.append(string_to_spv("matmul_q5_0_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q5_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_q5_0_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_1_defines, mulmat_body1, mulmat_load_q5_1, mulmat_body2))
+        tasks.append(string_to_spv("matmul_q5_1_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q5_1", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_q5_1_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_1", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q8_0_defines, mulmat_body1, mulmat_load_q8_0, mulmat_body2))
+        tasks.append(string_to_spv("matmul_q8_0_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q8_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_q8_0_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q8_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q2_K_defines, mulmat_body1, mulmat_load_q2_K, mulmat_body2))
+        tasks.append(string_to_spv("matmul_q2_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q2_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_q2_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q2_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q3_K_defines, mulmat_body1, mulmat_load_q3_K, mulmat_body2))
+        tasks.append(string_to_spv("matmul_q3_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q3_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_q3_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q3_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_K_defines, mulmat_body1, mulmat_load_q4_K, mulmat_body2))
+        tasks.append(string_to_spv("matmul_q4_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q4_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_q4_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_K_defines, mulmat_body1, mulmat_load_q5_K, mulmat_body2))
+        tasks.append(string_to_spv("matmul_q5_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q5_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_q5_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q6_K_defines, mulmat_body1, mulmat_load_q6_K, mulmat_body2))
+        tasks.append(string_to_spv("matmul_q6_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q6_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_q6_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q6_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        # MUL_MAT_ID
+        # stream.clear()
+        # stream.extend((mulmat_head, shader_float_type, mulmat_body1, mulmat_load_scalar, mulmat_body2))
+        # tasks.append(string_to_spv("matmul_id_f32", "".join(stream), {"MUL_MAT_ID": "1", "A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        # tasks.append(string_to_spv("matmul_id_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        # tasks.append(string_to_spv("matmul_id_f16", "".join(stream), {"MUL_MAT_ID": "1", "A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16))
+        # tasks.append(string_to_spv("matmul_id_f16_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16))
+
+        # tasks.append(string_to_spv("matmul_id_f16_f32", "".join(stream), {"MUL_MAT_ID": "1", "A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        # tasks.append(string_to_spv("matmul_id_f16_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        # stream.clear()
+        # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_0_defines, mulmat_body1, mulmat_load_q4_0, mulmat_body2))
+        # tasks.append(string_to_spv("matmul_id_q4_0_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q4_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        # tasks.append(string_to_spv("matmul_id_q4_0_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        # stream.clear()
+        # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_1_defines, mulmat_body1, mulmat_load_q4_1, mulmat_body2))
+        # tasks.append(string_to_spv("matmul_id_q4_1_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q4_1", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        # tasks.append(string_to_spv("matmul_id_q4_1_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_1", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        # stream.clear()
+        # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_0_defines, mulmat_body1, mulmat_load_q5_0, mulmat_body2))
+        # tasks.append(string_to_spv("matmul_id_q5_0_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q5_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        # tasks.append(string_to_spv("matmul_id_q5_0_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        # stream.clear()
+        # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_1_defines, mulmat_body1, mulmat_load_q5_1, mulmat_body2))
+        # tasks.append(string_to_spv("matmul_id_q5_1_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q5_1", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        # tasks.append(string_to_spv("matmul_id_q5_1_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_1", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        # stream.clear()
+        # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q8_0_defines, mulmat_body1, mulmat_load_q8_0, mulmat_body2))
+        # tasks.append(string_to_spv("matmul_id_q8_0_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q8_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        # tasks.append(string_to_spv("matmul_id_q8_0_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q8_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        # stream.clear()
+        # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q2_K_defines, mulmat_body1, mulmat_load_q2_K, mulmat_body2))
+        # tasks.append(string_to_spv("matmul_id_q2_k_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q2_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        # tasks.append(string_to_spv("matmul_id_q2_k_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q2_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        # stream.clear()
+        # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q3_K_defines, mulmat_body1, mulmat_load_q3_K, mulmat_body2))
+        # tasks.append(string_to_spv("matmul_id_q3_k_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q3_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        # tasks.append(string_to_spv("matmul_id_q3_k_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q3_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        # stream.clear()
+        # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_K_defines, mulmat_body1, mulmat_load_q4_K, mulmat_body2))
+        # tasks.append(string_to_spv("matmul_id_q4_k_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q4_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        # tasks.append(string_to_spv("matmul_id_q4_k_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        # stream.clear()
+        # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_K_defines, mulmat_body1, mulmat_load_q5_K, mulmat_body2))
+        # tasks.append(string_to_spv("matmul_id_q5_k_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q5_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        # tasks.append(string_to_spv("matmul_id_q5_k_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        # stream.clear()
+        # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q6_K_defines, mulmat_body1, mulmat_load_q6_K, mulmat_body2))
+        # tasks.append(string_to_spv("matmul_id_q6_k_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q6_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        # tasks.append(string_to_spv("matmul_id_q6_k_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q6_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
 
     # Shaders where precision is needed, so no fp16 version
 
@@ -2173,31 +2973,34 @@ async def main():
         stream.extend((mul_mat_vec_head, shader_int8_ext, shader_f32))
 
         if i == GGML_TYPE_F16:
-            stream.extend((shader_f16_defines, shader_f16_dequant_func, mul_mat_vec_body))
+            stream.extend((shader_f16_defines, mul_mat_vec_layout, shader_float_dequant_func, mul_mat_vec_body))
         elif i == GGML_TYPE_Q4_0:
-            stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, mul_mat_vec_body))
+            stream.extend((shader_q4_0_defines, mul_mat_vec_layout, shader_q4_0_dequant_func, mul_mat_vec_body))
         elif i == GGML_TYPE_Q4_1:
-            stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, mul_mat_vec_body))
+            stream.extend((shader_q4_1_defines, mul_mat_vec_layout, shader_q4_1_dequant_func, mul_mat_vec_body))
         elif i == GGML_TYPE_Q5_0:
-            stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, mul_mat_vec_body))
+            stream.extend((shader_q5_0_defines, mul_mat_vec_layout, shader_q5_0_dequant_func, mul_mat_vec_body))
         elif i == GGML_TYPE_Q5_1:
-            stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, mul_mat_vec_body))
+            stream.extend((shader_q5_1_defines, mul_mat_vec_layout, shader_q5_1_dequant_func, mul_mat_vec_body))
         elif i == GGML_TYPE_Q8_0:
-            stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, mul_mat_vec_body))
+            stream.extend((shader_q8_0_defines, mul_mat_vec_layout, shader_q8_0_dequant_func, mul_mat_vec_body))
         elif i == GGML_TYPE_Q2_K:
-            stream.extend((shader_q2_K_defines, mul_mat_vec_q2_K_body))
+            stream.extend((shader_q2_K_defines, mul_mat_vec_layout, mul_mat_vec_q2_K_body))
         elif i == GGML_TYPE_Q3_K:
-            stream.extend((shader_q3_K_defines, mul_mat_vec_q3_K_body))
+            stream.extend((shader_q3_K_defines, mul_mat_vec_layout, mul_mat_vec_q3_K_body))
         elif i == GGML_TYPE_Q4_K:
-            stream.extend((shader_q4_K_defines, mul_mat_vec_q4_K_body))
+            stream.extend((shader_q4_K_defines, mul_mat_vec_layout, mul_mat_vec_q4_K_body))
         elif i == GGML_TYPE_Q5_K:
-            stream.extend((shader_q5_K_defines, mul_mat_vec_q5_K_body))
+            stream.extend((shader_q5_K_defines, mul_mat_vec_layout, mul_mat_vec_q5_K_body))
         elif i == GGML_TYPE_Q6_K:
-            stream.extend((shader_q6_K_defines, mul_mat_vec_q6_K_body))
+            stream.extend((shader_q6_K_defines, mul_mat_vec_layout, mul_mat_vec_q6_K_body))
         else:
             continue
 
-        tasks.append(string_to_spv(f"mul_mat_vec_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION}))
+        tasks.append(string_to_spv(f"mul_mat_vec_{type_names[i]}_f32_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION}))
+        tasks.append(string_to_spv(f"mul_mat_vec_{type_names[i]}_f16_f32", "".join(stream), {"B_TYPE": "float16_t", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION}))
+
+        # tasks.append(string_to_spv(f"mul_mat_vec_id_{type_names[i]}_f32", "".join(stream), {"MUL_MAT_ID": "1", "B_TYPE": "float", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION}))
 
     # Dequant shaders
     for i in range(0, VK_NUM_TYPES):
@@ -2205,18 +3008,18 @@ async def main():
 
         stream.extend((dequant_head, shader_int8_ext, shader_f32))
 
-        if i == GGML_TYPE_F16:
-            stream.extend((shader_f16_defines,  shader_f16_dequant_func,  dequant_body))
+        if i == GGML_TYPE_F32:
+            stream.append(dequant_f32_body)
         elif i == GGML_TYPE_Q4_0:
-            stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, dequant_body))
+            stream.extend((shader_q4_0_defines, dequant_q4_0_body))
         elif i == GGML_TYPE_Q4_1:
-            stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, dequant_body))
+            stream.extend((shader_q4_1_defines, dequant_q4_1_body))
         elif i == GGML_TYPE_Q5_0:
-            stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, dequant_body))
+            stream.extend((shader_q5_0_defines, dequant_q5_0_body))
         elif i == GGML_TYPE_Q5_1:
-            stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, dequant_body))
+            stream.extend((shader_q5_1_defines, dequant_q5_1_body))
         elif i == GGML_TYPE_Q8_0:
-            stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, dequant_body))
+            stream.extend((shader_q8_0_defines, dequant_q8_0_body))
         elif i == GGML_TYPE_Q2_K:
             stream.extend((shader_q2_K_defines, dequant_q2_K_body))
         elif i == GGML_TYPE_Q3_K:
@@ -2232,30 +3035,35 @@ async def main():
 
         tasks.append(string_to_spv(f"dequant_{type_names[i]}", "".join(stream), {"D_TYPE": "float16_t"}))
 
-    tasks.append(string_to_spv("f32_to_f16", f32_to_f16_src, {}))
-
     # get_rows
     for i in range(0, VK_NUM_TYPES):
         stream.clear()
-        stream.extend((generic_head, shader_int8_ext, shader_f32))
+        stream.extend((generic_binary_op_head, shader_int8_ext, shader_f32))
+        optimization_workaround = False
 
-        if i == GGML_TYPE_F16:
-            stream.extend((shader_f16_defines,  shader_f16_dequant_func,  get_rows_body))
+        if i == GGML_TYPE_F32:
+            stream.extend((shader_f32_defines, generic_binary_op_layout, generic_binary_op_funcs, get_rows_float_body))
+        elif i == GGML_TYPE_F16:
+            stream.extend((shader_f16_defines, generic_binary_op_layout, generic_binary_op_funcs, get_rows_float_body))
+            optimization_workaround = True
         elif i == GGML_TYPE_Q4_0:
-            stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, get_rows_body))
+            stream.extend((shader_q4_0_defines, generic_binary_op_layout, shader_q4_0_dequant_func, generic_binary_op_funcs, get_rows_body))
         elif i == GGML_TYPE_Q4_1:
-            stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, get_rows_body))
+            stream.extend((shader_q4_1_defines, generic_binary_op_layout, shader_q4_1_dequant_func, generic_binary_op_funcs, get_rows_body))
         elif i == GGML_TYPE_Q5_0:
-            stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, get_rows_body))
+            stream.extend((shader_q5_0_defines, generic_binary_op_layout, shader_q5_0_dequant_func, generic_binary_op_funcs, get_rows_body))
         elif i == GGML_TYPE_Q5_1:
-            stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, get_rows_body))
+            stream.extend((shader_q5_1_defines, generic_binary_op_layout, shader_q5_1_dequant_func, generic_binary_op_funcs, get_rows_body))
         elif i == GGML_TYPE_Q8_0:
-            stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, get_rows_body))
+            stream.extend((shader_q8_0_defines, generic_binary_op_layout, shader_q8_0_dequant_func, generic_binary_op_funcs, get_rows_body))
         else:
             continue
 
-        tasks.append(string_to_spv(f"get_rows_{type_names[i]}", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float16_t"}))
-        tasks.append(string_to_spv(f"get_rows_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float"}))
+        if optimization_workaround:
+            tasks.append(string_to_spv(f"get_rows_{type_names[i]}", "".join(stream), {"B_TYPE": "int", "D_TYPE": "float16_t", "OPTIMIZATION_ERROR_WORKAROUND": "1"}))
+        else:
+            tasks.append(string_to_spv(f"get_rows_{type_names[i]}", "".join(stream), {"B_TYPE": "int", "D_TYPE": "float16_t"}))
+        tasks.append(string_to_spv(f"get_rows_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "int", "D_TYPE": "float"}))
 
     tasks.append(string_to_spv("mul_mat_vec_p021_f16_f32", mul_mat_p021_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}))
     tasks.append(string_to_spv("mul_mat_vec_nc_f16_f32", mul_mat_nc_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}))
@@ -2264,20 +3072,20 @@ async def main():
     tasks.append(string_to_spv("norm_f32", f"{generic_head}\n{shader_f32}\n{norm_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
     tasks.append(string_to_spv("rms_norm_f32", f"{generic_head}\n{shader_f32}\n{rms_norm_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
 
-    tasks.append(string_to_spv("cpy_f32_f32", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float"}))
-    tasks.append(string_to_spv("cpy_f32_f16", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float16_t"}))
-    tasks.append(string_to_spv("cpy_f16_f16", f"{cpy_src}\n{cpy_f16_f16_end}", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
+    tasks.append(string_to_spv("cpy_f32_f32", f"{generic_unary_op_combined}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("cpy_f32_f16", f"{generic_unary_op_combined}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float16_t"}))
+    tasks.append(string_to_spv("cpy_f16_f16", f"{generic_unary_op_combined}\n{cpy_f16_f16_end}", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
 
-    tasks.append(string_to_spv("add_f32", f"{generic_head}\n{shader_f32}\n{add_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("add_f32", f"{generic_binary_op_combined}\n{add_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
 
     tasks.append(string_to_spv("split_k_reduce", mulmat_split_k_reduce_src, {}))
-    tasks.append(string_to_spv("mul_f32", f"{generic_head}\n{shader_f32}\n{mul_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("mul_f32", f"{generic_binary_op_combined}\n{mul_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
 
-    tasks.append(string_to_spv("scale_f32", f"{generic_head}\n{shader_f32}\n{scale_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("scale_f32", f"{generic_unary_op_combined}\n{scale_body}", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
 
-    tasks.append(string_to_spv("sqr_f32", f"{generic_head}\n{shader_f32}\n{sqr_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("sqr_f32", f"{generic_unary_op_combined}\n{sqr_body}", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
 
-    tasks.append(string_to_spv("clamp_f32", f"{generic_head}\n{shader_f32}\n{clamp_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("clamp_f32", f"{generic_unary_op_combined}\n{clamp_body}", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
 
     tasks.append(string_to_spv("gelu_f32", f"{generic_head}\n{shader_f32}\n{gelu_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
     tasks.append(string_to_spv("silu_f32", f"{generic_head}\n{shader_f32}\n{silu_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
@@ -2285,7 +3093,8 @@ async def main():
 
     tasks.append(string_to_spv("diag_mask_inf_f32", f"{diag_mask_inf_head}\n{shader_f32}\n{diag_mask_inf_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
 
-    tasks.append(string_to_spv("soft_max_f32", f"{generic_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("soft_max_f32", f"{soft_max_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "C_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("soft_max_f32_f16", f"{soft_max_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float16_t", "C_TYPE": "float16_t", "D_TYPE": "float"}))
 
     tasks.append(string_to_spv("rope_f32", rope_src, {"A_TYPE": "float", "D_TYPE": "float"}))
     tasks.append(string_to_spv("rope_f16", rope_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
@@ -2293,6 +3102,8 @@ async def main():
     tasks.append(string_to_spv("rope_neox_f32", rope_neox_src, {"A_TYPE": "float", "D_TYPE": "float"}))
     tasks.append(string_to_spv("rope_neox_f16", rope_neox_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
 
+    tasks.append(string_to_spv("argsort_f32", argsort_src, {"A_TYPE": "float"}))
+
     # Helper to decorate tasks with semaphore acquisition.
     async def withSemaphore(sem, task):
         async with sem:
@@ -2326,9 +3137,12 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="GGML Vulkan Shader Generator")
 
     parser.add_argument("--glslc", help="Path to glslc")
+    parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
 
     args = parser.parse_args()
 
+    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
+
     if args.glslc:
         GLSLC = args.glslc
 
diff --git a/gguf-py/README.md b/gguf-py/README.md
index 22d7ffa52..a04c22759 100644
--- a/gguf-py/README.md
+++ b/gguf-py/README.md
@@ -21,6 +21,8 @@ pip install gguf
 
 [scripts/gguf-convert-endian.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-convert-endian.py) — Allows converting the endianness of GGUF files.
 
+[scripts/gguf-new-metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-new-metadata.py) — Copies a GGUF file with added/modified/removed metadata values.
+
 ## Development
 Maintainers who participate in development of this package are advised to install it in editable mode:
 
diff --git a/gguf-py/examples/reader.py b/gguf-py/examples/reader.py
index 62e0769da..d841048c6 100644
--- a/gguf-py/examples/reader.py
+++ b/gguf-py/examples/reader.py
@@ -1,8 +1,10 @@
 #!/usr/bin/env python3
+import logging
 import sys
 from pathlib import Path
 from gguf.gguf_reader import GGUFReader
 
+logger = logging.getLogger("reader")
 
 sys.path.insert(0, str(Path(__file__).parent.parent))
 
@@ -18,28 +20,28 @@ def read_gguf_file(gguf_file_path):
     reader = GGUFReader(gguf_file_path)
 
     # List all key-value pairs in a columnized format
-    print("Key-Value Pairs:")
+    print("Key-Value Pairs:") # noqa: NP100
     max_key_length = max(len(key) for key in reader.fields.keys())
     for key, field in reader.fields.items():
         value = field.parts[field.data[0]]
-        print(f"{key:{max_key_length}} : {value}")
-    print("----")
+        print(f"{key:{max_key_length}} : {value}") # noqa: NP100
+    print("----") # noqa: NP100
 
     # List all tensors
-    print("Tensors:")
+    print("Tensors:") # noqa: NP100
     tensor_info_format = "{:<30} | Shape: {:<15} | Size: {:<12} | Quantization: {}"
-    print(tensor_info_format.format("Tensor Name", "Shape", "Size", "Quantization"))
-    print("-" * 80)
+    print(tensor_info_format.format("Tensor Name", "Shape", "Size", "Quantization")) # noqa: NP100
+    print("-" * 80) # noqa: NP100
     for tensor in reader.tensors:
         shape_str = "x".join(map(str, tensor.shape))
         size_str = str(tensor.n_elements)
         quantization_str = tensor.tensor_type.name
-        print(tensor_info_format.format(tensor.name, shape_str, size_str, quantization_str))
+        print(tensor_info_format.format(tensor.name, shape_str, size_str, quantization_str)) # noqa: NP100
 
 
 if __name__ == '__main__':
     if len(sys.argv) < 2:
-        print("Usage: reader.py <path_to_gguf_file>")
+        logger.info("Usage: reader.py <path_to_gguf_file>")
         sys.exit(1)
     gguf_file_path = sys.argv[1]
     read_gguf_file(gguf_file_path)
diff --git a/gguf-py/gguf/__init__.py b/gguf-py/gguf/__init__.py
index 110ab342c..ea5146b16 100644
--- a/gguf-py/gguf/__init__.py
+++ b/gguf-py/gguf/__init__.py
@@ -1,5 +1,7 @@
 from .constants import *
+from .lazy import *
 from .gguf_reader import *
 from .gguf_writer import *
+from .quants import *
 from .tensor_mapping import *
 from .vocab import *
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 114a9a974..978fcada3 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import sys
 from enum import Enum, IntEnum, auto
 from typing import Any
 
@@ -11,6 +10,7 @@ from typing import Any
 GGUF_MAGIC             = 0x46554747  # "GGUF"
 GGUF_VERSION           = 3
 GGUF_DEFAULT_ALIGNMENT = 32
+GGML_QUANT_VERSION     = 2  # GGML_QNT_VERSION from ggml.h
 
 #
 # metadata keys
@@ -24,6 +24,7 @@ class Keys:
         ALIGNMENT            = "general.alignment"
         NAME                 = "general.name"
         AUTHOR               = "general.author"
+        VERSION              = "general.version"
         URL                  = "general.url"
         DESCRIPTION          = "general.description"
         LICENSE              = "general.license"
@@ -32,6 +33,7 @@ class Keys:
         FILE_TYPE            = "general.file_type"
 
     class LLM:
+        VOCAB_SIZE            = "{arch}.vocab_size"
         CONTEXT_LENGTH        = "{arch}.context_length"
         EMBEDDING_LENGTH      = "{arch}.embedding_length"
         BLOCK_COUNT           = "{arch}.block_count"
@@ -41,6 +43,7 @@ class Keys:
         EXPERT_COUNT          = "{arch}.expert_count"
         EXPERT_USED_COUNT     = "{arch}.expert_used_count"
         POOLING_TYPE          = "{arch}.pooling_type"
+        LOGIT_SCALE           = "{arch}.logit_scale"
 
     class Attention:
         HEAD_COUNT        = "{arch}.attention.head_count"
@@ -61,8 +64,15 @@ class Keys:
         SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length"
         SCALING_FINETUNED    = "{arch}.rope.scaling.finetuned"
 
+    class SSM:
+        CONV_KERNEL    = "{arch}.ssm.conv_kernel"
+        INNER_SIZE     = "{arch}.ssm.inner_size"
+        STATE_SIZE     = "{arch}.ssm.state_size"
+        TIME_STEP_RANK = "{arch}.ssm.time_step_rank"
+
     class Tokenizer:
         MODEL            = "tokenizer.ggml.model"
+        PRE              = "tokenizer.ggml.pre"
         LIST             = "tokenizer.ggml.tokens"
         TOKEN_TYPE       = "tokenizer.ggml.token_type"
         TOKEN_TYPE_COUNT = "tokenizer.ggml.token_type_count"  # for BERT-style token types
@@ -81,6 +91,13 @@ class Keys:
         HF_JSON          = "tokenizer.huggingface.json"
         RWKV             = "tokenizer.rwkv.world"
         CHAT_TEMPLATE    = "tokenizer.chat_template"
+        CHAT_TEMPLATE_N  = "tokenizer.chat_template.{name}"
+        CHAT_TEMPLATES   = "tokenizer.chat_templates"
+        # FIM/Infill special tokens constants
+        PREFIX_ID        = "tokenizer.ggml.prefix_token_id"
+        SUFFIX_ID        = "tokenizer.ggml.suffix_token_id"
+        MIDDLE_ID        = "tokenizer.ggml.middle_token_id"
+        EOT_ID           = "tokenizer.ggml.eot_token_id"
 
 
 #
@@ -92,6 +109,7 @@ class MODEL_ARCH(IntEnum):
     LLAMA      = auto()
     FALCON     = auto()
     BAICHUAN   = auto()
+    GROK       = auto()
     GPT2       = auto()
     GPTJ       = auto()
     GPTNEOX    = auto()
@@ -101,53 +119,75 @@ class MODEL_ARCH(IntEnum):
     REFACT     = auto()
     BERT       = auto()
     NOMIC_BERT = auto()
+    JINA_BERT_V2 = auto()
     BLOOM      = auto()
     STABLELM   = auto()
     QWEN       = auto()
     QWEN2      = auto()
+    QWEN2MOE   = auto()
     PHI2       = auto()
+    PHI3       = auto()
     PLAMO      = auto()
     CODESHELL  = auto()
     ORION      = auto()
     INTERNLM2  = auto()
     MINICPM    = auto()
+    GEMMA      = auto()
+    STARCODER2 = auto()
+    MAMBA      = auto()
+    XVERSE     = auto()
+    COMMAND_R  = auto()
+    DBRX       = auto()
+    OLMO       = auto()
 
 
 class MODEL_TENSOR(IntEnum):
-    TOKEN_EMBD      = auto()
-    TOKEN_EMBD_NORM = auto()
-    TOKEN_TYPES     = auto()
-    POS_EMBD        = auto()
-    OUTPUT          = auto()
-    OUTPUT_NORM     = auto()
-    ROPE_FREQS      = auto()
-    ATTN_Q          = auto()
-    ATTN_K          = auto()
-    ATTN_V          = auto()
-    ATTN_QKV        = auto()
-    ATTN_OUT        = auto()
-    ATTN_NORM       = auto()
-    ATTN_NORM_2     = auto()
-    ATTN_OUT_NORM   = auto()
-    ATTN_ROT_EMBD   = auto()
-    FFN_GATE_INP    = auto()
-    FFN_NORM        = auto()
-    FFN_GATE        = auto()
-    FFN_DOWN        = auto()
-    FFN_UP          = auto()
-    FFN_ACT         = auto()
-    FFN_GATE_EXP    = auto()
-    FFN_DOWN_EXP    = auto()
-    FFN_UP_EXP      = auto()
-    ATTN_Q_NORM     = auto()
-    ATTN_K_NORM     = auto()
-    LAYER_OUT_NORM  = auto()
+    TOKEN_EMBD         = auto()
+    TOKEN_EMBD_NORM    = auto()
+    TOKEN_TYPES        = auto()
+    POS_EMBD           = auto()
+    OUTPUT             = auto()
+    OUTPUT_NORM        = auto()
+    ROPE_FREQS         = auto()
+    ATTN_Q             = auto()
+    ATTN_K             = auto()
+    ATTN_V             = auto()
+    ATTN_QKV           = auto()
+    ATTN_OUT           = auto()
+    ATTN_NORM          = auto()
+    ATTN_NORM_2        = auto()
+    ATTN_OUT_NORM      = auto()
+    ATTN_ROT_EMBD      = auto()
+    FFN_GATE_INP       = auto()
+    FFN_GATE_INP_SHEXP = auto()
+    FFN_NORM           = auto()
+    FFN_GATE           = auto()
+    FFN_DOWN           = auto()
+    FFN_UP             = auto()
+    FFN_ACT            = auto()
+    FFN_GATE_EXP       = auto()
+    FFN_DOWN_EXP       = auto()
+    FFN_UP_EXP         = auto()
+    FFN_GATE_SHEXP     = auto()
+    FFN_DOWN_SHEXP     = auto()
+    FFN_UP_SHEXP       = auto()
+    ATTN_Q_NORM        = auto()
+    ATTN_K_NORM        = auto()
+    LAYER_OUT_NORM     = auto()
+    SSM_IN             = auto()
+    SSM_CONV1D         = auto()
+    SSM_X              = auto()
+    SSM_DT             = auto()
+    SSM_A              = auto()
+    SSM_D              = auto()
+    SSM_OUT            = auto()
 
 
 MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.LLAMA:          "llama",
     MODEL_ARCH.FALCON:         "falcon",
     MODEL_ARCH.BAICHUAN:       "baichuan",
+    MODEL_ARCH.GROK:           "grok",
     MODEL_ARCH.GPT2:           "gpt2",
     MODEL_ARCH.GPTJ:           "gptj",
     MODEL_ARCH.GPTNEOX:        "gptneox",
@@ -157,47 +197,68 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.REFACT:         "refact",
     MODEL_ARCH.BERT:           "bert",
     MODEL_ARCH.NOMIC_BERT:     "nomic-bert",
+    MODEL_ARCH.JINA_BERT_V2:   "jina-bert-v2",
     MODEL_ARCH.BLOOM:          "bloom",
     MODEL_ARCH.STABLELM:       "stablelm",
     MODEL_ARCH.QWEN:           "qwen",
     MODEL_ARCH.QWEN2:          "qwen2",
+    MODEL_ARCH.QWEN2MOE:       "qwen2moe",
     MODEL_ARCH.PHI2:           "phi2",
+    MODEL_ARCH.PHI3:           "phi3",
     MODEL_ARCH.PLAMO:          "plamo",
     MODEL_ARCH.CODESHELL:      "codeshell",
     MODEL_ARCH.ORION:          "orion",
     MODEL_ARCH.INTERNLM2:      "internlm2",
     MODEL_ARCH.MINICPM:        "minicpm",
+    MODEL_ARCH.GEMMA:          "gemma",
+    MODEL_ARCH.STARCODER2:     "starcoder2",
+    MODEL_ARCH.MAMBA:          "mamba",
+    MODEL_ARCH.XVERSE:         "xverse",
+    MODEL_ARCH.COMMAND_R:      "command-r",
+    MODEL_ARCH.DBRX:           "dbrx",
+    MODEL_ARCH.OLMO:           "olmo",
 }
 
 TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
-    MODEL_TENSOR.TOKEN_EMBD:      "token_embd",
-    MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm",
-    MODEL_TENSOR.TOKEN_TYPES:     "token_types",
-    MODEL_TENSOR.POS_EMBD:        "position_embd",
-    MODEL_TENSOR.OUTPUT_NORM:     "output_norm",
-    MODEL_TENSOR.OUTPUT:          "output",
-    MODEL_TENSOR.ROPE_FREQS:      "rope_freqs",
-    MODEL_TENSOR.ATTN_NORM:       "blk.{bid}.attn_norm",
-    MODEL_TENSOR.ATTN_NORM_2:     "blk.{bid}.attn_norm_2",
-    MODEL_TENSOR.ATTN_QKV:        "blk.{bid}.attn_qkv",
-    MODEL_TENSOR.ATTN_Q:          "blk.{bid}.attn_q",
-    MODEL_TENSOR.ATTN_K:          "blk.{bid}.attn_k",
-    MODEL_TENSOR.ATTN_V:          "blk.{bid}.attn_v",
-    MODEL_TENSOR.ATTN_OUT:        "blk.{bid}.attn_output",
-    MODEL_TENSOR.ATTN_ROT_EMBD:   "blk.{bid}.attn_rot_embd",
-    MODEL_TENSOR.ATTN_Q_NORM:     "blk.{bid}.attn_q_norm",
-    MODEL_TENSOR.ATTN_K_NORM:     "blk.{bid}.attn_k_norm",
-    MODEL_TENSOR.ATTN_OUT_NORM:   "blk.{bid}.attn_output_norm",
-    MODEL_TENSOR.FFN_GATE_INP:    "blk.{bid}.ffn_gate_inp",
-    MODEL_TENSOR.FFN_NORM:        "blk.{bid}.ffn_norm",
-    MODEL_TENSOR.FFN_GATE:        "blk.{bid}.ffn_gate",
-    MODEL_TENSOR.FFN_DOWN:        "blk.{bid}.ffn_down",
-    MODEL_TENSOR.FFN_UP:          "blk.{bid}.ffn_up",
-    MODEL_TENSOR.FFN_ACT:         "blk.{bid}.ffn",
-    MODEL_TENSOR.FFN_GATE_EXP:    "blk.{bid}.ffn_gate.{xid}",
-    MODEL_TENSOR.FFN_DOWN_EXP:    "blk.{bid}.ffn_down.{xid}",
-    MODEL_TENSOR.FFN_UP_EXP:      "blk.{bid}.ffn_up.{xid}",
-    MODEL_TENSOR.LAYER_OUT_NORM:  "blk.{bid}.layer_output_norm",
+    MODEL_TENSOR.TOKEN_EMBD:         "token_embd",
+    MODEL_TENSOR.TOKEN_EMBD_NORM:    "token_embd_norm",
+    MODEL_TENSOR.TOKEN_TYPES:        "token_types",
+    MODEL_TENSOR.POS_EMBD:           "position_embd",
+    MODEL_TENSOR.OUTPUT_NORM:        "output_norm",
+    MODEL_TENSOR.OUTPUT:             "output",
+    MODEL_TENSOR.ROPE_FREQS:         "rope_freqs",
+    MODEL_TENSOR.ATTN_NORM:          "blk.{bid}.attn_norm",
+    MODEL_TENSOR.ATTN_NORM_2:        "blk.{bid}.attn_norm_2",
+    MODEL_TENSOR.ATTN_QKV:           "blk.{bid}.attn_qkv",
+    MODEL_TENSOR.ATTN_Q:             "blk.{bid}.attn_q",
+    MODEL_TENSOR.ATTN_K:             "blk.{bid}.attn_k",
+    MODEL_TENSOR.ATTN_V:             "blk.{bid}.attn_v",
+    MODEL_TENSOR.ATTN_OUT:           "blk.{bid}.attn_output",
+    MODEL_TENSOR.ATTN_ROT_EMBD:      "blk.{bid}.attn_rot_embd",
+    MODEL_TENSOR.ATTN_Q_NORM:        "blk.{bid}.attn_q_norm",
+    MODEL_TENSOR.ATTN_K_NORM:        "blk.{bid}.attn_k_norm",
+    MODEL_TENSOR.ATTN_OUT_NORM:      "blk.{bid}.attn_output_norm",
+    MODEL_TENSOR.FFN_GATE_INP:       "blk.{bid}.ffn_gate_inp",
+    MODEL_TENSOR.FFN_GATE_INP_SHEXP: "blk.{bid}.ffn_gate_inp_shexp",
+    MODEL_TENSOR.FFN_NORM:           "blk.{bid}.ffn_norm",
+    MODEL_TENSOR.FFN_GATE:           "blk.{bid}.ffn_gate",
+    MODEL_TENSOR.FFN_DOWN:           "blk.{bid}.ffn_down",
+    MODEL_TENSOR.FFN_UP:             "blk.{bid}.ffn_up",
+    MODEL_TENSOR.FFN_GATE_SHEXP:     "blk.{bid}.ffn_gate_shexp",
+    MODEL_TENSOR.FFN_DOWN_SHEXP:     "blk.{bid}.ffn_down_shexp",
+    MODEL_TENSOR.FFN_UP_SHEXP:       "blk.{bid}.ffn_up_shexp",
+    MODEL_TENSOR.FFN_ACT:            "blk.{bid}.ffn",
+    MODEL_TENSOR.FFN_GATE_EXP:       "blk.{bid}.ffn_gate_exps",
+    MODEL_TENSOR.FFN_DOWN_EXP:       "blk.{bid}.ffn_down_exps",
+    MODEL_TENSOR.FFN_UP_EXP:         "blk.{bid}.ffn_up_exps",
+    MODEL_TENSOR.LAYER_OUT_NORM:     "blk.{bid}.layer_output_norm",
+    MODEL_TENSOR.SSM_IN:             "blk.{bid}.ssm_in",
+    MODEL_TENSOR.SSM_CONV1D:         "blk.{bid}.ssm_conv1d",
+    MODEL_TENSOR.SSM_X:              "blk.{bid}.ssm_x",
+    MODEL_TENSOR.SSM_DT:             "blk.{bid}.ssm_dt",
+    MODEL_TENSOR.SSM_A:              "blk.{bid}.ssm_a",
+    MODEL_TENSOR.SSM_D:              "blk.{bid}.ssm_d",
+    MODEL_TENSOR.SSM_OUT:            "blk.{bid}.ssm_out",
 }
 
 MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@@ -221,6 +282,28 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN_EXP,
         MODEL_TENSOR.FFN_UP_EXP,
     ],
+    MODEL_ARCH.GROK: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.ATTN_OUT_NORM,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.LAYER_OUT_NORM,
+    ],
     MODEL_ARCH.GPTNEOX: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -300,6 +383,22 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_UP,
         MODEL_TENSOR.LAYER_OUT_NORM,
     ],
+    MODEL_ARCH.JINA_BERT_V2: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.TOKEN_EMBD_NORM,
+        MODEL_TENSOR.TOKEN_TYPES,
+        MODEL_TENSOR.ATTN_OUT_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.LAYER_OUT_NORM,
+    ],
     MODEL_ARCH.MPT: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -311,6 +410,9 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
         MODEL_TENSOR.FFN_ACT,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.POS_EMBD,
     ],
     MODEL_ARCH.GPTJ: [
         MODEL_TENSOR.TOKEN_EMBD,
@@ -378,6 +480,8 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_GATE,
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K_NORM,
     ],
     MODEL_ARCH.QWEN: [
         MODEL_TENSOR.TOKEN_EMBD,
@@ -407,6 +511,25 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.QWEN2MOE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_INP_SHEXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+    ],
     MODEL_ARCH.PLAMO: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -448,6 +571,20 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.PHI3: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
     MODEL_ARCH.CODESHELL: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.POS_EMBD,
@@ -511,6 +648,101 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN_EXP,
         MODEL_TENSOR.FFN_UP_EXP,
     ],
+    MODEL_ARCH.GEMMA: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_NORM,
+    ],
+    MODEL_ARCH.STARCODER2: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
+    MODEL_ARCH.MAMBA: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.SSM_IN,
+        MODEL_TENSOR.SSM_CONV1D,
+        MODEL_TENSOR.SSM_X,
+        MODEL_TENSOR.SSM_DT,
+        MODEL_TENSOR.SSM_A,
+        MODEL_TENSOR.SSM_D,
+        MODEL_TENSOR.SSM_OUT,
+    ],
+    MODEL_ARCH.XVERSE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
+    MODEL_ARCH.COMMAND_R: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_Q_NORM,
+    ],
+    MODEL_ARCH.DBRX: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_OUT_NORM,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+    ],
+    MODEL_ARCH.OLMO: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
     # TODO
 }
 
@@ -539,6 +771,14 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.ROPE_FREQS,
         MODEL_TENSOR.ATTN_ROT_EMBD,
     ],
+    MODEL_ARCH.STARCODER2: [
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+    ],
+    MODEL_ARCH.XVERSE: [
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+    ],
 }
 
 #
@@ -568,20 +808,78 @@ class PoolingType(IntEnum):
 
 
 class GGMLQuantizationType(IntEnum):
-    F32  = 0
-    F16  = 1
-    Q4_0 = 2
-    Q4_1 = 3
-    Q5_0 = 6
-    Q5_1 = 7
-    Q8_0 = 8
-    Q8_1 = 9
-    Q2_K = 10
-    Q3_K = 11
-    Q4_K = 12
-    Q5_K = 13
-    Q6_K = 14
-    Q8_K = 15
+    F32     = 0
+    F16     = 1
+    Q4_0    = 2
+    Q4_1    = 3
+    Q5_0    = 6
+    Q5_1    = 7
+    Q8_0    = 8
+    Q8_1    = 9
+    Q2_K    = 10
+    Q3_K    = 11
+    Q4_K    = 12
+    Q5_K    = 13
+    Q6_K    = 14
+    Q8_K    = 15
+    IQ2_XXS = 16
+    IQ2_XS  = 17
+    IQ3_XXS = 18
+    IQ1_S   = 19
+    IQ4_NL  = 20
+    IQ3_S   = 21
+    IQ2_S   = 22
+    IQ4_XS  = 23
+    I8      = 24
+    I16     = 25
+    I32     = 26
+    I64     = 27
+    F64     = 28
+    IQ1_M   = 29
+    BF16    = 30
+
+
+# TODO: add GGMLFileType from ggml_ftype in ggml.h
+
+
+# from llama_ftype in llama.h
+# ALL VALUES SHOULD BE THE SAME HERE AS THEY ARE OVER THERE.
+class LlamaFileType(IntEnum):
+    ALL_F32              = 0
+    MOSTLY_F16           = 1   # except 1d tensors
+    MOSTLY_Q4_0          = 2   # except 1d tensors
+    MOSTLY_Q4_1          = 3   # except 1d tensors
+    MOSTLY_Q4_1_SOME_F16 = 4   # tok_embeddings.weight and output.weight are F16
+    # MOSTLY_Q4_2        = 5   # support has been removed
+    # MOSTLY_Q4_3        = 6   # support has been removed
+    MOSTLY_Q8_0          = 7   # except 1d tensors
+    MOSTLY_Q5_0          = 8   # except 1d tensors
+    MOSTLY_Q5_1          = 9   # except 1d tensors
+    MOSTLY_Q2_K          = 10  # except 1d tensors
+    MOSTLY_Q3_K_S        = 11  # except 1d tensors
+    MOSTLY_Q3_K_M        = 12  # except 1d tensors
+    MOSTLY_Q3_K_L        = 13  # except 1d tensors
+    MOSTLY_Q4_K_S        = 14  # except 1d tensors
+    MOSTLY_Q4_K_M        = 15  # except 1d tensors
+    MOSTLY_Q5_K_S        = 16  # except 1d tensors
+    MOSTLY_Q5_K_M        = 17  # except 1d tensors
+    MOSTLY_Q6_K          = 18  # except 1d tensors
+    MOSTLY_IQ2_XXS       = 19  # except 1d tensors
+    MOSTLY_IQ2_XS        = 20  # except 1d tensors
+    MOSTLY_Q2_K_S        = 21  # except 1d tensors
+    MOSTLY_IQ3_XS        = 22  # except 1d tensors
+    MOSTLY_IQ3_XXS       = 23  # except 1d tensors
+    MOSTLY_IQ1_S         = 24  # except 1d tensors
+    MOSTLY_IQ4_NL        = 25  # except 1d tensors
+    MOSTLY_IQ3_S         = 26  # except 1d tensors
+    MOSTLY_IQ3_M         = 27  # except 1d tensors
+    MOSTLY_IQ2_S         = 28  # except 1d tensors
+    MOSTLY_IQ2_M         = 29  # except 1d tensors
+    MOSTLY_IQ4_XS        = 30  # except 1d tensors
+    MOSTLY_IQ1_M         = 31  # except 1d tensors
+    MOSTLY_BF16          = 32  # except 1d tensors
+
+    GUESSED              = 1024  # not specified in the model file
 
 
 class GGUFEndian(IntEnum):
@@ -618,28 +916,42 @@ class GGUFValueType(IntEnum):
             return GGUFValueType.INT32
         # TODO: need help with 64-bit types in Python
         else:
-            print("Unknown type:", type(val))
-            sys.exit()
+            raise ValueError(f"Unknown type: {type(val)}")
 
 
 # Note: Does not support GGML_QKK_64
 QK_K = 256
 # Items here are (block size, type size)
-GGML_QUANT_SIZES = {
-    GGMLQuantizationType.F32:  (1, 4),
-    GGMLQuantizationType.F16:  (1, 2),
-    GGMLQuantizationType.Q4_0: (32, 2 + 16),
-    GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16),
-    GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16),
-    GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16),
-    GGMLQuantizationType.Q8_0: (32, 2 + 32),
-    GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32),
-    GGMLQuantizationType.Q2_K: (256, 2 + 2 + QK_K // 16 + QK_K // 4),
-    GGMLQuantizationType.Q3_K: (256, 2 + QK_K // 4 + QK_K // 8 + 12),
-    GGMLQuantizationType.Q4_K: (256, 2 + 2 + QK_K // 2 + 12),
-    GGMLQuantizationType.Q5_K: (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12),
-    GGMLQuantizationType.Q6_K: (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16),
-    GGMLQuantizationType.Q8_K: (256, 4 + QK_K + QK_K // 8),
+GGML_QUANT_SIZES: dict[GGMLQuantizationType, tuple[int, int]] = {
+    GGMLQuantizationType.F32:     (1, 4),
+    GGMLQuantizationType.F16:     (1, 2),
+    GGMLQuantizationType.Q4_0:    (32, 2 + 16),
+    GGMLQuantizationType.Q4_1:    (32, 2 + 2 + 16),
+    GGMLQuantizationType.Q5_0:    (32, 2 + 4 + 16),
+    GGMLQuantizationType.Q5_1:    (32, 2 + 2 + 4 + 16),
+    GGMLQuantizationType.Q8_0:    (32, 2 + 32),
+    GGMLQuantizationType.Q8_1:    (32, 4 + 4 + 32),
+    GGMLQuantizationType.Q2_K:    (256, 2 + 2 + QK_K // 16 + QK_K // 4),
+    GGMLQuantizationType.Q3_K:    (256, 2 + QK_K // 4 + QK_K // 8 + 12),
+    GGMLQuantizationType.Q4_K:    (256, 2 + 2 + QK_K // 2 + 12),
+    GGMLQuantizationType.Q5_K:    (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12),
+    GGMLQuantizationType.Q6_K:    (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16),
+    GGMLQuantizationType.Q8_K:    (256, 4 + QK_K + QK_K // 8),
+    GGMLQuantizationType.IQ2_XXS: (256, 2 + QK_K // 4),
+    GGMLQuantizationType.IQ2_XS:  (256, 2 + QK_K // 4 + QK_K // 32),
+    GGMLQuantizationType.IQ3_XXS: (256, 2 + QK_K // 4 + QK_K // 8),
+    GGMLQuantizationType.IQ1_S:   (256, 2 + QK_K // 8 + QK_K // 16),
+    GGMLQuantizationType.IQ4_NL:  (32, 2 + 16),
+    GGMLQuantizationType.IQ3_S:   (256, 2 + QK_K // 4 + QK_K // 8 + QK_K // 32 + 4),
+    GGMLQuantizationType.IQ2_S:   (256, 2 + QK_K // 4 + QK_K // 16),
+    GGMLQuantizationType.IQ4_XS:  (256, 2 + 2 + QK_K // 2 + QK_K // 64),
+    GGMLQuantizationType.I8:      (1, 1),
+    GGMLQuantizationType.I16:     (1, 2),
+    GGMLQuantizationType.I32:     (1, 4),
+    GGMLQuantizationType.I64:     (1, 8),
+    GGMLQuantizationType.F64:     (1, 8),
+    GGMLQuantizationType.IQ1_M:   (256, QK_K // 8 + QK_K // 16  + QK_K // 32),
+    GGMLQuantizationType.BF16:    (1, 2),
 }
 
 
@@ -659,6 +971,7 @@ KEY_GENERAL_SOURCE_HF_REPO       = Keys.General.SOURCE_HF_REPO
 KEY_GENERAL_FILE_TYPE            = Keys.General.FILE_TYPE
 
 # LLM
+KEY_VOCAB_SIZE            = Keys.LLM.VOCAB_SIZE
 KEY_CONTEXT_LENGTH        = Keys.LLM.CONTEXT_LENGTH
 KEY_EMBEDDING_LENGTH      = Keys.LLM.EMBEDDING_LENGTH
 KEY_BLOCK_COUNT           = Keys.LLM.BLOCK_COUNT
@@ -682,8 +995,15 @@ KEY_ROPE_SCALING_FACTOR       = Keys.Rope.SCALING_FACTOR
 KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN
 KEY_ROPE_SCALING_FINETUNED    = Keys.Rope.SCALING_FINETUNED
 
+# SSM
+KEY_SSM_CONV_KERNEL    = Keys.SSM.CONV_KERNEL
+KEY_SSM_INNER_SIZE     = Keys.SSM.INNER_SIZE
+KEY_SSM_STATE_SIZE     = Keys.SSM.STATE_SIZE
+KEY_SSM_TIME_STEP_RANK = Keys.SSM.TIME_STEP_RANK
+
 # tokenization
 KEY_TOKENIZER_MODEL      = Keys.Tokenizer.MODEL
+KEY_TOKENIZER_PRE        = Keys.Tokenizer.PRE
 KEY_TOKENIZER_LIST       = Keys.Tokenizer.LIST
 KEY_TOKENIZER_TOKEN_TYPE = Keys.Tokenizer.TOKEN_TYPE
 KEY_TOKENIZER_SCORES     = Keys.Tokenizer.SCORES
@@ -697,3 +1017,7 @@ KEY_TOKENIZER_CLS_ID     = Keys.Tokenizer.CLS_ID
 KEY_TOKENIZER_MASK_ID    = Keys.Tokenizer.MASK_ID
 KEY_TOKENIZER_HF_JSON    = Keys.Tokenizer.HF_JSON
 KEY_TOKENIZER_RWKV       = Keys.Tokenizer.RWKV
+KEY_TOKENIZER_PRIFIX_ID  = Keys.Tokenizer.PREFIX_ID
+KEY_TOKENIZER_SUFFIX_ID  = Keys.Tokenizer.SUFFIX_ID
+KEY_TOKENIZER_MIDDLE_ID  = Keys.Tokenizer.MIDDLE_ID
+KEY_TOKENIZER_EOT_ID     = Keys.Tokenizer.EOT_ID
diff --git a/gguf-py/gguf/gguf_reader.py b/gguf-py/gguf/gguf_reader.py
index 5b6d4ba6b..21b089f8a 100644
--- a/gguf-py/gguf/gguf_reader.py
+++ b/gguf-py/gguf/gguf_reader.py
@@ -4,6 +4,7 @@
 #
 from __future__ import annotations
 
+import logging
 import os
 from collections import OrderedDict
 from typing import Any, Literal, NamedTuple, TypeVar, Union
@@ -27,6 +28,7 @@ from gguf.constants import (
     GGUFValueType,
 )
 
+logger = logging.getLogger(__name__)
 
 READER_SUPPORTED_VERSIONS = [2, GGUF_VERSION]
 
@@ -63,7 +65,7 @@ class ReaderTensor(NamedTuple):
 
 class GGUFReader:
     # I - same as host, S - swapped
-    byte_order: Literal['I' | 'S'] = 'I'
+    byte_order: Literal['I'] | Literal['S'] = 'I'
     alignment: int = GGUF_DEFAULT_ALIGNMENT
 
     # Note: Internal helper, API may change.
@@ -81,7 +83,7 @@ class GGUFReader:
         GGUFValueType.BOOL:    np.bool_,
     }
 
-    def __init__(self, path: os.PathLike[str] | str, mode: Literal['r' | 'r+' | 'c'] = 'r'):
+    def __init__(self, path: os.PathLike[str] | str, mode: Literal['r'] | Literal['r+'] | Literal['c'] = 'r'):
         self.data = np.memmap(path, mode = mode)
         offs = 0
         if self._get(offs, np.uint32, override_order = '<')[0] != GGUF_MAGIC:
@@ -126,7 +128,7 @@ class GGUFReader:
         return self.tensors[idx]
 
     def _get(
-        self, offset: int, dtype: npt.DTypeLike, count: int = 1, override_order: None | Literal['I' | 'S' | '<'] = None,
+        self, offset: int, dtype: npt.DTypeLike, count: int = 1, override_order: None | Literal['I'] | Literal['S'] | Literal['<'] = None,
     ) -> npt.NDArray[Any]:
         count = int(count)
         itemsize = int(np.empty([], dtype = dtype).itemsize)
@@ -139,8 +141,13 @@ class GGUFReader:
 
     def _push_field(self, field: ReaderField, skip_sum: bool = False) -> int:
         if field.name in self.fields:
-            raise KeyError(f'Duplicate {field.name} already in list at offset {field.offset}')
-        self.fields[field.name] = field
+            # TODO: add option to generate error on duplicate keys
+            # raise KeyError(f'Duplicate {field.name} already in list at offset {field.offset}')
+
+            logger.warning(f'Duplicate key {field.name} at offset {field.offset}')
+            self.fields[field.name + '_{}'.format(field.offset)] = field
+        else:
+            self.fields[field.name] = field
         return 0 if skip_sum else sum(int(part.nbytes) for part in field.parts)
 
     def _get_str(self, offset: int) -> tuple[npt.NDArray[np.uint64], npt.NDArray[np.uint8]]:
@@ -234,25 +241,46 @@ class GGUFReader:
 
     def _build_tensors(self, start_offs: int, fields: list[ReaderField]) -> None:
         tensors = []
+        tensor_names = set() # keep track of name to prevent duplicated tensors
         for field in fields:
             _name_len, name_data, _n_dims, dims, raw_dtype, offset_tensor = field.parts
+            # check if there's any tensor having same name already in the list
+            tensor_name = str(bytes(name_data), encoding = 'utf-8')
+            if tensor_name in tensor_names:
+                raise ValueError(f'Found duplicated tensor with name {tensor_name}')
+            tensor_names.add(tensor_name)
             ggml_type = GGMLQuantizationType(raw_dtype[0])
-            n_elems = np.prod(dims)
+            n_elems = int(np.prod(dims))
             block_size, type_size = GGML_QUANT_SIZES[ggml_type]
             n_bytes = n_elems * type_size // block_size
             data_offs = int(start_offs + offset_tensor[0])
             item_type: npt.DTypeLike
-            if ggml_type == GGMLQuantizationType.F32:
-                item_count = n_elems
-                item_type = np.float32
-            elif ggml_type == GGMLQuantizationType.F16:
+            if ggml_type == GGMLQuantizationType.F16:
                 item_count = n_elems
                 item_type = np.float16
+            elif ggml_type == GGMLQuantizationType.F32:
+                item_count = n_elems
+                item_type = np.float32
+            elif ggml_type == GGMLQuantizationType.F64:
+                item_count = n_elems
+                item_type = np.float64
+            elif ggml_type == GGMLQuantizationType.I8:
+                item_count = n_elems
+                item_type = np.int8
+            elif ggml_type == GGMLQuantizationType.I16:
+                item_count = n_elems
+                item_type = np.int16
+            elif ggml_type == GGMLQuantizationType.I32:
+                item_count = n_elems
+                item_type = np.int32
+            elif ggml_type == GGMLQuantizationType.I64:
+                item_count = n_elems
+                item_type = np.int64
             else:
                 item_count = n_bytes
                 item_type = np.uint8
             tensors.append(ReaderTensor(
-                name = str(bytes(name_data), encoding = 'utf-8'),
+                name = tensor_name,
                 tensor_type = ggml_type,
                 shape = dims,
                 n_elements = n_elems,
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index e4681475c..d5e323a52 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -1,16 +1,19 @@
 from __future__ import annotations
 
+import logging
 import os
 import shutil
 import struct
 import tempfile
 from enum import Enum, auto
 from io import BufferedWriter
-from typing import IO, Any, Sequence
+from typing import IO, Any, Sequence, Mapping
+from string import ascii_letters, digits
 
 import numpy as np
 
 from .constants import (
+    GGML_QUANT_SIZES,
     GGUF_DEFAULT_ALIGNMENT,
     GGUF_MAGIC,
     GGUF_VERSION,
@@ -23,6 +26,8 @@ from .constants import (
     TokenType,
 )
 
+logger = logging.getLogger(__name__)
+
 
 class WriterState(Enum):
     EMPTY   = auto()
@@ -62,10 +67,11 @@ class GGUFWriter:
         self.kv_data_count = 0
         self.ti_data = bytearray()
         self.ti_data_count = 0
+        self.ti_names = set()
         self.use_temp_file = use_temp_file
         self.temp_file = None
         self.tensors = []
-        print("gguf: This GGUF file is for {0} Endian only".format(
+        logger.info("gguf: This GGUF file is for {0} Endian only".format(
             "Big" if self.endianess == GGUFEndian.BIG else "Little",
         ))
         self.state = WriterState.EMPTY
@@ -171,7 +177,7 @@ class GGUFWriter:
         if pack_fmt is not None:
             self.kv_data += self._pack(pack_fmt, val, skip_pack_prefix = vtype == GGUFValueType.BOOL)
         elif vtype == GGUFValueType.STRING:
-            encoded_val = val.encode("utf8") if isinstance(val, str) else val
+            encoded_val = val.encode("utf-8") if isinstance(val, str) else val
             self.kv_data += self._pack("Q", len(encoded_val))
             self.kv_data += encoded_val
         elif vtype == GGUFValueType.ARRAY and isinstance(val, Sequence) and val:
@@ -190,26 +196,47 @@ class GGUFWriter:
         return ((x + n - 1) // n) * n
 
     def add_tensor_info(
-        self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype[np.float16] | np.dtype[np.float32],
+        self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype,
         tensor_nbytes: int, raw_dtype: GGMLQuantizationType | None = None,
     ) -> None:
         if self.state is not WriterState.EMPTY:
             raise ValueError(f'Expected output file to be empty, got {self.state}')
 
-        if raw_dtype is None and tensor_dtype not in (np.float32, np.float16):
-            raise ValueError("Only F32 and F16 tensors are supported for now")
+        if name in self.ti_names:
+            raise ValueError(f'Duplicated tensor name {name}')
+        self.ti_names.add(name)
 
-        encoded_name = name.encode("utf8")
+        encoded_name = name.encode("utf-8")
         self.ti_data += self._pack("Q", len(encoded_name))
         self.ti_data += encoded_name
+        if raw_dtype is None:
+            if tensor_dtype == np.float16:
+                dtype = GGMLQuantizationType.F16
+            elif tensor_dtype == np.float32:
+                dtype = GGMLQuantizationType.F32
+            elif tensor_dtype == np.float64:
+                dtype = GGMLQuantizationType.F64
+            elif tensor_dtype == np.int8:
+                dtype = GGMLQuantizationType.I8
+            elif tensor_dtype == np.int16:
+                dtype = GGMLQuantizationType.I16
+            elif tensor_dtype == np.int32:
+                dtype = GGMLQuantizationType.I32
+            elif tensor_dtype == np.int64:
+                dtype = GGMLQuantizationType.I64
+            else:
+                raise ValueError("Only F16, F32, F64, I8, I16, I32, I64 tensors are supported for now")
+        else:
+            dtype = raw_dtype
+            if tensor_dtype == np.uint8:
+                block_size, type_size = GGML_QUANT_SIZES[raw_dtype]
+                if tensor_shape[-1] % type_size != 0:
+                    raise ValueError(f"Quantized tensor row size ({tensor_shape[-1]}) is not a multiple of {dtype.name} type size ({type_size})")
+                tensor_shape = tuple(tensor_shape[:-1]) + (tensor_shape[-1] // type_size * block_size,)
         n_dims = len(tensor_shape)
         self.ti_data += self._pack("I", n_dims)
         for i in range(n_dims):
             self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i])
-        if raw_dtype is None:
-            dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
-        else:
-            dtype = raw_dtype
         self.ti_data += self._pack("I", dtype)
         self.ti_data += self._pack("Q", self.offset_tensor)
         self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment)
@@ -251,15 +278,33 @@ class GGUFWriter:
         tensor.tofile(self.fout)
         self.write_padding(self.fout, tensor.nbytes)
 
-    def write_tensors_to_file(self) -> None:
+    def write_tensors_to_file(self, *, progress: bool = False) -> None:
         self.write_ti_data_to_file()
 
         self.write_padding(self.fout, self.fout.tell())
 
         if self.temp_file is None:
+            self.tensors.reverse()  # to pop from the "beginning" in constant time
+
+            if progress:
+                from tqdm import tqdm
+
+                total_bytes = sum(t.nbytes for t in self.tensors)
+
+                bar = tqdm(desc="Writing", total=total_bytes, unit="byte", unit_scale=True)
+
+                while True:
+                    try:
+                        tensor = self.tensors.pop()
+                    except IndexError:
+                        break
+                    tensor.tofile(self.fout)
+                    bar.update(tensor.nbytes)
+                    self.write_padding(self.fout, tensor.nbytes)
+                return
             while True:
                 try:
-                    tensor = self.tensors.pop(0)
+                    tensor = self.tensors.pop()
                 except IndexError:
                     break
                 tensor.tofile(self.fout)
@@ -284,6 +329,9 @@ class GGUFWriter:
     def add_author(self, author: str) -> None:
         self.add_string(Keys.General.AUTHOR, author)
 
+    def add_version(self, version: str) -> None:
+        self.add_string(Keys.General.VERSION, version)
+
     def add_tensor_data_layout(self, layout: str) -> None:
         self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
 
@@ -293,6 +341,9 @@ class GGUFWriter:
     def add_description(self, description: str) -> None:
         self.add_string(Keys.General.DESCRIPTION, description)
 
+    def add_licence(self, licence: str) -> None:
+        self.add_string(Keys.General.LICENSE, licence)
+
     def add_source_url(self, url: str) -> None:
         self.add_string(Keys.General.SOURCE_URL, url)
 
@@ -305,7 +356,7 @@ class GGUFWriter:
     def add_name(self, name: str) -> None:
         self.add_string(Keys.General.NAME, name)
 
-    def add_quantization_version(self, quantization_version: GGMLQuantizationType) -> None:
+    def add_quantization_version(self, quantization_version: int) -> None:
         self.add_uint32(
             Keys.General.QUANTIZATION_VERSION, quantization_version)
 
@@ -313,6 +364,9 @@ class GGUFWriter:
         self.data_alignment = alignment
         self.add_uint32(Keys.General.ALIGNMENT, alignment)
 
+    def add_vocab_size(self, size: int) -> None:
+        self.add_uint32(Keys.LLM.VOCAB_SIZE.format(arch=self.arch), size)
+
     def add_context_length(self, length: int) -> None:
         self.add_uint32(Keys.LLM.CONTEXT_LENGTH.format(arch=self.arch), length)
 
@@ -346,6 +400,9 @@ class GGUFWriter:
     def add_clamp_kqv(self, value: float) -> None:
         self.add_float32(Keys.Attention.CLAMP_KQV.format(arch=self.arch), value)
 
+    def add_logit_scale(self, value: float) -> None:
+        self.add_float32(Keys.LLM.LOGIT_SCALE.format(arch=self.arch), value)
+
     def add_expert_count(self, count: int) -> None:
         self.add_uint32(Keys.LLM.EXPERT_COUNT.format(arch=self.arch), count)
 
@@ -362,7 +419,7 @@ class GGUFWriter:
         self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value)
 
     def add_pooling_type(self, value: PoolingType) -> None:
-        self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value)
+        self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value)
 
     def add_rope_dimension_count(self, count: int) -> None:
         self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count)
@@ -382,9 +439,24 @@ class GGUFWriter:
     def add_rope_scaling_finetuned(self, value: bool) -> None:
         self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value)
 
+    def add_ssm_conv_kernel(self, value: int) -> None:
+        self.add_uint32(Keys.SSM.CONV_KERNEL.format(arch=self.arch), value)
+
+    def add_ssm_inner_size(self, value: int) -> None:
+        self.add_uint32(Keys.SSM.INNER_SIZE.format(arch=self.arch), value)
+
+    def add_ssm_state_size(self, value: int) -> None:
+        self.add_uint32(Keys.SSM.STATE_SIZE.format(arch=self.arch), value)
+
+    def add_ssm_time_step_rank(self, value: int) -> None:
+        self.add_uint32(Keys.SSM.TIME_STEP_RANK.format(arch=self.arch), value)
+
     def add_tokenizer_model(self, model: str) -> None:
         self.add_string(Keys.Tokenizer.MODEL, model)
 
+    def add_tokenizer_pre(self, pre: str) -> None:
+        self.add_string(Keys.Tokenizer.PRE, pre)
+
     def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None:
         self.add_array(Keys.Tokenizer.LIST, tokens)
 
@@ -430,9 +502,47 @@ class GGUFWriter:
     def add_add_space_prefix(self, value: bool) -> None:
         self.add_bool(Keys.Tokenizer.ADD_PREFIX, value)
 
-    def add_chat_template(self, value: str) -> None:
+    def add_chat_template(self, value: str | Sequence[Mapping[str, str]]) -> None:
+        if not isinstance(value, str):
+            template_default = None
+            template_names = set()
+
+            for choice in value:
+                name = choice.get('name', '')
+                template = choice.get('template')
+
+                # Allowing non-alphanumerical characters in template name is probably not a good idea, so filter it
+                name = ''.join((c if c in ascii_letters + digits else '_' for c in name))
+
+                if name and template is not None:
+                    if name == 'default':
+                        template_default = template
+                    else:
+                        template_names.add(name)
+                        self.add_string(Keys.Tokenizer.CHAT_TEMPLATE_N.format(name=name), template)
+
+            if template_names:
+                self.add_array(Keys.Tokenizer.CHAT_TEMPLATES, list(template_names))
+
+            if template_default is None:
+                return
+
+            value = template_default
+
         self.add_string(Keys.Tokenizer.CHAT_TEMPLATE, value)
 
+    def add_prefix_token_id(self, id: int) -> None:
+        self.add_uint32(Keys.Tokenizer.PREFIX_ID, id)
+
+    def add_suffix_token_id(self, id: int) -> None:
+        self.add_uint32(Keys.Tokenizer.SUFFIX_ID, id)
+
+    def add_middle_token_id(self, id: int) -> None:
+        self.add_uint32(Keys.Tokenizer.MIDDLE_ID, id)
+
+    def add_eot_token_id(self, id: int) -> None:
+        self.add_uint32(Keys.Tokenizer.EOT_ID, id)
+
     def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes:
         pack_prefix = ''
         if not skip_pack_prefix:
diff --git a/gguf-py/gguf/lazy.py b/gguf-py/gguf/lazy.py
new file mode 100644
index 000000000..1167335b8
--- /dev/null
+++ b/gguf-py/gguf/lazy.py
@@ -0,0 +1,236 @@
+from __future__ import annotations
+from abc import ABC, ABCMeta, abstractmethod
+
+import logging
+from typing import Any, Callable
+from collections import deque
+
+import numpy as np
+from numpy._typing import _Shape
+from numpy.typing import DTypeLike
+
+
+logger = logging.getLogger(__name__)
+
+
+class LazyMeta(ABCMeta):
+
+    def __new__(cls, name: str, bases: tuple[type, ...], namespace: dict[str, Any], **kwargs):
+        def __getattr__(self, __name: str) -> Any:
+            meta_attr = getattr(self._meta, __name)
+            if callable(meta_attr):
+                return type(self)._wrap_fn(
+                    (lambda s, *args, **kwargs: getattr(s, __name)(*args, **kwargs)),
+                    use_self=self,
+                )
+            elif isinstance(meta_attr, self._tensor_type):
+                # e.g. self.T with torch.Tensor should still be wrapped
+                return type(self)._wrap_fn(lambda s: getattr(s, __name))(self)
+            else:
+                # no need to wrap non-tensor properties,
+                # and they likely don't depend on the actual contents of the tensor
+                return meta_attr
+
+        namespace["__getattr__"] = __getattr__
+
+        # need to make a builder for the wrapped wrapper to copy the name,
+        # or else it fails with very cryptic error messages,
+        # because somehow the same string would end up in every closures
+        def mk_wrap(op_name: str, *, meta_noop: bool = False):
+            # need to wrap the wrapper to get self
+            def wrapped_special_op(self, *args, **kwargs):
+                return type(self)._wrap_fn(
+                    getattr(type(self)._tensor_type, op_name),
+                    meta_noop=meta_noop,
+                )(self, *args, **kwargs)
+            return wrapped_special_op
+
+        # special methods bypass __getattr__, so they need to be added manually
+        # ref: https://docs.python.org/3/reference/datamodel.html#special-lookup
+        # NOTE: doing this from a metaclass is very convenient
+        # TODO: make this even more comprehensive
+        for binary_op in (
+            "lt", "le", "eq", "ne", "ge", "gt", "not"
+            "abs", "add", "and", "floordiv", "invert", "lshift", "mod", "mul", "matmul",
+            "neg", "or", "pos", "pow", "rshift", "sub", "truediv", "xor",
+            "iadd", "iand", "ifloordiv", "ilshift", "imod", "imul", "ior", "irshift", "isub", "ixor",
+            "radd", "rand", "rfloordiv", "rmul", "ror", "rpow", "rsub", "rtruediv", "rxor",
+        ):
+            attr_name = f"__{binary_op}__"
+            # the result of these operators usually has the same shape and dtype as the input,
+            # so evaluation on the meta tensor can be skipped.
+            namespace[attr_name] = mk_wrap(attr_name, meta_noop=True)
+
+        for special_op in (
+            "getitem", "setitem", "len",
+        ):
+            attr_name = f"__{special_op}__"
+            namespace[attr_name] = mk_wrap(attr_name, meta_noop=False)
+
+        return super().__new__(cls, name, bases, namespace, **kwargs)
+
+
+# Tree of lazy tensors
+class LazyBase(ABC, metaclass=LazyMeta):
+    _tensor_type: type
+    _meta: Any
+    _data: Any | None
+    _lazy: deque[LazyBase]  # shared within a graph, to avoid deep recursion when making eager
+    _args: tuple
+    _func: Callable[[tuple], Any] | None
+
+    def __init__(self, *, meta: Any, data: Any | None = None, lazy: deque[LazyBase] | None = None, args: tuple = (), func: Callable[[tuple], Any] | None = None):
+        super().__init__()
+        self._meta = meta
+        self._data = data
+        self._lazy = lazy if lazy is not None else deque()
+        self._args = args
+        self._func = func
+        assert self._func is not None or self._data is not None
+        if self._data is None:
+            self._lazy.append(self)
+
+    def __init_subclass__(cls) -> None:
+        if "_tensor_type" not in cls.__dict__:
+            raise TypeError(f"property '_tensor_type' must be defined for {cls!r}")
+        return super().__init_subclass__()
+
+    @staticmethod
+    def _recurse_apply(o: Any, fn: Callable[[Any], Any]) -> Any:
+        # TODO: dict and set
+        if isinstance(o, (list, tuple)):
+            L = []
+            for item in o:
+                L.append(LazyBase._recurse_apply(item, fn))
+            if isinstance(o, tuple):
+                L = tuple(L)
+            return L
+        elif isinstance(o, LazyBase):
+            return fn(o)
+        else:
+            return o
+
+    @classmethod
+    def _wrap_fn(cls, fn: Callable, *, use_self: LazyBase | None = None, meta_noop: bool | DTypeLike | tuple[DTypeLike, Callable[[tuple[int, ...]], tuple[int, ...]]] = False) -> Callable[[Any], Any]:
+        def wrapped_fn(*args, **kwargs):
+            if kwargs is None:
+                kwargs = {}
+            args = ((use_self,) if use_self is not None else ()) + args
+
+            meta_args = LazyBase._recurse_apply(args, lambda t: t._meta)
+
+            if isinstance(meta_noop, bool) and not meta_noop:
+                try:
+                    res = fn(*meta_args, **kwargs)
+                except NotImplementedError:
+                    # running some operations on PyTorch's Meta tensors can cause this exception
+                    res = None
+            else:
+                # some operators don't need to actually run on the meta tensors
+                assert len(args) > 0
+                res = args[0]
+                assert isinstance(res, cls)
+                res = res._meta
+                # allow operations to override the dtype and shape
+                if meta_noop is not True:
+                    if isinstance(meta_noop, tuple):
+                        dtype, shape = meta_noop
+                        assert callable(shape)
+                        res = cls.meta_with_dtype_and_shape(dtype, shape(res.shape))
+                    else:
+                        res = cls.meta_with_dtype_and_shape(meta_noop, res.shape)
+
+            if isinstance(res, cls._tensor_type):
+                def collect_replace(t: LazyBase):
+                    if collect_replace.shared_lazy is None:
+                        collect_replace.shared_lazy = t._lazy
+                    else:
+                        collect_replace.shared_lazy.extend(t._lazy)
+                        t._lazy = collect_replace.shared_lazy
+
+                # emulating a static variable
+                collect_replace.shared_lazy = None
+
+                LazyBase._recurse_apply(args, collect_replace)
+
+                shared_lazy = collect_replace.shared_lazy
+
+                return cls(meta=cls.eager_to_meta(res), lazy=shared_lazy, args=args, func=lambda a: fn(*a, **kwargs))
+            else:
+                del res  # not needed
+                # non-tensor return likely relies on the contents of the args
+                # (e.g. the result of torch.equal)
+                eager_args = cls.to_eager(args)
+                return fn(*eager_args, **kwargs)
+        return wrapped_fn
+
+    @classmethod
+    def to_eager(cls, t: Any) -> Any:
+        def simple_to_eager(_t: LazyBase) -> Any:
+            def already_eager_to_eager(_t: LazyBase) -> Any:
+                assert _t._data is not None
+                return _t._data
+
+            while _t._data is None:
+                lt = _t._lazy.popleft()
+                if lt._data is not None:
+                    # Lazy tensor did not belong in the lazy queue.
+                    # Weirdly only happens with Bloom models...
+                    # likely because tensors aren't unique in the queue.
+                    # The final output is still the same as in eager mode,
+                    # so it's safe to ignore this.
+                    continue
+                assert lt._func is not None
+                lt._args = cls._recurse_apply(lt._args, already_eager_to_eager)
+                lt._data = lt._func(lt._args)
+                # sanity check
+                assert lt._data.dtype == lt._meta.dtype
+                assert lt._data.shape == lt._meta.shape
+
+            return _t._data
+
+        # recurse into lists and/or tuples, keeping their structure
+        return cls._recurse_apply(t, simple_to_eager)
+
+    @classmethod
+    def eager_to_meta(cls, t: Any) -> Any:
+        return cls.meta_with_dtype_and_shape(t.dtype, t.shape)
+
+    # must be overridden, meta tensor init is backend-specific
+    @classmethod
+    @abstractmethod
+    def meta_with_dtype_and_shape(cls, dtype: Any, shape: Any) -> Any: pass
+
+    @classmethod
+    def from_eager(cls, t: Any) -> Any:
+        if type(t) is cls:
+            # already eager
+            return t
+        elif isinstance(t, cls._tensor_type):
+            return cls(meta=cls.eager_to_meta(t), data=t)
+        else:
+            return TypeError(f"{type(t)!r} is not compatible with {cls._tensor_type!r}")
+
+
+class LazyNumpyTensor(LazyBase):
+    _tensor_type = np.ndarray
+
+    @classmethod
+    def meta_with_dtype_and_shape(cls, dtype: DTypeLike, shape: _Shape) -> np.ndarray[Any, Any]:
+        # The initial idea was to use np.nan as the fill value,
+        # but non-float types like np.int16 can't use that.
+        # So zero it is.
+        cheat = np.zeros(1, dtype)
+        return np.lib.stride_tricks.as_strided(cheat, shape, (0 for _ in shape))
+
+    def astype(self, dtype, *args, **kwargs):
+        meta = type(self).meta_with_dtype_and_shape(dtype, self._meta.shape)
+        full_args = (self, dtype,) + args
+        # very important to pass the shared _lazy deque, or else there's an infinite loop somewhere.
+        return type(self)(meta=meta, args=full_args, lazy=self._lazy, func=(lambda a: a[0].astype(*a[1:], **kwargs)))
+
+    def tofile(self, *args, **kwargs):
+        eager = LazyNumpyTensor.to_eager(self)
+        return eager.tofile(*args, **kwargs)
+
+    # TODO: __array_function__
diff --git a/gguf-py/gguf/quants.py b/gguf-py/gguf/quants.py
new file mode 100644
index 000000000..e7fc0eae3
--- /dev/null
+++ b/gguf-py/gguf/quants.py
@@ -0,0 +1,109 @@
+from __future__ import annotations
+from typing import Callable
+
+from numpy.typing import DTypeLike
+
+from .constants import GGML_QUANT_SIZES, GGMLQuantizationType
+from .lazy import LazyNumpyTensor
+
+import numpy as np
+
+
+# same as ggml_compute_fp32_to_bf16 in ggml-impl.h
+def __compute_fp32_to_bf16(n: np.ndarray) -> np.ndarray:
+    n = n.astype(np.float32, copy=False).view(np.int32)
+    # force nan to quiet
+    n = np.where((n & 0x7fffffff) > 0x7f800000, (n & 0xffff0000) | (64 << 16), n)
+    # flush subnormals to zero
+    n = np.where((n & 0x7f800000) == 0, n & 0x80000000, n)
+    # round to nearest even
+    n = (n + (0x7fff + ((n >> 16) & 1))) >> 16
+    return n.astype(np.int16)
+
+
+# This is faster than np.vectorize and np.apply_along_axis because it works on more than one row at a time
+def __apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.ndarray, otype: DTypeLike, oshape: tuple[int, ...]) -> np.ndarray:
+    rows = arr.reshape((-1, arr.shape[-1]))
+    osize = 1
+    for dim in oshape:
+        osize *= dim
+    out = np.empty(shape=osize, dtype=otype)
+    # compute over groups of 16 rows (arbitrary, but seems good for performance)
+    n_groups = rows.shape[0] // 16
+    np.concatenate([func(group).ravel() for group in np.array_split(rows, n_groups)], axis=0, out=out)
+    return out.reshape(oshape)
+
+
+def __quantize_bf16_array(n: np.ndarray) -> np.ndarray:
+    return __apply_over_grouped_rows(__compute_fp32_to_bf16, arr=n, otype=np.int16, oshape=n.shape)
+
+
+__quantize_bf16_lazy = LazyNumpyTensor._wrap_fn(__quantize_bf16_array, meta_noop=np.int16)
+
+
+def quantize_bf16(n: np.ndarray):
+    if type(n) is LazyNumpyTensor:
+        return __quantize_bf16_lazy(n)
+    else:
+        return __quantize_bf16_array(n)
+
+
+__q8_block_size, __q8_type_size = GGML_QUANT_SIZES[GGMLQuantizationType.Q8_0]
+
+
+def can_quantize_to_q8_0(n: np.ndarray) -> bool:
+    return n.shape[-1] % __q8_block_size == 0
+
+
+# round away from zero
+# ref: https://stackoverflow.com/a/59143326/22827863
+def np_roundf(n: np.ndarray) -> np.ndarray:
+    a = abs(n)
+    floored = np.floor(a)
+    b = floored + np.floor(2 * (a - floored))
+    return np.sign(n) * b
+
+
+def __quantize_q8_0_shape_change(s: tuple[int, ...]) -> tuple[int, ...]:
+    return (*s[:-1], s[-1] // __q8_block_size * __q8_type_size)
+
+
+# Implementation of Q8_0 with bit-exact same results as reference implementation in ggml-quants.c
+def __quantize_q8_0_rows(n: np.ndarray) -> np.ndarray:
+    shape = n.shape
+    assert shape[-1] % __q8_block_size == 0
+
+    n_blocks = n.size // __q8_block_size
+
+    blocks = n.reshape((n_blocks, __q8_block_size)).astype(np.float32, copy=False)
+
+    d = abs(blocks).max(axis=1, keepdims=True) / 127
+    with np.errstate(divide="ignore"):
+        id = np.where(d == 0, 0, 1 / d)
+    qs = np_roundf(blocks * id)
+
+    # (n_blocks, 2)
+    d = d.astype(np.float16).view(np.uint8)
+    # (n_blocks, block_size)
+    qs = qs.astype(np.int8).view(np.uint8)
+
+    assert d.shape[1] + qs.shape[1] == __q8_type_size
+
+    return np.concatenate([d, qs], axis=1).reshape(__quantize_q8_0_shape_change(shape))
+
+
+def __quantize_q8_0_array(n: np.ndarray) -> np.ndarray:
+    return __apply_over_grouped_rows(__quantize_q8_0_rows, arr=n, otype=np.uint8, oshape=__quantize_q8_0_shape_change(n.shape))
+
+
+__quantize_q8_0_lazy = LazyNumpyTensor._wrap_fn(
+    __quantize_q8_0_array,
+    meta_noop=(np.uint8, __quantize_q8_0_shape_change),
+)
+
+
+def quantize_q8_0(data: np.ndarray):
+    if type(data) is LazyNumpyTensor:
+        return __quantize_q8_0_lazy(data)
+    else:
+        return __quantize_q8_0_array(data)
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 861003776..8e1cac915 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -10,7 +10,7 @@ class TensorNameMap:
         # Token embeddings
         MODEL_TENSOR.TOKEN_EMBD: (
             "gpt_neox.embed_in",                         # gptneox
-            "transformer.wte",                           # gpt2 gpt-j mpt refact qwen
+            "transformer.wte",                           # gpt2 gpt-j mpt refact qwen dbrx
             "transformer.word_embeddings",               # falcon
             "word_embeddings",                           # bloom
             "model.embed_tokens",                        # llama-hf
@@ -20,6 +20,10 @@ class TensorNameMap:
             "wte",                                       # gpt2
             "transformer.embd.wte",                      # phi2
             "model.tok_embeddings",                      # internlm2
+            "model.embedding",                           # mamba-qbert
+            "backbone.embedding",                        # mamba
+            "backbone.embeddings",                       # mamba-hf
+            "transformer.in_out_embed",                  # Grok
         ),
 
         # Token type embeddings
@@ -44,7 +48,7 @@ class TensorNameMap:
         # Output
         MODEL_TENSOR.OUTPUT: (
             "embed_out",                 # gptneox
-            "lm_head",                   # gpt2 mpt falcon llama-hf baichuan qwen
+            "lm_head",                   # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx
             "output",                    # llama-pth bloom internlm2
             "word_embeddings_for_head",  # persimmon
             "lm_head.linear",            # phi2
@@ -56,11 +60,14 @@ class TensorNameMap:
             "transformer.ln_f",                        # gpt2 gpt-j falcon
             "model.norm",                              # llama-hf baichuan internlm2
             "norm",                                    # llama-pth
-            "transformer.norm_f",                      # mpt
+            "transformer.norm_f",                      # mpt dbrx
             "ln_f",                                    # refact bloom qwen gpt2
             "language_model.encoder.final_layernorm",  # persimmon
             "model.final_layernorm",                   # persimmon
             "lm_head.ln",                              # phi2
+            "model.norm_f",                            # mamba-qbert
+            "backbone.norm_f",                         # mamba
+            "transformer.rms_norm",                    # Grok
         ),
 
         # Rope frequencies
@@ -86,6 +93,10 @@ class TensorNameMap:
             "transformer.h.{bid}.ln",                               # phi2
             "model.layers.layers.{bid}.norm",                       # plamo
             "model.layers.{bid}.attention_norm",                    # internlm2
+            "model.layers.{bid}.norm",                              # mamba-qbert
+            "backbone.layers.{bid}.norm",                           # mamba
+            "transformer.decoder_layer.{bid}.rms_norm",             # Grok
+            "transformer.blocks.{bid}.norm_attn_norm.norm_1",       # dbrx
         ),
 
         # Attention norm 2
@@ -98,6 +109,7 @@ class TensorNameMap:
             "gpt_neox.layers.{bid}.attention.query_key_value",                     # gptneox
             "transformer.h.{bid}.attn.c_attn",                                     # gpt2 qwen
             "transformer.blocks.{bid}.attn.Wqkv",                                  # mpt
+            "transformer.blocks.{bid}.norm_attn_norm.attn.Wqkv",                   # dbrx
             "transformer.h.{bid}.self_attention.query_key_value",                  # falcon
             "h.{bid}.self_attention.query_key_value",                              # bloom
             "language_model.encoder.layers.{bid}.self_attention.query_key_value",  # persimmon
@@ -105,62 +117,72 @@ class TensorNameMap:
             "h.{bid}.attn.c_attn",                                                 # gpt2
             "transformer.h.{bid}.mixer.Wqkv",                                      # phi2
             "encoder.layers.{bid}.attn.Wqkv",                                      # nomic-bert
+            "model.layers.{bid}.self_attn.qkv_proj"                                # phi3
         ),
 
         # Attention query
         MODEL_TENSOR.ATTN_Q: (
-            "model.layers.{bid}.self_attn.q_proj",         # llama-hf
-            "layers.{bid}.attention.wq",                   # llama-pth
-            "encoder.layer.{bid}.attention.self.query",    # bert
-            "transformer.h.{bid}.attn.q_proj",             # gpt-j
-            "model.layers.layers.{bid}.self_attn.q_proj",  # plamo
-            "model.layers.{bid}.attention.wq"             # internlm2
+            "model.layers.{bid}.self_attn.q_proj",                       # llama-hf
+            "layers.{bid}.attention.wq",                                 # llama-pth
+            "encoder.layer.{bid}.attention.self.query",                  # bert
+            "transformer.h.{bid}.attn.q_proj",                           # gpt-j
+            "model.layers.layers.{bid}.self_attn.q_proj",                # plamo
+            "model.layers.{bid}.attention.wq",                           # internlm2
+            "transformer.decoder_layer.{bid}.multi_head_attention.query" # Grok
         ),
 
         # Attention key
         MODEL_TENSOR.ATTN_K: (
-            "model.layers.{bid}.self_attn.k_proj",         # llama-hf
-            "layers.{bid}.attention.wk",                   # llama-pth
-            "encoder.layer.{bid}.attention.self.key",      # bert
-            "transformer.h.{bid}.attn.k_proj",             # gpt-j
-            "model.layers.layers.{bid}.self_attn.k_proj",  # plamo
-            "model.layers.{bid}.attention.wk"             # internlm2
+            "model.layers.{bid}.self_attn.k_proj",                     # llama-hf
+            "layers.{bid}.attention.wk",                               # llama-pth
+            "encoder.layer.{bid}.attention.self.key",                  # bert
+            "transformer.h.{bid}.attn.k_proj",                         # gpt-j
+            "transformer.h.{bid}.attn.k",                              # refact
+            "model.layers.layers.{bid}.self_attn.k_proj",              # plamo
+            "model.layers.{bid}.attention.wk",                         # internlm2
+            "transformer.decoder_layer.{bid}.multi_head_attention.key" # Grok
         ),
 
         # Attention value
         MODEL_TENSOR.ATTN_V: (
-            "model.layers.{bid}.self_attn.v_proj",         # llama-hf
-            "layers.{bid}.attention.wv",                   # llama-pth
-            "encoder.layer.{bid}.attention.self.value",    # bert
-            "transformer.h.{bid}.attn.v_proj",             # gpt-j
-            "model.layers.layers.{bid}.self_attn.v_proj",  # plamo
-            "model.layers.{bid}.attention.wv"             # internlm2
+            "model.layers.{bid}.self_attn.v_proj",                       # llama-hf
+            "layers.{bid}.attention.wv",                                 # llama-pth
+            "encoder.layer.{bid}.attention.self.value",                  # bert
+            "transformer.h.{bid}.attn.v_proj",                           # gpt-j
+            "transformer.h.{bid}.attn.v",                                # refact
+            "model.layers.layers.{bid}.self_attn.v_proj",                # plamo
+            "model.layers.{bid}.attention.wv",                           # internlm2
+            "transformer.decoder_layer.{bid}.multi_head_attention.value" # Grok
         ),
 
         # Attention output
         MODEL_TENSOR.ATTN_OUT: (
-            "gpt_neox.layers.{bid}.attention.dense",                     # gptneox
-            "transformer.h.{bid}.attn.c_proj",                           # gpt2 refact qwen
-            "transformer.blocks.{bid}.attn.out_proj",                    # mpt
-            "transformer.h.{bid}.self_attention.dense",                  # falcon
-            "h.{bid}.self_attention.dense",                              # bloom
-            "model.layers.{bid}.self_attn.o_proj",                       # llama-hf
-            "layers.{bid}.attention.wo",                                 # llama-pth
-            "encoder.layer.{bid}.attention.output.dense",                # bert
-            "transformer.h.{bid}.attn.out_proj",                         # gpt-j
-            "language_model.encoder.layers.{bid}.self_attention.dense",  # persimmon
-            "model.layers.{bid}.self_attn.dense",                        # persimmon
-            "h.{bid}.attn.c_proj",                                       # gpt2
-            "transformer.h.{bid}.mixer.out_proj",                        # phi2
-            "model.layers.layers.{bid}.self_attn.o_proj",                # plamo
-            "model.layers.{bid}.attention.wo",                           # internlm2
-            "encoder.layers.{bid}.attn.out_proj",                        # nomic-bert
+            "gpt_neox.layers.{bid}.attention.dense",                        # gptneox
+            "transformer.h.{bid}.attn.c_proj",                              # gpt2 refact qwen
+            "transformer.blocks.{bid}.attn.out_proj",                       # mpt
+            "transformer.h.{bid}.self_attention.dense",                     # falcon
+            "h.{bid}.self_attention.dense",                                 # bloom
+            "model.layers.{bid}.self_attn.o_proj",                          # llama-hf
+            "layers.{bid}.attention.wo",                                    # llama-pth
+            "encoder.layer.{bid}.attention.output.dense",                   # bert
+            "transformer.h.{bid}.attn.out_proj",                            # gpt-j
+            "language_model.encoder.layers.{bid}.self_attention.dense",     # persimmon
+            "model.layers.{bid}.self_attn.dense",                           # persimmon
+            "h.{bid}.attn.c_proj",                                          # gpt2
+            "transformer.h.{bid}.mixer.out_proj",                           # phi2
+            "model.layers.layers.{bid}.self_attn.o_proj",                   # plamo
+            "model.layers.{bid}.attention.wo",                              # internlm2
+            "encoder.layers.{bid}.attn.out_proj",                           # nomic-bert
+            "transformer.decoder_layer.{bid}.multi_head_attention.linear",  # Grok
+            "transformer.blocks.{bid}.norm_attn_norm.attn.out_proj",        # dbrx
         ),
 
         # Attention output norm
         MODEL_TENSOR.ATTN_OUT_NORM: (
             "encoder.layer.{bid}.attention.output.LayerNorm",  # bert
             "encoder.layers.{bid}.norm1",                      # nomic-bert
+            "transformer.decoder_layer.{bid}.rms_norm_1",      # Grok
+            "transformer.blocks.{bid}.norm_attn_norm.norm_2",  # dbrx
         ),
 
         # Rotary embeddings
@@ -183,11 +205,19 @@ class TensorNameMap:
             "model.layers.{bid}.ln2",                                        # yi
             "h.{bid}.ln_2",                                                  # gpt2
             "model.layers.{bid}.ffn_norm",                                   # internlm2
+            "transformer.decoder_layer.{bid}.rms_norm_2",                    # Grok
         ),
 
         MODEL_TENSOR.FFN_GATE_INP: (
-            "layers.{bid}.feed_forward.gate",           # mixtral
-            "model.layers.{bid}.block_sparse_moe.gate", # mixtral
+            "layers.{bid}.feed_forward.gate",             # mixtral
+            "model.layers.{bid}.block_sparse_moe.gate",   # mixtral
+            "model.layers.{bid}.mlp.gate",                # qwen2moe
+            "transformer.decoder_layer.{bid}.router",     # Grok
+            "transformer.blocks.{bid}.ffn.router.layer",  # dbrx
+        ),
+
+        MODEL_TENSOR.FFN_GATE_INP_SHEXP: (
+            "model.layers.{bid}.mlp.shared_expert_gate", # qwen2moe
         ),
 
         # Feed-forward up
@@ -201,20 +231,30 @@ class TensorNameMap:
             "layers.{bid}.feed_forward.w3",                           # llama-pth
             "encoder.layer.{bid}.intermediate.dense",                 # bert
             "transformer.h.{bid}.mlp.fc_in",                          # gpt-j
+            "transformer.h.{bid}.mlp.linear_3",                       # refact
             "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h",  # persimmon
             "model.layers.{bid}.mlp.dense_h_to_4h",                   # persimmon
             "transformer.h.{bid}.mlp.w1",                             # qwen
             "h.{bid}.mlp.c_fc",                                       # gpt2
             "transformer.h.{bid}.mlp.fc1",                            # phi2
             "model.layers.{bid}.mlp.fc1",                             # phi2
+            "model.layers.{bid}.mlp.gate_up_proj",                    # phi3
             "model.layers.layers.{bid}.mlp.up_proj",                  # plamo
             "model.layers.{bid}.feed_forward.w3",                     # internlm2
             "encoder.layers.{bid}.mlp.fc11",                          # nomic-bert
+            "model.layers.{bid}.mlp.c_fc",                            # starcoder2
+            "encoder.layer.{bid}.mlp.gated_layers_v",                 # jina-bert-v2
         ),
 
         MODEL_TENSOR.FFN_UP_EXP: (
-            "layers.{bid}.feed_forward.experts.{xid}.w3",           # mixtral
-            "model.layers.{bid}.block_sparse_moe.experts.{xid}.w3", # mixtral
+            "layers.{bid}.feed_forward.experts.w3",          # mixtral (merged)
+            "transformer.decoder_layer.{bid}.moe.linear_v",  # Grok (merged)
+            "transformer.blocks.{bid}.ffn.experts.mlp.v1",   # dbrx
+            "model.layers.{bid}.mlp.experts.up_proj",        # qwen2moe (merged)
+        ),
+
+        MODEL_TENSOR.FFN_UP_SHEXP: (
+            "model.layers.{bid}.mlp.shared_expert.up_proj",  # qwen2moe
         ),
 
         # AWQ-activation gate
@@ -230,11 +270,19 @@ class TensorNameMap:
             "model.layers.layers.{bid}.mlp.gate_proj",    # plamo
             "model.layers.{bid}.feed_forward.w1",         # internlm2
             "encoder.layers.{bid}.mlp.fc12",              # nomic-bert
+            "encoder.layer.{bid}.mlp.gated_layers_w",     # jina-bert-v2
+            "transformer.h.{bid}.mlp.linear_1",           # refact
         ),
 
         MODEL_TENSOR.FFN_GATE_EXP: (
-            "layers.{bid}.feed_forward.experts.{xid}.w1",           # mixtral
-            "model.layers.{bid}.block_sparse_moe.experts.{xid}.w1", # mixtral
+            "layers.{bid}.feed_forward.experts.w1",         # mixtral (merged)
+            "transformer.decoder_layer.{bid}.moe.linear",   # Grok (merged)
+            "transformer.blocks.{bid}.ffn.experts.mlp.w1",  # dbrx
+            "model.layers.{bid}.mlp.experts.gate_proj",     # qwen2moe (merged)
+        ),
+
+        MODEL_TENSOR.FFN_GATE_SHEXP: (
+            "model.layers.{bid}.mlp.shared_expert.gate_proj",  # qwen2moe
         ),
 
         # Feed-forward down
@@ -256,21 +304,35 @@ class TensorNameMap:
             "model.layers.layers.{bid}.mlp.down_proj",                # plamo
             "model.layers.{bid}.feed_forward.w2",                     # internlm2
             "encoder.layers.{bid}.mlp.fc2",                           # nomic-bert
+            "model.layers.{bid}.mlp.c_proj",                          # starcoder2
+            "encoder.layer.{bid}.mlp.wo",                             # jina-bert-v2
         ),
 
         MODEL_TENSOR.FFN_DOWN_EXP: (
-            "layers.{bid}.feed_forward.experts.{xid}.w2",           # mixtral
-            "model.layers.{bid}.block_sparse_moe.experts.{xid}.w2", # mixtral
+            "layers.{bid}.feed_forward.experts.w2",          # mixtral (merged)
+            "transformer.decoder_layer.{bid}.moe.linear_1",  # Grok (merged)
+            "transformer.blocks.{bid}.ffn.experts.mlp.w2",   # dbrx
+            "model.layers.{bid}.mlp.experts.down_proj",      # qwen2moe (merged)
+        ),
+
+        MODEL_TENSOR.FFN_DOWN_SHEXP: (
+            "model.layers.{bid}.mlp.shared_expert.down_proj",  # qwen2moe
         ),
 
         MODEL_TENSOR.ATTN_Q_NORM: (
             "language_model.encoder.layers.{bid}.self_attention.q_layernorm",
             "model.layers.{bid}.self_attn.q_layernorm",                       # persimmon
+            "model.layers.{bid}.self_attn.q_norm",                            # cohere
+            "transformer.blocks.{bid}.attn.q_ln",                             # sea-lion
+            "encoder.layer.{bid}.attention.self.layer_norm_q"                 # jina-bert-v2
         ),
 
         MODEL_TENSOR.ATTN_K_NORM: (
             "language_model.encoder.layers.{bid}.self_attention.k_layernorm",
             "model.layers.{bid}.self_attn.k_layernorm",                       # persimmon
+            "model.layers.{bid}.self_attn.k_norm",                            # cohere
+            "transformer.blocks.{bid}.attn.k_ln",                             # sea-lion
+            "encoder.layer.{bid}.attention.self.layer_norm_k"                 # jina-bert-v2
         ),
 
         MODEL_TENSOR.ROPE_FREQS: (
@@ -278,9 +340,46 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.LAYER_OUT_NORM: (
-            "encoder.layer.{bid}.output.LayerNorm",  # bert
-            "encoder.layers.{bid}.norm2",            # nomic-bert
-        )
+            "encoder.layer.{bid}.output.LayerNorm",         # bert
+            "encoder.layers.{bid}.norm2",                   # nomic-bert
+            "transformer.decoder_layer.{bid}.rms_norm_3",   # Grok
+            "encoder.layer.{bid}.mlp.layernorm",            # jina-bert-v2
+        ),
+
+        MODEL_TENSOR.SSM_IN: (
+            "model.layers.{bid}.in_proj",
+            "backbone.layers.{bid}.mixer.in_proj",
+        ),
+
+        MODEL_TENSOR.SSM_CONV1D: (
+            "model.layers.{bid}.conv1d",
+            "backbone.layers.{bid}.mixer.conv1d",
+        ),
+
+        MODEL_TENSOR.SSM_X: (
+            "model.layers.{bid}.x_proj",
+            "backbone.layers.{bid}.mixer.x_proj",
+        ),
+
+        MODEL_TENSOR.SSM_DT: (
+            "model.layers.{bid}.dt_proj",
+            "backbone.layers.{bid}.mixer.dt_proj",
+        ),
+
+        MODEL_TENSOR.SSM_A: (
+            "model.layers.{bid}.A_log",
+            "backbone.layers.{bid}.mixer.A_log",
+        ),
+
+        MODEL_TENSOR.SSM_D: (
+            "model.layers.{bid}.D",
+            "backbone.layers.{bid}.mixer.D",
+        ),
+
+        MODEL_TENSOR.SSM_OUT: (
+            "model.layers.{bid}.out_proj",
+            "backbone.layers.{bid}.mixer.out_proj",
+        ),
     }
 
     mapping: dict[str, tuple[MODEL_TENSOR, str]]
@@ -299,7 +398,7 @@ class TensorNameMap:
                 if tensor not in MODEL_TENSORS[arch]:
                     continue
                 # TODO: make this configurable
-                n_experts = 8
+                n_experts = 60
                 for xid in range(n_experts):
                     tensor_name = TENSOR_NAMES[tensor].format(bid = bid, xid = xid)
                     self.mapping[tensor_name] = (tensor, tensor_name)
diff --git a/gguf-py/gguf/vocab.py b/gguf-py/gguf/vocab.py
index a23136b18..3ba99be4f 100644
--- a/gguf-py/gguf/vocab.py
+++ b/gguf-py/gguf/vocab.py
@@ -1,23 +1,25 @@
 from __future__ import annotations
 
+import logging
 import json
 import os
-import sys
 from pathlib import Path
-from typing import Any, Callable
+from typing import Any, Callable, Sequence, Mapping, Iterable
 
 from .gguf_writer import GGUFWriter
 
+logger = logging.getLogger(__name__)
+
 
 class SpecialVocab:
     merges: list[str]
     add_special_token: dict[str, bool]
     special_token_ids: dict[str, int]
-    chat_template: str | None
+    chat_template: str | Sequence[Mapping[str, str]] | None
 
     def __init__(
         self, path: str | os.PathLike[str], load_merges: bool = False,
-        special_token_types: tuple[str, ...] | None = None,
+        special_token_types: Iterable[str] | None = None,
         n_vocab: int | None = None,
     ):
         self.special_token_ids = {}
@@ -40,38 +42,29 @@ class SpecialVocab:
     def add_to_gguf(self, gw: GGUFWriter, quiet: bool = False) -> None:
         if self.merges:
             if not quiet:
-                print(f'gguf: Adding {len(self.merges)} merge(s).')
+                logger.info(f'Adding {len(self.merges)} merge(s).')
             gw.add_token_merges(self.merges)
         elif self.load_merges:
-            print(
-                'gguf: WARNING: Adding merges requested but no merges found, output may be non-functional.',
-                file = sys.stderr,
-            )
+            logger.warning('Adding merges requested but no merges found, output may be non-functional.')
         for typ, tokid in self.special_token_ids.items():
             id_handler: Callable[[int], None] | None = getattr(gw, f'add_{typ}_token_id', None)
             if id_handler is None:
-                print(
-                    f'gguf: WARNING: No handler for special token type {typ} with id {tokid} - skipping',
-                    file = sys.stderr,
-                )
+                logger.warning(f'No handler for special token type {typ} with id {tokid} - skipping')
                 continue
             if not quiet:
-                print(f'gguf: Setting special token type {typ} to {tokid}')
+                logger.info(f'Setting special token type {typ} to {tokid}')
             id_handler(tokid)
         for typ, value in self.add_special_token.items():
             add_handler: Callable[[bool], None] | None = getattr(gw, f'add_add_{typ}_token', None)
             if add_handler is None:
-                print(
-                    f'gguf: WARNING: No handler for add_{typ}_token with value {value} - skipping',
-                    file = sys.stderr,
-                )
+                logger.warning(f'No handler for add_{typ}_token with value {value} - skipping')
                 continue
             if not quiet:
-                print(f'gguf: Setting add_{typ}_token to {value}')
+                logger.info(f'Setting add_{typ}_token to {value}')
             add_handler(value)
         if self.chat_template is not None:
             if not quiet:
-                print(f'gguf: Setting chat_template to {self.chat_template}')
+                logger.info(f'Setting chat_template to {self.chat_template}')
             gw.add_chat_template(self.chat_template)
 
     def _load(self, path: Path) -> None:
@@ -99,10 +92,7 @@ class SpecialVocab:
                     continue
                 parts = line.split(None, 3)
                 if len(parts) != 2:
-                    print(
-                        f'gguf: WARNING: {merges_file.name}: Line {line_num}: Entry malformed, ignoring',
-                        file = sys.stderr,
-                    )
+                    logger.warning(f'{merges_file.name}: Line {line_num}: Entry malformed, ignoring')
                     continue
                 merges.append(f'{parts[0]} {parts[1]}')
         self.merges = merges
@@ -118,10 +108,7 @@ class SpecialVocab:
                 return
             self.special_token_ids[typ] = tid
             return
-        print(
-            f'gguf: WARNING: Special token type {typ}, id {tid} out of range, must be under {self.n_vocab} - skipping',
-            file = sys.stderr,
-        )
+        logger.warning(f'Special token type {typ}, id {tid} out of range, must be under {self.n_vocab} - skipping')
 
     def _try_load_from_tokenizer_json(self, path: Path) -> bool:
         tokenizer_file = path / 'tokenizer.json'
@@ -141,13 +128,10 @@ class SpecialVocab:
         with open(tokenizer_config_file, encoding = 'utf-8') as f:
             tokenizer_config = json.load(f)
         chat_template = tokenizer_config.get('chat_template')
-        if chat_template is None or isinstance(chat_template, str):
+        if chat_template is None or isinstance(chat_template, (str, list)):
             self.chat_template = chat_template
         else:
-            print(
-                f'gguf: WARNING: Bad type for chat_template field in {tokenizer_config_file!r} - ignoring',
-                file = sys.stderr
-            )
+            logger.warning(f'Bad type for chat_template field in {tokenizer_config_file!r} - ignoring')
         for typ in self.special_token_types:
             add_entry = tokenizer_config.get(f'add_{typ}_token')
             if isinstance(add_entry, bool):
diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml
index 9789c2c87..36e63ee3b 100644
--- a/gguf-py/pyproject.toml
+++ b/gguf-py/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "gguf"
-version = "0.7.0"
+version = "0.9.0"
 description = "Read and write ML models in GGUF for GGML"
 authors = ["GGML <ggml@ggml.ai>"]
 packages = [
@@ -21,6 +21,7 @@ classifiers = [
 [tool.poetry.dependencies]
 python = ">=3.8"
 numpy = ">=1.17"
+tqdm = ">=4.27"
 
 [tool.poetry.dev-dependencies]
 pytest = "^5.2"
@@ -33,3 +34,4 @@ build-backend = "poetry.core.masonry.api"
 gguf-convert-endian = "scripts:gguf_convert_endian_entrypoint"
 gguf-dump = "scripts:gguf_dump_entrypoint"
 gguf-set-metadata = "scripts:gguf_set_metadata_entrypoint"
+gguf-new-metadata = "scripts:gguf_new_metadata_entrypoint"
diff --git a/gguf-py/scripts/__init__.py b/gguf-py/scripts/__init__.py
index 77132db7a..1ad45639a 100644
--- a/gguf-py/scripts/__init__.py
+++ b/gguf-py/scripts/__init__.py
@@ -8,5 +8,6 @@ os.environ["NO_LOCAL_GGUF"] = "TRUE"
 gguf_convert_endian_entrypoint = import_module("scripts.gguf-convert-endian").main
 gguf_dump_entrypoint           = import_module("scripts.gguf-dump").main
 gguf_set_metadata_entrypoint   = import_module("scripts.gguf-set-metadata").main
+gguf_new_metadata_entrypoint   = import_module("scripts.gguf-new-metadata").main
 
 del import_module, os
diff --git a/gguf-py/scripts/gguf-convert-endian.py b/gguf-py/scripts/gguf-convert-endian.py
index 10a16ad06..b698af0fe 100755
--- a/gguf-py/scripts/gguf-convert-endian.py
+++ b/gguf-py/scripts/gguf-convert-endian.py
@@ -1,9 +1,11 @@
 #!/usr/bin/env python3
 from __future__ import annotations
 
+import logging
 import argparse
 import os
 import sys
+from tqdm import tqdm
 from pathlib import Path
 
 import numpy as np
@@ -14,6 +16,8 @@ if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent /
 
 import gguf
 
+logger = logging.getLogger("gguf-convert-endian")
+
 
 def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None:
     if np.uint32(1) == np.uint32(1).newbyteorder("<"):
@@ -29,11 +33,11 @@ def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None
     else:
         file_endian = host_endian
     order = host_endian if args.order == "native" else args.order
-    print(f"* Host is {host_endian.upper()} endian, GGUF file seems to be {file_endian.upper()} endian")
+    logger.info(f"* Host is {host_endian.upper()} endian, GGUF file seems to be {file_endian.upper()} endian")
     if file_endian == order:
-        print(f"* File is already {order.upper()} endian. Nothing to do.")
+        logger.info(f"* File is already {order.upper()} endian. Nothing to do.")
         sys.exit(0)
-    print("* Checking tensors for conversion compatibility")
+    logger.info("* Checking tensors for conversion compatibility")
     for tensor in reader.tensors:
         if tensor.tensor_type not in (
             gguf.GGMLQuantizationType.F32,
@@ -41,51 +45,64 @@ def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None
             gguf.GGMLQuantizationType.Q8_0,
         ):
             raise ValueError(f"Cannot handle type {tensor.tensor_type.name} for tensor {repr(tensor.name)}")
-    print(f"* Preparing to convert from {file_endian.upper()} to {order.upper()}")
+    logger.info(f"* Preparing to convert from {file_endian.upper()} to {order.upper()}")
     if args.dry_run:
         return
-    print("\n*** Warning *** Warning *** Warning **")
-    print("* This conversion process may damage the file. Ensure you have a backup.")
+    logger.warning("*** Warning *** Warning *** Warning **")
+    logger.warning("* This conversion process may damage the file. Ensure you have a backup.")
     if order != host_endian:
-        print("* Requested endian differs from host, you will not be able to load the model on this machine.")
-    print("* The file will be modified immediately, so if conversion fails or is interrupted")
-    print("* the file will be corrupted. Enter exactly YES if you are positive you want to proceed:")
+        logger.warning("* Requested endian differs from host, you will not be able to load the model on this machine.")
+    logger.warning("* The file will be modified immediately, so if conversion fails or is interrupted")
+    logger.warning("* the file will be corrupted. Enter exactly YES if you are positive you want to proceed:")
     response = input("YES, I am sure> ")
     if response != "YES":
-        print("You didn't enter YES. Okay then, see ya!")
+        logger.warning("You didn't enter YES. Okay then, see ya!")
         sys.exit(0)
-    print(f"\n* Converting fields ({len(reader.fields)})")
+    logger.info(f"* Converting fields ({len(reader.fields)})")
     for idx, field in enumerate(reader.fields.values()):
-        print(f"- {idx:4}: Converting field {repr(field.name)}, part count: {len(field.parts)}")
+        logger.info(f"- {idx:4}: Converting field {repr(field.name)}, part count: {len(field.parts)}")
         for part in field.parts:
             part.byteswap(inplace=True)
-    print(f"\n* Converting tensors ({len(reader.tensors)})")
-    for idx, tensor in enumerate(reader.tensors):
-        print(
-            f"  - {idx:4}: Converting tensor {repr(tensor.name)}, type={tensor.tensor_type.name}, "
-            f"elements={tensor.n_elements}... ",
-            end="",
+    logger.info(f"* Converting tensors ({len(reader.tensors)})")
+
+    for idx, tensor in enumerate(pbar := tqdm(reader.tensors, desc="Converting tensor")):
+        log_message = (
+            f"Converting tensor {repr(tensor.name)}, "
+            f"type={tensor.tensor_type.name}, "
+            f"elements={tensor.n_elements} "
         )
-        tensor_type = tensor.tensor_type
+
+        # Byte-swap each part of the tensor's field
         for part in tensor.field.parts:
             part.byteswap(inplace=True)
-        if tensor_type != gguf.GGMLQuantizationType.Q8_0:
+
+        # Byte-swap tensor data if necessary
+        if tensor.tensor_type == gguf.GGMLQuantizationType.Q8_0:
+            # Handle Q8_0 tensor blocks (block_q8_0)
+            # Specific handling of block_q8_0 is required.
+            # Each block_q8_0 consists of an f16 delta (scaling factor) followed by 32 int8 quantizations.
+
+            block_size = 34 # 34 bytes = <f16 delta scaling factor> + 32 * <int8 quant>
+
+            n_blocks = len(tensor.data) // block_size
+            for block_num in (inner_pbar := tqdm(range(n_blocks), desc="Byte-swapping Blocks", leave=False)):
+                block_offs = block_num * block_size
+
+                # Byte-Swap f16 sized delta field
+                delta = tensor.data[block_offs:block_offs + 2].view(dtype=np.uint16)
+                delta.byteswap(inplace=True)
+
+                # Byte-Swap Q8 weights
+                if block_num % 100000 == 0:
+                    inner_pbar.set_description(f"Byte-swapping Blocks [{(n_blocks - block_num) // n_blocks}]")
+
+        else:
+            # Handle other tensor types
             tensor.data.byteswap(inplace=True)
-            print()
-            continue
-        # A Q8_0 block consists of a f16 delta followed by 32 int8 quants, so 34 bytes
-        block_size = 34
-        n_blocks = len(tensor.data) // block_size
-        for block_num in range(n_blocks):
-            block_offs = block_num * block_size
-            # I know I said f16, but it doesn't matter here - any simple 16 bit type works.
-            delta = tensor.data[block_offs:block_offs + 2].view(dtype=np.uint16)
-            delta.byteswap(inplace=True)
-            if block_num % 100000 == 0:
-                print(f"[{(n_blocks - block_num) // 1000}K]", end="")
-                sys.stdout.flush()
-        print()
-    print("* Completion")
+
+        pbar.set_description(log_message)
+
+    logger.info("* Completion")
 
 
 def main() -> None:
@@ -102,8 +119,13 @@ def main() -> None:
         "--dry-run", action="store_true",
         help="Don't actually change anything",
     )
+    parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
+
     args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"])
-    print(f'* Loading: {args.model}')
+
+    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
+
+    logger.info(f'* Loading: {args.model}')
     reader = gguf.GGUFReader(args.model, 'r' if args.dry_run else 'r+')
     convert_byteorder(reader, args)
 
diff --git a/gguf-py/scripts/gguf-dump.py b/gguf-py/scripts/gguf-dump.py
index dbf891508..1a37a7b91 100755
--- a/gguf-py/scripts/gguf-dump.py
+++ b/gguf-py/scripts/gguf-dump.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 from __future__ import annotations
 
+import logging
 import argparse
 import os
 import sys
@@ -15,6 +16,8 @@ if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent /
 
 from gguf import GGUFReader, GGUFValueType  # noqa: E402
 
+logger = logging.getLogger("gguf-dump")
+
 
 def get_file_host_endian(reader: GGUFReader) -> tuple[str, str]:
     host_endian = 'LITTLE' if np.uint32(1) == np.uint32(1).newbyteorder("<") else 'BIG'
@@ -29,8 +32,8 @@ def get_file_host_endian(reader: GGUFReader) -> tuple[str, str]:
 # please see the comments in the modify_gguf.py example.
 def dump_metadata(reader: GGUFReader, args: argparse.Namespace) -> None:
     host_endian, file_endian = get_file_host_endian(reader)
-    print(f'* File is {file_endian} endian, script is running on a {host_endian} endian host.')
-    print(f'\n* Dumping {len(reader.fields)} key/value pair(s)')
+    print(f'* File is {file_endian} endian, script is running on a {host_endian} endian host.')  # noqa: NP100
+    print(f'* Dumping {len(reader.fields)} key/value pair(s)')  # noqa: NP100
     for n, field in enumerate(reader.fields.values(), 1):
         if not field.types:
             pretty_type = 'N/A'
@@ -39,20 +42,21 @@ def dump_metadata(reader: GGUFReader, args: argparse.Namespace) -> None:
             pretty_type = '[' * nest_count + str(field.types[-1].name) + ']' * nest_count
         else:
             pretty_type = str(field.types[-1].name)
-        print(f'  {n:5}: {pretty_type:10} | {len(field.data):8} | {field.name}', end = '')
+
+        log_message = f'  {n:5}: {pretty_type:10} | {len(field.data):8} | {field.name}'
         if len(field.types) == 1:
             curr_type = field.types[0]
             if curr_type == GGUFValueType.STRING:
-                print(' = {0}'.format(repr(str(bytes(field.parts[-1]), encoding='utf8')[:60])), end = '')
+                log_message += ' = {0}'.format(repr(str(bytes(field.parts[-1]), encoding='utf-8')[:60]))
             elif field.types[0] in reader.gguf_scalar_to_np:
-                print(' = {0}'.format(field.parts[-1][0]), end = '')
-        print()
+                log_message += ' = {0}'.format(field.parts[-1][0])
+        print(log_message)  # noqa: NP100
     if args.no_tensors:
         return
-    print(f'\n* Dumping {len(reader.tensors)} tensor(s)')
+    print(f'* Dumping {len(reader.tensors)} tensor(s)')  # noqa: NP100
     for n, tensor in enumerate(reader.tensors, 1):
         prettydims = ', '.join('{0:5}'.format(d) for d in list(tensor.shape) + [1] * (4 - len(tensor.shape)))
-        print(f'  {n:5}: {tensor.n_elements:10} | {prettydims} | {tensor.tensor_type.name:7} | {tensor.name}')
+        print(f'  {n:5}: {tensor.n_elements:10} | {prettydims} | {tensor.tensor_type.name:7} | {tensor.name}')  # noqa: NP100
 
 
 def dump_metadata_json(reader: GGUFReader, args: argparse.Namespace) -> None:
@@ -103,10 +107,17 @@ def main() -> None:
     parser.add_argument("--no-tensors", action="store_true", help="Don't dump tensor metadata")
     parser.add_argument("--json",       action="store_true", help="Produce JSON output")
     parser.add_argument("--json-array", action="store_true", help="Include full array values in JSON output (long)")
+    parser.add_argument("--verbose",    action="store_true", help="increase output verbosity")
+
     args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"])
+
+    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
+
     if not args.json:
-        print(f'* Loading: {args.model}')
+        logger.info(f'* Loading: {args.model}')
+
     reader = GGUFReader(args.model, 'r')
+
     if args.json:
         dump_metadata_json(reader, args)
     else:
diff --git a/gguf-py/scripts/gguf-new-metadata.py b/gguf-py/scripts/gguf-new-metadata.py
new file mode 100755
index 000000000..63d3c5d8f
--- /dev/null
+++ b/gguf-py/scripts/gguf-new-metadata.py
@@ -0,0 +1,242 @@
+#!/usr/bin/env python3
+import logging
+import argparse
+import os
+import sys
+import json
+from pathlib import Path
+
+import numpy as np
+from tqdm import tqdm
+from typing import Any, Sequence, NamedTuple
+
+# Necessary to load the local gguf package
+if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
+    sys.path.insert(0, str(Path(__file__).parent.parent))
+
+import gguf
+
+logger = logging.getLogger("gguf-new-metadata")
+
+
+class MetadataDetails(NamedTuple):
+    type: gguf.GGUFValueType
+    value: Any
+    description: str = ''
+
+
+def get_byteorder(reader: gguf.GGUFReader) -> gguf.GGUFEndian:
+    if np.uint32(1) == np.uint32(1).newbyteorder("<"):
+        # Host is little endian
+        host_endian = gguf.GGUFEndian.LITTLE
+        swapped_endian = gguf.GGUFEndian.BIG
+    else:
+        # Sorry PDP or other weird systems that don't use BE or LE.
+        host_endian = gguf.GGUFEndian.BIG
+        swapped_endian = gguf.GGUFEndian.LITTLE
+
+    if reader.byte_order == "S":
+        return swapped_endian
+    else:
+        return host_endian
+
+
+def decode_field(field: gguf.ReaderField | None) -> Any:
+    if field and field.types:
+        main_type = field.types[0]
+
+        if main_type == gguf.GGUFValueType.ARRAY:
+            sub_type = field.types[-1]
+
+            if sub_type == gguf.GGUFValueType.STRING:
+                return [str(bytes(field.parts[idx]), encoding='utf-8') for idx in field.data]
+            else:
+                return [pv for idx in field.data for pv in field.parts[idx].tolist()]
+        if main_type == gguf.GGUFValueType.STRING:
+            return str(bytes(field.parts[-1]), encoding='utf-8')
+        else:
+            return field.parts[-1][0]
+
+    return None
+
+
+def get_field_data(reader: gguf.GGUFReader, key: str) -> Any:
+    field = reader.get_field(key)
+
+    return decode_field(field)
+
+
+def find_token(token_list: Sequence[int], token: str) -> Sequence[int]:
+    token_ids = [index for index, value in enumerate(token_list) if value == token]
+
+    if len(token_ids) == 0:
+        raise LookupError(f'Unable to find "{token}" in token list!')
+
+    return token_ids
+
+
+def copy_with_new_metadata(reader: gguf.GGUFReader, writer: gguf.GGUFWriter, new_metadata: dict[str, MetadataDetails], remove_metadata: Sequence[str]) -> None:
+    for field in reader.fields.values():
+        # Suppress virtual fields and fields written by GGUFWriter
+        if field.name == gguf.Keys.General.ARCHITECTURE or field.name.startswith('GGUF.'):
+            logger.debug(f'Suppressing {field.name}')
+            continue
+
+        # Skip old chat templates if we have new ones
+        if field.name.startswith(gguf.Keys.Tokenizer.CHAT_TEMPLATE) and gguf.Keys.Tokenizer.CHAT_TEMPLATE in new_metadata:
+            logger.debug(f'Skipping {field.name}')
+            continue
+
+        if field.name in remove_metadata:
+            logger.debug(f'Removing {field.name}')
+            continue
+
+        old_val = MetadataDetails(field.types[0], decode_field(field))
+        val = new_metadata.get(field.name, old_val)
+
+        if field.name in new_metadata:
+            logger.debug(f'Modifying {field.name}: "{old_val.value}" -> "{val.value}" {val.description}')
+            del new_metadata[field.name]
+        elif val.value is not None:
+            logger.debug(f'Copying {field.name}')
+
+        if val.value is not None:
+            writer.add_key(field.name)
+            writer.add_val(val.value, val.type)
+
+    if gguf.Keys.Tokenizer.CHAT_TEMPLATE in new_metadata:
+        logger.debug('Adding chat template(s)')
+        writer.add_chat_template(new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE].value)
+        del new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE]
+
+    for key, val in new_metadata.items():
+        logger.debug(f'Adding {key}: "{val.value}" {val.description}')
+        writer.add_key(key)
+        writer.add_val(val.value, val.type)
+
+    total_bytes = 0
+
+    for tensor in reader.tensors:
+        total_bytes += tensor.n_bytes
+        # Dimensions are written in reverse order, so flip them first
+        shape = np.flipud(tensor.shape).tolist()
+        writer.add_tensor_info(tensor.name, shape, tensor.data.dtype, tensor.data.nbytes, tensor.tensor_type)
+
+    bar = tqdm(desc="Writing", total=total_bytes, unit="byte", unit_scale=True)
+
+    writer.write_header_to_file()
+    writer.write_kv_data_to_file()
+    writer.write_ti_data_to_file()
+
+    for tensor in reader.tensors:
+        writer.write_tensor_data(tensor.data)
+        bar.update(tensor.n_bytes)
+
+    writer.close()
+
+
+def main() -> None:
+    tokenizer_metadata = (getattr(gguf.Keys.Tokenizer, n) for n in gguf.Keys.Tokenizer.__dict__.keys() if not n.startswith('_'))
+    token_names = dict((n.split('.')[-1][:-len('_token_id')], n) for n in tokenizer_metadata if n.endswith('_token_id'))
+
+    parser = argparse.ArgumentParser(description="Make a copy of a GGUF file with new metadata")
+    parser.add_argument("input",                                       type=Path, help="GGUF format model input filename")
+    parser.add_argument("output",                                      type=Path, help="GGUF format model output filename")
+    parser.add_argument("--general-name",                              type=str,  help="The models general.name", metavar='"name"')
+    parser.add_argument("--general-description",                       type=str,  help="The models general.description", metavar='"Description ..."')
+    parser.add_argument("--chat-template",                             type=str,  help="Chat template string (or JSON string containing templates)", metavar='"{% ... %} ..."')
+    parser.add_argument("--chat-template-config",                      type=Path, help="Config file containing chat template(s)", metavar='tokenizer_config.json')
+    parser.add_argument("--remove-metadata",      action="append",     type=str,  help="Remove metadata (by key name) from output model", metavar='general.url')
+    parser.add_argument("--special-token",        action="append",     type=str,  help="Special token by value", nargs=2, metavar=(' | '.join(token_names.keys()), '"<token>"'))
+    parser.add_argument("--special-token-by-id",  action="append",     type=str,  help="Special token by id", nargs=2, metavar=(' | '.join(token_names.keys()), '0'))
+    parser.add_argument("--force",                action="store_true",            help="Bypass warnings without confirmation")
+    parser.add_argument("--verbose",              action="store_true",            help="Increase output verbosity")
+    args = parser.parse_args(None if len(sys.argv) > 2 else ["--help"])
+
+    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
+
+    new_metadata = {}
+    remove_metadata = args.remove_metadata or []
+
+    if args.general_name:
+        new_metadata[gguf.Keys.General.NAME] = MetadataDetails(gguf.GGUFValueType.STRING, args.general_name)
+
+    if args.general_description:
+        new_metadata[gguf.Keys.General.DESCRIPTION] = MetadataDetails(gguf.GGUFValueType.STRING, args.general_description)
+
+    if args.chat_template:
+        new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] = MetadataDetails(gguf.GGUFValueType.STRING, json.loads(args.chat_template) if args.chat_template.startswith('[') else args.chat_template)
+
+    if args.chat_template_config:
+        with open(args.chat_template_config, 'r') as fp:
+            config = json.load(fp)
+            template = config.get('chat_template')
+            if template:
+                new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] = MetadataDetails(gguf.GGUFValueType.STRING, template)
+
+    if remove_metadata:
+        logger.warning('*** Warning *** Warning *** Warning **')
+        logger.warning('* Most metadata is required for a fully functional GGUF file,')
+        logger.warning('* removing crucial metadata may result in a corrupt output file!')
+
+        if not args.force:
+            logger.warning('* Enter exactly YES if you are positive you want to proceed:')
+            response = input('YES, I am sure> ')
+            if response != 'YES':
+                logger.info("You didn't enter YES. Okay then, see ya!")
+                sys.exit(0)
+
+    logger.info(f'* Loading: {args.input}')
+    reader = gguf.GGUFReader(args.input, 'r')
+
+    arch = get_field_data(reader, gguf.Keys.General.ARCHITECTURE)
+    endianess = get_byteorder(reader)
+
+    token_list = get_field_data(reader, gguf.Keys.Tokenizer.LIST) or []
+
+    for name, token in args.special_token or []:
+        if name not in token_names:
+            logger.warning(f'Unknown special token "{name}", ignoring...')
+        else:
+            ids = find_token(token_list, token)
+            new_metadata[token_names[name]] = MetadataDetails(gguf.GGUFValueType.UINT32, ids[0], f'= {token}')
+
+            if len(ids) > 1:
+                logger.warning(f'Multiple "{token}" tokens found, choosing ID {ids[0]}, use --special-token-by-id if you want another:')
+                logger.warning(', '.join(str(i) for i in ids))
+
+    for name, id_string in args.special_token_by_id or []:
+        if name not in token_names:
+            logger.warning(f'Unknown special token "{name}", ignoring...')
+        elif not id_string.isdecimal():
+            raise LookupError(f'Token ID "{id_string}" is not a valid ID!')
+        else:
+            id_int = int(id_string)
+
+            if id_int >= 0 and id_int < len(token_list):
+                new_metadata[token_names[name]] = MetadataDetails(gguf.GGUFValueType.UINT32, id_int, f'= {token_list[id_int]}')
+            else:
+                raise LookupError(f'Token ID {id_int} is not within token list!')
+
+    if os.path.isfile(args.output) and not args.force:
+        logger.warning('*** Warning *** Warning *** Warning **')
+        logger.warning(f'* The "{args.output}" GGUF file already exists, it will be overwritten!')
+        logger.warning('* Enter exactly YES if you are positive you want to proceed:')
+        response = input('YES, I am sure> ')
+        if response != 'YES':
+            logger.info("You didn't enter YES. Okay then, see ya!")
+            sys.exit(0)
+
+    logger.info(f'* Writing: {args.output}')
+    writer = gguf.GGUFWriter(args.output, arch=arch, endianess=endianess)
+
+    alignment = get_field_data(reader, gguf.Keys.General.ALIGNMENT)
+    if alignment is not None:
+        logger.debug(f'Setting custom alignment: {alignment}')
+        writer.data_alignment = alignment
+
+    copy_with_new_metadata(reader, writer, new_metadata, remove_metadata)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/gguf-py/scripts/gguf-set-metadata.py b/gguf-py/scripts/gguf-set-metadata.py
index 3ebdfa898..e35b651b8 100755
--- a/gguf-py/scripts/gguf-set-metadata.py
+++ b/gguf-py/scripts/gguf-set-metadata.py
@@ -1,4 +1,5 @@
 #!/usr/bin/env python3
+import logging
 import argparse
 import os
 import sys
@@ -10,6 +11,8 @@ if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent /
 
 from gguf import GGUFReader  # noqa: E402
 
+logger = logging.getLogger("gguf-set-metadata")
+
 
 def minimal_example(filename: str) -> None:
     reader = GGUFReader(filename, 'r+')
@@ -41,36 +44,33 @@ def minimal_example(filename: str) -> None:
 def set_metadata(reader: GGUFReader, args: argparse.Namespace) -> None:
     field = reader.get_field(args.key)
     if field is None:
-        print(f'! Field {repr(args.key)} not found', file = sys.stderr)
+        logger.error(f'! Field {repr(args.key)} not found')
         sys.exit(1)
     # Note that field.types is a list of types. This is because the GGUF
     # format supports arrays. For example, an array of UINT32 would
     # look like [GGUFValueType.ARRAY, GGUFValueType.UINT32]
     handler = reader.gguf_scalar_to_np.get(field.types[0]) if field.types else None
     if handler is None:
-        print(
-            f'! This tool only supports changing simple values, {repr(args.key)} has unsupported type {field.types}',
-            file = sys.stderr,
-        )
+        logger.error(f'! This tool only supports changing simple values, {repr(args.key)} has unsupported type {field.types}')
         sys.exit(1)
     current_value = field.parts[field.data[0]][0]
     new_value = handler(args.value)
-    print(f'* Preparing to change field {repr(args.key)} from {current_value} to {new_value}')
+    logger.info(f'* Preparing to change field {repr(args.key)} from {current_value} to {new_value}')
     if current_value == new_value:
-        print(f'- Key {repr(args.key)} already set to requested value {current_value}')
+        logger.info(f'- Key {repr(args.key)} already set to requested value {current_value}')
         sys.exit(0)
     if args.dry_run:
         sys.exit(0)
     if not args.force:
-        print('*** Warning *** Warning *** Warning **')
-        print('* Changing fields in a GGUF file can make it unusable. Proceed at your own risk.')
-        print('* Enter exactly YES if you are positive you want to proceed:')
+        logger.warning('*** Warning *** Warning *** Warning **')
+        logger.warning('* Changing fields in a GGUF file can make it unusable. Proceed at your own risk.')
+        logger.warning('* Enter exactly YES if you are positive you want to proceed:')
         response = input('YES, I am sure> ')
         if response != 'YES':
-            print("You didn't enter YES. Okay then, see ya!")
+            logger.info("You didn't enter YES. Okay then, see ya!")
             sys.exit(0)
     field.parts[field.data[0]][0] = new_value
-    print('* Field changed. Successful completion.')
+    logger.info('* Field changed. Successful completion.')
 
 
 def main() -> None:
@@ -80,8 +80,13 @@ def main() -> None:
     parser.add_argument("value",     type=str,            help="Metadata value to set")
     parser.add_argument("--dry-run", action="store_true", help="Don't actually change anything")
     parser.add_argument("--force",   action="store_true", help="Change the field without confirmation")
+    parser.add_argument("--verbose",      action="store_true",    help="increase output verbosity")
+
     args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"])
-    print(f'* Loading: {args.model}')
+
+    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
+
+    logger.info(f'* Loading: {args.model}')
     reader = GGUFReader(args.model, 'r' if args.dry_run else 'r+')
     set_metadata(reader, args)
 
diff --git a/grammars/README.md b/grammars/README.md
index e1383fa5c..2b8384d9d 100644
--- a/grammars/README.md
+++ b/grammars/README.md
@@ -51,7 +51,7 @@ single-line ::= [^\n]+ "\n"`
 
 ## Sequences and Alternatives
 
-The order of symbols in a sequence matter. For example, in `"1. " move " " move "\n"`, the `"1. "` must come before the first `move`, etc.
+The order of symbols in a sequence matters. For example, in `"1. " move " " move "\n"`, the `"1. "` must come before the first `move`, etc.
 
 Alternatives, denoted by `|`, give different sequences that are acceptable. For example, in `move ::= pawn | nonpawn | castle`, `move` can be a `pawn` move, a `nonpawn` move, or a `castle`.
 
@@ -89,3 +89,13 @@ This guide provides a brief overview. Check out the GBNF files in this directory
 ```
 ./main -m <model> --grammar-file grammars/some-grammar.gbnf -p 'Some prompt'
 ```
+
+## Troubleshooting
+
+Grammars currently have performance gotchas (see https://github.com/ggerganov/llama.cpp/issues/4218).
+
+### Efficient optional repetitions
+
+A common pattern is to allow repetitions of a pattern `x` up to N times.
+
+While semantically correct, the syntax `x? x? x?.... x?` (with N repetitions) will result in extremely slow inference. Instead, you can write `(x (x (x ... (x)?...)?)?)?` (w/ N-deep nesting)
diff --git a/grammars/json.gbnf b/grammars/json.gbnf
index a9537cdf9..a8a80752e 100644
--- a/grammars/json.gbnf
+++ b/grammars/json.gbnf
@@ -15,7 +15,7 @@ array  ::=
 
 string ::=
   "\"" (
-    [^"\\] |
+    [^"\\\x7F\x00-\x1F] |
     "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
   )* "\"" ws
 
diff --git a/grammars/json_arr.gbnf b/grammars/json_arr.gbnf
index ef53e77a0..31a3202f8 100644
--- a/grammars/json_arr.gbnf
+++ b/grammars/json_arr.gbnf
@@ -24,7 +24,7 @@ array  ::=
 
 string ::=
   "\"" (
-    [^"\\] |
+    [^"\\\x7F\x00-\x1F] |
     "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
   )* "\"" ws
 
diff --git a/llama.cpp b/llama.cpp
index 5de07dfa9..202bf94c8 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -7,7 +7,7 @@
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
 
-#ifdef GGML_USE_CUBLAS
+#ifdef GGML_USE_CUDA
 #  include "ggml-cuda.h"
 #elif defined(GGML_USE_CLBLAST)
 #  include "ggml-opencl.h"
@@ -52,12 +52,16 @@
         #define NOMINMAX
     #endif
     #include <windows.h>
+    #ifndef PATH_MAX
+        #define PATH_MAX MAX_PATH
+    #endif
     #include <io.h>
 #endif
 
 #include <algorithm>
 #include <array>
 #include <cassert>
+#include <cctype>
 #include <cfloat>
 #include <cinttypes>
 #include <climits>
@@ -71,7 +75,9 @@
 #include <forward_list>
 #include <fstream>
 #include <functional>
+#include <future>
 #include <initializer_list>
+#include <locale>
 #include <map>
 #include <memory>
 #include <mutex>
@@ -100,7 +106,7 @@
 #endif
 
 #define LLAMA_MAX_NODES   8192
-#define LLAMA_MAX_EXPERTS 8
+#define LLAMA_MAX_EXPERTS 60
 
 //
 // logging
@@ -189,6 +195,7 @@ enum llm_arch {
     LLM_ARCH_LLAMA,
     LLM_ARCH_FALCON,
     LLM_ARCH_BAICHUAN,
+    LLM_ARCH_GROK,
     LLM_ARCH_GPT2,
     LLM_ARCH_GPTJ,
     LLM_ARCH_GPTNEOX,
@@ -198,42 +205,64 @@ enum llm_arch {
     LLM_ARCH_REFACT,
     LLM_ARCH_BERT,
     LLM_ARCH_NOMIC_BERT,
+    LLM_ARCH_JINA_BERT_V2,
     LLM_ARCH_BLOOM,
     LLM_ARCH_STABLELM,
     LLM_ARCH_QWEN,
     LLM_ARCH_QWEN2,
+    LLM_ARCH_QWEN2MOE,
     LLM_ARCH_PHI2,
+    LLM_ARCH_PHI3,
     LLM_ARCH_PLAMO,
     LLM_ARCH_CODESHELL,
     LLM_ARCH_ORION,
     LLM_ARCH_INTERNLM2,
     LLM_ARCH_MINICPM,
+    LLM_ARCH_GEMMA,
+    LLM_ARCH_STARCODER2,
+    LLM_ARCH_MAMBA,
+    LLM_ARCH_XVERSE,
+    LLM_ARCH_COMMAND_R,
+    LLM_ARCH_DBRX,
+    LLM_ARCH_OLMO,
     LLM_ARCH_UNKNOWN,
 };
 
-static std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
-    { LLM_ARCH_LLAMA,           "llama"      },
-    { LLM_ARCH_FALCON,          "falcon"     },
-    { LLM_ARCH_GPT2,            "gpt2"       },
-    { LLM_ARCH_GPTJ,            "gptj"       },
-    { LLM_ARCH_GPTNEOX,         "gptneox"    },
-    { LLM_ARCH_MPT,             "mpt"        },
-    { LLM_ARCH_BAICHUAN,        "baichuan"   },
-    { LLM_ARCH_STARCODER,       "starcoder"  },
-    { LLM_ARCH_PERSIMMON,       "persimmon"  },
-    { LLM_ARCH_REFACT,          "refact"     },
-    { LLM_ARCH_BERT,            "bert"       },
-    { LLM_ARCH_NOMIC_BERT,      "nomic-bert" },
-    { LLM_ARCH_BLOOM,           "bloom"      },
-    { LLM_ARCH_STABLELM,        "stablelm"   },
-    { LLM_ARCH_QWEN,            "qwen"       },
-    { LLM_ARCH_QWEN2,           "qwen2"      },
-    { LLM_ARCH_PHI2,            "phi2"       },
-    { LLM_ARCH_PLAMO,           "plamo"      },
-    { LLM_ARCH_CODESHELL,       "codeshell"  },
-    { LLM_ARCH_ORION,           "orion"      },
-    { LLM_ARCH_INTERNLM2,       "internlm2"  },
-    { LLM_ARCH_MINICPM,         "minicpm"    },
+static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
+    { LLM_ARCH_LLAMA,           "llama"        },
+    { LLM_ARCH_FALCON,          "falcon"       },
+    { LLM_ARCH_GROK,            "grok"         },
+    { LLM_ARCH_GPT2,            "gpt2"         },
+    { LLM_ARCH_GPTJ,            "gptj"         },
+    { LLM_ARCH_GPTNEOX,         "gptneox"      },
+    { LLM_ARCH_MPT,             "mpt"          },
+    { LLM_ARCH_BAICHUAN,        "baichuan"     },
+    { LLM_ARCH_STARCODER,       "starcoder"    },
+    { LLM_ARCH_PERSIMMON,       "persimmon"    },
+    { LLM_ARCH_REFACT,          "refact"       },
+    { LLM_ARCH_BERT,            "bert"         },
+    { LLM_ARCH_NOMIC_BERT,      "nomic-bert"   },
+    { LLM_ARCH_JINA_BERT_V2,    "jina-bert-v2" },
+    { LLM_ARCH_BLOOM,           "bloom"        },
+    { LLM_ARCH_STABLELM,        "stablelm"     },
+    { LLM_ARCH_QWEN,            "qwen"         },
+    { LLM_ARCH_QWEN2,           "qwen2"        },
+    { LLM_ARCH_QWEN2MOE,        "qwen2moe"     },
+    { LLM_ARCH_PHI2,            "phi2"         },
+    { LLM_ARCH_PHI3,            "phi3"         },
+    { LLM_ARCH_PLAMO,           "plamo"        },
+    { LLM_ARCH_CODESHELL,       "codeshell"    },
+    { LLM_ARCH_ORION,           "orion"        },
+    { LLM_ARCH_INTERNLM2,       "internlm2"    },
+    { LLM_ARCH_MINICPM,         "minicpm"      },
+    { LLM_ARCH_GEMMA,           "gemma"        },
+    { LLM_ARCH_STARCODER2,      "starcoder2"   },
+    { LLM_ARCH_MAMBA,           "mamba"        },
+    { LLM_ARCH_XVERSE,          "xverse"       },
+    { LLM_ARCH_COMMAND_R,       "command-r"    },
+    { LLM_ARCH_DBRX,            "dbrx"         },
+    { LLM_ARCH_OLMO,            "olmo"         },
+    { LLM_ARCH_UNKNOWN,         "(unknown)"    },
 };
 
 enum llm_kv {
@@ -242,12 +271,14 @@ enum llm_kv {
     LLM_KV_GENERAL_ALIGNMENT,
     LLM_KV_GENERAL_NAME,
     LLM_KV_GENERAL_AUTHOR,
+    LLM_KV_GENERAL_VERSION,
     LLM_KV_GENERAL_URL,
     LLM_KV_GENERAL_DESCRIPTION,
     LLM_KV_GENERAL_LICENSE,
     LLM_KV_GENERAL_SOURCE_URL,
     LLM_KV_GENERAL_SOURCE_HF_REPO,
 
+    LLM_KV_VOCAB_SIZE,
     LLM_KV_CONTEXT_LENGTH,
     LLM_KV_EMBEDDING_LENGTH,
     LLM_KV_BLOCK_COUNT,
@@ -257,6 +288,7 @@ enum llm_kv {
     LLM_KV_EXPERT_COUNT,
     LLM_KV_EXPERT_USED_COUNT,
     LLM_KV_POOLING_TYPE,
+    LLM_KV_LOGIT_SCALE,
 
     LLM_KV_ATTENTION_HEAD_COUNT,
     LLM_KV_ATTENTION_HEAD_COUNT_KV,
@@ -276,7 +308,17 @@ enum llm_kv {
     LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,
     LLM_KV_ROPE_SCALING_FINETUNED,
 
+    LLM_KV_SPLIT_NO,
+    LLM_KV_SPLIT_COUNT,
+    LLM_KV_SPLIT_TENSORS_COUNT,
+
+    LLM_KV_SSM_INNER_SIZE,
+    LLM_KV_SSM_CONV_KERNEL,
+    LLM_KV_SSM_STATE_SIZE,
+    LLM_KV_SSM_TIME_STEP_RANK,
+
     LLM_KV_TOKENIZER_MODEL,
+    LLM_KV_TOKENIZER_PRE,
     LLM_KV_TOKENIZER_LIST,
     LLM_KV_TOKENIZER_TOKEN_TYPE,
     LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,
@@ -287,25 +329,33 @@ enum llm_kv {
     LLM_KV_TOKENIZER_UNK_ID,
     LLM_KV_TOKENIZER_SEP_ID,
     LLM_KV_TOKENIZER_PAD_ID,
+    LLM_KV_TOKENIZER_CLS_ID,
+    LLM_KV_TOKENIZER_MASK_ID,
     LLM_KV_TOKENIZER_ADD_BOS,
     LLM_KV_TOKENIZER_ADD_EOS,
     LLM_KV_TOKENIZER_ADD_PREFIX,
     LLM_KV_TOKENIZER_HF_JSON,
     LLM_KV_TOKENIZER_RWKV,
+    LLM_KV_TOKENIZER_PREFIX_ID,
+    LLM_KV_TOKENIZER_SUFFIX_ID,
+    LLM_KV_TOKENIZER_MIDDLE_ID,
+    LLM_KV_TOKENIZER_EOT_ID,
 };
 
-static std::map<llm_kv, const char *> LLM_KV_NAMES = {
+static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_GENERAL_ARCHITECTURE,          "general.architecture"                  },
     { LLM_KV_GENERAL_QUANTIZATION_VERSION,  "general.quantization_version"          },
     { LLM_KV_GENERAL_ALIGNMENT,             "general.alignment"                     },
     { LLM_KV_GENERAL_NAME,                  "general.name"                          },
     { LLM_KV_GENERAL_AUTHOR,                "general.author"                        },
+    { LLM_KV_GENERAL_VERSION,               "general.version"                       },
     { LLM_KV_GENERAL_URL,                   "general.url"                           },
     { LLM_KV_GENERAL_DESCRIPTION,           "general.description"                   },
     { LLM_KV_GENERAL_LICENSE,               "general.license"                       },
     { LLM_KV_GENERAL_SOURCE_URL,            "general.source.url"                    },
     { LLM_KV_GENERAL_SOURCE_HF_REPO,        "general.source.huggingface.repository" },
 
+    { LLM_KV_VOCAB_SIZE,                    "%s.vocab_size"            },
     { LLM_KV_CONTEXT_LENGTH,                "%s.context_length"        },
     { LLM_KV_EMBEDDING_LENGTH,              "%s.embedding_length"      },
     { LLM_KV_BLOCK_COUNT,                   "%s.block_count"           },
@@ -315,6 +365,7 @@ static std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_EXPERT_COUNT,                  "%s.expert_count"          },
     { LLM_KV_EXPERT_USED_COUNT,             "%s.expert_used_count"     },
     { LLM_KV_POOLING_TYPE ,                 "%s.pooling_type"          },
+    { LLM_KV_LOGIT_SCALE,                   "%s.logit_scale"           },
 
     { LLM_KV_ATTENTION_HEAD_COUNT,          "%s.attention.head_count"             },
     { LLM_KV_ATTENTION_HEAD_COUNT_KV,       "%s.attention.head_count_kv"          },
@@ -334,7 +385,17 @@ static std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,     "%s.rope.scaling.original_context_length" },
     { LLM_KV_ROPE_SCALING_FINETUNED,        "%s.rope.scaling.finetuned"               },
 
+    { LLM_KV_SPLIT_NO,                      "split.no"            },
+    { LLM_KV_SPLIT_COUNT,                   "split.count"         },
+    { LLM_KV_SPLIT_TENSORS_COUNT,           "split.tensors.count" },
+
+    { LLM_KV_SSM_CONV_KERNEL,               "%s.ssm.conv_kernel"    },
+    { LLM_KV_SSM_INNER_SIZE,                "%s.ssm.inner_size"     },
+    { LLM_KV_SSM_STATE_SIZE,                "%s.ssm.state_size"     },
+    { LLM_KV_SSM_TIME_STEP_RANK,            "%s.ssm.time_step_rank" },
+
     { LLM_KV_TOKENIZER_MODEL,               "tokenizer.ggml.model"              },
+    { LLM_KV_TOKENIZER_PRE,                 "tokenizer.ggml.pre"                },
     { LLM_KV_TOKENIZER_LIST,                "tokenizer.ggml.tokens"             },
     { LLM_KV_TOKENIZER_TOKEN_TYPE,          "tokenizer.ggml.token_type"         },
     { LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,    "tokenizer.ggml.token_type_count"   },
@@ -345,11 +406,17 @@ static std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_TOKENIZER_UNK_ID,              "tokenizer.ggml.unknown_token_id"   },
     { LLM_KV_TOKENIZER_SEP_ID,              "tokenizer.ggml.seperator_token_id" },
     { LLM_KV_TOKENIZER_PAD_ID,              "tokenizer.ggml.padding_token_id"   },
+    { LLM_KV_TOKENIZER_CLS_ID,              "tokenizer.ggml.cls_token_id"       },
+    { LLM_KV_TOKENIZER_MASK_ID,             "tokenizer.ggml.mask_token_id"      },
     { LLM_KV_TOKENIZER_ADD_BOS,             "tokenizer.ggml.add_bos_token"      },
     { LLM_KV_TOKENIZER_ADD_EOS,             "tokenizer.ggml.add_eos_token"      },
     { LLM_KV_TOKENIZER_ADD_PREFIX,          "tokenizer.ggml.add_space_prefix"   },
     { LLM_KV_TOKENIZER_HF_JSON,             "tokenizer.huggingface.json"        },
     { LLM_KV_TOKENIZER_RWKV,                "tokenizer.rwkv.world"              },
+    { LLM_KV_TOKENIZER_PREFIX_ID,           "tokenizer.ggml.prefix_token_id"    },
+    { LLM_KV_TOKENIZER_SUFFIX_ID,           "tokenizer.ggml.suffix_token_id"    },
+    { LLM_KV_TOKENIZER_MIDDLE_ID,           "tokenizer.ggml.middle_token_id"    },
+    { LLM_KV_TOKENIZER_EOT_ID,              "tokenizer.ggml.eot_token_id"       },
 };
 
 struct LLM_KV {
@@ -358,7 +425,7 @@ struct LLM_KV {
     llm_arch arch;
 
     std::string operator()(llm_kv kv) const {
-        return ::format(LLM_KV_NAMES[kv], LLM_ARCH_NAMES[arch]);
+        return ::format(LLM_KV_NAMES.at(kv), LLM_ARCH_NAMES.at(arch));
     }
 };
 
@@ -380,20 +447,34 @@ enum llm_tensor {
     LLM_TENSOR_ATTN_OUT_NORM,
     LLM_TENSOR_ATTN_ROT_EMBD,
     LLM_TENSOR_FFN_GATE_INP,
+    LLM_TENSOR_FFN_GATE_INP_SHEXP,
     LLM_TENSOR_FFN_NORM,
     LLM_TENSOR_FFN_GATE,
     LLM_TENSOR_FFN_DOWN,
     LLM_TENSOR_FFN_UP,
     LLM_TENSOR_FFN_ACT,
-    LLM_TENSOR_FFN_DOWN_EXP,
+    LLM_TENSOR_FFN_DOWN_EXP,  // split experts for backward compatibility
     LLM_TENSOR_FFN_GATE_EXP,
     LLM_TENSOR_FFN_UP_EXP,
+    LLM_TENSOR_FFN_DOWN_EXPS, // merged experts
+    LLM_TENSOR_FFN_GATE_EXPS,
+    LLM_TENSOR_FFN_UP_EXPS,
+    LLM_TENSOR_FFN_DOWN_SHEXP,
+    LLM_TENSOR_FFN_GATE_SHEXP,
+    LLM_TENSOR_FFN_UP_SHEXP,
     LLM_TENSOR_ATTN_Q_NORM,
     LLM_TENSOR_ATTN_K_NORM,
     LLM_TENSOR_LAYER_OUT_NORM,
+    LLM_TENSOR_SSM_IN,
+    LLM_TENSOR_SSM_CONV1D,
+    LLM_TENSOR_SSM_X,
+    LLM_TENSOR_SSM_DT,
+    LLM_TENSOR_SSM_A,
+    LLM_TENSOR_SSM_D,
+    LLM_TENSOR_SSM_OUT,
 };
 
-static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
+static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
     {
         LLM_ARCH_LLAMA,
         {
@@ -415,6 +496,9 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
             { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
             { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
             { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
+            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
         },
     },
     {
@@ -450,6 +534,31 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_GROK,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
+            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
+            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
+            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
+            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
+            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
+        },
+    },
     {
         LLM_ARCH_GPT2,
         {
@@ -507,7 +616,7 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
         {
             { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
             { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_OUTPUT,          "output"},
             { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
             { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
             { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
@@ -515,6 +624,9 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
             { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
             { LLM_TENSOR_FFN_ACT,         "blk.%d.ffn.act" },
+            { LLM_TENSOR_POS_EMBD,        "position_embd" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm"},
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm"},
         },
     },
     {
@@ -581,6 +693,25 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_JINA_BERT_V2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
+            { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
+            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
     {
         LLM_ARCH_BLOOM,
         {
@@ -612,6 +743,8 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
             { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
             { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
         },
     },
     {
@@ -647,6 +780,28 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_QWEN2MOE,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
+            { LLM_TENSOR_OUTPUT,             "output" },
+            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
+            { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
+            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
+            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
+            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
+        },
+    },
     {
         LLM_ARCH_PHI2,
         {
@@ -663,6 +818,23 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_PHI3,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
     {
         LLM_ARCH_PLAMO,
         {
@@ -760,6 +932,122 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
             { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
         },
     },
+    {
+        LLM_ARCH_GEMMA,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_STARCODER2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_MAMBA,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_SSM_IN,          "blk.%d.ssm_in" },
+            { LLM_TENSOR_SSM_CONV1D,      "blk.%d.ssm_conv1d" },
+            { LLM_TENSOR_SSM_X,           "blk.%d.ssm_x" },
+            { LLM_TENSOR_SSM_DT,          "blk.%d.ssm_dt" },
+            { LLM_TENSOR_SSM_A,           "blk.%d.ssm_a" },
+            { LLM_TENSOR_SSM_D,           "blk.%d.ssm_d" },
+            { LLM_TENSOR_SSM_OUT,         "blk.%d.ssm_out" },
+        },
+    },
+    {
+        LLM_ARCH_XVERSE,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_COMMAND_R,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
+        },
+    },
+    {
+        LLM_ARCH_DBRX,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
+            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
+        },
+    },
+    {
+        LLM_ARCH_OLMO,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
     {
         LLM_ARCH_UNKNOWN,
         {
@@ -793,38 +1081,38 @@ struct LLM_TN {
     llm_arch arch;
 
     std::string operator()(llm_tensor tensor) const {
-        if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) {
+        if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
             return "__missing__";
         }
-        return LLM_TENSOR_NAMES[arch].at(tensor);
+        return LLM_TENSOR_NAMES.at(arch).at(tensor);
     }
 
     std::string operator()(llm_tensor tensor, const std::string & suffix) const {
-        if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) {
+        if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
             return "__missing__";
         }
-        return LLM_TENSOR_NAMES[arch].at(tensor) + "." + suffix;
+        return LLM_TENSOR_NAMES.at(arch).at(tensor) + "." + suffix;
     }
 
     std::string operator()(llm_tensor tensor, int bid) const {
-        if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) {
+        if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
             return "__missing__";
         }
-        return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid);
+        return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid);
     }
 
     std::string operator()(llm_tensor tensor, const std::string & suffix, int bid) const {
-        if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) {
+        if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
             return "__missing__";
         }
-        return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid) + "." + suffix;
+        return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid) + "." + suffix;
     }
 
     std::string operator()(llm_tensor tensor, const std::string & suffix, int bid, int xid) const {
-        if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) {
+        if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
             return "__missing__";
         }
-        return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid, xid) + "." + suffix;
+        return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid, xid) + "." + suffix;
     }
 };
 
@@ -832,20 +1120,20 @@ struct LLM_TN {
 // gguf helpers
 //
 
-static std::map<int32_t, const char *> LLAMA_ROPE_SCALING_TYPES = {
-    { LLAMA_ROPE_SCALING_NONE,   "none"   },
-    { LLAMA_ROPE_SCALING_LINEAR, "linear" },
-    { LLAMA_ROPE_SCALING_YARN,   "yarn"   },
+static const std::map<llama_rope_scaling_type, const char *> LLAMA_ROPE_SCALING_TYPES = {
+    { LLAMA_ROPE_SCALING_TYPE_NONE,   "none"   },
+    { LLAMA_ROPE_SCALING_TYPE_LINEAR, "linear" },
+    { LLAMA_ROPE_SCALING_TYPE_YARN,   "yarn"   },
 };
 
-static int32_t llama_rope_scaling_type_from_string(const std::string & name) {
+static llama_rope_scaling_type llama_rope_scaling_type_from_string(const std::string & name) {
     for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) {
         if (kv.second == name) {
-            return kv.first;
+            return (llama_rope_scaling_type) kv.first;
         }
     }
 
-    return LLAMA_ROPE_SCALING_UNSPECIFIED;
+    return LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
 }
 
 static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) {
@@ -902,21 +1190,6 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
     }
 }
 
-//
-// ggml helpers
-//
-
-static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
-    struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
-
-    if (plan.work_size > 0) {
-        buf.resize(plan.work_size);
-        plan.work_data = buf.data();
-    }
-
-    ggml_graph_compute(graph, &plan);
-}
-
 //
 // llama helpers
 //
@@ -947,7 +1220,7 @@ struct llama_file {
     size_t size;
 
     llama_file(const char * fname, const char * mode) {
-        fp = std::fopen(fname, mode);
+        fp = ggml_fopen(fname, mode);
         if (fp == NULL) {
             throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
         }
@@ -1016,6 +1289,7 @@ struct llama_file {
         }
     }
 };
+using llama_files = std::vector<std::unique_ptr<llama_file>>;
 
 struct llama_mmap {
     void * addr;
@@ -1216,6 +1490,7 @@ struct llama_mmap {
     }
 #endif
 };
+using llama_mmaps = std::vector<std::unique_ptr<llama_mmap>>;
 
 // Represents some region of memory being locked using mlock or VirtualLock;
 // will automatically unlock on destruction.
@@ -1365,13 +1640,14 @@ struct llama_mlock {
     static void raw_unlock(const void * addr, size_t len) {}
 #endif
 };
+using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;
 
-static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
+static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
     std::vector<char> result(8, 0);
-    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
+    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
     if (n_tokens < 0) {
         result.resize(-n_tokens);
-        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
+        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
         GGML_ASSERT(check == -n_tokens);
     }
     else {
@@ -1384,13 +1660,15 @@ static std::string llama_token_to_piece(const struct llama_context * ctx, llama_
 static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer) {
     ggml_backend_buffer_type_t buft = nullptr;
 
-#if defined(GGML_USE_CUBLAS)
+#if defined(GGML_USE_CUDA)
     // host buffers should only be used when data is expected to be copied to/from the GPU
     if (host_buffer) {
         buft = ggml_backend_cuda_host_buffer_type();
     }
 #elif defined(GGML_USE_SYCL)
-    buft = ggml_backend_sycl_host_buffer_type();
+    if (host_buffer) {
+        buft = ggml_backend_sycl_host_buffer_type();
+    }
 #elif defined(GGML_USE_CPU_HBM)
     buft = ggml_backend_cpu_hbm_buffer_type();
 #elif defined(GGML_USE_VULKAN)
@@ -1412,7 +1690,7 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) {
 
 #ifdef GGML_USE_METAL
     buft = ggml_backend_metal_buffer_type();
-#elif defined(GGML_USE_CUBLAS)
+#elif defined(GGML_USE_CUDA)
     buft = ggml_backend_cuda_buffer_type(gpu);
 #elif defined(GGML_USE_VULKAN)
     buft = ggml_backend_vk_buffer_type(gpu);
@@ -1438,12 +1716,18 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) {
 static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_gpu, const float * tensor_split) {
     ggml_backend_buffer_type_t buft = nullptr;
 
-#ifdef GGML_USE_CUBLAS
+#ifdef GGML_USE_CUDA
     if (ggml_backend_cuda_get_device_count() > 1) {
         buft = ggml_backend_cuda_split_buffer_type(tensor_split);
     }
 #endif
 
+#ifdef GGML_USE_SYCL
+    if (ggml_backend_sycl_get_device_count() > 1) {
+        buft = ggml_backend_sycl_split_buffer_type(tensor_split);
+    }
+#endif
+
     if (buft == nullptr) {
         buft = llama_default_buffer_type_offload(fallback_gpu);
     }
@@ -1453,8 +1737,10 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_g
 }
 
 static size_t llama_get_device_count() {
-#if defined(GGML_USE_CUBLAS)
+#if defined(GGML_USE_CUDA)
     return ggml_backend_cuda_get_device_count();
+#elif defined(GGML_USE_SYCL)
+    return ggml_backend_sycl_get_device_count();
 #elif defined(GGML_USE_VULKAN)
     return ggml_backend_vk_get_device_count();
 #else
@@ -1463,15 +1749,20 @@ static size_t llama_get_device_count() {
 }
 
 static size_t llama_get_device_memory(int device) {
-#if defined(GGML_USE_CUBLAS)
+#if defined(GGML_USE_CUDA)
     size_t total;
     size_t free;
-    ggml_backend_cuda_get_device_memory(device, &total, &free);
+    ggml_backend_cuda_get_device_memory(device, &free, &total);
+    return free;
+#elif defined(GGML_USE_SYCL)
+    size_t total;
+    size_t free;
+    ggml_backend_sycl_get_device_memory(device, &free, &total);
     return free;
 #elif defined(GGML_USE_VULKAN)
     size_t total;
     size_t free;
-    ggml_backend_vk_get_device_memory(device, &total, &free);
+    ggml_backend_vk_get_device_memory(device, &free, &total);
     return free;
 #else
     return 1;
@@ -1513,19 +1804,26 @@ enum e_model {
     MODEL_4B,
     MODEL_7B,
     MODEL_8B,
+    MODEL_12B,
     MODEL_13B,
     MODEL_14B,
     MODEL_15B,
     MODEL_20B,
     MODEL_30B,
     MODEL_34B,
+    MODEL_35B,
     MODEL_40B,
     MODEL_65B,
     MODEL_70B,
+    MODEL_314B,
     MODEL_SMALL,
     MODEL_MEDIUM,
     MODEL_LARGE,
     MODEL_XL,
+    MODEL_A2_7B,
+    MODEL_8x7B,
+    MODEL_8x22B,
+    MODEL_16x12B,
 };
 
 static const size_t kiB = 1024;
@@ -1533,8 +1831,9 @@ static const size_t MiB = 1024*kiB;
 static const size_t GiB = 1024*MiB;
 
 struct llama_hparams {
-    bool     vocab_only;
-    bool     rope_finetuned;
+    bool vocab_only;
+    bool rope_finetuned;
+
     uint32_t n_vocab;
     uint32_t n_ctx_train; // context size the model was trained on
     uint32_t n_embd;
@@ -1555,15 +1854,23 @@ struct llama_hparams {
     float    rope_freq_base_train;
     float    rope_freq_scale_train;
     uint32_t n_yarn_orig_ctx;
-    int32_t  rope_scaling_type_train;
+
+    // for State Space Models
+    uint32_t ssm_d_conv  = 0;
+    uint32_t ssm_d_inner = 0;
+    uint32_t ssm_d_state = 0;
+    uint32_t ssm_dt_rank = 0;
 
     float f_clamp_kqv      = 0.0f;
     float f_max_alibi_bias = 0.0f;
+    float f_logit_scale    = 0.0f;
 
     bool causal_attn = true;
-    bool need_kq_pos = false;
+    bool use_alibi   = false;
 
-    uint32_t pooling_type = LLAMA_POOLING_NONE;
+    enum llama_pooling_type      pooling_type            = LLAMA_POOLING_TYPE_NONE;
+    enum llama_rope_type         rope_type               = LLAMA_ROPE_TYPE_NONE;
+    enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;
 
     bool operator!=(const llama_hparams & other) const {
         if (this->vocab_only    != other.vocab_only)    return true;
@@ -1583,6 +1890,11 @@ struct llama_hparams {
         if (this->rope_finetuned  != other.rope_finetuned)  return true;
         if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true;
 
+        if (this->ssm_d_conv  != other.ssm_d_conv)  return true;
+        if (this->ssm_d_inner != other.ssm_d_inner) return true;
+        if (this->ssm_d_state != other.ssm_d_state) return true;
+        if (this->ssm_dt_rank != other.ssm_dt_rank) return true;
+
         const float EPSILON = 1e-9f;
 
         if (!is_float_close(this->f_norm_eps,            other.f_norm_eps,            EPSILON)) return true;
@@ -1594,6 +1906,9 @@ struct llama_hparams {
     }
 
     uint32_t n_gqa() const {
+        if (n_head_kv == 0) {
+            return 0;
+        }
         return n_head/n_head_kv;
     }
 
@@ -1604,16 +1919,30 @@ struct llama_hparams {
     uint32_t n_embd_v_gqa() const { // dimension of value embeddings across all k-v heads
         return n_embd_head_v * n_head_kv;
     }
+
+    uint32_t n_embd_k_s() const { // dimension of the rolling state embeddings
+        // corresponds to Mamba's conv_states size
+        // TODO: maybe support other convolution strides than 1
+        // NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed
+        return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * ssm_d_inner;
+    }
+
+    uint32_t n_embd_v_s() const { // dimension of the recurrent state embeddings
+        // corresponds to Mamba's ssm_states size
+        return ssm_d_state * ssm_d_inner;
+    }
 };
 
 struct llama_cparams {
-    uint32_t n_ctx;       // context size used during inference
+    uint32_t n_ctx;           // context size used during inference
     uint32_t n_batch;
+    uint32_t n_ubatch;
+    uint32_t n_seq_max;
     uint32_t n_threads;       // number of threads to use for generation
     uint32_t n_threads_batch; // number of threads to use for batch processing
 
-    float    rope_freq_base;
-    float    rope_freq_scale;
+    float rope_freq_base;
+    float rope_freq_scale;
 
     uint32_t n_yarn_orig_ctx;
     // These hyperparameters are not exposed in GGUF, because all
@@ -1622,10 +1951,14 @@ struct llama_cparams {
     float yarn_attn_factor;
     float yarn_beta_fast;
     float yarn_beta_slow;
+    float defrag_thold;
 
-    bool mul_mat_q;
+    bool embeddings;
+    bool causal_attn;
     bool offload_kqv;
-    bool do_pooling;
+    bool flash_attn;
+
+    enum llama_pooling_type pooling_type;
 
     ggml_backend_sched_eval_callback cb_eval;
     void * cb_eval_user_data;
@@ -1671,30 +2004,64 @@ struct llama_layer {
 
     // ff MoE
     struct ggml_tensor * ffn_gate_inp;
-    struct ggml_tensor * ffn_gate_exp[LLAMA_MAX_EXPERTS];
-    struct ggml_tensor * ffn_down_exp[LLAMA_MAX_EXPERTS];
-    struct ggml_tensor * ffn_up_exp  [LLAMA_MAX_EXPERTS];
+    struct ggml_tensor * ffn_gate_exps;
+    struct ggml_tensor * ffn_down_exps;
+    struct ggml_tensor * ffn_up_exps ;
+
+    // ff shared expert (shexp)
+    struct ggml_tensor * ffn_gate_inp_shexp;
+    struct ggml_tensor * ffn_gate_shexp;
+    struct ggml_tensor * ffn_down_shexp;
+    struct ggml_tensor * ffn_up_shexp;
 
     // ff bias
     struct ggml_tensor * ffn_down_b; // b2
     struct ggml_tensor * ffn_up_b;   // b3
     struct ggml_tensor * ffn_act;
+
+    // mamba proj
+    struct ggml_tensor * ssm_in;
+    struct ggml_tensor * ssm_x;
+    struct ggml_tensor * ssm_dt;
+    struct ggml_tensor * ssm_out;
+
+    // mamba
+    struct ggml_tensor * ssm_conv1d;
+    struct ggml_tensor * ssm_a;
+    struct ggml_tensor * ssm_d;
+
+    // mamba bias
+    struct ggml_tensor * ssm_conv1d_b;
+    struct ggml_tensor * ssm_dt_b;
 };
 
 struct llama_kv_cell {
     llama_pos pos   = -1;
     llama_pos delta = 0;
+    int32_t   src   = 0; // used by recurrent state models to copy states
 
     std::set<llama_seq_id> seq_id;
 
     bool has_seq_id(const llama_seq_id & id) const {
         return seq_id.find(id) != seq_id.end();
     }
+
+    bool is_empty() const {
+        return seq_id.empty();
+    }
+
+    bool is_same_seq(const llama_kv_cell & other) const {
+        return seq_id == other.seq_id;
+    }
 };
 
 // ring-buffer of cached KV data
 struct llama_kv_cache {
     bool has_shift = false;
+    bool do_defrag = false;
+    bool do_copy   = false;
+    bool recurrent = false; // with recurrent state models, a cell can hold the state for more than one past token
+    bool v_trans   = true;  // the value tensor is transposed
 
     // Note: The value of head isn't only used to optimize searching
     // for a free KV slot. llama_decode_internal also uses it, so it
@@ -1706,6 +2073,9 @@ struct llama_kv_cache {
     // computed before each graph build
     uint32_t n = 0;
 
+    ggml_type type_k = GGML_TYPE_F16;
+    ggml_type type_v = GGML_TYPE_F16;
+
     std::vector<llama_kv_cell> cells;
 
     std::vector<struct ggml_tensor *> k_l; // per layer
@@ -1732,6 +2102,31 @@ struct llama_kv_cache {
     }
 };
 
+struct llama_control_vector {
+    std::vector<struct ggml_tensor *> tensors; // per layer
+    std::vector<struct ggml_context *> ctxs;
+    std::vector<ggml_backend_buffer_t> bufs;
+
+    int32_t layer_start = -1;
+    int32_t layer_end   = -1;
+
+    ggml_tensor * tensor_for(int il) const {
+        if (il < 0 || il < layer_start || il > layer_end || (size_t) il >= tensors.size()) {
+            return nullptr;
+        }
+        return tensors[il];
+    }
+
+    ~llama_control_vector() {
+        for (struct ggml_context * ctx : ctxs) {
+            ggml_free(ctx);
+        }
+        for (ggml_backend_buffer_t buf : bufs) {
+            ggml_backend_buffer_free(buf);
+        }
+    }
+};
+
 struct llama_vocab {
     using id    = int32_t;
     using token = std::string;
@@ -1743,7 +2138,8 @@ struct llama_vocab {
         ttype type;
     };
 
-    enum llama_vocab_type type = LLAMA_VOCAB_TYPE_SPM;
+    enum llama_vocab_type     type     = LLAMA_VOCAB_TYPE_SPM;
+    enum llama_vocab_pre_type type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
 
     std::unordered_map<token, id> token_to_id;
     std::vector<token_data>       id_to_token;
@@ -1753,20 +2149,22 @@ struct llama_vocab {
     std::map<std::pair<std::string, std::string>, int> bpe_ranks;
 
     // default LLaMA special tokens
-    id special_bos_id = 1;
-    id special_eos_id = 2;
-    id special_unk_id = 0;
-    id special_sep_id = -1;
-    id special_pad_id = -1;
+    id special_bos_id  = 1;
+    id special_eos_id  = 2;
+    id special_unk_id  = 0;
+    id special_sep_id  = -1;
+    id special_pad_id  = -1;
+    id special_cls_id  = -1;
+    id special_mask_id = -1;
 
     int special_add_bos = -1; // -1 unknown, 1 add, 0 don't add.
     int special_add_eos = -1; // -1 unknown, 1 add, 0 don't add.
 
     id linefeed_id       = 13;
-    id special_prefix_id = 32007;
-    id special_middle_id = 32009;
-    id special_suffix_id = 32008;
-    id special_eot_id    = 32010;
+    id special_prefix_id = -1;
+    id special_suffix_id = -1;
+    id special_middle_id = -1;
+    id special_eot_id    = -1; // TODO: move above after "eos_id", and here add "file separator" token
 
     bool add_space_prefix = true;
 
@@ -1835,12 +2233,12 @@ struct llama_model {
     // the model memory buffers for the tensor data
     std::vector<ggml_backend_buffer_t> bufs;
 
-    // model memory mapped file
-    std::unique_ptr<llama_mmap> mapping;
+    // model memory mapped files
+    llama_mmaps mappings;
 
     // objects representing data potentially being locked in memory
-    std::vector<std::unique_ptr<llama_mlock>> mlock_bufs;
-    llama_mlock mlock_mmap;
+    llama_mlocks mlock_bufs;
+    llama_mlocks mlock_mmaps;
 
     // for quantize-stats only
     std::vector<std::pair<std::string, struct ggml_tensor *>> tensors_by_name;
@@ -1853,6 +2251,11 @@ struct llama_model {
             ggml_free(ctx);
         }
         for (ggml_backend_buffer_t buf : bufs) {
+#ifdef GGML_USE_CUDA
+            if (ggml_backend_buffer_get_type(buf) == ggml_backend_cpu_buffer_type()) {
+                ggml_backend_cuda_unregister_host_buffer(ggml_backend_buffer_get_base(buf));
+            }
+#endif
             ggml_backend_buffer_free(buf);
         }
     }
@@ -1867,12 +2270,7 @@ struct llama_context {
             ggml_backend_free(backend);
         }
 
-#ifdef GGML_USE_VULKAN
-        ggml_vk_free_cpu_assist();
-#endif
-
-        ggml_backend_buffer_free(buf_input);
-        ggml_free(ctx_input);
+        ggml_backend_buffer_free(buf_output);
     }
 
     llama_cparams cparams;
@@ -1898,36 +2296,57 @@ struct llama_context {
     int64_t t_p_eval_us = 0;
     int64_t t_eval_us   = 0;
 
+    int64_t t_compute_start_us = 0;
+    int64_t n_queued_tokens = 0;
+
     int32_t n_sample = 0; // number of tokens sampled
     int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
     int32_t n_eval   = 0; // number of eval calls
 
-    // decode output (2-dimensional array: [n_tokens][n_vocab])
-    std::vector<float> logits;
-#ifndef NDEBUG
-    // guard against access to unset logits
-    std::vector<bool>  logits_valid;
-#endif
+    // host buffer for the model output (logits and embeddings)
+    ggml_backend_buffer_t buf_output = nullptr;
+
+    // decode output (2-dimensional array: [n_outputs][n_vocab])
+    size_t  logits_size = 0; // capacity (of floats) for logits
+    float * logits      = nullptr;
+
+    std::vector<int32_t> output_ids; // map batch token positions to ids of the logits and embd buffers
+    size_t  output_size = 0; // capacity (of tokens positions) for the output buffers
+    int32_t n_outputs   = 0; // number of actually-used outputs in the current ubatch or last logical batch
+
     bool logits_all = false;
 
-    // input embedding (1-dimensional array: [n_embd])
-    std::vector<float> embedding;
+    // embeddings output (2-dimensional array: [n_outputs][n_embd])
+    // populated only when pooling_type == LLAMA_POOLING_TYPE_NONE
+    size_t  embd_size = 0; // capacity (of floats) for embeddings
+    float * embd      = nullptr;
+
+    // sequence embeddings output (map of [n_embd] vectors)
+    // populated only when pooling_type != LLAMA_POOLING_TYPE_NONE
+    std::map<llama_seq_id, std::vector<float>> embd_seq;
 
     // memory buffers used to evaluate the model
     std::vector<uint8_t> buf_compute_meta;
     ggml_backend_sched_t sched = nullptr;
 
+    ggml_abort_callback abort_callback      = nullptr;
+    void *              abort_callback_data = nullptr;
+
     // input tensors
-    ggml_backend_buffer_t buf_input = nullptr;
-    ggml_context * ctx_input = nullptr;
     struct ggml_tensor * inp_tokens;    // I32 [n_batch]
     struct ggml_tensor * inp_embd;      // F32 [n_embd, n_batch]
     struct ggml_tensor * inp_pos;       // I32 [n_batch]
-    struct ggml_tensor * inp_KQ_mask;   // F32 [n_ctx, n_batch]
-    struct ggml_tensor * inp_KQ_pos;    // F32 [n_ctx]
-    struct ggml_tensor * inp_K_shift;   // I32 [n_ctx]
+    struct ggml_tensor * inp_out_ids;   // I32 [n_outputs]
+    struct ggml_tensor * inp_KQ_mask;   // F32 [kv_size, n_batch]
+    struct ggml_tensor * inp_K_shift;   // I32 [kv_size]
     struct ggml_tensor * inp_mean;      // F32 [n_batch, n_batch]
     struct ggml_tensor * inp_cls;       // I32 [n_batch]
+    struct ggml_tensor * inp_s_copy;    // I32 [kv_size]
+    struct ggml_tensor * inp_s_mask;    // F32 [1, n_kv]
+    struct ggml_tensor * inp_s_seq;     // I32 [n_kv, n_batch]
+
+    // control vectors
+    struct llama_control_vector cvec;
 
 #ifdef GGML_USE_MPI
     ggml_mpi_context * ctx_mpi = NULL;
@@ -1940,25 +2359,49 @@ struct llama_context {
 
 static bool llama_kv_cache_init(
              struct llama_kv_cache & cache,
-                 const llama_model & model,
-                         ggml_type   ktype,
-                         ggml_type   vtype,
-                          uint32_t   n_ctx,
+               const llama_context * ctx,
+                         ggml_type   type_k,
+                         ggml_type   type_v,
+                          uint32_t   kv_size,
                               bool   offload) {
+    const llama_model & model = ctx->model;
+    const llama_cparams & cparams = ctx->cparams;
+
     const struct llama_hparams & hparams = model.hparams;
 
-    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa();
-    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
+    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
+    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
     const int64_t  n_layer      = hparams.n_layer;
 
     cache.has_shift = false;
 
+    // TODO: find a nicer way to add other recurrent model architectures
+    cache.recurrent = model.arch == LLM_ARCH_MAMBA;
+    cache.v_trans   = !cparams.flash_attn;
+
+    // TODO: support mixed recurrent Transformer architectures
+    // NOTE: (!a || b) is a logical implication (a -> b)
+    GGML_ASSERT(!cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_s());
+    GGML_ASSERT(!cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_s());
+    GGML_ASSERT( cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_gqa());
+    GGML_ASSERT( cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_gqa());
+
     cache.head = 0;
-    cache.size = n_ctx;
+    cache.size = kv_size;
     cache.used = 0;
 
+    cache.type_k = type_k;
+    cache.type_v = type_v;
+
     cache.cells.clear();
-    cache.cells.resize(n_ctx);
+    cache.cells.resize(kv_size);
+
+    if (cache.recurrent) {
+        // init state copy sources
+        for (uint32_t i = 0; i < cache.size; ++i) {
+            cache.cells[i].src = i;
+        }
+    }
 
 #ifdef GGML_USE_CLBLAST
     offload = false;
@@ -1997,8 +2440,8 @@ static bool llama_kv_cache_init(
 
     for (int i = 0; i < (int) n_layer; i++) {
         struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front();
-        ggml_tensor * k = ggml_new_tensor_1d(ctx, ktype, n_embd_k_gqa*n_ctx);
-        ggml_tensor * v = ggml_new_tensor_1d(ctx, vtype, n_embd_v_gqa*n_ctx);
+        ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
+        ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size);
         ggml_format_name(k, "cache_k_l%d", i);
         ggml_format_name(v, "cache_v_l%d", i);
         cache.k_l.push_back(k);
@@ -2032,6 +2475,54 @@ static bool llama_kv_cache_find_slot(
     const uint32_t n_ctx    = cache.size;
     const uint32_t n_tokens = batch.n_tokens;
 
+    if (cache.recurrent) {
+        // For recurrent state architectures (like Mamba),
+        // each KV cache cell can store the state for a whole sequence.
+
+        llama_seq_id min = cache.size - 1;
+        llama_seq_id max = 0;
+
+        for (uint32_t i = 0; i < n_tokens; ++i) {
+            for (int32_t j = 0; j < batch.n_seq_id[i]; ++j) {
+                llama_seq_id seq_id = batch.seq_id[i][j];
+                // make sure it's a valid seq_id
+                if ((uint32_t) seq_id < cache.size) {
+                    if (seq_id > max) {
+                        max = seq_id;
+                    }
+                    if (seq_id < min) {
+                        min = seq_id;
+                    }
+                    // Assuming the tokens are in-order
+                    if (batch.pos[i] != cache.cells[seq_id].pos + 1) {
+                        // What should happen when the pos backtracks or skips a value?
+                        // Clearing the state mid-batch would require special-casing which isn't done.
+                        LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d\n",
+                            __func__, batch.pos[i], cache.cells[seq_id].pos, seq_id);
+                    }
+                    if (cache.cells[seq_id].pos < 0 && 0 <= batch.pos[i]) {
+                        cache.used += 1;
+                    }
+                    cache.cells[seq_id].pos = batch.pos[i];
+                    // NOTE: seq_ids are not inserted here; they are handled when the input tensors are set
+                } else {
+                    // too big seq_id
+                    // TODO: would it be possible to resize the KV cache size instead?
+                    LLAMA_LOG_ERROR("%s: seq_id=%d >= kv_size=%d Try using a bigger --parallel value\n", __func__, seq_id, cache.size);
+                    return false;
+                }
+            }
+        }
+
+        // allow getting the range of used cells, from head to head + n
+        cache.head = min;
+        cache.n    = max - min + 1;
+
+        // sanity check
+        return max >= min;
+    }
+    // otherwise, one cell per token.
+
     if (n_tokens > n_ctx) {
         LLAMA_LOG_ERROR("%s: n_tokens=%d > n_ctx=%d\n", __func__, n_tokens, n_ctx);
         return false;
@@ -2080,10 +2571,12 @@ static bool llama_kv_cache_find_slot(
 }
 
 // find how many cells are currently in use
-static int32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) {
-    for (uint32_t i = cache.size - 1; i > 0; --i) {
-        if (cache.cells[i].pos >= 0 && !cache.cells[i].seq_id.empty()) {
-            return i + 1;
+static uint32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) {
+    for (uint32_t i = cache.size; i > 0; --i) {
+        const llama_kv_cell & cell = cache.cells[i - 1];
+
+        if (cell.pos >= 0 && !cell.is_empty()) {
+            return i;
         }
     }
 
@@ -2097,9 +2590,13 @@ static void llama_kv_cache_clear(struct llama_kv_cache & cache) {
     }
     cache.head = 0;
     cache.used = 0;
+
+    for (auto & buf : cache.bufs) {
+        ggml_backend_buffer_clear(buf, 0);
+    }
 }
 
-static void llama_kv_cache_seq_rm(
+static bool llama_kv_cache_seq_rm(
         struct llama_kv_cache & cache,
                  llama_seq_id   seq_id,
                     llama_pos   p0,
@@ -2109,6 +2606,25 @@ static void llama_kv_cache_seq_rm(
     if (p0 < 0) p0 = 0;
     if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
 
+    // models like Mamba can't have a state partially erased
+    if (cache.recurrent) {
+        if (seq_id >= (int64_t) cache.size) {
+            // could be fatal
+            return false;
+        }
+        if (0 <= seq_id) {
+            // partial intersection is invalid
+            if ((0 < p0 && p0 <= cache.cells[seq_id].pos) || (0 < p1 && p1 <= cache.cells[seq_id].pos)) {
+                return false;
+            }
+        } else {
+            // seq_id is negative, then the range should include everything or nothing
+            if (p0 != p1 && (p0 != 0 || p1 != std::numeric_limits<llama_pos>::max())) {
+                return false;
+            }
+        }
+    }
+
     for (uint32_t i = 0; i < cache.size; ++i) {
         if (cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
             if (seq_id < 0) {
@@ -2118,7 +2634,7 @@ static void llama_kv_cache_seq_rm(
             } else {
                 continue;
             }
-            if (cache.cells[i].seq_id.empty()) {
+            if (cache.cells[i].is_empty()) {
                 // keep count of the number of used cells
                 if (cache.cells[i].pos >= 0) cache.used--;
 
@@ -2130,6 +2646,8 @@ static void llama_kv_cache_seq_rm(
 
     // If we freed up a slot, set head to it so searching can start there.
     if (new_head != cache.size && new_head < cache.head) cache.head = new_head;
+
+    return true;
 }
 
 static void llama_kv_cache_seq_cp(
@@ -2141,6 +2659,29 @@ static void llama_kv_cache_seq_cp(
     if (p0 < 0) p0 = 0;
     if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
 
+    if (cache.recurrent) {
+        if ((uint32_t) seq_id_dst < cache.size && (uint32_t) seq_id_src < cache.size) {
+            seq_id_src = cache.cells[seq_id_src].src;
+            GGML_ASSERT((uint32_t) seq_id_src < cache.size);
+            // intent to "copy from"
+            // supports copy chains thanks to taking the source of the source
+            cache.cells[seq_id_dst].src = seq_id_src;
+
+            // preserve the "keep or clear" status of the copied sequence
+            if (cache.cells[seq_id_src].has_seq_id(seq_id_src)) {
+                cache.cells[seq_id_dst].seq_id.insert(seq_id_dst);
+            } else {
+                cache.cells[seq_id_dst].seq_id.erase(seq_id_dst);
+            }
+
+            cache.do_copy = true;
+
+            cache.cells[seq_id_dst].pos = cache.cells[seq_id_src].pos;
+        }
+        return;
+    }
+    // otherwise, this is the KV cache of a Transformer-like model
+
     cache.head = 0;
 
     for (uint32_t i = 0; i < cache.size; ++i) {
@@ -2169,7 +2710,7 @@ static void llama_kv_cache_seq_keep(struct llama_kv_cache & cache, llama_seq_id
     if (new_head != cache.size && new_head < cache.head) cache.head = new_head;
 }
 
-static void llama_kv_cache_seq_shift(
+static void llama_kv_cache_seq_add(
         struct llama_kv_cache & cache,
                  llama_seq_id   seq_id,
                     llama_pos   p0,
@@ -2180,6 +2721,17 @@ static void llama_kv_cache_seq_shift(
     if (p0 < 0) p0 = 0;
     if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
 
+    if (cache.recurrent) {
+        // for Mamba-like models, only the pos needs to be shifted
+        if (0 <= seq_id && seq_id < (int64_t) cache.size) {
+            llama_kv_cell & cell = cache.cells[seq_id];
+            if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
+                cell.pos += delta;
+            }
+        }
+        return;
+    }
+
     for (uint32_t i = 0; i < cache.size; ++i) {
         if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
             cache.has_shift = true;
@@ -2187,10 +2739,14 @@ static void llama_kv_cache_seq_shift(
             cache.cells[i].delta += delta;
 
             if (cache.cells[i].pos < 0) {
-                if (!cache.cells[i].seq_id.empty()) cache.used--;
+                if (!cache.cells[i].is_empty()) {
+                    cache.used--;
+                }
                 cache.cells[i].pos = -1;
                 cache.cells[i].seq_id.clear();
-                if (new_head == cache.size) new_head = i;
+                if (new_head == cache.size) {
+                    new_head = i;
+                }
             }
         }
     }
@@ -2209,6 +2765,17 @@ static void llama_kv_cache_seq_div(
     if (p0 < 0) p0 = 0;
     if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
 
+    if (cache.recurrent) {
+        // for Mamba-like models, only the pos needs to be changed
+        if (0 <= seq_id && seq_id < (int64_t) cache.size) {
+            llama_kv_cell & cell = cache.cells[seq_id];
+            if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
+                cell.pos /= d;
+            }
+        }
+        return;
+    }
+
     for (uint32_t i = 0; i < cache.size; ++i) {
         if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
             cache.has_shift = true;
@@ -2222,6 +2789,27 @@ static void llama_kv_cache_seq_div(
     }
 }
 
+static llama_pos llama_kv_cache_seq_pos_max(struct llama_kv_cache & cache, llama_seq_id seq_id) {
+    llama_pos result = 0;
+
+    for (uint32_t i = 0; i < cache.size; ++i) {
+        if (cache.cells[i].has_seq_id(seq_id)) {
+            result = std::max(result, cache.cells[i].pos);
+        }
+    }
+
+    return result;
+}
+
+static void llama_kv_cache_defrag(struct llama_kv_cache & cache) {
+    cache.do_defrag = true;
+}
+
+static uint32_t llama_kv_cache_get_padding(const struct llama_cparams & cparams) {
+    // the FA kernels require padding to avoid extra runtime boundary checks
+    return cparams.flash_attn ? 256u : 32u;
+}
+
 //
 // model loading and saving
 //
@@ -2293,7 +2881,7 @@ namespace GGUFMeta {
         }
     };
 
-    struct ArrayInfo{
+    struct ArrayInfo {
         const gguf_type gt;
         const size_t length;
         const void * data;
@@ -2312,7 +2900,7 @@ namespace GGUFMeta {
     };
 
     template<typename T>
-    class GKV: public GKV_Base<T> {
+    class GKV : public GKV_Base<T> {
         GKV() = delete;
 
         public:
@@ -2328,46 +2916,50 @@ namespace GGUFMeta {
 
         static const char * override_type_to_str(const llama_model_kv_override_type ty) {
             switch (ty) {
-                case LLAMA_KV_OVERRIDE_BOOL:  return "bool";
-                case LLAMA_KV_OVERRIDE_INT:   return "int";
-                case LLAMA_KV_OVERRIDE_FLOAT: return "float";
+                case LLAMA_KV_OVERRIDE_TYPE_BOOL:  return "bool";
+                case LLAMA_KV_OVERRIDE_TYPE_INT:   return "int";
+                case LLAMA_KV_OVERRIDE_TYPE_FLOAT: return "float";
+                case LLAMA_KV_OVERRIDE_TYPE_STR:   return "str";
             }
             return "unknown";
         }
 
-        static bool validate_override(const llama_model_kv_override_type expected_type, const struct llama_model_kv_override *override) {
-            if (!override) { return false; }
-            if (override->tag == expected_type) {
+        static bool validate_override(const llama_model_kv_override_type expected_type, const struct llama_model_kv_override * ovrd) {
+            if (!ovrd) { return false; }
+            if (ovrd->tag == expected_type) {
                 LLAMA_LOG_INFO("%s: Using metadata override (%5s) '%s' = ",
-                    __func__, override_type_to_str(override->tag), override->key);
-                switch (override->tag) {
-                    case LLAMA_KV_OVERRIDE_BOOL:  {
-                        LLAMA_LOG_INFO("%s\n", override->bool_value ? "true" : "false");
+                    __func__, override_type_to_str(ovrd->tag), ovrd->key);
+                switch (ovrd->tag) {
+                    case LLAMA_KV_OVERRIDE_TYPE_BOOL:  {
+                        LLAMA_LOG_INFO("%s\n", ovrd->val_bool ? "true" : "false");
                     } break;
-                    case LLAMA_KV_OVERRIDE_INT:   {
-                        LLAMA_LOG_INFO("%" PRId64 "\n", override->int_value);
+                    case LLAMA_KV_OVERRIDE_TYPE_INT:   {
+                        LLAMA_LOG_INFO("%" PRId64 "\n", ovrd->val_i64);
                     } break;
-                    case LLAMA_KV_OVERRIDE_FLOAT: {
-                        LLAMA_LOG_INFO("%.6f\n", override->float_value);
+                    case LLAMA_KV_OVERRIDE_TYPE_FLOAT: {
+                        LLAMA_LOG_INFO("%.6f\n", ovrd->val_f64);
+                    } break;
+                    case LLAMA_KV_OVERRIDE_TYPE_STR: {
+                        LLAMA_LOG_INFO("%s\n", ovrd->val_str);
                     } break;
                     default:
                         // Shouldn't be possible to end up here, but just in case...
                         throw std::runtime_error(
                             format("Unsupported attempt to override %s type for metadata key %s\n",
-                                override_type_to_str(override->tag), override->key));
+                                override_type_to_str(ovrd->tag), ovrd->key));
                 }
                 return true;
             }
             LLAMA_LOG_WARN("%s: Warning: Bad metadata override type for key '%s', expected %s but got %s\n",
-                __func__, override->key, override_type_to_str(expected_type), override_type_to_str(override->tag));
+                __func__, ovrd->key, override_type_to_str(expected_type), override_type_to_str(ovrd->tag));
             return false;
         }
 
         template<typename OT>
         static typename std::enable_if<std::is_same<OT, bool>::value, bool>::type
-        try_override(OT & target, const struct llama_model_kv_override *override) {
-            if (validate_override(LLAMA_KV_OVERRIDE_BOOL, override)) {
-                target = override->bool_value;
+        try_override(OT & target, const struct llama_model_kv_override * ovrd) {
+            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_BOOL, ovrd)) {
+                target = ovrd->val_bool;
                 return true;
             }
             return false;
@@ -2375,9 +2967,9 @@ namespace GGUFMeta {
 
         template<typename OT>
         static typename std::enable_if<!std::is_same<OT, bool>::value && std::is_integral<OT>::value, bool>::type
-        try_override(OT & target, const struct llama_model_kv_override *override) {
-            if (validate_override(LLAMA_KV_OVERRIDE_INT, override)) {
-                target = override->int_value;
+        try_override(OT & target, const struct llama_model_kv_override * ovrd) {
+            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_INT, ovrd)) {
+                target = ovrd->val_i64;
                 return true;
             }
             return false;
@@ -2385,9 +2977,9 @@ namespace GGUFMeta {
 
         template<typename OT>
         static typename std::enable_if<std::is_floating_point<OT>::value, bool>::type
-        try_override(T & target, const struct llama_model_kv_override *override) {
-            if (validate_override(LLAMA_KV_OVERRIDE_FLOAT, override)) {
-                target = override->float_value;
+        try_override(T & target, const struct llama_model_kv_override * ovrd) {
+            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_FLOAT, ovrd)) {
+                target = ovrd->val_f64;
                 return true;
             }
             return false;
@@ -2395,17 +2987,16 @@ namespace GGUFMeta {
 
         template<typename OT>
         static typename std::enable_if<std::is_same<OT, std::string>::value, bool>::type
-        try_override(T & target, const struct llama_model_kv_override *override) {
-            (void)target;
-            (void)override;
-            if (!override) { return false; }
-            // Currently, we should never end up here so it would be a bug if we do.
-            throw std::runtime_error(format("Unsupported attempt to override string type for metadata key %s\n",
-                override ? override->key : "NULL"));
+        try_override(T & target, const struct llama_model_kv_override * ovrd) {
+            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_STR, ovrd)) {
+                target = ovrd->val_str;
+                return true;
+            }
+            return false;
         }
 
-        static bool set(const gguf_context * ctx, const int k, T & target, const struct llama_model_kv_override *override = nullptr) {
-            if (try_override<T>(target, override)) {
+        static bool set(const gguf_context * ctx, const int k, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
+            if (try_override<T>(target, ovrd)) {
                 return true;
             }
             if (k < 0) { return false; }
@@ -2413,16 +3004,18 @@ namespace GGUFMeta {
             return true;
         }
 
-        static bool set(const gguf_context * ctx, const char * key, T & target, const struct llama_model_kv_override *override = nullptr) {
-            return set(ctx, gguf_find_key(ctx, key), target, override);
+        static bool set(const gguf_context * ctx, const char * key, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
+            return set(ctx, gguf_find_key(ctx, key), target, ovrd);
         }
 
-        static bool set(const gguf_context * ctx, const std::string & key, T & target, const struct llama_model_kv_override *override = nullptr) {
-            return set(ctx, key.c_str(), target, override);
+        static bool set(const gguf_context * ctx, const std::string & key, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
+            return set(ctx, key.c_str(), target, ovrd);
         }
     };
 }
 
+using llama_buf_map = std::unordered_map<uint32_t, ggml_backend_buffer_t>;
+
 struct llama_model_loader {
     int n_kv      = 0;
     int n_tensors = 0;
@@ -2432,55 +3025,148 @@ struct llama_model_loader {
     size_t  n_bytes    = 0;
 
     bool use_mmap = false;
+    bool check_tensors;
 
-    llama_file  file;
+    llama_files files;
     llama_ftype ftype;
     llama_fver  fver;
 
-    std::unique_ptr<llama_mmap> mapping;
+    llama_mmaps mappings;
+
+    // Holds information on a model weight
+    struct llama_tensor_weight {
+        uint16_t  idx; // source file index
+        size_t   offs; // tensor data offset in the original file
+
+        ggml_tensor * tensor;
+
+        llama_tensor_weight(const llama_file * file, uint16_t idx, const char * name, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) {
+            const int tensor_idx = gguf_find_tensor(gguf_ctx, name);
+            offs = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, tensor_idx);
+
+            if (offs + ggml_nbytes(tensor) < offs || offs + ggml_nbytes(tensor) > file->size) {
+                throw std::runtime_error(format("tensor '%s' data is not within the file bounds, model is corrupted or incomplete", name));
+            }
+        }
+    };
+    std::vector<llama_tensor_weight> weights;
+
     std::unordered_map<std::string, struct llama_model_kv_override> kv_overrides;
 
-    struct gguf_context * ctx_gguf = NULL;
-    struct ggml_context * ctx_meta = NULL;
+    struct gguf_context * meta = NULL;
+    std::vector<ggml_context *> contexts;
 
     std::string arch_name;
     LLM_KV      llm_kv    = LLM_KV(LLM_ARCH_UNKNOWN);
 
-    llama_model_loader(const std::string & fname, bool use_mmap, const struct llama_model_kv_override * param_overrides_p) : file(fname.c_str(), "rb") {
+    llama_model_loader(const std::string & fname, bool use_mmap, bool check_tensors, const struct llama_model_kv_override * param_overrides_p) {
         int trace = 0;
         if (getenv("LLAMA_TRACE")) {
             trace = atoi(getenv("LLAMA_TRACE"));
         }
 
-        struct gguf_init_params params = {
-            /*.no_alloc = */ true,
-            /*.ctx      = */ &ctx_meta,
-        };
-
         if (param_overrides_p != nullptr) {
             for (const struct llama_model_kv_override *p = param_overrides_p; p->key[0] != 0; p++) {
                 kv_overrides.insert({std::string(p->key), *p});
             }
         }
 
-        ctx_gguf = gguf_init_from_file(fname.c_str(), params);
-        if (!ctx_gguf) {
+        struct ggml_context * ctx = NULL;
+        struct gguf_init_params params = {
+            /*.no_alloc = */ true,
+            /*.ctx      = */ &ctx,
+        };
+
+        meta = gguf_init_from_file(fname.c_str(), params);
+        if (!meta) {
             throw std::runtime_error(format("%s: failed to load model from %s\n", __func__, fname.c_str()));
         }
 
         get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false);
         llm_kv = LLM_KV(llm_arch_from_string(arch_name));
 
-        n_kv      = gguf_get_n_kv(ctx_gguf);
-        n_tensors = gguf_get_n_tensors(ctx_gguf);
+        files.emplace_back(new llama_file(fname.c_str(), "rb"));
+        contexts.emplace_back(ctx);
 
-        fver = (enum llama_fver ) gguf_get_version(ctx_gguf);
+        // Save tensors data offset of the main file.
+        // For subsidiary files, `meta` tensor data offset must not be used,
+        // so we build a unified tensors index for weights.
+        for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
+            weights.emplace_back(files.back().get(), 0, cur->name, meta, cur);
+        }
+        uint16_t n_split = 0;
+        get_key(llm_kv(LLM_KV_SPLIT_COUNT), n_split, false);
 
-        for (int i = 0; i < n_tensors; i++) {
-            const char * name = gguf_get_tensor_name(ctx_gguf, i);
-            struct ggml_tensor * t = ggml_get_tensor(ctx_meta, name);
-            n_elements += ggml_nelements(t);
-            n_bytes    += ggml_nbytes(t);
+        // Load additional GGML contexts
+        if (n_split > 1) {
+            uint16_t idx = 0;
+            get_key(llm_kv(LLM_KV_SPLIT_NO), idx);
+            if (idx != 0) {
+                throw std::runtime_error(format("illegal split file: %d, model must be loaded with the first split", idx));
+            }
+
+            char split_prefix[PATH_MAX] = {0};
+            if (!llama_split_prefix(split_prefix, sizeof(split_prefix), fname.c_str(), idx, n_split)) {
+                throw std::runtime_error(format("invalid split file: %s", fname.c_str()));
+            }
+
+            if (trace > 0) {
+                LLAMA_LOG_INFO("%s: loading additional %d GGUFs\n", __func__, n_split);
+            }
+
+            char split_path[PATH_MAX] = {0};
+            for (idx = 1; idx < n_split; idx++) {
+                llama_split_path(split_path, sizeof(split_path), split_prefix, idx, n_split);
+
+                struct gguf_init_params split_params = {
+                    /*.no_alloc = */ true,
+                    /*.ctx      = */ &ctx,
+                };
+                struct gguf_context * ctx_gguf = gguf_init_from_file(split_path, split_params);
+                if (!ctx_gguf) {
+                    throw std::runtime_error(format("%s: failed to load GGUF split from %s\n", __func__, split_path));
+                }
+
+                files.emplace_back(new llama_file(split_path, "rb"));
+                contexts.emplace_back(ctx);
+
+                // Save tensors data offset info of the shard.
+                for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
+                    weights.emplace_back(files.back().get(), idx, cur->name, ctx_gguf, cur);
+                }
+
+                gguf_free(ctx_gguf);
+            }
+
+            get_key(llm_kv(LLM_KV_SPLIT_TENSORS_COUNT), n_tensors);
+
+            // sanity check
+            {
+                const int n_tensors_loaded = (int) weights.size();
+                if (n_tensors != n_tensors_loaded) {
+                    throw std::runtime_error(format("corrupted model: %d tensors expected but %d found", n_tensors, n_tensors_loaded));
+                }
+            }
+
+            LLAMA_LOG_INFO("%s: additional %d GGUFs metadata loaded.\n",  __func__, n_split - 1);
+        }
+
+        n_kv      = gguf_get_n_kv(meta);
+        n_tensors = weights.size();
+
+        fver = (enum llama_fver) gguf_get_version(meta);
+
+        std::set<std::string> tensor_names;
+        for (auto & w : weights) {
+            n_elements += ggml_nelements(w.tensor);
+            n_bytes    += ggml_nbytes(w.tensor);
+            // make sure there is no duplicated tensor names
+            const std::string name(w.tensor->name);
+            auto found = tensor_names.find(name);
+            if (found != tensor_names.end()) {
+                throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", w.tensor->name));
+            }
+            tensor_names.insert(name);
         }
 
         LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n",
@@ -2495,7 +3181,8 @@ struct llama_model_loader {
             enum ggml_type type_max = GGML_TYPE_F32;
 
             for (int i = 0; i < n_tensors; i++) {
-                enum ggml_type type = gguf_get_tensor_type(ctx_gguf, i);
+                const ggml_tensor * tensor = weights.at(i).tensor;
+                enum ggml_type type = tensor->type;
 
                 n_type[type]++;
 
@@ -2505,14 +3192,15 @@ struct llama_model_loader {
                 }
 
                 if (trace > 0) {
-                    struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
-                    LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, ggml_get_name(meta), ggml_type_name(type), llama_format_tensor_shape(meta).c_str());
+                    const uint16_t sid = weights.at(i).idx;
+                    LLAMA_LOG_INFO("%s: - tensor %4d, split %2d: %32s %-8s [ %s ]\n", __func__, i, sid, ggml_get_name(tensor), ggml_type_name(type), llama_format_tensor_shape(tensor).c_str());
                 }
             }
 
             switch (type_max) {
                 case GGML_TYPE_F32:     ftype = LLAMA_FTYPE_ALL_F32;        break;
                 case GGML_TYPE_F16:     ftype = LLAMA_FTYPE_MOSTLY_F16;     break;
+                case GGML_TYPE_BF16:    ftype = LLAMA_FTYPE_MOSTLY_BF16;    break;
                 case GGML_TYPE_Q4_0:    ftype = LLAMA_FTYPE_MOSTLY_Q4_0;    break;
                 case GGML_TYPE_Q4_1:    ftype = LLAMA_FTYPE_MOSTLY_Q4_1;    break;
                 case GGML_TYPE_Q5_0:    ftype = LLAMA_FTYPE_MOSTLY_Q5_0;    break;
@@ -2525,8 +3213,13 @@ struct llama_model_loader {
                 case GGML_TYPE_Q6_K:    ftype = LLAMA_FTYPE_MOSTLY_Q6_K;    break;
                 case GGML_TYPE_IQ2_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS; break;
                 case GGML_TYPE_IQ2_XS:  ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS;  break;
+                case GGML_TYPE_IQ2_S:   ftype = LLAMA_FTYPE_MOSTLY_IQ2_S;   break;
                 case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break;
                 case GGML_TYPE_IQ1_S:   ftype = LLAMA_FTYPE_MOSTLY_IQ1_S;   break;
+                case GGML_TYPE_IQ1_M:   ftype = LLAMA_FTYPE_MOSTLY_IQ1_M;   break;
+                case GGML_TYPE_IQ4_NL:  ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL;  break;
+                case GGML_TYPE_IQ4_XS:  ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS;  break;
+                case GGML_TYPE_IQ3_S:   ftype = LLAMA_FTYPE_MOSTLY_IQ3_S;   break;
                 default:
                     {
                         LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max));
@@ -2538,22 +3231,23 @@ struct llama_model_loader {
             ftype = (llama_ftype) (ftype | LLAMA_FTYPE_GUESSED);
 
             {
-                const int kid = gguf_find_key(ctx_gguf, "general.file_type");
+                const int kid = gguf_find_key(meta, "general.file_type");
                 if (kid >= 0) {
-                    ftype = (llama_ftype) gguf_get_val_u32(ctx_gguf, kid);
+                    ftype = (llama_ftype) gguf_get_val_u32(meta, kid);
                 }
             }
 
             LLAMA_LOG_INFO("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
+
             for (int i = 0; i < n_kv; i++) {
-                const char * name           = gguf_get_key(ctx_gguf, i);
-                const enum gguf_type type   = gguf_get_kv_type(ctx_gguf, i);
+                const char * name           = gguf_get_key(meta, i);
+                const enum gguf_type type   = gguf_get_kv_type(meta, i);
                 const std::string type_name =
                     type == GGUF_TYPE_ARRAY
-                    ? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(ctx_gguf, i)), gguf_get_arr_n(ctx_gguf, i))
+                    ? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(meta, i)), gguf_get_arr_n(meta, i))
                     : gguf_type_name(type);
 
-                std::string value          = gguf_kv_to_str(ctx_gguf, i);
+                std::string value          = gguf_kv_to_str(meta, i);
                 const size_t MAX_VALUE_LEN = 40;
                 if (value.size() > MAX_VALUE_LEN) {
                     value = format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str());
@@ -2579,21 +3273,22 @@ struct llama_model_loader {
         }
 
         this->use_mmap = use_mmap;
+        this->check_tensors = check_tensors;
     }
 
     ~llama_model_loader() {
-        if (ctx_gguf) {
-            gguf_free(ctx_gguf);
+        if (meta) {
+            gguf_free(meta);
         }
-        if (ctx_meta) {
-            ggml_free(ctx_meta);
+        for (auto * ctx : contexts) {
+            ggml_free(ctx);
         }
     }
 
     template<typename T>
     typename std::enable_if<std::is_integral<T>::value, bool>::type
     get_arr_n(const std::string & key, T & result, const bool required = true) {
-        const int kid = gguf_find_key(ctx_gguf, key.c_str());
+        const int kid = gguf_find_key(meta, key.c_str());
 
         if (kid < 0) {
             if (required) {
@@ -2603,7 +3298,7 @@ struct llama_model_loader {
         }
 
         struct GGUFMeta::ArrayInfo arr_info =
-            GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(ctx_gguf, kid);
+            GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta, kid);
 
 
         result = arr_info.length;
@@ -2623,7 +3318,7 @@ struct llama_model_loader {
         const struct llama_model_kv_override * override =
             it != kv_overrides.end() ? &it->second : nullptr;
 
-        const bool found = GGUFMeta::GKV<T>::set(ctx_gguf, key, result, override);
+        const bool found = GGUFMeta::GKV<T>::set(meta, key, result, override);
 
         if (required && !found) {
             throw std::runtime_error(format("key not found in model: %s", key.c_str()));
@@ -2646,28 +3341,61 @@ struct llama_model_loader {
     }
 
     const char * get_tensor_name(int i) const {
-        return gguf_get_tensor_name(ctx_gguf, i);
+        return weights.at(i).tensor->name;
+    }
+
+    const llama_tensor_weight * get_weight(const char * name) const {
+        for (const auto & weight : weights) {
+            if (strcmp(name, weight.tensor->name) == 0) {
+                return &weight;
+            }
+        }
+        return nullptr;
+    }
+
+    const llama_tensor_weight * get_weight(int i) const {
+        return get_weight(get_tensor_name(i));
+    }
+
+    const llama_tensor_weight & require_weight(const char * name) const {
+        const llama_tensor_weight * weight = get_weight(name);
+        if (!weight) {
+            throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name));
+        }
+        return *weight;
     }
 
     struct ggml_tensor * get_tensor_meta(const char * name) const {
-        return ggml_get_tensor(ctx_meta, name);
+        const auto * weight = get_weight(name);
+        if (!weight) {
+            return nullptr;
+        }
+        return weight->tensor;
+    }
+
+    struct ggml_tensor * require_tensor_meta(const char * name) const {
+        struct ggml_tensor * tensor = get_tensor_meta(name);
+        if (!tensor) {
+            throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name));
+        }
+        return tensor;
     }
 
     struct ggml_tensor * get_tensor_meta(int i) const {
         return get_tensor_meta(get_tensor_name(i));
     }
 
-    struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, struct ggml_tensor * meta) {
-        struct ggml_tensor * tensor = ggml_dup_tensor(ctx, meta);
-        ggml_set_name(tensor, ggml_get_name(meta));
+    struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, const struct ggml_tensor * cur) {
+        struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur);
+        ggml_set_name(tensor, ggml_get_name(cur));
 
         n_created++;
 
         return tensor;
     }
 
-    struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, bool required = true) {
-        struct ggml_tensor * cur = ggml_get_tensor(ctx_meta, name.c_str());
+    const struct ggml_tensor * check_tensor_dims(const std::string & name, const std::vector<int64_t> & ne, bool required) const {
+        const struct ggml_tensor * cur = get_tensor_meta(name.c_str());
 
         if (cur == NULL) {
             if (!required) {
@@ -2678,8 +3406,8 @@ struct llama_model_loader {
 
         {
             bool is_ok = true;
-            for (size_t i = 0; i < ne.size(); ++i) {
-                if (ne[i] != cur->ne[i]) {
+            for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
+                if ((i < ne.size() && ne[i] != cur->ne[i]) || (i >= ne.size() && cur->ne[i] != 1)) {
                     is_ok = false;
                     break;
                 }
@@ -2693,89 +3421,143 @@ struct llama_model_loader {
             }
         }
 
+        return cur;
+    }
+
+    struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, bool required = true) {
+        const struct ggml_tensor * cur = check_tensor_dims(name, ne, required);
+
+        if (cur == NULL) {
+            return NULL;
+        }
+
         return create_tensor_for(ctx, cur);
     }
 
+    struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::vector<int64_t> & ne, size_t offset, bool required = true) {
+        const struct ggml_tensor * cur = check_tensor_dims(name, ne, required);
+
+        if (cur == NULL) {
+            return NULL;
+        }
+
+        if (cur->type != base->type) {
+            throw std::runtime_error(format("%s: tensor '%s' has wrong type; expected %s, got %s", __func__, name.c_str(), ggml_type_name(base->type), ggml_type_name(cur->type)));
+        }
+
+        std::array<int64_t, GGML_MAX_DIMS> dims;
+        for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
+            dims[i] = i < ne.size() ? ne[i] : 1;
+        }
+
+        struct ggml_tensor * tensor = ggml_view_4d(ctx, base,
+                                        dims[0], dims[1], dims[2], dims[3],
+                                        cur->nb[1], cur->nb[2], cur->nb[3],
+                                        offset);
+
+        ggml_set_name(tensor, name.c_str());
+
+        n_created++;
+
+        return tensor;
+    }
+
     void done_getting_tensors() const {
         if (n_created != n_tensors) {
             throw std::runtime_error(format("%s: wrong number of tensors; expected %d, got %d", __func__, n_tensors, n_created));
         }
     }
 
-    size_t file_offset(const char * name) const {
-        const int idx = gguf_find_tensor(ctx_gguf, name);
-
-        if (idx < 0) {
-            throw std::runtime_error(format("%s: tensor '%s' not found in the file", __func__, name));
-        }
-
-        return gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, idx);
-    }
-
-    void init_mapping(bool prefetch = true, llama_mlock * lmlock = nullptr) {
-        // prefetch the whole file - all the data is needed anyway
+    void init_mappings(bool prefetch = true, llama_mlocks * mlock_mmaps = nullptr) {
         if (use_mmap) {
-            mapping.reset(new llama_mmap(&file, prefetch ? -1 : 0, ggml_is_numa()));
+            mappings.reserve(files.size());
+            mmaps_used.reserve(files.size());
+            for (const auto & file : files) {
+                std::unique_ptr<llama_mmap> mapping(new llama_mmap(file.get(), prefetch ? -1 : 0, ggml_is_numa()));
+                mmaps_used.emplace_back(mapping->size, 0);
+                if (mlock_mmaps) {
+                    std::unique_ptr<llama_mlock> mlock_mmap(new llama_mlock());
+                    mlock_mmap->init(mapping->addr);
+                    mlock_mmaps->emplace_back(std::move(mlock_mmap));
+                }
+                mappings.emplace_back(std::move(mapping));
+            }
         }
 
         // compute the total size of all tensors for progress reporting
-        for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
-            struct ggml_tensor * cur = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
-            size_data += ggml_nbytes(cur);
-        }
-
-        if (use_mmap && mapping) {
-            if (lmlock) {
-                lmlock->init(mapping->addr);
-            }
-            mmap_used_first = mapping->size;
+        for (auto & w : weights) {
+            size_data += ggml_nbytes(w.tensor);
         }
     }
 
-    void get_mapping_range(size_t * first, size_t * last, ggml_context * ctx) const {
-        GGML_ASSERT(mapping);
+    void get_mapping_range(size_t * first, size_t * last, void ** addr, int idx, ggml_context * ctx) const {
+        GGML_ASSERT(!mappings.empty());
+        const auto & mapping = mappings.at(idx);
 
         *first = mapping->size;
         *last  = 0;
+        *addr = mapping->addr;
         for (ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor; tensor = ggml_get_next_tensor(ctx, tensor)) {
-            const size_t offs = file_offset(ggml_get_name(tensor));
-            *first = std::min(*first, offs);
-            *last  = std::max(*last,  offs + ggml_nbytes(tensor));
+            try {
+                const auto * weight = get_weight(ggml_get_name(tensor));
+                if (!weight) {
+                    continue;
+                }
+                if (weight->idx != idx) {
+                    continue;
+                }
+                *first = std::min(*first, weight->offs);
+                *last  = std::max(*last,  weight->offs + ggml_nbytes(tensor));
+            } catch(...) {
+                // the tensor is not in the model
+            }
         }
     }
 
     // for backwards compatibility, does not support ggml-backend
     void load_data_for(struct ggml_tensor * cur) const {
-        const size_t offs = file_offset(ggml_get_name(cur));
+        const auto & w = require_weight(ggml_get_name(cur));
 
-        if (use_mmap && mapping) {
+        if (use_mmap) {
+            const auto & mapping = mappings.at(w.idx);
             if (cur->data == nullptr) {
-                cur->data = (uint8_t *)mapping->addr + offs;
+                cur->data = (uint8_t *)mapping->addr + w.offs;
             } else {
-                memcpy(cur->data, (uint8_t *)mapping->addr + offs, ggml_nbytes(cur));
+                memcpy(cur->data, (uint8_t *)mapping->addr + w.offs, ggml_nbytes(cur));
             }
         } else {
             GGML_ASSERT(cur->data != nullptr);
-            file.seek(offs, SEEK_SET);
-            file.read_raw(cur->data, ggml_nbytes(cur));
+            GGML_ASSERT(w.idx < files.size());
+            const auto & file = files.at(w.idx);
+            file->seek(w.offs, SEEK_SET);
+            file->read_raw(cur->data, ggml_nbytes(cur));
+        }
+
+        if (check_tensors && !ggml_validate_row_data(cur->type, cur->data, ggml_nbytes(cur))) {
+            throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
         }
     }
 
     size_t size_done = 0;
     size_t size_data = 0;
-    size_t mmap_used_first = -1;
-    size_t mmap_used_last  = 0;
+    std::vector<std::pair<size_t, size_t>> mmaps_used;
 
     // Returns false if cancelled by progress_callback
-    bool load_all_data(struct ggml_context * ctx, llama_progress_callback progress_callback, void * progress_callback_user_data, ggml_backend_buffer_t buf_mmap, llama_mlock * lmlock) {
-        GGML_ASSERT(size_data != 0 && "call init_mapping() first");
+    bool load_all_data(
+            struct ggml_context * ctx,
+            llama_buf_map & bufs_mmap,
+            llama_mlocks * lmlocks,
+            llama_progress_callback progress_callback,
+            void * progress_callback_user_data) {
+        GGML_ASSERT(size_data != 0 && "call init_mappings() first");
 
         std::vector<no_init<uint8_t>> read_buf;
+        std::vector<std::future<std::pair<ggml_tensor *, bool>>> validation_result;
 
-        for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
-            struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i));
-            if (!cur) {
-                // some tensors may be allocated in a different context
+        for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
+            const auto * weight = get_weight(ggml_get_name(cur));
+            if (weight == nullptr) {
+                // this can happen with split experts models
                 continue;
             }
 
@@ -2785,41 +3567,85 @@ struct llama_model_loader {
                 }
             }
 
-            const size_t offs = file_offset(ggml_get_name(cur));
+            size_t n_size = ggml_nbytes(cur);
 
-            if (use_mmap && mapping) {
+            if (use_mmap) {
+                const auto & mapping = mappings.at(weight->idx);
+                ggml_backend_buffer_t buf_mmap = nullptr;
+                if (bufs_mmap.count(weight->idx)) {
+                    buf_mmap = bufs_mmap.at(weight->idx);
+                }
+                uint8_t * data = (uint8_t *) mapping->addr + weight->offs;
+
+                if (check_tensors) {
+                    validation_result.emplace_back(std::async(std::launch::async, [cur, data, n_size] {
+                        return std::make_pair(cur, ggml_validate_row_data(cur->type, data, n_size));
+                    }));
+                }
+
+                GGML_ASSERT(buf_mmap || cur->data); // either we have a buffer to allocate the tensor in, or it is already allocated
                 if (buf_mmap && cur->data == nullptr) {
-                    ggml_backend_tensor_alloc(buf_mmap, cur, (uint8_t *) mapping->addr + offs);
-                    if (lmlock) {
-                        lmlock->grow_to(offs + ggml_nbytes(cur));
+                    ggml_backend_tensor_alloc(buf_mmap, cur, data);
+                    if (lmlocks) {
+                        const auto & lmlock = lmlocks->at(weight->idx);
+                        lmlock->grow_to(weight->offs + n_size);
                     }
-                    mmap_used_first = std::min(mmap_used_first, offs);
-                    mmap_used_last  = std::max(mmap_used_last,  offs + ggml_nbytes(cur));
+
+                    auto & mmap_used = mmaps_used[weight->idx];
+                    mmap_used.first  = std::min(mmap_used.first,  weight->offs);
+                    mmap_used.second = std::max(mmap_used.second, weight->offs + n_size);
                 } else {
-                    ggml_backend_tensor_set(cur, (uint8_t *) mapping->addr + offs, 0, ggml_nbytes(cur));
+                    ggml_backend_tensor_set(cur, data, 0, n_size);
                 }
             } else {
+                GGML_ASSERT(weight->idx < files.size());
+                const auto & file = files.at(weight->idx);
                 if (ggml_backend_buffer_is_host(cur->buffer)) {
-                    file.seek(offs, SEEK_SET);
-                    file.read_raw(cur->data, ggml_nbytes(cur));
+                    file->seek(weight->offs, SEEK_SET);
+                    file->read_raw(cur->data, n_size);
+                    if (check_tensors) {
+                        validation_result.emplace_back(std::async(std::launch::async, [cur, n_size] {
+                            return std::make_pair(cur, ggml_validate_row_data(cur->type, cur->data, n_size));
+                        }));
+                    }
                 } else {
-                    read_buf.resize(ggml_nbytes(cur));
-                    file.seek(offs, SEEK_SET);
-                    file.read_raw(read_buf.data(), ggml_nbytes(cur));
-                    ggml_backend_tensor_set(cur, read_buf.data(), 0, ggml_nbytes(cur));
+                    read_buf.resize(n_size);
+                    file->seek(weight->offs, SEEK_SET);
+                    file->read_raw(read_buf.data(), n_size);
+                    ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size);
+                    if (check_tensors && !ggml_validate_row_data(cur->type, read_buf.data(), n_size)) {
+                        throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
+                    }
                 }
             }
 
-            size_done += ggml_nbytes(cur);
+            size_done += n_size;
+        }
+
+        // check validation results
+        bool validation_failed = false;
+        for (auto & future : validation_result) {
+            auto result = future.get();
+            if (!result.second) {
+                LLAMA_LOG_ERROR("%s: tensor '%s' has invalid data\n", __func__, ggml_get_name(result.first));
+                validation_failed = true;
+            }
+        }
+        if (validation_failed) {
+            throw std::runtime_error("found tensors with invalid data");
         }
 
         // check if this is the last call and do final cleanup
         if (size_done >= size_data) {
             // unmap offloaded tensors and metadata
-            if (use_mmap && mapping) {
-                mapping->unmap_fragment(0, mmap_used_first);
-                if (mmap_used_last != 0) {
-                    mapping->unmap_fragment(mmap_used_last, mapping->size);
+            if (use_mmap) {
+                for (uint32_t idx = 0; idx < mappings.size(); idx++) {
+                    const auto & mmap_used = mmaps_used.at(idx);
+                    auto & mapping = mappings.at(idx);
+                    mapping->unmap_fragment(0, mmap_used.first);
+                    if (mmap_used.second != 0) {
+                        mapping->unmap_fragment(mmap_used.second, mapping->size);
+                    }
                 }
             }
             if (progress_callback) {
@@ -2833,6 +3659,19 @@ struct llama_model_loader {
     }
 };
 
+template<>
+bool llama_model_loader::get_key(const enum llm_kv kid, enum llama_pooling_type & result, const bool required) {
+    uint32_t tmp;
+    const bool found = get_key(kid, tmp, required);
+    if (found) {
+        result = (enum llama_pooling_type) tmp;
+    } else {
+        result = LLAMA_POOLING_TYPE_UNSPECIFIED;
+    }
+    return found;
+}
+
+
 //
 // load LLaMA models
 //
@@ -2853,6 +3692,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
     switch (ftype) {
         case LLAMA_FTYPE_ALL_F32:     return "all F32";
         case LLAMA_FTYPE_MOSTLY_F16:  return "F16";
+        case LLAMA_FTYPE_MOSTLY_BF16: return "BF16";
         case LLAMA_FTYPE_MOSTLY_Q4_0: return "Q4_0";
         case LLAMA_FTYPE_MOSTLY_Q4_1: return "Q4_1";
         case LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16:
@@ -2874,9 +3714,16 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
         case LLAMA_FTYPE_MOSTLY_Q6_K:   return "Q6_K";
         case LLAMA_FTYPE_MOSTLY_IQ2_XXS:return "IQ2_XXS - 2.0625 bpw";
         case LLAMA_FTYPE_MOSTLY_IQ2_XS: return "IQ2_XS - 2.3125 bpw";
-        case LLAMA_FTYPE_MOSTLY_Q3_K_XS:return "Q3_K - Extra small";
+        case LLAMA_FTYPE_MOSTLY_IQ2_S:  return "IQ2_S - 2.5 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ2_M:  return "IQ2_M - 2.7 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_XS: return "IQ3_XS - 3.3 bpw";
         case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XXS - 3.0625 bpw";
         case LLAMA_FTYPE_MOSTLY_IQ1_S  :return "IQ1_S - 1.5625 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ1_M  :return "IQ1_M - 1.75 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ4_XS: return "IQ4_XS - 4.25 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_S:  return "IQ3_S - 3.4375 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_M:  return "IQ3_S mix - 3.66 bpw";
 
         default: return "unknown, may not work";
     }
@@ -2894,32 +3741,40 @@ static const char * llama_model_type_name(e_model type) {
         case MODEL_3B:     return "3B";
         case MODEL_7B:     return "7B";
         case MODEL_8B:     return "8B";
+        case MODEL_12B:    return "12B";
         case MODEL_13B:    return "13B";
         case MODEL_14B:    return "14B";
         case MODEL_15B:    return "15B";
         case MODEL_20B:    return "20B";
         case MODEL_30B:    return "30B";
         case MODEL_34B:    return "34B";
+        case MODEL_35B:    return "35B";
         case MODEL_40B:    return "40B";
         case MODEL_65B:    return "65B";
         case MODEL_70B:    return "70B";
+        case MODEL_314B:   return "314B";
         case MODEL_SMALL:  return "0.1B";
         case MODEL_MEDIUM: return "0.4B";
         case MODEL_LARGE:  return "0.8B";
         case MODEL_XL:     return "1.5B";
+        case MODEL_A2_7B:  return "A2.7B";
+        case MODEL_8x7B:   return "8x7B";
+        case MODEL_8x22B:  return "8x22B";
+        case MODEL_16x12B: return "16x12B";
         default:           return "?B";
     }
 }
+
 static const char * llama_model_vocab_type_name(enum llama_vocab_type type){
     switch (type) {
-        case LLAMA_VOCAB_TYPE_SPM:         return "SPM";
-        case LLAMA_VOCAB_TYPE_BPE:         return "BPE";
-        case LLAMA_VOCAB_TYPE_WPM:         return "WPM";
-        default:                           return "unknown";
+        case LLAMA_VOCAB_TYPE_NONE: return "no vocab";
+        case LLAMA_VOCAB_TYPE_SPM:  return "SPM";
+        case LLAMA_VOCAB_TYPE_BPE:  return "BPE";
+        case LLAMA_VOCAB_TYPE_WPM:  return "WPM";
+        default:                    return "unknown";
     }
 }
 
-
 static void llm_load_arch(llama_model_loader & ml, llama_model & model) {
     model.arch = ml.get_arch();
     if (model.arch == LLM_ARCH_UNKNOWN) {
@@ -2931,7 +3786,7 @@ static void llm_load_hparams(
         llama_model_loader & ml,
         llama_model & model) {
     auto & hparams = model.hparams;
-    const gguf_context * ctx = ml.ctx_gguf;
+    const gguf_context * ctx = ml.meta;
 
     // get metadata as string
     for (int i = 0; i < gguf_get_n_kv(ctx); i++) {
@@ -2948,14 +3803,20 @@ static void llm_load_hparams(
     ml.get_key(LLM_KV_GENERAL_NAME, model.name, false);
 
     // get hparams kv
-    ml.get_arr_n(LLM_KV_TOKENIZER_LIST,       hparams.n_vocab);
-    ml.get_key  (LLM_KV_CONTEXT_LENGTH,       hparams.n_ctx_train);
-    ml.get_key  (LLM_KV_EMBEDDING_LENGTH,     hparams.n_embd);
-    ml.get_key  (LLM_KV_FEED_FORWARD_LENGTH,  hparams.n_ff);
-    ml.get_key  (LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head);
-    ml.get_key  (LLM_KV_BLOCK_COUNT,          hparams.n_layer);
-    ml.get_key  (LLM_KV_EXPERT_COUNT,         hparams.n_expert,      false);
-    ml.get_key  (LLM_KV_EXPERT_USED_COUNT,    hparams.n_expert_used, false);
+    ml.get_key(LLM_KV_VOCAB_SIZE,           hparams.n_vocab,       false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab);
+
+    // everything past this point is not vocab-related
+    if (hparams.vocab_only) {
+        return;
+    }
+
+    ml.get_key(LLM_KV_CONTEXT_LENGTH,       hparams.n_ctx_train);
+    ml.get_key(LLM_KV_EMBEDDING_LENGTH,     hparams.n_embd);
+    ml.get_key(LLM_KV_FEED_FORWARD_LENGTH,  hparams.n_ff);
+    ml.get_key(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head);
+    ml.get_key(LLM_KV_BLOCK_COUNT,          hparams.n_layer);
+    ml.get_key(LLM_KV_EXPERT_COUNT,         hparams.n_expert,      false);
+    ml.get_key(LLM_KV_EXPERT_USED_COUNT,    hparams.n_expert_used, false);
 
     GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS);
     GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert);
@@ -2983,7 +3844,7 @@ static void llm_load_hparams(
     std::string rope_scaling("linear");
     ml.get_key(LLM_KV_ROPE_SCALING_TYPE, rope_scaling, false);
     hparams.rope_scaling_type_train = llama_rope_scaling_type_from_string(rope_scaling);
-    GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_UNSPECIFIED);
+    GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED);
 
     // rope_freq_scale (inverse of the kv) is optional
     float ropescale = 0.0f;
@@ -2995,7 +3856,7 @@ static void llm_load_hparams(
 
     // sanity check for n_rot (optional)
     {
-        hparams.n_rot = hparams.n_embd / hparams.n_head;
+        hparams.n_rot = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head;
 
         ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
 
@@ -3008,10 +3869,10 @@ static void llm_load_hparams(
         // gpt-j n_rot = rotary_dim
     }
 
-    hparams.n_embd_head_k = hparams.n_embd / hparams.n_head;
+    hparams.n_embd_head_k = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head;
     ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false);
 
-    hparams.n_embd_head_v = hparams.n_embd / hparams.n_head;
+    hparams.n_embd_head_v = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head;
     ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);
 
     // arch-specific KVs
@@ -3020,15 +3881,23 @@ static void llm_load_hparams(
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
-                switch (hparams.n_layer) {
-                    case 22: model.type = e_model::MODEL_1B; break;
-                    case 26: model.type = e_model::MODEL_3B; break;
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 40: model.type = e_model::MODEL_13B; break;
-                    case 48: model.type = e_model::MODEL_34B; break;
-                    case 60: model.type = e_model::MODEL_30B; break;
-                    case 80: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_65B : e_model::MODEL_70B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                if (hparams.n_expert == 8) {
+                    switch (hparams.n_layer) {
+                        case 32: model.type = e_model::MODEL_8x7B; break;
+                        case 56: model.type = e_model::MODEL_8x22B; break;
+                        default: model.type = e_model::MODEL_UNKNOWN;
+                    }
+                } else {
+                    switch (hparams.n_layer) {
+                        case 22: model.type = e_model::MODEL_1B; break;
+                        case 26: model.type = e_model::MODEL_3B; break;
+                        case 32: model.type = hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B; break;
+                        case 40: model.type = e_model::MODEL_13B; break;
+                        case 48: model.type = e_model::MODEL_34B; break;
+                        case 60: model.type = e_model::MODEL_30B; break;
+                        case 80: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_65B : e_model::MODEL_70B; break;
+                        default: model.type = e_model::MODEL_UNKNOWN;
+                    }
                 }
             } break;
         case LLM_ARCH_MINICPM:
@@ -3040,6 +3909,15 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_GROK:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 64: model.type = e_model::MODEL_314B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
         case LLM_ARCH_FALCON:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@@ -3096,10 +3974,10 @@ static void llm_load_hparams(
             } break;
         case LLM_ARCH_BERT:
             {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
+                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
                 ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
-                ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
+                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type, false);
 
                 switch (hparams.n_layer) {
                     case 3:
@@ -3115,12 +3993,25 @@ static void llm_load_hparams(
                         model.type = e_model::MODEL_335M; break; // bge-large
                 }
             } break;
+        case LLM_ARCH_JINA_BERT_V2:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
+                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
+                ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
+                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type);
+                hparams.f_max_alibi_bias = 8.0f;
+
+                switch (hparams.n_layer) {
+                    case 4: model.type = e_model::MODEL_33M; break; // jina-embeddings-small
+                    case 12: model.type = e_model::MODEL_137M; break; // jina-embeddings-base
+                }
+            } break;
         case LLM_ARCH_NOMIC_BERT:
             {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
+                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
                 ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
-                ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
+                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type);
 
                 if (hparams.n_layer == 12 && hparams.n_embd == 768) {
                     model.type = e_model::MODEL_137M;
@@ -3161,6 +4052,7 @@ static void llm_load_hparams(
                 switch (hparams.n_layer) {
                     case 24: model.type = e_model::MODEL_1B; break;
                     case 32: model.type = e_model::MODEL_3B; break;
+                    case 40: model.type = e_model::MODEL_12B; break;
                     default: model.type = e_model::MODEL_UNKNOWN;
                }
             } break;
@@ -3185,10 +4077,28 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_QWEN2MOE:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                switch (hparams.n_layer) {
+                    case 24: model.type = e_model::MODEL_A2_7B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
         case LLM_ARCH_PHI2:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
 
+                switch (hparams.n_layer) {
+                    case 24: model.type = e_model::MODEL_1B; break;
+                    case 32: model.type = e_model::MODEL_3B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_PHI3:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
                 switch (hparams.n_layer) {
                     case 24: model.type = e_model::MODEL_1B; break;
                     case 32: model.type = e_model::MODEL_3B; break;
@@ -3241,18 +4151,113 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_GEMMA:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 18: model.type = e_model::MODEL_2B; break;
+                    case 28: model.type = e_model::MODEL_7B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+               }
+            } break;
+        case LLM_ARCH_STARCODER2:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                switch (hparams.n_layer) {
+                    case 30: model.type = e_model::MODEL_3B; break;
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 40: model.type = e_model::MODEL_15B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_MAMBA:
+            {
+                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
+                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
+                ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
+                ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 24:
+                        switch (hparams.n_embd) {
+                            case 768: model.type = e_model::MODEL_SMALL; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 48:
+                        switch (hparams.n_embd) {
+                            case 1024: model.type = e_model::MODEL_MEDIUM; break;
+                            case 1536: model.type = e_model::MODEL_LARGE; break;
+                            case 2048: model.type = e_model::MODEL_XL; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 64:
+                        switch (hparams.n_embd) {
+                            case 2560: model.type = e_model::MODEL_3B; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_XVERSE:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 40: model.type = e_model::MODEL_13B; break;
+                    case 80: model.type = e_model::MODEL_65B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_COMMAND_R:
+            {
+                ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                switch (hparams.n_layer) {
+                    case 40: model.type = e_model::MODEL_35B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_DBRX:
+        {
+            ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,  hparams.f_norm_eps);
+            ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV,      hparams.f_clamp_kqv);
+
+            switch (hparams.n_layer) {
+                case 40: model.type = e_model::MODEL_16x12B; break;
+                default: model.type = e_model::MODEL_UNKNOWN;
+            }
+        } break;
+        case LLM_ARCH_OLMO:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV,     hparams.f_clamp_kqv, false);
+
+                switch (hparams.n_layer) {
+                    case 22: model.type = e_model::MODEL_1B; break;
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 80: model.type = e_model::MODEL_70B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
         default: (void)0;
     }
 
     model.ftype = ml.ftype;
 
     if (hparams.f_max_alibi_bias > 0.0f) {
-        hparams.need_kq_pos = true;
+        hparams.use_alibi = true;
     }
+
+    hparams.rope_type = llama_rope_type(&model);
 }
 
 // TODO: This should probably be in llama.h
-static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos, bool special = false);
+static std::vector<llama_vocab::id> llama_tokenize_internal(
+    const llama_vocab & vocab, std::string raw_text, bool add_special, bool parse_special = false
+);
 static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch);
 
 static void llm_load_vocab(
@@ -3260,10 +4265,197 @@ static void llm_load_vocab(
         llama_model & model) {
     auto & vocab = model.vocab;
 
-    struct gguf_context * ctx = ml.ctx_gguf;
+    struct gguf_context * ctx = ml.meta;
 
     const auto kv = LLM_KV(model.arch);
 
+    // determine vocab type
+    {
+        std::string tokenizer_model;
+        std::string tokenizer_pre;
+
+        ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_model);
+        ml.get_key(LLM_KV_TOKENIZER_PRE,   tokenizer_pre, false);
+
+        if (tokenizer_model == "no_vocab") {
+            vocab.type = LLAMA_VOCAB_TYPE_NONE;
+
+            // default special tokens
+            vocab.special_bos_id  = -1;
+            vocab.special_eos_id  = -1;
+            vocab.special_unk_id  = -1;
+            vocab.special_sep_id  = -1;
+            vocab.special_pad_id  = -1;
+            vocab.special_cls_id  = -1;
+            vocab.special_mask_id = -1;
+            vocab.linefeed_id     = -1;
+
+            return;
+        } else if (tokenizer_model == "llama") {
+            vocab.type = LLAMA_VOCAB_TYPE_SPM;
+
+            // default special tokens
+            vocab.special_bos_id  = 1;
+            vocab.special_eos_id  = 2;
+            vocab.special_unk_id  = 0;
+            vocab.special_sep_id  = -1;
+            vocab.special_pad_id  = -1;
+            vocab.special_cls_id  = -1;
+            vocab.special_mask_id = -1;
+
+            // For Fill-In-the-Middle (FIM)/infill models which where converted
+            // prior to support of FIM special tokens in GGUF, the following
+            // will allow those models to continue to work. The general names
+            // of the known models are currently CodeLlama (LLM_ARCH_LLAMA) and
+            // CodeGemma (LLM_ARCH_GEMMA). This can potentially be removed once
+            // new versions of these models have been published.
+            std::string gen_name;
+            ml.get_key(LLM_KV_GENERAL_NAME, gen_name, false);
+
+            std::transform(gen_name.begin(), gen_name.end(), gen_name.begin(),
+                [](unsigned char c){ return std::tolower(c); });
+
+            if (gen_name.find("code") != std::string::npos) {
+                if (model.arch == LLM_ARCH_LLAMA) {
+                    vocab.special_prefix_id = 32007;
+                    vocab.special_suffix_id = 32008;
+                    vocab.special_middle_id = 32009;
+                    vocab.special_eot_id    = 32010;
+                } else if (model.arch == LLM_ARCH_GEMMA) {
+                    vocab.special_prefix_id = 67;
+                    vocab.special_suffix_id = 69;
+                    vocab.special_middle_id = 68;
+                    // TODO: this is not EOT, it is "file separator" token, needs fix
+                    //       https://huggingface.co/google/codegemma-7b-it/blob/9b1d9231388358c04d90bd003458f5070d97db44/tokenizer_config.json#L565-L572
+                    //vocab.special_eot_id    = 70;
+                    vocab.special_eot_id    = 107;
+                }
+            }
+
+            const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
+            if (add_space_prefix_keyidx != -1) {
+                vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
+            } // The default value of add_space_prefix is true.
+        } else if (tokenizer_model == "bert") {
+            vocab.type = LLAMA_VOCAB_TYPE_WPM;
+
+            // default special tokens
+            vocab.special_bos_id  = -1;
+            vocab.special_eos_id  = -1;
+            vocab.special_unk_id  = 100;
+            vocab.special_sep_id  = 102;
+            vocab.special_pad_id  = 0;
+            vocab.special_cls_id  = 101;
+            vocab.special_mask_id = 103;
+            vocab.add_space_prefix = false;
+        } else {
+            if (tokenizer_model == "gpt2") {
+                vocab.type = LLAMA_VOCAB_TYPE_BPE;
+            } else {
+                LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_model.c_str());
+                LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__);
+                vocab.type = LLAMA_VOCAB_TYPE_SPM;
+                return;
+            }
+            // read bpe merges and populate bpe ranks
+            const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
+            if (merges_keyidx == -1) {
+                throw std::runtime_error("cannot find tokenizer merges in model file\n");
+            }
+
+            const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);
+
+            for (int i = 0; i < n_merges; i++) {
+                const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
+                GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
+
+                std::string first;
+                std::string second;
+
+                const size_t pos = word.find(' ', 1);
+
+                if (pos != std::string::npos) {
+                    first  = word.substr(0, pos);
+                    second = word.substr(pos + 1);
+                }
+
+                vocab.bpe_ranks.emplace(std::make_pair(first, second), i);
+            }
+
+            // default special tokens
+            vocab.special_bos_id  = 11;
+            vocab.special_eos_id  = 11;
+            vocab.special_unk_id  = -1;
+            vocab.special_sep_id  = -1;
+            vocab.special_pad_id  = -1;
+            vocab.special_cls_id  = -1;
+            vocab.special_mask_id = -1;
+        }
+
+        // for now, only BPE models have pre-tokenizers
+        if (vocab.type == LLAMA_VOCAB_TYPE_BPE) {
+            if (tokenizer_pre.empty()) {
+                LLAMA_LOG_WARN("%s: missing pre-tokenizer type, using: 'default'\n", __func__);
+                LLAMA_LOG_WARN("%s:                                             \n", __func__);
+                LLAMA_LOG_WARN("%s: ************************************        \n", __func__);
+                LLAMA_LOG_WARN("%s: GENERATION QUALITY WILL BE DEGRADED!        \n", __func__);
+                LLAMA_LOG_WARN("%s: CONSIDER REGENERATING THE MODEL             \n", __func__);
+                LLAMA_LOG_WARN("%s: ************************************        \n", __func__);
+                LLAMA_LOG_WARN("%s:                                             \n", __func__);
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+            } else if (
+                    tokenizer_pre == "default") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+            } else if (
+                    tokenizer_pre == "llama3"   ||
+                    tokenizer_pre == "llama-v3" ||
+                    tokenizer_pre == "llama-bpe") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
+            } else if (
+                    tokenizer_pre == "deepseek-llm") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM;
+            } else if (
+                    tokenizer_pre == "deepseek-coder") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER;
+            } else if (
+                    tokenizer_pre == "falcon") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_FALCON;
+            } else if (
+                    tokenizer_pre == "mpt") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_MPT;
+            } else if (
+                    tokenizer_pre == "starcoder") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STARCODER;
+            } else if (
+                    tokenizer_pre == "gpt-2"   ||
+                    tokenizer_pre == "jina-es" ||
+                    tokenizer_pre == "jina-de" ||
+                    tokenizer_pre == "jina-v2-es" ||
+                    tokenizer_pre == "jina-v2-de") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT2;
+            } else if (
+                    tokenizer_pre == "refact") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_REFACT;
+            } else if (
+                tokenizer_pre == "command-r") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_COMMAND_R;
+            } else if (
+                tokenizer_pre == "qwen2") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2;
+            } else if (
+                tokenizer_pre == "olmo") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_OLMO;
+            } else if (
+                tokenizer_pre == "dbrx") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DBRX;
+            } else {
+                throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
+            }
+        } else {
+            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+        }
+    }
+
     const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str());
     if (token_idx == -1) {
         throw std::runtime_error("cannot find tokenizer vocab in model file\n");
@@ -3281,85 +4473,13 @@ static void llm_load_vocab(
         toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
     }
 
-    // determine vocab type
-    {
-        std::string tokenizer_name;
-
-        ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_name);
-
-        if (tokenizer_name == "llama") {
-            vocab.type = LLAMA_VOCAB_TYPE_SPM;
-
-            // default special tokens
-            vocab.special_bos_id = 1;
-            vocab.special_eos_id = 2;
-            vocab.special_unk_id = 0;
-            vocab.special_sep_id = -1;
-            vocab.special_pad_id = -1;
-
-            const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
-            if (add_space_prefix_keyidx != -1) {
-                vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
-            } // The default value of add_space_prefix is true.
-        } else if (tokenizer_name == "gpt2") {
-            vocab.type = LLAMA_VOCAB_TYPE_BPE;
-
-            // read bpe merges and populate bpe ranks
-            const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
-            if (merges_keyidx == -1) {
-                throw std::runtime_error("cannot find tokenizer merges in model file\n");
-            }
-
-            const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);
-
-            for (int i = 0; i < n_merges; i++) {
-                const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
-                GGML_ASSERT(codepoints_from_utf8(word).size() > 0);
-
-                std::string first;
-                std::string second;
-
-                const size_t pos = word.find(' ', 1);
-
-                if (pos != std::string::npos) {
-                    first  = word.substr(0, pos);
-                    second = word.substr(pos + 1);
-                }
-
-                vocab.bpe_ranks.emplace(std::make_pair(first, second), i);
-            }
-
-            // default special tokens
-            vocab.special_bos_id = 11;
-            vocab.special_eos_id = 11;
-            vocab.special_unk_id = -1;
-            vocab.special_sep_id = -1;
-            vocab.special_pad_id = -1;
-        } else if (tokenizer_name == "bert") {
-            vocab.type = LLAMA_VOCAB_TYPE_WPM;
-
-            // default special tokens
-            vocab.special_bos_id = 101;
-            vocab.special_eos_id = 102;
-            vocab.special_unk_id = 100;
-            vocab.special_sep_id = -1;
-            vocab.special_pad_id = -1;
-            vocab.add_space_prefix = false;
-        } else {
-            LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_name.c_str());
-            LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__);
-
-            vocab.type = LLAMA_VOCAB_TYPE_SPM;
-        }
-    }
-
     const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);
 
     vocab.id_to_token.resize(n_vocab);
 
     for (uint32_t i = 0; i < n_vocab; i++) {
         std::string word = gguf_get_arr_str(ctx, token_idx, i);
-        GGML_ASSERT(codepoints_from_utf8(word).size() > 0);
+        GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
 
         vocab.token_to_id[word] = i;
 
@@ -3381,7 +4501,7 @@ static void llm_load_vocab(
     } else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) {
         vocab.linefeed_id = vocab.special_pad_id;
     } else {
-        const std::vector<int> ids = llama_tokenize_internal(vocab, "\u010A", false);
+        const std::vector<int> ids = llama_tokenize_internal(vocab, "\xC4\x8A", false); // U+010A
         GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
         vocab.linefeed_id = ids[0];
     }
@@ -3389,12 +4509,19 @@ static void llm_load_vocab(
     // special tokens
     {
         const std::vector<std::pair<enum llm_kv, int32_t &>> special_token_types = {
-            { LLM_KV_TOKENIZER_BOS_ID, vocab.special_bos_id },
-            { LLM_KV_TOKENIZER_EOS_ID, vocab.special_eos_id },
-            { LLM_KV_TOKENIZER_UNK_ID, vocab.special_unk_id },
-            { LLM_KV_TOKENIZER_SEP_ID, vocab.special_sep_id },
-            { LLM_KV_TOKENIZER_PAD_ID, vocab.special_pad_id },
+            { LLM_KV_TOKENIZER_BOS_ID,    vocab.special_bos_id    },
+            { LLM_KV_TOKENIZER_EOS_ID,    vocab.special_eos_id    },
+            { LLM_KV_TOKENIZER_UNK_ID,    vocab.special_unk_id    },
+            { LLM_KV_TOKENIZER_SEP_ID,    vocab.special_sep_id    },
+            { LLM_KV_TOKENIZER_PAD_ID,    vocab.special_pad_id    },
+            { LLM_KV_TOKENIZER_CLS_ID,    vocab.special_cls_id    },
+            { LLM_KV_TOKENIZER_MASK_ID,   vocab.special_mask_id   },
+            { LLM_KV_TOKENIZER_PREFIX_ID, vocab.special_prefix_id },
+            { LLM_KV_TOKENIZER_SUFFIX_ID, vocab.special_suffix_id },
+            { LLM_KV_TOKENIZER_MIDDLE_ID, vocab.special_middle_id },
+            { LLM_KV_TOKENIZER_EOT_ID,    vocab.special_eot_id    },
         };
+
         for (const auto & it : special_token_types) {
             const std::string & key = kv(std::get<0>(it));
             int32_t & id = std::get<1>(it);
@@ -3409,7 +4536,6 @@ static void llm_load_vocab(
             } else {
                 id = new_id;
             }
-
         }
 
         // Handle add_bos_token and add_eos_token
@@ -3423,6 +4549,28 @@ static void llm_load_vocab(
                 vocab.special_add_eos = int(temp);
             }
         }
+
+        // find EOT token: "<|eot_id|>", "<|im_end|>", "<end_of_turn>", etc.
+        //
+        // TODO: convert scripts should provide this token through the KV metadata LLAMA_KV_TOKENIZER_EOT_ID
+        //       for now, we apply this workaround to find the EOT token based on its text
+        if (vocab.special_eot_id == -1) {
+            for (const auto & t : vocab.token_to_id) {
+                if (
+                        // TODO: gemma "<end_of_turn>" is exported as a normal token, so the following check does not work
+                        //       need to fix convert script
+                        //vocab.id_to_token[t.second].type == LLAMA_TOKEN_TYPE_CONTROL &&
+                        (t.first == "<|eot_id|>" ||
+                         t.first == "<|im_end|>" ||
+                         t.first == "<|end|>" ||
+                         t.first == "<end_of_turn>"
+                        )
+                   ) {
+                    vocab.special_eot_id = t.second;
+                    break;
+                }
+            }
+        }
     }
 
     // build special tokens cache
@@ -3548,14 +4696,22 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     LLAMA_LOG_INFO("%s: f_norm_rms_eps   = %.1e\n",   __func__, hparams.f_norm_rms_eps);
     LLAMA_LOG_INFO("%s: f_clamp_kqv      = %.1e\n",   __func__, hparams.f_clamp_kqv);
     LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n",   __func__, hparams.f_max_alibi_bias);
+    LLAMA_LOG_INFO("%s: f_logit_scale    = %.1e\n",   __func__, hparams.f_logit_scale);
     LLAMA_LOG_INFO("%s: n_ff             = %u\n",     __func__, hparams.n_ff);
     LLAMA_LOG_INFO("%s: n_expert         = %u\n",     __func__, hparams.n_expert);
     LLAMA_LOG_INFO("%s: n_expert_used    = %u\n",     __func__, hparams.n_expert_used);
+    LLAMA_LOG_INFO("%s: causal attn      = %d\n",     __func__, hparams.causal_attn);
+    LLAMA_LOG_INFO("%s: pooling type     = %d\n",     __func__, hparams.pooling_type);
+    LLAMA_LOG_INFO("%s: rope type        = %d\n",     __func__, hparams.rope_type);
     LLAMA_LOG_INFO("%s: rope scaling     = %s\n",     __func__, rope_scaling_type);
     LLAMA_LOG_INFO("%s: freq_base_train  = %.1f\n",   __func__, hparams.rope_freq_base_train);
     LLAMA_LOG_INFO("%s: freq_scale_train = %g\n",     __func__, hparams.rope_freq_scale_train);
     LLAMA_LOG_INFO("%s: n_yarn_orig_ctx  = %u\n",     __func__, hparams.n_yarn_orig_ctx);
     LLAMA_LOG_INFO("%s: rope_finetuned   = %s\n",     __func__, hparams.rope_finetuned ? "yes" : "unknown");
+    LLAMA_LOG_INFO("%s: ssm_d_conv       = %u\n",     __func__, hparams.ssm_d_conv);
+    LLAMA_LOG_INFO("%s: ssm_d_inner      = %u\n",     __func__, hparams.ssm_d_inner);
+    LLAMA_LOG_INFO("%s: ssm_d_state      = %u\n",     __func__, hparams.ssm_d_state);
+    LLAMA_LOG_INFO("%s: ssm_dt_rank      = %u\n",     __func__, hparams.ssm_dt_rank);
     LLAMA_LOG_INFO("%s: model type       = %s\n",     __func__, llama_model_type_name(model.type));
     LLAMA_LOG_INFO("%s: model ftype      = %s\n",     __func__, llama_model_ftype_name(model.ftype).c_str());
     if (ml.n_elements >= 1e12) {
@@ -3577,12 +4733,19 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     LLAMA_LOG_INFO("%s: general.name     = %s\n",    __func__, model.name.c_str());
 
     // special tokens
-    if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token        = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); }
-    if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token        = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); }
-    if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token        = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); }
-    if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token        = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); }
-    if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token        = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); }
-    if (vocab.linefeed_id    != -1) { LLAMA_LOG_INFO( "%s: LF token         = %d '%s'\n", __func__, vocab.linefeed_id,    vocab.id_to_token[vocab.linefeed_id].text.c_str() );    }
+    if (vocab.special_bos_id    != -1) { LLAMA_LOG_INFO( "%s: BOS token        = %d '%s'\n", __func__, vocab.special_bos_id,  vocab.id_to_token[vocab.special_bos_id].text.c_str() );  }
+    if (vocab.special_eos_id    != -1) { LLAMA_LOG_INFO( "%s: EOS token        = %d '%s'\n", __func__, vocab.special_eos_id,  vocab.id_to_token[vocab.special_eos_id].text.c_str() );  }
+    if (vocab.special_unk_id    != -1) { LLAMA_LOG_INFO( "%s: UNK token        = %d '%s'\n", __func__, vocab.special_unk_id,  vocab.id_to_token[vocab.special_unk_id].text.c_str() );  }
+    if (vocab.special_sep_id    != -1) { LLAMA_LOG_INFO( "%s: SEP token        = %d '%s'\n", __func__, vocab.special_sep_id,  vocab.id_to_token[vocab.special_sep_id].text.c_str() );  }
+    if (vocab.special_pad_id    != -1) { LLAMA_LOG_INFO( "%s: PAD token        = %d '%s'\n", __func__, vocab.special_pad_id,  vocab.id_to_token[vocab.special_pad_id].text.c_str() );  }
+    if (vocab.special_cls_id    != -1) { LLAMA_LOG_INFO( "%s: CLS token        = %d '%s'\n", __func__, vocab.special_cls_id,  vocab.id_to_token[vocab.special_cls_id].text.c_str() );  }
+    if (vocab.special_mask_id   != -1) { LLAMA_LOG_INFO( "%s: MASK token       = %d '%s'\n", __func__, vocab.special_mask_id, vocab.id_to_token[vocab.special_mask_id].text.c_str() ); }
+
+    if (vocab.linefeed_id       != -1) { LLAMA_LOG_INFO( "%s: LF token         = %d '%s'\n", __func__, vocab.linefeed_id,       vocab.id_to_token[vocab.linefeed_id].text.c_str() );       }
+    if (vocab.special_prefix_id != -1) { LLAMA_LOG_INFO( "%s: PRE token        = %d '%s'\n", __func__, vocab.special_prefix_id, vocab.id_to_token[vocab.special_prefix_id].text.c_str() ); }
+    if (vocab.special_suffix_id != -1) { LLAMA_LOG_INFO( "%s: SUF token        = %d '%s'\n", __func__, vocab.special_suffix_id, vocab.id_to_token[vocab.special_suffix_id].text.c_str() ); }
+    if (vocab.special_middle_id != -1) { LLAMA_LOG_INFO( "%s: MID token        = %d '%s'\n", __func__, vocab.special_middle_id, vocab.id_to_token[vocab.special_middle_id].text.c_str() ); }
+    if (vocab.special_eot_id    != -1) { LLAMA_LOG_INFO( "%s: EOT token        = %d '%s'\n", __func__, vocab.special_eot_id,    vocab.id_to_token[vocab.special_eot_id].text.c_str() );    }
 }
 
 // Returns false if cancelled by progress_callback
@@ -3600,15 +4763,24 @@ static bool llm_load_tensors(
 
     auto & hparams = model.hparams;
 
+#ifdef GGML_USE_SYCL
+    // disable MoE with SYCL until mul_mat_id is updated
+    if (hparams.n_expert > 0) {
+        n_gpu_layers = 0;
+    }
+#endif
+
     model.split_mode   = split_mode;
     model.main_gpu     = main_gpu;
     model.n_gpu_layers = n_gpu_layers;
 
     const int64_t n_layer     = hparams.n_layer;
     const int64_t i_gpu_start = std::max((int64_t) hparams.n_layer - n_gpu_layers, (int64_t) 0);
+    bool use_mmap_buffer = true;
 
     // there is very little benefit to offloading the input layer, so always keep it on the CPU
     model.buft_input = llama_default_buffer_type_cpu(true);
+    //model.buft_input = llama_default_buffer_type_offload(main_gpu);
 
     model.buft_layer.resize(n_layer);
 
@@ -3617,7 +4789,7 @@ static bool llm_load_tensors(
         model.buft_layer[i] = llama_default_buffer_type_cpu(true);
     }
 
-    if (split_mode == LLAMA_SPLIT_LAYER) {
+    if (split_mode == LLAMA_SPLIT_MODE_LAYER) {
         // calculate the split points
         int device_count = llama_get_device_count();
         bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; });
@@ -3656,10 +4828,10 @@ static bool llm_load_tensors(
         }
     } else {
         ggml_backend_buffer_type_t split_buft;
-        if (split_mode == LLAMA_SPLIT_ROW) {
+        if (split_mode == LLAMA_SPLIT_MODE_ROW) {
             split_buft = llama_default_buffer_type_split(main_gpu, tensor_split);
         } else {
-            // LLAMA_SPLIT_NONE or LLAMA_SPLIT_LAYER in backends where it is not supported
+            // LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_LAYER in backends where it is not supported
             split_buft = llama_default_buffer_type_offload(main_gpu);
         }
         // assign the repeating layers
@@ -3692,7 +4864,11 @@ static bool llm_load_tensors(
     }
 
     // create one context per buffer type
-    size_t ctx_size = ggml_tensor_overhead()*ml.n_tensors;
+    size_t ctx_size = ggml_tensor_overhead()*(ml.n_tensors + 1); // +1 for models where tok_embd is duplicated as output
+
+    // for moe merged tensors
+    ctx_size += ggml_tensor_overhead()*n_layer*3;
+
     std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
     for (auto & it : buft_layer_count) {
         struct ggml_init_params params = {
@@ -3719,6 +4895,11 @@ static bool llm_load_tensors(
         const int64_t n_vocab      = hparams.n_vocab;
         const int64_t n_vocab_type = hparams.n_vocab_type;
         const int64_t n_ff         = hparams.n_ff;
+        const int64_t n_expert     = hparams.n_expert;
+
+        if (n_expert > 0 && hparams.n_expert_used == 0) {
+            throw std::runtime_error("model has expert layers but no expert layers are used");
+        }
 
         GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
@@ -3742,7 +4923,13 @@ static bool llm_load_tensors(
                     {
                         model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                         if (model.arch != LLM_ARCH_MINICPM){
-                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
+                            // if output is NULL, init from the input tok embed
+                            if (model.output == NULL) {
+                                model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+                                ml.n_created--; // artificial tensor
+                                ml.size_data += ggml_nbytes(model.output);
+                            }
                         }
                     }
 
@@ -3767,28 +4954,148 @@ static bool llm_load_tensors(
 
                         layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
 
-                        layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd}, false);
-
-                        if (layer.ffn_gate_inp == nullptr) {
-                            GGML_ASSERT(hparams.n_expert      == 0);
-                            GGML_ASSERT(hparams.n_expert_used == 0);
-
+                        if (n_expert == 0) {
                             layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                             layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
                             layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                         } else {
-                            GGML_ASSERT(hparams.n_expert      > 0);
-                            GGML_ASSERT(hparams.n_expert_used > 0);
+                            layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
 
-                            // MoE branch
-                            for (uint32_t x = 0; x < hparams.n_expert; ++x) {
-                                layer.ffn_gate_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), {n_embd,   n_ff});
-                                layer.ffn_down_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), {  n_ff, n_embd});
-                                layer.ffn_up_exp[x]   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXP,   "weight", i, x), {n_embd,   n_ff});
+                            layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, false);
+                            if (layer.ffn_gate_exps) {
+                                layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert});
+                                layer.ffn_up_exps   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert});
+                            } else {
+                                // merge split expert into a single tensor for compatibility with older models
+                                // requires disabling mmap
+                                use_mmap_buffer = false;
+
+                                ggml_type type_gate = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, 0).c_str())->type;
+                                ggml_type type_down = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, 0).c_str())->type;
+                                ggml_type type_up   = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_UP_EXP,   "weight", i, 0).c_str())->type;
+
+                                layer.ffn_gate_exps = ggml_new_tensor_3d(ctx_split, type_gate, n_embd,   n_ff, n_expert);
+                                layer.ffn_down_exps = ggml_new_tensor_3d(ctx_split, type_down,   n_ff, n_embd, n_expert);
+                                layer.ffn_up_exps   = ggml_new_tensor_3d(ctx_split, type_up,   n_embd,   n_ff, n_expert);
+
+                                ggml_set_name(layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i).c_str());
+                                ggml_set_name(layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i).c_str());
+                                ggml_set_name(layer.ffn_up_exps,   tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i).c_str());
+
+                                for (uint32_t x = 0; x < n_expert; ++x) {
+                                    // the individual experts are loaded into a view of the merged tensor
+                                    ml.create_tensor_as_view(ctx_split, layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_gate_exps->nb[2]*x);
+                                    ml.create_tensor_as_view(ctx_split, layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd }, layer.ffn_down_exps->nb[2]*x);
+                                    ml.create_tensor_as_view(ctx_split, layer.ffn_up_exps,   tn(LLM_TENSOR_FFN_UP_EXP,   "weight", i, x), { n_embd, n_ff }, layer.ffn_up_exps->nb[2]*x);
+                                }
                             }
                         }
                     }
                 } break;
+            case LLM_ARCH_GROK:
+                {
+                    if (n_expert == 0) {
+                        throw std::runtime_error("Grok model cannot have zero experts");
+                    }
+
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, false);
+                        // if output is NULL, init from the input tok embed
+                        if (model.output == NULL) {
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+                            ml.n_created--; // artificial tensor
+                            ml.size_data += ggml_nbytes(model.output);
+                        }
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+
+                        layer.attn_out_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
+
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+
+                        layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
+
+                        layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false);
+                        if (layer.ffn_gate_exps) {
+                            layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert});
+                            layer.ffn_up_exps   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert});
+                        } else {
+                            // merge split expert into a single tensor for compatibility with older models
+                            // requires disabling mmap
+                            use_mmap_buffer = false;
+
+                            ggml_type type_gate = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, 0).c_str())->type;
+                            ggml_type type_down = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, 0).c_str())->type;
+                            ggml_type type_up   = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_UP_EXP,   "weight", i, 0).c_str())->type;
+
+                            layer.ffn_gate_exps = ggml_new_tensor_3d(ctx_split, type_gate, n_embd,   n_ff, n_expert);
+                            layer.ffn_down_exps = ggml_new_tensor_3d(ctx_split, type_down,   n_ff, n_embd, n_expert);
+                            layer.ffn_up_exps   = ggml_new_tensor_3d(ctx_split, type_up,   n_embd,   n_ff, n_expert);
+
+                            ggml_set_name(layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i).c_str());
+                            ggml_set_name(layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i).c_str());
+                            ggml_set_name(layer.ffn_up_exps,   tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i).c_str());
+
+                            for (uint32_t x = 0; x < n_expert; ++x) {
+                                // the individual experts are loaded into a view of the merged tensor
+                                ml.create_tensor_as_view(ctx_split, layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_gate_exps->nb[2]*x);
+                                ml.create_tensor_as_view(ctx_split, layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd }, layer.ffn_down_exps->nb[2]*x);
+                                ml.create_tensor_as_view(ctx_split, layer.ffn_up_exps,   tn(LLM_TENSOR_FFN_UP_EXP,   "weight", i, x), { n_embd, n_ff }, layer.ffn_up_exps->nb[2]*x);
+                            }
+                        }
+
+                        layer.layer_out_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
+                    }
+                } break;
+            case LLM_ARCH_DBRX:
+            {
+                if (n_expert == 0) {
+                    throw std::runtime_error("DBRX model cannot have zero experts");
+                }
+
+                model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                // output
+                {
+                    model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                    model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                }
+
+                for (int i = 0; i < n_layer; ++i) {
+                    ggml_context * ctx_layer = ctx_for_layer(i);
+                    ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                    auto & layer = model.layers[i];
+
+                    layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM,  "weight", i), {n_embd});
+
+                    layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+                    layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+
+                    layer.attn_out_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
+
+                    layer.ffn_gate_inp  = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert});
+                    layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert});
+                    layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff,   n_embd, n_expert});
+                    layer.ffn_up_exps   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff,   n_expert});
+                }
+            } break;
             case LLM_ARCH_BAICHUAN:
                 {
                     model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
@@ -3825,11 +5132,12 @@ static bool llm_load_tensors(
                     {
                         model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                         model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
-                        if (gguf_find_tensor(ml.ctx_gguf, tn(LLM_TENSOR_OUTPUT, "weight").c_str()) >= 0) {
-                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,     "weight"), {n_embd, n_vocab});
-                        } else {
+
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, false);
+                        if (!model.output) {
                             model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU
                             ml.n_created--; // artificial tensor
+                            ml.size_data += ggml_nbytes(model.output);
                         }
                     }
 
@@ -3842,10 +5150,8 @@ static bool llm_load_tensors(
                         layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
                         layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});
 
-                        if (gguf_find_tensor(ml.ctx_gguf, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i).c_str()) >= 0) {
-                            layer.attn_norm_2   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd});
-                            layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd});
-                        }
+                        layer.attn_norm_2   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, false);
+                        layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, false);
 
                         layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
                         layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
@@ -3984,6 +5290,50 @@ static bool llm_load_tensors(
                         layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i),   {n_embd});
                     }
                 } break;
+            case LLM_ARCH_JINA_BERT_V2:
+                {
+                    model.tok_embd     = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}); // word_embeddings
+                    model.type_embd    = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}); //token_type_embeddings
+                    model.tok_norm   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); // LayerNorm
+                    model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd}); //LayerNorm bias
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i]; // JinaBertLayer
+
+                        layer.wq   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.bq   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i),   {n_embd});
+
+                        layer.attn_q_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, false);
+                        layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, false);
+
+                        layer.wk   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.bk   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i),   {n_embd_gqa});
+
+                        layer.attn_k_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, false);
+                        layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, false);
+
+                        layer.wv   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.bv   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i),   {n_embd_gqa});
+
+                        layer.wo              = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {n_embd, n_embd}); //output_dens
+                        layer.bo              = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT,      "bias", i), {n_embd}); //output_dens
+
+                        layer.attn_out_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); //output_norm
+                        layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i),   {n_embd});
+
+                        layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,        "weight", i), {n_embd, n_ff});
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE,    "weight", i), {n_embd, n_ff});
+
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN,        "weight", i), {n_ff, n_embd});
+                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN,      "bias", i), {n_embd});
+
+                        layer.layer_out_norm = ml.create_tensor(ctx_split, tn(LLM_TENSOR_LAYER_OUT_NORM,        "weight", i), {n_embd});
+                        layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM,        "bias", i), {n_embd});
+                    }
+                } break;
             case LLM_ARCH_BLOOM:
                 {
                     model.tok_embd   = ml.create_tensor(ctx_input,  tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab});
@@ -4025,11 +5375,19 @@ static bool llm_load_tensors(
             case LLM_ARCH_MPT:
                 {
                     model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+                    model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, hparams.n_ctx_train}, false);
 
                     // output
                     {
                         model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
-                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, false);
+
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, false);
+                        if (!model.output) {
+                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU
+                            ml.n_created--; // artificial tensor
+                            ml.size_data += ggml_nbytes(model.output);
+                        }
                     }
 
                     for (int i = 0; i < n_layer; ++i) {
@@ -4038,14 +5396,29 @@ static bool llm_load_tensors(
 
                         auto & layer = model.layers[i];
 
-                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, false);
 
                         layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
-                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, false);
 
-                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
-                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
-                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, false);
+
+                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, false);
+
+                        layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, false);
+
+                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, false);
+
+                        layer.attn_q_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, false);
+                        layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {n_embd}, false);
+
+                        layer.attn_k_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, false);
+                        layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {n_embd}, false);
 
                         // AWQ ScaleActivation layer
                         layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, false);
@@ -4081,8 +5454,13 @@ static bool llm_load_tensors(
                         layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, false);
                         layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, false);
 
-                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
-                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});
+                        // optional q and k layernorms, present in StableLM 2 12B
+                        layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head}, false);
+                        layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv}, false);
+
+                        // optional FFN norm, not present in StableLM 2 12B which uses parallel residual
+                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, false);
+                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, false);
 
                         layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                         layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
@@ -4125,7 +5503,13 @@ static bool llm_load_tensors(
                     // output
                     {
                         model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
-                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, false);
+                        // if output is NULL, init from the input tok embed
+                        if (model.output == NULL) {
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+                            ml.n_created--; // artificial tensor
+                            ml.size_data += ggml_nbytes(model.output);
+                        }
                     }
 
                     for (int i = 0; i < n_layer; ++i) {
@@ -4153,6 +5537,54 @@ static bool llm_load_tensors(
                         layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                     }
                 } break;
+            case LLM_ARCH_QWEN2MOE:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+
+                        // optional bias tensors
+                        layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd});
+                        layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa});
+                        layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa});
+
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+
+                        layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
+
+                        GGML_ASSERT(hparams.n_expert      > 0);
+                        GGML_ASSERT(hparams.n_expert_used > 0);
+
+                        // MoE branch
+                        auto n_ff_exp = n_ff / hparams.n_expert_used;
+                        layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert});
+                        layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert});
+                        layer.ffn_up_exps   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert});
+
+                        // Shared expert branch
+                        layer.ffn_gate_inp_shexp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd});
+                        layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd,   n_ff});
+                        layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_up_shexp   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd,   n_ff});
+                    }
+                } break;
             case LLM_ARCH_PHI2:
                 {
                     model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
@@ -4198,6 +5630,33 @@ static bool llm_load_tensors(
                         layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff});
                     }
                 } break;
+            case LLM_ARCH_PHI3:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab });
+
+                    // output
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd });
+                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab });
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context* ctx_layer = ctx_for_layer(i);
+                        ggml_context* ctx_split = ctx_for_layer_split(i);
+
+                        auto& layer = model.layers[i];
+
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd });
+
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, false);
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd });
+
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd });
+
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd });
+                        layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff });
+                    }
+                } break;
             case LLM_ARCH_PLAMO:
                 {
                     model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
@@ -4353,6 +5812,230 @@ static bool llm_load_tensors(
 
                         layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
                         layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+                    }
+                } break;
+            case LLM_ARCH_GEMMA:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
+                    model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                    model.output      = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}); // same as tok_embd, duplicated to allow offloading
+                    ml.n_created--; // artificial tensor
+                    ml.size_data += ggml_nbytes(model.output);
+
+                    const int64_t n_ff          = hparams.n_ff;
+                    const int64_t n_embd_head_k = hparams.n_embd_head_k;
+                    const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
+                    const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa();
+
+                    for (uint32_t i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * hparams.n_head});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * hparams.n_head, n_embd});
+
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                    }
+                } break;
+            case LLM_ARCH_STARCODER2:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
+                    {
+                        model.output_norm   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
+
+                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
+                        // if output is NULL, init from the input tok embed
+                        if (model.output == NULL) {
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+                            ml.n_created--; // artificial tensor
+                            ml.size_data += ggml_nbytes(model.output);
+                        }
+
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});
+
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+
+                        // optional bias tensors
+                        layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd});
+                        layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa});
+                        layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa});
+                        layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
+
+                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});
+
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+
+                        // optional bias tensors
+                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
+                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP ,  "bias", i), {  n_ff});
+                    }
+                } break;
+            case LLM_ARCH_MAMBA:
+                {
+                    const int64_t d_conv  = hparams.ssm_d_conv;
+                    const int64_t d_inner = hparams.ssm_d_inner;
+                    const int64_t d_state = hparams.ssm_d_state;
+                    const int64_t dt_rank = hparams.ssm_dt_rank;
+                    // only an expansion factor of 2 is supported for now
+                    GGML_ASSERT(2 * n_embd == d_inner);
+
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+
+                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
+                        // if output is NULL, init from the input tok embed, duplicated to allow offloading
+                        if (model.output == NULL) {
+                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+                            ml.n_created--; // artificial tensor
+                            ml.size_data += ggml_nbytes(model.output);
+                        }
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        // norm
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+
+                        layer.ssm_in = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner});
+
+                        layer.ssm_conv1d = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner});
+                        layer.ssm_conv1d_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner});
+
+                        layer.ssm_x = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state});
+
+                        layer.ssm_dt = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner});
+                        layer.ssm_dt_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner});
+
+                        // no "weight" suffix for these
+                        layer.ssm_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner});
+                        layer.ssm_d = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_D, i), {d_inner});
+
+                        // out_proj
+                        layer.ssm_out = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd});
+                    }
+                } break;
+            case LLM_ARCH_XVERSE:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                    }
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+                        auto & layer = model.layers[i];
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+                    }
+                } break;
+            case LLM_ARCH_COMMAND_R:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        // init output from the input tok embed
+                        model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+                        ml.n_created--; // artificial tensor
+                        ml.size_data += ggml_nbytes(model.output);
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+
+                        if (n_layer >= 64){
+                            layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head});
+                            layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv});
+                        }
+
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+                    }
+                } break;
+            case LLM_ARCH_OLMO:  // adapted from LLM_ARCH_LLAMA with norm params removed
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
+                    {
+                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
+                        // if output is NULL, init from the input tok embed
+                        if (model.output == NULL) {
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+                            ml.n_created--; // artificial tensor
+                            ml.size_data += ggml_nbytes(model.output);
+                        }
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+
+
                         layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                         layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
                         layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
@@ -4365,49 +6048,97 @@ static bool llm_load_tensors(
 
     ml.done_getting_tensors();
 
-    ml.init_mapping(true, use_mlock ? &model.mlock_mmap : nullptr);
+    ml.init_mappings(true, use_mlock ? &model.mlock_mmaps : nullptr);
+    model.mappings.reserve(ml.mappings.size());
 
     // create the backend buffers
-    std::vector<std::pair<ggml_context *, ggml_backend_buffer_t>> ctx_bufs;
+    std::vector<std::pair<ggml_context *, llama_buf_map>> ctx_bufs;
+    ctx_bufs.reserve(ctx_map.size());
+
+    // Ensure we have enough capacity for the maximum backend buffer we will potentially create
+    size_t n_max_backend_buffer = ctx_map.size() * ml.files.size();
+    model.bufs.reserve(n_max_backend_buffer);
 
     for (auto & it : ctx_map) {
         ggml_backend_buffer_type_t buft = it.first;
-        ggml_context * ctx = it.second;
-        ggml_backend_buffer_t buf = nullptr;
+        ggml_context * ctx              = it.second;
+
+        llama_buf_map bufs;
+        bufs.reserve(n_max_backend_buffer);
 
         // only the mmap region containing the tensors in the model is mapped to the backend buffer
         // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers
         // this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
-        if (ml.use_mmap && buft == llama_default_buffer_type_cpu(true)) {
-            size_t first, last;
-            ml.get_mapping_range(&first, &last, ctx);
-            buf = ggml_backend_cpu_buffer_from_ptr((char *) ml.mapping->addr + first, last - first);
+        if (ml.use_mmap && use_mmap_buffer && buft == llama_default_buffer_type_cpu(true)) {
+            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
+                void * addr = nullptr;
+                size_t first, last;
+                ml.get_mapping_range(&first, &last, &addr, idx, ctx);
+                if (first >= last) {
+                    continue;
+                }
+                ggml_backend_buffer_t buf = ggml_backend_cpu_buffer_from_ptr((char *) addr + first, last - first);
+                if (buf == nullptr) {
+                    throw std::runtime_error("unable to allocate backend CPU buffer");
+                }
+                model.bufs.push_back(buf);
+                bufs.emplace(idx, buf);
+#ifdef GGML_USE_CUDA
+                if (n_layer >= n_gpu_layers) {
+                    ggml_backend_cuda_register_host_buffer(
+                        ggml_backend_buffer_get_base(buf),
+                        ggml_backend_buffer_get_size(buf));
+                }
+#endif
+            }
         }
 #ifdef GGML_USE_METAL
-        else if (ml.use_mmap && buft == ggml_backend_metal_buffer_type()) {
-            const size_t max_size = ggml_get_max_tensor_size(ctx);
-            size_t first, last;
-            ml.get_mapping_range(&first, &last, ctx);
-            buf = ggml_backend_metal_buffer_from_ptr((char *) ml.mapping->addr + first, last - first, max_size);
+        else if (ml.use_mmap && use_mmap_buffer && buft == ggml_backend_metal_buffer_type()) {
+            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
+                const size_t max_size = ggml_get_max_tensor_size(ctx);
+                void * addr = nullptr;
+                size_t first, last;
+                ml.get_mapping_range(&first, &last, &addr, idx, ctx);
+                if (first >= last) {
+                    continue;
+                }
+                ggml_backend_buffer_t buf = ggml_backend_metal_buffer_from_ptr((char *) addr + first, last - first, max_size);
+                if (buf == nullptr) {
+                    throw std::runtime_error("unable to allocate backend metal buffer");
+                }
+                model.bufs.push_back(buf);
+                bufs.emplace(idx, buf);
+            }
         }
 #endif
         else {
-            buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
-            if (buf != nullptr && use_mlock && ggml_backend_buffer_is_host(buf)) {
+            ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
+            if (buf == nullptr) {
+                throw std::runtime_error("unable to allocate backend buffer");
+            }
+            model.bufs.push_back(buf);
+            if (use_mlock && ggml_backend_buffer_is_host(buf)) {
                 model.mlock_bufs.emplace_back(new llama_mlock);
                 auto & mlock_buf = model.mlock_bufs.back();
                 mlock_buf->init   (ggml_backend_buffer_get_base(buf));
                 mlock_buf->grow_to(ggml_backend_buffer_get_size(buf));
             }
+            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
+                bufs.emplace(idx, buf);
+            }
         }
-        if (buf == nullptr) {
+
+        if (bufs.empty()) {
             throw std::runtime_error("failed to allocate buffer");
         }
-        // indicate that this buffer contains weights
-        // this is used by ggml_backend_sched to improve op scheduling -> ops that use a weight are preferably scheduled to the backend that contains the weight
-        ggml_backend_buffer_set_usage(buf, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
-        model.bufs.push_back(buf);
-        ctx_bufs.emplace_back(ctx, buf);
+
+        for (auto & buf : bufs) {
+            // indicate that this buffer contains weights
+            // this is used by ggml_backend_sched to improve op scheduling -> ops that use a weight are preferably scheduled to the backend that contains the weight
+            ggml_backend_buffer_set_usage(buf.second, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
+        }
+
+        ctx_bufs.emplace_back(ctx, bufs);
     }
 
     if (llama_supports_gpu_offload()) {
@@ -4439,13 +6170,17 @@ static bool llm_load_tensors(
     // load tensor data
     for (auto & it : ctx_bufs) {
         ggml_context * ctx = it.first;
-        ggml_backend_buffer_t buf = it.second;
-        if (!ml.load_all_data(ctx, progress_callback, progress_callback_user_data, buf, use_mlock ? &model.mlock_mmap : NULL)) {
+        auto & bufs = it.second;
+        if (!ml.load_all_data(ctx, bufs, use_mlock ? &model.mlock_mmaps : NULL, progress_callback, progress_callback_user_data)) {
             return false;
         }
     }
 
-    model.mapping = std::move(ml.mapping);
+    if (use_mmap_buffer) {
+        for (auto & mapping : ml.mappings) {
+            model.mappings.emplace_back(std::move(mapping));
+        }
+    }
 
     // loading time will be recalculate after the first eval, so
     // we take page faults deferred by mmap() into consideration
@@ -4456,7 +6191,7 @@ static bool llm_load_tensors(
 // Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
 static int llama_model_load(const std::string & fname, llama_model & model, llama_model_params & params) {
     try {
-        llama_model_loader ml(fname, params.use_mmap, params.kv_overrides);
+        llama_model_loader ml(fname, params.use_mmap, params.check_tensors, params.kv_overrides);
 
         model.hparams.vocab_only = params.vocab_only;
 
@@ -4478,7 +6213,8 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
 
         llm_load_print_meta(ml, model);
 
-        if (model.hparams.n_vocab != model.vocab.id_to_token.size()) {
+        if (model.vocab.type != LLAMA_VOCAB_TYPE_NONE &&
+            model.hparams.n_vocab != model.vocab.id_to_token.size()) {
             throw std::runtime_error("vocab size mismatch");
         }
 
@@ -4493,6 +6229,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
             || !(
                 model.ftype == LLAMA_FTYPE_ALL_F32 ||
                 model.ftype == LLAMA_FTYPE_MOSTLY_F16 ||
+                model.ftype == LLAMA_FTYPE_MOSTLY_BF16 ||
                 model.ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ||
                 model.ftype == LLAMA_FTYPE_MOSTLY_Q4_1
             )
@@ -4503,6 +6240,16 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
         }
 #endif
 
+#ifdef GGML_USE_SYCL
+        if (params.split_mode == LLAMA_SPLIT_MODE_NONE) {
+            ggml_backend_sycl_set_single_device_mode(params.main_gpu);
+            //SYCL use device index (0, 1, 2) directly, uer input device id, then convert to device index.
+            params.main_gpu = ggml_backend_sycl_get_device_index(params.main_gpu);
+        } else {
+            ggml_backend_sycl_set_mul_device_mode();
+        }
+#endif
+
         if (!llm_load_tensors(
             ml, model, params.n_gpu_layers, params.split_mode,  params.main_gpu, params.tensor_split, params.use_mlock,
             params.progress_callback, params.progress_callback_user_data
@@ -4523,12 +6270,6 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
 
 using llm_build_cb = std::function<void(struct ggml_tensor * cur, const char * name, int nl)>;
 
-enum llm_rope_type {
-    LLM_ROPE,
-    LLM_ROPE_NEOX,
-    LLM_ROPE_GLM,
-};
-
 enum llm_ffn_op_type {
     LLM_FFN_SILU,
     LLM_FFN_GELU,
@@ -4548,113 +6289,79 @@ enum llm_norm_type {
 
 static struct ggml_tensor * llm_build_inp_embd(
         struct ggml_context * ctx,
+       struct llama_context & lctx,
         const llama_hparams & hparams,
           const llama_batch & batch,
          struct ggml_tensor * tok_embd,
-         struct ggml_tensor * inp_tokens,
-         struct ggml_tensor * inp_embd,
          const llm_build_cb & cb) {
     const int64_t n_embd = hparams.n_embd;
 
     struct ggml_tensor * inpL;
 
     if (batch.token) {
-        struct ggml_tensor * inp_tokens_v = ggml_view_1d(ctx, inp_tokens, batch.n_tokens, 0);
-        cb(inp_tokens, "inp_tokens", -1);
+        lctx.inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, batch.n_tokens);
+        cb(lctx.inp_tokens, "inp_tokens", -1);
+        ggml_set_input(lctx.inp_tokens);
 
-        inpL = ggml_get_rows(ctx, tok_embd, inp_tokens_v);
+        inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens);
     } else {
 #ifdef GGML_USE_MPI
         GGML_ASSERT(false && "not implemented");
 #endif
-
-        inpL = ggml_view_2d(ctx, inp_embd, n_embd, batch.n_tokens, inp_embd->nb[1], 0);
+        lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
+        inpL = lctx.inp_embd;
+        ggml_set_input(lctx.inp_embd);
     }
 
+    cb(inpL, "inp_embd", -1);
+
     return inpL;
 }
 
-// Persimmon: n_rot = n_embd_head_k/2
-// Other:     n_rot = n_embd_head_k
-static void llm_build_k_shift(
-      struct ggml_context * ctx,
-      const llama_hparams & hparams,
-      const llama_cparams & cparams,
-     const llama_kv_cache & kv,
-       struct ggml_cgraph * graph,
-       struct ggml_tensor * K_shift,
-            llm_rope_type   type,
-                  int64_t   n_ctx,
-                  float     freq_base,
-                  float     freq_scale,
-       const llm_build_cb & cb) {
-    const int64_t n_layer       = hparams.n_layer;
-    const int64_t n_head_kv     = hparams.n_head_kv;
-    const int64_t n_embd_head_k = hparams.n_embd_head_k;
-    const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
-    const int32_t n_rot         = hparams.n_rot;
-    const int32_t n_orig_ctx    = cparams.n_yarn_orig_ctx;
-    const float   ext_factor    = cparams.yarn_ext_factor;
-    const float   attn_factor   = cparams.yarn_attn_factor;
-    const float   beta_fast     = cparams.yarn_beta_fast;
-    const float   beta_slow     = cparams.yarn_beta_slow;
-
-    int rope_type = 0;
-
-    switch (type) {
-        case LLM_ROPE:      rope_type = 0; break;
-        case LLM_ROPE_NEOX: rope_type = 2; break;
-        case LLM_ROPE_GLM:  rope_type = 4; break;
-    }
-
-    for (int il = 0; il < n_layer; ++il) {
-        struct ggml_tensor * tmp =
-            // we rotate only the first n_rot dimensions
-            ggml_rope_custom_inplace(ctx,
-                    ggml_view_3d(ctx, kv.k_l[il],
-                        n_embd_head_k, n_head_kv, n_ctx,
-                        ggml_row_size(kv.k_l[il]->type, n_embd_head_k),
-                        ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa),
-                        0),
-                    K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
-                    ext_factor, attn_factor, beta_fast, beta_slow);
-        cb(tmp, "K_shifted", il);
-        ggml_build_forward_expand(graph, tmp);
-    }
-}
-
 static void llm_build_kv_store(
         struct ggml_context * ctx,
         const llama_hparams & hparams,
+        const llama_cparams & cparams,
        const llama_kv_cache & kv,
          struct ggml_cgraph * graph,
          struct ggml_tensor * k_cur,
          struct ggml_tensor * v_cur,
-                    int64_t   n_ctx,
                     int32_t   n_tokens,
                     int32_t   kv_head,
          const llm_build_cb & cb,
                     int64_t   il) {
+    const int64_t n_ctx = cparams.n_ctx;
+
     const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
     const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
 
-    // compute the transposed [n_tokens, n_embd] V matrix
-    struct ggml_tensor * v_cur_t = ggml_transpose(ctx, ggml_reshape_2d(ctx, v_cur, n_embd_v_gqa, n_tokens));
-    //struct ggml_tensor * v_cur_t = ggml_transpose(ctx, v_cur); // TODO: reshape above is likely not needed
-    cb(v_cur_t, "v_cur_t", il);
+    GGML_ASSERT(kv.size == n_ctx);
 
     struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k_l[il], n_tokens*n_embd_k_gqa,
             (ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa))*kv_head);
     cb(k_cache_view, "k_cache_view", il);
 
-    struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, kv.v_l[il], n_tokens, n_embd_v_gqa,
-            (  n_ctx)*ggml_element_size(kv.v_l[il]),
-            (kv_head)*ggml_element_size(kv.v_l[il]));
+    // note: storing RoPE-ed version of K in the KV cache
+    ggml_build_forward_expand(graph, ggml_cpy(ctx, k_cur, k_cache_view));
+
+    assert(v_cur->ne[0] == n_embd_v_gqa && v_cur->ne[1] == n_tokens);
+
+    struct ggml_tensor * v_cache_view = nullptr;
+
+    if (cparams.flash_attn) {
+        v_cache_view = ggml_view_1d(ctx, kv.v_l[il], n_tokens*n_embd_v_gqa,
+                (kv_head)*ggml_row_size(kv.v_l[il]->type, n_embd_v_gqa));
+    } else {
+        // note: the V cache is transposed when not using flash attention
+        v_cache_view = ggml_view_2d(ctx, kv.v_l[il], n_tokens, n_embd_v_gqa,
+                (  n_ctx)*ggml_element_size(kv.v_l[il]),
+                (kv_head)*ggml_element_size(kv.v_l[il]));
+
+        v_cur = ggml_transpose(ctx, v_cur);
+    }
     cb(v_cache_view, "v_cache_view", il);
 
-    // important: storing RoPE-ed version of K in the KV cache!
-    ggml_build_forward_expand(graph, ggml_cpy(ctx, k_cur,   k_cache_view));
-    ggml_build_forward_expand(graph, ggml_cpy(ctx, v_cur_t, v_cache_view));
+    ggml_build_forward_expand(graph, ggml_cpy(ctx, v_cur, v_cache_view));
 }
 
 static struct ggml_tensor * llm_build_norm(
@@ -4703,7 +6410,7 @@ static struct ggml_tensor * llm_build_ffn(
           llm_ffn_gate_type   type_gate,
          const llm_build_cb & cb,
                         int   il) {
-    struct ggml_tensor * tmp = ggml_mul_mat(ctx, up, cur);
+    struct ggml_tensor * tmp = up ? ggml_mul_mat(ctx, up, cur) : cur;
     cb(tmp, "ffn_up", il);
 
     if (up_b) {
@@ -4780,24 +6487,117 @@ static struct ggml_tensor * llm_build_ffn(
     return cur;
 }
 
-// if max_alibi_bias > 0 then apply ALiBi
+static struct ggml_tensor * llm_build_moe_ffn(
+        struct ggml_context * ctx,
+         struct ggml_tensor * cur,
+         struct ggml_tensor * gate_inp,
+         struct ggml_tensor * up_exps,
+         struct ggml_tensor * gate_exps,
+         struct ggml_tensor * down_exps,
+                    int64_t   n_expert,
+                    int64_t   n_expert_used,
+            llm_ffn_op_type   type_op,
+                       bool   norm_w,
+         const llm_build_cb & cb,
+                        int   il) {
+    int64_t n_embd = cur->ne[0];
+    int64_t n_tokens = cur->ne[1];
+
+    ggml_tensor * logits = ggml_mul_mat(ctx, gate_inp, cur); // [n_expert, n_tokens]
+    cb(logits, "ffn_moe_logits", il);
+
+    ggml_tensor * probs = ggml_soft_max(ctx, logits); // [n_expert, n_tokens]
+    cb(probs, "ffn_moe_probs", il);
+
+    // select experts
+    ggml_tensor * selected_experts = ggml_top_k(ctx, probs, n_expert_used); // [n_expert_used, n_tokens]
+    cb(selected_experts->src[0], "ffn_moe_argsort", il);
+    cb(selected_experts, "ffn_moe_topk", il);
+
+    ggml_tensor * weights = ggml_get_rows(ctx,
+            ggml_reshape_3d(ctx, probs, 1, n_expert, n_tokens), selected_experts); // [1, n_expert_used, n_tokens]
+    cb(weights, "ffn_moe_weights", il);
+
+    if (norm_w) {
+        weights = ggml_reshape_2d(ctx, weights, n_expert_used, n_tokens);
+
+        ggml_tensor * weights_sum = ggml_sum_rows(ctx, weights); // [1, n_tokens]
+        cb(weights_sum, "ffn_moe_weights_sum", il);
+
+        weights = ggml_div(ctx, weights, weights_sum); // [n_expert_used, n_tokens]
+        cb(weights, "ffn_moe_weights_norm", il);
+
+        weights = ggml_reshape_3d(ctx, weights, 1, n_expert_used, n_tokens);
+    }
+
+    cur = ggml_reshape_3d(ctx, cur, n_embd, 1, n_tokens);
+    ggml_tensor * up = ggml_mul_mat_id(ctx, up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
+    cb(up, "ffn_moe_up", il);
+
+    ggml_tensor * gate = ggml_mul_mat_id(ctx, gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
+    cb(gate, "ffn_moe_gate", il);
+
+    switch (type_op) {
+        case LLM_FFN_SILU:
+            {
+                gate = ggml_silu(ctx, gate);
+                cb(gate, "ffn_moe_silu", il);
+            } break;
+        case LLM_FFN_GELU:
+            {
+                gate = ggml_gelu(ctx, gate);
+                cb(gate, "ffn_moe_gelu", il);
+            } break;
+        default:
+            GGML_ASSERT(false);
+    }
+
+    ggml_tensor * par = ggml_mul(ctx, up, gate); // [n_ff, n_expert_used, n_tokens]
+    cb(par, "ffn_moe_gate_par", il);
+
+    ggml_tensor * experts = ggml_mul_mat_id(ctx, down_exps, par, selected_experts); // [n_embd, n_expert_used, n_tokens]
+    cb(experts, "ffn_moe_down", il);
+
+    experts = ggml_mul(ctx, experts, weights);
+
+    // aggregate experts
+    ggml_tensor * moe_out = nullptr;
+    for (int i = 0; i < n_expert_used; ++i) {
+        ggml_tensor * cur_expert = ggml_view_2d(ctx, experts, n_embd, n_tokens,
+                experts->nb[2], i*experts->nb[1]);
+
+        if (i == 0) {
+            moe_out = cur_expert;
+        } else {
+            moe_out = ggml_add(ctx, moe_out, cur_expert);
+        }
+    }
+
+    if (n_expert_used == 1) {
+        // avoid returning a non-contiguous tensor
+        moe_out = ggml_cont(ctx, moe_out);
+    }
+
+    return moe_out;
+}
+
 static struct ggml_tensor * llm_build_kqv(
         struct ggml_context * ctx,
           const llama_model & model,
         const llama_hparams & hparams,
+        const llama_cparams & cparams,
        const llama_kv_cache & kv,
          struct ggml_cgraph * graph,
          struct ggml_tensor * wo,
          struct ggml_tensor * wo_b,
          struct ggml_tensor * q_cur,
          struct ggml_tensor * kq_mask,
-         struct ggml_tensor * kq_pos,
-                    int64_t   n_ctx,
                     int32_t   n_tokens,
                     int32_t   n_kv,
                     float     kq_scale,
          const llm_build_cb & cb,
                     int       il) {
+    const int64_t n_ctx         = cparams.n_ctx;
     const int64_t n_head        = hparams.n_head;
     const int64_t n_head_kv     = hparams.n_head_kv;
     const int64_t n_embd_head_k = hparams.n_embd_head_k;
@@ -4815,56 +6615,76 @@ static struct ggml_tensor * llm_build_kqv(
                 0);
     cb(k, "k", il);
 
-    struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
-    cb(kq, "kq", il);
+    struct ggml_tensor * cur;
 
-    if (model.arch == LLM_ARCH_PHI2) {
-        // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs
-        // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847
-        ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
-    }
+    if (cparams.flash_attn) {
+        GGML_UNUSED(model);
+        GGML_UNUSED(n_ctx);
 
-#if defined(GGML_USE_VULKAN) || defined(GGML_USE_KOMPUTE) || defined(GGML_USE_SYCL)
-#pragma message("TODO: ALiBi support in ggml_soft_max_ext is not implemented for Vulkan, Kompute, and SYCL")
-#pragma message("      Falling back to ggml_alibi(). Will become an error in Mar 2024")
-#pragma message("ref:  https://github.com/ggerganov/llama.cpp/pull/5488")
-    if (hparams.f_max_alibi_bias > 0.0f) {
-        kq = ggml_scale(ctx, kq, kq_scale);
-        cb(kq, "kq_scaled", il);
+        // split cached v into n_head heads (not transposed)
+        struct ggml_tensor * v =
+            ggml_view_3d(ctx, kv.v_l[il],
+                    n_embd_head_v, n_kv, n_head_kv,
+                    ggml_row_size(kv.v_l[il]->type, n_embd_k_gqa),
+                    ggml_row_size(kv.v_l[il]->type, n_embd_head_k),
+                    0);
+        cb(v, "v", il);
 
-        kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, hparams.f_max_alibi_bias);
-        cb(kq, "kq_scaled_alibi", il);
+        cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias);
 
-        kq = ggml_add(ctx, kq, kq_mask);
-        cb(kq, "kq_masked", il);
+        if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3) {
+            ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
+        }
 
-        kq = ggml_soft_max(ctx, kq);
-        cb(kq, "kq_soft_max", il);
-    } else
-#endif
-    {
-        kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_pos, kq_scale, hparams.f_max_alibi_bias);
+        cur = ggml_reshape_2d(ctx, cur, n_embd_head_k*n_head, n_tokens);
+    } else {
+        struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
+        cb(kq, "kq", il);
+
+        if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3) {
+            // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs
+            // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847
+            ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
+        }
+
+        if (model.arch == LLM_ARCH_GROK) {
+            // need to do the following:
+            // multiply by attn_output_multiplyer of 0.08838834764831845
+            // and then :
+            // kq = 30 * tanh(kq / 30)
+            // before the softmax below
+
+            //try from phi2
+            //ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
+
+            kq = ggml_tanh(ctx, ggml_scale(ctx, kq, 0.08838834764831845f/30.0f));
+            kq = ggml_scale(ctx, kq, 30);
+        }
+
+        kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_scale, hparams.f_max_alibi_bias);
         cb(kq, "kq_soft_max_ext", il);
+
+        GGML_ASSERT(kv.size == n_ctx);
+
+        // split cached v into n_head heads
+        struct ggml_tensor * v =
+            ggml_view_3d(ctx, kv.v_l[il],
+                    n_kv, n_embd_head_v, n_head_kv,
+                    ggml_element_size(kv.v_l[il])*n_ctx,
+                    ggml_element_size(kv.v_l[il])*n_ctx*n_embd_head_v,
+                    0);
+        cb(v, "v", il);
+
+        struct ggml_tensor * kqv = ggml_mul_mat(ctx, v, kq);
+        cb(kqv, "kqv", il);
+
+        struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3);
+        cb(kqv_merged, "kqv_merged", il);
+
+        cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_k*n_head, n_tokens);
+        cb(cur, "kqv_merged_cont", il);
     }
 
-    // split cached v into n_head heads
-    struct ggml_tensor * v =
-        ggml_view_3d(ctx, kv.v_l[il],
-                n_kv, n_embd_head_v, n_head_kv,
-                ggml_element_size(kv.v_l[il])*n_ctx,
-                ggml_element_size(kv.v_l[il])*n_ctx*n_embd_head_v,
-                0);
-    cb(v, "v", il);
-
-    struct ggml_tensor * kqv = ggml_mul_mat(ctx, v, kq);
-    cb(kqv, "kqv", il);
-
-    struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3);
-    cb(kqv_merged, "kqv_merged", il);
-
-    struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_k*n_head, n_tokens);
-    cb(cur, "kqv_merged_cont", il);
-
     ggml_build_forward_expand(graph, cur);
 
     cur = ggml_mul_mat(ctx, wo, cur);
@@ -4883,6 +6703,7 @@ static struct ggml_tensor * llm_build_kv(
         struct ggml_context * ctx,
           const llama_model & model,
         const llama_hparams & hparams,
+        const llama_cparams & cparams,
        const llama_kv_cache & kv,
          struct ggml_cgraph * graph,
          struct ggml_tensor * wo,
@@ -4891,8 +6712,6 @@ static struct ggml_tensor * llm_build_kv(
          struct ggml_tensor * v_cur,
          struct ggml_tensor * q_cur,
          struct ggml_tensor * kq_mask,
-         struct ggml_tensor * kq_pos,
-                    int64_t   n_ctx,
                     int32_t   n_tokens,
                     int32_t   kv_head,
                     int32_t   n_kv,
@@ -4906,11 +6725,12 @@ static struct ggml_tensor * llm_build_kv(
     ggml_build_forward_expand(graph, k_cur);
     ggml_build_forward_expand(graph, v_cur);
 
-    llm_build_kv_store(ctx, hparams, kv, graph, k_cur, v_cur, n_ctx, n_tokens, kv_head, cb, il);
+    llm_build_kv_store(ctx, hparams, cparams, kv, graph, k_cur, v_cur, n_tokens, kv_head, cb, il);
 
     struct ggml_tensor * cur;
-    cur  = llm_build_kqv(ctx, model, hparams, kv, graph, wo, wo_b,
-            q_cur, kq_mask, kq_pos, n_ctx, n_tokens, n_kv, kq_scale, cb, il);
+
+    cur  = llm_build_kqv(ctx, model, hparams, cparams, kv, graph, wo, wo_b,
+            q_cur, kq_mask, n_tokens, n_kv, kq_scale, cb, il);
     cb(cur, "kqv_out", il);
 
     return cur;
@@ -4918,7 +6738,7 @@ static struct ggml_tensor * llm_build_kv(
 
 struct llm_build_context {
     const llama_model    & model;
-    const llama_context  & lctx;
+          llama_context  & lctx;
     const llama_hparams  & hparams;
     const llama_cparams  & cparams;
     const llama_batch    & batch;
@@ -4926,6 +6746,7 @@ struct llm_build_context {
 
     const int64_t n_embd;
     const int64_t n_layer;
+    const int64_t n_rot;
     const int64_t n_ctx;       // user-specified context size (can be different from n_ctx_train)
     const int64_t n_head;
     const int64_t n_head_kv;
@@ -4946,12 +6767,15 @@ struct llm_build_context {
     const float norm_rms_eps;
 
     const int32_t n_tokens;
-    const int32_t n_kv;     // size of KV cache to consider (n_kv <= n_ctx)
+    const int32_t n_kv;     // size of KV cache to consider (n_kv <= kv_self.size)
+    const int32_t n_outputs;
     const int32_t kv_head;  // index of where we store new KV data in the cache
     const int32_t n_orig_ctx;
 
-    const bool do_rope_shift;
-    const uint32_t pooling_type;
+    const bool flash_attn;
+
+    const enum llama_pooling_type pooling_type;
+    const enum llama_rope_type    rope_type;
 
     const llm_build_cb & cb;
 
@@ -4973,6 +6797,7 @@ struct llm_build_context {
         kv_self          (lctx.kv_self),
         n_embd           (hparams.n_embd),
         n_layer          (hparams.n_layer),
+        n_rot            (hparams.n_rot),
         n_ctx            (cparams.n_ctx),
         n_head           (hparams.n_head),
         n_head_kv        (hparams.n_head_kv),
@@ -4991,11 +6816,13 @@ struct llm_build_context {
         norm_eps         (hparams.f_norm_eps),
         norm_rms_eps     (hparams.f_norm_rms_eps),
         n_tokens         (batch.n_tokens),
-        n_kv             (worst_case ? n_ctx            : kv_self.n),
-        kv_head          (worst_case ? n_ctx - n_tokens : kv_self.head),
+        n_kv             (worst_case ? kv_self.size : kv_self.n),
+        n_outputs        (worst_case ? n_tokens : lctx.n_outputs),
+        kv_head          (worst_case ? (kv_self.recurrent ? 0 : kv_self.size - n_tokens) : kv_self.head),
         n_orig_ctx       (cparams.n_yarn_orig_ctx),
-        do_rope_shift    (worst_case || kv_self.has_shift),
-        pooling_type     (cparams.do_pooling ? hparams.pooling_type : (uint32_t)LLAMA_POOLING_NONE),
+        flash_attn       (cparams.flash_attn),
+        pooling_type     (cparams.pooling_type),
+        rope_type        (hparams.rope_type),
         cb               (cb),
         buf_compute_meta (lctx.buf_compute_meta) {
             // all initializations should be done in init()
@@ -5009,6 +6836,18 @@ struct llm_build_context {
         };
 
         ctx0 = ggml_init(params);
+
+        lctx.inp_tokens  = nullptr;
+        lctx.inp_embd    = nullptr;
+        lctx.inp_pos     = nullptr;
+        lctx.inp_out_ids = nullptr;
+        lctx.inp_KQ_mask = nullptr;
+        lctx.inp_K_shift = nullptr;
+        lctx.inp_mean    = nullptr;
+        lctx.inp_cls     = nullptr;
+        lctx.inp_s_copy  = nullptr;
+        lctx.inp_s_mask  = nullptr;
+        lctx.inp_s_seq   = nullptr;
     }
 
     void free() {
@@ -5018,9 +6857,187 @@ struct llm_build_context {
         }
     }
 
+    struct ggml_cgraph * build_k_shift() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        GGML_ASSERT(kv_self.size == n_ctx);
+
+        lctx.inp_K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
+        cb(lctx.inp_K_shift, "K_shift", -1);
+        ggml_set_input(lctx.inp_K_shift);
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * tmp =
+                // we rotate only the first n_rot dimensions
+                ggml_rope_custom_inplace(ctx0,
+                        ggml_view_3d(ctx0, kv_self.k_l[il],
+                            n_embd_head_k, n_head_kv, n_ctx,
+                            ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
+                            ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
+                            0),
+                        lctx.inp_K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
+            cb(tmp, "K_shifted", il);
+            ggml_build_forward_expand(gf, tmp);
+        }
+
+        return gf;
+    }
+
+    struct ggml_cgraph * build_s_copy() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        GGML_ASSERT(kv_self.recurrent);
+
+        struct ggml_tensor * state_copy = build_inp_s_copy();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size);
+            struct ggml_tensor * ssm_states  = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size);
+
+            conv_states = ggml_get_rows(ctx0, conv_states, state_copy);
+            ssm_states  = ggml_get_rows(ctx0,  ssm_states, state_copy);
+
+            // TODO: name the intermediate tensors with cb()
+
+            ggml_build_forward_expand(gf, ggml_cpy(ctx0, conv_states, kv_self.k_l[il]));
+            ggml_build_forward_expand(gf, ggml_cpy(ctx0,  ssm_states, kv_self.v_l[il]));
+        }
+
+        return gf;
+    }
+
+    struct ggml_cgraph * build_defrag(const std::vector<uint32_t> & ids) {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        for (uint32_t i = 0; i < ids.size(); ++i) {
+            const uint32_t id = ids[i];
+
+            if (i == id || id == ids.size()) {
+                continue;
+            }
+
+            uint32_t nm = 1;
+
+            while (i + nm < ids.size() && ids[i + nm] == id + nm) {
+                nm++;
+            }
+
+            for (int il = 0; il < n_layer; ++il) {
+                ggml_tensor * view_k_src = ggml_view_2d(ctx0, kv_self.k_l[il],
+                        n_embd_k_gqa, nm,
+                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
+                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*i));
+
+                ggml_tensor * view_k_dst = ggml_view_2d(ctx0, kv_self.k_l[il],
+                        n_embd_k_gqa, nm,
+                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
+                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*id));
+
+                ggml_tensor * view_v_src;
+                ggml_tensor * view_v_dst;
+
+                if (flash_attn) {
+                    // NOTE: the V cache is not transposed when using flash attention
+                    view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il],
+                            n_embd_v_gqa, nm,
+                            ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa),
+                            ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*i));
+
+                    view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il],
+                            n_embd_v_gqa, nm,
+                            ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa),
+                            ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*id));
+                } else {
+                    view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il],
+                            nm, n_embd_v_gqa,
+                            ggml_row_size(kv_self.v_l[il]->type, kv_self.size),
+                            ggml_row_size(kv_self.v_l[il]->type, i));
+
+                    view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il],
+                            nm, n_embd_v_gqa,
+                            ggml_row_size(kv_self.v_l[il]->type, kv_self.size),
+                            ggml_row_size(kv_self.v_l[il]->type, id));
+                }
+
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_k_src, view_k_dst));
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_v_src, view_v_dst));
+            }
+
+            i += nm - 1;
+        }
+
+        //LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes);
+
+        return gf;
+    }
+
+    struct ggml_tensor * build_inp_pos() {
+        lctx.inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+        cb(lctx.inp_pos, "inp_pos", -1);
+        ggml_set_input(lctx.inp_pos);
+        return lctx.inp_pos;
+    }
+
+    struct ggml_tensor * build_inp_out_ids() {
+        lctx.inp_out_ids = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_outputs);
+        cb(lctx.inp_out_ids, "inp_out_ids", -1);
+        ggml_set_input(lctx.inp_out_ids);
+        return lctx.inp_out_ids;
+    }
+
+    struct ggml_tensor * build_inp_KQ_mask(bool causal = true) {
+        if (causal) {
+            lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv,     GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
+        } else {
+            lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
+        }
+        cb(lctx.inp_KQ_mask, "KQ_mask", -1);
+        ggml_set_input(lctx.inp_KQ_mask);
+        return flash_attn ? ggml_cast(ctx0, lctx.inp_KQ_mask, GGML_TYPE_F16) : lctx.inp_KQ_mask;
+    }
+
+    struct ggml_tensor * build_inp_mean() {
+        lctx.inp_mean = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens);
+        cb(lctx.inp_mean, "inp_mean", -1);
+        ggml_set_input(lctx.inp_mean);
+        return lctx.inp_mean;
+    }
+
+    struct ggml_tensor * build_inp_cls() {
+        lctx.inp_cls = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+        cb(lctx.inp_cls, "inp_cls", -1);
+        ggml_set_input(lctx.inp_cls);
+        return lctx.inp_cls;
+    }
+
+    struct ggml_tensor * build_inp_s_copy() {
+        lctx.inp_s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, kv_self.size);
+        cb(lctx.inp_s_copy, "inp_s_copy", -1);
+        ggml_set_input(lctx.inp_s_copy);
+        return lctx.inp_s_copy;
+    }
+
+    struct ggml_tensor * build_inp_s_mask() {
+        lctx.inp_s_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_kv);
+        cb(lctx.inp_s_mask, "inp_s_mask", -1);
+        ggml_set_input(lctx.inp_s_mask);
+        return lctx.inp_s_mask;
+    }
+
+    struct ggml_tensor * build_inp_s_seq() {
+        lctx.inp_s_seq = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_kv, n_tokens);
+        cb(lctx.inp_s_seq, "inp_s_seq", -1);
+        ggml_set_input(lctx.inp_s_seq);
+        return lctx.inp_s_seq;
+    }
+
     struct ggml_cgraph * build_llama() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        // mutable variable, needed during the last layer of the computation to skip unused tokens
+        int32_t n_tokens = this->n_tokens;
+
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
         GGML_ASSERT(n_embd_head == hparams.n_rot);
@@ -5028,21 +7045,13 @@ struct llm_build_context {
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // shift the entire K-cache if needed
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb);
-        }
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * inpSA = inpL;
@@ -5079,22 +7088,29 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_custom(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                    hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_custom(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
-                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                n_tokens = n_outputs;
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
             }
 
             struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
@@ -5121,65 +7137,24 @@ struct llm_build_context {
                         LLM_NORM_RMS, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts]
-                cb(logits, "ffn_moe_logits", il);
-
-                ggml_tensor * probs = ggml_soft_max(ctx0, logits); // [n_tokens, num_experts]
-                cb(probs, "ffn_moe_probs", il);
-
-                // select experts
-                ggml_tensor * selected_experts = ggml_top_k(ctx0, probs, n_expert_used); // [n_tokens, num_experts_per_tok]
-                cb(selected_experts->src[0], "ffn_moe_argsort", il);
-
-                ggml_tensor * weights = ggml_get_rows(ctx0,
-                        ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts);
-                cb(weights, "ffn_moe_weights", il);
-
-                weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_tokens, num_experts_per_tok]
-
-                ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights);
-                cb(weights_sum, "ffn_moe_weights_sum", il);
-
-                weights = ggml_div(ctx0, weights, weights_sum); // [n_tokens, num_experts_per_tok]
-                cb(weights, "ffn_moe_weights_norm", il);
-
-                // compute expert outputs
-                ggml_tensor * moe_out = nullptr;
-
-                for (int i = 0; i < n_expert_used; ++i) {
-                    ggml_tensor * cur_expert;
-
-                    ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exp, n_expert, selected_experts, i, cur);
-                    cb(cur_up, "ffn_moe_up", il);
-
-                    ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exp, n_expert, selected_experts, i, cur);
-                    cb(cur_gate, "ffn_moe_gate", il);
-
-                    cur_gate = ggml_silu(ctx0, cur_gate);
-                    cb(cur_gate, "ffn_moe_silu", il);
-
-                    cur_expert = ggml_mul(ctx0, cur_up, cur_gate); // [n_tokens, n_embd]
-                    cb(cur_expert, "ffn_moe_gate_par", il);
-
-                    cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exp, n_expert, selected_experts, i, cur_expert); // [n_tokens, n_embd]
-                    cb(cur_expert, "ffn_moe_down", il);
-
-                    cur_expert = ggml_mul(ctx0, cur_expert,
-                            ggml_view_2d(ctx0, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
-                    cb(cur_expert, "ffn_moe_weighted", il);
-
-                    if (i == 0) {
-                        moe_out = cur_expert;
-                    } else {
-                        moe_out = ggml_add(ctx0, moe_out, cur_expert);
-                        cb(moe_out, "ffn_moe_out", il);
-                    }
-                }
-
-                cur = moe_out;
+                cur = llm_build_moe_ffn(ctx0, cur,
+                        model.layers[il].ffn_gate_inp,
+                        model.layers[il].ffn_up_exps,
+                        model.layers[il].ffn_gate_exps,
+                        model.layers[il].ffn_down_exps,
+                        n_expert, n_expert_used,
+                        LLM_FFN_SILU, true,
+                        cb, il);
+                cb(cur, "ffn_moe_out", il);
             }
 
             cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
+            if (layer_dir != nullptr) {
+                cur = ggml_add(ctx0, cur, layer_dir);
+            }
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -5212,25 +7187,13 @@ struct llm_build_context {
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = model.type == MODEL_7B ? build_inp_pos() : nullptr;
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // positions of the tokens in the KV cache
-        struct ggml_tensor * KQ_pos = ggml_view_1d(ctx0, lctx.inp_KQ_pos, n_kv, 0);
-        cb(KQ_pos, "KQ_pos", -1);
-
-        // shift the entire K-cache if needed
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb);
-        }
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * inpSA = inpL;
@@ -5255,12 +7218,12 @@ struct llm_build_context {
                     case MODEL_7B:
                         Qcur = ggml_rope_custom(
                             ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                            hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale,
+                            n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                             ext_factor, attn_factor, beta_fast, beta_slow
                         );
                         Kcur = ggml_rope_custom(
                             ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                            hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale,
+                            n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                             ext_factor, attn_factor, beta_fast, beta_slow
                         );
                         break;
@@ -5274,11 +7237,16 @@ struct llm_build_context {
                 cb(Qcur, "Qcur", il);
                 cb(Kcur, "Kcur", il);
 
-
-                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
                         model.layers[il].wo, NULL,
-                        Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
             }
 
             struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
@@ -5323,6 +7291,108 @@ struct llm_build_context {
         return gf;
     }
 
+    struct ggml_cgraph * build_xverse() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                        model.layers[il].wo, NULL,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,      cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   NULL,
+                        model.layers[il].ffn_gate, NULL,
+                        model.layers[il].ffn_down, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            }
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
     struct ggml_cgraph * build_falcon() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
@@ -5334,21 +7404,13 @@ struct llm_build_context {
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // shift the entire K-cache if needed
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb);
-        }
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * attn_norm;
@@ -5388,21 +7450,28 @@ struct llm_build_context {
 
                 // using mode = 2 for neox mode
                 Qcur = ggml_rope_custom(
-                    ctx0, Qcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx,
+                    ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_custom(
-                    ctx0, Kcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx,
+                    ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
                         model.layers[il].wo, NULL,
-                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur       = ggml_get_rows(ctx0,       cur, inp_out_ids);
+                inpL      = ggml_get_rows(ctx0,      inpL, inp_out_ids);
+                attn_norm = ggml_get_rows(ctx0, attn_norm, inp_out_ids);
             }
 
             struct ggml_tensor * ffn_inp = cur;
@@ -5445,6 +7514,293 @@ struct llm_build_context {
         return gf;
     }
 
+    struct ggml_cgraph * build_grok() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        // mutable variable, needed during the last layer of the computation to skip unused tokens
+        int32_t n_tokens = this->n_tokens;
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // multiply by embedding_multiplier_scale of 78.38367176906169
+        inpL = ggml_scale(ctx0, inpL, 78.38367176906169f);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
+
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                Qcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                n_tokens = n_outputs;
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            // Grok
+            // if attn_out_norm is present then apply it before adding the input
+            if (model.layers[il].attn_out_norm) {
+                cur = llm_build_norm(ctx0, cur, hparams,
+                        model.layers[il].attn_out_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "attn_out_norm", il);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            // MoE branch
+            cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = llm_build_moe_ffn(ctx0, cur,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    model.layers[il].ffn_gate_exps,
+                    model.layers[il].ffn_down_exps,
+                    n_expert, n_expert_used,
+                    LLM_FFN_GELU, true,
+                    cb, il);
+            cb(cur, "ffn_moe_out", il);
+
+            // Grok
+            // if layer_out_norm is present then apply it before adding the input
+            // Idea: maybe ffn_out_norm is a better name
+            if (model.layers[il].layer_out_norm) {
+                cur = llm_build_norm(ctx0, cur, hparams,
+                        model.layers[il].layer_out_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "layer_out_norm", il);
+            }
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
+            if (layer_dir != nullptr) {
+                cur = ggml_add(ctx0, cur, layer_dir);
+            }
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+
+        // Grok
+        // multiply logits by output_multiplier_scale of 0.5773502691896257
+
+        cur = ggml_scale(ctx0, cur, 0.5773502691896257f);
+
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
+    struct ggml_cgraph * build_dbrx() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        // mutable variable, needed during the last layer of the computation to skip unused tokens
+        int32_t n_tokens = this->n_tokens;
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                                 model.layers[il].attn_norm, NULL,
+                                 LLM_NORM, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                struct ggml_tensor * Qcur = nullptr;
+                struct ggml_tensor * Kcur = nullptr;
+                struct ggml_tensor * Vcur = nullptr;
+
+                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
+
+                cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
+                cb(cur, "wqkv_clamped", il);
+
+                Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+                Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+                Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                        model.layers[il].wo, NULL,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                n_tokens = n_outputs;
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            // MoE branch
+            cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                                 model.layers[il].attn_out_norm, NULL,
+                                 LLM_NORM, cb, il);
+            cb(cur, "attn_out_norm", il);
+
+            cur = llm_build_moe_ffn(ctx0, cur,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    model.layers[il].ffn_gate_exps,
+                    model.layers[il].ffn_down_exps,
+                    n_expert, n_expert_used,
+                    LLM_FFN_SILU, true,
+                    cb, il);
+            cb(cur, "ffn_moe_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
+            if (layer_dir != nullptr) {
+                cur = ggml_add(ctx0, cur, layer_dir);
+            }
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                             model.output_norm, NULL,
+                             LLM_NORM, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
     struct ggml_cgraph * build_starcoder() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
@@ -5453,21 +7809,17 @@ struct llm_build_context {
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
 
         struct ggml_tensor * cur;
-        struct ggml_tensor * pos;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
-        cb(KQ_mask, "KQ_mask", -1);
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
-        pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
+        struct ggml_tensor * pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
         cb(pos, "pos_embd", -1);
 
         inpL = ggml_add(ctx0, inpL, pos);
@@ -5498,10 +7850,16 @@ struct llm_build_context {
 
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
-                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
             }
 
             // add the input
@@ -5553,20 +7911,13 @@ struct llm_build_context {
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb);
-        }
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * residual = inpL;
@@ -5625,7 +7976,7 @@ struct llm_build_context {
 
                 // RoPE the first n_rot of q/k, pass the other half, and concat.
                 struct ggml_tensor * qrot = ggml_view_3d(
-                        ctx0, tmpq, hparams.n_rot, n_head, n_tokens,
+                        ctx0, tmpq, n_rot, n_head, n_tokens,
                         ggml_element_size(tmpq) * n_embd_head,
                         ggml_element_size(tmpq) * n_embd_head * n_head,
                         0
@@ -5633,7 +7984,7 @@ struct llm_build_context {
                 cb(qrot, "qrot", il);
 
                 struct ggml_tensor * krot = ggml_view_3d(
-                        ctx0, tmpk, hparams.n_rot, n_head, n_tokens,
+                        ctx0, tmpk, n_rot, n_head, n_tokens,
                         ggml_element_size(tmpk) * n_embd_head,
                         ggml_element_size(tmpk) * n_embd_head * n_head,
                         0
@@ -5642,29 +7993,29 @@ struct llm_build_context {
 
                 // get the second half of tmpq, e.g tmpq[n_rot:, :, :]
                 struct ggml_tensor * qpass = ggml_view_3d(
-                        ctx0, tmpq, hparams.n_rot, n_head, n_tokens,
+                        ctx0, tmpq, n_rot, n_head, n_tokens,
                         ggml_element_size(tmpq) * n_embd_head,
                         ggml_element_size(tmpq) * n_embd_head * n_head,
-                        ggml_element_size(tmpq) * hparams.n_rot
+                        ggml_element_size(tmpq) * n_rot
                         );
                 cb(qpass, "qpass", il);
 
                 struct ggml_tensor * kpass = ggml_view_3d(
-                        ctx0, tmpk, hparams.n_rot, n_head, n_tokens,
+                        ctx0, tmpk, n_rot, n_head, n_tokens,
                         ggml_element_size(tmpk) * n_embd_head,
                         ggml_element_size(tmpk) * n_embd_head * n_head,
-                        ggml_element_size(tmpk) * hparams.n_rot
+                        ggml_element_size(tmpk) * n_rot
                         );
                 cb(kpass, "kpass", il);
 
                 struct ggml_tensor * qrotated = ggml_rope_custom(
-                    ctx0, qrot, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx,
+                    ctx0, qrot, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(qrotated, "qrotated", il);
 
                 struct ggml_tensor * krotated = ggml_rope_custom(
-                    ctx0, krot, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx,
+                    ctx0, krot, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(krotated, "krotated", il);
@@ -5703,10 +8054,16 @@ struct llm_build_context {
                         );
                 cb(Vcur, "Vcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
-                        Kcur, Vcur, Q, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Q, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur      = ggml_get_rows(ctx0,      cur, inp_out_ids);
+                residual = ggml_get_rows(ctx0, residual, inp_out_ids);
             }
 
             struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur);
@@ -5760,16 +8117,10 @@ struct llm_build_context {
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // positions of the tokens in the KV cache
-        struct ggml_tensor * KQ_pos = ggml_view_1d(ctx0, lctx.inp_KQ_pos, n_kv, 0);
-        cb(KQ_pos, "KQ_pos", -1);
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * inpSA = inpL;
@@ -5796,10 +8147,16 @@ struct llm_build_context {
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                 cb(Qcur, "Qcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
                         model.layers[il].wo, NULL,
-                        Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
             }
 
             struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
@@ -5849,19 +8206,21 @@ struct llm_build_context {
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
 
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
+        struct ggml_tensor * inp_pos = nullptr;
 
-        // get input vectors with right size
-        const size_t stride1 = n_tokens * ggml_type_size(lctx.inp_tokens->type);
-        struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
-        struct ggml_tensor * inp_mean = ggml_view_2d(ctx0, lctx.inp_mean, n_tokens, n_tokens, stride1, 0);
-        struct ggml_tensor * inp_cls = ggml_view_1d(ctx0, lctx.inp_cls, n_tokens, 0);
+        if (model.arch != LLM_ARCH_JINA_BERT_V2) {
+            inp_pos = build_inp_pos();
+        }
+        struct ggml_tensor * inp_mean = build_inp_mean();
+        struct ggml_tensor * inp_cls  = build_inp_cls();
 
         // construct input embeddings (token, type, position)
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // token types are hardcoded to zero ("Sentence A")
         struct ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0);
@@ -5876,39 +8235,50 @@ struct llm_build_context {
         cb(inpL, "inp_norm", -1);
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
-        cb(KQ_mask, "KQ_mask", -1); // [n_kv, n_tokens]
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask(false);
 
         // iterate layers
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * cur = inpL;
 
+            struct ggml_tensor * Qcur;
+            struct ggml_tensor * Kcur;
+            struct ggml_tensor * Vcur;
+
             // self-attention
-            if (model.arch == LLM_ARCH_BERT) {
-                struct ggml_tensor * Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq);
+            if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_JINA_BERT_V2) {
+                Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq);
                 cb(Qcur, "Qcur", il);
 
-                struct ggml_tensor * Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), model.layers[il].bk);
+                if (model.layers[il].attn_q_norm) {
+                    Qcur = llm_build_norm(ctx0, Qcur, hparams,
+                            model.layers[il].attn_q_norm,
+                            model.layers[il].attn_q_norm_b,
+                            LLM_NORM, cb, il);
+                }
+
+                Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), model.layers[il].bk);
                 cb(Kcur, "Kcur", il);
 
-                struct ggml_tensor * Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), model.layers[il].bv);
+                if (model.layers[il].attn_k_norm) {
+                    Kcur = llm_build_norm(ctx0, Kcur, hparams,
+                            model.layers[il].attn_k_norm,
+                            model.layers[il].attn_k_norm_b,
+                            LLM_NORM, cb, il);
+                }
+                Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), model.layers[il].bv);
                 cb(Vcur, "Vcur", il);
 
-                // seems like we just need to do this for Q?
-                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
-
-                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
             } else {
                 // compute Q and K and RoPE them
                 cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
-                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
-                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
-                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+                Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+                Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+                Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
 
                 cb(Qcur, "Qcur", il);
                 cb(Kcur, "Kcur", il);
@@ -5916,22 +8286,57 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_custom(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
-                    hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_custom(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
+            }
 
-                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+            struct ggml_tensor * q =                 ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+            struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
+
+            struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+            cb(kq, "kq", il);
+
+            kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, 1.0f/sqrtf(float(n_embd_head)), hparams.f_max_alibi_bias);
+            cb(kq, "kq_soft_max_ext", il);
+
+            struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_tokens)));
+            cb(v, "v", il);
+
+            struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_tokens, n_embd_head, n_head_kv), kq);
+            cb(kqv, "kqv", il);
+
+            struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
+            cb(kqv_merged, "kqv_merged", il);
+
+            cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
+            cb(cur, "kqv_merged_cont", il);
+
+            ggml_build_forward_expand(gf, cur);
+
+            cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
+            if (model.layers[il].bo) {
+                cb(cur, "kqv_wo", il);
+            }
+
+            if (model.layers[il].bo) {
+                cur = ggml_add(ctx0, cur, model.layers[il].bo);
+            }
+            cb(cur, "kqv_out", il);
+
+            if (il == n_layer - 1 && pooling_type == LLAMA_POOLING_TYPE_NONE) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
             }
 
             // re-add the layer input
@@ -5951,6 +8356,13 @@ struct llm_build_context {
                         model.layers[il].ffn_down, model.layers[il].ffn_down_b,
                         NULL,
                         LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+            } else if (model.arch == LLM_ARCH_JINA_BERT_V2) {
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   NULL,
+                        model.layers[il].ffn_gate, NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                        NULL,
+                        LLM_FFN_GELU, LLM_FFN_PAR, cb, il);
             } else {
                 cur = llm_build_ffn(ctx0, cur,
                         model.layers[il].ffn_up,   NULL,
@@ -5973,16 +8385,29 @@ struct llm_build_context {
 
         // final output
         cur = inpL;
+        cb(cur, "result_embd", -1);
 
         // pooling layer
-        if (pooling_type == LLAMA_POOLING_MEAN) {
-            cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean);
-        } else if (pooling_type == LLAMA_POOLING_CLS) {
-            cur = ggml_get_rows(ctx0, cur, inp_cls);
-        } else {
-            GGML_ASSERT(pooling_type == LLAMA_POOLING_NONE && "Invalid pooling type");
+        switch (pooling_type) {
+            case LLAMA_POOLING_TYPE_NONE:
+                {
+                    // nop
+                } break;
+            case LLAMA_POOLING_TYPE_MEAN:
+                {
+                    cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean);
+                    cb(cur, "result_embd_pooled", -1);
+                } break;
+            case LLAMA_POOLING_TYPE_CLS:
+                {
+                    cur = ggml_get_rows(ctx0, cur, inp_cls);
+                    cb(cur, "result_embd_pooled", -1);
+                } break;
+            case LLAMA_POOLING_TYPE_UNSPECIFIED:
+                {
+                    GGML_ASSERT(false && "Invalid pooling type");
+                } break;
         }
-        cb(cur, "result_embd", -1);
 
         ggml_build_forward_expand(gf, cur);
 
@@ -5999,16 +8424,10 @@ struct llm_build_context {
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // positions of the tokens in the KV cache
-        struct ggml_tensor * KQ_pos = ggml_view_1d(ctx0, lctx.inp_KQ_pos, n_kv, 0);
-        cb(KQ_pos, "KQ_pos", -1);
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         inpL = llm_build_norm(ctx0, inpL, hparams,
                 model.tok_norm,
@@ -6041,10 +8460,16 @@ struct llm_build_context {
 
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
-                        Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
             }
 
             // Add the input
@@ -6094,25 +8519,30 @@ struct llm_build_context {
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
 
         struct ggml_tensor * cur;
+        struct ggml_tensor * pos;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
-        cb(KQ_mask, "KQ_mask", -1);
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
-        // positions of the tokens in the KV cache
-        struct ggml_tensor * KQ_pos = ggml_view_1d(ctx0, lctx.inp_KQ_pos, n_kv, 0);
-        cb(KQ_pos, "KQ_pos", -1);
+        if (model.pos_embd) {
+            // inp_pos - contains the positions
+            struct ggml_tensor * inp_pos = build_inp_pos();
+            pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
+            cb(pos, "pos_embd", -1);
+
+            inpL = ggml_add(ctx0, inpL, pos);
+            cb(inpL, "inpL", -1);
+        }
 
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * attn_norm;
 
             attn_norm = llm_build_norm(ctx0, inpL, hparams,
                     model.layers[il].attn_norm,
-                    NULL,
+                    model.layers[il].attn_norm_b,
                     LLM_NORM, cb, il);
             cb(attn_norm, "attn_norm", il);
 
@@ -6123,6 +8553,11 @@ struct llm_build_context {
                 cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
+                if (model.layers[il].bqkv){
+                    cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+                    cb(cur, "bqkv", il);
+                }
+
                 if (hparams.f_clamp_kqv > 0.0f) {
                     cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
                     cb(cur, "wqkv_clamped", il);
@@ -6136,12 +8571,40 @@ struct llm_build_context {
                 cb(Kcur, "Kcur", il);
                 cb(Vcur, "Vcur", il);
 
-                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+                // Q/K Layernorm
+                if (model.layers[il].attn_q_norm) {
+                    Qcur = llm_build_norm(ctx0, Qcur, hparams,
+                            model.layers[il].attn_q_norm,
+                            model.layers[il].attn_q_norm_b,
+                            LLM_NORM, cb, il);
+                    cb(Qcur, "Qcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
-                        model.layers[il].wo, NULL,
-                        Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                    Kcur = llm_build_norm(ctx0, Kcur, hparams,
+                            model.layers[il].attn_k_norm,
+                            model.layers[il].attn_k_norm_b,
+                            LLM_NORM, cb, il);
+                    cb(Kcur, "Kcur", il);
+
+                    Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+
+                    cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                            model.layers[il].wo, model.layers[il].bo,
+                            Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+                } else {
+                    Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+
+                    cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                            model.layers[il].wo, model.layers[il].bo,
+                            Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+                }
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
             }
 
             // Add the input
@@ -6152,13 +8615,13 @@ struct llm_build_context {
             {
                 cur = llm_build_norm(ctx0, ffn_inp, hparams,
                         model.layers[il].ffn_norm,
-                        NULL,
+                        model.layers[il].ffn_norm_b,
                         LLM_NORM, cb, il);
                 cb(cur, "ffn_norm", il);
                 cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   NULL,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
                         NULL,                      NULL,
-                        model.layers[il].ffn_down, NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
                         model.layers[il].ffn_act,
                         LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                 cb(cur, "ffn_out", il);
@@ -6175,7 +8638,7 @@ struct llm_build_context {
 
         cur = llm_build_norm(ctx0, cur, hparams,
                 model.output_norm,
-                NULL,
+                model.output_norm_b,
                 LLM_NORM, cb, -1);
         cb(cur, "result_norm", -1);
 
@@ -6196,24 +8659,16 @@ struct llm_build_context {
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // shift the entire K-cache if needed
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb);
-        }
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
-            struct ggml_tensor * inpSA = inpL;
+
 
             // norm
             cur = llm_build_norm(ctx0, inpL, hparams,
@@ -6222,6 +8677,8 @@ struct llm_build_context {
                     LLM_NORM, cb, il);
             cb(cur, "attn_norm", il);
 
+            struct ggml_tensor * inpSA = cur;
+
             // self-attention
             {
                 // compute Q and K and RoPE them
@@ -6246,37 +8703,69 @@ struct llm_build_context {
                     cb(Vcur, "Vcur", il);
                 }
 
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                cb(Qcur, "Qcur", il);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                cb(Kcur, "Kcur", il);
+
+                if (model.layers[il].attn_q_norm) {
+                    Qcur = llm_build_norm(ctx0, Qcur, hparams,
+                            model.layers[il].attn_q_norm,
+                            NULL,
+                            LLM_NORM, cb, il);
+                    cb(Qcur, "Qcur", il);
+                }
+                if (model.layers[il].attn_k_norm) {
+                    Kcur = llm_build_norm(ctx0, Kcur, hparams,
+                            model.layers[il].attn_k_norm,
+                            NULL,
+                            LLM_NORM, cb, il);
+                    cb(Kcur, "Kcur", il);
+                }
+
+
                 Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
-                    hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
+                    ctx0, Qcur, inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
+                    ctx0, Kcur, inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
                         model.layers[il].wo, NULL,
-                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
 
-            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpL  = ggml_get_rows(ctx0,  inpL, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
             cb(ffn_inp, "ffn_inp", il);
 
             // feed-forward network
             {
-                cur = llm_build_norm(ctx0, ffn_inp, hparams,
-                        model.layers[il].ffn_norm,
-                        model.layers[il].ffn_norm_b,
-                        LLM_NORM, cb, il);
-                cb(cur, "ffn_norm", il);
-
+                if (model.layers[il].ffn_norm) {
+                    cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                            model.layers[il].ffn_norm,
+                            model.layers[il].ffn_norm_b,
+                            LLM_NORM, cb, il);
+                    cb(cur, "ffn_norm", il);
+                } else {
+                    // parallel residual
+                    cur = inpSA;
+                }
                 cur = llm_build_ffn(ctx0, cur,
                         model.layers[il].ffn_up,   NULL,
                         model.layers[il].ffn_gate, NULL,
@@ -6319,21 +8808,13 @@ struct llm_build_context {
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // shift the entire K-cache if needed
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb);
-        }
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * inpSA = inpL;
@@ -6364,21 +8845,27 @@ struct llm_build_context {
 
                 // using mode = 2 for neox mode
                 Qcur = ggml_rope_custom(
-                    ctx0, Qcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx,
+                    ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_custom(
-                    ctx0, Kcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx,
+                    ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
                         model.layers[il].wo, NULL,
-                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
             }
 
             struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
@@ -6433,21 +8920,13 @@ struct llm_build_context {
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // shift the entire K-cache if needed
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb);
-        }
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * inpSA = inpL;
@@ -6476,30 +8955,30 @@ struct llm_build_context {
                 Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                 cb(Vcur, "Vcur", il);
 
-                // these nodes are added to the graph together so that they are not reordered
-                // by doing so, the number of splits in the graph is reduced
-                ggml_build_forward_expand(gf, Qcur);
-                ggml_build_forward_expand(gf, Kcur);
-                ggml_build_forward_expand(gf, Vcur);
-
                 Qcur = ggml_rope_custom(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
-                    hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_custom(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
-                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
             }
 
             struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
@@ -6542,6 +9021,150 @@ struct llm_build_context {
         return gf;
     }
 
+    struct ggml_cgraph * build_qwen2moe() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        // mutable variable, needed during the last layer of the computation to skip unused tokens
+        int32_t n_tokens = this->n_tokens;
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self_attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                cb(Qcur, "Qcur", il);
+
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                cb(Kcur, "Kcur", il);
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                n_tokens = n_outputs;
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // MoE branch
+            cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            ggml_tensor * moe_out =
+                    llm_build_moe_ffn(ctx0, cur,
+                        model.layers[il].ffn_gate_inp,
+                        model.layers[il].ffn_up_exps,
+                        model.layers[il].ffn_gate_exps,
+                        model.layers[il].ffn_down_exps,
+                        n_expert, n_expert_used,
+                        LLM_FFN_SILU, false,
+                        cb, il);
+            cb(cur, "ffn_moe_out", il);
+
+            // FFN shared expert
+            {
+                ggml_tensor * cur_gate_inp = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp_shexp, cur);
+                cb(cur_gate_inp, "ffn_shexp_gate_inp", il);
+
+                // sigmoid
+                ggml_tensor * cur_gate = ggml_div(ctx0, ggml_silu(ctx0, cur_gate_inp), cur_gate_inp);
+                cb(cur_gate, "ffn_shexp_gate", il);
+
+                ggml_tensor * cur_ffn = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up_shexp,   NULL,
+                        model.layers[il].ffn_gate_shexp, NULL,
+                        model.layers[il].ffn_down_shexp, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur_ffn, "ffn_shexp", il);
+
+                ggml_tensor * ffn_shexp_out = ggml_mul(ctx0, cur_ffn, cur_gate);
+                cb(ffn_shexp_out, "ffn_shexp_out", il);
+
+                moe_out = ggml_add(ctx0, moe_out, ffn_shexp_out);
+                cb(moe_out, "ffn_out", il);
+
+                cur = moe_out;
+            }
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
     struct ggml_cgraph * build_phi2() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
@@ -6554,21 +9177,13 @@ struct llm_build_context {
         struct ggml_tensor * ffn_output;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // shift the entire K-cache if needed
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb);
-        }
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
             attn_norm_output = llm_build_norm(ctx0, inpL, hparams,
@@ -6607,7 +9222,7 @@ struct llm_build_context {
                 Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 
                 Qcur = ggml_rope_custom(
-                    ctx0, Qcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx,
+                    ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
@@ -6618,15 +9233,22 @@ struct llm_build_context {
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_custom(
-                    ctx0, Kcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx,
+                    ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
-                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur              = ggml_get_rows(ctx0,              cur, inp_out_ids);
+                inpL             = ggml_get_rows(ctx0,             inpL, inp_out_ids);
+                attn_norm_output = ggml_get_rows(ctx0, attn_norm_output, inp_out_ids);
             }
 
             // FF
@@ -6660,12 +9282,140 @@ struct llm_build_context {
 
         cur = ggml_add(ctx0, cur, model.output_b);
         cb(cur, "result_output", -1);
+        ggml_build_forward_expand(gf, cur);
+        return gf;
+    }
+
+    struct ggml_cgraph * build_phi3() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            auto residual = inpL;
+
+            // self-attention
+            {
+                struct ggml_tensor* attn_norm_output = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm,
+                    NULL,
+                    LLM_NORM_RMS, cb, il);
+                cb(attn_norm_output, "attn_norm", il);
+
+                struct ggml_tensor * Qcur = nullptr;
+                struct ggml_tensor * Kcur = nullptr;
+                struct ggml_tensor * Vcur = nullptr;
+
+                if (model.layers[il].wqkv) {
+                    cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output);
+                    cb(cur, "wqkv", il);
+
+                    Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0 * sizeof(float) * (n_embd)));
+                    Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd)));
+                    Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa)));
+                }
+                else {
+                    Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
+                    Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
+                    Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
+                }
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+
+                Qcur = ggml_rope_custom(
+                    ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
+                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head)));
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_custom(
+                    ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
+                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor* inp_out_ids = build_inp_out_ids();
+                cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+                residual = ggml_get_rows(ctx0, residual, inp_out_ids);
+            }
+
+            cur = ggml_add(ctx0, cur, residual);
+            residual = cur;
+
+            cur = llm_build_norm(ctx0, cur, hparams,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            // FF
+            // special-case: the up and gate tensors are merged into a single tensor
+            // TOOD: support into llm_build_ffn
+            {
+                struct ggml_tensor* up = ggml_mul_mat(ctx0, model.layers[il].ffn_up, cur);
+                cb(up, "ffn_up", il);
+
+                auto g = ggml_cont(ctx0, ggml_view_2d(ctx0, up, up->ne[0] / 2, up->ne[1], ggml_row_size(up->type, up->ne[0]), 0));
+                auto y = ggml_cont(ctx0, ggml_view_2d(ctx0, up, up->ne[0] / 2, up->ne[1], ggml_row_size(up->type, up->ne[0]), up->nb[1] / 2));
+
+                y = ggml_mul(ctx0, y, ggml_silu(ctx0, g));
+                cb(y, "ffn_gate", il);
+
+                auto down = ggml_mul_mat(ctx0, model.layers[il].ffn_down, y);
+                cb(down, "ffn_down", il);
+
+                cur = down;
+                cb(cur, "ffn_out", il);
+            }
+
+            cur = ggml_add(ctx0, residual, cur);
+            cb(cur, "l_out", il);
+
+            inpL = cur;
+        }
+
+        cur = llm_build_norm(ctx0, inpL, hparams,
+            model.output_norm,
+            NULL,
+            LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
 
         return gf;
     }
 
+
     struct ggml_cgraph * build_plamo() {
         struct ggml_cgraph * gf = ggml_new_graph(ctx0);
 
@@ -6676,21 +9426,13 @@ struct llm_build_context {
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // shift the entire K-cache if needed
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb);
-        }
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
 
@@ -6715,26 +9457,33 @@ struct llm_build_context {
                 cb(Vcur, "Vcur", il);
 
                 Qcur = ggml_rope_custom(
-                        ctx0, ggml_reshape_3d(ctx0, Qcur, hparams.n_rot, n_head,    n_tokens), inp_pos,
-                        n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale,
+                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_rot, n_head,    n_tokens), inp_pos,
+                        n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_custom(
-                        ctx0, ggml_reshape_3d(ctx0, Kcur, hparams.n_rot, n_head_kv, n_tokens), inp_pos,
-                        n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale,
+                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens), inp_pos,
+                        n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
                         model.layers[il].wo, NULL,
-                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
             struct ggml_tensor * sa_out = cur;
 
             cur = attention_norm;
 
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur    = ggml_get_rows(ctx0,    cur, inp_out_ids);
+                sa_out = ggml_get_rows(ctx0, sa_out, inp_out_ids);
+                inpL   = ggml_get_rows(ctx0,   inpL, inp_out_ids);
+            }
+
             // feed-forward network
             {
                 cur = llm_build_ffn(ctx0, cur,
@@ -6783,16 +9532,13 @@ struct llm_build_context {
         struct ggml_tensor * pos;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
-        cb(KQ_mask, "KQ_mask", -1);
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
         cb(pos, "pos_embd", -1);
@@ -6825,10 +9571,16 @@ struct llm_build_context {
 
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
-                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
             }
 
             // add the input
@@ -6881,21 +9633,13 @@ struct llm_build_context {
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // shift the entire K-cache if needed
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb);
-        }
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
             cur = llm_build_norm(ctx0, inpL, hparams,
@@ -6922,22 +9666,28 @@ struct llm_build_context {
 
                 struct ggml_tensor * Qcur = ggml_rope_custom(
                     ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head,    n_tokens), inp_pos,
-                    hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 struct ggml_tensor * Kcur = ggml_rope_custom(
                     ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
-                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
             }
 
             // add the input
@@ -6989,21 +9739,13 @@ struct llm_build_context {
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // shift the entire K-cache if needed
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb);
-        }
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * inpSA = inpL;
@@ -7040,22 +9782,28 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_custom(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
-                    hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_custom(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
                         model.layers[il].wo, NULL,
-                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
             }
 
             struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
@@ -7108,21 +9856,13 @@ struct llm_build_context {
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // shift the entire K-cache if needed
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb);
-        }
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * inpSA = inpL;
@@ -7159,22 +9899,28 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_custom(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
-                    hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_custom(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
-                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
             }
 
             struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
@@ -7236,25 +9982,17 @@ struct llm_build_context {
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // scale the input embeddings
         inpL = ggml_scale(ctx0, inpL, scale_embd);
         cb(inpL, "inp_scaled", -1);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // shift the entire K-cache if needed
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb);
-        }
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * inpSA = inpL;
@@ -7291,22 +10029,28 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_custom(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
-                    hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_custom(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
-                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
             }
 
             // scale_res - scale the hidden states for residual connection
@@ -7364,8 +10108,711 @@ struct llm_build_context {
 
         return gf;
     }
+
+    struct ggml_cgraph * build_gemma() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t n_embd_head_k = hparams.n_embd_head_k;
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
+        cb(inpL, "inp_scaled", -1);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_rope_custom(
+                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head,    n_tokens), inp_pos,
+                        n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
+                cb(Qcur, "Qcur", il);
+
+                Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));
+                cb(Qcur, "Qcur_scaled", il);
+
+                Kcur = ggml_rope_custom(
+                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos,
+                        n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                        model.layers[il].wo, NULL,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+            }
+
+            struct ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
+            cb(sa_out, "sa_out", il);
+
+            cur = llm_build_norm(ctx0, sa_out, hparams,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            // feed-forward network
+            {
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up, NULL,
+                        model.layers[il].ffn_gate, NULL,
+                        model.layers[il].ffn_down, NULL,
+                        NULL,
+                        LLM_FFN_GELU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            }
+
+            cur = ggml_add(ctx0, cur, sa_out);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
+    struct ggml_cgraph * build_starcoder2() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, model.layers[il].attn_norm_b,
+                    LLM_NORM, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
+
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                Qcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+
+            cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                    model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
+                    LLM_NORM, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
+                        NULL,                      NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                        NULL,
+                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+            cb(cur, "ffn_out", il);
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, model.output_norm_b,
+                LLM_NORM, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
+    struct ggml_cgraph * build_mamba() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t d_model = n_embd;
+        const int64_t d_conv  = hparams.ssm_d_conv;
+        const int64_t d_inner = hparams.ssm_d_inner;
+        GGML_ASSERT(2 * d_model == d_inner);
+        const int64_t d_state = hparams.ssm_d_state;
+        const int64_t dt_rank = hparams.ssm_dt_rank;
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        // {n_embd, n_tokens}
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        struct ggml_tensor * state_mask = build_inp_s_mask();
+        struct ggml_tensor * state_seq  = build_inp_s_seq();
+
+        for (int il = 0; il < n_layer; ++il) {
+            // (ab)using the KV cache to store the states
+            struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size);
+            struct ggml_tensor * ssm_states  = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size);
+
+            // clear states of sequences which are starting at the beginning of this batch
+            {
+                conv_states = ggml_mul(ctx0,
+                    ggml_view_2d(ctx0, conv_states, conv_states->ne[0], n_kv, conv_states->nb[1], kv_head*conv_states->nb[1]),
+                    state_mask);
+                ssm_states  = ggml_mul(ctx0,
+                    ggml_view_2d(ctx0, ssm_states, ssm_states->ne[0], n_kv, ssm_states->nb[1], kv_head*ssm_states->nb[1]),
+                    state_mask);
+            }
+
+            conv_states = ggml_reshape_3d(ctx0, conv_states, d_conv - 1, d_inner, n_kv);
+            ssm_states  = ggml_reshape_3d(ctx0,  ssm_states,    d_state, d_inner, n_kv);
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // {n_embd, 2*d_inner} * {n_embd, n_tokens} => {2*d_inner, n_tokens}
+            struct ggml_tensor * xz = ggml_mul_mat(ctx0, model.layers[il].ssm_in, cur);
+            // split the above in two
+            // => {d_inner, n_tokens}
+            struct ggml_tensor * x = ggml_view_2d(ctx0, xz, d_inner, xz->ne[1], xz->nb[1], 0);
+            struct ggml_tensor * z = ggml_view_2d(ctx0, xz, d_inner, xz->ne[1], xz->nb[1], ggml_element_size(xz)*d_inner);
+
+            // conv
+            {
+                // Custom operator which is needed only to ease simultaneous sequence processing.
+                // For a single sequence, the equivalent is to concatenate the columns of conv_states and x,
+                // then make a self-overlapping view of that over d_conv columns at each stride in the 3rd dimension,
+                // then element-wise multiply that with the conv1d weigth,
+                // then sum the elements of each row,
+                // (the last two steps are a dot product over rows (also doable with mul_mat))
+                // then permute away the ne[0] dimension,
+                // and then you're left with the resulting x tensor.
+                // The new conv_states is the last (d_conv - 1) columns
+                // of the last 3rd dimensional "layer" of the self-overlapping view.
+                // For simultaneous sequences, it's more complicated.
+                struct ggml_tensor * x_conv = ggml_ssm_conv(ctx0, conv_states, x, model.layers[il].ssm_conv1d, state_seq);
+
+                // store last (d_conv - 1) columns of the conv_state part of x_conv back into the KV cache
+                ggml_build_forward_expand(gf,
+                    ggml_cpy(ctx0,
+                        ggml_view_2d(ctx0, x_conv, d_conv - 1, d_inner*n_kv, d_conv*ggml_element_size(x_conv), (1+d_inner*n_tokens)*ggml_element_size(x_conv)),
+                        ggml_view_1d(ctx0, kv_self.k_l[il], (d_conv - 1)*(d_inner)*(n_kv), kv_head*(d_conv - 1)*(d_inner)*ggml_element_size(x_conv))));
+
+                // extract x from x_conv
+                x = ggml_view_2d(ctx0, x_conv, d_inner, n_tokens, d_inner*ggml_element_size(x_conv), 0);
+
+                // bias
+                x = ggml_add(ctx0, x, model.layers[il].ssm_conv1d_b);
+
+                x = ggml_silu(ctx0, x);
+            }
+
+            // ssm
+            {
+                // {d_inner, dt_rank + 2*d_state} * {d_inner, n_tokens} => {dt_rank + 2*d_state, n_tokens}
+                struct ggml_tensor * x_db = ggml_mul_mat(ctx0, model.layers[il].ssm_x, x);
+                // split
+                struct ggml_tensor * dt = ggml_view_2d(ctx0, x_db, dt_rank, n_tokens, x_db->nb[1], 0);
+                struct ggml_tensor * B  = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*dt_rank);
+                struct ggml_tensor * C  = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*(dt_rank+d_state));
+
+                // {dt_rank, d_inner} * {dt_rank, n_tokens} => {d_inner, n_tokens}
+                dt = ggml_mul_mat(ctx0, model.layers[il].ssm_dt, dt);
+                dt = ggml_add(ctx0, dt, model.layers[il].ssm_dt_b);
+
+                // Custom operator to optimize the parallel associative scan
+                // as described in the Annex D of the Mamba paper.
+                // => {d_inner, n_tokens} and {d_state, d_inner, n_kv} combined,
+                // because only a single tensor can be returned.
+                struct ggml_tensor * y_ssm_states = ggml_ssm_scan(ctx0, ssm_states, x, dt, model.layers[il].ssm_a, B, C, state_seq);
+
+                // store last states (the second part of y_ssm_states)
+                ggml_build_forward_expand(gf,
+                    ggml_cpy(ctx0,
+                        ggml_view_1d(ctx0, y_ssm_states, d_state*d_inner*n_kv, d_inner*n_tokens*ggml_element_size(y_ssm_states)),
+                        ggml_view_1d(ctx0, kv_self.v_l[il], d_state*d_inner*n_kv, kv_head*d_state*d_inner*ggml_element_size(ssm_states))));
+
+                struct ggml_tensor * y = ggml_view_2d(ctx0, y_ssm_states, d_inner, n_tokens, d_inner*ggml_element_size(y_ssm_states), 0);
+
+                if (il == n_layer - 1) {
+                    // skip computing output for unused tokens
+                    struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                    x    = ggml_get_rows(ctx0,    x, inp_out_ids);
+                    y    = ggml_get_rows(ctx0,    y, inp_out_ids);
+                    z    = ggml_get_rows(ctx0,    z, inp_out_ids);
+                    inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+                }
+
+                // {d_inner, n_tokens} * {d_inner} => {d_inner, n_tokens}
+                y = ggml_add(ctx0, y, ggml_mul(ctx0, x, model.layers[il].ssm_d));
+                y = ggml_mul(ctx0, y, ggml_silu(ctx0, z));
+
+                // {d_inner, n_embd} * {d_inner, n_tokens} => {n_embd, n_tokens}
+                cur = ggml_mul_mat(ctx0, model.layers[il].ssm_out, y);
+            }
+
+            // residual
+            cur = ggml_add(ctx0, cur, inpL);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        // final rmsnorm
+        cur = llm_build_norm(ctx0, inpL, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
+    struct ggml_cgraph * build_command_r() {
+
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        const float f_logit_scale = hparams.f_logit_scale;
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM, cb, il);
+            cb(cur, "attn_norm", il);
+            struct ggml_tensor * ffn_inp = cur;
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
+
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                if (model.layers[il].attn_q_norm) {
+                    Qcur = ggml_view_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens,
+                                ggml_element_size(Qcur) * n_embd_head,
+                                ggml_element_size(Qcur) * n_embd_head * n_head,
+                                0);
+                    cb(Qcur, "Qcur", il);
+                    Kcur = ggml_view_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens,
+                                ggml_element_size(Kcur) * n_embd_head,
+                                ggml_element_size(Kcur) * n_embd_head * n_head_kv,
+                                0);
+                    cb(Kcur, "Kcur", il);
+
+                    Qcur = llm_build_norm(ctx0, Qcur, hparams,
+                                model.layers[il].attn_q_norm,
+                                NULL,
+                                LLM_NORM, cb, il);
+                    cb(Qcur, "Qcur", il);
+
+                    Kcur = llm_build_norm(ctx0, Kcur, hparams,
+                            model.layers[il].attn_k_norm,
+                            NULL,
+                            LLM_NORM, cb, il);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                Qcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur     = ggml_get_rows(ctx0,     cur, inp_out_ids);
+                inpL    = ggml_get_rows(ctx0,    inpL, inp_out_ids);
+                ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
+            }
+
+            struct ggml_tensor * attn_out = cur;
+
+            // feed-forward network
+            {
+                cur = llm_build_ffn(ctx0, ffn_inp,
+                        model.layers[il].ffn_up,   NULL,
+                        model.layers[il].ffn_gate, NULL,
+                        model.layers[il].ffn_down, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            }
+
+            // add together residual + FFN + self-attention
+            cur = ggml_add(ctx0, cur, inpL);
+            cur = ggml_add(ctx0, cur, attn_out);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+
+        if (f_logit_scale) {
+            cur = ggml_scale(ctx0, cur, f_logit_scale);
+        }
+
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+
+    }
+
+    // ref: https://allenai.org/olmo
+    // based on the original build_llama() function, changes:
+    //   * non-parametric layer norm
+    //   * clamp qkv
+    //   * removed bias
+    //   * removed MoE
+    struct ggml_cgraph * build_olmo() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        // mutable variable, needed during the last layer of the computation to skip unused tokens
+        int32_t n_tokens = this->n_tokens;
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    NULL, NULL,
+                    LLM_NORM, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (hparams.f_clamp_kqv > 0.0f) {
+                    Qcur = ggml_clamp(ctx0, Qcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
+                    cb(Qcur, "Qcur", il);
+                }
+
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (hparams.f_clamp_kqv > 0.0f) {
+                    Kcur = ggml_clamp(ctx0, Kcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (hparams.f_clamp_kqv > 0.0f) {
+                    Vcur = ggml_clamp(ctx0, Vcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                Qcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                        model.layers[il].wo, nullptr,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                n_tokens = n_outputs;
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                    NULL, NULL,
+                    LLM_NORM, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = llm_build_ffn(ctx0, cur,
+                    model.layers[il].ffn_up,   NULL,
+                    model.layers[il].ffn_gate, NULL,
+                    model.layers[il].ffn_down, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
+            if (layer_dir != nullptr) {
+                cur = ggml_add(ctx0, cur, layer_dir);
+            }
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                NULL, NULL,
+                LLM_NORM, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
 };
 
+static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
+    llama_batch dummy;
+    dummy.n_tokens = 0;
+
+    llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
+
+    struct llm_build_context llm(lctx, dummy, cb, false);
+
+    llm.init();
+
+    struct ggml_cgraph * result = llm.build_defrag(ids);
+
+    llm.free();
+
+    return result;
+}
+
+static struct ggml_cgraph * llama_build_graph_k_shift(llama_context & lctx) {
+    llama_batch dummy;
+    dummy.n_tokens = 0;
+
+    llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
+
+    struct llm_build_context llm(lctx, dummy, cb, false);
+
+    llm.init();
+
+    struct ggml_cgraph * result = llm.build_k_shift();
+
+    llm.free();
+
+    return result;
+}
+
+static struct ggml_cgraph * llama_build_graph_s_copy(llama_context & lctx) {
+    llama_batch dummy;
+    dummy.n_tokens = 0;
+
+    llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
+
+    struct llm_build_context llm(lctx, dummy, cb, false);
+
+    llm.init();
+
+    struct ggml_cgraph * result = llm.build_s_copy();
+
+    llm.free();
+
+    return result;
+}
+
 static struct ggml_cgraph * llama_build_graph(
          llama_context & lctx,
      const llama_batch & batch,
@@ -7383,7 +10830,21 @@ static struct ggml_cgraph * llama_build_graph(
         if (!lctx.cparams.offload_kqv) {
             if (strcmp(name, "kqv_merged_cont") == 0) {
                 // all nodes between the KV store and the attention output are run on the CPU
-                ggml_backend_sched_set_node_backend(lctx.sched, cur, lctx.backend_cpu);
+                ggml_backend_sched_set_tensor_backend(lctx.sched, cur, lctx.backend_cpu);
+            }
+        }
+
+        // norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends
+        // FIXME: fix in ggml_backend_sched
+        const bool full_offload = lctx.model.n_gpu_layers > (int)lctx.model.hparams.n_layer;
+        if (batch.n_tokens < 32 || full_offload) {
+            if (il != -1 && strcmp(name, "norm") == 0) {
+                for (auto * backend : lctx.backends) {
+                    if (ggml_backend_buft_supports_backend(lctx.model.buft_layer[il].buft, backend)) {
+                        ggml_backend_sched_set_tensor_backend(lctx.sched, cur, backend);
+                        break;
+                    }
+                }
             }
         }
     };
@@ -7407,6 +10868,10 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_falcon();
             } break;
+        case LLM_ARCH_GROK:
+            {
+                result = llm.build_grok();
+            } break;
         case LLM_ARCH_STARCODER:
             {
                 result = llm.build_starcoder();
@@ -7420,6 +10885,7 @@ static struct ggml_cgraph * llama_build_graph(
                 result = llm.build_refact();
             } break;
         case LLM_ARCH_BERT:
+        case LLM_ARCH_JINA_BERT_V2:
         case LLM_ARCH_NOMIC_BERT:
             {
                 result = llm.build_bert();
@@ -7444,10 +10910,18 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_qwen2();
             } break;
+        case LLM_ARCH_QWEN2MOE:
+            {
+                result = llm.build_qwen2moe();
+            } break;
         case LLM_ARCH_PHI2:
             {
                 result = llm.build_phi2();
             } break;
+        case LLM_ARCH_PHI3:
+            {
+                result = llm.build_phi3();
+            } break;
         case LLM_ARCH_PLAMO:
             {
                 result = llm.build_plamo();
@@ -7472,6 +10946,34 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_minicpm();
             } break;
+        case LLM_ARCH_GEMMA:
+            {
+                result = llm.build_gemma();
+            } break;
+        case LLM_ARCH_STARCODER2:
+            {
+                result = llm.build_starcoder2();
+            } break;
+        case LLM_ARCH_MAMBA:
+            {
+                result = llm.build_mamba();
+            } break;
+        case LLM_ARCH_XVERSE:
+            {
+                result = llm.build_xverse();
+            } break;
+        case LLM_ARCH_COMMAND_R:
+            {
+                result = llm.build_command_r();
+            } break;
+        case LLM_ARCH_DBRX:
+            {
+                result = llm.build_dbrx();
+            } break;
+        case LLM_ARCH_OLMO:
+            {
+                result = llm.build_olmo();
+            } break;
         default:
             GGML_ASSERT(false);
     }
@@ -7481,6 +10983,30 @@ static struct ggml_cgraph * llama_build_graph(
     return result;
 }
 
+static void llama_set_k_shift(llama_context & lctx) {
+    const int64_t kv_size = lctx.kv_self.size;
+
+    assert(ggml_backend_buffer_is_host(lctx.inp_K_shift->buffer));
+
+    int32_t * data = (int32_t *) lctx.inp_K_shift->data;
+
+    for (int i = 0; i < kv_size; ++i) {
+        data[i] = lctx.kv_self.cells[i].delta;
+    }
+}
+
+static void llama_set_s_copy(llama_context & lctx) {
+    const int64_t kv_size = lctx.kv_self.size;
+
+    assert(ggml_backend_buffer_is_host(lctx.inp_s_copy->buffer));
+
+    int32_t * data = (int32_t *) lctx.inp_s_copy->data;
+
+    for (int i = 0; i < kv_size; ++i) {
+        data[i] = lctx.kv_self.cells[i].src;
+    }
+}
+
 static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
     //
     // set input data
@@ -7503,74 +11029,138 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
         ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
     }
 
-    if (batch.pos) {
+    if (batch.pos && lctx.inp_pos) {
         const int64_t n_tokens = batch.n_tokens;
 
         ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos));
     }
 
-    {
-        const int64_t n_kv     = kv_self.n;
+    if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) {
+        GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs");
         const int64_t n_tokens = batch.n_tokens;
 
-        assert(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
+        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_out_ids->buffer));
+        int32_t * data = (int32_t *) lctx.inp_out_ids->data;
 
-        float * data = (float *) lctx.inp_KQ_mask->data;
+        if (lctx.n_outputs == n_tokens) {
+            for (int i = 0; i < n_tokens; ++i) {
+                data[i] = i;
+            }
+        } else if (batch.logits) {
+            int32_t n_outputs = 0;
+            for (int i = 0; i < n_tokens; ++i) {
+                if (batch.logits[i]) {
+                    data[n_outputs++] = i;
+                }
+            }
+            // the graph needs to have been passed the correct number of outputs
+            GGML_ASSERT(lctx.n_outputs == n_outputs);
+        } else if (lctx.n_outputs == 1) {
+            // only keep last output
+            data[0] = n_tokens - 1;
+        } else {
+            GGML_ASSERT(lctx.n_outputs == 0);
+        }
+    }
 
-        for (int h = 0; h < 1; ++h) {
-            for (int j = 0; j < n_tokens; ++j) {
-                const llama_pos    pos    = batch.pos[j];
-                const llama_seq_id seq_id = batch.seq_id[j][0];
+    GGML_ASSERT(
+        // (!a || b) is a logical implication (a -> b)
+        // !hparams.causal_attn -> !cparams.causal_attn
+        (hparams.causal_attn || !cparams.causal_attn) &&
+        "causal attention with embedding models is not supported"
+    );
 
-                for (int i = 0; i < n_kv; ++i) {
-                    float f;
-                    if (!lctx.kv_self.cells[i].has_seq_id(seq_id) ||
-                        (hparams.causal_attn && lctx.kv_self.cells[i].pos > pos)) {
-                        f = -INFINITY;
-                    } else {
-                        f = 0;
+    if (lctx.inp_KQ_mask) {
+        // NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache.
+        if (cparams.causal_attn) {
+            const int64_t n_kv     = kv_self.n;
+            const int64_t n_tokens = batch.n_tokens;
+
+            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
+
+            float * data = (float *) lctx.inp_KQ_mask->data;
+
+            // For causal attention, use only the previous KV cells
+            // of the correct sequence for each token of the batch.
+            // It's assumed that if a token in the batch has multiple sequences, they are equivalent.
+            for (int h = 0; h < 1; ++h) {
+                for (int j = 0; j < n_tokens; ++j) {
+                    const llama_pos    pos    = batch.pos[j];
+                    const llama_seq_id seq_id = batch.seq_id[j][0];
+
+                    for (int i = 0; i < n_kv; ++i) {
+                        float f;
+                        if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) {
+                            f = -INFINITY;
+                        } else {
+                            if (hparams.use_alibi) {
+                                f = -fabs(lctx.kv_self.cells[i].pos - pos);
+                            } else {
+                                f = 0.0f;
+                            }
+                        }
+                        data[h*(n_kv*n_tokens) + j*n_kv + i] = f;
+                    }
+                }
+
+                for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
+                    for (int j = 0; j < n_kv; ++j) {
+                        data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
+                    }
+                }
+            }
+        } else {
+            // when using kv cache, the mask needs to match the kv cache size
+            const int64_t n_tokens = batch.n_tokens;
+            const int64_t n_stride = hparams.causal_attn ? kv_self.n : n_tokens;
+
+            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
+
+            float * data = (float *) lctx.inp_KQ_mask->data;
+
+            for (int h = 0; h < 1; ++h) {
+                for (int j = 0; j < n_tokens; ++j) {
+                    const llama_seq_id seq_id = batch.seq_id[j][0];
+
+                    for (int i = 0; i < n_tokens; ++i) {
+                        float f = -INFINITY;
+                        for (int s = 0; s < batch.n_seq_id[i]; ++s) {
+                            if (batch.seq_id[i][s] == seq_id) {
+                                if (hparams.use_alibi) {
+                                    f = -fabs(batch.pos[i] - batch.pos[j]);
+                                } else {
+                                    f = 0.0f;
+                                }
+                                break;
+                            }
+                        }
+
+                        data[h*(n_tokens*n_tokens) + j*n_stride + i] = f;
+                    }
+
+                    for (int i = n_tokens; i < n_stride; ++i) {
+                        data[h*(n_tokens*n_tokens) + j*n_stride + i] = -INFINITY;
                     }
-                    data[h*(n_kv*n_tokens) + j*n_kv + i] = f;
                 }
             }
         }
     }
 
-    if (hparams.need_kq_pos) {
-        const int64_t n_kv = kv_self.n;
-
-        assert(ggml_backend_buffer_is_host(lctx.inp_KQ_pos->buffer));
-
-        float * data = (float *) lctx.inp_KQ_pos->data;
-
-        for (int i = 0; i < n_kv; ++i) {
-            data[i] = float(lctx.kv_self.cells[i].pos);
-        }
-    }
-
-    if (kv_self.has_shift) {
-        const int64_t n_ctx = cparams.n_ctx;
-
-        assert(ggml_backend_buffer_is_host(lctx.inp_K_shift->buffer));
-
-        int32_t * data = (int32_t *) lctx.inp_K_shift->data;
-
-        for (int i = 0; i < n_ctx; ++i) {
-            data[i] = lctx.kv_self.cells[i].delta;
-        }
-    }
-
-    if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_MEAN) {
+    if (cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) {
         const int64_t n_tokens = batch.n_tokens;
 
+        GGML_ASSERT(lctx.inp_mean);
         GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer));
-        float * data = (float *) lctx.inp_mean->data;
 
+        float * data = (float *) lctx.inp_mean->data;
         memset(lctx.inp_mean->data, 0, n_tokens * n_tokens * ggml_element_size(lctx.inp_mean));
 
         std::vector<uint64_t> sum(n_tokens, 0);
         for (int i = 0; i < n_tokens; ++i) {
             const llama_seq_id seq_id = batch.seq_id[i][0];
+
+            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN");
+
             sum[seq_id] += 1;
         }
 
@@ -7588,20 +11178,170 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
         }
     }
 
-    if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_CLS) {
+    if (cparams.pooling_type == LLAMA_POOLING_TYPE_CLS) {
         const int64_t n_tokens = batch.n_tokens;
 
+        GGML_ASSERT(lctx.inp_cls);
         GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
+
         uint32_t * data = (uint32_t *) lctx.inp_cls->data;
+        memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls));
 
         for (int i = 0; i < n_tokens; ++i) {
             const llama_seq_id seq_id = batch.seq_id[i][0];
-            const llama_pos pos = batch.pos[i];
+            const llama_pos    pos    = batch.pos[i];
+
+            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS");
+
             if (pos == 0) {
                 data[seq_id] = i;
             }
         }
     }
+
+    if (kv_self.recurrent) {
+        const int64_t n_kv = kv_self.n;
+
+        if (lctx.inp_s_mask) {
+            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_mask->buffer));
+            float * data = (float *) lctx.inp_s_mask->data;
+
+            // states which are not affected by the current batch are left untouched
+            for (int i = 0; i < n_kv; ++i) {
+                llama_seq_id    seq_id       = i + lctx.kv_self.head;
+                llama_kv_cell & kv_cell      = lctx.kv_self.cells[seq_id];
+                bool            has_self_seq = kv_cell.has_seq_id(seq_id);
+
+                data[i] = (float) has_self_seq;
+
+                // ensure current sequences will be kept
+                if (!has_self_seq && kv_cell.pos >= 0) {
+                    kv_cell.seq_id.insert(seq_id);
+                }
+            }
+        }
+        // For Mamba (and other recurrent architectures),
+        // update the correct state(s)/sequence(s) for each token of the batch.
+        // Like with the KQ_mask, if a token in the batch has multiple sequences,
+        // they are assumed to be equivalent (not here, but in ggml_ssm_scan and ggml_ssm_conv).
+        if (lctx.inp_s_seq) {
+            const int64_t n_tokens = batch.n_tokens;
+
+            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_seq->buffer));
+            int32_t * data = (int32_t *) lctx.inp_s_seq->data;
+
+            for (int j = 0; j < n_tokens; ++j) {
+                const int32_t n_seq = batch.n_seq_id[j];
+                GGML_ASSERT(0 < n_seq); // a token should be part of at least 1 sequence
+
+                for (int i = 0; i < n_kv; ++i) {
+                    if (i < n_seq) {
+                        // for this type of model, the head is the minimum seq_id of the batch
+                        data[j*n_kv + i] = batch.seq_id[j][i] - kv_self.head;
+                    } else {
+                        data[j*n_kv + i] = -1;
+                    }
+                }
+            }
+        }
+    }
+}
+
+// Make sure enough space is available for outputs.
+// Returns max number of outputs for which space was reserved.
+static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) {
+    const auto & cparams = lctx.cparams;
+    const auto & hparams = lctx.model.hparams;
+
+    const size_t n_outputs_max = std::max(n_outputs, (size_t) cparams.n_seq_max);
+
+    const auto n_batch = cparams.n_batch;
+    const auto n_vocab = hparams.n_vocab;
+    const auto n_embd  = hparams.n_embd;
+
+    // TODO: use a per-batch flag for logits presence instead
+    const bool has_logits = cparams.causal_attn;
+    const bool has_embd   = cparams.embeddings && (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);
+
+    const size_t logits_size = has_logits ? n_vocab*n_outputs_max : 0;
+    const size_t embd_size   = has_embd   ?  n_embd*n_outputs_max : 0;
+
+    if (lctx.output_ids.empty()) {
+        // init, never resized afterwards
+        lctx.output_ids.resize(n_batch);
+    }
+
+    const size_t prev_size = lctx.buf_output ? ggml_backend_buffer_get_size(lctx.buf_output) : 0;
+    const size_t new_size  = (logits_size + embd_size) * sizeof(float);
+
+    // alloc only when more than the current capacity is required
+    // TODO: also consider shrinking the buffer
+    if (!lctx.buf_output || prev_size < new_size) {
+        if (lctx.buf_output) {
+#ifndef NDEBUG
+            // This doesn't happen often, but may be annoying in some cases (like the HellaSwag benchmark)
+            LLAMA_LOG_INFO("%s: reallocating output buffer from size %.02f MiB to %.02f MiB\n", __func__, prev_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
+#endif
+            ggml_backend_buffer_free(lctx.buf_output);
+            lctx.buf_output = nullptr;
+            lctx.logits = nullptr;
+            lctx.embd = nullptr;
+        }
+
+        lctx.buf_output = ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(true), new_size);
+        if (lctx.buf_output == nullptr) {
+            LLAMA_LOG_ERROR("%s: failed to allocate output buffer of size %.2f MiB\n", __func__, new_size / (1024.0 * 1024.0));
+            return 0;
+        }
+    }
+
+    float * output_base = (float *) ggml_backend_buffer_get_base(lctx.buf_output);
+
+    lctx.logits = has_logits ? output_base               : nullptr;
+    lctx.embd   = has_embd   ? output_base + logits_size : nullptr;
+
+    lctx.output_size = n_outputs_max;
+    lctx.logits_size = logits_size;
+    lctx.embd_size   = embd_size;
+
+    // set all ids as invalid (negative)
+    std::fill(lctx.output_ids.begin(), lctx.output_ids.end(), -1);
+
+    ggml_backend_buffer_clear(lctx.buf_output, 0);
+
+    lctx.n_outputs = 0;
+
+    return n_outputs_max;
+}
+
+
+static void llama_graph_compute(
+        llama_context & lctx,
+          ggml_cgraph * gf,
+                  int   n_threads) {
+#ifdef GGML_USE_MPI
+    const int64_t n_layer = lctx.model.hparams.n_layer;
+    ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer);
+#endif
+
+#ifdef GGML_USE_METAL
+    if (ggml_backend_is_metal(lctx.backend_metal)) {
+        ggml_backend_metal_set_n_cb(lctx.backend_metal, n_threads);
+    }
+#endif
+
+    if (lctx.backend_cpu != nullptr) {
+        ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads);
+        ggml_backend_cpu_set_abort_callback(lctx.backend_cpu, lctx.abort_callback, lctx.abort_callback_data);
+    }
+
+    ggml_backend_sched_graph_compute_async(lctx.sched, gf);
+
+    // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched));
+
+#ifdef GGML_USE_MPI
+    ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer);
+#endif
 }
 
 // decode a batch of tokens by evaluating the transformer
@@ -7615,10 +11355,11 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
 //
 static int llama_decode_internal(
          llama_context & lctx,
-           llama_batch   batch) {
-    const uint32_t n_tokens = batch.n_tokens;
+           llama_batch   batch_all) { // TODO: rename back to batch
 
-    if (n_tokens == 0) {
+    const uint32_t n_tokens_all = batch_all.n_tokens;
+
+    if (n_tokens_all == 0) {
         LLAMA_LOG_ERROR("%s: n_tokens == 0", __func__);
         return -1;
     }
@@ -7627,14 +11368,16 @@ static int llama_decode_internal(
     const auto & hparams = model.hparams;
     const auto & cparams = lctx.cparams;
 
-    const auto n_batch = cparams.n_batch;
+    GGML_ASSERT((!batch_all.token && batch_all.embd) || (batch_all.token && !batch_all.embd)); // NOLINT
 
-    GGML_ASSERT(n_tokens <= n_batch);
+    GGML_ASSERT(n_tokens_all <= cparams.n_batch);
 
-    int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
-    GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
+    GGML_ASSERT((cparams.causal_attn || cparams.n_ubatch >= n_tokens_all) && "non-causal attention requires n_ubatch >= n_tokens");
 
-    const int64_t t_start_us = ggml_time_us();
+    if (lctx.t_compute_start_us == 0) {
+        lctx.t_compute_start_us = ggml_time_us();
+    }
+    lctx.n_queued_tokens += n_tokens_all;
 
 #ifdef GGML_USE_MPI
     // TODO: needs fix after #3228
@@ -7642,224 +11385,614 @@ static int llama_decode_internal(
     //ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads);
 #endif
 
-    GGML_ASSERT(n_threads > 0);
-
     auto & kv_self = lctx.kv_self;
 
     const int64_t n_embd  = hparams.n_embd;
     const int64_t n_vocab = hparams.n_vocab;
 
-    // helpers for smoother batch API transition
-    // after deprecating the llama_eval calls, these will be removed
-    std::vector<llama_pos> pos;
+    uint32_t n_outputs = 0;
+    uint32_t n_outputs_prev = 0;
 
+    const auto n_ubatch = cparams.n_ubatch;
+
+    std::vector<llama_pos> pos;
     std::vector<int32_t>                   n_seq_id;
     std::vector<llama_seq_id *>            seq_id_arr;
     std::vector<std::vector<llama_seq_id>> seq_id;
 
-    if (batch.pos == nullptr) {
-        pos.resize(n_tokens);
-        for (uint32_t i = 0; i < n_tokens; i++) {
-            pos[i] = batch.all_pos_0 + i*batch.all_pos_1;
+    // count outputs
+    if (batch_all.logits) {
+        for (uint32_t i = 0; i < n_tokens_all; ++i) {
+            n_outputs += batch_all.logits[i] != 0;
+        }
+    } else if (lctx.logits_all || (cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE)) {
+        n_outputs = n_tokens_all;
+    } else {
+        // keep last output only
+        n_outputs = 1;
+    }
+
+    // reserve output buffer
+    if (llama_output_reserve(lctx, n_outputs) < n_outputs) {
+        LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_outputs);
+        return -2;
+    };
+
+    // set output mappings
+    if (batch_all.logits) {
+        int32_t i_logits = 0;
+        for (uint32_t i = 0; i < n_tokens_all; ++i) {
+            if (batch_all.logits[i]) {
+                lctx.output_ids[i] = i_logits++;
+            }
+        }
+    } else {
+        for (uint32_t i = 0; i < n_outputs; ++i) {
+            lctx.output_ids[i] = i;
+        }
+    }
+
+    for (uint32_t cur_token = 0; cur_token < n_tokens_all; cur_token += n_ubatch) {
+        const uint32_t n_tokens = std::min(n_ubatch, n_tokens_all - cur_token);
+        llama_batch u_batch = {
+            /* .n_tokens   = */ (int32_t) n_tokens,
+            /* .token      = */ batch_all.token     ? batch_all.token    + cur_token        : nullptr,
+            /* .embd       = */ batch_all.embd      ? batch_all.embd     + cur_token*n_embd : nullptr,
+            /* .pos        = */ batch_all.pos       ? batch_all.pos      + cur_token        : nullptr,
+            /* .n_seq_id   = */ batch_all.n_seq_id  ? batch_all.n_seq_id + cur_token        : nullptr,
+            /* .seq_id     = */ batch_all.seq_id    ? batch_all.seq_id   + cur_token        : nullptr,
+            /* .logits     = */ batch_all.logits    ? batch_all.logits   + cur_token        : nullptr,
+            /* .all_pos_0  = */ batch_all.all_pos_0 + (llama_pos) cur_token*batch_all.all_pos_1,
+            /* .all_pos_1  = */ batch_all.all_pos_1,
+            /* .all_seq_id = */ batch_all.all_seq_id,
+        };
+
+        // count the outputs in this u_batch
+        {
+            int32_t n_outputs_new = 0;
+
+            if (u_batch.logits) {
+                for (uint32_t i = 0; i < n_tokens; i++) {
+                    n_outputs_new += u_batch.logits[i] != 0;
+                }
+            } else if (n_outputs == n_tokens_all) {
+                n_outputs_new = n_tokens;
+            } else {
+                // keep last output only
+                if (cur_token + n_tokens >= n_tokens_all) {
+                    n_outputs_new = 1;
+                }
+            }
+
+            // needs to happen before the graph is built
+            lctx.n_outputs = n_outputs_new;
         }
 
-        batch.pos = pos.data();
-    }
+        int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
+        GGML_ASSERT(n_threads > 0);
 
-    if (batch.seq_id == nullptr) {
-        n_seq_id.resize(n_tokens);
-        seq_id.resize(n_tokens);
-        seq_id_arr.resize(n_tokens);
-        for (uint32_t i = 0; i < n_tokens; i++) {
-            n_seq_id[i] = 1;
-            seq_id[i].resize(1);
-            seq_id[i][0] = batch.all_seq_id;
-            seq_id_arr[i] = seq_id[i].data();
+        // helpers for smoother batch API transition
+        // after deprecating the llama_eval calls, these will be removed
+        if (u_batch.pos == nullptr) {
+            pos.resize(n_tokens);
+            for (uint32_t i = 0; i < n_tokens; i++) {
+                pos[i] = u_batch.all_pos_0 + i*u_batch.all_pos_1;
+            }
+
+            u_batch.pos = pos.data();
         }
 
-        batch.n_seq_id = n_seq_id.data();
-        batch.seq_id = seq_id_arr.data();
+        if (u_batch.seq_id == nullptr) {
+            n_seq_id.resize(n_tokens);
+            seq_id.resize(n_tokens);
+            seq_id_arr.resize(n_tokens);
+            for (uint32_t i = 0; i < n_tokens; i++) {
+                n_seq_id[i] = 1;
+                seq_id[i].resize(1);
+                seq_id[i][0] = u_batch.all_seq_id;
+                seq_id_arr[i] = seq_id[i].data();
+            }
+
+            u_batch.n_seq_id = n_seq_id.data();
+            u_batch.seq_id = seq_id_arr.data();
+        }
+
+        // non-causal masks do not use the KV cache
+        if (hparams.causal_attn) {
+            llama_kv_cache_update(&lctx);
+
+            // if we have enough unused cells before the current head ->
+            //   better to start searching from the beginning of the cache, hoping to fill it
+            if (kv_self.head > kv_self.used + 2*n_tokens) {
+                kv_self.head = 0;
+            }
+
+            if (!llama_kv_cache_find_slot(kv_self, u_batch)) {
+                return 1;
+            }
+
+            if (!kv_self.recurrent) {
+                // a heuristic, to avoid attending the full cache if it is not yet utilized
+                // after enough generations, the benefit from this heuristic disappears
+                // if we start defragmenting the cache, the benefit from this will be more important
+                const uint32_t pad = llama_kv_cache_get_padding(cparams);
+                kv_self.n = std::min(kv_self.size, std::max(pad, GGML_PAD(llama_kv_cache_cell_max(kv_self), pad)));
+                //kv_self.n = llama_kv_cache_cell_max(kv_self);
+            }
+        }
+
+        //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head);
+
+        ggml_backend_sched_reset(lctx.sched);
+        ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data);
+
+        ggml_cgraph * gf = llama_build_graph(lctx, u_batch, false);
+
+        // the output is always the last tensor in the graph
+        struct ggml_tensor * res  = gf->nodes[gf->n_nodes - 1];
+        struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 2];
+
+        if (lctx.n_outputs == 0) {
+            // no output
+            res  = nullptr;
+            embd = nullptr;
+        } else if (!hparams.causal_attn) {
+            res = nullptr; // do not extract logits for embedding models such as BERT
+
+            // token or sequence embeddings
+            embd = gf->nodes[gf->n_nodes - 1];
+
+            GGML_ASSERT(strcmp(embd->name, "result_embd") == 0 || strcmp(embd->name, "result_embd_pooled") == 0);
+        } else if (cparams.embeddings) {
+            // the embeddings could be in the second to last tensor, or any of the previous tensors
+            int i_embd = gf->n_nodes - 2;
+            for (int i = 3; strcmp(embd->name, "result_norm") != 0; ++i) {
+                i_embd = gf->n_nodes - i;
+                if (i_embd < 0) { break; }
+                embd = gf->nodes[i_embd];
+            }
+            GGML_ASSERT(i_embd >= 0 && "missing result_norm tensor");
+
+            // TODO: use a per-batch flag to know when to skip logits while keeping embeddings
+            if (!cparams.causal_attn) {
+                res = nullptr; // do not extract logits when not needed
+                // skip computing logits
+                // TODO: is this safe?
+                gf->n_nodes = i_embd + 1;
+            }
+        } else {
+            embd = nullptr; // do not extract embeddings when not needed
+            GGML_ASSERT(strcmp(res->name, "result_output") == 0 && "missing result_output tensor");
+        }
+        // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
+
+        // for big prompts, if BLAS is enabled, it is better to use only one thread
+        // otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
+        // TODO: this is mostly important for Apple Silicon where CBLAS is still performing very well
+        //       we still need some threads to process all non-mul_mat ops, but not too much to avoid interfering
+        //       with the BLAS calls. need a better solution
+        // MoE Special Case: This logic applies when hparams.n_expert == 0, i.e. the model is NOT an MoE model. When an MoE is
+        //                   being processed then Accelerate/BLAS will not be involved, so capping would limit performance.
+        if (n_tokens >= 32 && hparams.n_expert == 0 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) {
+            n_threads = std::min(4, n_threads);
+        }
+
+        ggml_backend_sched_alloc_graph(lctx.sched, gf);
+
+        llama_set_inputs(lctx, u_batch);
+
+        llama_graph_compute(lctx, gf, n_threads);
+
+        // update the kv ring buffer
+        {
+            kv_self.head += n_tokens;
+
+            // Ensure kv cache head points to a valid index.
+            if (kv_self.head >= kv_self.size) {
+                kv_self.head = 0;
+            }
+        }
+
+#ifdef GGML_PERF
+        // print timing information per ggml operation (for debugging purposes)
+        // requires GGML_PERF to be defined
+        ggml_graph_print(gf);
+#endif
+
+        // plot the computation graph in dot format (for debugging purposes)
+        //if (n_past%100 == 0) {
+        //    ggml_graph_dump_dot(gf, NULL, "llama.dot");
+        //}
+
+        // extract logits
+        if (res) {
+            ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(lctx.sched, res);
+            GGML_ASSERT(backend_res != nullptr);
+            GGML_ASSERT(lctx.logits != nullptr);
+
+            float * logits_out = lctx.logits + n_outputs_prev*n_vocab;
+            const int32_t n_outputs_new = lctx.n_outputs;
+
+            if (n_outputs_new) {
+                GGML_ASSERT( n_outputs_prev + n_outputs_new <= n_outputs);
+                GGML_ASSERT((n_outputs_prev + n_outputs_new)*n_vocab <= (int64_t) lctx.logits_size);
+                ggml_backend_tensor_get_async(backend_res, res, logits_out, 0, n_outputs_new*n_vocab*sizeof(float));
+            }
+        }
+
+        // extract embeddings
+        if (embd) {
+            ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd);
+            GGML_ASSERT(backend_embd != nullptr);
+
+            switch (cparams.pooling_type) {
+                case LLAMA_POOLING_TYPE_NONE:
+                    {
+                        // extract token embeddings
+                        GGML_ASSERT(lctx.embd != nullptr);
+                        float * embd_out = lctx.embd + n_outputs_prev*n_embd;
+                        const int32_t n_outputs_new = lctx.n_outputs;
+
+                        if (n_outputs_new) {
+                            GGML_ASSERT( n_outputs_prev + n_outputs_new <= n_outputs);
+                            GGML_ASSERT((n_outputs_prev + n_outputs_new)*n_embd <= (int64_t) lctx.embd_size);
+                            ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_outputs_new*n_embd*sizeof(float));
+                        }
+                    } break;
+                case LLAMA_POOLING_TYPE_CLS:
+                case LLAMA_POOLING_TYPE_MEAN:
+                    {
+                        GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0);
+
+                        // extract sequence embeddings
+                        auto & embd_seq_out = lctx.embd_seq;
+                        embd_seq_out.clear();
+
+                        for (uint32_t i = 0; i < n_tokens; i++) {
+                            const llama_seq_id seq_id = u_batch.seq_id[i][0];
+                            if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
+                                continue;
+                            }
+                            embd_seq_out[seq_id].resize(n_embd);
+                            ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
+                        }
+                    } break;
+                case LLAMA_POOLING_TYPE_UNSPECIFIED:
+                    {
+                        GGML_ASSERT(false && "unknown pooling type");
+                    } break;
+            }
+        }
+        n_outputs_prev += lctx.n_outputs;
     }
 
-    // if we have enough unused cells before the current head ->
-    //   better to start searching from the beginning of the cache, hoping to fill it
-    if (kv_self.head > kv_self.used + 2*n_tokens) {
-        kv_self.head = 0;
+    // set to total number of outputs in the batch, for use in llama_get_logits_ith
+    lctx.n_outputs = n_outputs;
+
+    // wait for the computation to finish (automatically done when obtaining the model output)
+    //llama_synchronize(&lctx);
+
+    // decide if we need to defrag the kv cache
+    if (cparams.causal_attn && cparams.defrag_thold >= 0.0f) {
+        const float fragmentation = kv_self.n >= 128 ? 1.0f - float(kv_self.used)/float(kv_self.n) : 0.0f;
+
+        // queue defragmentation for next llama_kv_cache_update
+        if (fragmentation > cparams.defrag_thold) {
+            //LLAMA_LOG_INFO("fragmentation: %.2f\n", fragmentation);
+
+            llama_kv_cache_defrag(kv_self);
+        }
     }
 
-    if (!llama_kv_cache_find_slot(kv_self, batch)) {
-        return 1;
+    // Reset state for the next token before backend sync, to allow the CPU activities in the reset to
+    // overlap with device computation.
+    ggml_backend_sched_reset(lctx.sched);
+
+    return 0;
+}
+
+
+// find holes from the beginning of the KV cache and fill them by moving data from the end of the cache
+static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
+    auto & kv_self = lctx.kv_self;
+
+    const auto & hparams = lctx.model.hparams;
+
+    const uint32_t n_layer = hparams.n_layer;
+
+    const uint32_t n_kv   = llama_kv_cache_cell_max(kv_self);
+    const uint32_t n_used = kv_self.used;
+
+    assert(n_used <= n_kv);
+
+    //const int64_t t_start = ggml_time_us();
+
+    // number of cells moved
+    uint32_t n_moves = 0;
+
+    // each move requires 6*n_layer tensors (see build_defrag)
+    //   - source view, destination view, copy operation
+    //   - x2 for keys and values
+    //const uint32_t max_moves = LLAMA_MAX_NODES/(6*n_layer);
+    // TODO: tmp fix https://github.com/ggerganov/llama.cpp/issues/6685#issuecomment-2057579516
+    const uint32_t max_moves = (LLAMA_MAX_NODES - 2*n_layer)/(6*n_layer);
+
+    // determine which KV cells to move where
+    //
+    //  cell i moves to ids[i]
+    //
+    //  if ids[i] == i || ids[i] == n_kv, then cell i is not moved
+    //
+    std::vector<uint32_t> ids(n_kv, n_kv);
+
+    for (uint32_t i0 = 0; i0 < n_used; ++i0) {
+        const auto & cell0 = kv_self.cells[i0];
+
+        if (!cell0.is_empty()) {
+            ids[i0] = i0;
+
+            continue;
+        }
+
+        // found a hole - fill it with data from the end of the cache
+
+        uint32_t nh = 1;
+
+        // determine the size of the hole
+        while (i0 + nh < n_used && kv_self.cells[i0 + nh].is_empty()) {
+            nh++;
+        }
+
+        uint32_t nf = 0;
+        uint32_t is = n_kv - 1;
+
+        // starting from the end, find nh non-empty cells
+        for (; is > i0; --is) {
+            const auto & cell1 = kv_self.cells[is];
+
+            if (cell1.is_empty() || ids[is] != n_kv) {
+                continue;
+            }
+
+            // non-empty cell which is not yet moved
+            nf++;
+
+            if (nf == nh) {
+                break;
+            }
+        }
+
+        // this can only happen if `n_used` is not accurate, which would be a bug
+        GGML_ASSERT(nf == nh && "KV defrag bug: nf != nh");
+
+        nf = 0;
+
+        uint32_t i1 = is;
+
+        // are we moving a continuous block of memory?
+        bool cont = false;
+
+        // should we stop searching for the next move?
+        bool stop = false;
+
+        // go back and move the nf cells to the hole
+        for (; i1 < n_kv; ++i1) {
+            auto & cell1 = kv_self.cells[i1];
+
+            if (cell1.is_empty() || ids[i1] != n_kv) {
+                if (n_moves == max_moves) {
+                    stop = true;
+                    break;
+                }
+
+                cont = false;
+                continue;
+            }
+
+            // this cell goes to (i0 + nf)
+            ids[i1] = i0 + nf;
+
+            // move the cell meta data
+            kv_self.cells[i0 + nf] = cell1;
+
+            // clear the old cell and move the head there
+            cell1 = llama_kv_cell();
+            kv_self.head = n_used;
+
+            if (!cont) {
+                n_moves++;
+                cont = true;
+            }
+
+            nf++;
+
+            if (nf == nh) {
+                break;
+            }
+        }
+
+        if (stop || n_moves == max_moves) {
+            break;
+        }
+
+        //LLAMA_LOG_INFO("(tmp log) KV defrag: move [%u, %u) to [%u, %u)\n", is, i1 + 1, i0, i0 + nh);
+
+        i0 += nh - 1;
     }
 
-    // a heuristic, to avoid attending the full cache if it is not yet utilized
-    // after enough generations, the benefit from this heuristic disappears
-    // if we start defragmenting the cache, the benefit from this will be more important
-    kv_self.n = std::min((int32_t) cparams.n_ctx, std::max(32, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32)));
-    //kv_self.n = llama_kv_cache_cell_max(kv_self);
+    if (n_moves == 0) {
+        return;
+    }
 
-    //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head);
+    //LLAMA_LOG_INFO("(tmp log) KV defrag cell moves: %u\n", n_moves);
+
+    //LLAMA_LOG_INFO("expected gf nodes: %u\n", 6*n_moves*n_layer);
+
+#if 0
+    // CPU defrag
+    //
+    // TODO: optimizations are possible:
+    //       - multiple threads
+    //       - avoid copying to the host memory when already there
+    //
+    // likely not worth the effort, as we have ggml_graph based defrag
+    //
+
+    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa();
+    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
+
+    const uint32_t kv_size = kv_self.size;
+
+    std::vector<uint8_t> buf_k;
+    std::vector<uint8_t> buf_v;
+
+    for (uint32_t il = 0; il < n_layer; ++il) {
+        const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
+        const size_t k_size     = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_size);
+
+        const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
+        const size_t v_size    = ggml_row_size (kv_self.v_l[il]->type, n_embd_v_gqa*kv_size);
+
+        buf_k.resize(k_size);
+        buf_v.resize(v_size);
+
+        ggml_backend_tensor_get(kv_self.k_l[il], buf_k.data(), 0, buf_k.size());
+        ggml_backend_tensor_get(kv_self.v_l[il], buf_v.data(), 0, buf_v.size());
+
+        // batch move [i, i+nm) to [id, id+nm)
+        // note: cells can move only to a lower index
+        for (uint32_t i = 0; i < n_kv; ++i) {
+            const uint32_t id = ids[i];
+
+            if (i == id || id == n_kv) {
+                continue;
+            }
+
+            uint32_t nm = 1;
+
+            while (i + nm < n_kv && ids[i + nm] == id + nm) {
+                nm++;
+            }
+
+            // move keys
+            {
+                const int64_t os =  i*k_size_row;
+                const int64_t od = id*k_size_row;
+
+                memcpy(buf_k.data() + od, buf_k.data() + os, nm*k_size_row);
+            }
+
+            // move values (note: they are transposed)
+            {
+                const int64_t os =  i;
+                const int64_t od = id;
+
+                for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+                    memcpy(buf_v.data() + (od + j*kv_size)*v_size_el, buf_v.data() + (os + j*kv_size)*v_size_el, nm*v_size_el);
+                }
+            }
+
+            i += nm - 1;
+        }
+
+        ggml_backend_tensor_set(kv_self.k_l[il], buf_k.data(), 0, buf_k.size());
+        ggml_backend_tensor_set(kv_self.v_l[il], buf_v.data(), 0, buf_v.size());
+    }
+#else
+    // ggml_graph defrag
 
     ggml_backend_sched_reset(lctx.sched);
-    ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data);
 
-    ggml_cgraph * gf = llama_build_graph(lctx, batch, false);
+    ggml_cgraph * gf = llama_build_graph_defrag(lctx, ids);
 
-    // the output is always the last tensor in the graph
-    struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
-    struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2];
-    if (strcmp(res->name, "result_output") == 0) {
-        // the embeddings could be the second to last tensor, or the third to last tensor
-        if (strcmp(embeddings->name, "result_norm") != 0) {
-            embeddings = gf->nodes[gf->n_nodes - 3];
-            GGML_ASSERT(strcmp(embeddings->name, "result_norm") == 0);
+    llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
+#endif
+
+    //const int64_t t_end = ggml_time_us();
+
+    //LLAMA_LOG_INFO("(tmp log) KV defrag time: %.3f ms\n", (t_end - t_start)/1000.0);
+}
+
+static void llama_kv_cache_update_internal(struct llama_context & lctx) {
+    bool need_reserve = false;
+
+    // apply K-shift if needed
+    if (lctx.model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE && lctx.kv_self.has_shift) {
+        {
+            ggml_backend_sched_reset(lctx.sched);
+
+            ggml_cgraph * gf = llama_build_graph_k_shift(lctx);
+
+            ggml_backend_sched_alloc_graph(lctx.sched, gf);
+
+            llama_set_k_shift(lctx);
+
+            llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
+
+            need_reserve = true;
         }
-    } else if (strcmp(res->name, "result_embd") == 0) {
-        embeddings = res;
-        res = nullptr;
-    } else {
-        GGML_ASSERT(false);
-    }
 
-    // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
+        {
+            auto & kv_self = lctx.kv_self;
 
-    // for big prompts, if BLAS is enabled, it is better to use only one thread
-    // otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
-    // TODO: this is mostly important for Apple Silicon where CBLAS is still performing very well
-    //       we still need some threads to process all non-mul_mat ops, but not too much to avoid interfering
-    //       with the BLAS calls. need a better solution
-    // MoE Special Case: This logic applies when hparams.n_expert == 0, i.e. the model is NOT an MoE model. When an MoE is
-    //                   being processed then Accelerate/BLAS will not be involved, so capping would limit performance.
-    if (n_tokens >= 32 && hparams.n_expert == 0 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) {
-        n_threads = std::min(4, n_threads);
-    }
-
-#ifdef GGML_USE_MPI
-    const int64_t n_layer = hparams.n_layer;
-    ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer);
-#endif
-
-#ifdef GGML_USE_METAL
-    if (ggml_backend_is_metal(lctx.backend_metal)) {
-        ggml_backend_metal_set_n_cb(lctx.backend_metal, n_threads);
-    }
-#endif
-
-    if (lctx.backend_cpu != nullptr) {
-        ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads);
-    }
-
-    llama_set_inputs(lctx, batch);
-
-    ggml_backend_sched_graph_compute(lctx.sched, gf);
-
-    // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched));
-
-#ifdef GGML_USE_MPI
-    ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer);
-#endif
-
-    // update the kv ring buffer
-    {
-        if (kv_self.has_shift) {
             kv_self.has_shift = false;
+
             for (uint32_t i = 0; i < kv_self.size; ++i) {
                 kv_self.cells[i].delta = 0;
             }
         }
-
-        kv_self.head += n_tokens;
-
-        // Ensure kv cache head points to a valid index.
-        if (kv_self.head >= kv_self.size) {
-            kv_self.head = 0;
-        }
     }
 
-#ifdef GGML_PERF
-    // print timing information per ggml operation (for debugging purposes)
-    // requires GGML_PERF to be defined
-    ggml_graph_print(gf);
-#endif
+    if (lctx.kv_self.recurrent && lctx.kv_self.do_copy) {
+        {
+            ggml_backend_sched_reset(lctx.sched);
 
-    // plot the computation graph in dot format (for debugging purposes)
-    //if (n_past%100 == 0) {
-    //    ggml_graph_dump_dot(gf, NULL, "llama.dot");
-    //}
+            ggml_cgraph * gf = llama_build_graph_s_copy(lctx);
 
-    // extract logits
-    // TODO: do not compute and extract logits if only embeddings are needed
-    //       need to update the graphs to skip "result_output"
-    if (res) {
-        auto & logits_out = lctx.logits;
+            ggml_backend_sched_alloc_graph(lctx.sched, gf);
 
-#ifndef NDEBUG
-        auto & logits_valid = lctx.logits_valid;
-        logits_valid.clear();
-        logits_valid.resize(n_tokens);
+            llama_set_s_copy(lctx);
 
-        logits_out.clear();
-#endif
+            llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
 
-        ggml_backend_t res_backend = ggml_backend_sched_get_node_backend(lctx.sched, res);
-        GGML_ASSERT(res_backend != nullptr);
-        if (batch.logits) {
-            logits_out.resize(n_vocab * n_tokens);
-            for (uint32_t i = 0; i < n_tokens; i++) {
-                if (batch.logits[i] == 0) {
-                    continue;
-                }
-                ggml_backend_tensor_get_async(res_backend, res, logits_out.data() + (n_vocab*i), (n_vocab*i)*sizeof(float), n_vocab*sizeof(float));
-#ifndef NDEBUG
-                logits_valid[i] = true;
-#endif
+            need_reserve = true;
+        }
+
+        {
+            auto & kv_self = lctx.kv_self;
+
+            kv_self.do_copy = false;
+
+            for (uint32_t i = 0; i < kv_self.size; ++i) {
+                kv_self.cells[i].src = i;
             }
-        } else if (lctx.logits_all) {
-            logits_out.resize(n_vocab * n_tokens);
-            ggml_backend_tensor_get_async(res_backend, res, logits_out.data(), 0, n_vocab*n_tokens*sizeof(float));
-#ifndef NDEBUG
-            std::fill(logits_valid.begin(), logits_valid.end(), true);
-#endif
-        } else {
-            logits_out.resize(n_vocab);
-            ggml_backend_tensor_get_async(res_backend, res, logits_out.data(), (n_vocab*(n_tokens - 1))*sizeof(float), n_vocab*sizeof(float));
-#ifndef NDEBUG
-            logits_valid[0] = true;
-#endif
         }
-        ggml_backend_synchronize(res_backend);
     }
 
-    // extract embeddings
-    if (!lctx.embedding.empty()) {
-        auto & embedding_out = lctx.embedding;
+    // defragment the KV cache if needed
+    if (lctx.kv_self.do_defrag) {
+        llama_kv_cache_defrag_internal(lctx);
 
-        const int64_t embd_pos  = res ? n_embd * (n_tokens-1) : 0;
-        const int64_t embd_size = res ? n_embd : n_embd * n_tokens;
+        need_reserve = true;
 
-        embedding_out.resize(embd_size);
-        ggml_backend_t embeddings_backend = ggml_backend_sched_get_node_backend(lctx.sched, embeddings);
-        ggml_backend_tensor_get_async(embeddings_backend, embeddings, embedding_out.data(), embd_pos*sizeof(float), embd_size*sizeof(float));
-        ggml_backend_synchronize(embeddings_backend);
+        lctx.kv_self.do_defrag = false;
     }
 
-    // measure the performance only for the single-token evals
-    if (n_tokens == 1) {
-        lctx.t_eval_us += ggml_time_us() - t_start_us;
-        lctx.n_eval++;
-    }
-    else if (n_tokens > 1) {
-        lctx.t_p_eval_us += ggml_time_us() - t_start_us;
-        lctx.n_p_eval += n_tokens;
-    }
+    // reserve a worst case graph again
+    if (need_reserve) {
+        // TODO: extract to a function
+        // build worst-case graph
+        int n_tokens = (int)std::min(lctx.cparams.n_ctx, lctx.cparams.n_ubatch);
+        int n_past = lctx.cparams.n_ctx - n_tokens;
+        llama_token token = llama_token_bos(&lctx.model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
+        ggml_cgraph * gf = llama_build_graph(lctx, llama_batch_get_one(&token, n_tokens, n_past, 0), true);
 
-    // get a more accurate load time, upon first eval
-    // TODO: fix this
-    if (!lctx.has_evaluated_once) {
-        lctx.t_load_us = ggml_time_us() - lctx.t_start_us;
-        lctx.has_evaluated_once = true;
+        // initialize scheduler with the worst-case graph
+        ggml_backend_sched_reset(lctx.sched);
+        if (!ggml_backend_sched_reserve(lctx.sched, gf)) {
+            LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
+        }
     }
-
-    return 0;
 }
 
 //
@@ -7871,46 +12004,53 @@ static enum llama_vocab_type llama_vocab_get_type(const llama_vocab & vocab) {
 }
 
 static bool llama_is_normal_token(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
     return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL;
 }
 
 static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
     return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_UNKNOWN;
 }
 
 static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
     return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_CONTROL;
 }
 
 static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
     return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_BYTE;
 }
 
 static bool llama_is_user_defined_token(const llama_vocab& vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
     return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_USER_DEFINED;
 }
 
 static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) {
+    GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
     GGML_ASSERT(llama_is_byte_token(vocab, id));
-    const auto& token_data = vocab.id_to_token.at(id);
+    const auto & token_data = vocab.id_to_token.at(id);
     switch (llama_vocab_get_type(vocab)) {
-    case LLAMA_VOCAB_TYPE_SPM: {
-        auto buf = token_data.text.substr(3, 2);
-        return strtol(buf.c_str(), NULL, 16);
-    }
-    case LLAMA_VOCAB_TYPE_BPE: {
-        GGML_ASSERT(false);
-        return unicode_to_bytes_bpe(token_data.text);
-    }
-    case LLAMA_VOCAB_TYPE_WPM: {
-        GGML_ASSERT(false);
-    }
-    default:
-        GGML_ASSERT(false);
+        case LLAMA_VOCAB_TYPE_SPM: {
+            auto buf = token_data.text.substr(3, 2);
+            return strtol(buf.c_str(), NULL, 16);
+        }
+        case LLAMA_VOCAB_TYPE_BPE: {
+            GGML_ASSERT(false);
+            return unicode_utf8_to_byte(token_data.text); // TODO: why is this here after GGML_ASSERT?
+        }
+        case LLAMA_VOCAB_TYPE_WPM: {
+            GGML_ASSERT(false);
+        }
+        default:
+            GGML_ASSERT(false);
     }
 }
 
 static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
+    GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
     static const char * hex = "0123456789ABCDEF";
     switch (llama_vocab_get_type(vocab)) {
         case LLAMA_VOCAB_TYPE_SPM: {
@@ -7925,7 +12065,7 @@ static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
         }
         case LLAMA_VOCAB_TYPE_WPM:
         case LLAMA_VOCAB_TYPE_BPE: {
-            return vocab.token_to_id.at(bytes_to_unicode_bpe(ch));
+            return vocab.token_to_id.at(unicode_byte_to_utf8(ch));
         }
         default:
             GGML_ASSERT(false);
@@ -8121,7 +12261,101 @@ struct llm_tokenizer_bpe {
 
     void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
         int final_prev_index = -1;
-        auto word_collection = bpe_gpt2_preprocess(text);
+        bool ignore_merges = false;
+
+        std::vector<std::string> word_collection;
+        switch (vocab.type) {
+            case LLAMA_VOCAB_TYPE_BPE:
+                switch (vocab.type_pre) {
+                    case LLAMA_VOCAB_PRE_TYPE_LLAMA3:
+                        ignore_merges = true;
+                        word_collection = unicode_regex_split(text, {
+                            // original regex from tokenizer.json
+                            //"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+
+                            // adapted: https://github.com/ggerganov/llama.cpp/pull/6920#issuecomment-2080233989
+                            "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                        });
+                        break;
+                    case LLAMA_VOCAB_PRE_TYPE_DBRX:
+                        word_collection = unicode_regex_split(text, {
+                            // same as llama3
+                            "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                        });
+                        break;
+                    case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM:
+                        word_collection = unicode_regex_split(text, {
+                            "[\r\n]",
+                            "\\s?[A-Za-zµÀ-ÖØ-öø-ƺƼ-ƿDŽ-ʓʕ-ʯͰ-ͳͶͷͻ-ͽͿΆΈ-ΊΌΎ-ΡΣ-ϵϷ-ҁҊ-ԯԱ-ՖႠ-ჅᎠ-Ᏽᏸ-ᏽᲐ-ᲺᲽ-Ჿᴀ-ᴫᵫ-ᵷᵹ-ᶚḀ-ἕἘ-Ἕἠ-ὅὈ-Ὅὐ-ὗὙὛὝὟ-ώᾀ-ᾴᾶ-ᾼιῂ-ῄῆ-ῌῐ-ΐῖ-Ίῠ-Ῥῲ-ῴῶ-ῼℂℇℊ-ℓℕℙ-ℝℤΩℨK-ℭℯ-ℴℹℼ-ℿⅅ-ⅉⅎↃↄⰀ-ⱻⱾ-ⳤⳫ-ⳮⳲⳳꙀ-ꙭꚀ-ꚛꜢ-ꝯꝱ-ꞇꞋ-ꞎꭰ-ꮿff-stﬓ-ﬗＡ-Ｚａ-ｚ𐐀-𐑏𐒰-𐓓𐓘-𐓻𐲀-𐲲𐳀-𐳲𑢠-𑣟𞤀-𞥃]+",
+                            "\\s?[!-/:-~！-／：-～‘-‟　-。]+",
+                            "\\s+$",
+                            "[一-龥ࠀ-一가-퟿]+",
+                            "\\p{N}+",
+                        });
+                        break;
+                    case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER:
+                        word_collection = unicode_regex_split(text, {
+                            "[\r\n]",
+                            "\\s?\\p{L}+",
+                            "\\s?\\p{P}+",
+                            "[一-龥ࠀ-一가-퟿]+",
+                            "\\p{N}",
+                        });
+                        break;
+                    case LLAMA_VOCAB_PRE_TYPE_FALCON:
+                        word_collection = unicode_regex_split(text, {
+                            "[\\p{P}\\$\\+<=>\\^~\\|]+",
+                            "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
+                            "[0-9][0-9][0-9]",
+                        });
+                        break;
+                    case LLAMA_VOCAB_PRE_TYPE_MPT:
+                        // TODO: MPT pre-tokenization regexes are unknown
+                        //       the following are close, but not exact. run the following:
+                        //       ./bin/test-tokenizer-0 ../models/ggml-vocab-mpt.gguf
+                        GGML_ASSERT("MPT pre-tokenization regexes are unknown - fixes needed");
+                        word_collection = unicode_regex_split(text, {
+                            "\\s?\\p{L}+",
+                            "\\s?\\p{P}+",
+                            "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
+                        });
+                        break;
+                    case LLAMA_VOCAB_PRE_TYPE_STARCODER:
+                    case LLAMA_VOCAB_PRE_TYPE_REFACT:
+                    case LLAMA_VOCAB_PRE_TYPE_COMMAND_R:
+                        word_collection = unicode_regex_split(text, {
+                            "\\p{N}",
+                            "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
+                        });
+                        break;
+                    case LLAMA_VOCAB_PRE_TYPE_GPT2:
+                    case LLAMA_VOCAB_PRE_TYPE_OLMO:
+                        word_collection = unicode_regex_split(text, {
+                            "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
+                        });
+                        break;
+                    case LLAMA_VOCAB_PRE_TYPE_QWEN2:
+                        word_collection = unicode_regex_split(text, {
+                            // original regex from tokenizer.json
+                            // "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
+                            "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                        });
+                        break;
+                    default:
+                        // default regex for BPE tokenization pre-processing
+                        word_collection = unicode_regex_split(text, {
+                            "[\\p{P}\\$\\+<=>\\^~\\|]+",
+                            "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
+                            "\\p{N}+",
+                            "[0-9][0-9][0-9]",
+                        });
+                        break;
+                }
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
 
         symbols_final.clear();
 
@@ -8132,6 +12366,11 @@ struct llm_tokenizer_bpe {
             int index = 0;
             size_t offset = 0;
 
+            if (ignore_merges && vocab.token_to_id.find(word) != vocab.token_to_id.end()) {
+                symbols.emplace_back(llm_symbol{-1, -1, word.c_str(), word.size()});
+                offset = word.size();
+            }
+
             while (offset < word.size()) {
                 llm_symbol sym;
                 size_t char_len = std::min(word.size() - offset, (size_t) ::utf8_len(word[offset]));
@@ -8178,7 +12417,7 @@ struct llm_tokenizer_bpe {
                 add_new_bigram(bigram.left, left_symbol.next);  // right side of current symbol
             }
 
-            // add the fnished tokens to the final list keeping correct order for next and prev
+            // add the finished tokens to the final list keeping correct order for next and prev
             for (auto & sym : symbols) {
                 if (sym.n > 0) {
                     sym.prev = final_prev_index;
@@ -8248,145 +12487,6 @@ private:
         work_queue.push(bigram);
     }
 
-    std::vector<std::string> bpe_gpt2_preprocess(const std::string & text) {
-        std::vector<std::string> bpe_words;
-        std::vector<std::string> bpe_encoded_words;
-
-        std::string token = "";
-        // GPT2 system regex:  's|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+
-        bool collecting_numeric = false;
-        bool collecting_letter = false;
-        bool collecting_special = false;
-        bool collecting_whitespace_lookahead = false;
-        bool collecting = false;
-
-        std::vector<std::string> text_utf;
-        text_utf.reserve(text.size());
-        bpe_words.reserve(text.size());
-        bpe_encoded_words.reserve(text.size());
-
-        auto cps = codepoints_from_utf8(text);
-        for (size_t i = 0; i < cps.size(); ++i)
-            text_utf.emplace_back(codepoint_to_utf8(cps[i]));
-
-        for (int i = 0; i < (int)text_utf.size(); i++) {
-            const std::string & utf_char = text_utf[i];
-            bool split_condition = false;
-            int bytes_remain = text_utf.size() - i;
-            // forward backward lookups
-            const std::string & utf_char_next = (i + 1 < (int)text_utf.size()) ? text_utf[i + 1] : "";
-            const std::string & utf_char_next_next = (i + 2 < (int)text_utf.size()) ? text_utf[i + 2] : "";
-
-            // handling contractions
-            if (!split_condition && bytes_remain >= 2) {
-                // 's|'t|'m|'d
-                if (utf_char == "\'" && (utf_char_next == "s" || utf_char_next == "t" || utf_char_next == "m" || utf_char_next == "d")) {
-                    split_condition = true;
-                }
-                if (split_condition) {
-                    if (token.size()) {
-                        bpe_words.emplace_back(token); // push previous content as token
-                    }
-                    token = utf_char + utf_char_next;
-                    bpe_words.emplace_back(token);
-                    token = "";
-                    i++;
-                    continue;
-                }
-            }
-            if (!split_condition && bytes_remain >= 3) {
-                // 're|'ve|'ll
-                if (utf_char == "\'" && (
-                    (utf_char_next == "r" && utf_char_next_next == "e") ||
-                    (utf_char_next == "v" && utf_char_next_next == "e") ||
-                    (utf_char_next == "l" && utf_char_next_next == "l"))
-                    ) {
-                    split_condition = true;
-                }
-                if (split_condition) {
-                    // current token + next token can be defined
-                    if (token.size()) {
-                        bpe_words.emplace_back(token); // push previous content as token
-                    }
-                    token = utf_char + utf_char_next + utf_char_next_next;
-                    bpe_words.emplace_back(token); // the contraction
-                    token = "";
-                    i += 2;
-                    continue;
-                }
-            }
-
-            if (!split_condition && !collecting) {
-                if (codepoint_type(utf_char) == CODEPOINT_TYPE_LETTER || (!token.size() && utf_char == " " && codepoint_type(utf_char_next) == CODEPOINT_TYPE_LETTER)) {
-                    collecting_letter = true;
-                    collecting = true;
-                }
-                else if (codepoint_type(utf_char) == CODEPOINT_TYPE_DIGIT || (!token.size() && utf_char == " " && codepoint_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) {
-                    collecting_numeric = true;
-                    collecting = true;
-                }
-                else if (
-                    ((codepoint_type(utf_char) != CODEPOINT_TYPE_LETTER && codepoint_type(utf_char) != CODEPOINT_TYPE_DIGIT) && (codepoint_type(utf_char) != CODEPOINT_TYPE_WHITESPACE)) ||
-                    (!token.size() && utf_char == " " && codepoint_type(utf_char_next) != CODEPOINT_TYPE_LETTER && codepoint_type(utf_char_next) != CODEPOINT_TYPE_DIGIT && codepoint_type(utf_char_next) != CODEPOINT_TYPE_WHITESPACE)
-                    ) {
-                    collecting_special = true;
-                    collecting = true;
-                }
-                else if (codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE && codepoint_type(utf_char_next) == CODEPOINT_TYPE_WHITESPACE) {
-                    collecting_whitespace_lookahead = true;
-                    collecting = true;
-                }
-                else if (codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE) {
-                    split_condition = true;
-                }
-            }
-            else if (!split_condition && collecting) {
-                if (collecting_letter && codepoint_type(utf_char) != CODEPOINT_TYPE_LETTER) {
-                    split_condition = true;
-                }
-                else if (collecting_numeric && codepoint_type(utf_char) != CODEPOINT_TYPE_DIGIT) {
-                    split_condition = true;
-                }
-                else if (collecting_special && (codepoint_type(utf_char) == CODEPOINT_TYPE_LETTER || codepoint_type(utf_char) == CODEPOINT_TYPE_DIGIT || codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE)) {
-                    split_condition = true;
-                }
-                else if (collecting_whitespace_lookahead && (codepoint_type(utf_char_next) == CODEPOINT_TYPE_LETTER || codepoint_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) {
-                    split_condition = true;
-                }
-            }
-
-            if (utf_char_next == "") {
-                split_condition = true; // final
-                token += utf_char;
-            }
-
-            if (split_condition) {
-                if (token.size()) {
-                    bpe_words.emplace_back(token);
-                }
-                token = utf_char;
-                collecting = false;
-                collecting_letter = false;
-                collecting_numeric = false;
-                collecting_special = false;
-                collecting_whitespace_lookahead = false;
-            }
-            else {
-                token += utf_char;
-            }
-        }
-
-        for (std::string & word : bpe_words) {
-            std::string encoded_token = "";
-            for (char & c : word) {
-                encoded_token += bytes_to_unicode_bpe(c);
-            }
-            bpe_encoded_words.emplace_back(encoded_token);
-        }
-
-        return bpe_encoded_words;
-    }
-
     const llama_vocab & vocab;
 
     std::vector<llm_symbol> symbols;
@@ -8447,51 +12547,44 @@ struct llm_tokenizer_wpm {
                 output.push_back(vocab.special_unk_id);
             }
         }
-
-        // append eos token
-        output.push_back(vocab.special_eos_id);
     }
 
     std::vector<std::string> preprocess(const std::string & text) {
-        std::string ori_str = normalize(text);
-        uint64_t ori_size = ori_str.size();
+        std::vector<uint32_t> cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text));
 
-        // single punct / single symbol / single digit
-        // baseline: add whitespace on the left and right of punct and chinese characters
-        std::vector<std::string> words;
+        // strip accents, strip control, uniformize whitespace,
+        // to lowercase, pad chinese characters, pad punctuation
         std::string new_str = "";
-        uint64_t i = 0;
-        while (i < ori_size) {
-            int utf_char_len = utf8_len(ori_str[i]);
-            if ((utf_char_len == 1) && ispunct(ori_str[i])) {
-                new_str += " ";
-                new_str += ori_str[i];
-                new_str += " ";
-                i += 1;
+        for (uint32_t code : cpts_nfd) {
+            int type = unicode_cpt_type(code);
+            if (type == CODEPOINT_TYPE_ACCENT_MARK || type == CODEPOINT_TYPE_CONTROL) {
+                continue;
             }
-            else if ((utf_char_len == 3) && is_chinese_char(ori_str.substr(i, 3))) {
-                new_str += " ";
-                new_str += ori_str.substr(i, 3);
-                new_str += " ";
-                i += 3;
+            code = unicode_tolower(code);
+            if (type == CODEPOINT_TYPE_SEPARATOR) {
+                code = ' ';
             }
-            else {
-                new_str += ori_str[i];
-                i += 1;
+            std::string s = unicode_cpt_to_utf8(code);
+            if (type == CODEPOINT_TYPE_PUNCTUATION || is_ascii_punct(code) || is_chinese_char(code)) {
+                new_str += " ";
+                new_str += s;
+                new_str += " ";
+            } else {
+                new_str += s;
             }
         }
 
         // split by whitespace
         uint64_t l = 0;
         uint64_t r = 0;
+        std::vector<std::string> words;
         while (r < new_str.size()) {
             // if is whitespace
-            if (isspace(new_str[r])) {
+            if (isspace(new_str[r], std::locale::classic())) {
                 if (r > l) words.push_back(new_str.substr(l, (r - l)));
                 l = r + 1;
                 r = l;
-            }
-            else {
+            } else {
                 r += 1;
             }
         }
@@ -8501,97 +12594,30 @@ struct llm_tokenizer_wpm {
         return words;
     }
 
-    std::string normalize(const std::string & text) {
-        // TODO: handle chinese characters? https://github.com/huggingface/tokenizers/blob/ef5f50605ddf9f8caef1598c0e4853862b9707a7/tokenizers/src/normalizers/bert.rs#L98
-        std::string text2 = strip_accents(text);
-        for (size_t i = 0; i < text2.size(); i += utf8_len(text2[i])) {
-            char c = text2[i];
-            if (c >= 'A' && c <= 'Z') {
-                text2[i] = c - 'A' + 'a';
-            }
+    bool is_ascii_punct(uint32_t code) {
+        if (code > 0xFF) {
+            return false;
         }
-        return text2;
+        auto c = char(static_cast<unsigned char>(code));
+        return ispunct(c, std::locale::classic());
     }
 
-    bool is_chinese_char(const std::string & str) {
-        int len = str.length();
-        unsigned int codepoint = 0;
-        int num_bytes = 0;
-        int i = 0;
-        unsigned char ch = static_cast<unsigned char>(str[i]);
-        if (ch <= 0x7f) {
-            codepoint = ch;
-            num_bytes = 1;
-        } else if ((ch >> 5) == 0x06) {
-            codepoint = ch & 0x1f;
-            num_bytes = 2;
-        } else if ((ch >> 4) == 0x0e) {
-            codepoint = ch & 0x0f;
-            num_bytes = 3;
-        } else if ((ch >> 3) == 0x1e) {
-            codepoint = ch & 0x07;
-            num_bytes = 4;
-        }
-        for (int j = 1; j < num_bytes; ++j) {
-            if (i + j >= len) {
-                return false; // incomplete UTF-8 character
-            }
-            unsigned char next_ch = static_cast<unsigned char>(str[i + j]);
-            if ((next_ch >> 6) != 0x02) {
-                return false; // invalid trailing byte
-            }
-            codepoint = (codepoint << 6) | (next_ch & 0x3f);
-        }
-        if ((codepoint >= 0x4E00  && codepoint <= 0x9FFF)  ||
-            (codepoint >= 0x3400  && codepoint <= 0x4DBF)  ||
-            (codepoint >= 0x20000 && codepoint <= 0x2A6DF) ||
-            (codepoint >= 0x2A700 && codepoint <= 0x2B73F) ||
-            (codepoint >= 0x2B740 && codepoint <= 0x2B81F) ||
-            (codepoint >= 0x2B920 && codepoint <= 0x2CEAF) || // this should be 0x2B820 but in hf rust code it is 0x2B920
-            (codepoint >= 0xF900  && codepoint <= 0xFAFF)  ||
-            (codepoint >= 0x2F800 && codepoint <= 0x2FA1F) ||
-            (codepoint >= 0x3000  && codepoint <= 0x303F)  ||
-            (codepoint >= 0xFF00  && codepoint <= 0xFFEF)) {
+    bool is_chinese_char(uint32_t cpt) {
+        if ((cpt >= 0x4E00  && cpt <= 0x9FFF)  ||
+            (cpt >= 0x3400  && cpt <= 0x4DBF)  ||
+            (cpt >= 0x20000 && cpt <= 0x2A6DF) ||
+            (cpt >= 0x2A700 && cpt <= 0x2B73F) ||
+            (cpt >= 0x2B740 && cpt <= 0x2B81F) ||
+            (cpt >= 0x2B920 && cpt <= 0x2CEAF) || // this should be 0x2B820 but in hf rust code it is 0x2B920
+            (cpt >= 0xF900  && cpt <= 0xFAFF)  ||
+            (cpt >= 0x2F800 && cpt <= 0x2FA1F) ||
+            (cpt >= 0x3000  && cpt <= 0x303F)  ||
+            (cpt >= 0xFF00  && cpt <= 0xFFEF)) {
             return true; // NOLINT
         }
         return false;
     }
 
-    std::string strip_accents(const std::string & input_string) {
-        std::string resultString;
-        std::map<std::string, char> accent_map = {
-            {"À", 'A'}, {"Á", 'A'}, {"Â", 'A'}, {"Ã", 'A'}, {"Ä", 'A'}, {"Å", 'A'},
-            {"à", 'a'}, {"á", 'a'}, {"â", 'a'}, {"ã", 'a'}, {"ä", 'a'}, {"å", 'a'},
-            {"È", 'E'}, {"É", 'E'}, {"Ê", 'E'}, {"Ë", 'E'}, {"è", 'e'}, {"é", 'e'},
-            {"ê", 'e'}, {"ë", 'e'}, {"Ì", 'I'}, {"Í", 'I'}, {"Î", 'I'}, {"Ï", 'I'},
-            {"ì", 'i'}, {"í", 'i'}, {"î", 'i'}, {"ï", 'i'}, {"Ò", 'O'}, {"Ó", 'O'},
-            {"Ô", 'O'}, {"Õ", 'O'}, {"Ö", 'O'}, {"ò", 'o'}, {"ó", 'o'}, {"ô", 'o'},
-            {"õ", 'o'}, {"ö", 'o'}, {"Ù", 'U'}, {"Ú", 'U'}, {"Û", 'U'}, {"Ü", 'U'},
-            {"ù", 'u'}, {"ú", 'u'}, {"û", 'u'}, {"ü", 'u'}, {"Ý", 'Y'}, {"ý", 'y'},
-            {"Ç", 'C'}, {"ç", 'c'}, {"Ñ", 'N'}, {"ñ", 'n'},
-        };
-
-        for (size_t i = 0; i <  input_string.length();) {
-            int len = utf8_len(input_string[i]);
-            std::string curChar = input_string.substr(i, len);
-            auto iter = accent_map.find(curChar);
-            if (iter != accent_map.end()) {
-                resultString += iter->second;
-            } else {
-                resultString += curChar;
-            }
-            i += len;
-        }
-
-        return resultString;
-    }
-
-    static size_t utf8_len(char src) {
-        const size_t lookup[] = {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4};
-        uint8_t highbits = static_cast<uint8_t>(src) >> 4;
-        return lookup[highbits];
-    }
-
     const llama_vocab & vocab;
 };
 
@@ -8725,30 +12751,28 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
     }
 }
 
-static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos, bool special) {
+static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool add_special, bool parse_special) {
     std::vector<llama_vocab::id> output;
-
-    // OG tokenizer behavior:
-    //
-    // tokenizer.encode('', add_bos=True)  returns [1]
-    // tokenizer.encode('', add_bos=False) returns []
-
-    if (bos && vocab.special_bos_id != -1) {
-        output.push_back(vocab.special_bos_id);
-    }
-
-    if (raw_text.empty()) {
-        return output;
-    }
-
     std::forward_list<fragment_buffer_variant> fragment_buffer;
-    fragment_buffer.emplace_front(raw_text, 0, raw_text.length());
 
-    if (special) tokenizer_st_partition(vocab, fragment_buffer);
+    if (!raw_text.empty()) {
+        fragment_buffer.emplace_front(raw_text, 0, raw_text.length());
+        if (parse_special) tokenizer_st_partition(vocab, fragment_buffer);
+    }
 
     switch (vocab.type) {
         case LLAMA_VOCAB_TYPE_SPM:
             {
+                // OG tokenizer behavior:
+                //
+                // tokenizer.encode('', add_special_tokens=True)  returns [1]
+                // tokenizer.encode('', add_special_tokens=False) returns []
+
+                if (add_special && vocab.special_add_bos != 0) {
+                    GGML_ASSERT(vocab.special_bos_id != -1);
+                    output.push_back(vocab.special_bos_id);
+                }
+
                 for (const auto & fragment : fragment_buffer) {
                     if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
                         // without adding this leading whitespace, we do not get the same results as the original tokenizer
@@ -8774,9 +12798,19 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
                         output.push_back(fragment.token);
                     }
                 }
+
+                if (add_special && vocab.special_add_eos == 1) {
+                    GGML_ASSERT(vocab.special_eos_id != -1);
+                    output.push_back(vocab.special_eos_id);
+                }
             } break;
         case LLAMA_VOCAB_TYPE_BPE:
             {
+                if (add_special && vocab.special_add_bos != 0) {
+                    GGML_ASSERT(vocab.special_bos_id != -1);
+                    output.push_back(vocab.special_bos_id);
+                }
+
                 for (const auto & fragment : fragment_buffer) {
                     if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
                         auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
@@ -8790,9 +12824,19 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
                         output.push_back(fragment.token);
                     }
                 }
+
+                if (add_special && vocab.special_add_eos == 1) {
+                    GGML_ASSERT(vocab.special_add_eos != -1);
+                    output.push_back(vocab.special_eos_id);
+                }
             } break;
         case LLAMA_VOCAB_TYPE_WPM:
             {
+                if (add_special) {
+                    GGML_ASSERT(vocab.special_cls_id != -1);
+                    output.push_back(vocab.special_cls_id);
+                }
+
                 for (const auto & fragment : fragment_buffer) {
                     if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
                         auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
@@ -8806,7 +12850,14 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
                         output.push_back(fragment.token);
                     }
                 }
+
+                if (add_special) {
+                    GGML_ASSERT(vocab.special_sep_id != -1);
+                    output.push_back(vocab.special_sep_id);
+                }
             } break;
+        case LLAMA_VOCAB_TYPE_NONE:
+            GGML_ASSERT(false);
     }
 
     return output;
@@ -8816,28 +12867,10 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
 // grammar - internal
 //
 
-struct llama_partial_utf8 {
-    uint32_t value;    // bit value so far (unshifted)
-    int      n_remain; // num bytes remaining; -1 indicates invalid sequence
-};
-
-struct llama_grammar {
-    const std::vector<std::vector<llama_grammar_element>>   rules;
-    std::vector<std::vector<const llama_grammar_element *>> stacks;
-
-    // buffer for partially generated UTF-8 sequence from accepted tokens
-    llama_partial_utf8                                      partial_utf8;
-};
-
-struct llama_grammar_candidate {
-    size_t               index;
-    const uint32_t     * code_points;
-    llama_partial_utf8   partial_utf8;
-};
 
 // Decodes a UTF-8 string which may end in an incomplete sequence. Adds a terminating 0 for use as
 // pointer. If an invalid sequence is encountered, returns `llama_partial_utf8.n_remain == -1`.
-static std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
+std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
         const std::string & src,
         llama_partial_utf8   partial_start) {
     static const int      lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 3, 4 };
@@ -8988,7 +13021,9 @@ static void llama_grammar_advance_stack(
         std::vector<std::vector<const llama_grammar_element *>> & new_stacks) {
 
     if (stack.empty()) {
-        new_stacks.emplace_back(stack);
+        if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) {
+            new_stacks.emplace_back(stack);
+        }
         return;
     }
 
@@ -9025,7 +13060,10 @@ static void llama_grammar_advance_stack(
         }
         case LLAMA_GRETYPE_CHAR:
         case LLAMA_GRETYPE_CHAR_NOT:
-            new_stacks.emplace_back(stack);
+            if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) {
+                // only add the stack if it's not a duplicate of one we already have
+                new_stacks.emplace_back(stack);
+            }
             break;
         default:
             // end of alternate (LLAMA_GRETYPE_END, LLAMA_GRETYPE_ALT) or middle of char range
@@ -9039,12 +13077,13 @@ static void llama_grammar_advance_stack(
 // be positioned at a character range (see `llama_grammar_advance_stack`), and
 // produces the N possible stacks if the given char is accepted at those
 // positions
-static std::vector<std::vector<const llama_grammar_element *>> llama_grammar_accept(
+void llama_grammar_accept(
         const std::vector<std::vector<llama_grammar_element>>         & rules,
         const std::vector<std::vector<const llama_grammar_element *>> & stacks,
-        const uint32_t                                                  chr) {
+        const uint32_t                                                  chr,
+        std::vector<std::vector<const llama_grammar_element *>>       & new_stacks) {
 
-    std::vector<std::vector<const llama_grammar_element *>> new_stacks;
+    new_stacks.clear();
 
     for (const auto & stack : stacks) {
         if (stack.empty()) {
@@ -9063,8 +13102,6 @@ static std::vector<std::vector<const llama_grammar_element *>> llama_grammar_acc
             llama_grammar_advance_stack(rules, new_stack, new_stacks);
         }
     }
-
-    return new_stacks;
 }
 
 static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
@@ -9078,6 +13115,7 @@ static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_
         const std::vector<llama_grammar_candidate>            & candidates) {
 
     std::vector<llama_grammar_candidate> rejects;
+    rejects.reserve(candidates.size());
 
     if (stack.empty()) {
         for (const auto & tok : candidates) {
@@ -9091,6 +13129,8 @@ static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_
     const llama_grammar_element * stack_pos = stack.back();
 
     std::vector<llama_grammar_candidate> next_candidates;
+    next_candidates.reserve(candidates.size());
+
     for (const auto & tok : candidates) {
         if (*tok.code_points == 0) {
             // reached end of full codepoints in token, reject iff it ended in a partial sequence
@@ -9163,7 +13203,7 @@ struct llama_grammar * llama_grammar_init(
 
     // loop over alternates of start rule to build initial stacks
     std::vector<std::vector<const llama_grammar_element *>> stacks;
-    pos = rules[start_rule_index];
+    pos = vec_rules[start_rule_index].data();
     do {
         std::vector<const llama_grammar_element *> stack;
         if (!llama_grammar_is_end_of_sequence(pos)) {
@@ -9625,10 +13665,6 @@ void llama_sample_temp(struct llama_context * ctx, llama_token_data_array * cand
     }
 }
 
-void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
-    llama_sample_temp(ctx, candidates_p, temp);
-}
-
 void llama_sample_repetition_penalties(
             struct llama_context * ctx,
           llama_token_data_array * candidates,
@@ -9680,16 +13716,14 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
     GGML_ASSERT(ctx);
     const int64_t t_start_sample_us = ggml_time_us();
 
-    bool allow_eos = false;
+    bool allow_eog = false;
     for (const auto & stack : grammar->stacks) {
         if (stack.empty()) {
-            allow_eos = true;
+            allow_eog = true;
             break;
         }
     }
 
-    const llama_token eos = llama_token_eos(&ctx->model);
-
     std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
     candidates_decoded.reserve(candidates->size);
     std::vector<llama_grammar_candidate>                              candidates_grammar;
@@ -9697,9 +13731,10 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
 
     for (size_t i = 0; i < candidates->size; ++i) {
         const llama_token id    = candidates->data[i].id;
-        const std::string piece = llama_token_to_piece(ctx, id);
-        if (id == eos) {
-            if (!allow_eos) {
+        const std::string piece = llama_token_to_piece(ctx, id, false);
+
+        if (llama_token_is_eog(&ctx->model, id)) {
+            if (!allow_eog) {
                 candidates->data[i].logit = -INFINITY;
             }
         } else if (piece.empty() || piece[0] == 0) {
@@ -9755,38 +13790,6 @@ void llama_sample_apply_guidance(
     ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
 }
 
-void llama_sample_classifier_free_guidance(
-          struct llama_context * ctx,
-        llama_token_data_array * candidates,
-          struct llama_context * guidance_ctx,
-                         float   scale) {
-    GGML_ASSERT(ctx);
-    int64_t t_start_sample_us;
-
-    t_start_sample_us = ggml_time_us();
-    const size_t n_vocab = llama_n_vocab(llama_get_model(ctx));
-
-    GGML_ASSERT(n_vocab == candidates->size);
-    GGML_ASSERT(!candidates->sorted);
-
-    std::vector<float> logits_base(n_vocab);
-    for (size_t i = 0; i < n_vocab; ++i) {
-        logits_base[i] = candidates->data[i].logit;
-    }
-
-    float * logits_guidance = llama_get_logits(guidance_ctx);
-
-    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    llama_sample_apply_guidance(ctx, logits_base.data(), logits_guidance, scale);
-    t_start_sample_us = ggml_time_us();
-
-    for (size_t i = 0; i < n_vocab; ++i) {
-        candidates->data[i].logit = logits_base[i];
-    }
-
-    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-}
-
 llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) {
     GGML_ASSERT(ctx);
 
@@ -9894,7 +13897,7 @@ llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_da
     return result;
 }
 
-llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates) {
+llama_token llama_sample_token_with_rng(struct llama_context * ctx, llama_token_data_array * candidates, std::mt19937 & rng) {
     GGML_ASSERT(ctx);
 
     const int64_t t_start_sample_us = ggml_time_us();
@@ -9907,7 +13910,6 @@ llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_arra
     }
 
     std::discrete_distribution<> dist(probs.begin(), probs.end());
-    auto & rng = ctx->rng;
     int idx = dist(rng);
 
     llama_token result = candidates->data[idx].id;
@@ -9917,10 +13919,14 @@ llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_arra
     return result;
 }
 
+llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates) {
+    return llama_sample_token_with_rng(ctx, candidates, ctx->rng);
+}
+
 void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token) {
     const int64_t t_start_sample_us = ggml_time_us();
 
-    if (token == llama_token_eos(&ctx->model)) {
+    if (llama_token_is_eog(&ctx->model, token)) {
         for (const auto & stack : grammar->stacks) {
             if (stack.empty()) {
                 return;
@@ -9929,13 +13935,15 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
         GGML_ASSERT(false);
     }
 
-    const std::string piece = llama_token_to_piece(ctx, token);
+    const std::string piece = llama_token_to_piece(ctx, token, false);
 
     // Note terminating 0 in decoded string
     const auto   decoded     = decode_utf8(piece, grammar->partial_utf8);
     const auto & code_points = decoded.first;
+    std::vector<std::vector<const llama_grammar_element *>> tmp_new_stacks;
     for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
-        grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it);
+        llama_grammar_accept(grammar->rules, grammar->stacks, *it, tmp_new_stacks);
+        grammar->stacks = tmp_new_stacks;
     }
     grammar->partial_utf8 = decoded.second;
     GGML_ASSERT(!grammar->stacks.empty());
@@ -10069,6 +14077,11 @@ struct llama_beam_search_data {
             }
             llama_logit_info logit_info(ctx);
             std::vector<llama_token_data> next_tokens = logit_info.top_k(n_beams);
+
+            // Clear the kv slot so that other beams may try different tokens at this position. The llama_decode()
+            // call in loop() will conclusively fill in the kv slot once the beams converge at this position.
+            llama_kv_cache_seq_rm(ctx, 0, n_past, -1);
+
             size_t i=0;
             if (next_beams.size() < n_beams) {
                 for (; next_beams.size() < n_beams ; ++i) {
@@ -10214,13 +14227,16 @@ struct quantize_state_internal {
 
     bool has_imatrix      = false;
 
+    // used to figure out if a model shares tok_embd with the output weight
+    bool has_output       = false;
+
     quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params)
         : model(model)
         , params(params)
         {}
 };
 
-static void llama_convert_tensor_internal(
+static void llama_tensor_dequantize_internal(
     struct ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
     const size_t nelements, const int nthread
 ) {
@@ -10235,13 +14251,16 @@ static void llama_convert_tensor_internal(
         if (qtype.to_float == NULL) {
             throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor->type)));
         }
-    } else if (tensor->type != GGML_TYPE_F16) {
+    } else if (tensor->type != GGML_TYPE_F16 &&
+               tensor->type != GGML_TYPE_BF16) {
         throw std::runtime_error(format("cannot dequantize/convert tensor type %s", ggml_type_name(tensor->type)));
     }
 
     if (nthread < 2) {
         if (tensor->type == GGML_TYPE_F16) {
             ggml_fp16_to_fp32_row((ggml_fp16_t *)tensor->data, f32_output, nelements);
+        } else if (tensor->type == GGML_TYPE_BF16) {
+            ggml_bf16_to_fp32_row((ggml_bf16_t *)tensor->data, f32_output, nelements);
         } else if (ggml_is_quantized(tensor->type)) {
             qtype.to_float(tensor->data, f32_output, nelements);
         } else {
@@ -10250,7 +14269,14 @@ static void llama_convert_tensor_internal(
         return;
     }
 
-    size_t block_size = tensor->type == GGML_TYPE_F16 ? 1 : (size_t)ggml_blck_size(tensor->type);
+    size_t block_size;
+    if (tensor->type == GGML_TYPE_F16 ||
+        tensor->type == GGML_TYPE_BF16) {
+        block_size = 1;
+    } else {
+        block_size = (size_t)ggml_blck_size(tensor->type);
+    }
+
     size_t block_size_bytes = ggml_type_size(tensor->type);
 
     GGML_ASSERT(nelements % block_size == 0);
@@ -10269,6 +14295,8 @@ static void llama_convert_tensor_internal(
         auto compute = [qtype] (ggml_type typ, uint8_t * inbuf, float * outbuf, int nels) {
             if (typ == GGML_TYPE_F16) {
                 ggml_fp16_to_fp32_row((ggml_fp16_t *)inbuf, outbuf, nels);
+            } else if (typ == GGML_TYPE_BF16) {
+                ggml_bf16_to_fp32_row((ggml_bf16_t *)inbuf, outbuf, nels);
             } else {
                 qtype.to_float(inbuf, outbuf, nels);
             }
@@ -10281,7 +14309,7 @@ static void llama_convert_tensor_internal(
     workers.clear();
 }
 
-static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
+static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
     const std::string name = ggml_get_name(tensor);
 
     // TODO: avoid hardcoded tensor names - use the TN_* constants
@@ -10298,7 +14326,6 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
             // sprinkled in the model. Hence, simply dividing i_ffn_down by n_expert does not work
             // for getting the current layer as I initially thought, and we need to resort to parsing the
             // tensor name.
-            n_layer /= n_expert;
             if (sscanf(name, "blk.%d.", &i_layer) != 1) {
                 throw std::runtime_error(format("Failed to determine layer for tensor %s", name));
             }
@@ -10309,36 +14336,63 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
         return std::make_pair(i_layer, n_layer);
     };
 
-    if (name == tn(LLM_TENSOR_OUTPUT, "weight")) {
-        int nx = tensor->ne[0];
-        if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
-            new_type = GGML_TYPE_Q8_0;
-        }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) {
-            new_type = GGML_TYPE_Q5_K;
-        }
-        else if (new_type != GGML_TYPE_Q8_0) {
-            new_type = GGML_TYPE_Q6_K;
+    // for arches that share the same tensor between the token embeddings and the output, we quantize the token embeddings
+    // with the quantization of the output tensor
+    if (name == tn(LLM_TENSOR_OUTPUT, "weight") || (!qs.has_output && name == tn(LLM_TENSOR_TOKEN_EMBD, "weight"))) {
+        if (qs.params->output_tensor_type < GGML_TYPE_COUNT) {
+            new_type = qs.params->output_tensor_type;
+        } else {
+            int nx = tensor->ne[0];
+            if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
+                new_type = GGML_TYPE_Q8_0;
+            }
+            else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
+                     ftype == LLAMA_FTYPE_MOSTLY_IQ1_S   || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S  || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M   ||
+                     ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
+                new_type = GGML_TYPE_Q5_K;
+            }
+            else if (new_type != GGML_TYPE_Q8_0) {
+                new_type = GGML_TYPE_Q6_K;
+            }
         }
     } else if (name == "token_embd.weight") {
-        if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) {
-            new_type = GGML_TYPE_Q2_K;
+        if (qs.params->token_embedding_type < GGML_TYPE_COUNT) {
+            new_type = qs.params->token_embedding_type;
+        } else {
+            if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS ||
+                ftype == LLAMA_FTYPE_MOSTLY_IQ1_S   || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
+                new_type = GGML_TYPE_Q2_K;
+            }
+            else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
+                new_type = GGML_TYPE_IQ3_S;
+            }
+            else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+                new_type = GGML_TYPE_IQ3_S;
+            }
         }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
-            new_type = GGML_TYPE_Q4_K;
-        }
-    } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) {
+    } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
+               ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M    || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
         if (name.find("attn_v.weight") != std::string::npos) {
             if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K;
-            else new_type = GGML_TYPE_Q2_K;
+            else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
             ++qs.i_attention_wv;
         }
+        else if (qs.model.hparams.n_expert == 8 && name.find("attn_k.weight") != std::string::npos) {
+            new_type = GGML_TYPE_Q4_K;
+        }
         else if (name.find("ffn_down") != std::string::npos) {
-            if (qs.i_ffn_down < qs.n_ffn_down/8) new_type = GGML_TYPE_Q2_K;
+            if (qs.i_ffn_down < qs.n_ffn_down/8) {
+                new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
+            }
             ++qs.i_ffn_down;
         }
         else if (name.find("attn_output.weight") != std::string::npos) {
-            if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) new_type = GGML_TYPE_IQ2_XXS;
+            if (qs.model.hparams.n_expert == 8) {
+                new_type = GGML_TYPE_Q5_K;
+            } else {
+                if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) new_type = GGML_TYPE_IQ2_XXS;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_S;
+            }
         }
     } else if (name.find("attn_v.weight") != std::string::npos) {
         if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
@@ -10348,12 +14402,21 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
             new_type = GGML_TYPE_Q4_K;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
-            new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_Q3_K : GGML_TYPE_IQ3_XXS;
+            new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_IQ3_S : GGML_TYPE_IQ3_XXS;
+        }
+        else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S) && qs.model.hparams.n_gqa() >= 4) {
+            new_type = GGML_TYPE_Q4_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
+            new_type = GGML_TYPE_Q4_K;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
             new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
+        else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && qs.model.hparams.n_gqa() >= 4) {
+            new_type = GGML_TYPE_Q5_K;
+        }
         else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
                 use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K;
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
@@ -10377,14 +14440,24 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
             // TODO: explore better strategies
             new_type = GGML_TYPE_Q8_0;
         }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS) {
-            new_type = GGML_TYPE_Q2_K;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
+            new_type = GGML_TYPE_IQ3_XXS;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+            new_type = GGML_TYPE_IQ2_S;
+        }
+    } else if (name.find("attn_q.weight") != std::string::npos) {
+        if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
+            new_type = GGML_TYPE_IQ3_XXS;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+            new_type = GGML_TYPE_IQ2_S;
         }
     } else if (name.find("ffn_down") != std::string::npos) {
         auto info = layer_info(qs.i_ffn_down, qs.n_ffn_down, name.c_str());
         int i_layer = info.first, n_layer = info.second;
         if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS) {
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) {
             if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && !qs.has_imatrix) {
@@ -10395,6 +14468,10 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
                      : arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K
                      : GGML_TYPE_Q3_K;
         }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M && (i_layer < n_layer/8 ||
+                    (qs.model.hparams.n_expert == 8 && use_more_bits(i_layer, n_layer)))) {
+            new_type = GGML_TYPE_Q4_K;
+        }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
             new_type = arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K;
         }
@@ -10406,6 +14483,9 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
                 if (use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
             }
         }
+        else if (i_layer < n_layer/8 && (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && !qs.has_imatrix) {
+            new_type = GGML_TYPE_Q5_K;
+        }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && i_layer < n_layer/8) {
             new_type = GGML_TYPE_Q5_K;
@@ -10421,39 +14501,43 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
     } else if (name.find("attn_output.weight") != std::string::npos) {
         if (arch != LLM_ARCH_FALCON) {
             if (qs.model.hparams.n_expert == 8) {
-                if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K   || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
-                    ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M ||
-                    ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
+                if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K   || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
+                    ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL  ||
+                    ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S  ||
+                    ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) {
                     new_type = GGML_TYPE_Q5_K;
                 }
             } else {
-                if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K  ) new_type = GGML_TYPE_Q3_K;
-                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) new_type = GGML_TYPE_Q3_K;
-                else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K;
-                else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
+                if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K   ) new_type = GGML_TYPE_Q3_K;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) new_type = GGML_TYPE_IQ3_S;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M ) new_type = GGML_TYPE_Q4_K;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L ) new_type = GGML_TYPE_Q5_K;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  ) new_type = GGML_TYPE_Q4_K;
             }
         } else {
             if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
         }
     }
     else if (name.find("attn_qkv.weight") != std::string::npos) {
-        if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
+        if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
+            new_type = GGML_TYPE_Q4_K;
+        }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q5_K;
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K;
     }
     else if (name.find("ffn_gate") != std::string::npos) {
         auto info = layer_info(qs.i_ffn_gate, qs.n_ffn_gate, name.c_str());
         int i_layer = info.first, n_layer = info.second;
-        if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS && !use_more_bits(i_layer, n_layer)) {
-            new_type = GGML_TYPE_Q2_K;
+        if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
+            new_type = GGML_TYPE_IQ3_XXS;
         }
         ++qs.i_ffn_gate;
     }
     else if (name.find("ffn_up") != std::string::npos) {
         auto info = layer_info(qs.i_ffn_up, qs.n_ffn_up, name.c_str());
         int i_layer = info.first, n_layer = info.second;
-        if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS && !use_more_bits(i_layer, n_layer)) {
-            new_type = GGML_TYPE_Q2_K;
+        if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
+            new_type = GGML_TYPE_IQ3_XXS;
         }
         ++qs.i_ffn_up;
     }
@@ -10471,9 +14555,10 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
     //}
     bool convert_incompatible_tensor = false;
     if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K ||
-        new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K ||
-        new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS ||
-        new_type == GGML_TYPE_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) {
+        new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K || new_type == GGML_TYPE_IQ4_XS ||
+        new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_S ||
+        new_type == GGML_TYPE_IQ3_XXS || new_type == GGML_TYPE_IQ1_S || new_type == GGML_TYPE_IQ3_S ||
+        new_type == GGML_TYPE_IQ1_M) {
         int nx = tensor->ne[0];
         int ny = tensor->ne[1];
         if (nx % QK_K != 0) {
@@ -10487,13 +14572,17 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
         switch (new_type) {
             case GGML_TYPE_IQ2_XXS:
             case GGML_TYPE_IQ2_XS:
+            case GGML_TYPE_IQ2_S:
             case GGML_TYPE_IQ3_XXS:
+            case GGML_TYPE_IQ3_S:
             case GGML_TYPE_IQ1_S:
-            case GGML_TYPE_Q2_K: new_type = GGML_TYPE_Q4_0; break;
-            case GGML_TYPE_Q3_K: new_type = GGML_TYPE_Q4_1; break;
-            case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break;
-            case GGML_TYPE_Q5_K: new_type = GGML_TYPE_Q5_1; break;
-            case GGML_TYPE_Q6_K: new_type = GGML_TYPE_Q8_0; break;
+            case GGML_TYPE_IQ1_M:
+            case GGML_TYPE_Q2_K:
+            case GGML_TYPE_Q3_K:
+            case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break;
+            case GGML_TYPE_Q4_K:   new_type = GGML_TYPE_Q5_0;   break;
+            case GGML_TYPE_Q5_K:   new_type = GGML_TYPE_Q5_1;   break;
+            case GGML_TYPE_Q6_K:   new_type = GGML_TYPE_Q8_0;   break;
             default: throw std::runtime_error("\nUnsupported tensor size encountered\n");
         }
         LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type));
@@ -10503,35 +14592,97 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
     return new_type;
 }
 
+static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int64_t chunk_size, int64_t nrows, int64_t n_per_row, const float * imatrix, std::vector<std::thread> & workers, const int nthread) {
+    if (nthread < 2) {
+        // single-thread
+        size_t new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix);
+        if (!ggml_validate_row_data(new_type, new_data, new_size)) {
+            throw std::runtime_error("quantized data validation failed");
+        }
+        return new_size;
+    }
+
+    std::mutex mutex;
+    int64_t counter = 0;
+    size_t new_size = 0;
+    bool valid = true;
+    auto compute = [&mutex, &counter, &new_size, &valid, new_type, f32_data, new_data, chunk_size,
+            nrows, n_per_row, imatrix]() {
+        const int64_t nrows_per_chunk = chunk_size / n_per_row;
+        size_t local_size = 0;
+        while (true) {
+            std::unique_lock<std::mutex> lock(mutex);
+            int64_t first_row = counter; counter += nrows_per_chunk;
+            if (first_row >= nrows) {
+                if (local_size > 0) {
+                    new_size += local_size;
+                }
+                break;
+            }
+            lock.unlock();
+            const int64_t this_nrow = std::min(nrows - first_row, nrows_per_chunk);
+            size_t this_size = ggml_quantize_chunk(new_type, f32_data, new_data, first_row * n_per_row, this_nrow, n_per_row, imatrix);
+            local_size += this_size;
+
+            // validate the quantized data
+            const size_t row_size  = ggml_row_size(new_type, n_per_row);
+            void * this_data = (char *) new_data + first_row * row_size;
+            if (!ggml_validate_row_data(new_type, this_data, this_size)) {
+                std::unique_lock<std::mutex> lock(mutex);
+                valid = false;
+                break;
+            }
+        }
+    };
+    for (int it = 0; it < nthread - 1; ++it) {
+        workers.emplace_back(compute);
+    }
+    compute();
+    for (auto & w : workers) { w.join(); }
+    workers.clear();
+    if (!valid) {
+        throw std::runtime_error("quantized data validation failed");
+    }
+    return new_size;
+}
+
 static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
-    ggml_type quantized_type;
+    ggml_type default_type;
     llama_ftype ftype = params->ftype;
 
     switch (params->ftype) {
-        case LLAMA_FTYPE_MOSTLY_Q4_0: quantized_type = GGML_TYPE_Q4_0; break;
-        case LLAMA_FTYPE_MOSTLY_Q4_1: quantized_type = GGML_TYPE_Q4_1; break;
-        case LLAMA_FTYPE_MOSTLY_Q5_0: quantized_type = GGML_TYPE_Q5_0; break;
-        case LLAMA_FTYPE_MOSTLY_Q5_1: quantized_type = GGML_TYPE_Q5_1; break;
-        case LLAMA_FTYPE_MOSTLY_Q8_0: quantized_type = GGML_TYPE_Q8_0; break;
-        case LLAMA_FTYPE_MOSTLY_F16:  quantized_type = GGML_TYPE_F16;  break;
-        case LLAMA_FTYPE_ALL_F32:     quantized_type = GGML_TYPE_F32;  break;
+        case LLAMA_FTYPE_MOSTLY_Q4_0: default_type = GGML_TYPE_Q4_0; break;
+        case LLAMA_FTYPE_MOSTLY_Q4_1: default_type = GGML_TYPE_Q4_1; break;
+        case LLAMA_FTYPE_MOSTLY_Q5_0: default_type = GGML_TYPE_Q5_0; break;
+        case LLAMA_FTYPE_MOSTLY_Q5_1: default_type = GGML_TYPE_Q5_1; break;
+        case LLAMA_FTYPE_MOSTLY_Q8_0: default_type = GGML_TYPE_Q8_0; break;
+        case LLAMA_FTYPE_MOSTLY_F16:  default_type = GGML_TYPE_F16;  break;
+        case LLAMA_FTYPE_MOSTLY_BF16: default_type = GGML_TYPE_BF16; break;
+        case LLAMA_FTYPE_ALL_F32:     default_type = GGML_TYPE_F32;  break;
 
         // K-quants
         case LLAMA_FTYPE_MOSTLY_Q2_K_S:
-        case LLAMA_FTYPE_MOSTLY_Q2_K:    quantized_type = GGML_TYPE_Q2_K;    break;
-        case LLAMA_FTYPE_MOSTLY_Q3_K_XS:
+        case LLAMA_FTYPE_MOSTLY_Q2_K:    default_type = GGML_TYPE_Q2_K;    break;
+        case LLAMA_FTYPE_MOSTLY_IQ3_XS:  default_type = GGML_TYPE_IQ3_S;   break;
         case LLAMA_FTYPE_MOSTLY_Q3_K_S:
         case LLAMA_FTYPE_MOSTLY_Q3_K_M:
-        case LLAMA_FTYPE_MOSTLY_Q3_K_L:  quantized_type = GGML_TYPE_Q3_K;    break;
+        case LLAMA_FTYPE_MOSTLY_Q3_K_L:  default_type = GGML_TYPE_Q3_K;    break;
         case LLAMA_FTYPE_MOSTLY_Q4_K_S:
-        case LLAMA_FTYPE_MOSTLY_Q4_K_M:  quantized_type = GGML_TYPE_Q4_K;    break;
+        case LLAMA_FTYPE_MOSTLY_Q4_K_M:  default_type = GGML_TYPE_Q4_K;    break;
         case LLAMA_FTYPE_MOSTLY_Q5_K_S:
-        case LLAMA_FTYPE_MOSTLY_Q5_K_M:  quantized_type = GGML_TYPE_Q5_K;    break;
-        case LLAMA_FTYPE_MOSTLY_Q6_K:    quantized_type = GGML_TYPE_Q6_K;    break;
-        case LLAMA_FTYPE_MOSTLY_IQ2_XXS: quantized_type = GGML_TYPE_IQ2_XXS; break;
-        case LLAMA_FTYPE_MOSTLY_IQ2_XS:  quantized_type = GGML_TYPE_IQ2_XS;  break;
-        case LLAMA_FTYPE_MOSTLY_IQ3_XXS: quantized_type = GGML_TYPE_IQ3_XXS; break;
-        case LLAMA_FTYPE_MOSTLY_IQ1_S:   quantized_type = GGML_TYPE_IQ1_S  ; break;
+        case LLAMA_FTYPE_MOSTLY_Q5_K_M:  default_type = GGML_TYPE_Q5_K;    break;
+        case LLAMA_FTYPE_MOSTLY_Q6_K:    default_type = GGML_TYPE_Q6_K;    break;
+        case LLAMA_FTYPE_MOSTLY_IQ2_XXS: default_type = GGML_TYPE_IQ2_XXS; break;
+        case LLAMA_FTYPE_MOSTLY_IQ2_XS:  default_type = GGML_TYPE_IQ2_XS;  break;
+        case LLAMA_FTYPE_MOSTLY_IQ2_S:   default_type = GGML_TYPE_IQ2_XS;  break;
+        case LLAMA_FTYPE_MOSTLY_IQ2_M:   default_type = GGML_TYPE_IQ2_S;   break;
+        case LLAMA_FTYPE_MOSTLY_IQ3_XXS: default_type = GGML_TYPE_IQ3_XXS; break;
+        case LLAMA_FTYPE_MOSTLY_IQ1_S:   default_type = GGML_TYPE_IQ1_S;   break;
+        case LLAMA_FTYPE_MOSTLY_IQ1_M:   default_type = GGML_TYPE_IQ1_M;   break;
+        case LLAMA_FTYPE_MOSTLY_IQ4_NL:  default_type = GGML_TYPE_IQ4_NL;  break;
+        case LLAMA_FTYPE_MOSTLY_IQ4_XS:  default_type = GGML_TYPE_IQ4_XS;  break;
+        case LLAMA_FTYPE_MOSTLY_IQ3_S:   default_type = GGML_TYPE_IQ3_S;   break;
+        case LLAMA_FTYPE_MOSTLY_IQ3_M:   default_type = GGML_TYPE_IQ3_S;   break;
 
         default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
     }
@@ -10550,8 +14701,13 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
     constexpr bool use_mmap = false;
 #endif
 
-    llama_model_loader ml(fname_inp, use_mmap, NULL);
-    ml.init_mapping(false); // no prefetching?
+    llama_model_kv_override * kv_overrides = nullptr;
+    if (params->kv_overrides) {
+        auto v = (std::vector<llama_model_kv_override>*)params->kv_overrides;
+        kv_overrides = v->data();
+    }
+    llama_model_loader ml(fname_inp, use_mmap, /*check_tensors*/ true, kv_overrides);
+    ml.init_mappings(false); // no prefetching
 
     llama_model model;
     llm_load_arch(ml, model);
@@ -10575,41 +14731,60 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
     struct gguf_context * ctx_out = gguf_init_empty();
 
     // copy the KV pairs from the input file
-    gguf_set_kv     (ctx_out, ml.ctx_gguf);
+    gguf_set_kv     (ctx_out, ml.meta);
     gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
     gguf_set_val_u32(ctx_out, "general.file_type", ftype);
+    // Remove split metadata
+    gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_NO).c_str());
+    gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str());
+    gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str());
+
+    if (params->kv_overrides) {
+        const std::vector<llama_model_kv_override> & overrides = *(const std::vector<llama_model_kv_override> *)params->kv_overrides;
+        for (auto & o : overrides) {
+            if (o.key[0] == 0) break;
+            if (o.tag == LLAMA_KV_OVERRIDE_TYPE_FLOAT) {
+                gguf_set_val_f32(ctx_out, o.key, o.val_f64);
+            } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_INT) {
+                gguf_set_val_i32(ctx_out, o.key, o.val_i64);
+            } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_BOOL) {
+                gguf_set_val_bool(ctx_out, o.key, o.val_bool);
+            } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_STR) {
+                gguf_set_val_str(ctx_out, o.key, o.val_str);
+            } else {
+                LLAMA_LOG_WARN("%s: unknown KV override type for key %s\n", __func__, o.key);
+            }
+        }
+    }
 
     for (int i = 0; i < ml.n_tensors; ++i) {
-        struct ggml_tensor * meta = ml.get_tensor_meta(i);
+        const struct ggml_tensor * meta = ml.get_tensor_meta(i);
 
         const std::string name = ggml_get_name(meta);
 
         // TODO: avoid hardcoded tensor names - use the TN_* constants
-        if (name.find("attn_v.weight") != std::string::npos || name.find("attn_qkv.weight") != std::string::npos) {
+        if (name.find("attn_v.weight")   != std::string::npos ||
+            name.find("attn_qkv.weight") != std::string::npos) {
             ++qs.n_attention_wv;
-        }
-        else if (name.find("ffn_down") != std::string::npos) {
-            ++qs.n_ffn_down;
-        }
-        else if (name.find("ffn_gate") != std::string::npos) {
-            ++qs.n_ffn_gate;
-        }
-        else if (name.find("ffn_up") != std::string::npos) {
-            ++qs.n_ffn_up;
+        } else if (name == LLM_TN(model.arch)(LLM_TENSOR_OUTPUT, "weight")) {
+            qs.has_output = true;
         }
     }
-    if (qs.n_attention_wv != qs.n_ffn_down || (uint32_t)qs.n_attention_wv != model.hparams.n_layer) {
-        LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_ffn_down = %d, hparams.n_layer = %d\n",
-                __func__, qs.n_attention_wv, qs.n_ffn_down, model.hparams.n_layer);
-    }
+
+    qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)model.hparams.n_layer;
+
+    // sanity checks
+    //
+    //  - qs.n_attention_wv == 0                     for Mamba       models
+    //  - qs.n_attention_wv == model.hparams.n_layer for Transformer models
+    //
+    GGML_ASSERT((qs.n_attention_wv == 0 || qs.n_attention_wv == (int)model.hparams.n_layer) && "n_attention_wv is unexpected");
 
     size_t total_size_org = 0;
     size_t total_size_new = 0;
-    std::vector<int64_t> hist_all(1 << 4, 0);
 
     std::vector<std::thread> workers;
     workers.reserve(nthread);
-    std::mutex mutex;
 
     int idx = 0;
 
@@ -10617,24 +14792,74 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
     std::vector<no_init<uint8_t>> work;
     std::vector<no_init<float>> f32_conv_buf;
 
+    uint16_t n_split = 1;
+    // Assume split index is continuous
+    if (params->keep_split) {
+        for (int i = 0; i < ml.n_tensors; ++i) {
+            n_split = std::max(uint16_t(ml.get_weight(i)->idx+1), n_split);
+        }
+    }
+    std::vector<gguf_context*> ctx_outs(n_split, NULL);
+    ctx_outs[0] = ctx_out;
+
     // populate the original tensors so we get an initial meta data
     for (int i = 0; i < ml.n_tensors; ++i) {
-        struct ggml_tensor * meta = ml.get_tensor_meta(i);
-        gguf_add_tensor(ctx_out, meta);
+        auto weight = ml.get_weight(i);
+        uint16_t i_split = params->keep_split ? weight->idx : 0;
+        struct ggml_tensor * tensor = weight->tensor;
+        if (ctx_outs[i_split] == NULL) {
+            ctx_outs[i_split] = gguf_init_empty();
+        }
+        gguf_add_tensor(ctx_outs[i_split], tensor);
     }
 
-    std::ofstream fout(fname_out, std::ios::binary);
-    fout.exceptions(std::ofstream::failbit); // fail fast on write errors
+    // Set split info if needed
+    if (n_split > 1) {
+        for (size_t i = 0; i < ctx_outs.size(); ++i) {
+            gguf_set_val_u16(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_NO).c_str(), i);
+            gguf_set_val_u16(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str(), n_split);
+            gguf_set_val_i32(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str(), ml.n_tensors);
+        }
+    }
 
-    const size_t meta_size = gguf_get_meta_size(ctx_out);
+    int cur_split = -1;
+    std::ofstream fout;
+    auto close_ofstream = [&]() {
+        // Write metadata and close file handler
+        if (fout.is_open()) {
+            fout.seekp(0);
+            std::vector<uint8_t> data(gguf_get_meta_size(ctx_outs[cur_split]));
+            gguf_get_meta_data(ctx_outs[cur_split], data.data());
+            fout.write((const char *) data.data(), data.size());
+            fout.close();
+        }
+    };
+    auto new_ofstream = [&](int index) {
+        cur_split = index;
+        GGML_ASSERT(ctx_outs[cur_split] && "Find uninitialized gguf_context");
+        std::string fname = fname_out;
+        if (params->keep_split) {
+            char split_path[PATH_MAX] = {0};
+            llama_split_path(split_path, sizeof(split_path), fname_out.c_str(), cur_split, n_split);
+            fname = std::string(split_path);
+        }
 
-    LLAMA_LOG_INFO("%s: meta size = %zu bytes\n", __func__, meta_size);
-
-    // placeholder for the meta data
-    ::zeros(fout, meta_size);
+        fout = std::ofstream(fname, std::ios::binary);
+        fout.exceptions(std::ofstream::failbit); // fail fast on write errors
+        const size_t meta_size = gguf_get_meta_size(ctx_outs[cur_split]);
+        // placeholder for the meta data
+        ::zeros(fout, meta_size);
+    };
 
+    const auto tn = LLM_TN(model.arch);
+    new_ofstream(0);
     for (int i = 0; i < ml.n_tensors; ++i) {
-        struct ggml_tensor * tensor = ml.get_tensor_meta(i);
+        auto weight = ml.get_weight(i);
+        struct ggml_tensor * tensor = weight->tensor;
+        if (weight->idx != cur_split && params->keep_split) {
+            close_ofstream();
+            new_ofstream(weight->idx);
+        }
 
         const std::string name = ggml_get_name(tensor);
 
@@ -10655,39 +14880,59 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         // This used to be a regex, but <regex> has an extreme cost to compile times.
         bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?
 
-        // quantize only 2D tensors
-        quantize &= (ggml_n_dims(tensor) == 2);
+        // quantize only 2D and 3D tensors (experts)
+        quantize &= (ggml_n_dims(tensor) >= 2);
+
+        // do not quantize norm tensors
+        quantize &= name.find("_norm.weight") == std::string::npos;
+
         quantize &= params->quantize_output_tensor || name != "output.weight";
         quantize &= !params->only_copy;
 
         // do not quantize expert gating tensors
-        quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_FFN_GATE_INP, "weight");
+        // NOTE: can't use LLM_TN here because the layer number is not known
+        quantize &= name.find("ffn_gate_inp.weight") == std::string::npos;
 
         // do not quantize positional embeddings and token types (BERT)
         quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_POS_EMBD,    "weight");
         quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_TOKEN_TYPES, "weight");
 
+        // do not quantize Mamba's small yet 2D weights
+        // NOTE: can't use LLM_TN here because the layer number is not known
+        quantize &= name.find("ssm_conv1d.weight") == std::string::npos;
+        quantize &= name.find("ssm_x.weight")      == std::string::npos;
+        quantize &= name.find("ssm_dt.weight")     == std::string::npos;
+
         enum ggml_type new_type;
         void * new_data;
         size_t new_size;
 
         if (quantize) {
-            new_type = quantized_type;
-            if (!params->pure) {
-                new_type = get_k_quant_type(qs, new_type, tensor, ftype);
+            new_type = default_type;
+
+            // get more optimal quantization type based on the tensor shape, layer, etc.
+            if (!params->pure && ggml_is_quantized(default_type)) {
+                new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
+            }
+            if (params->token_embedding_type < GGML_TYPE_COUNT && strcmp(tensor->name, "token_embd.weight") == 0) {
+                new_type = params->token_embedding_type;
+            }
+            if (params->output_tensor_type < GGML_TYPE_COUNT && strcmp(tensor->name, "output.weight") == 0) {
+                new_type = params->output_tensor_type;
             }
 
             // If we've decided to quantize to the same type the tensor is already
             // in then there's nothing to do.
             quantize = tensor->type != new_type;
         }
+
         if (!quantize) {
             new_type = tensor->type;
             new_data = tensor->data;
             new_size = ggml_nbytes(tensor);
             LLAMA_LOG_INFO("size = %8.3f MB\n", ggml_nbytes(tensor)/1024.0/1024.0);
         } else {
-            const size_t nelements = ggml_nelements(tensor);
+            const int64_t nelements = ggml_nelements(tensor);
 
             const float * imatrix = nullptr;
             if (imatrix_data) {
@@ -10695,17 +14940,28 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
                 if (it == imatrix_data->end()) {
                     LLAMA_LOG_INFO("\n====== %s: did not find weights for %s\n", __func__, tensor->name);
                 } else {
-                    if (it->second.size() == (size_t)tensor->ne[0]) {
+                    if (it->second.size() == (size_t)tensor->ne[0]*tensor->ne[2]) {
                         imatrix = it->second.data();
                     } else {
                         LLAMA_LOG_INFO("\n====== %s: imatrix size %d is different from tensor size %d for %s\n", __func__,
-                                int(it->second.size()), int(tensor->ne[0]), tensor->name);
+                                int(it->second.size()), int(tensor->ne[0]*tensor->ne[2]), tensor->name);
+
+                        // this can happen when quantizing an old mixtral model with split tensors with a new incompatible imatrix
+                        // this is a significant error and it may be good idea to abort the process if this happens,
+                        // since many people will miss the error and not realize that most of the model is being quantized without an imatrix
+                        // tok_embd should be ignored in this case, since it always causes this warning
+                        if (name != tn(LLM_TENSOR_TOKEN_EMBD, "weight")) {
+                            throw std::runtime_error(format("imatrix size %d is different from tensor size %d for %s",
+                                    int(it->second.size()), int(tensor->ne[0]*tensor->ne[2]), tensor->name));
+                        }
                     }
                 }
             }
             if ((new_type == GGML_TYPE_IQ2_XXS ||
                  new_type == GGML_TYPE_IQ2_XS  ||
+                 new_type == GGML_TYPE_IQ2_S   ||
                  new_type == GGML_TYPE_IQ1_S   ||
+                (new_type == GGML_TYPE_IQ1_M && strcmp(tensor->name, "token_embd.weight") && strcmp(tensor->name, "output.weight"))  ||
                 (new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0)) && !imatrix) {
                 LLAMA_LOG_ERROR("\n\n============================================================\n");
                 LLAMA_LOG_ERROR("Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name);
@@ -10721,123 +14977,60 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
             } else if (ggml_is_quantized(tensor->type) && !params->allow_requantize) {
                 throw std::runtime_error(format("requantizing from type %s is disabled", ggml_type_name(tensor->type)));
             } else {
-                llama_convert_tensor_internal(tensor, f32_conv_buf, workers, nelements, nthread);
+                llama_tensor_dequantize_internal(tensor, f32_conv_buf, workers, nelements, nthread);
                 f32_data = (float *) f32_conv_buf.data();
             }
 
-            LLAMA_LOG_INFO("quantizing to %s .. ", ggml_type_name(new_type));
+            LLAMA_LOG_INFO("converting to %s .. ", ggml_type_name(new_type));
             fflush(stdout);
 
-            if (work.size() < nelements * 4) {
+            if (work.size() < (size_t)nelements * 4) {
                 work.resize(nelements * 4); // upper bound on size
             }
             new_data = work.data();
-            std::array<int64_t, 1 << 4> hist_cur = {};
 
-            const int n_per_row = tensor->ne[0];
-            const int nrows = nelements / n_per_row;
+            const int64_t n_per_row = tensor->ne[0];
+            const int64_t nrows = tensor->ne[1];
 
-            static const int min_chunk_size = 32 * 512;
-            const int chunk_size = n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row);
+            static const int64_t min_chunk_size = 32 * 512;
+            const int64_t chunk_size = n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row);
 
-            const int nchunk = (nelements + chunk_size - 1)/chunk_size;
-            const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1;
-            if (nthread_use < 2) {
-                new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, hist_cur.data(), imatrix);
-            } else {
-                int counter = 0;
-                new_size = 0;
-                auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, chunk_size,
-                     nrows, n_per_row, imatrix]() {
-                    std::array<int64_t, 1 << 4> local_hist = {};
-                    const int nrows_per_chunk = chunk_size / n_per_row;
-                    size_t local_size = 0;
-                    while (true) {
-                        std::unique_lock<std::mutex> lock(mutex);
-                        int first_row = counter; counter += nrows_per_chunk;
-                        if (first_row >= nrows) {
-                            if (local_size > 0) {
-                                for (int j=0; j<int(local_hist.size()); ++j) {
-                                    hist_cur[j] += local_hist[j];
-                                }
-                                new_size += local_size;
-                            }
-                            break;
-                        }
-                        lock.unlock();
-                        const int this_nrow = std::min(nrows - first_row, nrows_per_chunk);
-                        local_size += ggml_quantize_chunk(new_type, f32_data, new_data,
-                                first_row * n_per_row, this_nrow, n_per_row, local_hist.data(), imatrix);
-                    }
-                };
-                for (int it = 0; it < nthread_use - 1; ++it) {
-                    workers.emplace_back(compute);
-                }
-                compute();
-                for (auto & w : workers) { w.join(); }
-                workers.clear();
+            const int64_t nelements_matrix = tensor->ne[0] * tensor->ne[1];
+            const int64_t nchunk = (nelements_matrix + chunk_size - 1)/chunk_size;
+            const int64_t nthread_use = nthread > 1 ? std::max((int64_t)1, std::min((int64_t)nthread, nchunk)) : 1;
+
+            // quantize each expert separately since they have different importance matrices
+            new_size = 0;
+            for (int64_t i03 = 0; i03 < tensor->ne[2]; ++i03) {
+                const float * f32_data_03 = f32_data + i03 * nelements_matrix;
+                void * new_data_03 = (char *)new_data + ggml_row_size(new_type, n_per_row) * i03 * nrows;
+                const float * imatrix_03 = imatrix ? imatrix + i03 * n_per_row : nullptr;
+
+                new_size += llama_tensor_quantize_internal(new_type, f32_data_03, new_data_03, chunk_size, nrows, n_per_row, imatrix_03, workers, nthread_use);
             }
-
-            LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
-            int64_t tot_count = 0;
-            for (size_t i = 0; i < hist_cur.size(); i++) {
-                hist_all[i] += hist_cur[i];
-                tot_count += hist_cur[i];
-            }
-
-            if (tot_count > 0) {
-                LLAMA_LOG_INFO(" | hist: ");
-                for (size_t i = 0; i < hist_cur.size(); i++) {
-                    LLAMA_LOG_INFO("%5.3f ", hist_cur[i] / float(nelements));
-                }
-            }
-            LLAMA_LOG_INFO("\n");
+            LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB\n", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
         }
         total_size_org += ggml_nbytes(tensor);
         total_size_new += new_size;
 
         // update the gguf meta data as we go
-        gguf_set_tensor_type(ctx_out, name.c_str(), new_type);
-        gguf_set_tensor_data(ctx_out, name.c_str(), new_data, new_size);
+        gguf_set_tensor_type(ctx_outs[cur_split], name.c_str(), new_type);
+        gguf_set_tensor_data(ctx_outs[cur_split], name.c_str(), new_data, new_size);
 
         // write tensor data + padding
         fout.write((const char *) new_data, new_size);
         zeros(fout, GGML_PAD(new_size, align) - new_size);
     }
-
-    // go back to beginning of file and write the updated meta data
-    {
-        fout.seekp(0);
-        std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
-        gguf_get_meta_data(ctx_out, data.data());
-        fout.write((const char *) data.data(), data.size());
+    close_ofstream();
+    for (auto & c:ctx_outs) {
+        gguf_free(c);
     }
 
-    fout.close();
-
-    gguf_free(ctx_out);
-
     LLAMA_LOG_INFO("%s: model size  = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
     LLAMA_LOG_INFO("%s: quant size  = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);
 
-    // print histogram for all tensors
-    {
-        int64_t sum_all = 0;
-        for (size_t i = 0; i < hist_all.size(); i++) {
-            sum_all += hist_all[i];
-        }
-
-        if (sum_all > 0) {
-            LLAMA_LOG_INFO("%s: hist: ", __func__);
-            for (size_t i = 0; i < hist_all.size(); i++) {
-                LLAMA_LOG_INFO("%5.3f ", hist_all[i] / float(sum_all));
-            }
-            LLAMA_LOG_INFO("\n");
-        }
-    }
-
     if (qs.n_fallback > 0) {
-        LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) incompatible with k-quants and required fallback quantization\n",
+        LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) required fallback quantization\n",
                 __func__, qs.n_fallback, qs.n_k_quantized + qs.n_fallback);
     }
 }
@@ -10876,8 +15069,8 @@ static int llama_apply_lora_from_file_internal(
     std::unique_ptr<llama_model_loader> ml;
     if (path_base_model) {
         LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model);
-        ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*kv_overrides*/ nullptr));
-        ml->init_mapping(/*prefetch*/ false); // no prefetching
+        ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*check_tensors*/ false, /*kv_overrides*/ nullptr));
+        ml->init_mappings(/*prefetch*/ false); // no prefetching
     }
 
     struct tensor_meta {
@@ -10998,7 +15191,7 @@ static int llama_apply_lora_from_file_internal(
 
         ggml_tensor * base_t;
         if (ml) {
-            if (gguf_find_tensor(ml->ctx_gguf, base_name.c_str()) < 0) {
+            if (!ml->get_tensor_meta(base_name.c_str())) {
                 LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
                 return 1;
             }
@@ -11126,7 +15319,7 @@ static int llama_apply_lora_from_file_internal(
 struct llama_model_params llama_model_default_params() {
     struct llama_model_params result = {
         /*.n_gpu_layers                =*/ 0,
-        /*.split_mode                  =*/ LLAMA_SPLIT_LAYER,
+        /*.split_mode                  =*/ LLAMA_SPLIT_MODE_LAYER,
         /*.main_gpu                    =*/ 0,
         /*.tensor_split                =*/ nullptr,
         /*.progress_callback           =*/ nullptr,
@@ -11135,6 +15328,7 @@ struct llama_model_params llama_model_default_params() {
         /*.vocab_only                  =*/ false,
         /*.use_mmap                    =*/ true,
         /*.use_mlock                   =*/ false,
+        /*.check_tensors               =*/ false,
     };
 
 #ifdef GGML_USE_METAL
@@ -11149,10 +15343,13 @@ struct llama_context_params llama_context_default_params() {
     struct llama_context_params result = {
         /*.seed                        =*/ LLAMA_DEFAULT_SEED,
         /*.n_ctx                       =*/ 512,
-        /*.n_batch                     =*/ 512,
+        /*.n_batch                     =*/ 2048,
+        /*.n_ubatch                    =*/ 512,
+        /*.n_seq_max                   =*/ 1,
         /*.n_threads                   =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
         /*.n_threads_batch             =*/ GGML_DEFAULT_N_THREADS,
-        /*.rope_scaling_type           =*/ LLAMA_ROPE_SCALING_UNSPECIFIED,
+        /*.rope_scaling_type           =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
+        /*.pooling_type                =*/ LLAMA_POOLING_TYPE_UNSPECIFIED,
         /*.rope_freq_base              =*/ 0.0f,
         /*.rope_freq_scale             =*/ 0.0f,
         /*.yarn_ext_factor             =*/ -1.0f,
@@ -11160,15 +15357,17 @@ struct llama_context_params llama_context_default_params() {
         /*.yarn_beta_fast              =*/ 32.0f,
         /*.yarn_beta_slow              =*/ 1.0f,
         /*.yarn_orig_ctx               =*/ 0,
+        /*.defrag_thold                =*/ -1.0f,
         /*.cb_eval                     =*/ nullptr,
         /*.cb_eval_user_data           =*/ nullptr,
         /*.type_k                      =*/ GGML_TYPE_F16,
         /*.type_v                      =*/ GGML_TYPE_F16,
-        /*.mul_mat_q                   =*/ true,
         /*.logits_all                  =*/ false,
-        /*.embedding                   =*/ false,
+        /*.embeddings                  =*/ false,
         /*.offload_kqv                 =*/ true,
-        /*.do_pooling                  =*/ true,
+        /*.flash_attn                  =*/ false,
+        /*.abort_callback              =*/ nullptr,
+        /*.abort_callback_data         =*/ nullptr,
     };
 
     return result;
@@ -11178,11 +15377,15 @@ struct llama_model_quantize_params llama_model_quantize_default_params() {
     struct llama_model_quantize_params result = {
         /*.nthread                     =*/ 0,
         /*.ftype                       =*/ LLAMA_FTYPE_MOSTLY_Q5_1,
+        /*.output_tensor_type          =*/ GGML_TYPE_COUNT,
+        /*.token_embedding_type        =*/ GGML_TYPE_COUNT,
         /*.allow_requantize            =*/ false,
         /*.quantize_output_tensor      =*/ true,
         /*.only_copy                   =*/ false,
         /*.pure                        =*/ false,
+        /*.keep_split                  =*/ false,
         /*.imatrix                     =*/ nullptr,
+        /*.kv_overrides                =*/ nullptr,
     };
 
     return result;
@@ -11191,7 +15394,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() {
 size_t llama_max_devices(void) {
 #if defined(GGML_USE_METAL)
     return 1;
-#elif defined(GGML_USE_CUBLAS)
+#elif defined(GGML_USE_CUDA)
     return GGML_CUDA_MAX_DEVICES;
 #elif defined(GGML_USE_SYCL)
     return GGML_SYCL_MAX_DEVICES;
@@ -11211,8 +15414,8 @@ bool llama_supports_mlock(void) {
 }
 
 bool llama_supports_gpu_offload(void) {
-#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \
-    defined(GGML_USE_SYCL)   || defined(GGML_USE_KOMPUTE)
+#if defined(GGML_USE_CUDA) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \
+    defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE)
     // Defined when llama.cpp is compiled with support for offloading model layers to GPU.
     return true;
 #else
@@ -11220,15 +15423,6 @@ bool llama_supports_gpu_offload(void) {
 #endif
 }
 
-// deprecated:
-bool llama_mmap_supported(void) {
-    return llama_supports_mmap();
-}
-
-bool llama_mlock_supported(void) {
-    return llama_supports_mlock();
-}
-
 void llama_backend_init(void) {
     ggml_time_init();
 
@@ -11309,29 +15503,63 @@ struct llama_context * llama_new_context_with_model(
         struct llama_context_params   params) {
 
     if (!model) {
+        LLAMA_LOG_ERROR("%s: model cannot be NULL\n", __func__);
         return nullptr;
     }
 
+    if (params.n_batch == 0 && params.n_ubatch == 0) {
+        LLAMA_LOG_ERROR("%s: n_batch and n_ubatch cannot both be zero\n", __func__);
+        return nullptr;
+    }
+
+    if (params.n_ctx == 0 && model->hparams.n_ctx_train == 0) {
+        LLAMA_LOG_ERROR("%s: n_ctx and model->hparams.n_ctx_train cannot both be zero\n", __func__);
+        return nullptr;
+    }
+
+    if (params.flash_attn && model->arch == LLM_ARCH_GROK) {
+        LLAMA_LOG_WARN("%s: flash_attn is not compatible with Grok - forcing off\n", __func__);
+        params.flash_attn = false;
+    }
+
     llama_context * ctx = new llama_context(*model);
 
     const auto & hparams = model->hparams;
     auto       & cparams = ctx->cparams;
 
-    cparams.n_batch          = params.n_batch;
+    cparams.n_seq_max        = std::max(1u, params.n_seq_max);
     cparams.n_threads        = params.n_threads;
     cparams.n_threads_batch  = params.n_threads_batch;
     cparams.yarn_ext_factor  = params.yarn_ext_factor;
     cparams.yarn_attn_factor = params.yarn_attn_factor;
     cparams.yarn_beta_fast   = params.yarn_beta_fast;
     cparams.yarn_beta_slow   = params.yarn_beta_slow;
-    cparams.mul_mat_q        = params.mul_mat_q;
+    cparams.defrag_thold     = params.defrag_thold;
+    cparams.embeddings       = params.embeddings;
     cparams.offload_kqv      = params.offload_kqv;
-    cparams.do_pooling       = params.do_pooling;
+    cparams.flash_attn       = params.flash_attn;
+    cparams.pooling_type     = params.pooling_type;
 
     cparams.n_ctx            = params.n_ctx           == 0    ? hparams.n_ctx_train           : params.n_ctx;
     cparams.rope_freq_base   = params.rope_freq_base  == 0.0f ? hparams.rope_freq_base_train  : params.rope_freq_base;
     cparams.rope_freq_scale  = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale;
 
+    // this is necessary due to kv_self.n being padded later during inference
+    cparams.n_ctx            = GGML_PAD(cparams.n_ctx, llama_kv_cache_get_padding(cparams));
+
+    // with causal attention, the batch size is limited by the context size
+    cparams.n_batch          = hparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch;
+
+    // the batch has to be at least GGML_KQ_MASK_PAD because we will be padding the KQ_mask
+    // this is required by GPU kernels in order to avoid out-of-bounds accesses (e.g. ggml_flash_attn_ext)
+    // ref: https://github.com/ggerganov/llama.cpp/pull/5021
+    if (cparams.n_batch < GGML_KQ_MASK_PAD) {
+        LLAMA_LOG_WARN("%s: n_batch is less than GGML_KQ_MASK_PAD - increasing to %d\n", __func__, GGML_KQ_MASK_PAD);
+        cparams.n_batch = GGML_KQ_MASK_PAD;
+    }
+
+    cparams.n_ubatch         = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);
+
     cparams.n_yarn_orig_ctx  = params.yarn_orig_ctx    != 0 ? params.yarn_orig_ctx    :
                                hparams.n_yarn_orig_ctx != 0 ? hparams.n_yarn_orig_ctx :
                                                               hparams.n_ctx_train;
@@ -11340,16 +15568,26 @@ struct llama_context * llama_new_context_with_model(
     cparams.cb_eval_user_data = params.cb_eval_user_data;
 
     auto rope_scaling_type = params.rope_scaling_type;
-    if (rope_scaling_type == LLAMA_ROPE_SCALING_UNSPECIFIED) {
+    if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED) {
         rope_scaling_type = hparams.rope_scaling_type_train;
     }
 
-    if (rope_scaling_type == LLAMA_ROPE_SCALING_NONE) {
+    if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_NONE) {
         cparams.rope_freq_scale = 1.0f; // never scale if scaling type is none
     }
 
     if (cparams.yarn_ext_factor < 0.0f) { // negative indicates 'not set'
-        cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_YARN ? 1.0f : 0.0f;
+        cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f;
+    }
+
+    cparams.causal_attn = hparams.causal_attn;
+
+    if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
+        if (hparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
+            cparams.pooling_type = LLAMA_POOLING_TYPE_NONE;
+        } else {
+            cparams.pooling_type = hparams.pooling_type;
+        }
     }
 
     if (params.seed == LLAMA_DEFAULT_SEED) {
@@ -11357,14 +15595,30 @@ struct llama_context * llama_new_context_with_model(
     }
 
     LLAMA_LOG_INFO("%s: n_ctx      = %u\n",     __func__, cparams.n_ctx);
+    LLAMA_LOG_INFO("%s: n_batch    = %u\n",     __func__, cparams.n_batch);
+    LLAMA_LOG_INFO("%s: n_ubatch   = %u\n",     __func__, cparams.n_ubatch);
+    LLAMA_LOG_INFO("%s: flash_attn = %d\n",     __func__, cparams.flash_attn);
     LLAMA_LOG_INFO("%s: freq_base  = %.1f\n",   __func__, cparams.rope_freq_base);
     LLAMA_LOG_INFO("%s: freq_scale = %g\n",     __func__, cparams.rope_freq_scale);
 
-    ctx->rng = std::mt19937(params.seed);
-    ctx->logits_all = params.logits_all;
+    ctx->abort_callback      = params.abort_callback;
+    ctx->abort_callback_data = params.abort_callback_data;
 
-    const ggml_type type_k = params.type_k;
-    const ggml_type type_v = params.type_v;
+    ctx->rng                 = std::mt19937(params.seed);
+    ctx->logits_all          = params.logits_all;
+
+    uint32_t kv_size = cparams.n_ctx;
+    ggml_type type_k = params.type_k;
+    ggml_type type_v = params.type_v;
+
+    // Mamba only needs a constant number of KV cache cells per sequence
+    if (model->arch == LLM_ARCH_MAMBA) {
+        // Mamba needs at least as many KV cells as there are sequences kept at any time
+        kv_size = std::max((uint32_t) 1, params.n_seq_max);
+        // it's probably best to keep as much precision as possible for the states
+        type_k = GGML_TYPE_F32; // required by ggml_ssm_conv for Mamba's conv_states
+        type_v = GGML_TYPE_F32; // required by ggml_ssm_scan for Mamba's ssm_states
+    }
 
     GGML_ASSERT(hparams.n_embd_head_k % ggml_blck_size(type_k) == 0);
     GGML_ASSERT(hparams.n_embd_head_v % ggml_blck_size(type_v) == 0);
@@ -11381,32 +15635,43 @@ struct llama_context * llama_new_context_with_model(
             }
             ctx->backends.push_back(ctx->backend_metal);
         }
-#elif defined(GGML_USE_CUBLAS)
-        if (model->n_gpu_layers > 0) {
-            // with split_mode LLAMA_SPLIT_NONE or LLAMA_SPLIT_ROW, only the main GPU backend is used
-            if (model->split_mode == LLAMA_SPLIT_NONE || model->split_mode == LLAMA_SPLIT_ROW) {
-                ggml_backend_t backend = ggml_backend_cuda_init(model->main_gpu);
+#elif defined(GGML_USE_CUDA)
+        if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
+            // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
+            ggml_backend_t backend = ggml_backend_cuda_init(model->main_gpu);
+            if (backend == nullptr) {
+                LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, model->main_gpu);
+                llama_free(ctx);
+                return nullptr;
+            }
+            ctx->backends.push_back(backend);
+        } else {
+            // LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU
+            for (int device = 0; device < ggml_backend_cuda_get_device_count(); ++device) {
+                ggml_backend_t backend = ggml_backend_cuda_init(device);
                 if (backend == nullptr) {
-                    LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, model->main_gpu);
+                    LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, device);
                     llama_free(ctx);
                     return nullptr;
                 }
                 ctx->backends.push_back(backend);
-            } else {
-                // LLAMA_SPLIT_LAYER requires a backend for each GPU
-                for (int device = 0; device < ggml_backend_cuda_get_device_count(); ++device) {
-                    ggml_backend_t backend = ggml_backend_cuda_init(device);
-                    if (backend == nullptr) {
-                        LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, device);
-                        llama_free(ctx);
-                        return nullptr;
-                    }
-                    ctx->backends.push_back(backend);
-                }
             }
         }
 #elif defined(GGML_USE_VULKAN)
-        if (model->n_gpu_layers > 0) {
+        if (model->split_mode == LLAMA_SPLIT_MODE_ROW) {
+            LLAMA_LOG_ERROR("%s: Row split not supported. Failed to initialize Vulkan backend\n", __func__);
+            llama_free(ctx);
+            return nullptr;
+        }
+        if (model->split_mode == LLAMA_SPLIT_MODE_NONE) {
+            ggml_backend_t backend = ggml_backend_vk_init(0);
+            if (backend == nullptr) {
+                LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__);
+                llama_free(ctx);
+                return nullptr;
+            }
+            ctx->backends.push_back(backend);
+        } else {
             for (int device = 0; device < ggml_backend_vk_get_device_count(); ++device) {
                 ggml_backend_t backend = ggml_backend_vk_init(device);
                 if (backend == nullptr) {
@@ -11418,14 +15683,29 @@ struct llama_context * llama_new_context_with_model(
             }
         }
 #elif defined(GGML_USE_SYCL)
-        if (model->n_gpu_layers > 0) {
+        // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
+        if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
             ggml_backend_t backend = ggml_backend_sycl_init(model->main_gpu);
             if (backend == nullptr) {
-                LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d backend\n", __func__, model->main_gpu);
+                int main_gpu_id = ggml_backend_sycl_get_device_id(model->main_gpu);
+                LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d) backend\n", __func__, main_gpu_id, model->main_gpu);
                 llama_free(ctx);
                 return nullptr;
             }
             ctx->backends.push_back(backend);
+        } else {
+            // LLAMA_SPLIT_LAYER requires a backend for each GPU
+            for (int i = 0; i < ggml_backend_sycl_get_device_count(); ++i) {
+                ggml_backend_t backend = ggml_backend_sycl_init(i);
+                if (backend == nullptr) {
+                    int id_list[GGML_SYCL_MAX_DEVICES];
+                    ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES);
+                    LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d) backend\n", __func__, id_list[i], i);
+                    llama_free(ctx);
+                    return nullptr;
+                }
+                ctx->backends.push_back(backend);
+            }
         }
 #elif defined(GGML_USE_KOMPUTE)
         if (model->n_gpu_layers > 0) {
@@ -11446,8 +15726,7 @@ struct llama_context * llama_new_context_with_model(
         }
         ctx->backends.push_back(ctx->backend_cpu);
 
-        if (!llama_kv_cache_init(ctx->kv_self, ctx->model, type_k, type_v,
-                cparams.n_ctx, cparams.offload_kqv)) {
+        if (!llama_kv_cache_init(ctx->kv_self, ctx, type_k, type_v, kv_size, cparams.offload_kqv)) {
             LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__);
             llama_free(ctx);
             return nullptr;
@@ -11471,45 +15750,18 @@ struct llama_context * llama_new_context_with_model(
                 ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f));
         }
 
-        // resized during inference, reserve maximum
-        ctx->logits.reserve(hparams.n_vocab*cparams.n_batch);
-
-        if (params.embedding) {
-            ctx->embedding.resize(hparams.n_embd);
-        }
-
-        // graph inputs
+        // graph outputs buffer
         {
-            ggml_init_params init_params = {
-                /* .mem_size   */ ggml_tensor_overhead()*8,
-                /* .mem_buffer */ nullptr,
-                /* .no_alloc   */ true,
-            };
-            ctx->ctx_input = ggml_init(init_params);
+            // resized during inference when a batch uses more outputs
+            if (llama_output_reserve(*ctx, params.n_seq_max) < params.n_seq_max) {
+                LLAMA_LOG_ERROR("%s: failed to reserve initial output buffer\n", __func__);
+                llama_free(ctx);
+                return nullptr;
+            }
 
-            ctx->inp_tokens  = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch);
-            ctx->inp_embd    = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, hparams.n_embd, cparams.n_batch);
-            ctx->inp_pos     = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch);
-            ctx->inp_KQ_mask = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_ctx, cparams.n_batch);
-            ctx->inp_KQ_pos  = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_ctx);
-            ctx->inp_K_shift = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_ctx);
-            ctx->inp_mean    = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_batch, cparams.n_batch);
-            ctx->inp_cls     = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch);
-
-            ggml_set_name(ctx->inp_tokens,  "inp_tokens");
-            ggml_set_name(ctx->inp_embd,    "inp_embd");
-            ggml_set_name(ctx->inp_pos,     "inp_pos");
-            ggml_set_name(ctx->inp_KQ_mask, "inp_KQ_mask");
-            ggml_set_name(ctx->inp_KQ_pos,  "inp_KQ_pos");
-            ggml_set_name(ctx->inp_K_shift, "inp_K_shift");
-            ggml_set_name(ctx->inp_mean,    "inp_mean");
-            ggml_set_name(ctx->inp_cls,     "inp_cls");
-
-            ctx->buf_input = ggml_backend_alloc_ctx_tensors_from_buft(ctx->ctx_input, llama_default_buffer_type_cpu(true));
-
-            LLAMA_LOG_INFO("%s: %10s input buffer size   = %8.2f MiB\n", __func__,
-                    ggml_backend_buffer_name(ctx->buf_input),
-                    ggml_backend_buffer_get_size(ctx->buf_input) / 1024.0 / 1024.0);
+            LLAMA_LOG_INFO("%s: %10s  output buffer size = %8.2f MiB\n", __func__,
+                    ggml_backend_buffer_name(ctx->buf_output),
+                    ggml_backend_buffer_get_size(ctx->buf_output) / 1024.0 / 1024.0);
         }
 
         // scheduler and compute buffers
@@ -11526,12 +15778,23 @@ struct llama_context * llama_new_context_with_model(
             }
 
             // buffer used to store the computation graph and the tensor meta data
-            ctx->buf_compute_meta.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead());
+            ctx->buf_compute_meta.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead_custom(LLAMA_MAX_NODES, false));
 
-            ctx->sched = ggml_backend_sched_new(ctx->backends.data(), backend_buft.data(), ctx->backends.size(), LLAMA_MAX_NODES);
+            // enabling pipeline parallelism in the scheduler increases memory usage, so it is only done when necessary
+            bool pipeline_parallel = llama_get_device_count() > 1 && model->n_gpu_layers > (int)model->hparams.n_layer && model->split_mode == LLAMA_SPLIT_MODE_LAYER;
+#ifndef GGML_USE_CUDA
+            // pipeline parallelism requires support for async compute and events
+            // currently this is only implemented in the CUDA backend
+            pipeline_parallel = false;
+#endif
+            ctx->sched = ggml_backend_sched_new(ctx->backends.data(), backend_buft.data(), ctx->backends.size(), LLAMA_MAX_NODES, pipeline_parallel);
+
+            if (pipeline_parallel) {
+                LLAMA_LOG_INFO("%s: pipeline parallelism enabled (n_copies=%d)\n", __func__, ggml_backend_sched_get_n_copies(ctx->sched));
+            }
 
             // build worst-case graph
-            int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_batch);
+            int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_ubatch);
             int n_past = cparams.n_ctx - n_tokens;
             llama_token token = llama_token_bos(&ctx->model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
             ggml_cgraph * gf = llama_build_graph(*ctx, llama_batch_get_one(&token, n_tokens, n_past, 0), true);
@@ -11547,14 +15810,17 @@ struct llama_context * llama_new_context_with_model(
                 ggml_backend_t backend = ctx->backends[i];
                 ggml_backend_buffer_type_t buft = backend_buft[i];
                 size_t size = ggml_backend_sched_get_buffer_size(ctx->sched, backend);
-                LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__,
-                        ggml_backend_buft_name(buft),
-                        size / 1024.0 / 1024.0);
+                if (size > 1) {
+                    LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__,
+                            ggml_backend_buft_name(buft),
+                            size / 1024.0 / 1024.0);
+                }
             }
 
             // note: the number of splits during measure is higher than during inference due to the kv shift
             int n_splits = ggml_backend_sched_get_n_splits(ctx->sched);
-            LLAMA_LOG_INFO("%s: graph splits (measure): %d\n", __func__, n_splits);
+            LLAMA_LOG_INFO("%s: graph nodes  = %d\n", __func__, gf->n_nodes);
+            LLAMA_LOG_INFO("%s: graph splits = %d\n", __func__, n_splits);
         }
     }
 
@@ -11591,12 +15857,77 @@ uint32_t llama_n_batch(const struct llama_context * ctx) {
     return ctx->cparams.n_batch;
 }
 
+uint32_t llama_n_ubatch(const struct llama_context * ctx) {
+    return ctx->cparams.n_ubatch;
+}
+
+uint32_t llama_n_seq_max(const struct llama_context * ctx) {
+    return ctx->kv_self.size;
+}
+
 enum llama_vocab_type llama_vocab_type(const struct llama_model * model) {
     return model->vocab.type;
 }
 
+enum llama_rope_type llama_rope_type(const struct llama_model * model) {
+    switch (model->arch) {
+        // these models do not use RoPE
+        case LLM_ARCH_GPT2:
+        case LLM_ARCH_GPTJ:
+        case LLM_ARCH_GPTNEOX:
+        case LLM_ARCH_MPT:
+        case LLM_ARCH_REFACT:
+        case LLM_ARCH_BLOOM:
+        case LLM_ARCH_MAMBA:
+        case LLM_ARCH_JINA_BERT_V2:
+            return LLAMA_ROPE_TYPE_NONE;
+
+        // use what we call a normal RoPE, operating on pairs of consecutive head values
+        case LLM_ARCH_LLAMA:
+        case LLM_ARCH_BAICHUAN:
+        case LLM_ARCH_STARCODER:
+        case LLM_ARCH_PLAMO:
+        case LLM_ARCH_CODESHELL:
+        case LLM_ARCH_ORION:
+        case LLM_ARCH_INTERNLM2:
+        case LLM_ARCH_MINICPM:
+        case LLM_ARCH_XVERSE:
+        case LLM_ARCH_COMMAND_R:
+        case LLM_ARCH_OLMO:
+            return LLAMA_ROPE_TYPE_NORM;
+
+        // the pairs of head values are offset by n_rot/2
+        case LLM_ARCH_FALCON:
+        case LLM_ARCH_GROK:
+        case LLM_ARCH_DBRX:
+        case LLM_ARCH_PERSIMMON:
+        case LLM_ARCH_BERT:
+        case LLM_ARCH_NOMIC_BERT:
+        case LLM_ARCH_STABLELM:
+        case LLM_ARCH_QWEN:
+        case LLM_ARCH_QWEN2:
+        case LLM_ARCH_QWEN2MOE:
+        case LLM_ARCH_PHI2:
+        case LLM_ARCH_PHI3:
+        case LLM_ARCH_GEMMA:
+        case LLM_ARCH_STARCODER2:
+            return LLAMA_ROPE_TYPE_NEOX;
+
+        // all model arches should be listed explicitly here
+        case LLM_ARCH_UNKNOWN:
+            GGML_ASSERT(false && "unknown architecture");
+            break;
+    }
+
+    return LLAMA_ROPE_TYPE_NONE;
+}
+
+enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx) {
+    return ctx->cparams.pooling_type;
+}
+
 int32_t llama_n_vocab(const struct llama_model * model) {
-    return model->vocab.id_to_token.size();
+    return model->hparams.n_vocab;
 }
 
 int32_t llama_n_ctx_train(const struct llama_model * model) {
@@ -11607,6 +15938,10 @@ int32_t llama_n_embd(const struct llama_model * model) {
     return model->hparams.n_embd;
 }
 
+int32_t llama_n_layer(const struct llama_model * model) {
+    return model->hparams.n_layer;
+}
+
 float llama_rope_freq_scale_train(const struct llama_model * model) {
     return model->hparams.rope_freq_scale_train;
 }
@@ -11697,15 +16032,6 @@ uint32_t llama_model_quantize(
     }
 }
 
-int32_t llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) {
-    try {
-        return llama_apply_lora_from_file_internal(ctx->model, path_lora, scale, path_base_model, n_threads);
-    } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
-        return 1;
-    }
-}
-
 int32_t llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) {
     try {
         return llama_apply_lora_from_file_internal(*model, path_lora, scale, path_base_model, n_threads);
@@ -11715,10 +16041,100 @@ int32_t llama_model_apply_lora_from_file(const struct llama_model * model, const
     }
 }
 
-struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_max_seq) {
+static bool llama_control_vector_init(struct llama_control_vector & cvec, const llama_model & model) {
+    GGML_ASSERT(cvec.tensors.empty());
+    GGML_ASSERT(cvec.ctxs.empty());
+    GGML_ASSERT(cvec.bufs.empty());
+
+    // count layer buffer types
+    std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
+    for (int64_t i = 0; i < model.hparams.n_layer; i++) {
+        buft_layer_count[model.buft_layer[i].buft]++;
+    }
+
+    // allocate contexts
+    std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
+    for (auto & it : buft_layer_count) {
+        int n_layers = it.second;
+        struct ggml_init_params params = {
+            /*.mem_size   =*/ n_layers * ggml_tensor_overhead(),
+            /*.mem_buffer =*/ NULL,
+            /*.no_alloc   =*/ true,
+        };
+        ggml_context * ctx = ggml_init(params);
+        if (!ctx) {
+            LLAMA_LOG_ERROR("%s: failed to allocate context for control vector\n", __func__);
+            return 1;
+        }
+        ctx_map[it.first] = ctx;
+    }
+
+    // make tensors
+    cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0
+    for (size_t il = 1; il < model.hparams.n_layer; il++) {
+        struct ggml_context * ctx = ctx_map.at(model.buft_layer[il].buft);
+        ggml_tensor * tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd);
+        cvec.tensors.push_back(tensor);
+    }
+
+    // allocate tensors / buffers and zero
+    for (auto it : ctx_map) {
+        ggml_backend_buffer_type_t buft = it.first;
+        ggml_context * ctx = it.second;
+        ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
+        if (!buf) {
+            LLAMA_LOG_ERROR("%s: failed to allocate buffer for control vector\n", __func__);
+            return false;
+        }
+        ggml_backend_buffer_clear(buf, 0);
+        cvec.ctxs.push_back(ctx);
+        cvec.bufs.push_back(buf);
+    }
+
+    return true;
+}
+
+int32_t llama_control_vector_apply(struct llama_context * lctx, const float * data, size_t len, int32_t n_embd, int32_t il_start, int32_t il_end) {
+    const llama_model & model = lctx->model;
+    llama_control_vector & cvec = lctx->cvec;
+
+    if (data == nullptr) {
+        // disable the current control vector (but leave allocated for later)
+        cvec.layer_start = -1;
+        cvec.layer_end   = -1;
+        return 0;
+    }
+
+    if (n_embd != (int) model.hparams.n_embd) {
+        LLAMA_LOG_ERROR("%s: control vector n_embd does not match model\n", __func__);
+        return 1;
+    }
+
+    if (cvec.tensors.empty()) {
+        if (!llama_control_vector_init(cvec, model)) {
+            return 1;
+        }
+    }
+
+    cvec.layer_start = il_start;
+    cvec.layer_end   = il_end;
+
+    for (size_t il = 1; il < model.hparams.n_layer; il++) {
+        assert(cvec.tensors[il] != nullptr);
+
+        const size_t off = n_embd * (il - 1); // buffer doesn't have data for layer 0, since it's never present
+        if (off + n_embd <= len) {
+            ggml_backend_tensor_set(cvec.tensors[il], data + off, 0, n_embd * ggml_element_size(cvec.tensors[il]));
+        }
+    }
+
+    return 0;
+}
+
+struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max) {
     struct llama_kv_cache_view result = {
         /*.n_cells            = */ 0,
-        /*.n_max_seq          = */ n_max_seq,
+        /*.n_seq_max          = */ n_seq_max,
         /*.token_count        = */ 0,
         /*.used_cells         = */ llama_get_kv_cache_used_cells(ctx),
         /*.max_contiguous     = */ 0,
@@ -11746,7 +16162,7 @@ void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_k
         void * p = realloc(view->cells, sizeof(struct llama_kv_cache_view_cell) * view->n_cells);
         GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells");
         view->cells = (struct llama_kv_cache_view_cell *)p;
-        p = realloc(view->cells_sequences, sizeof(llama_seq_id) * view->n_max_seq * view->n_cells);
+        p = realloc(view->cells_sequences, sizeof(llama_seq_id) * view->n_seq_max * view->n_cells);
         GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells sequences");
         view->cells_sequences = (llama_seq_id *)p;
     }
@@ -11760,7 +16176,7 @@ void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_k
     uint32_t max_contig = 0;
     int32_t max_contig_idx = -1;
 
-    for (int32_t i = 0; i < int32_t(ctx->kv_self.size); i++, c_curr++, cs_curr += view->n_max_seq) {
+    for (int32_t i = 0; i < int32_t(ctx->kv_self.size); i++, c_curr++, cs_curr += view->n_seq_max) {
         const size_t curr_size = kv_cells[i].seq_id.size();
         token_count += curr_size;
         c_curr->pos = kv_cells[i].pos + kv_cells[i].delta;
@@ -11777,7 +16193,7 @@ void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_k
 
         int seq_idx = 0;
         for (const llama_seq_id it : kv_cells[i].seq_id) {
-            if (seq_idx >= view->n_max_seq) {
+            if (seq_idx >= view->n_seq_max) {
                 break;
             }
             cs_curr[seq_idx] = it;
@@ -11786,7 +16202,7 @@ void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_k
         if (seq_idx != 0) {
             used_cells++;
         }
-        for (; seq_idx < view->n_max_seq; seq_idx++) {
+        for (; seq_idx < view->n_seq_max; seq_idx++) {
             cs_curr[seq_idx] = -1;
         }
     }
@@ -11822,8 +16238,8 @@ void llama_kv_cache_clear(struct llama_context * ctx) {
     llama_kv_cache_clear(ctx->kv_self);
 }
 
-void llama_kv_cache_seq_rm(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
-    llama_kv_cache_seq_rm(ctx->kv_self, seq_id, p0, p1);
+bool llama_kv_cache_seq_rm(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
+    return llama_kv_cache_seq_rm(ctx->kv_self, seq_id, p0, p1);
 }
 
 void llama_kv_cache_seq_cp(struct llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
@@ -11837,12 +16253,12 @@ void llama_kv_cache_seq_keep(struct llama_context * ctx, llama_seq_id seq_id) {
     llama_kv_cache_seq_keep(ctx->kv_self, seq_id);
 }
 
-void llama_kv_cache_seq_shift(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
+void llama_kv_cache_seq_add(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
     if (delta == 0) {
         return;
     }
 
-    llama_kv_cache_seq_shift(ctx->kv_self, seq_id, p0, p1, delta);
+    llama_kv_cache_seq_add(ctx->kv_self, seq_id, p0, p1, delta);
 }
 
 void llama_kv_cache_seq_div(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
@@ -11853,33 +16269,89 @@ void llama_kv_cache_seq_div(struct llama_context * ctx, llama_seq_id seq_id, lla
     llama_kv_cache_seq_div(ctx->kv_self, seq_id, p0, p1, d);
 }
 
-// Returns the *maximum* size of the state
+llama_pos llama_kv_cache_seq_pos_max(struct llama_context * ctx, llama_seq_id seq_id) {
+    return llama_kv_cache_seq_pos_max(ctx->kv_self, seq_id);
+}
+
+void llama_kv_cache_defrag(struct llama_context * ctx) {
+    llama_kv_cache_defrag(ctx->kv_self);
+}
+
+void llama_kv_cache_update(struct llama_context * ctx) {
+    llama_kv_cache_update_internal(*ctx);
+}
+
+// deprecated
 size_t llama_get_state_size(const struct llama_context * ctx) {
+    return llama_state_get_size(ctx);
+}
+
+// deprecated
+size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
+    return llama_state_get_data(ctx, dst);
+}
+
+// deprecated
+size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
+    return llama_state_set_data(ctx, src);
+}
+
+// deprecated
+bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+    return llama_state_load_file(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
+}
+
+// deprecated
+bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
+    return llama_state_save_file(ctx, path_session, tokens, n_token_count);
+}
+
+// Returns the *maximum* size of the state
+size_t llama_state_get_size(const struct llama_context * ctx) {
+    const auto & cparams = ctx->cparams;
+    const auto & hparams = ctx->model.hparams;
+
     // we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
     // for reference, std::mt19937(1337) serializes to 6701 bytes.
     const size_t s_rng_size        = sizeof(size_t);
     const size_t s_rng             = LLAMA_MAX_RNG_STATE;
+    const size_t s_n_outputs       = sizeof(size_t);
+    // assume worst case for outputs although only currently set ones are serialized
+    const size_t s_output_pos      = ctx->cparams.n_batch * sizeof(int32_t);
     const size_t s_logits_size     = sizeof(size_t);
-    // assume worst case for logits although only currently set ones are serialized
-    const size_t s_logits          = ctx->logits.capacity() * sizeof(float);
+    const size_t s_logits          = ctx->logits_size ? cparams.n_batch * hparams.n_vocab * sizeof(float) : 0;
     const size_t s_embedding_size  = sizeof(size_t);
-    const size_t s_embedding       = ctx->embedding.size() * sizeof(float);
-    const size_t s_kv_size         = sizeof(size_t);
-    const size_t s_kv_ntok         = sizeof(int);
+    const size_t s_embedding       = ctx->embd_size   ? cparams.n_batch * hparams.n_embd  * sizeof(float) : 0;
+    const size_t s_kv_buf_size     = sizeof(size_t);
+    const size_t s_kv_head         = sizeof(uint32_t);
+    const size_t s_kv_size         = sizeof(uint32_t);
+    const size_t s_kv_used         = sizeof(uint32_t);
+    const size_t s_v_trans         = sizeof(uint32_t);
     const size_t s_kv              = ctx->kv_self.total_size();
+    const size_t s_kv_cell         = sizeof(llama_pos) + sizeof(size_t) + cparams.n_seq_max*sizeof(llama_seq_id);
+    const size_t s_kv_cells        = ctx->kv_self.size * s_kv_cell;
 
     const size_t s_total = (
         + s_rng_size
         + s_rng
+        + s_n_outputs
+        + s_output_pos
         + s_logits_size
         + s_logits
         + s_embedding_size
         + s_embedding
+        + s_kv_buf_size
+        + s_kv_head
         + s_kv_size
-        + s_kv_ntok
+        + s_kv_used
+        + s_v_trans
         + s_kv
+        + s_kv_cells
     );
 
+    // on session change it is very likely that the state size has changed - so we need to update this function
+    static_assert(LLAMA_SESSION_VERSION == 6, "So you just bumped the session version - good. But did you remember to update llama_state_get_size?");
+
     return s_total;
 }
 
@@ -11928,21 +16400,23 @@ struct llama_data_file_context : llama_data_context {
  * file context:
  * llama_file file("/path", "wb");
  * llama_data_file_context data_ctx(&file);
- * llama_copy_state_data(ctx, &data_ctx);
+ * llama_state_get_data(ctx, &data_ctx);
  *
  * buffer context:
  * std::vector<uint8_t> buf(max_size, 0);
  * llama_data_buffer_context data_ctx(&buf.data());
- * llama_copy_state_data(ctx, &data_ctx);
+ * llama_state_get_data(ctx, &data_ctx);
  *
 */
-static void llama_copy_state_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) {
+static void llama_state_get_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) {
+    llama_synchronize(ctx);
+
     // copy rng
     {
         std::ostringstream rng_ss;
         rng_ss << ctx->rng;
 
-        const std::string & rng_str = rng_ss.str();
+        const std::string & rng_str  = rng_ss.str();
         const size_t        rng_size = rng_str.size();
 
         GGML_ASSERT(rng_size <= LLAMA_MAX_RNG_STATE);
@@ -11951,25 +16425,61 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat
         data_ctx->write(rng_str.data(), rng_size);
     }
 
-    // copy logits
+    // copy outputs
     {
-        const size_t logits_size = ctx->logits.size();
+        // Can't use ctx->n_outputs because it's not for the
+        // entire last batch when n_ubatch is smaller than n_batch
+        size_t n_outputs = 0;
 
-        data_ctx->write(&logits_size, sizeof(logits_size));
+        // copy output ids
+        {
+            std::vector<int32_t> output_pos;
 
-        if (logits_size) {
-            data_ctx->write(ctx->logits.data(), logits_size * sizeof(float));
+            const size_t    n_batch = ctx->cparams.n_batch;
+            const auto & output_ids = ctx->output_ids;
+
+            output_pos.resize(ctx->output_size);
+
+            // build a more compact representation of the output ids
+            for (size_t i = 0; i < n_batch; ++i) {
+                // map an output id to a position in the batch
+                int32_t pos = output_ids[i];
+                if (pos >= 0) {
+                    if ((size_t) pos >= n_outputs) {
+                        n_outputs = pos + 1;
+                    }
+                    GGML_ASSERT((size_t) pos < ctx->output_size);
+                    output_pos[pos] = i;
+                }
+            }
+
+            data_ctx->write(&n_outputs, sizeof(n_outputs));
+
+            if (n_outputs) {
+                data_ctx->write(output_pos.data(), n_outputs * sizeof(int32_t));
+            }
         }
-    }
 
-    // copy embeddings
-    {
-        const size_t embedding_size = ctx->embedding.size();
+        // copy logits
+        {
+            const size_t logits_size = std::min(ctx->logits_size, n_outputs * ctx->model.hparams.n_vocab);
 
-        data_ctx->write(&embedding_size, sizeof(embedding_size));
+            data_ctx->write(&logits_size, sizeof(logits_size));
 
-        if (embedding_size) {
-            data_ctx->write(ctx->embedding.data(), embedding_size * sizeof(float));
+            if (logits_size) {
+                data_ctx->write(ctx->logits, logits_size * sizeof(float));
+            }
+        }
+
+        // copy embeddings
+        {
+            const size_t embeddings_size = std::min(ctx->embd_size, n_outputs * ctx->model.hparams.n_embd);
+
+            data_ctx->write(&embeddings_size, sizeof(embeddings_size));
+
+            if (embeddings_size) {
+                data_ctx->write(ctx->embd, embeddings_size * sizeof(float));
+            }
         }
     }
 
@@ -11977,42 +16487,60 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat
     {
         const auto & kv_self = ctx->kv_self;
         const auto & hparams = ctx->model.hparams;
-        const auto & cparams = ctx->cparams;
 
-        const auto   n_layer      = hparams.n_layer;
-        const auto   n_embd_k_gqa = hparams.n_embd_k_gqa();
-        const auto   n_embd_v_gqa = hparams.n_embd_v_gqa();
-        const auto   n_ctx        = cparams.n_ctx;
+        const uint32_t n_layer      = hparams.n_layer;
+        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
+        const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
 
-        const size_t   kv_buf_size = kv_self.total_size();
-        const uint32_t kv_head     = kv_self.head;
+        // NOTE: kv_size and kv_buf_size are mostly used for sanity checks
+        const uint32_t kv_head     = llama_kv_cache_cell_max(kv_self);
         const uint32_t kv_size     = kv_self.size;
+        const size_t   kv_buf_size = kv_self.total_size() / (kv_size ? kv_size : 1) * kv_head;
         const uint32_t kv_used     = kv_self.used;
+        const uint32_t v_trans     = kv_self.v_trans ? 1 : 0;
 
         data_ctx->write(&kv_buf_size, sizeof(kv_buf_size));
         data_ctx->write(&kv_head,     sizeof(kv_head));
         data_ctx->write(&kv_size,     sizeof(kv_size));
         data_ctx->write(&kv_used,     sizeof(kv_used));
+        data_ctx->write(&v_trans,     sizeof(v_trans));
 
         if (kv_buf_size) {
-            const size_t elt_size = ggml_element_size(kv_self.k_l[0]);
+            const size_t pre_kv_buf_size = data_ctx->get_size_written();
 
             std::vector<uint8_t> tmp_buf;
             for (int il = 0; il < (int) n_layer; ++il) {
-                tmp_buf.resize(elt_size*n_embd_k_gqa*kv_head);
+                const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head);
+
+                tmp_buf.resize(k_size);
                 ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), 0, tmp_buf.size());
                 data_ctx->write(tmp_buf.data(), tmp_buf.size());
 
+                if (kv_self.recurrent || !kv_self.v_trans) {
+                    // v is contiguous for recurrent models
+                    // TODO: use other tensors for state models than k and v
+                    const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head);
+
+                    tmp_buf.resize(v_size);
+                    ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), 0, tmp_buf.size());
+                    data_ctx->write(tmp_buf.data(), tmp_buf.size());
+                    continue;
+                }
+
                 // v is not contiguous, copy row by row
-                tmp_buf.resize(elt_size*kv_head);
+                const size_t v_row_size   = ggml_row_size(kv_self.v_l[il]->type, kv_head);
+                const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_size);
+
+                tmp_buf.resize(v_row_size);
                 for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) {
-                    ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), ir*elt_size*n_ctx, tmp_buf.size());
+                    ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), ir*v_row_stride, tmp_buf.size());
                     data_ctx->write(tmp_buf.data(), tmp_buf.size());
                 }
             }
+            GGML_ASSERT(kv_buf_size == data_ctx->get_size_written() - pre_kv_buf_size);
         }
 
-        for (uint32_t i = 0; i < kv_size; ++i) {
+        for (uint32_t i = 0; i < kv_head; ++i) {
             const auto & cell = kv_self.cells[i];
 
             const llama_pos pos         = cell.pos;
@@ -12028,16 +16556,18 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat
     }
 }
 
-size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
+size_t llama_state_get_data(struct llama_context * ctx, uint8_t * dst) {
     llama_data_buffer_context data_ctx(dst);
-    llama_copy_state_data_internal(ctx, &data_ctx);
+    llama_state_get_data_internal(ctx, &data_ctx);
 
     return data_ctx.get_size_written();
 }
 
 // Sets the state reading from the specified source address
-size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
-    uint8_t * inp = src;
+size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src) {
+    llama_synchronize(ctx);
+
+    const uint8_t * inp = src;
 
     // set rng
     {
@@ -12046,7 +16576,7 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
 
         GGML_ASSERT(rng_size <= LLAMA_MAX_RNG_STATE);
 
-        std::string rng_str((char *)inp, rng_size); inp += rng_size;
+        std::string rng_str((const char *)inp, rng_size); inp += rng_size;
 
         std::istringstream rng_ss(rng_str);
         rng_ss >> ctx->rng;
@@ -12054,33 +16584,55 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
         GGML_ASSERT(!rng_ss.fail());
     }
 
+    // set output ids
+    {
+        size_t n_outputs;
+        std::vector<int32_t> output_pos;
+
+        memcpy(&n_outputs, inp, sizeof(n_outputs)); inp += sizeof(n_outputs);
+
+        GGML_ASSERT(n_outputs <= llama_output_reserve(*ctx, n_outputs));
+
+        if (n_outputs) {
+            output_pos.resize(n_outputs);
+            memcpy(output_pos.data(), inp, n_outputs * sizeof(int32_t));
+            inp += n_outputs * sizeof(int32_t);
+
+            for (int32_t i = 0; i < (int32_t) output_pos.size(); ++i) {
+                int32_t id = output_pos[i];
+                GGML_ASSERT((uint32_t) id < ctx->cparams.n_batch);
+                ctx->output_ids[id] = i;
+            }
+
+            ctx->n_outputs = n_outputs;
+        }
+    }
+
     // set logits
     {
         size_t logits_size;
 
         memcpy(&logits_size, inp, sizeof(logits_size)); inp += sizeof(logits_size);
 
-        GGML_ASSERT(ctx->logits.capacity() >= logits_size);
+        GGML_ASSERT(ctx->logits_size >= logits_size);
 
         if (logits_size) {
-            ctx->logits.resize(logits_size);
-
-            memcpy(ctx->logits.data(), inp, logits_size * sizeof(float));
+            memcpy(ctx->logits, inp, logits_size * sizeof(float));
             inp += logits_size * sizeof(float);
         }
     }
 
     // set embeddings
     {
-        size_t embedding_size;
+        size_t embeddings_size;
 
-        memcpy(&embedding_size, inp, sizeof(embedding_size)); inp += sizeof(embedding_size);
+        memcpy(&embeddings_size, inp, sizeof(embeddings_size)); inp += sizeof(embeddings_size);
 
-        GGML_ASSERT(ctx->embedding.capacity() == embedding_size);
+        GGML_ASSERT(ctx->embd_size >= embeddings_size);
 
-        if (embedding_size) {
-            memcpy(ctx->embedding.data(), inp, embedding_size * sizeof(float));
-            inp += embedding_size * sizeof(float);
+        if (embeddings_size) {
+            memcpy(ctx->embd, inp, embeddings_size * sizeof(float));
+            inp += embeddings_size * sizeof(float);
         }
     }
 
@@ -12088,49 +16640,72 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
     {
         const auto & kv_self = ctx->kv_self;
         const auto & hparams = ctx->model.hparams;
-        const auto & cparams = ctx->cparams;
 
-        const int    n_layer      = hparams.n_layer;
-        const int    n_embd_k_gqa = hparams.n_embd_k_gqa();
-        const int    n_embd_v_gqa = hparams.n_embd_v_gqa();
-        const int    n_ctx        = cparams.n_ctx;
+        const uint32_t n_layer      = hparams.n_layer;
+        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
+        const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
 
         size_t   kv_buf_size;
         uint32_t kv_head;
         uint32_t kv_size;
         uint32_t kv_used;
+        uint32_t v_trans;
 
         memcpy(&kv_buf_size, inp, sizeof(kv_buf_size)); inp += sizeof(kv_buf_size);
         memcpy(&kv_head,     inp, sizeof(kv_head));     inp += sizeof(kv_head);
         memcpy(&kv_size,     inp, sizeof(kv_size));     inp += sizeof(kv_size);
         memcpy(&kv_used,     inp, sizeof(kv_used));     inp += sizeof(kv_used);
+        memcpy(&v_trans,     inp, sizeof(v_trans));     inp += sizeof(v_trans);
+
+        GGML_ASSERT(kv_self.v_trans == (bool) v_trans); // incompatible V transposition
+
+        if (kv_self.size != kv_size) {
+            // the KV cache needs to be big enough to load all the KV cells from the saved state
+            GGML_ASSERT(kv_self.size >= kv_head);
+
+            LLAMA_LOG_INFO("%s: state contains %d KV cells, was saved with kv_size=%d, but is loaded with kv_size=%d (fine, but different)\n",
+                __func__, kv_head, kv_size, kv_self.size);
+        }
+
+        llama_kv_cache_clear(ctx);
 
         if (kv_buf_size) {
-            GGML_ASSERT(kv_self.total_size() == kv_buf_size);
+            const size_t pre_kv_buf_size = inp - src;
 
-            const size_t elt_size = ggml_element_size(kv_self.k_l[0]);
+            GGML_ASSERT(kv_self.total_size() >= kv_buf_size);
 
             for (int il = 0; il < (int) n_layer; ++il) {
-                size_t k_size = elt_size*n_embd_k_gqa*kv_head;
+                const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head);
+
                 ggml_backend_tensor_set(kv_self.k_l[il], inp, 0, k_size);
                 inp += k_size;
 
+                if (kv_self.recurrent || !kv_self.v_trans) {
+                    // v is contiguous for recurrent models
+                    // TODO: use other tensors for state models than k and v
+                    const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head);
+
+                    ggml_backend_tensor_set(kv_self.v_l[il], inp, 0, v_size);
+                    inp += v_size;
+                    continue;
+                }
+
                 // v is not contiguous, copy row by row
-                size_t v_row_size = elt_size*kv_head;
+                const size_t v_row_size   = ggml_row_size(kv_self.v_l[il]->type, kv_head);
+                const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_self.size);
+
                 for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) {
-                    ggml_backend_tensor_set(kv_self.v_l[il], inp, ir*elt_size*n_ctx, v_row_size);
+                    ggml_backend_tensor_set(kv_self.v_l[il], inp, ir*v_row_stride, v_row_size);
                     inp += v_row_size;
                 }
             }
+            GGML_ASSERT(kv_buf_size == inp - src - pre_kv_buf_size);
         }
 
         ctx->kv_self.head = kv_head;
-        ctx->kv_self.size = kv_size;
         ctx->kv_self.used = kv_used;
 
-        ctx->kv_self.cells.resize(kv_size);
-
-        for (uint32_t i = 0; i < kv_size; ++i) {
+        for (uint32_t i = 0; i < kv_head; ++i) {
             llama_pos pos;
             size_t    seq_id_size;
 
@@ -12149,14 +16724,14 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
     }
 
     const size_t nread    = inp - src;
-    const size_t max_size = llama_get_state_size(ctx);
+    const size_t max_size = llama_state_get_size(ctx);
 
     GGML_ASSERT(nread <= max_size);
 
     return nread;
 }
 
-static bool llama_load_session_file_internal(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+static bool llama_state_load_file_internal(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
     llama_file file(path_session, "rb");
 
     // sanity checks
@@ -12194,7 +16769,7 @@ static bool llama_load_session_file_internal(struct llama_context * ctx, const c
     // restore the context state
     {
         const size_t n_state_size_cur = file.size - file.tell();
-        const size_t n_state_size_max = llama_get_state_size(ctx);
+        const size_t n_state_size_max = llama_state_get_size(ctx);
 
         if (n_state_size_cur > n_state_size_max) {
             LLAMA_LOG_ERROR("%s : the state size in session file is too big! max %zu, got %zu\n", __func__, n_state_size_max, n_state_size_cur);
@@ -12204,22 +16779,22 @@ static bool llama_load_session_file_internal(struct llama_context * ctx, const c
         std::vector<uint8_t> state_data(n_state_size_max);
         file.read_raw(state_data.data(), n_state_size_cur);
 
-        llama_set_state_data(ctx, state_data.data());
+        llama_state_set_data(ctx, state_data.data());
     }
 
     return true;
 }
 
-bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+bool llama_state_load_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
     try {
-        return llama_load_session_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
+        return llama_state_load_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("error loading session file: %s\n", err.what());
         return false;
     }
 }
 
-bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
+static bool llama_state_save_file_internal(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
     llama_file file(path_session, "wb");
 
     file.write_u32(LLAMA_SESSION_MAGIC);
@@ -12233,41 +16808,477 @@ bool llama_save_session_file(struct llama_context * ctx, const char * path_sessi
 
     // save the context state using stream saving
     llama_data_file_context data_ctx(&file);
-    llama_copy_state_data_internal(ctx, &data_ctx);
+    llama_state_get_data_internal(ctx, &data_ctx);
 
     return true;
 }
 
-int llama_eval(
-        struct llama_context * ctx,
-                 llama_token * tokens,
-                     int32_t   n_tokens,
-                     int32_t   n_past) {
-    llama_kv_cache_seq_rm(ctx->kv_self, -1, n_past, -1);
-
-    const int ret = llama_decode_internal(*ctx, llama_batch_get_one(tokens, n_tokens, n_past, 0));
-    if (ret < 0) {
-        LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
+bool llama_state_save_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
+    try {
+        return llama_state_save_file_internal(ctx, path_session, tokens, n_token_count);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("error saving session file: %s\n", err.what());
+        return false;
     }
-
-    return ret;
 }
 
-int llama_eval_embd(
-            struct llama_context * ctx,
-                           float * embd,
-                         int32_t   n_tokens,
-                         int32_t   n_past) {
-    llama_kv_cache_seq_rm(ctx->kv_self, -1, n_past, -1);
+size_t llama_state_seq_get_size(struct llama_context* ctx, llama_seq_id seq_id) {
+    // save the size of size_t as a uint32_t for safety check
+    const size_t size_t_size_size = sizeof(uint32_t);
 
-    llama_batch batch = { n_tokens, nullptr, embd, nullptr, nullptr, nullptr, nullptr, n_past, 1, 0, };
+    // other values
+    const size_t s_cell_count_size = sizeof(uint32_t);
+    const size_t s_layer_count_size = sizeof(uint32_t);
+    const size_t n_embd_v_gqa_size = sizeof(uint32_t);
 
-    const int ret = llama_decode_internal(*ctx, batch);
-    if (ret < 0) {
-        LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
+    size_t s_cell_count = 0;
+    size_t s_cell_data_size = 0;
+    const auto & kv_self = ctx->kv_self;
+    const auto & hparams = ctx->model.hparams;
+
+    const uint32_t n_layer = hparams.n_layer;
+    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
+    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
+
+    for (uint32_t i = 0; i < kv_self.size; ++i) {
+        const auto & cell = kv_self.cells[i];
+        if (cell.seq_id.count(seq_id) > 0) {
+            ++s_cell_count;
+            s_cell_data_size += sizeof(llama_pos);
+        }
     }
 
-    return ret;
+    for (int il = 0; il < (int)n_layer; ++il) {
+        // types of keys and values
+        s_cell_data_size += sizeof(int32_t) * 2;
+        // k_size_row and v_size_el values of layer
+        s_cell_data_size += sizeof(size_t) * 2;
+
+        // keys
+        const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
+        s_cell_data_size += k_size_row * s_cell_count;
+
+        // values (transposed)
+        const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
+        s_cell_data_size += v_size_el * s_cell_count * n_embd_v_gqa;
+    }
+
+    const size_t s_total = (
+        size_t_size_size +
+        s_cell_count_size +
+        s_layer_count_size +
+        n_embd_v_gqa_size +
+        s_cell_data_size
+        );
+
+    return s_total;
+}
+
+static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llama_data_context & data_ctx, llama_seq_id seq_id) {
+    llama_synchronize(ctx);
+
+    const auto & kv_self = ctx->kv_self;
+    GGML_ASSERT(!kv_self.recurrent); // not implemented
+
+    // Save the size of size_t as a uint32_t for safety check
+    const uint32_t size_t_size = sizeof(size_t);
+    data_ctx.write(&size_t_size, sizeof(size_t_size));
+
+    std::vector<std::pair<uint32_t, uint32_t>> cell_ranges; // ranges, from inclusive, to exclusive
+    uint32_t cell_count = 0;
+
+    // Count the number of cells with the specified seq_id
+    // Find all the ranges of cells with this seq id
+    {
+        uint32_t cell_range_begin = kv_self.size;
+        for (uint32_t i = 0; i < kv_self.size; ++i) {
+            const auto & cell = kv_self.cells[i];
+            if (cell.has_seq_id(seq_id)) {
+                ++cell_count;
+                if (cell_range_begin == kv_self.size) {
+                    cell_range_begin = i;
+                }
+            }
+            else {
+                if (cell_range_begin != kv_self.size) {
+                    cell_ranges.push_back({ cell_range_begin, i });
+                    cell_range_begin = kv_self.size;
+                }
+            }
+        }
+        if (cell_range_begin != kv_self.size) {
+            cell_ranges.push_back({ cell_range_begin, kv_self.size });
+        }
+
+        // DEBUG CHECK: Sum of cell counts in ranges should equal the total cell count
+        uint32_t cell_count_check = 0;
+        for (const auto & range : cell_ranges) {
+            cell_count_check += range.second - range.first;
+        }
+        GGML_ASSERT(cell_count == cell_count_check);
+    }
+
+    // Write the cell count
+    data_ctx.write(&cell_count, sizeof(cell_count));
+
+    const auto & hparams = ctx->model.hparams;
+    const uint32_t n_layer = hparams.n_layer;
+    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
+    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
+
+    // Write the layer count
+    data_ctx.write(&n_layer, sizeof(n_layer));
+
+    // Write n_embd_v_gqa
+    data_ctx.write(&n_embd_v_gqa, sizeof(n_embd_v_gqa));
+
+    // Iterate the ranges and write all the pos (this is the token position in the prompt)
+    for (const auto & range : cell_ranges) {
+        for (uint32_t i = range.first; i < range.second; ++i) {
+            const auto & cell = kv_self.cells[i];
+            data_ctx.write(&cell.pos, sizeof(cell.pos));
+        }
+    }
+
+    // Iterate and write all the keys first, each row is a cell
+    // Get whole range at a time
+    std::vector<uint8_t> tmp_buf;
+    for (int il = 0; il < (int)n_layer; ++il) {
+        // Write key type
+        const int32_t k_type_i = (int32_t)kv_self.k_l[il]->type;
+        data_ctx.write(&k_type_i, sizeof(k_type_i));
+
+        // Write row size of key
+        const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
+        data_ctx.write(&k_size_row, sizeof(k_size_row));
+
+        // Read each range of cells of k_size length each into tmp_buf and write out
+        for (const auto & range : cell_ranges) {
+            const size_t range_size = range.second - range.first;
+            tmp_buf.resize(range_size * k_size_row);
+            ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), range.first * k_size_row, range_size * k_size_row);
+            data_ctx.write(tmp_buf.data(), tmp_buf.size());
+        }
+    }
+
+    // TODO: simplify, reduce copy-paste
+    if (!kv_self.v_trans) {
+        for (int il = 0; il < (int)n_layer; ++il) {
+            // Write value type
+            const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
+            data_ctx.write(&v_type_i, sizeof(v_type_i));
+
+            // Write row size of value
+            const size_t v_size_row = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa);
+            data_ctx.write(&v_size_row, sizeof(v_size_row));
+
+            // Read each range of cells of v_size length each into tmp_buf and write out
+            for (const auto & range : cell_ranges) {
+                const size_t range_size = range.second - range.first;
+                tmp_buf.resize(range_size * v_size_row);
+                ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), range.first * v_size_row, range_size * v_size_row);
+                data_ctx.write(tmp_buf.data(), tmp_buf.size());
+            }
+        }
+    } else {
+        // For the values, they are transposed, so we also need the element size and get the element ranges from each row
+        const uint32_t kv_size = kv_self.size;
+        for (int il = 0; il < (int)n_layer; ++il) {
+            // Write value type
+            const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
+            data_ctx.write(&v_type_i, sizeof(v_type_i));
+
+            // Write element size
+            const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
+            data_ctx.write(&v_size_el, sizeof(v_size_el));
+
+            // For each row, we get the element values of each cell
+            for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+                // Read each range of cells of v_size_el length each into tmp_buf and write out
+                for (const auto & range : cell_ranges) {
+                    const size_t range_size = range.second - range.first;
+                    const size_t src_offset = (range.first + j * kv_size) * v_size_el;
+                    tmp_buf.resize(range_size * v_size_el);
+                    ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), src_offset, tmp_buf.size());
+                    data_ctx.write(tmp_buf.data(), tmp_buf.size());
+                }
+            }
+        }
+    }
+
+    return data_ctx.get_size_written();
+}
+
+size_t llama_state_seq_get_data(struct llama_context* ctx, uint8_t* dst, llama_seq_id seq_id) {
+    llama_data_buffer_context data_ctx(dst);
+    return llama_state_seq_get_data_internal(ctx, data_ctx, seq_id);
+}
+
+size_t llama_state_seq_set_data(struct llama_context * ctx, const uint8_t * src, llama_seq_id dest_seq_id) {
+    llama_synchronize(ctx);
+
+    auto & kv_self = ctx->kv_self;
+    GGML_ASSERT(!kv_self.recurrent); // not implemented
+
+    // Wipe the slot
+    llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+
+    const uint8_t * inp = src;
+
+    // Read size of size_t
+    uint32_t size_t_size;
+    memcpy(&size_t_size, inp, sizeof(size_t_size));
+    inp += sizeof(size_t_size);
+    if (size_t_size != sizeof(size_t)) {
+        LLAMA_LOG_ERROR("%s: size_t size mismatch\n", __func__);
+        return 0;
+    }
+
+    // Read the cell count
+    uint32_t cell_count;
+    memcpy(&cell_count, inp, sizeof(cell_count));
+    inp += sizeof(cell_count);
+
+    // Read the layer count
+    uint32_t n_layer_ref;
+    memcpy(&n_layer_ref, inp, sizeof(n_layer_ref));
+    inp += sizeof(n_layer_ref);
+
+    // Read n_embd_v_gqa
+    uint32_t n_embd_v_gqa_ref;
+    memcpy(&n_embd_v_gqa_ref, inp, sizeof(n_embd_v_gqa_ref));
+    inp += sizeof(n_embd_v_gqa_ref);
+
+    // Sanity check model compatibility
+    const auto & hparams = ctx->model.hparams;
+    const uint32_t n_layer = hparams.n_layer;
+    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
+    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
+    if (n_layer != n_layer_ref) {
+        LLAMA_LOG_ERROR("%s: mismatched n_layer (%d != %d)\n", __func__, n_layer, n_layer_ref);
+        return 0;
+    }
+    if (n_embd_v_gqa != n_embd_v_gqa_ref) {
+        LLAMA_LOG_ERROR("%s: mismatched n_embd_v_gqa (%d != %d)\n", __func__, n_embd_v_gqa, n_embd_v_gqa_ref);
+        return 0;
+    }
+
+    // Allocate the new cells for the slot
+    if (cell_count) {
+        llama_batch batch = llama_batch_init(cell_count, 0, 1);
+        batch.n_tokens = cell_count;
+        for (uint32_t i = 0; i < cell_count; ++i) {
+            llama_pos pos;
+            memcpy(&pos, inp, sizeof(pos));
+            inp += sizeof(pos);
+
+            batch.pos[i] = pos;
+            batch.n_seq_id[i] = 1;
+            batch.seq_id[i][0] = dest_seq_id;
+        }
+        if (!llama_kv_cache_find_slot(kv_self, batch)) {
+            llama_batch_free(batch);
+            LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__);
+            return 0;
+        }
+
+        // DEBUG CHECK: kv_self.head should be our first cell, kv_self.head + cell_count - 1 should be our last cell (verify seq_id and pos values)
+        // Assume that this is one contiguous block of cells
+        GGML_ASSERT(kv_self.head + cell_count <= kv_self.size);
+        GGML_ASSERT(kv_self.cells[kv_self.head].pos == batch.pos[0]);
+        GGML_ASSERT(kv_self.cells[kv_self.head + cell_count - 1].pos == batch.pos[cell_count - 1]);
+        GGML_ASSERT(kv_self.cells[kv_self.head].has_seq_id(dest_seq_id));
+        GGML_ASSERT(kv_self.cells[kv_self.head + cell_count - 1].has_seq_id(dest_seq_id));
+
+        // Cleanup
+        llama_batch_free(batch);
+    }
+
+    const uint32_t kv_size = kv_self.size;
+    const uint32_t kv_head = kv_self.head;
+
+    // For each layer, read the keys for each cell, one row is one cell, read as one contiguous blo
+    for (int il = 0; il < (int)n_layer; ++il) {
+        // Read type of key
+        int32_t k_type_i_ref;
+        memcpy(&k_type_i_ref, inp, sizeof(k_type_i_ref));
+        inp += sizeof(k_type_i_ref);
+        const int32_t k_type_i = (int32_t)kv_self.k_l[il]->type;
+        if (k_type_i != k_type_i_ref) {
+            llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+            LLAMA_LOG_ERROR("%s: mismatched key type (%d != %d, layer %d)\n", __func__, k_type_i, k_type_i_ref, il);
+            return 0;
+        }
+
+        // Read row size of key
+        size_t k_size_row_ref;
+        memcpy(&k_size_row_ref, inp, sizeof(k_size_row_ref));
+        inp += sizeof(k_size_row_ref);
+        const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
+        if (k_size_row != k_size_row_ref) {
+            llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+            LLAMA_LOG_ERROR("%s: mismatched key row size (%zu != %zu, layer %d)\n", __func__, k_size_row, k_size_row_ref, il);
+            return 0;
+        }
+
+        if (cell_count) {
+            // Read and set the keys for the whole cell range
+            ggml_backend_tensor_set(kv_self.k_l[il], inp, kv_head * k_size_row, cell_count * k_size_row);
+            inp += cell_count * k_size_row;
+        }
+    }
+
+    // TODO: simplify, reduce copy-paste
+    if (!kv_self.v_trans) {
+        for (int il = 0; il < (int)n_layer; ++il) {
+            // Read type of value
+            int32_t v_type_i_ref;
+            memcpy(&v_type_i_ref, inp, sizeof(v_type_i_ref));
+            inp += sizeof(v_type_i_ref);
+            const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
+            if (v_type_i != v_type_i_ref) {
+                llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+                LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il);
+                return 0;
+            }
+
+            // Read row size of value
+            size_t v_size_row_ref;
+            memcpy(&v_size_row_ref, inp, sizeof(v_size_row_ref));
+            inp += sizeof(v_size_row_ref);
+            const size_t v_size_row = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa);
+            if (v_size_row != v_size_row_ref) {
+                llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+                LLAMA_LOG_ERROR("%s: mismatched value row size (%zu != %zu, layer %d)\n", __func__, v_size_row, v_size_row_ref, il);
+                return 0;
+            }
+
+            if (cell_count) {
+                // Read and set the values for the whole cell range
+                ggml_backend_tensor_set(kv_self.v_l[il], inp, kv_head * v_size_row, cell_count * v_size_row);
+                inp += cell_count * v_size_row;
+            }
+        }
+    } else {
+        // For each layer, read the values for each cell (transposed)
+        for (int il = 0; il < (int)n_layer; ++il) {
+            // Read type of value
+            int32_t v_type_i_ref;
+            memcpy(&v_type_i_ref, inp, sizeof(v_type_i_ref));
+            inp += sizeof(v_type_i_ref);
+            const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
+            if (v_type_i != v_type_i_ref) {
+                llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+                LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il);
+                return 0;
+            }
+
+            // Read element size of value
+            size_t v_size_el_ref;
+            memcpy(&v_size_el_ref, inp, sizeof(v_size_el_ref));
+            inp += sizeof(v_size_el_ref);
+            const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
+            if (v_size_el != v_size_el_ref) {
+                llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+                LLAMA_LOG_ERROR("%s: mismatched value element size (%zu != %zu, layer %d)\n", __func__, v_size_el, v_size_el_ref, il);
+                return 0;
+            }
+
+            if (cell_count) {
+                // For each row in the transposed matrix, read the values for the whole cell range
+                for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+                    const size_t dst_offset = (kv_head + j * kv_size) * v_size_el;
+                    ggml_backend_tensor_set(kv_self.v_l[il], inp, dst_offset, cell_count * v_size_el);
+                    inp += cell_count * v_size_el;
+                }
+            }
+        }
+    }
+
+    const size_t nread = inp - src;
+
+    return nread;
+}
+
+static size_t llama_state_seq_save_file_internal(struct llama_context * ctx, const char * filepath, llama_seq_id seq_id, const llama_token * tokens, size_t n_token_count) {
+    llama_file file(filepath, "wb");
+
+    file.write_u32(LLAMA_STATE_SEQ_MAGIC);
+    file.write_u32(LLAMA_STATE_SEQ_VERSION);
+
+    // save the prompt
+    file.write_u32((uint32_t)n_token_count);
+    file.write_raw(tokens, sizeof(llama_token) * n_token_count);
+
+    // save the context state using stream saving
+    llama_data_file_context data_ctx(&file);
+    llama_state_seq_get_data_internal(ctx, data_ctx, seq_id);
+
+    const size_t res = file.tell();
+    GGML_ASSERT(res == sizeof(uint32_t) * 3 + sizeof(llama_token) * n_token_count + data_ctx.get_size_written());
+    return res;
+}
+
+static size_t llama_state_seq_load_file_internal(struct llama_context * ctx, const char * filepath, llama_seq_id dest_seq_id, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+    llama_file file(filepath, "rb");
+
+    // version checks
+    {
+        const uint32_t magic   = file.read_u32();
+        const uint32_t version = file.read_u32();
+
+        if (magic != LLAMA_STATE_SEQ_MAGIC || version != LLAMA_STATE_SEQ_VERSION) {
+            LLAMA_LOG_ERROR("%s: unknown (magic, version) for sequence state file: %08x, %08x\n", __func__, magic, version);
+            return 0;
+        }
+    }
+
+    // load the prompt
+    {
+        const uint32_t n_token_count = file.read_u32();
+
+        if (n_token_count > n_token_capacity) {
+            LLAMA_LOG_ERROR("%s: token count in sequence state file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
+            return 0;
+        }
+
+        file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
+        *n_token_count_out = n_token_count;
+    }
+
+    // restore the context state
+    {
+        const size_t state_size = file.size - file.tell();
+        std::vector<uint8_t> state_data(state_size);
+        file.read_raw(state_data.data(), state_size);
+        const size_t nread = llama_state_seq_set_data(ctx, state_data.data(), dest_seq_id);
+        if (!nread) {
+            LLAMA_LOG_ERROR("%s: failed to restore sequence state\n", __func__);
+            return 0;
+        }
+        GGML_ASSERT(nread <= state_size);
+        GGML_ASSERT(nread + sizeof(uint32_t) * 3 + sizeof(llama_token) * *n_token_count_out == file.tell());
+    }
+
+    return file.tell();
+}
+
+size_t llama_state_seq_save_file(struct llama_context * ctx, const char * filepath, llama_seq_id seq_id, const llama_token * tokens, size_t n_token_count) {
+    try {
+        return llama_state_seq_save_file_internal(ctx, filepath, seq_id, tokens, n_token_count);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("error saving sequence state file: %s\n", err.what());
+        return 0;
+    }
+}
+
+size_t llama_state_seq_load_file(struct llama_context * ctx, const char * filepath, llama_seq_id dest_seq_id, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+    try {
+        return llama_state_seq_load_file_internal(ctx, filepath, dest_seq_id, tokens_out, n_token_capacity, n_token_count_out);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("error loading sequence state file: %s\n", err.what());
+        return 0;
+    }
 }
 
 void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch) {
@@ -12275,6 +17286,15 @@ void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_
     ctx->cparams.n_threads_batch = n_threads_batch;
 }
 
+void llama_set_abort_callback(struct llama_context * ctx, bool (*abort_callback)(void * data), void * abort_callback_data) {
+    ctx->abort_callback      = abort_callback;
+    ctx->abort_callback_data = abort_callback_data;
+}
+
+void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) {
+    ctx->cparams.causal_attn = causal_attn;
+}
+
 struct llama_batch llama_batch_get_one(
              llama_token * tokens,
                  int32_t   n_tokens,
@@ -12341,35 +17361,154 @@ int32_t llama_decode(
     return ret;
 }
 
+void llama_synchronize(struct llama_context * ctx) {
+    ggml_backend_sched_synchronize(ctx->sched);
+
+    // FIXME: if multiple single tokens are evaluated without a synchronization,
+    // the stats will be added to the prompt evaluation stats
+    // this should only happen when using batch size 1 to evaluate a batch
+
+    // add the evaluation to the stats
+    if (ctx->n_queued_tokens == 1) {
+        ctx->t_eval_us += ggml_time_us() - ctx->t_compute_start_us;
+        ctx->n_eval++;
+    } else if (ctx->n_queued_tokens > 1) {
+        ctx->t_p_eval_us += ggml_time_us() - ctx->t_compute_start_us;
+        ctx->n_p_eval += ctx->n_queued_tokens;
+    }
+
+    // get a more accurate load time, upon first eval
+    if (ctx->n_queued_tokens > 0 && !ctx->has_evaluated_once) {
+        ctx->t_load_us = ggml_time_us() - ctx->t_start_us;
+        ctx->has_evaluated_once = true;
+    }
+
+    ctx->n_queued_tokens = 0;
+    ctx->t_compute_start_us = 0;
+}
+
 float * llama_get_logits(struct llama_context * ctx) {
-    return ctx->logits.data();
+    llama_synchronize(ctx);
+
+    return ctx->logits;
 }
 
 float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) {
-    assert(ctx->logits_valid.at(i));
-    return ctx->logits.data() + i*ctx->model.hparams.n_vocab;
+    int32_t j = -1;
+    llama_synchronize(ctx);
+
+    try {
+        if (ctx->logits == nullptr) {
+            throw std::runtime_error("no logits");
+        }
+
+        if (i < 0) {
+            j = ctx->n_outputs + i;
+            if (j < 0) {
+                throw std::runtime_error(format("negative index out of range [0, %d)", ctx->n_outputs));
+            }
+        } else if ((size_t) i >= ctx->output_ids.size()) {
+            throw std::runtime_error(format("out of range [0, %lu)", ctx->output_ids.size()));
+        } else {
+            j = ctx->output_ids[i];
+        }
+
+        if (j < 0) {
+            throw std::runtime_error(format("batch.logits[%d] != true", i));
+        }
+        if (j >= ctx->n_outputs) {
+            // This should not happen
+            throw std::runtime_error(format("corrupt output buffer (j=%d, n_outputs=%d)", j, ctx->n_outputs));
+        }
+
+        return ctx->logits + j*ctx->model.hparams.n_vocab;
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: invalid logits id %d, reason: %s\n", __func__, i, err.what());
+#ifndef NDEBUG
+        GGML_ASSERT(false);
+#endif
+        return nullptr;
+    }
 }
 
 float * llama_get_embeddings(struct llama_context * ctx) {
-    return ctx->embedding.data();
+    llama_synchronize(ctx);
+
+    return ctx->embd;
 }
 
 float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i) {
-    return ctx->embedding.data() + i*ctx->model.hparams.n_embd;
+    int32_t j = -1;
+
+    llama_synchronize(ctx);
+
+    try {
+        if (ctx->embd == nullptr) {
+            throw std::runtime_error("no embeddings");
+        }
+
+        if (i < 0) {
+            j = ctx->n_outputs + i;
+            if (j < 0) {
+                throw std::runtime_error(format("negative index out of range [0, %d)", ctx->n_outputs));
+            }
+        } else if ((size_t) i >= ctx->output_ids.size()) {
+            throw std::runtime_error(format("out of range [0, %lu)", ctx->output_ids.size()));
+        } else {
+            j = ctx->output_ids[i];
+        }
+
+        if (j < 0) {
+            throw std::runtime_error(format("batch.logits[%d] != true", i));
+        }
+        if (j >= ctx->n_outputs) {
+            // This should not happen
+            throw std::runtime_error(format("corrupt output buffer (j=%d, n_outputs=%d)", j, ctx->n_outputs));
+        }
+
+        return ctx->embd + j*ctx->model.hparams.n_embd;
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: invalid embeddings id %d, reason: %s\n", __func__, i, err.what());
+#ifndef NDEBUG
+        GGML_ASSERT(false);
+#endif
+        return nullptr;
+    }
+}
+
+float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id) {
+    llama_synchronize(ctx);
+
+    auto it = ctx->embd_seq.find(seq_id);
+    if (it == ctx->embd_seq.end()) {
+        return nullptr;
+    }
+
+    return it->second.data();
 }
 
 const char * llama_token_get_text(const struct llama_model * model, llama_token token) {
+    GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
     return model->vocab.id_to_token[token].text.c_str();
 }
 
 float llama_token_get_score(const struct llama_model * model, llama_token token) {
+    GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
     return model->vocab.id_to_token[token].score;
 }
 
 llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token) {
+    GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
     return model->vocab.id_to_token[token].type;
 }
 
+bool llama_token_is_eog(const struct llama_model * model, llama_token token) {
+    return token != -1 && (
+        token == llama_token_eos(model) ||
+        token == llama_token_eot(model)
+    );
+}
+
 llama_token llama_token_bos(const struct llama_model * model) {
     return model->vocab.special_bos_id;
 }
@@ -12378,6 +17517,14 @@ llama_token llama_token_eos(const struct llama_model * model) {
     return model->vocab.special_eos_id;
 }
 
+llama_token llama_token_cls(const struct llama_model * model) {
+    return model->vocab.special_cls_id;
+}
+
+llama_token llama_token_sep(const struct llama_model * model) {
+    return model->vocab.special_sep_id;
+}
+
 llama_token llama_token_nl(const struct llama_model * model) {
     return model->vocab.linefeed_id;
 }
@@ -12411,12 +17558,12 @@ int32_t llama_tokenize(
                   const char * text,
                      int32_t   text_len,
                  llama_token * tokens,
-                     int32_t   n_max_tokens,
-                        bool   add_bos,
-                        bool   special) {
-    auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_bos, special);
+                     int32_t   n_tokens_max,
+                        bool   add_special,
+                        bool   parse_special) {
+    auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_special, parse_special);
 
-    if (n_max_tokens < (int) res.size()) {
+    if (n_tokens_max < (int) res.size()) {
         // LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
         return -((int) res.size());
     }
@@ -12430,16 +17577,17 @@ int32_t llama_tokenize(
 
 static std::string llama_decode_text(const std::string & text) {
     std::string decoded_text;
-    auto unicode_sequences = codepoints_from_utf8(text);
-    for (auto& unicode_sequence : unicode_sequences) {
-        decoded_text += unicode_to_bytes_bpe(codepoint_to_utf8(unicode_sequence));
+
+    const auto cpts = unicode_cpts_from_utf8(text);
+    for (const auto cpt : cpts) {
+        decoded_text += unicode_utf8_to_byte(unicode_cpt_to_utf8(cpt));
     }
 
     return decoded_text;
 }
 
 // does not write null-terminator to buf
-int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length) {
+int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length, bool special) {
     if (0 <= token && token < llama_n_vocab(model)) {
         switch (llama_vocab_get_type(model->vocab)) {
         case LLAMA_VOCAB_TYPE_WPM:
@@ -12454,10 +17602,12 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
                 }
                 memcpy(buf, result.c_str(), result.length());
                 return result.length();
-            } else if (llama_is_user_defined_token(model->vocab, token)) {
+            } else if (
+                    (llama_is_user_defined_token(model->vocab, token)) ||
+                    (llama_is_control_token     (model->vocab, token) && special)) {
                 std::string result = model->vocab.id_to_token[token].text;
                 if (length < (int) result.length()) {
-                    return -result.length();
+                    return -(int) result.length();
                 }
                 memcpy(buf, result.c_str(), result.length());
                 return result.length();
@@ -12467,8 +17617,6 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
                 }
                 memcpy(buf, "\xe2\x96\x85", 3);
                 return 3;
-            } else if (llama_is_control_token(model->vocab, token)) {
-                ;
             } else if (llama_is_byte_token(model->vocab, token)) {
                 if (length < 1) {
                     return -1;
@@ -12489,15 +17637,15 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
                 }
                 memcpy(buf, result.c_str(), result.length());
                 return result.length();
-            } else if (llama_is_user_defined_token(model->vocab, token)) {
+            } else if (
+                    (llama_is_user_defined_token(model->vocab, token)) ||
+                    (llama_is_control_token     (model->vocab, token) && special)) {
                 std::string result = model->vocab.id_to_token[token].text;
                 if (length < (int) result.length()) {
-                    return -result.length();
+                    return -(int) result.length();
                 }
                 memcpy(buf, result.c_str(), result.length());
                 return result.length();
-            } else if (llama_is_control_token(model->vocab, token)) {
-                ;
             }
             break;
         }
@@ -12529,7 +17677,7 @@ static int32_t llama_chat_apply_template_internal(
     std::string & dest, bool add_ass) {
     // Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527
     std::stringstream ss;
-    if (tmpl.find("<|im_start|>") != std::string::npos) {
+    if (tmpl == "chatml" || tmpl.find("<|im_start|>") != std::string::npos) {
         // chatml template
         for (auto message : chat) {
             ss << "<|im_start|>" << message->role << "\n" << message->content << "<|im_end|>\n";
@@ -12537,7 +17685,7 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|im_start|>assistant\n";
         }
-    } else if (tmpl.find("[INST]") != std::string::npos) {
+    } else if (tmpl == "llama2" || tmpl.find("[INST]") != std::string::npos) {
         // llama2 template and its variants
         // [variant] support system message
         bool support_system_message = tmpl.find("<<SYS>>") != std::string::npos;
@@ -12572,7 +17720,7 @@ static int32_t llama_chat_apply_template_internal(
             }
         }
         // llama2 templates seem to not care about "add_generation_prompt"
-    } else if (tmpl.find("<|user|>") != std::string::npos) {
+    } else if (tmpl == "zephyr" || tmpl.find("<|user|>") != std::string::npos) {
         // zephyr template
         for (auto message : chat) {
             ss << "<|" << message->role << "|>" << "\n" << message->content << "<|endoftext|>\n";
@@ -12580,6 +17728,139 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|assistant|>\n";
         }
+    } else if (tmpl == "monarch" || tmpl.find("bos_token + message['role']") != std::string::npos) {
+        // mlabonne/AlphaMonarch-7B template (the <s> is included inside history)
+        for (auto message : chat) {
+            std::string bos = (message == chat.front()) ? "" : "<s>"; // skip BOS for first message
+            ss << bos << message->role << "\n" << message->content << "</s>\n";
+        }
+        if (add_ass) {
+            ss << "<s>assistant\n";
+        }
+    } else if (tmpl == "gemma" || tmpl.find("<start_of_turn>") != std::string::npos) {
+        // google/gemma-7b-it
+        std::string system_prompt = "";
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                // there is no system message for gemma, but we will merge it with user prompt, so nothing is broken
+                system_prompt = trim(message->content);
+                continue;
+            }
+            // in gemma, "assistant" is "model"
+            role = role == "assistant" ? "model" : message->role;
+            ss << "<start_of_turn>" << role << "\n";
+            if (!system_prompt.empty() && role != "model") {
+                ss << system_prompt << "\n\n";
+                system_prompt = "";
+            }
+            ss << trim(message->content) << "<end_of_turn>\n";
+        }
+        if (add_ass) {
+            ss << "<start_of_turn>model\n";
+        }
+    } else if (tmpl == "orion" || tmpl.find("'\\n\\nAssistant: ' + eos_token") != std::string::npos) {
+        // OrionStarAI/Orion-14B-Chat
+        std::string system_prompt = "";
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                // there is no system message support, we will merge it with user prompt
+                system_prompt = message->content;
+                continue;
+            } else if (role == "user") {
+                ss << "Human: ";
+                if (!system_prompt.empty()) {
+                    ss << system_prompt << "\n\n";
+                    system_prompt = "";
+                }
+                ss << message->content << "\n\nAssistant: </s>";
+            } else {
+                ss << message->content << "</s>";
+            }
+        }
+    } else if (tmpl == "openchat" || tmpl.find("GPT4 Correct ") != std::string::npos) {
+        // openchat/openchat-3.5-0106,
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                ss << message->content << "<|end_of_turn|>";
+            } else {
+                role[0] = toupper(role[0]);
+                ss << "GPT4 Correct " << role << ": " << message->content << "<|end_of_turn|>";
+            }
+        }
+        if (add_ass) {
+            ss << "GPT4 Correct Assistant:";
+        }
+    } else if (tmpl == "vicuna" || tmpl == "vicuna-orca" || (tmpl.find("USER: ") != std::string::npos && tmpl.find("ASSISTANT: ") != std::string::npos)) {
+        // eachadea/vicuna-13b-1.1 (and Orca variant)
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                // Orca-Vicuna variant uses a system prefix
+                if (tmpl == "vicuna-orca" || tmpl.find("SYSTEM: ") != std::string::npos) {
+                    ss << "SYSTEM: " << message->content << "\n";
+                } else {
+                    ss << message->content << "\n\n";
+                }
+            } else if (role == "user") {
+                ss << "USER: " << message->content << "\n";
+            } else if (role == "assistant") {
+                ss << "ASSISTANT: " << message->content << "</s>\n";
+            }
+        }
+        if (add_ass) {
+            ss << "ASSISTANT:";
+        }
+    } else if (tmpl == "deepseek" || (tmpl.find("### Instruction:") != std::string::npos && tmpl.find("<|EOT|>") != std::string::npos)) {
+        // deepseek-ai/deepseek-coder-33b-instruct
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                ss << message->content;
+            } else if (role == "user") {
+                ss << "### Instruction:\n" << message->content << "\n";
+            } else if (role == "assistant") {
+                ss << "### Response:\n" << message->content << "\n<|EOT|>\n";
+            }
+        }
+        if (add_ass) {
+            ss << "### Response:\n";
+        }
+    } else if (tmpl == "command-r" || (tmpl.find("<|START_OF_TURN_TOKEN|>") != std::string::npos && tmpl.find("<|USER_TOKEN|>") != std::string::npos)) {
+        // CohereForAI/c4ai-command-r-plus
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                ss << "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>";
+            } else if (role == "user") {
+                ss << "<|START_OF_TURN_TOKEN|><|USER_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>";
+            } else if (role == "assistant") {
+                ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>";
+            }
+        }
+        if (add_ass) {
+            ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>";
+        }
+    } else if (tmpl == "llama3" || (tmpl.find("<|start_header_id|>") != std::string::npos && tmpl.find("<|end_header_id|>") != std::string::npos)) {
+        // Llama 3
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|start_header_id|>" << role << "<|end_header_id|>\n\n" << trim(message->content) << "<|eot_id|>";
+        }
+        if (add_ass) {
+            ss << "<|start_header_id|>assistant<|end_header_id|>\n\n";
+        }
+    } else if (tmpl == "phi3" || (tmpl.find("<|assistant|>") != std::string::npos && tmpl.find("<|end|>") != std::string::npos )) {
+        // Phi 3
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|" << role << "|>\n" << trim(message->content) << "<|end|>\n";
+        }
+        if (add_ass) {
+            ss << "<|assistant|>\n";
+        }
     } else {
         // template not supported
         return -1;
@@ -12602,29 +17883,57 @@ LLAMA_API int32_t llama_chat_apply_template(
         // load template from model
         std::vector<char> model_template(2048, 0); // longest known template is about 1200 bytes
         std::string template_key = "tokenizer.chat_template";
-        int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), curr_tmpl.size());
+        int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size());
         if (res < 0) {
             // worst case: there is no information about template, we will use chatml by default
-            curr_tmpl = "<|im_start|>"; // see llama_chat_apply_template_internal
+            curr_tmpl = "chatml"; // see llama_chat_apply_template_internal
         } else {
             curr_tmpl = std::string(model_template.data(), model_template.size());
         }
     }
+
     // format the chat to string
     std::vector<const llama_chat_message *> chat_vec;
     chat_vec.resize(n_msg);
     for (size_t i = 0; i < n_msg; i++) {
         chat_vec[i] = &chat[i];
     }
+
     std::string formatted_chat;
     int32_t res = llama_chat_apply_template_internal(curr_tmpl, chat_vec, formatted_chat, add_ass);
     if (res < 0) {
         return res;
     }
-    strncpy(buf, formatted_chat.c_str(), length);
+    if (buf && length > 0) {
+        strncpy(buf, formatted_chat.c_str(), length);
+    }
     return res;
 }
 
+LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count) {
+    static const char * const SPLIT_PATH_FORMAT = "%s-%05d-of-%05d.gguf";
+    if (snprintf(split_path, maxlen, SPLIT_PATH_FORMAT, path_prefix, split_no + 1, split_count)) {
+        return strlen(split_path);
+    }
+    return 0;
+}
+
+int llama_split_prefix(char * dest, size_t maxlen, const char * split_path, int split_no, int split_count) {
+    std::string str_split_path(split_path);
+    char postfix[32];
+    snprintf(postfix, 32, "-%05d-of-%05d.gguf", split_no + 1, split_count);
+    std::string str_postfix(postfix);
+
+    // check if dest ends with postfix
+    int size_prefix = str_split_path.size() - str_postfix.size();
+    if (size_prefix > 0 && str_split_path.find(str_postfix, size_prefix) != std::string::npos) {
+        snprintf(dest, std::min((size_t) size_prefix + 1, maxlen), "%s", split_path);
+        return size_prefix;
+    }
+
+    return 0;
+}
+
 struct llama_timings llama_get_timings(struct llama_context * ctx) {
     struct llama_timings result = {
         /*.t_start_ms  =*/ 1e-3 * ctx->t_start_us,
@@ -12635,7 +17944,7 @@ struct llama_timings llama_get_timings(struct llama_context * ctx) {
         /*.t_eval_ms   =*/ 1e-3 * ctx->t_eval_us,
 
         /*.n_sample =*/ std::max(1, ctx->n_sample),
-        /*.n_p_eval =*/ std::max(1, ctx->n_p_eval),
+        /*.n_p_eval =*/ std::max(0, ctx->n_p_eval),
         /*.n_eval   =*/ std::max(1, ctx->n_eval),
     };
 
@@ -12684,6 +17993,11 @@ const char * llama_print_system_info(void) {
     s += "SSSE3 = "       + std::to_string(ggml_cpu_has_ssse3())       + " | ";
     s += "VSX = "         + std::to_string(ggml_cpu_has_vsx())         + " | ";
     s += "MATMUL_INT8 = " + std::to_string(ggml_cpu_has_matmul_int8()) + " | ";
+#ifdef GGML_USE_LLAMAFILE
+    s += "LLAMAFILE = 1 | ";
+#else
+    s += "LLAMAFILE = 0 | ";
+#endif
 
     return s.c_str();
 }
diff --git a/llama.h b/llama.h
index 77a84c18a..0b2e708d0 100644
--- a/llama.h
+++ b/llama.h
@@ -37,9 +37,13 @@
 
 #define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
 #define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
+#define LLAMA_FILE_MAGIC_GGSQ 0x67677371u // 'ggsq'
 
 #define LLAMA_SESSION_MAGIC   LLAMA_FILE_MAGIC_GGSN
-#define LLAMA_SESSION_VERSION 4
+#define LLAMA_SESSION_VERSION 6
+
+#define LLAMA_STATE_SEQ_MAGIC   LLAMA_FILE_MAGIC_GGSQ
+#define LLAMA_STATE_SEQ_VERSION 1
 
 #ifdef __cplusplus
 extern "C" {
@@ -59,9 +63,36 @@ extern "C" {
     typedef int32_t llama_seq_id;
 
     enum llama_vocab_type {
-        LLAMA_VOCAB_TYPE_SPM = 0, // SentencePiece
-        LLAMA_VOCAB_TYPE_BPE = 1, // Byte Pair Encoding
-        LLAMA_VOCAB_TYPE_WPM = 2, // WordPiece
+        LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab
+        LLAMA_VOCAB_TYPE_SPM  = 1, // LLaMA tokenizer based on byte-level BPE with byte fallback
+        LLAMA_VOCAB_TYPE_BPE  = 2, // GPT-2 tokenizer based on byte-level BPE
+        LLAMA_VOCAB_TYPE_WPM  = 3, // BERT tokenizer based on WordPiece
+    };
+
+    // pre-tokenization types
+    enum llama_vocab_pre_type {
+        LLAMA_VOCAB_PRE_TYPE_DEFAULT        = 0,
+        LLAMA_VOCAB_PRE_TYPE_LLAMA3         = 1,
+        LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM   = 2,
+        LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER = 3,
+        LLAMA_VOCAB_PRE_TYPE_FALCON         = 4,
+        LLAMA_VOCAB_PRE_TYPE_MPT            = 5,
+        LLAMA_VOCAB_PRE_TYPE_STARCODER      = 6,
+        LLAMA_VOCAB_PRE_TYPE_GPT2           = 7,
+        LLAMA_VOCAB_PRE_TYPE_REFACT         = 8,
+        LLAMA_VOCAB_PRE_TYPE_COMMAND_R      = 9,
+        LLAMA_VOCAB_PRE_TYPE_QWEN2          = 10,
+        LLAMA_VOCAB_PRE_TYPE_OLMO           = 11,
+        LLAMA_VOCAB_PRE_TYPE_DBRX           = 12,
+    };
+
+    // note: these values should be synchronized with ggml_rope
+    // TODO: maybe move this enum to ggml.h (ggml_rope_type)
+    enum llama_rope_type {
+        LLAMA_ROPE_TYPE_NONE = -1,
+        LLAMA_ROPE_TYPE_NORM =  0,
+        LLAMA_ROPE_TYPE_NEOX =  2,
+        LLAMA_ROPE_TYPE_GLM  =  4,
     };
 
     enum llama_token_type {
@@ -98,31 +129,40 @@ extern "C" {
         LLAMA_FTYPE_MOSTLY_IQ2_XXS       = 19, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_IQ2_XS        = 20, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_Q2_K_S        = 21, // except 1d tensors
-        LLAMA_FTYPE_MOSTLY_Q3_K_XS       = 22, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_IQ3_XS        = 22, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_IQ3_XXS       = 23, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_IQ1_S         = 24, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_IQ4_NL        = 25, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_IQ3_S         = 26, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_IQ3_M         = 27, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_IQ2_S         = 28, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_IQ2_M         = 29, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_IQ4_XS        = 30, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_IQ1_M         = 31, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_BF16          = 32, // except 1d tensors
 
         LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
     };
 
     enum llama_rope_scaling_type {
-        LLAMA_ROPE_SCALING_UNSPECIFIED = -1,
-        LLAMA_ROPE_SCALING_NONE        = 0,
-        LLAMA_ROPE_SCALING_LINEAR      = 1,
-        LLAMA_ROPE_SCALING_YARN        = 2,
-        LLAMA_ROPE_SCALING_MAX_VALUE   = LLAMA_ROPE_SCALING_YARN,
+        LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED = -1,
+        LLAMA_ROPE_SCALING_TYPE_NONE        = 0,
+        LLAMA_ROPE_SCALING_TYPE_LINEAR      = 1,
+        LLAMA_ROPE_SCALING_TYPE_YARN        = 2,
+        LLAMA_ROPE_SCALING_TYPE_MAX_VALUE   = LLAMA_ROPE_SCALING_TYPE_YARN,
     };
 
     enum llama_pooling_type {
-        LLAMA_POOLING_NONE = 0,
-        LLAMA_POOLING_MEAN = 1,
-        LLAMA_POOLING_CLS  = 2,
+        LLAMA_POOLING_TYPE_UNSPECIFIED = -1,
+        LLAMA_POOLING_TYPE_NONE = 0,
+        LLAMA_POOLING_TYPE_MEAN = 1,
+        LLAMA_POOLING_TYPE_CLS  = 2,
     };
 
     enum llama_split_mode {
-        LLAMA_SPLIT_NONE    = 0, // single GPU
-        LLAMA_SPLIT_LAYER   = 1, // split layers and KV across GPUs
-        LLAMA_SPLIT_ROW     = 2, // split rows across GPUs
+        LLAMA_SPLIT_MODE_NONE    = 0, // single GPU
+        LLAMA_SPLIT_MODE_LAYER   = 1, // split layers and KV across GPUs
+        LLAMA_SPLIT_MODE_ROW     = 2, // split rows across GPUs
     };
 
     typedef struct llama_token_data {
@@ -137,7 +177,7 @@ extern "C" {
         bool sorted;
     } llama_token_data_array;
 
-    typedef bool (*llama_progress_callback)(float progress, void *ctx);
+    typedef bool (*llama_progress_callback)(float progress, void * user_data);
 
     // Input data for llama_decode
     // A llama_batch object can contain input about one or many sequences
@@ -147,7 +187,7 @@ extern "C" {
     // - embd   : token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
     // - pos    : the positions of the respective token in the sequence
     // - seq_id : the sequence to which the respective token belongs
-    // - logits : if zero, the logits for the respective token will not be output
+    // - logits : if zero, the logits (and/or the embeddings) for the respective token will not be output
     //
     typedef struct llama_batch {
         int32_t n_tokens;
@@ -157,7 +197,7 @@ extern "C" {
         llama_pos    *  pos;
         int32_t      *  n_seq_id;
         llama_seq_id ** seq_id;
-        int8_t       *  logits;
+        int8_t       *  logits; // TODO: rename this to "output"
 
         // NOTE: helpers for smooth API transition - can be deprecated in the future
         //       for future-proof code, use the above fields instead and ignore everything below
@@ -170,18 +210,22 @@ extern "C" {
     } llama_batch;
 
     enum llama_model_kv_override_type {
-        LLAMA_KV_OVERRIDE_INT,
-        LLAMA_KV_OVERRIDE_FLOAT,
-        LLAMA_KV_OVERRIDE_BOOL,
+        LLAMA_KV_OVERRIDE_TYPE_INT,
+        LLAMA_KV_OVERRIDE_TYPE_FLOAT,
+        LLAMA_KV_OVERRIDE_TYPE_BOOL,
+        LLAMA_KV_OVERRIDE_TYPE_STR,
     };
 
     struct llama_model_kv_override {
-        char key[128];
         enum llama_model_kv_override_type tag;
+
+        char key[128];
+
         union {
-            int64_t int_value;
-            double float_value;
-            bool bool_value;
+            int64_t val_i64;
+            double  val_f64;
+            bool    val_bool;
+            char    val_str[128];
         };
     };
 
@@ -210,18 +254,24 @@ extern "C" {
         const struct llama_model_kv_override * kv_overrides;
 
         // Keep the booleans together to avoid misalignment during copy-by-value.
-        bool vocab_only; // only load the vocabulary, no weights
-        bool use_mmap;   // use mmap if possible
-        bool use_mlock;  // force system to keep model in RAM
+        bool vocab_only;    // only load the vocabulary, no weights
+        bool use_mmap;      // use mmap if possible
+        bool use_mlock;     // force system to keep model in RAM
+        bool check_tensors; // validate model tensor data
     };
 
     struct llama_context_params {
         uint32_t seed;              // RNG seed, -1 for random
         uint32_t n_ctx;             // text context, 0 = from model
-        uint32_t n_batch;           // prompt processing maximum batch size
+        uint32_t n_batch;           // logical maximum batch size that can be submitted to llama_decode
+        uint32_t n_ubatch;          // physical maximum batch size
+        uint32_t n_seq_max;         // max number of sequences (i.e. distinct states for recurrent models)
         uint32_t n_threads;         // number of threads to use for generation
         uint32_t n_threads_batch;   // number of threads to use for batch processing
-        int32_t  rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
+
+        enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
+        enum llama_pooling_type      pooling_type;      // whether to pool (sum) embedding results by sequence id
+                                                        // (ignored if no pooling layer)
 
         // ref: https://github.com/ggerganov/llama.cpp/pull/2054
         float    rope_freq_base;   // RoPE base frequency, 0 = from model
@@ -231,6 +281,7 @@ extern "C" {
         float    yarn_beta_fast;   // YaRN low correction dim
         float    yarn_beta_slow;   // YaRN high correction dim
         uint32_t yarn_orig_ctx;    // YaRN original context size
+        float    defrag_thold;     // defragment the KV cache if holes/size > thold, < 0 disabled (default)
 
         ggml_backend_sched_eval_callback cb_eval;
         void * cb_eval_user_data;
@@ -239,22 +290,31 @@ extern "C" {
         enum ggml_type type_v; // data type for V cache
 
         // Keep the booleans together to avoid misalignment during copy-by-value.
-        bool mul_mat_q;   // if true, use experimental mul_mat_q kernels (DEPRECATED - always true)
-        bool logits_all;  // the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
-        bool embedding;   // embedding mode only
+        bool logits_all;  // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
+        bool embeddings;  // if true, extract embeddings (together with logits)
         bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
-        bool do_pooling;  // whether to pool (sum) embedding results by sequence id (ignored if no pooling layer)
+        bool flash_attn;  // whether to use flash attention
+
+        // Abort callback
+        // if it returns true, execution of llama_decode() will be aborted
+        // currently works only with CPU execution
+        ggml_abort_callback abort_callback;
+        void *              abort_callback_data;
     };
 
     // model quantization parameters
     typedef struct llama_model_quantize_params {
-        int32_t nthread;             // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
-        enum llama_ftype ftype;      // quantize to this llama_ftype
-        bool allow_requantize;       // allow quantizing non-f32/f16 tensors
-        bool quantize_output_tensor; // quantize output.weight
-        bool only_copy;              // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
-        bool pure;                   // disable k-quant mixtures and quantize all tensors to the same type
-        void * imatrix;              // pointer to importance matrix data
+        int32_t nthread;                     // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
+        enum llama_ftype ftype;              // quantize to this llama_ftype
+        enum ggml_type output_tensor_type;   // output tensor type
+        enum ggml_type token_embedding_type; // itoken embeddings tensor type
+        bool allow_requantize;               // allow quantizing non-f32/f16 tensors
+        bool quantize_output_tensor;         // quantize output.weight
+        bool only_copy;                      // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
+        bool pure;                           // quantize all tensors to the default type
+        bool keep_split;                     // quantize to the same number of shards
+        void * imatrix;                      // pointer to importance matrix data
+        void * kv_overrides;                 // pointer to vector containing overrides
     } llama_model_quantize_params;
 
     // grammar types
@@ -348,19 +408,22 @@ extern "C" {
     LLAMA_API bool llama_supports_mlock      (void);
     LLAMA_API bool llama_supports_gpu_offload(void);
 
-    LLAMA_API DEPRECATED(bool llama_mmap_supported (void), "use llama_supports_mmap() instead");
-    LLAMA_API DEPRECATED(bool llama_mlock_supported(void), "use llama_supports_mlock() instead");
-
     LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
 
     LLAMA_API uint32_t llama_n_ctx      (const struct llama_context * ctx);
     LLAMA_API uint32_t llama_n_batch    (const struct llama_context * ctx);
+    LLAMA_API uint32_t llama_n_ubatch   (const struct llama_context * ctx);
+    LLAMA_API uint32_t llama_n_seq_max  (const struct llama_context * ctx);
 
-    LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
+    LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx);
+
+    LLAMA_API enum llama_vocab_type   llama_vocab_type  (const struct llama_model   * model);
+    LLAMA_API enum llama_rope_type    llama_rope_type   (const struct llama_model   * model);
 
     LLAMA_API int32_t llama_n_vocab    (const struct llama_model * model);
     LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
     LLAMA_API int32_t llama_n_embd     (const struct llama_model * model);
+    LLAMA_API int32_t llama_n_layer    (const struct llama_model * model);
 
     // Get the model's RoPE frequency scaling factor
     LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);
@@ -406,20 +469,26 @@ extern "C" {
     // The model needs to be reloaded before applying a new adapter, otherwise the adapter
     // will be applied on top of the previous one
     // Returns 0 on success
-    LLAMA_API DEPRECATED(int32_t llama_apply_lora_from_file(
-            struct llama_context * ctx,
-                      const char * path_lora,
-                           float   scale,
-                      const char * path_base_model,
-                         int32_t   n_threads),
-            "use llama_model_apply_lora_from_file instead");
-
     LLAMA_API int32_t llama_model_apply_lora_from_file(
             const struct llama_model * model,
-                      const char * path_lora,
-                           float   scale,
-                      const char * path_base_model,
-                         int32_t   n_threads);
+                          const char * path_lora,
+                               float   scale,
+                          const char * path_base_model,
+                             int32_t   n_threads);
+
+    // Apply a loaded control vector to a llama_context, or if data is NULL, clear
+    // the currently loaded vector.
+    // n_embd should be the size of a single layer's control, and data should point
+    // to an n_embd x n_layers buffer starting from layer 1.
+    // il_start and il_end are the layer range the vector should apply to (both inclusive)
+    // See llama_control_vector_load in common to load a control vector.
+    LLAMA_API int32_t llama_control_vector_apply(
+            struct llama_context * lctx,
+                     const float * data,
+                          size_t   len,
+                         int32_t   n_embd,
+                         int32_t   il_start,
+                         int32_t   il_end);
 
     //
     // KV cache
@@ -440,7 +509,7 @@ extern "C" {
         // Maximum number of sequences that can exist in a cell. It's not an error
         // if there are more sequences in a cell than this value, however they will
         // not be visible in the view cells_sequences.
-        int32_t n_max_seq;
+        int32_t n_seq_max;
 
         // Number of tokens in the cache. For example, if there are two populated
         // cells, the first with 1 sequence id in it and the second with 2 sequence
@@ -460,12 +529,12 @@ extern "C" {
         // Information for an individual cell.
         struct llama_kv_cache_view_cell * cells;
 
-        // The sequences for each cell. There will be n_max_seq items per cell.
+        // The sequences for each cell. There will be n_seq_max items per cell.
         llama_seq_id * cells_sequences;
     };
 
     // Create an empty KV cache view. (use only for debugging purposes)
-    LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_max_seq);
+    LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max);
 
     // Free a KV cache view. (use only for debugging purposes)
     LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view);
@@ -480,15 +549,16 @@ extern "C" {
     // Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
     LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx);
 
-    // Clear the KV cache
+    // Clear the KV cache - both cell info is erased and KV data is zeroed
     LLAMA_API void llama_kv_cache_clear(
             struct llama_context * ctx);
 
     // Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
+    // Returns false if a partial sequence cannot be removed. Removing a whole sequence never fails
     // seq_id < 0 : match any sequence
     // p0 < 0     : [0,  p1]
     // p1 < 0     : [p0, inf)
-    LLAMA_API void llama_kv_cache_seq_rm(
+    LLAMA_API bool llama_kv_cache_seq_rm(
             struct llama_context * ctx,
                     llama_seq_id   seq_id,
                        llama_pos   p0,
@@ -511,10 +581,12 @@ extern "C" {
                     llama_seq_id   seq_id);
 
     // Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
-    // If the KV cache is RoPEd, the KV data is updated accordingly
+    // If the KV cache is RoPEd, the KV data is updated accordingly:
+    //   - lazily on next llama_decode()
+    //   - explicitly with llama_kv_cache_update()
     // p0 < 0 : [0,  p1]
     // p1 < 0 : [p0, inf)
-    LLAMA_API void llama_kv_cache_seq_shift(
+    LLAMA_API void llama_kv_cache_seq_add(
             struct llama_context * ctx,
                     llama_seq_id   seq_id,
                        llama_pos   p0,
@@ -522,7 +594,9 @@ extern "C" {
                        llama_pos   delta);
 
     // Integer division of the positions by factor of `d > 1`
-    // If the KV cache is RoPEd, the KV data is updated accordingly
+    // If the KV cache is RoPEd, the KV data is updated accordingly:
+    //   - lazily on next llama_decode()
+    //   - explicitly with llama_kv_cache_update()
     // p0 < 0 : [0,  p1]
     // p1 < 0 : [p0, inf)
     LLAMA_API void llama_kv_cache_seq_div(
@@ -532,66 +606,117 @@ extern "C" {
                        llama_pos   p1,
                              int   d);
 
+    // Returns the largest position present in the KV cache for the specified sequence
+    LLAMA_API llama_pos llama_kv_cache_seq_pos_max(
+            struct llama_context * ctx,
+                    llama_seq_id   seq_id);
+
+    // Defragment the KV cache
+    // This will be applied:
+    //   - lazily on next llama_decode()
+    //   - explicitly with llama_kv_cache_update()
+    LLAMA_API void llama_kv_cache_defrag(struct llama_context * ctx);
+
+    // Apply the KV cache updates (such as K-shifts, defragmentation, etc.)
+    LLAMA_API void llama_kv_cache_update(struct llama_context * ctx);
+
     //
     // State / sessions
     //
 
     // Returns the maximum size in bytes of the state (rng, logits, embedding
     // and kv_cache) - will often be smaller after compacting tokens
-    LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx);
+    LLAMA_API size_t llama_state_get_size(const struct llama_context * ctx);
+    LLAMA_API DEPRECATED(size_t llama_get_state_size(const struct llama_context * ctx),
+        "use llama_state_get_size instead");
 
     // Copies the state to the specified destination address.
     // Destination needs to have allocated enough memory.
     // Returns the number of bytes copied
-    LLAMA_API size_t llama_copy_state_data(
+    LLAMA_API size_t llama_state_get_data(
             struct llama_context * ctx,
                          uint8_t * dst);
+    LLAMA_API DEPRECATED(size_t llama_copy_state_data(
+            struct llama_context * ctx,
+                         uint8_t * dst),
+        "use llama_state_get_data instead");
 
     // Set the state reading from the specified address
     // Returns the number of bytes read
-    LLAMA_API size_t llama_set_state_data(
+    LLAMA_API size_t llama_state_set_data(
             struct llama_context * ctx,
-                         uint8_t * src);
+                   const uint8_t * src);
+    LLAMA_API DEPRECATED(size_t llama_set_state_data(
+            struct llama_context * ctx,
+                   const uint8_t * src),
+        "use llama_state_set_data instead");
 
     // Save/load session file
-    LLAMA_API bool llama_load_session_file(
+    LLAMA_API bool llama_state_load_file(
             struct llama_context * ctx,
                       const char * path_session,
                      llama_token * tokens_out,
                           size_t   n_token_capacity,
                           size_t * n_token_count_out);
+    LLAMA_API DEPRECATED(bool llama_load_session_file(
+            struct llama_context * ctx,
+                      const char * path_session,
+                     llama_token * tokens_out,
+                          size_t   n_token_capacity,
+                          size_t * n_token_count_out),
+        "use llama_state_load_file instead");
 
-    LLAMA_API bool llama_save_session_file(
+    LLAMA_API bool llama_state_save_file(
             struct llama_context * ctx,
                       const char * path_session,
                const llama_token * tokens,
                           size_t   n_token_count);
+    LLAMA_API DEPRECATED(bool llama_save_session_file(
+            struct llama_context * ctx,
+                      const char * path_session,
+               const llama_token * tokens,
+                          size_t   n_token_count),
+        "use llama_state_save_file instead");
+
+    // Get the exact size needed to copy the KV cache of a single sequence
+    LLAMA_API size_t llama_state_seq_get_size(
+            struct llama_context * ctx,
+                    llama_seq_id   seq_id);
+
+    // Copy the KV cache of a single sequence into the specified buffer
+    LLAMA_API size_t llama_state_seq_get_data(
+            struct llama_context * ctx,
+                         uint8_t * dst,
+                    llama_seq_id   seq_id);
+
+    // Copy the sequence data (originally copied with `llama_state_seq_get_data`) into the specified sequence
+    // Returns:
+    //  - Positive: Ok
+    //  - Zero: Failed to load
+    LLAMA_API size_t llama_state_seq_set_data(
+            struct llama_context * ctx,
+                   const uint8_t * src,
+                    llama_seq_id   dest_seq_id);
+
+    LLAMA_API size_t llama_state_seq_save_file(
+            struct llama_context * ctx,
+                      const char * filepath,
+                    llama_seq_id   seq_id,
+               const llama_token * tokens,
+                          size_t   n_token_count);
+
+    LLAMA_API size_t llama_state_seq_load_file(
+            struct llama_context * ctx,
+                      const char * filepath,
+                    llama_seq_id   dest_seq_id,
+                     llama_token * tokens_out,
+                          size_t   n_token_capacity,
+                          size_t * n_token_count_out);
 
     //
     // Decoding
     //
 
-    // Run the llama inference to obtain the logits and probabilities for the next token(s).
-    // tokens + n_tokens is the provided batch of new tokens to process
-    // n_past is the number of tokens to use from previous eval calls
-    // Returns 0 on success
-    // DEPRECATED: use llama_decode() instead
-    LLAMA_API DEPRECATED(int llama_eval(
-            struct llama_context * ctx,
-                     llama_token * tokens,
-                         int32_t   n_tokens,
-                         int32_t   n_past),
-            "use llama_decode() instead");
-
-    // Same as llama_eval, but use float matrix input directly.
-    // DEPRECATED: use llama_decode() instead
-    LLAMA_API DEPRECATED(int llama_eval_embd(
-            struct llama_context * ctx,
-                           float * embd,
-                         int32_t   n_tokens,
-                         int32_t   n_past),
-            "use llama_decode() instead");
-
     // Return batch for single sequence of tokens starting at pos_0
     //
     // NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it
@@ -630,25 +755,51 @@ extern "C" {
     // n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens)
     LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch);
 
-    // Token logits obtained from the last call to llama_eval()
-    // The logits for the last token are stored in the last row
-    // Logits for which llama_batch.logits[i] == 0 are undefined
-    // Rows: n_tokens provided with llama_batch
+    // Set whether to use causal attention or not
+    // If set to true, the model will only attend to the past tokens
+    LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn);
+
+    // Set abort callback
+    LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data);
+
+    // Wait until all computations are finished
+    // This is automatically done when using one of the functions below to obtain the computation results
+    // and is not necessary to call it explicitly in most cases
+    LLAMA_API void llama_synchronize(struct llama_context * ctx);
+
+    // Token logits obtained from the last call to llama_decode()
+    // The logits for which llama_batch.logits[i] != 0 are stored contiguously
+    // in the order they have appeared in the batch.
+    // Rows: number of tokens for which llama_batch.logits[i] != 0
     // Cols: n_vocab
     LLAMA_API float * llama_get_logits(struct llama_context * ctx);
 
-    // Logits for the ith token. Equivalent to:
-    // llama_get_logits(ctx) + i*n_vocab
+    // Logits for the ith token. For positive indices, Equivalent to:
+    // llama_get_logits(ctx) + ctx->output_ids[i]*n_vocab
+    // Negative indicies can be used to access logits in reverse order, -1 is the last logit.
+    // returns NULL for invalid ids.
     LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i);
 
-    // Get the embeddings for the input
-    // shape: [n_embd] (1-dimensional)
+    // Get all output token embeddings.
+    // when pooling_type == LLAMA_POOLING_TYPE_NONE or when using a generative model,
+    // the embeddings for which llama_batch.logits[i] != 0 are stored contiguously
+    // in the order they have appeared in the batch.
+    // shape: [n_outputs*n_embd]
+    // Otherwise, returns NULL.
     LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
 
-    // Get the embeddings for the ith sequence
-    // llama_get_embeddings(ctx) + i*n_embd
+    // Get the embeddings for the ith token. For positive indices, Equivalent to:
+    // llama_get_embeddings(ctx) + ctx->output_ids[i]*n_embd
+    // Negative indicies can be used to access embeddings in reverse order, -1 is the last embedding.
+    // shape: [n_embd] (1-dimensional)
+    // returns NULL for invalid ids.
     LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i);
 
+    // Get the embeddings for a sequence id
+    // Returns NULL if pooling_type is LLAMA_POOLING_TYPE_NONE
+    // shape: [n_embd] (1-dimensional)
+    LLAMA_API float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id);
+
     //
     // Vocab
     //
@@ -659,9 +810,14 @@ extern "C" {
 
     LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token);
 
+    // Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.)
+    LLAMA_API bool llama_token_is_eog(const struct llama_model * model, llama_token token);
+
     // Special tokens
     LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
     LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
+    LLAMA_API llama_token llama_token_cls(const struct llama_model * model); // classification
+    LLAMA_API llama_token llama_token_sep(const struct llama_model * model); // sentence separator
     LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
 
     // Returns -1 if unknown, 1 for true or 0 for false.
@@ -670,7 +826,7 @@ extern "C" {
     // Returns -1 if unknown, 1 for true or 0 for false.
     LLAMA_API int32_t         llama_add_eos_token(const struct llama_model * model);
 
-    // codellama infill tokens
+    // Codellama infill tokens
     LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix
     LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle
     LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix
@@ -682,32 +838,34 @@ extern "C" {
 
     /// @details Convert the provided text into tokens.
     /// @param tokens The tokens pointer must be large enough to hold the resulting tokens.
-    /// @return Returns the number of tokens on success, no more than n_max_tokens
+    /// @return Returns the number of tokens on success, no more than n_tokens_max
     /// @return Returns a negative number on failure - the number of tokens that would have been returned
-    /// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.
-    ///                Does not insert a leading space.
+    /// @param parse_special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated
+    ///                      as plaintext. Does not insert a leading space.
     LLAMA_API int32_t llama_tokenize(
         const struct llama_model * model,
                       const char * text,
                          int32_t   text_len,
                      llama_token * tokens,
-                         int32_t   n_max_tokens,
-                            bool   add_bos,
-                            bool   special);
+                         int32_t   n_tokens_max,
+                            bool   add_special,
+                            bool   parse_special);
 
     // Token Id -> Piece.
     // Uses the vocabulary in the provided context.
     // Does not write null terminator to the buffer.
     // User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
+    // @param special If true, special tokens are rendered in the output.
     LLAMA_API int32_t llama_token_to_piece(
               const struct llama_model * model,
                            llama_token   token,
                                   char * buf,
-                               int32_t   length);
+                               int32_t   length,
+                                  bool   special);
 
     /// Apply chat template. Inspired by hf apply_chat_template() on python.
     /// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model"
-    /// NOTE: This function only support some known jinja templates. It is not a jinja parser.
+    /// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
     /// @param tmpl A Jinja template to use for this chat. If this is nullptr, the model’s default chat template will be used instead.
     /// @param chat Pointer to a list of multiple llama_chat_message
     /// @param n_msg Number of llama_chat_message in this chat
@@ -765,13 +923,6 @@ extern "C" {
                              float * logits_guidance,
                              float   scale);
 
-    LLAMA_API DEPRECATED(void llama_sample_classifier_free_guidance(
-              struct llama_context * ctx,
-            llama_token_data_array * candidates,
-              struct llama_context * guidance_ctx,
-                             float   scale),
-              "use llama_sample_apply_guidance() instead");
-
     /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
     LLAMA_API void llama_sample_softmax(
             struct llama_context * ctx,
@@ -825,12 +976,6 @@ extern "C" {
           llama_token_data_array * candidates,
                            float   temp);
 
-    LLAMA_API DEPRECATED(void llama_sample_temperature(
-                struct llama_context * ctx,
-              llama_token_data_array * candidates,
-                               float   temp),
-            "use llama_sample_temp instead");
-
     /// @details Apply constraints from grammar
     LLAMA_API void llama_sample_grammar(
             struct llama_context * ctx,
@@ -869,7 +1014,7 @@ extern "C" {
             struct llama_context * ctx,
           llama_token_data_array * candidates);
 
-    /// @details Randomly selects a token from the candidates based on their probabilities.
+    /// @details Randomly selects a token from the candidates based on their probabilities using the RNG of ctx.
     LLAMA_API llama_token llama_sample_token(
             struct llama_context * ctx,
           llama_token_data_array * candidates);
@@ -924,6 +1069,16 @@ extern "C" {
                                 int32_t   n_past,
                                 int32_t   n_predict);
 
+    /// @details Build a split GGUF final path for this chunk.
+    ///          llama_split_path(split_path, sizeof(split_path), "/models/ggml-model-q4_0", 2, 4) => split_path = "/models/ggml-model-q4_0-00002-of-00004.gguf"
+    //  Returns the split_path length.
+    LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count);
+
+    /// @details Extract the path prefix from the split_path if and only if the split_no and split_count match.
+    ///          llama_split_prefix(split_prefix, 64, "/models/ggml-model-q4_0-00002-of-00004.gguf", 2, 4) => split_prefix = "/models/ggml-model-q4_0"
+    //  Returns the split_prefix length.
+    LLAMA_API int llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int split_no, int split_count);
+
     // Performance information
     LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
 
@@ -946,15 +1101,49 @@ extern "C" {
 // Internal API to be implemented by llama.cpp and used by tests/benchmarks only
 #ifdef LLAMA_API_INTERNAL
 
-#include <vector>
+#include <random>
 #include <string>
+#include <vector>
 
 struct ggml_tensor;
 
+struct llama_partial_utf8 {
+    uint32_t value;    // bit value so far (unshifted)
+    int      n_remain; // num bytes remaining; -1 indicates invalid sequence
+};
+
+struct llama_grammar {
+    const std::vector<std::vector<llama_grammar_element>>   rules;
+    std::vector<std::vector<const llama_grammar_element *>> stacks;
+
+    // buffer for partially generated UTF-8 sequence from accepted tokens
+    llama_partial_utf8                                      partial_utf8;
+};
+
+struct llama_grammar_candidate {
+    size_t               index;
+    const uint32_t     * code_points;
+    llama_partial_utf8   partial_utf8;
+};
+
 const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
     struct llama_context * ctx
 );
 
+void llama_grammar_accept(
+        const std::vector<std::vector<llama_grammar_element>>         & rules,
+        const std::vector<std::vector<const llama_grammar_element *>> & stacks,
+        const uint32_t                                                  chr,
+        std::vector<std::vector<const llama_grammar_element *>>       & new_stacks);
+
+std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
+        const std::string & src,
+        llama_partial_utf8   partial_start);
+
+// Randomly selects a token from the candidates based on their probabilities using given std::mt19937.
+// This is a temporary workaround in order to fix race conditions when sampling with multiple sequences.
+llama_token llama_sample_token_with_rng(struct llama_context * ctx, llama_token_data_array * candidates, std::mt19937 & rng);
+
 #endif // LLAMA_API_INTERNAL
 
 #endif // LLAMA_H
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new file mode 100644
index 000000000..1d6cb4bb7
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diff --git a/models/ggml-vocab-bert-bge.gguf b/models/ggml-vocab-bert-bge.gguf
new file mode 100644
index 000000000..b2cbd5df6
Binary files /dev/null and b/models/ggml-vocab-bert-bge.gguf differ
diff --git a/models/ggml-vocab-bert-bge.gguf.inp b/models/ggml-vocab-bert-bge.gguf.inp
new file mode 100644
index 000000000..0a89107c6
--- /dev/null
+++ b/models/ggml-vocab-bert-bge.gguf.inp
@@ -0,0 +1,106 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
+
+__ggml_vocab_test__
+ 
+__ggml_vocab_test__
+  
+__ggml_vocab_test__
+   
+__ggml_vocab_test__
+	
+__ggml_vocab_test__
+
+
+__ggml_vocab_test__
+
+
+
+__ggml_vocab_test__
+
+
+
+
+__ggml_vocab_test__
+	
+
+__ggml_vocab_test__
+Hello world
+__ggml_vocab_test__
+ Hello world
+__ggml_vocab_test__
+Hello World
+__ggml_vocab_test__
+ Hello World
+__ggml_vocab_test__
+ Hello World!
+__ggml_vocab_test__
+Hello, world!
+__ggml_vocab_test__
+ Hello, world!
+__ggml_vocab_test__
+ this is 🦙.cpp
+__ggml_vocab_test__
+w048 7tuijk dsdfhu
+__ggml_vocab_test__
+нещо на Български
+__ggml_vocab_test__
+កាន់តែពិសេសអាចខលចេញ
+__ggml_vocab_test__
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)
+__ggml_vocab_test__
+Hello
+__ggml_vocab_test__
+ Hello
+__ggml_vocab_test__
+  Hello
+__ggml_vocab_test__
+   Hello
+__ggml_vocab_test__
+    Hello
+__ggml_vocab_test__
+    Hello
+    Hello
+__ggml_vocab_test__
+ (
+__ggml_vocab_test__
+
+ =
+__ggml_vocab_test__
+' era
+__ggml_vocab_test__
+Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
+__ggml_vocab_test__
+3
+__ggml_vocab_test__
+33
+__ggml_vocab_test__
+333
+__ggml_vocab_test__
+3333
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+33333
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+333333
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+3333333
+__ggml_vocab_test__
+33333333
+__ggml_vocab_test__
+333333333
+__ggml_vocab_test__
+
+ 
+
+ 
+
+
+ 	 		 	
+  
+   
+    
+     
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
+__ggml_vocab_test__
diff --git a/models/ggml-vocab-bert-bge.gguf.out b/models/ggml-vocab-bert-bge.gguf.out
new file mode 100644
index 000000000..e4a76cdb0
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@@ -0,0 +1,43 @@
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diff --git a/models/ggml-vocab-command-r.gguf b/models/ggml-vocab-command-r.gguf
new file mode 100644
index 000000000..b553eab33
Binary files /dev/null and b/models/ggml-vocab-command-r.gguf differ
diff --git a/models/ggml-vocab-command-r.gguf.inp b/models/ggml-vocab-command-r.gguf.inp
new file mode 100644
index 000000000..0a89107c6
--- /dev/null
+++ b/models/ggml-vocab-command-r.gguf.inp
@@ -0,0 +1,106 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
+
+__ggml_vocab_test__
+ 
+__ggml_vocab_test__
+  
+__ggml_vocab_test__
+   
+__ggml_vocab_test__
+	
+__ggml_vocab_test__
+
+
+__ggml_vocab_test__
+
+
+
+__ggml_vocab_test__
+
+
+
+
+__ggml_vocab_test__
+	
+
+__ggml_vocab_test__
+Hello world
+__ggml_vocab_test__
+ Hello world
+__ggml_vocab_test__
+Hello World
+__ggml_vocab_test__
+ Hello World
+__ggml_vocab_test__
+ Hello World!
+__ggml_vocab_test__
+Hello, world!
+__ggml_vocab_test__
+ Hello, world!
+__ggml_vocab_test__
+ this is 🦙.cpp
+__ggml_vocab_test__
+w048 7tuijk dsdfhu
+__ggml_vocab_test__
+нещо на Български
+__ggml_vocab_test__
+កាន់តែពិសេសអាចខលចេញ
+__ggml_vocab_test__
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)
+__ggml_vocab_test__
+Hello
+__ggml_vocab_test__
+ Hello
+__ggml_vocab_test__
+  Hello
+__ggml_vocab_test__
+   Hello
+__ggml_vocab_test__
+    Hello
+__ggml_vocab_test__
+    Hello
+    Hello
+__ggml_vocab_test__
+ (
+__ggml_vocab_test__
+
+ =
+__ggml_vocab_test__
+' era
+__ggml_vocab_test__
+Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
+__ggml_vocab_test__
+3
+__ggml_vocab_test__
+33
+__ggml_vocab_test__
+333
+__ggml_vocab_test__
+3333
+__ggml_vocab_test__
+33333
+__ggml_vocab_test__
+333333
+__ggml_vocab_test__
+3333333
+__ggml_vocab_test__
+33333333
+__ggml_vocab_test__
+333333333
+__ggml_vocab_test__
+
+ 
+
+ 
+
+
+ 	 		 	
+  
+   
+    
+     
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
+__ggml_vocab_test__
diff --git a/models/ggml-vocab-command-r.gguf.out b/models/ggml-vocab-command-r.gguf.out
new file mode 100644
index 000000000..cc4277daa
--- /dev/null
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@@ -0,0 +1,43 @@
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diff --git a/models/ggml-vocab-deepseek-coder.gguf b/models/ggml-vocab-deepseek-coder.gguf
new file mode 100644
index 000000000..6728cd747
Binary files /dev/null and b/models/ggml-vocab-deepseek-coder.gguf differ
diff --git a/models/ggml-vocab-deepseek-coder.gguf.inp b/models/ggml-vocab-deepseek-coder.gguf.inp
new file mode 100644
index 000000000..0a89107c6
--- /dev/null
+++ b/models/ggml-vocab-deepseek-coder.gguf.inp
@@ -0,0 +1,106 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
+
+__ggml_vocab_test__
+ 
+__ggml_vocab_test__
+  
+__ggml_vocab_test__
+   
+__ggml_vocab_test__
+	
+__ggml_vocab_test__
+
+
+__ggml_vocab_test__
+
+
+
+__ggml_vocab_test__
+
+
+
+
+__ggml_vocab_test__
+	
+
+__ggml_vocab_test__
+Hello world
+__ggml_vocab_test__
+ Hello world
+__ggml_vocab_test__
+Hello World
+__ggml_vocab_test__
+ Hello World
+__ggml_vocab_test__
+ Hello World!
+__ggml_vocab_test__
+Hello, world!
+__ggml_vocab_test__
+ Hello, world!
+__ggml_vocab_test__
+ this is 🦙.cpp
+__ggml_vocab_test__
+w048 7tuijk dsdfhu
+__ggml_vocab_test__
+нещо на Български
+__ggml_vocab_test__
+កាន់តែពិសេសអាចខលចេញ
+__ggml_vocab_test__
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)
+__ggml_vocab_test__
+Hello
+__ggml_vocab_test__
+ Hello
+__ggml_vocab_test__
+  Hello
+__ggml_vocab_test__
+   Hello
+__ggml_vocab_test__
+    Hello
+__ggml_vocab_test__
+    Hello
+    Hello
+__ggml_vocab_test__
+ (
+__ggml_vocab_test__
+
+ =
+__ggml_vocab_test__
+' era
+__ggml_vocab_test__
+Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
+__ggml_vocab_test__
+3
+__ggml_vocab_test__
+33
+__ggml_vocab_test__
+333
+__ggml_vocab_test__
+3333
+__ggml_vocab_test__
+33333
+__ggml_vocab_test__
+333333
+__ggml_vocab_test__
+3333333
+__ggml_vocab_test__
+33333333
+__ggml_vocab_test__
+333333333
+__ggml_vocab_test__
+
+ 
+
+ 
+
+
+ 	 		 	
+  
+   
+    
+     
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
+__ggml_vocab_test__
diff --git a/models/ggml-vocab-deepseek-coder.gguf.out b/models/ggml-vocab-deepseek-coder.gguf.out
new file mode 100644
index 000000000..9ccc560d6
--- /dev/null
+++ b/models/ggml-vocab-deepseek-coder.gguf.out
@@ -0,0 +1,43 @@
+ 1050 207 19 207 19192 4217
+ 37 32009 71 6247
+
+ 207
+ 243
+ 315
+ 184
+ 185
+ 185 185
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+ 184 185
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+ 414 9489 1835
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+ 414 9489 5414
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+ 17535 11 1835 0
+ 414 9489 11 1835 0
+ 437 317 12394 99 234 13 14789
+ 86 15 19 23 207 22 83 3963 27659 26078 3934 14072
+ 1593 6478 616 2251 14994
+ 155 239 209 155 239 114 155 239 228 155 240 220 155 239 224 155 240 211 155 239 231 155 239 115 155 239 240 155 240 210 155 239 240 155 239 95 155 239 114 155 239 214 155 239 210 155 239 236 155 239 214 155 240 210 155 239 218
+ 10047 235 209 334 8760 8 12394 233 114 350 222 10047 221 104 169 116 224 334 4684 3909 992 24330 262 29651 612 8 207 156 237 214 334 5950 992 78 12896 344 638 891 1372 10736 8
+ 17535
+ 414 9489
+ 207 414 9489
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+ 315 414 9489
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+ 334
+ 185 405
+ 6 2895
+ 17535 11 320 6 435 0 1717 417 340 12394 233 210 3015 19100 608 9413 2668 16 18 16 19 16 20 16 1393 169 121 239
+ 18
+ 18 18
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+ 18 18 18 18 18 18
+ 18 18 18 18 18 18 18
+ 18 18 18 18 18 18 18 18
+ 18 18 18 18 18 18 18 18 18
+ 185 207 185 185 207 185 185 185 207 12405 459 22758 185 243 185 315 185 251 185 730 185 10047 235 209 334 8760 8 12394 233 114 350 222 10047 221 104 169 116 224 334 4684 3909 992 24330 262 29651 612 8 207 156 237 214 12394 99 234 10047 99 234 207 18 207 18 18 207 18 18 18 207 18 18 18 18 207 18 18 18 18 18 207 18 18 18 18 18 18 207 18 18 18 18 18 18 18 207 18 18 18 18 18 18 18 18 207 18 13 18 207 18 524 18 207 18 1202 18 207 155 239 209 155 239 114 155 239 228 155 240 220 155 239 224 155 240 211 155 239 231 155 239 115 155 239 240 155 240 210 155 239 240 155 239 95 155 239 114 155 239 214 10047 233 210 3015 19100 608 9413 2668 16 18 16 19 16 20 16 1393 169 121 239 18155 374 17194 28 2861 6478 616 2251 14994 31269 4191 6 4686 4686 10252 3358 3358 3409 524 15330 3023 15031 5668 303 6 312 798 651 83 839 362 6 82 741 11 651 1369 340 2037 30 651 44 441 2037 303 6 642 1098 359 11 651 35 340 833 738 10860 30 998 6 10709 245 6 75 43
diff --git a/models/ggml-vocab-deepseek-llm.gguf b/models/ggml-vocab-deepseek-llm.gguf
new file mode 100644
index 000000000..5d66091c4
Binary files /dev/null and b/models/ggml-vocab-deepseek-llm.gguf differ
diff --git a/models/ggml-vocab-deepseek-llm.gguf.inp b/models/ggml-vocab-deepseek-llm.gguf.inp
new file mode 100644
index 000000000..0a89107c6
--- /dev/null
+++ b/models/ggml-vocab-deepseek-llm.gguf.inp
@@ -0,0 +1,106 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
+
+__ggml_vocab_test__
+ 
+__ggml_vocab_test__
+  
+__ggml_vocab_test__
+   
+__ggml_vocab_test__
+	
+__ggml_vocab_test__
+
+
+__ggml_vocab_test__
+
+
+
+__ggml_vocab_test__
+
+
+
+
+__ggml_vocab_test__
+	
+
+__ggml_vocab_test__
+Hello world
+__ggml_vocab_test__
+ Hello world
+__ggml_vocab_test__
+Hello World
+__ggml_vocab_test__
+ Hello World
+__ggml_vocab_test__
+ Hello World!
+__ggml_vocab_test__
+Hello, world!
+__ggml_vocab_test__
+ Hello, world!
+__ggml_vocab_test__
+ this is 🦙.cpp
+__ggml_vocab_test__
+w048 7tuijk dsdfhu
+__ggml_vocab_test__
+нещо на Български
+__ggml_vocab_test__
+កាន់តែពិសេសអាចខលចេញ
+__ggml_vocab_test__
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)
+__ggml_vocab_test__
+Hello
+__ggml_vocab_test__
+ Hello
+__ggml_vocab_test__
+  Hello
+__ggml_vocab_test__
+   Hello
+__ggml_vocab_test__
+    Hello
+__ggml_vocab_test__
+    Hello
+    Hello
+__ggml_vocab_test__
+ (
+__ggml_vocab_test__
+
+ =
+__ggml_vocab_test__
+' era
+__ggml_vocab_test__
+Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
+__ggml_vocab_test__
+3
+__ggml_vocab_test__
+33
+__ggml_vocab_test__
+333
+__ggml_vocab_test__
+3333
+__ggml_vocab_test__
+33333
+__ggml_vocab_test__
+333333
+__ggml_vocab_test__
+3333333
+__ggml_vocab_test__
+33333333
+__ggml_vocab_test__
+333333333
+__ggml_vocab_test__
+
+ 
+
+ 
+
+
+ 	 		 	
+  
+   
+    
+     
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
+__ggml_vocab_test__
diff --git a/models/ggml-vocab-deepseek-llm.gguf.out b/models/ggml-vocab-deepseek-llm.gguf.out
new file mode 100644
index 000000000..fd94b896d
--- /dev/null
+++ b/models/ggml-vocab-deepseek-llm.gguf.out
@@ -0,0 +1,43 @@
+ 1052 207 19 207 19109 4223
+ 37 100014 71 6245
+
+ 207
+ 243
+ 300
+ 184
+ 185
+ 185 185
+ 185 185 185
+ 184 185
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+ 37727 1843
+ 17464 5427
+ 37727 5427
+ 37727 5427 0
+ 17464 11 1843 0
+ 37727 11 1843 0
+ 437 317 12356 99 234 13 14743
+ 86 15 19 23 207 22 83 3970 27519 26016 3944 14025
+ 1603 6476 620 91754
+ 71374 209 71374 114 71374 228 155 240 220 71374 224 155 240 211 71374 231 71374 115 71374 240 155 240 210 71374 240 71374 95 71374 114 71374 214 71374 210 71374 236 71374 214 155 240 210 71374 218
+ 10044 95300 334 8754 8 33701 114 350 222 10044 221 104 46713 334 34732 996 24250 262 80923 8 207 37103 214 334 5956 89213 344 643 895 1377 10728 8
+ 17464
+ 37727
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+ 334
+ 185 403
+ 6 2906
+ 17464 11 320 6 436 0 1724 418 340 33701 210 3025 19017 612 9407 2681 16 18 16 19 16 20 16 1398 68940 239
+ 18
+ 18 18
+ 18 18 18
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+ 185 207 185 185 207 185 185 185 207 11969 486 22504 185 243 185 300 185 251 185 663 185 10044 95300 334 8754 8 33701 114 350 222 10044 221 104 46713 334 34732 996 24250 262 80923 8 207 37103 214 12356 99 234 10044 99 234 207 18 207 18 18 207 18 18 18 207 18 18 18 18 207 18 18 18 18 18 207 18 18 18 18 18 18 207 18 18 18 18 18 18 18 207 18 18 18 18 18 18 18 18 207 18 13 18 207 18 526 18 207 18 1204 18 207 71374 209 71374 114 71374 228 155 240 220 71374 224 155 240 211 71374 231 71374 115 71374 240 155 240 210 71374 240 71374 95 71374 114 71374 214 71899 210 3025 19017 612 9407 2681 16 18 16 19 16 20 16 1398 68940 239 78827 55170 76659 620 91754 31116 36804 4885 4885 10897 4390 4390 41047 15278 3033 14986 5675 304 6 313 803 655 33326 362 6 82 745 11 655 1374 340 2049 30 655 44 441 2049 304 6 647 1099 359 11 655 35 340 837 742 10842 30 1003 6 10699 245 6 75 43
diff --git a/models/ggml-vocab-falcon.gguf b/models/ggml-vocab-falcon.gguf
index d4ea2e822..334d50da5 100644
Binary files a/models/ggml-vocab-falcon.gguf and b/models/ggml-vocab-falcon.gguf differ
diff --git a/models/ggml-vocab-falcon.gguf.inp b/models/ggml-vocab-falcon.gguf.inp
new file mode 100644
index 000000000..0a89107c6
--- /dev/null
+++ b/models/ggml-vocab-falcon.gguf.inp
@@ -0,0 +1,106 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
+
+__ggml_vocab_test__
+ 
+__ggml_vocab_test__
+  
+__ggml_vocab_test__
+   
+__ggml_vocab_test__
+	
+__ggml_vocab_test__
+
+
+__ggml_vocab_test__
+
+
+
+__ggml_vocab_test__
+
+
+
+
+__ggml_vocab_test__
+	
+
+__ggml_vocab_test__
+Hello world
+__ggml_vocab_test__
+ Hello world
+__ggml_vocab_test__
+Hello World
+__ggml_vocab_test__
+ Hello World
+__ggml_vocab_test__
+ Hello World!
+__ggml_vocab_test__
+Hello, world!
+__ggml_vocab_test__
+ Hello, world!
+__ggml_vocab_test__
+ this is 🦙.cpp
+__ggml_vocab_test__
+w048 7tuijk dsdfhu
+__ggml_vocab_test__
+нещо на Български
+__ggml_vocab_test__
+កាន់តែពិសេសអាចខលចេញ
+__ggml_vocab_test__
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)
+__ggml_vocab_test__
+Hello
+__ggml_vocab_test__
+ Hello
+__ggml_vocab_test__
+  Hello
+__ggml_vocab_test__
+   Hello
+__ggml_vocab_test__
+    Hello
+__ggml_vocab_test__
+    Hello
+    Hello
+__ggml_vocab_test__
+ (
+__ggml_vocab_test__
+
+ =
+__ggml_vocab_test__
+' era
+__ggml_vocab_test__
+Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
+__ggml_vocab_test__
+3
+__ggml_vocab_test__
+33
+__ggml_vocab_test__
+333
+__ggml_vocab_test__
+3333
+__ggml_vocab_test__
+33333
+__ggml_vocab_test__
+333333
+__ggml_vocab_test__
+3333333
+__ggml_vocab_test__
+33333333
+__ggml_vocab_test__
+333333333
+__ggml_vocab_test__
+
+ 
+
+ 
+
+
+ 	 		 	
+  
+   
+    
+     
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
+__ggml_vocab_test__
diff --git a/models/ggml-vocab-falcon.gguf.out b/models/ggml-vocab-falcon.gguf.out
new file mode 100644
index 000000000..209b04cda
--- /dev/null
+++ b/models/ggml-vocab-falcon.gguf.out
@@ -0,0 +1,43 @@
+ 878 204 31 3068 133 2137
+ 28611 132 30042
+
+ 204
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+ 466
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+ 193
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diff --git a/models/ggml-vocab-gpt-2.gguf b/models/ggml-vocab-gpt-2.gguf
new file mode 100644
index 000000000..5ea85cf52
Binary files /dev/null and b/models/ggml-vocab-gpt-2.gguf differ
diff --git a/models/ggml-vocab-gpt-2.gguf.inp b/models/ggml-vocab-gpt-2.gguf.inp
new file mode 100644
index 000000000..0a89107c6
--- /dev/null
+++ b/models/ggml-vocab-gpt-2.gguf.inp
@@ -0,0 +1,106 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
+
+__ggml_vocab_test__
+ 
+__ggml_vocab_test__
+  
+__ggml_vocab_test__
+   
+__ggml_vocab_test__
+	
+__ggml_vocab_test__
+
+
+__ggml_vocab_test__
+
+
+
+__ggml_vocab_test__
+
+
+
+
+__ggml_vocab_test__
+	
+
+__ggml_vocab_test__
+Hello world
+__ggml_vocab_test__
+ Hello world
+__ggml_vocab_test__
+Hello World
+__ggml_vocab_test__
+ Hello World
+__ggml_vocab_test__
+ Hello World!
+__ggml_vocab_test__
+Hello, world!
+__ggml_vocab_test__
+ Hello, world!
+__ggml_vocab_test__
+ this is 🦙.cpp
+__ggml_vocab_test__
+w048 7tuijk dsdfhu
+__ggml_vocab_test__
+нещо на Български
+__ggml_vocab_test__
+កាន់តែពិសេសអាចខលចេញ
+__ggml_vocab_test__
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)
+__ggml_vocab_test__
+Hello
+__ggml_vocab_test__
+ Hello
+__ggml_vocab_test__
+  Hello
+__ggml_vocab_test__
+   Hello
+__ggml_vocab_test__
+    Hello
+__ggml_vocab_test__
+    Hello
+    Hello
+__ggml_vocab_test__
+ (
+__ggml_vocab_test__
+
+ =
+__ggml_vocab_test__
+' era
+__ggml_vocab_test__
+Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
+__ggml_vocab_test__
+3
+__ggml_vocab_test__
+33
+__ggml_vocab_test__
+333
+__ggml_vocab_test__
+3333
+__ggml_vocab_test__
+33333
+__ggml_vocab_test__
+333333
+__ggml_vocab_test__
+3333333
+__ggml_vocab_test__
+33333333
+__ggml_vocab_test__
+333333333
+__ggml_vocab_test__
+
+ 
+
+ 
+
+
+ 	 		 	
+  
+   
+    
+     
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
+__ggml_vocab_test__
diff --git a/models/ggml-vocab-gpt-2.gguf.out b/models/ggml-vocab-gpt-2.gguf.out
new file mode 100644
index 000000000..78430f0d3
--- /dev/null
+++ b/models/ggml-vocab-gpt-2.gguf.out
@@ -0,0 +1,43 @@
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+
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diff --git a/models/ggml-vocab-llama-bpe.gguf b/models/ggml-vocab-llama-bpe.gguf
new file mode 100644
index 000000000..e51a99118
Binary files /dev/null and b/models/ggml-vocab-llama-bpe.gguf differ
diff --git a/models/ggml-vocab-llama-bpe.gguf.inp b/models/ggml-vocab-llama-bpe.gguf.inp
new file mode 100644
index 000000000..9380bf355
--- /dev/null
+++ b/models/ggml-vocab-llama-bpe.gguf.inp
@@ -0,0 +1,108 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
+
+__ggml_vocab_test__
+ 
+__ggml_vocab_test__
+  
+__ggml_vocab_test__
+   
+__ggml_vocab_test__
+	
+__ggml_vocab_test__
+
+
+__ggml_vocab_test__
+
+
+
+__ggml_vocab_test__
+
+
+
+
+__ggml_vocab_test__
+	
+
+__ggml_vocab_test__
+Hello world
+__ggml_vocab_test__
+ Hello world
+__ggml_vocab_test__
+Hello World
+__ggml_vocab_test__
+ Hello World
+__ggml_vocab_test__
+ Hello World!
+__ggml_vocab_test__
+Hello, world!
+__ggml_vocab_test__
+ Hello, world!
+__ggml_vocab_test__
+ this is 🦙.cpp
+__ggml_vocab_test__
+w048 7tuijk dsdfhu
+__ggml_vocab_test__
+нещо на Български
+__ggml_vocab_test__
+កាន់តែពិសេសអាចខលចេញ
+__ggml_vocab_test__
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)
+__ggml_vocab_test__
+Hello
+__ggml_vocab_test__
+ Hello
+__ggml_vocab_test__
+  Hello
+__ggml_vocab_test__
+   Hello
+__ggml_vocab_test__
+    Hello
+__ggml_vocab_test__
+    Hello
+    Hello
+__ggml_vocab_test__
+ (
+__ggml_vocab_test__
+
+ =
+__ggml_vocab_test__
+' era
+__ggml_vocab_test__
+Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
+__ggml_vocab_test__
+3
+__ggml_vocab_test__
+33
+__ggml_vocab_test__
+333
+__ggml_vocab_test__
+3333
+__ggml_vocab_test__
+33333
+__ggml_vocab_test__
+333333
+__ggml_vocab_test__
+3333333
+__ggml_vocab_test__
+33333333
+__ggml_vocab_test__
+333333333
+__ggml_vocab_test__
+
+ 
+
+ 
+
+
+ 	 		 	
+  
+   
+    
+     
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
+__ggml_vocab_test__
+ Việt
+__ggml_vocab_test__
diff --git a/models/ggml-vocab-llama-bpe.gguf.out b/models/ggml-vocab-llama-bpe.gguf.out
new file mode 100644
index 000000000..1f3607fb6
--- /dev/null
+++ b/models/ggml-vocab-llama-bpe.gguf.out
@@ -0,0 +1,44 @@
+ 1142 220 19 220 27154 4038
+ 37 51853 261
+
+ 220
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+ 262
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+ 9468 248 222 320 8416 8 27623 114 102470 9468 234 104 31643 320 36773 100166 98634 8 26602 227 320 3323 43465 430 706 1202 1866 4037 8
+ 9906
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+ 320
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+ 101798
diff --git a/models/ggml-vocab-llama.gguf b/models/ggml-vocab-llama-spm.gguf
similarity index 99%
rename from models/ggml-vocab-llama.gguf
rename to models/ggml-vocab-llama-spm.gguf
index 549eed8c5..658295a5d 100644
Binary files a/models/ggml-vocab-llama.gguf and b/models/ggml-vocab-llama-spm.gguf differ
diff --git a/models/ggml-vocab-llama-spm.gguf.inp b/models/ggml-vocab-llama-spm.gguf.inp
new file mode 100644
index 000000000..0a89107c6
--- /dev/null
+++ b/models/ggml-vocab-llama-spm.gguf.inp
@@ -0,0 +1,106 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
+
+__ggml_vocab_test__
+ 
+__ggml_vocab_test__
+  
+__ggml_vocab_test__
+   
+__ggml_vocab_test__
+	
+__ggml_vocab_test__
+
+
+__ggml_vocab_test__
+
+
+
+__ggml_vocab_test__
+
+
+
+
+__ggml_vocab_test__
+	
+
+__ggml_vocab_test__
+Hello world
+__ggml_vocab_test__
+ Hello world
+__ggml_vocab_test__
+Hello World
+__ggml_vocab_test__
+ Hello World
+__ggml_vocab_test__
+ Hello World!
+__ggml_vocab_test__
+Hello, world!
+__ggml_vocab_test__
+ Hello, world!
+__ggml_vocab_test__
+ this is 🦙.cpp
+__ggml_vocab_test__
+w048 7tuijk dsdfhu
+__ggml_vocab_test__
+нещо на Български
+__ggml_vocab_test__
+កាន់តែពិសេសអាចខលចេញ
+__ggml_vocab_test__
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)
+__ggml_vocab_test__
+Hello
+__ggml_vocab_test__
+ Hello
+__ggml_vocab_test__
+  Hello
+__ggml_vocab_test__
+   Hello
+__ggml_vocab_test__
+    Hello
+__ggml_vocab_test__
+    Hello
+    Hello
+__ggml_vocab_test__
+ (
+__ggml_vocab_test__
+
+ =
+__ggml_vocab_test__
+' era
+__ggml_vocab_test__
+Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
+__ggml_vocab_test__
+3
+__ggml_vocab_test__
+33
+__ggml_vocab_test__
+333
+__ggml_vocab_test__
+3333
+__ggml_vocab_test__
+33333
+__ggml_vocab_test__
+333333
+__ggml_vocab_test__
+3333333
+__ggml_vocab_test__
+33333333
+__ggml_vocab_test__
+333333333
+__ggml_vocab_test__
+
+ 
+
+ 
+
+
+ 	 		 	
+  
+   
+    
+     
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
+__ggml_vocab_test__
diff --git a/models/ggml-vocab-llama-spm.gguf.out b/models/ggml-vocab-llama-spm.gguf.out
new file mode 100644
index 000000000..9c3327cb5
--- /dev/null
+++ b/models/ggml-vocab-llama-spm.gguf.out
@@ -0,0 +1,43 @@
+ 474 287 29871 29946 29871 30226 7378
+ 383 4000 261
+
+ 259
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+ 268
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diff --git a/models/ggml-vocab-mpt.gguf b/models/ggml-vocab-mpt.gguf
index 6affa34bd..f42f56dec 100644
Binary files a/models/ggml-vocab-mpt.gguf and b/models/ggml-vocab-mpt.gguf differ
diff --git a/models/ggml-vocab-mpt.gguf.inp b/models/ggml-vocab-mpt.gguf.inp
new file mode 100644
index 000000000..0a89107c6
--- /dev/null
+++ b/models/ggml-vocab-mpt.gguf.inp
@@ -0,0 +1,106 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
+
+__ggml_vocab_test__
+ 
+__ggml_vocab_test__
+  
+__ggml_vocab_test__
+   
+__ggml_vocab_test__
+	
+__ggml_vocab_test__
+
+
+__ggml_vocab_test__
+
+
+
+__ggml_vocab_test__
+
+
+
+
+__ggml_vocab_test__
+	
+
+__ggml_vocab_test__
+Hello world
+__ggml_vocab_test__
+ Hello world
+__ggml_vocab_test__
+Hello World
+__ggml_vocab_test__
+ Hello World
+__ggml_vocab_test__
+ Hello World!
+__ggml_vocab_test__
+Hello, world!
+__ggml_vocab_test__
+ Hello, world!
+__ggml_vocab_test__
+ this is 🦙.cpp
+__ggml_vocab_test__
+w048 7tuijk dsdfhu
+__ggml_vocab_test__
+нещо на Български
+__ggml_vocab_test__
+កាន់តែពិសេសអាចខលចេញ
+__ggml_vocab_test__
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)
+__ggml_vocab_test__
+Hello
+__ggml_vocab_test__
+ Hello
+__ggml_vocab_test__
+  Hello
+__ggml_vocab_test__
+   Hello
+__ggml_vocab_test__
+    Hello
+__ggml_vocab_test__
+    Hello
+    Hello
+__ggml_vocab_test__
+ (
+__ggml_vocab_test__
+
+ =
+__ggml_vocab_test__
+' era
+__ggml_vocab_test__
+Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
+__ggml_vocab_test__
+3
+__ggml_vocab_test__
+33
+__ggml_vocab_test__
+333
+__ggml_vocab_test__
+3333
+__ggml_vocab_test__
+33333
+__ggml_vocab_test__
+333333
+__ggml_vocab_test__
+3333333
+__ggml_vocab_test__
+33333333
+__ggml_vocab_test__
+333333333
+__ggml_vocab_test__
+
+ 
+
+ 
+
+
+ 	 		 	
+  
+   
+    
+     
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
+__ggml_vocab_test__
diff --git a/models/ggml-vocab-mpt.gguf.out b/models/ggml-vocab-mpt.gguf.out
new file mode 100644
index 000000000..d8d0fe909
--- /dev/null
+++ b/models/ggml-vocab-mpt.gguf.out
@@ -0,0 +1,43 @@
+ 728 577 24142 2607
+ 39 26288 6554
+
+ 209
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diff --git a/models/ggml-vocab-phi-3.gguf b/models/ggml-vocab-phi-3.gguf
new file mode 100644
index 000000000..f8022a385
Binary files /dev/null and b/models/ggml-vocab-phi-3.gguf differ
diff --git a/models/ggml-vocab-phi-3.gguf.inp b/models/ggml-vocab-phi-3.gguf.inp
new file mode 100644
index 000000000..0a89107c6
--- /dev/null
+++ b/models/ggml-vocab-phi-3.gguf.inp
@@ -0,0 +1,106 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
+
+__ggml_vocab_test__
+ 
+__ggml_vocab_test__
+  
+__ggml_vocab_test__
+   
+__ggml_vocab_test__
+	
+__ggml_vocab_test__
+
+
+__ggml_vocab_test__
+
+
+
+__ggml_vocab_test__
+
+
+
+
+__ggml_vocab_test__
+	
+
+__ggml_vocab_test__
+Hello world
+__ggml_vocab_test__
+ Hello world
+__ggml_vocab_test__
+Hello World
+__ggml_vocab_test__
+ Hello World
+__ggml_vocab_test__
+ Hello World!
+__ggml_vocab_test__
+Hello, world!
+__ggml_vocab_test__
+ Hello, world!
+__ggml_vocab_test__
+ this is 🦙.cpp
+__ggml_vocab_test__
+w048 7tuijk dsdfhu
+__ggml_vocab_test__
+нещо на Български
+__ggml_vocab_test__
+កាន់តែពិសេសអាចខលចេញ
+__ggml_vocab_test__
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)
+__ggml_vocab_test__
+Hello
+__ggml_vocab_test__
+ Hello
+__ggml_vocab_test__
+  Hello
+__ggml_vocab_test__
+   Hello
+__ggml_vocab_test__
+    Hello
+__ggml_vocab_test__
+    Hello
+    Hello
+__ggml_vocab_test__
+ (
+__ggml_vocab_test__
+
+ =
+__ggml_vocab_test__
+' era
+__ggml_vocab_test__
+Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
+__ggml_vocab_test__
+3
+__ggml_vocab_test__
+33
+__ggml_vocab_test__
+333
+__ggml_vocab_test__
+3333
+__ggml_vocab_test__
+33333
+__ggml_vocab_test__
+333333
+__ggml_vocab_test__
+3333333
+__ggml_vocab_test__
+33333333
+__ggml_vocab_test__
+333333333
+__ggml_vocab_test__
+
+ 
+
+ 
+
+
+ 	 		 	
+  
+   
+    
+     
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
+__ggml_vocab_test__
diff --git a/models/ggml-vocab-phi-3.gguf.out b/models/ggml-vocab-phi-3.gguf.out
new file mode 100644
index 000000000..9c3327cb5
--- /dev/null
+++ b/models/ggml-vocab-phi-3.gguf.out
@@ -0,0 +1,43 @@
+ 474 287 29871 29946 29871 30226 7378
+ 383 4000 261
+
+ 259
+ 1678
+ 268
+ 29871 12
+ 29871 13
+ 29871 13 13
+ 29871 13 13 13
+ 29871 12 13
+ 15043 3186
+ 29871 15043 3186
+ 15043 2787
+ 29871 15043 2787
+ 29871 15043 2787 29991
+ 15043 29892 3186 29991
+ 29871 15043 29892 3186 29991
+ 29871 445 338 29871 243 162 169 156 29889 8223
+ 281 29900 29946 29947 29871 29955 9161 13535 18031 2176 6905
+ 1538 4851 665 1386 29713 1305
+ 29871 31849 31324 31934 228 162 142 228 161 146 228 162 133 228 161 153 228 161 186 31708 228 162 132 31708 228 161 165 31324 228 161 136 228 161 132 228 161 158 228 161 136 228 162 132 228 161 140
+ 29871 243 162 157 131 313 8945 29897 29871 243 162 155 185 30722 243 162 143 174 30598 313 20787 953 3848 275 16125 630 29897 29871 31681 313 6194 953 29877 2397 393 756 967 1914 5993 29897
+ 15043
+ 29871 15043
+ 259 15043
+ 1678 15043
+ 268 15043
+ 268 15043 13 1678 15043
+ 29871 313
+ 29871 13 353
+ 525 3152
+ 15043 29892 343 29915 497 29991 1128 526 366 29871 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739
+ 29871 29941
+ 29871 29941 29941
+ 29871 29941 29941 29941
+ 29871 29941 29941 29941 29941
+ 29871 29941 29941 29941 29941 29941
+ 29871 29941 29941 29941 29941 29941 29941
+ 29871 29941 29941 29941 29941 29941 29941 29941
+ 29871 29941 29941 29941 29941 29941 29941 29941 29941
+ 29871 29941 29941 29941 29941 29941 29941 29941 29941 29941
+ 29871 13 29871 13 13 29871 13 13 13 29871 12 29871 12 12 29871 12 13 259 13 1678 13 268 13 418 13 243 162 157 131 313 8945 29897 29871 243 162 155 185 30722 243 162 143 174 30598 313 20787 953 3848 275 16125 630 29897 29871 31681 29871 243 162 169 156 243 162 169 156 29871 29941 29871 29941 29941 29871 29941 29941 29941 29871 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29941 29871 29941 29889 29941 29871 29941 636 29941 29871 29941 856 29941 29871 31849 31324 31934 228 162 142 228 161 146 228 162 133 228 161 153 228 161 186 31708 228 162 132 31708 228 161 165 31324 228 161 136 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739 448 23648 2751 25512 1538 4851 665 1386 29713 1305 14550 4907 11120 16159 16159 16159 15945 15945 3045 636 6824 6824 6824 8773 8773 8773 306 29915 345 1063 525 29873 1025 540 29915 29879 727 29892 525 1525 366 1854 29973 525 29924 451 1854 306 29915 645 1207 372 29892 525 29928 366 763 777 23429 29973 1334 29915 29963 29872 263 29915 29880 29931
diff --git a/models/ggml-vocab-qwen2.gguf b/models/ggml-vocab-qwen2.gguf
new file mode 100644
index 000000000..541e475bc
Binary files /dev/null and b/models/ggml-vocab-qwen2.gguf differ
diff --git a/models/ggml-vocab-qwen2.gguf.inp b/models/ggml-vocab-qwen2.gguf.inp
new file mode 100644
index 000000000..0a89107c6
--- /dev/null
+++ b/models/ggml-vocab-qwen2.gguf.inp
@@ -0,0 +1,106 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
+
+__ggml_vocab_test__
+ 
+__ggml_vocab_test__
+  
+__ggml_vocab_test__
+   
+__ggml_vocab_test__
+	
+__ggml_vocab_test__
+
+
+__ggml_vocab_test__
+
+
+
+__ggml_vocab_test__
+
+
+
+
+__ggml_vocab_test__
+	
+
+__ggml_vocab_test__
+Hello world
+__ggml_vocab_test__
+ Hello world
+__ggml_vocab_test__
+Hello World
+__ggml_vocab_test__
+ Hello World
+__ggml_vocab_test__
+ Hello World!
+__ggml_vocab_test__
+Hello, world!
+__ggml_vocab_test__
+ Hello, world!
+__ggml_vocab_test__
+ this is 🦙.cpp
+__ggml_vocab_test__
+w048 7tuijk dsdfhu
+__ggml_vocab_test__
+нещо на Български
+__ggml_vocab_test__
+កាន់តែពិសេសអាចខលចេញ
+__ggml_vocab_test__
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)
+__ggml_vocab_test__
+Hello
+__ggml_vocab_test__
+ Hello
+__ggml_vocab_test__
+  Hello
+__ggml_vocab_test__
+   Hello
+__ggml_vocab_test__
+    Hello
+__ggml_vocab_test__
+    Hello
+    Hello
+__ggml_vocab_test__
+ (
+__ggml_vocab_test__
+
+ =
+__ggml_vocab_test__
+' era
+__ggml_vocab_test__
+Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
+__ggml_vocab_test__
+3
+__ggml_vocab_test__
+33
+__ggml_vocab_test__
+333
+__ggml_vocab_test__
+3333
+__ggml_vocab_test__
+33333
+__ggml_vocab_test__
+333333
+__ggml_vocab_test__
+3333333
+__ggml_vocab_test__
+33333333
+__ggml_vocab_test__
+333333333
+__ggml_vocab_test__
+
+ 
+
+ 
+
+
+ 	 		 	
+  
+   
+    
+     
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
+__ggml_vocab_test__
diff --git a/models/ggml-vocab-qwen2.gguf.out b/models/ggml-vocab-qwen2.gguf.out
new file mode 100644
index 000000000..401a510e8
--- /dev/null
+++ b/models/ggml-vocab-qwen2.gguf.out
@@ -0,0 +1,43 @@
+ 1122 220 19 220 26062 3951
+ 37 50753 261
+
+ 220
+ 256
+ 262
+ 197
+ 198
+ 271
+ 1406
+ 1572
+ 9707 1879
+ 21927 1879
+ 9707 4337
+ 21927 4337
+ 21927 4337 0
+ 9707 11 1879 0
+ 21927 11 1879 0
+ 419 374 11162 99 247 13 10821
+ 86 15 19 23 220 22 83 1963 41808 11472 2940 16739
+ 78762 14144 1456 13073 63471 33594 3038 133178 79012
+ 146394 97529 241 44258 233 146568 44258 224 147603 20879 115 146280 44258 223 146280 147272 97529 227 147805 148301 147270 44258 223 146848
+ 145836 320 8252 8 26525 114 378 235 149921 30543 320 35673 99066 97534 8 25521 227 320 3243 42365 429 702 1181 1828 3950 8
+ 9707
+ 21927
+ 220 21927
+ 256 21927
+ 262 21927
+ 262 21927 198 262 21927
+ 320
+ 198 284
+ 6 11385
+ 9707 11 379 64848 0 2585 525 498 26525 223 937 104100 18493 22377 99257 16 18 16 19 16 20 16 35727 21216
+ 18
+ 18 18
+ 18 18 18
+ 18 18 18 18
+ 18 18 18 18 18
+ 18 18 18 18 18 18
+ 18 18 18 18 18 18 18
+ 18 18 18 18 18 18 18 18
+ 18 18 18 18 18 18 18 18 18
+ 198 4710 14731 65497 7847 1572 2303 78672 10947 145836 320 8252 8 26525 114 378 235 149921 30543 320 35673 99066 97534 8 25521 227 11162 99 247 149955 220 18 220 18 18 220 18 18 18 220 18 18 18 18 220 18 18 18 18 18 220 18 18 18 18 18 18 220 18 18 18 18 18 18 18 220 18 18 18 18 18 18 18 18 220 18 13 18 220 18 496 18 220 18 1112 18 220 146394 97529 241 44258 233 146568 44258 224 147603 20879 115 146280 44258 223 146280 147272 97529 227 144534 937 104100 18493 22377 99257 16 18 16 19 16 20 16 35727 21216 55460 53237 18658 14144 1456 13073 63471 33594 3038 133178 79012 3355 4605 4605 13874 13874 73594 3014 3014 28149 17085 2928 26610 7646 358 3003 1012 364 83 813 566 594 1052 11 364 787 498 2704 30 364 44 537 2704 358 3278 1281 432 11 364 35 498 1075 1045 15243 30 1205 6 42612 264 63866 43
diff --git a/models/ggml-vocab-refact.gguf b/models/ggml-vocab-refact.gguf
index 8f26cfb76..52afcf01a 100644
Binary files a/models/ggml-vocab-refact.gguf and b/models/ggml-vocab-refact.gguf differ
diff --git a/models/ggml-vocab-refact.gguf.inp b/models/ggml-vocab-refact.gguf.inp
new file mode 100644
index 000000000..0a89107c6
--- /dev/null
+++ b/models/ggml-vocab-refact.gguf.inp
@@ -0,0 +1,106 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
+
+__ggml_vocab_test__
+ 
+__ggml_vocab_test__
+  
+__ggml_vocab_test__
+   
+__ggml_vocab_test__
+	
+__ggml_vocab_test__
+
+
+__ggml_vocab_test__
+
+
+
+__ggml_vocab_test__
+
+
+
+
+__ggml_vocab_test__
+	
+
+__ggml_vocab_test__
+Hello world
+__ggml_vocab_test__
+ Hello world
+__ggml_vocab_test__
+Hello World
+__ggml_vocab_test__
+ Hello World
+__ggml_vocab_test__
+ Hello World!
+__ggml_vocab_test__
+Hello, world!
+__ggml_vocab_test__
+ Hello, world!
+__ggml_vocab_test__
+ this is 🦙.cpp
+__ggml_vocab_test__
+w048 7tuijk dsdfhu
+__ggml_vocab_test__
+нещо на Български
+__ggml_vocab_test__
+កាន់តែពិសេសអាចខលចេញ
+__ggml_vocab_test__
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)
+__ggml_vocab_test__
+Hello
+__ggml_vocab_test__
+ Hello
+__ggml_vocab_test__
+  Hello
+__ggml_vocab_test__
+   Hello
+__ggml_vocab_test__
+    Hello
+__ggml_vocab_test__
+    Hello
+    Hello
+__ggml_vocab_test__
+ (
+__ggml_vocab_test__
+
+ =
+__ggml_vocab_test__
+' era
+__ggml_vocab_test__
+Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
+__ggml_vocab_test__
+3
+__ggml_vocab_test__
+33
+__ggml_vocab_test__
+333
+__ggml_vocab_test__
+3333
+__ggml_vocab_test__
+33333
+__ggml_vocab_test__
+333333
+__ggml_vocab_test__
+3333333
+__ggml_vocab_test__
+33333333
+__ggml_vocab_test__
+333333333
+__ggml_vocab_test__
+
+ 
+
+ 
+
+
+ 	 		 	
+  
+   
+    
+     
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
+__ggml_vocab_test__
diff --git a/models/ggml-vocab-refact.gguf.out b/models/ggml-vocab-refact.gguf.out
new file mode 100644
index 000000000..06b15c090
--- /dev/null
+++ b/models/ggml-vocab-refact.gguf.out
@@ -0,0 +1,43 @@
+ 4833 225 38 225 143 140 17723
+ 56 2006 3935 265
+
+ 225
+ 261
+ 264
+ 202
+ 203
+ 478
+ 2831
+ 15773
+ 8279 5788
+ 12000 5788
+ 8279 10896
+ 12000 10896
+ 12000 10896 19
+ 8279 30 5788 19
+ 12000 30 5788 19
+ 458 438 5945 118 252 32 3766
+ 105 34 38 42 225 41 102 1707 12530 10180 1479 8278
+ 39862 8372 1039 9446 40242 13852 2053 8949 12531 1520 10700
+ 14574 227 14574 133 14574 246 30457 238 14574 242 30457 229 14574 249 14574 134 14574 258 30457 228 14574 258 14574 114 14574 133 14574 232 14574 228 14574 254 14574 232 30457 228 14574 236
+ 3807 253 227 308 4382 27 18458 133 46113 44967 123 13868 308 12565 19775 33071 40824 733 27 41889 308 2585 22680 688 1401 2819 4369 2404 27
+ 8279
+ 12000
+ 225 12000
+ 261 12000
+ 264 12000
+ 264 12000 284 12000
+ 308
+ 203 280
+ 25 34666
+ 8279 30 533 25 464 19 4971 884 844 18458 228 1018 4982 13368 2909 9513 17827 35 37 35 38 35 39 35 11873 47838
+ 37
+ 37 37
+ 37 37 37
+ 37 37 37 37
+ 37 37 37 37 37
+ 37 37 37 37 37 37
+ 37 37 37 37 37 37 37
+ 37 37 37 37 37 37 37 37
+ 37 37 37 37 37 37 37 37 37
+ 334 719 8878 202 10885 4222 16104 28570 203 3807 253 227 308 4382 27 18458 133 46113 44967 123 13868 308 12565 19775 33071 40824 733 27 41889 5945 118 252 3807 118 252 225 37 225 37 37 225 37 37 37 225 37 37 37 37 225 37 37 37 37 37 225 37 37 37 37 37 37 225 37 37 37 37 37 37 37 225 37 37 37 37 37 37 37 37 225 37 32 37 225 37 497 37 225 37 1179 37 225 14574 227 14574 133 14574 246 30457 238 14574 242 30457 229 14574 249 14574 134 14574 258 30457 228 14574 258 14574 114 14574 133 14574 232 36628 228 1018 4982 13368 2909 9513 17827 35 37 35 38 35 39 35 11873 47838 20921 16623 13028 8372 1039 9446 40242 13852 2053 8949 12531 1520 10700 5881 9592 13299 914 31753 31359 9163 3202 35472 10397 439 4763 2583 330 102 1455 938 1182 2017 30 330 613 844 3654 49 330 63 646 3654 439 4621 1930 561 30 330 54 844 2124 1629 35993 49 2688 25 7709 312 25 94 62
diff --git a/models/ggml-vocab-stablelm-3b-4e1t.gguf b/models/ggml-vocab-stablelm.gguf
similarity index 100%
rename from models/ggml-vocab-stablelm-3b-4e1t.gguf
rename to models/ggml-vocab-stablelm.gguf
diff --git a/models/ggml-vocab-starcoder.gguf b/models/ggml-vocab-starcoder.gguf
index a52983fdb..7a7e7742a 100644
Binary files a/models/ggml-vocab-starcoder.gguf and b/models/ggml-vocab-starcoder.gguf differ
diff --git a/models/ggml-vocab-starcoder.gguf.inp b/models/ggml-vocab-starcoder.gguf.inp
new file mode 100644
index 000000000..0a89107c6
--- /dev/null
+++ b/models/ggml-vocab-starcoder.gguf.inp
@@ -0,0 +1,106 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
+
+__ggml_vocab_test__
+ 
+__ggml_vocab_test__
+  
+__ggml_vocab_test__
+   
+__ggml_vocab_test__
+	
+__ggml_vocab_test__
+
+
+__ggml_vocab_test__
+
+
+
+__ggml_vocab_test__
+
+
+
+
+__ggml_vocab_test__
+	
+
+__ggml_vocab_test__
+Hello world
+__ggml_vocab_test__
+ Hello world
+__ggml_vocab_test__
+Hello World
+__ggml_vocab_test__
+ Hello World
+__ggml_vocab_test__
+ Hello World!
+__ggml_vocab_test__
+Hello, world!
+__ggml_vocab_test__
+ Hello, world!
+__ggml_vocab_test__
+ this is 🦙.cpp
+__ggml_vocab_test__
+w048 7tuijk dsdfhu
+__ggml_vocab_test__
+нещо на Български
+__ggml_vocab_test__
+កាន់តែពិសេសអាចខលចេញ
+__ggml_vocab_test__
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)
+__ggml_vocab_test__
+Hello
+__ggml_vocab_test__
+ Hello
+__ggml_vocab_test__
+  Hello
+__ggml_vocab_test__
+   Hello
+__ggml_vocab_test__
+    Hello
+__ggml_vocab_test__
+    Hello
+    Hello
+__ggml_vocab_test__
+ (
+__ggml_vocab_test__
+
+ =
+__ggml_vocab_test__
+' era
+__ggml_vocab_test__
+Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
+__ggml_vocab_test__
+3
+__ggml_vocab_test__
+33
+__ggml_vocab_test__
+333
+__ggml_vocab_test__
+3333
+__ggml_vocab_test__
+33333
+__ggml_vocab_test__
+333333
+__ggml_vocab_test__
+3333333
+__ggml_vocab_test__
+33333333
+__ggml_vocab_test__
+333333333
+__ggml_vocab_test__
+
+ 
+
+ 
+
+
+ 	 		 	
+  
+   
+    
+     
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
+__ggml_vocab_test__
diff --git a/models/ggml-vocab-starcoder.gguf.out b/models/ggml-vocab-starcoder.gguf.out
new file mode 100644
index 000000000..ccb55c7fe
--- /dev/null
+++ b/models/ggml-vocab-starcoder.gguf.out
@@ -0,0 +1,43 @@
+ 4850 244 57 244 162 159 17722
+ 75 2022 3943 284
+
+ 244
+ 280
+ 283
+ 221
+ 222
+ 499
+ 3067
+ 15767
+ 8302 5810
+ 12009 5810
+ 8302 10914
+ 12009 10914
+ 12009 10914 38
+ 8302 49 5810 38
+ 12009 49 5810 38
+ 477 458 5954 137 271 51 3779
+ 124 53 57 61 244 60 121 1726 12568 10240 1519 8290
+ 39916 8389 1059 9504 40216 13858 2073 8983 12571 1539 10721
+ 14566 246 14566 152 14566 265 30428 257 14566 261 30428 248 14566 268 14566 153 14566 277 30428 247 14566 277 14566 133 14566 152 14566 251 14566 247 14566 273 14566 251 30428 247 14566 255
+ 3822 272 246 327 4434 46 18445 152 46030 45022 142 13878 327 12585 19884 33773 40920 751 46 41839 327 2605 22716 708 1421 2840 4387 2421 46
+ 8302
+ 12009
+ 244 12009
+ 280 12009
+ 283 12009
+ 283 12009 303 12009
+ 327
+ 222 299
+ 44 34719
+ 8302 49 553 44 483 38 4998 904 863 18445 247 1037 4995 13379 2924 9515 17823 54 56 54 57 54 58 54 11904 47892
+ 56
+ 56 56
+ 56 56 56
+ 56 56 56 56
+ 56 56 56 56 56
+ 56 56 56 56 56 56
+ 56 56 56 56 56 56 56
+ 56 56 56 56 56 56 56 56
+ 56 56 56 56 56 56 56 56 56
+ 353 736 8886 221 10883 4238 16101 28540 222 3822 272 246 327 4434 46 18445 152 46030 45022 142 13878 327 12585 19884 33773 40920 751 46 41839 5954 137 271 3822 137 271 244 56 244 56 56 244 56 56 56 244 56 56 56 56 244 56 56 56 56 56 244 56 56 56 56 56 56 244 56 56 56 56 56 56 56 244 56 56 56 56 56 56 56 56 244 56 51 56 244 56 516 56 244 56 1198 56 244 14566 246 14566 152 14566 265 30428 257 14566 261 30428 248 14566 268 14566 153 14566 277 30428 247 14566 277 14566 133 14566 152 14566 251 36570 247 1037 4995 13379 2924 9515 17823 54 56 54 57 54 58 54 11904 47892 20895 16625 13047 8389 1059 9504 40216 13858 2073 8983 12571 1539 10721 5918 9643 13298 932 31723 31330 9221 3226 35426 10400 457 4783 2602 349 121 1477 957 1200 2038 49 349 632 863 3673 68 349 82 666 3673 457 4650 1949 580 49 349 73 863 2144 1649 35941 68 2726 44 7728 331 44 113 81
diff --git a/pyrightconfig.json b/pyrightconfig.json
new file mode 100644
index 000000000..020a71a4e
--- /dev/null
+++ b/pyrightconfig.json
@@ -0,0 +1,3 @@
+{
+  "extraPaths": ["gguf-py"],
+}
diff --git a/requirements.txt b/requirements.txt
index d36f74520..e7d14e16a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,6 +7,6 @@
 -r ./requirements/requirements-convert.txt
 
 -r ./requirements/requirements-convert-hf-to-gguf.txt
+-r ./requirements/requirements-convert-hf-to-gguf-update.txt
 -r ./requirements/requirements-convert-llama-ggml-to-gguf.txt
--r ./requirements/requirements-convert-lora-to-ggml.txt
 -r ./requirements/requirements-convert-persimmon-to-gguf.txt
diff --git a/requirements/requirements-convert-lora-to-ggml.txt b/requirements/requirements-convert-hf-to-gguf-update.txt
similarity index 100%
rename from requirements/requirements-convert-lora-to-ggml.txt
rename to requirements/requirements-convert-hf-to-gguf-update.txt
diff --git a/requirements/requirements-convert.txt b/requirements/requirements-convert.txt
index a3d6ecec0..7ab1228cb 100644
--- a/requirements/requirements-convert.txt
+++ b/requirements/requirements-convert.txt
@@ -1,5 +1,5 @@
 numpy~=1.24.4
-sentencepiece~=0.1.98
-transformers>=4.35.2,<5.0.0
+sentencepiece~=0.2.0
+transformers>=4.40.1,<5.0.0
 gguf>=0.1.0
 protobuf>=4.21.0,<5.0.0
diff --git a/scripts/LlamaConfig.cmake.in b/scripts/LlamaConfig.cmake.in
index 6a6d8e39e..f842c7137 100644
--- a/scripts/LlamaConfig.cmake.in
+++ b/scripts/LlamaConfig.cmake.in
@@ -3,7 +3,7 @@ set(LLAMA_BUILD_COMMIT @LLAMA_BUILD_COMMIT@)
 set(LLAMA_BUILD_NUMBER @LLAMA_BUILD_NUMBER@)
 set(LLAMA_SHARED_LIB @BUILD_SHARED_LIBS@)
 set(LLAMA_BLAS @LLAMA_BLAS@)
-set(LLAMA_CUBLAS @LLAMA_CUBLAS@)
+set(LLAMA_CUDA @LLAMA_CUDA@)
 set(LLAMA_METAL @LLAMA_METAL@)
 set(LLAMA_MPI @LLAMA_MPI@)
 set(LLAMA_CLBLAST @LLAMA_CLBLAST@)
@@ -27,7 +27,7 @@ if (LLAMA_BLAS)
     find_package(BLAS REQUIRED)
 endif()
 
-if (LLAMA_CUBLAS)
+if (LLAMA_CUDA)
     find_package(CUDAToolkit REQUIRED)
 endif()
 
diff --git a/scripts/check-requirements.sh b/scripts/check-requirements.sh
index af7bab753..6a7400d3c 100755
--- a/scripts/check-requirements.sh
+++ b/scripts/check-requirements.sh
@@ -168,6 +168,11 @@ fi
 
 check_convert_script convert.py
 for py in convert-*.py; do
+    # skip convert-hf-to-gguf-update.py
+    # TODO: the check is failing for some reason:
+    #       https://github.com/ggerganov/llama.cpp/actions/runs/8875330981/job/24364557177?pr=6920
+    [[ $py == convert-hf-to-gguf-update.py ]] && continue
+
     check_convert_script "$py"
 done
 
diff --git a/scripts/compare-commits.sh b/scripts/compare-commits.sh
index 331c4b9ce..fd0ee88b2 100755
--- a/scripts/compare-commits.sh
+++ b/scripts/compare-commits.sh
@@ -12,19 +12,7 @@ bench_args="${@:3}"
 
 rm -f llama-bench.sqlite
 
-backend="cpu"
-
-if [[ "$OSTYPE" == "darwin"* ]]; then
-    backend="metal"
-elif command -v nvcc &> /dev/null; then
-    backend="cuda"
-fi
-
-make_opts=""
-
-if [[ "$backend" == "cuda" ]]; then
-    make_opts="LLAMA_CUBLAS=1"
-fi
+# to test a backend, call the script with the corresponding environment variable (e.g. LLAMA_CUDA=1 ./scripts/compare-commits.sh ...)
 
 git checkout $1
 make clean && make -j32 $make_opts llama-bench
diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py
index 70737f976..0ede9e67c 100755
--- a/scripts/compare-llama-bench.py
+++ b/scripts/compare-llama-bench.py
@@ -1,5 +1,6 @@
 #!/usr/bin/env python3
 
+import logging
 import argparse
 import heapq
 import sys
@@ -11,14 +12,16 @@ try:
     import git
     from tabulate import tabulate
 except ImportError as e:
-    print("ERROR: the following Python libraries are required: GitPython, tabulate.")
+    print("the following Python libraries are required: GitPython, tabulate.") # noqa: NP100
     raise e
 
+logger = logging.getLogger("compare-llama-bench")
+
 # Properties by which to differentiate results per commit:
 KEY_PROPERTIES = [
     "cpu_info", "gpu_info", "n_gpu_layers", "main_gpu", "cuda", "opencl", "metal", "gpu_blas",
     "blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads",
-    "type_k", "type_v", "no_kv_offload", "mul_mat_q", "tensor_split", "n_prompt", "n_gen"
+    "type_k", "type_v", "no_kv_offload", "tensor_split", "n_prompt", "n_gen"
 ]
 
 # Properties that are boolean and are converted to Yes/No for the table:
@@ -31,7 +34,7 @@ PRETTY_NAMES = {
     "model_size": "Model Size [GiB]", "model_n_params": "Num. of Parameters",
     "n_batch": "Batch size", "n_threads": "Threads", "type_k": "K type", "type_v": "V type",
     "n_gpu_layers": "GPU layers", "main_gpu": "Main GPU", "no_kv_offload": "NKVO",
-    "mul_mat_q": "MMQ", "tensor_split": "Tensor split"
+    "tensor_split": "Tensor split"
 }
 
 DEFAULT_SHOW = ["model_type"]  # Always show these properties by default.
@@ -90,12 +93,14 @@ help_s = (
     "specified values are averaged WITHOUT weighing by the --repetitions parameter of llama-bench."
 )
 parser.add_argument("-s", "--show", help=help_s)
+parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
 
 known_args, unknown_args = parser.parse_known_args()
 
+logging.basicConfig(level=logging.DEBUG if known_args.verbose else logging.INFO)
+
 if unknown_args:
-    print(f"ERROR: Received unknown args: {unknown_args}.")
-    print()
+    logger.error(f"Received unknown args: {unknown_args}.\n")
     parser.print_help()
     sys.exit(1)
 
@@ -108,8 +113,7 @@ if input_file is None:
         input_file = sqlite_files[0]
 
 if input_file is None:
-    print("ERROR: Cannot find a suitable input file, please provide one.")
-    print()
+    logger.error("Cannot find a suitable input file, please provide one.\n")
     parser.print_help()
     sys.exit(1)
 
@@ -178,6 +182,9 @@ def get_commit_hexsha8(name):
     for t in repo.tags:
         if t.name == name:
             return t.commit.hexsha[:8]
+    for c in repo.iter_commits("--all"):
+        if c.hexsha[:8] == name[:8]:
+            return c.hexsha[:8]
     return None
 
 
@@ -191,23 +198,19 @@ if known_args.baseline is not None:
         hexsha8_baseline = get_commit_hexsha8(known_args.baseline)
         name_baseline = known_args.baseline
     if hexsha8_baseline is None:
-        print(f"ERROR: cannot find data for baseline={known_args.baseline}.")
+        logger.error(f"cannot find data for baseline={known_args.baseline}.")
         sys.exit(1)
 # Otherwise, search for the most recent parent of master for which there is data:
 elif repo is not None:
     hexsha8_baseline = find_parent_in_data(repo.heads.master.commit)
 
     if hexsha8_baseline is None:
-        print("ERROR: No baseline was provided and did not find data for any master branch commits.")
-        print()
+        logger.error("No baseline was provided and did not find data for any master branch commits.\n")
         parser.print_help()
         sys.exit(1)
 else:
-    print(
-        "ERROR: No baseline was provided and the current working directory "
-        "is not part of a git repository from which a baseline could be inferred."
-    )
-    print()
+    logger.error("No baseline was provided and the current working directory "
+                 "is not part of a git repository from which a baseline could be inferred.\n")
     parser.print_help()
     sys.exit(1)
 
@@ -224,7 +227,7 @@ if known_args.compare is not None:
         hexsha8_compare = get_commit_hexsha8(known_args.compare)
         name_compare = known_args.compare
     if hexsha8_compare is None:
-        print(f"ERROR: cannot find data for baseline={known_args.compare}.")
+        logger.error(f"cannot find data for compare={known_args.compare}.")
         sys.exit(1)
 # Otherwise, search for the commit for llama-bench was most recently run
 # and that is not a parent of master:
@@ -238,16 +241,12 @@ elif repo is not None:
             break
 
     if hexsha8_compare is None:
-        print("ERROR: No compare target was provided and did not find data for any non-master commits.")
-        print()
+        logger.error("No compare target was provided and did not find data for any non-master commits.\n")
         parser.print_help()
         sys.exit(1)
 else:
-    print(
-        "ERROR: No compare target was provided and the current working directory "
-        "is not part of a git repository from which a compare target could be inferred."
-    )
-    print()
+    logger.error("No compare target was provided and the current working directory "
+                 "is not part of a git repository from which a compare target could be inferred.\n")
     parser.print_help()
     sys.exit(1)
 
@@ -281,8 +280,7 @@ if known_args.show is not None:
         if prop not in KEY_PROPERTIES[:-2]:  # Last two values are n_prompt, n_gen.
             unknown_cols.append(prop)
     if unknown_cols:
-        print(f"ERROR: Unknown values for --show: {', '.join(unknown_cols)}")
-        print()
+        logger.error(f"Unknown values for --show: {', '.join(unknown_cols)}")
         parser.print_usage()
         sys.exit(1)
     rows_show = get_rows(show)
@@ -327,8 +325,12 @@ table = []
 for row in rows_show:
     n_prompt = int(row[-4])
     n_gen    = int(row[-3])
-    assert n_prompt == 0 or n_gen == 0
-    test_name = f"tg{n_gen}" if n_prompt == 0 else f"pp{n_prompt}"
+    if n_prompt != 0 and n_gen == 0:
+        test_name = f"pp{n_prompt}"
+    elif n_prompt == 0 and n_gen != 0:
+        test_name = f"tg{n_gen}"
+    else:
+        test_name = f"pp{n_prompt}+tg{n_gen}"
     #           Regular columns    test name    avg t/s values              Speedup
     #            VVVVVVVVVVVVV     VVVVVVVVV    VVVVVVVVVVVVVV              VVVVVVV
     table.append(list(row[:-4]) + [test_name] + list(row[-2:]) + [float(row[-1]) / float(row[-2])])
@@ -366,7 +368,7 @@ if "gpu_info" in show:
 headers  = [PRETTY_NAMES[p] for p in show]
 headers += ["Test", f"t/s {name_baseline}", f"t/s {name_compare}", "Speedup"]
 
-print(tabulate(
+print(tabulate( # noqa: NP100
     table,
     headers=headers,
     floatfmt=".2f",
diff --git a/scripts/debug-test.sh b/scripts/debug-test.sh
new file mode 100755
index 000000000..231a23d69
--- /dev/null
+++ b/scripts/debug-test.sh
@@ -0,0 +1,117 @@
+#!/bin/bash
+test_suite=${1:-}
+test_number=${2:-}
+
+PROG=${0##*/}
+build_dir="build-ci-debug"
+
+if [ x"$1" = x"-h" ] || [ x"$1" = x"--help" ]; then
+    echo "Usage: $PROG [OPTION]... <test_regex> (test_number)"
+    echo "Debug specific ctest program."
+    echo
+    echo "Options:"
+    echo "  -h, --help       Display this help and exit"
+    echo
+    echo "Arguments:"
+    echo "  <test_regex>     (Mandatory) Supply one regex to the script to filter tests"
+    echo "  (test_number)    (Optional) Test number to run a specific test"
+    echo
+    echo "Example:"
+    echo "  $PROG test-tokenizer"
+    echo "  $PROG test-tokenizer 3"
+    echo
+    exit 0
+fi
+
+# Function to select and debug a test
+function select_test() {
+    test_suite=${1:-test}
+    test_number=${2:-}
+
+    # Sanity Check If Tests Is Detected
+    printf "\n\nGathering tests that fit REGEX: ${test_suite} ...\n"
+    tests=($(ctest -R ${test_suite} -V -N | grep -E " +Test +#[0-9]+*" | cut -d':' -f2 | awk '{$1=$1};1'))
+    if [ ${#tests[@]} -eq 0 ]
+    then
+        echo "No tests avaliable... check your compliation process..."
+        echo "Exiting."
+        exit 1
+    fi
+
+    if [ -z $test_number ]
+    then
+        # List out avaliable tests
+        printf "Which test would you like to debug?\n"
+        id=0
+        for s in "${tests[@]}"
+        do
+            echo "Test# ${id}"
+            echo "  $s"
+            ((id++))
+        done
+
+        # Prompt user which test they wanted to run
+        printf "\nRun test#? "
+        read test_number
+    else
+        printf "\nUser Already Requested #${test_number}"
+    fi
+
+    # Start GDB with the requested test binary and arguments
+    printf "Debugging(GDB) test: ${tests[test_number]}\n"
+    # Change IFS (Internal Field Separator)
+    sIFS=$IFS
+    IFS=$'\n'
+
+    # Get test args
+    gdb_args=($(ctest -R ${test_suite} -V -N | grep "Test command" | cut -d':' -f3 | awk '{$1=$1};1' ))
+    IFS=$sIFS
+    printf "Debug arguments: ${gdb_args[test_number]}\n\n"
+
+    # Expand paths if needed
+    args=()
+    for x in $(echo ${gdb_args[test_number]} | sed -e 's/"\/\<//' -e 's/\>"//')
+    do
+        args+=($(echo $x | sed -e 's/.*\/..\//..\//'))
+    done
+
+    # Execute debugger
+    echo "gdb args: ${args[@]}"
+    gdb --args ${args[@]}
+}
+
+# Step 0: Check the args
+if [ -z "$test_suite" ]
+then
+    echo "Usage: $PROG [OPTION]... <test_regex> (test_number)"
+    echo "Supply one regex to the script to filter tests,"
+    echo "and optionally a test number to run a specific test."
+    echo "Use --help flag for full instructions"
+    exit 1
+fi
+
+# Step 1: Reset and Setup folder context
+## Sanity check that we are actually in a git repo
+repo_root=$(git rev-parse --show-toplevel)
+if [ ! -d "$repo_root" ]; then
+    echo "Error: Not in a Git repository."
+    exit 1
+fi
+
+## Reset folder to root context of git repo
+pushd "$repo_root" || exit 1
+
+## Create and enter build directory
+rm -rf "$build_dir" && mkdir "$build_dir" || exit 1
+
+# Step 2: Setup Build Environment and Compile Test Binaries
+cmake -B "./$build_dir" -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_FATAL_WARNINGS=ON || exit 1
+pushd "$build_dir" && make -j || exit 1
+
+# Step 3: Debug the Test
+select_test "$test_suite" "$test_number"
+
+# Step 4: Return to the directory from which the user ran the command.
+popd || exit 1
+popd || exit 1
+popd || exit 1
diff --git a/scripts/gen-authors.sh b/scripts/gen-authors.sh
new file mode 100755
index 000000000..3ef8391cc
--- /dev/null
+++ b/scripts/gen-authors.sh
@@ -0,0 +1,9 @@
+#!/bin/bash
+
+printf "# date: $(date)\n" > AUTHORS
+printf "# this file is auto-generated by scripts/gen-authors.sh\n\n" >> AUTHORS
+
+git log --format='%an <%ae>' --reverse --date=short master | awk '!seen[$0]++' | sort >> AUTHORS
+
+# if necessary, update your name here. for example: jdoe -> John Doe
+sed -i '' 's/^jdoe/John Doe/g' AUTHORS
diff --git a/scripts/gen-unicode-data.py b/scripts/gen-unicode-data.py
new file mode 100644
index 000000000..37d1e396c
--- /dev/null
+++ b/scripts/gen-unicode-data.py
@@ -0,0 +1,64 @@
+import regex
+
+
+def get_matches(regex_expr):
+    regex_expr_compiled = regex.compile(regex_expr)
+    unicode_ranges = []
+    current_range = None
+
+    for codepoint in range(0x110000):
+        char = chr(codepoint)
+        if regex_expr_compiled.match(char):
+            if current_range is None:
+                current_range = [codepoint, codepoint]
+            else:
+                current_range[1] = codepoint
+        elif current_range is not None:
+            unicode_ranges.append(tuple(current_range))
+            current_range = None
+
+    if current_range is not None:
+        unicode_ranges.append(tuple(current_range))
+
+    return unicode_ranges
+
+
+def print_cat(mode, cat, ranges):
+    if mode == "range":
+        print("const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_{} = {{".format(cat)) # noqa: NP100
+    if mode == "map":
+        print("const std::map<uint32_t, uint32_t> unicode_map_{} = {{".format(cat)) # noqa: NP100
+    for i, values in enumerate(ranges):
+        end = ",\n" if (i % 4 == 3 or i + 1 == len(ranges)) else ", "
+        values = ["0x%08X" % value for value in values]
+        print("{" + ", ".join(values) + "}", end=end) # noqa: NP100
+    print("};") # noqa: NP100
+    print("") # noqa: NP100
+
+
+print_cat("range", "number",      get_matches(r'\p{N}'))
+print_cat("range", "letter",      get_matches(r'\p{L}'))
+print_cat("range", "separator",   get_matches(r'\p{Z}'))
+print_cat("range", "accent_mark", get_matches(r'\p{M}'))
+print_cat("range", "punctuation", get_matches(r'\p{P}'))
+print_cat("range", "symbol",      get_matches(r'\p{S}'))
+print_cat("range", "control",     get_matches(r'\p{C}'))
+
+print_cat("range", "whitespace",  get_matches(r'\s'))
+
+
+map_lowercase = []
+map_uppercase = []
+for codepoint in range(0x110000):
+    char = chr(codepoint)
+    lower = ord(char.lower()[0])
+    upper = ord(char.upper()[0])
+    if codepoint != lower:
+        map_lowercase.append((codepoint, lower))
+    if codepoint != upper:
+        map_uppercase.append((codepoint, upper))
+print_cat("map", "lowercase", map_lowercase)
+print_cat("map", "uppercase", map_uppercase)
+
+
+# TODO: generate unicode_map_nfd
diff --git a/scripts/get-wikitext-103.sh b/scripts/get-wikitext-103.sh
new file mode 100755
index 000000000..880dd5cbe
--- /dev/null
+++ b/scripts/get-wikitext-103.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+
+wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip
+
+echo "Usage:"
+echo ""
+echo "  ./perplexity -m model.gguf -f wiki.test.raw [other params]"
+echo ""
+
+exit 0
diff --git a/scripts/get-wikitext-2.sh b/scripts/get-wikitext-2.sh
index 7ca760fa6..b01476a46 100755
--- a/scripts/get-wikitext-2.sh
+++ b/scripts/get-wikitext-2.sh
@@ -1,10 +1,11 @@
 #!/bin/bash
 
 wget https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
+unzip wikitext-2-raw-v1.zip
 
 echo "Usage:"
 echo ""
-echo "  ./perplexity -m model.gguf -f wiki.test.raw [other params]"
+echo "  ./perplexity -m model.gguf -f wikitext-2-raw/wiki.test.raw [other params]"
 echo ""
 
 exit 0
diff --git a/scripts/hf.sh b/scripts/hf.sh
index 1e9e5a6ea..58f83d6fe 100755
--- a/scripts/hf.sh
+++ b/scripts/hf.sh
@@ -3,9 +3,9 @@
 # Shortcut for downloading HF models
 #
 # Usage:
-#   ./main -m $(./examples/hf.sh https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
-#   ./main -m $(./examples/hf.sh --url https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/blob/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
-#   ./main -m $(./examples/hf.sh --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf)
+#   ./main -m $(./scripts/hf.sh https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
+#   ./main -m $(./scripts/hf.sh --url https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/blob/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
+#   ./main -m $(./scripts/hf.sh --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf)
 #
 
 # all logs go to stderr
@@ -14,7 +14,7 @@ function log {
 }
 
 function usage {
-    log "Usage: $0 [[--url] <url>] [--repo <repo>] [--file <file>] [-h|--help]"
+    log "Usage: $0 [[--url] <url>] [--repo <repo>] [--file <file>] [--outdir <dir> [-h|--help]"
     exit 1
 }
 
@@ -26,9 +26,9 @@ function has_cmd {
 }
 
 if has_cmd wget; then
-    cmd="wget -q --show-progress -c -O %s %s"
+    cmd="wget -q --show-progress -c -O %s/%s %s"
 elif has_cmd curl; then
-    cmd="curl -C - -f -o %s -L %s"
+    cmd="curl -C - -f --output-dir %s -o %s -L %s"
 else
     log "[E] curl or wget not found"
     exit 1
@@ -37,6 +37,7 @@ fi
 url=""
 repo=""
 file=""
+outdir="."
 
 # parse args
 while [[ $# -gt 0 ]]; do
@@ -53,6 +54,10 @@ while [[ $# -gt 0 ]]; do
             file="$2"
             shift 2
             ;;
+        --outdir)
+            outdir="$2"
+            shift 2
+            ;;
         -h|--help)
             usage
             ;;
@@ -94,10 +99,10 @@ basename=$(basename $url)
 log "[+] attempting to download $basename"
 
 if [ -n "$cmd" ]; then
-    cmd=$(printf "$cmd" "$basename" "$url")
+    cmd=$(printf "$cmd" "$outdir" "$basename" "$url")
     log "[+] $cmd"
     if $cmd; then
-        echo $basename
+        echo $outdir/$basename
         exit 0
     fi
 fi
diff --git a/scripts/pod-llama.sh b/scripts/pod-llama.sh
new file mode 100644
index 000000000..2058ceabf
--- /dev/null
+++ b/scripts/pod-llama.sh
@@ -0,0 +1,213 @@
+#!/bin/bash
+#
+# Use this script only on fresh pods (runpod.io)!
+# Otherwise, it can break your environment!
+#
+
+if [ -z "$1" ]; then
+    echo "Usage: $0 <data>"
+    echo "  0: no models"
+    echo "  1: tinyllama-1b"
+    echo "  2: codellama-7b"
+    echo "  3: codellama-13b"
+    echo "  4: codellama-34b"
+    echo "  5: codellama-7b-instruct"
+    echo "  6: codellama-13b-instruct"
+    echo "  7: codellama-34b-instruct"
+
+    exit 1
+fi
+
+set -x
+
+# setup deps
+apt-get update
+apt-get install -y git-lfs cmake cmake-curses-gui vim ruby
+git-lfs install
+
+if [ ! -d "/workspace" ]; then
+    ln -sfn $(pwd) /workspace
+fi
+
+# download data
+cd /workspace
+
+# this is useful to git clone repos without doubling the disk size due to .git
+git clone https://github.com/iboB/git-lfs-download
+ln -sfn /workspace/git-lfs-download/git-lfs-download /usr/local/bin/git-lfs-download
+
+# llama.cpp
+cd /workspace
+git clone https://github.com/ggerganov/llama.cpp
+
+cd llama.cpp
+
+LLAMA_CUDA=1 make -j
+
+ln -sfn /workspace/TinyLlama-1.1B-Chat-v0.3  ./models/tinyllama-1b
+ln -sfn /workspace/CodeLlama-7b-hf           ./models/codellama-7b
+ln -sfn /workspace/CodeLlama-13b-hf          ./models/codellama-13b
+ln -sfn /workspace/CodeLlama-34b-hf          ./models/codellama-34b
+ln -sfn /workspace/CodeLlama-7b-Instruct-hf  ./models/codellama-7b-instruct
+ln -sfn /workspace/CodeLlama-13b-Instruct-hf ./models/codellama-13b-instruct
+ln -sfn /workspace/CodeLlama-34b-Instruct-hf ./models/codellama-34b-instruct
+
+pip install -r requirements.txt
+
+# cmake
+cd /workspace/llama.cpp
+
+mkdir build-cublas
+cd build-cublas
+
+cmake -DLLAMA_CUDA=1 ../
+make -j
+
+if [ "$1" -eq "0" ]; then
+    exit 0
+fi
+
+# more models
+if [ "$1" -eq "1" ]; then
+    cd /workspace
+
+    git-lfs-download https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3
+
+    cd /workspace/llama.cpp
+
+    python3 convert.py ./models/tinyllama-1b  --outfile ./models/tinyllama-1b/ggml-model-f16.gguf  --outtype f16
+
+    ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_0.gguf q4_0
+    ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_k.gguf q4_k
+    ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q8_0.gguf q8_0
+fi
+
+if [ "$1" -eq "2" ]; then
+    cd /workspace
+
+    git-lfs-download https://huggingface.co/codellama/CodeLlama-7b-hf  --without *safetensors*
+    rm -v ./CodeLlama-7b-hf/*safetensors*
+
+    cd /workspace/llama.cpp
+
+    python3 convert.py ./models/codellama-7b  --outfile ./models/codellama-7b/ggml-model-f16.gguf  --outtype f16
+
+    ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_0.gguf q4_0
+    ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_k.gguf q4_k
+    ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q8_0.gguf q8_0
+fi
+
+if [ "$1" -eq "3" ]; then
+    cd /workspace
+
+    git-lfs-download https://huggingface.co/codellama/CodeLlama-13b-hf --without *safetensors*
+    rm -v ./CodeLlama-13b-hf/*safetensors*
+
+    cd /workspace/llama.cpp
+
+    python3 convert.py ./models/codellama-13b --outfile ./models/codellama-13b/ggml-model-f16.gguf --outtype f16
+
+    ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_0.gguf q4_0
+    ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_k.gguf q4_k
+    ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q8_0.gguf q8_0
+fi
+
+if [ "$1" -eq "4" ]; then
+    cd /workspace
+
+    git-lfs-download https://huggingface.co/codellama/CodeLlama-34b-hf --without *safetensors*
+    rm -v ./CodeLlama-34b-hf/*safetensors*
+
+    cd /workspace/llama.cpp
+
+    python3 convert.py ./models/codellama-34b --outfile ./models/codellama-34b/ggml-model-f16.gguf --outtype f16
+
+    ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_0.gguf q4_0
+    ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_k.gguf q4_k
+    ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q8_0.gguf q8_0
+fi
+
+if [ "$1" -eq "5" ]; then
+    cd /workspace
+
+    git-lfs-download https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf  --without *safetensors*
+    rm -v ./CodeLlama-7b-Instruct-hf/*safetensors*
+
+    cd /workspace/llama.cpp
+
+    python3 convert.py ./models/codellama-7b-instruct  --outfile ./models/codellama-7b-instruct/ggml-model-f16.gguf  --outtype f16
+
+    ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_0.gguf q4_0
+    ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_k.gguf q4_k
+    ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q8_0.gguf q8_0
+fi
+
+if [ "$1" -eq "6" ]; then
+    cd /workspace
+
+    git-lfs-download https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf --without *safetensors*
+    rm -v ./CodeLlama-13b-Instruct-hf/*safetensors*
+
+    cd /workspace/llama.cpp
+
+    python3 convert.py ./models/codellama-13b-instruct --outfile ./models/codellama-13b-instruct/ggml-model-f16.gguf --outtype f16
+
+    ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_0.gguf q4_0
+    ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_k.gguf q4_k
+    ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q8_0.gguf q8_0
+fi
+
+if [ "$1" -eq "7" ]; then
+    cd /workspace
+
+    git-lfs-download https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf --without *safetensors*
+    rm -v ./CodeLlama-34b-Instruct-hf/*safetensors*
+
+    cd /workspace/llama.cpp
+
+    python3 convert.py ./models/codellama-34b-instruct --outfile ./models/codellama-34b-instruct/ggml-model-f16.gguf --outtype f16
+
+    ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_0.gguf q4_0
+    ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_k.gguf q4_k
+    ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q8_0.gguf q8_0
+fi
+
+if [ "$1" -eq "1" ]; then
+    # perf + perplexity
+    cd /workspace/llama.cpp/build-cublas
+
+    make -j && ../scripts/run-all-perf.sh tinyllama-1b "f16" "-ngl 99 -t 1 -p 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,32,64,128,256,512,1024,2048 -n 128"
+
+    ../scripts/get-wikitext-2.sh
+    unzip wikitext-2-raw-v1.zip
+
+    make -j && ./bin/perplexity -m ../models/tinyllama-1b/ggml-model-f16.gguf -f ./wikitext-2-raw/wiki.test.raw -ngl 100 --chunks 32
+
+    # batched
+    cd /workspace/llama.cpp
+
+    LLAMA_CUDA=1 make -j && ./batched ./models/tinyllama-1b/ggml-model-f16.gguf "Hello, my name is" 8 128 999
+
+    # batched-bench
+    cd /workspace/llama.cpp
+
+    LLAMA_CUDA=1 make -j && ./batched-bench ./models/tinyllama-1b/ggml-model-f16.gguf 4608 1 99 0 512 128 1,2,3,4,5,6,7,8,16,32
+
+    # parallel
+    cd /workspace/llama.cpp
+
+    LLAMA_CUDA=1 make -j && ./parallel -m ./models/tinyllama-1b/ggml-model-f16.gguf -t 1 -ngl 100 -c 4096 -b 512 -s 1 -np 8 -ns 128 -n 100 -cb
+
+fi
+
+# speculative
+#if [ "$1" -eq "7" ]; then
+#    cd /workspace/llama.cpp
+#
+#    LLAMA_CUDA=1 make -j && ./speculative -m ./models/codellama-34b-instruct/ggml-model-f16.gguf -md ./models/codellama-7b-instruct/ggml-model-q4_0.gguf -p "# Dijkstra's shortest path algorithm in Python (4 spaces indentation) + complexity analysis:\n\n" -e -ngl 999 -ngld 999 -t 4 -n 512 -c 4096 -s 21 --draft 16 -np 1 --temp 0.0
+#fi
+
+# more benches
+#LLAMA_CUDA=1 make -j && ./batched-bench ./models/codellama-7b/ggml-model-q4_k.gguf  4096 1 99 1 512,3200 128,128,800 1
+#LLAMA_CUDA=1 make -j && ./batched-bench ./models/codellama-13b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1
+
diff --git a/scripts/run-with-preset.py b/scripts/run-with-preset.py
index a18252730..e986a3604 100755
--- a/scripts/run-with-preset.py
+++ b/scripts/run-with-preset.py
@@ -1,5 +1,6 @@
 #!/usr/bin/env python3
 
+import logging
 import argparse
 import os
 import subprocess
@@ -7,6 +8,8 @@ import sys
 
 import yaml
 
+logger = logging.getLogger("run-with-preset")
+
 CLI_ARGS_MAIN_PERPLEXITY = [
     "batch-size", "cfg-negative-prompt", "cfg-scale", "chunks", "color", "ctx-size", "escape",
     "export", "file", "frequency-penalty", "grammar", "grammar-file", "hellaswag",
@@ -56,6 +59,7 @@ parser.add_argument("-bin", "--binary", help="The binary to run.")
 parser.add_argument("yaml_files", nargs="*",
                     help="Arbitrary number of YAML files from which to read preset values. "
                     "If two files specify the same values the later one will be used.")
+parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
 
 known_args, unknown_args = parser.parse_known_args()
 
@@ -63,6 +67,8 @@ if not known_args.yaml_files and not unknown_args:
     parser.print_help()
     sys.exit(0)
 
+logging.basicConfig(level=logging.DEBUG if known_args.verbose else logging.INFO)
+
 props = dict()
 
 for yaml_file in known_args.yaml_files:
@@ -85,7 +91,7 @@ elif binary.lower().endswith("llama-bench"):
 elif binary.lower().endswith("server"):
     cli_args = CLI_ARGS_SERVER
 else:
-    print(f"Unknown binary: {binary}")
+    logger.error(f"Unknown binary: {binary}")
     sys.exit(1)
 
 command_list = [binary]
@@ -121,11 +127,11 @@ for cli_arg in cli_args:
 
 num_unused = len(props)
 if num_unused > 10:
-    print(f"The preset file contained a total of {num_unused} unused properties.")
+    logger.info(f"The preset file contained a total of {num_unused} unused properties.")
 elif num_unused > 0:
-    print("The preset file contained the following unused properties:")
+    logger.info("The preset file contained the following unused properties:")
     for prop, value in props.items():
-        print(f"  {prop}: {value}")
+        logger.info(f"  {prop}: {value}")
 
 command_list += unknown_args
 
diff --git a/scripts/server-llm.sh b/scripts/server-llm.sh
index 30bbac321..eb6ce458e 100644
--- a/scripts/server-llm.sh
+++ b/scripts/server-llm.sh
@@ -380,7 +380,7 @@ fi
 
 if [[ "$backend" == "cuda" ]]; then
     printf "[+] Building with CUDA backend\n"
-    LLAMA_CUBLAS=1 make -j server $log
+    LLAMA_CUDA=1 make -j server $log
 elif [[ "$backend" == "cpu" ]]; then
     printf "[+] Building with CPU backend\n"
     make -j server $log
diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh
index 2c391e641..7c157eb4c 100755
--- a/scripts/sync-ggml-am.sh
+++ b/scripts/sync-ggml-am.sh
@@ -60,11 +60,14 @@ while read c; do
         src/ggml*.m \
         src/ggml*.metal \
         src/ggml*.cu \
+        src/ggml-cuda/* \
         tests/test-opt.cpp \
         tests/test-grad0.cpp \
         tests/test-quantize-fns.cpp \
         tests/test-quantize-perf.cpp \
         tests/test-backend-ops.cpp \
+        LICENSE \
+        scripts/gen-authors.sh \
         >> $SRC_LLAMA/ggml-src.patch
 done < $SRC_LLAMA/ggml-commits
 
@@ -94,6 +97,8 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
     # src/ggml-alloc.c            -> ggml-alloc.c
     # src/ggml-backend-impl.h     -> ggml-backend-impl.h
     # src/ggml-backend.c          -> ggml-backend.c
+    # src/ggml-common.h           -> ggml-common.h
+    # src/ggml-cuda/*             -> ggml-cuda/
     # src/ggml-cuda.cu            -> ggml-cuda.cu
     # src/ggml-cuda.h             -> ggml-cuda.h
     # src/ggml-impl.h             -> ggml-impl.h
@@ -120,12 +125,17 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
     # tests/test-quantize-fns.cpp  -> tests/test-quantize-fns.cpp
     # tests/test-quantize-perf.cpp -> tests/test-quantize-perf.cpp
     # tests/test-backend-ops.cpp   -> tests/test-backend-ops.cpp
+    #
+    # LICENSE                      -> LICENSE
+    # scripts/gen-authors.sh       -> scripts/gen-authors.sh
 
     cat ggml-src.patch | sed \
         -e 's/src\/ggml\.c/ggml.c/g' \
         -e 's/src\/ggml-alloc\.c/ggml-alloc.c/g' \
         -e 's/src\/ggml-backend-impl\.h/ggml-backend-impl.h/g' \
         -e 's/src\/ggml-backend\.c/ggml-backend.c/g' \
+        -e 's/src\/ggml-common\.h/ggml-common.h/g' \
+        -e 's/src\/ggml-cuda\//ggml-cuda\//g' \
         -e 's/src\/ggml-cuda\.cu/ggml-cuda.cu/g' \
         -e 's/src\/ggml-cuda\.h/ggml-cuda.h/g' \
         -e 's/src\/ggml-impl\.h/ggml-impl.h/g' \
@@ -151,6 +161,8 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
         -e 's/tests\/test-quantize-fns\.cpp/tests\/test-quantize-fns.cpp/g' \
         -e 's/tests\/test-quantize-perf\.cpp/tests\/test-quantize-perf.cpp/g' \
         -e 's/tests\/test-backend-ops\.cpp/tests\/test-backend-ops.cpp/g' \
+        -e 's/LICENSE/LICENSE/g' \
+        -e 's/scripts\/gen-authors\.sh/scripts\/gen-authors.sh/g' \
         > ggml-src.patch.tmp
     mv ggml-src.patch.tmp ggml-src.patch
 
diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index 733d8f95b..1ea320429 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-818eeb8a3be99125746a90ec63af8f51516a2ec6
+30f54cbb3ada3e4c5bc6924de3e5918e5be4ff11
diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh
index feb34bbc8..80c43976f 100755
--- a/scripts/sync-ggml.sh
+++ b/scripts/sync-ggml.sh
@@ -4,6 +4,8 @@ cp -rpv ../ggml/src/ggml.c                  ./ggml.c
 cp -rpv ../ggml/src/ggml-alloc.c            ./ggml-alloc.c
 cp -rpv ../ggml/src/ggml-backend-impl.h     ./ggml-backend-impl.h
 cp -rpv ../ggml/src/ggml-backend.c          ./ggml-backend.c
+cp -rpv ../ggml/src/ggml-common.h           ./ggml-common.h
+cp -rpv ../ggml/src/ggml-cuda/*             ./ggml-cuda/
 cp -rpv ../ggml/src/ggml-cuda.cu            ./ggml-cuda.cu
 cp -rpv ../ggml/src/ggml-cuda.h             ./ggml-cuda.h
 cp -rpv ../ggml/src/ggml-impl.h             ./ggml-impl.h
@@ -29,3 +31,6 @@ cp -rpv ../ggml/include/ggml/ggml-backend.h ./ggml-backend.h
 cp -rpv ../ggml/tests/test-opt.cpp         ./tests/test-opt.cpp
 cp -rpv ../ggml/tests/test-grad0.cpp       ./tests/test-grad0.cpp
 cp -rpv ../ggml/tests/test-backend-ops.cpp ./tests/test-backend-ops.cpp
+
+cp -rpv ../LICENSE                         ./LICENSE
+cp -rpv ../ggml/scripts/gen-authors.sh     ./scripts/gen-authors.sh
diff --git a/scripts/verify-checksum-models.py b/scripts/verify-checksum-models.py
index dff4b4734..0b5b9aafa 100755
--- a/scripts/verify-checksum-models.py
+++ b/scripts/verify-checksum-models.py
@@ -1,8 +1,11 @@
 #!/usr/bin/env python3
 
+import logging
 import os
 import hashlib
 
+logger = logging.getLogger("verify-checksum-models")
+
 
 def sha256sum(file):
     block_size = 16 * 1024 * 1024  # 16 MB block size
@@ -27,7 +30,7 @@ hash_list_file = os.path.join(llama_path, "SHA256SUMS")
 
 # Check if the hash list file exists
 if not os.path.exists(hash_list_file):
-    print(f"Hash list file not found: {hash_list_file}")
+    logger.error(f"Hash list file not found: {hash_list_file}")
     exit(1)
 
 # Read the hash file content and split it into an array of lines
@@ -46,7 +49,7 @@ for line in hash_list:
     file_path = os.path.join(llama_path, filename)
 
     # Informing user of the progress of the integrity check
-    print(f"Verifying the checksum of {file_path}")
+    logger.info(f"Verifying the checksum of {file_path}")
 
     # Check if the file exists
     if os.path.exists(file_path):
@@ -73,9 +76,9 @@ for line in hash_list:
 
 
 # Print column headers for results table
-print("\n" + "filename".ljust(40) + "valid checksum".center(20) + "file missing".center(20))
-print("-" * 80)
+print("filename".ljust(40) + "valid checksum".center(20) + "file missing".center(20)) # noqa: NP100
+print("-" * 80) # noqa: NP100
 
 # Output the results as a table
 for r in results:
-    print(f"{r['filename']:40} {r['valid checksum']:^20} {r['file missing']:^20}")
+    print(f"{r['filename']:40} {r['valid checksum']:^20} {r['file missing']:^20}") # noqa: NP100
diff --git a/scripts/xxd.cmake b/scripts/xxd.cmake
new file mode 100644
index 000000000..f5ad6ab9b
--- /dev/null
+++ b/scripts/xxd.cmake
@@ -0,0 +1,16 @@
+# CMake equivalent of `xxd -i ${INPUT} ${OUTPUT}`
+# Usage: cmake -DINPUT=examples/server/public/index.html -DOUTPUT=examples/server/index.html.hpp -P scripts/xxd.cmake
+
+SET(INPUT "" CACHE STRING "Input File")
+SET(OUTPUT "" CACHE STRING "Output File")
+
+get_filename_component(filename "${INPUT}" NAME)
+string(REGEX REPLACE "\\.|-" "_" name "${filename}")
+
+file(READ "${INPUT}" hex_data HEX)
+string(REGEX REPLACE "([0-9a-f][0-9a-f])" "0x\\1," hex_sequence "${hex_data}")
+
+string(LENGTH ${hex_data} hex_len)
+math(EXPR len "${hex_len} / 2")
+
+file(WRITE "${OUTPUT}" "unsigned char ${name}[] = {${hex_sequence}};\nunsigned int ${name}_len = ${len};\n")
diff --git a/sgemm.cpp b/sgemm.cpp
new file mode 100644
index 000000000..40ba9d7e9
--- /dev/null
+++ b/sgemm.cpp
@@ -0,0 +1,1030 @@
+// Copyright 2024 Mozilla Foundation
+//
+// Permission is hereby granted, free of charge, to any person obtaining
+// a copy of this software and associated documentation files (the
+// "Software"), to deal in the Software without restriction, including
+// without limitation the rights to use, copy, modify, merge, publish,
+// distribute, sublicense, and/or sell copies of the Software, and to
+// permit persons to whom the Software is furnished to do so, subject to
+// the following conditions:
+//
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+//
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
+// BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
+// ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+// CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+// SOFTWARE.
+
+//
+//                   _   _          ___ _      _   ___
+//                  | |_(_)_ _ _  _| _ ) |    /_\ / __|
+//                  |  _| | ' \ || | _ \ |__ / _ \\__ \.
+//                   \__|_|_||_\_, |___/____/_/ \_\___/
+//                             |__/
+//
+//                    BASIC LINEAR ALGEBRA SUBPROGRAMS
+//
+//
+// This file implements multithreaded CPU matrix multiplication for the
+// common contiguous use case C = Aᵀ * B. These kernels are designed to
+// have excellent performance[1] for matrices that fit in the CPU cache
+// without imposing any overhead such as cache filling or malloc calls.
+//
+// This implementation does not guarantee any upper bound with rounding
+// errors, which grow along with k. Our goal's to maximally exploit the
+// hardware for performance, and then use whatever resources remain for
+// improving numerical accuracy.
+//
+// [1] J. Tunney, ‘LLaMA Now Goes Faster on CPUs’, Mar. 2024. [Online].
+//     Available: https://justine.lol/matmul/. [Accessed: 29-Mar-2024].
+
+#pragma GCC diagnostic ignored "-Wpedantic"
+#pragma GCC diagnostic ignored "-Wignored-attributes"
+
+#include "sgemm.h"
+#include "ggml-impl.h"
+#include "ggml-quants.h"
+
+#ifdef _MSC_VER
+#define NOINLINE __declspec(noinline)
+#else
+#define NOINLINE __attribute__((__noinline__))
+#endif
+
+#if defined(__ARM_NEON) || defined(__AVX512F__)
+#define VECTOR_REGISTERS 32
+#else
+#define VECTOR_REGISTERS 16
+#endif
+
+#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1)
+
+namespace {
+
+inline float unhalf(ggml_fp16_t d) {
+    return GGML_FP16_TO_FP32(d);
+}
+
+////////////////////////////////////////////////////////////////////////////////////////////////////
+// VECTORIZED ARITHMETIC OPERATIONS
+
+#if defined(__SSE__) || defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
+inline __m128 add(__m128 x, __m128 y) { return _mm_add_ps(x, y); }
+inline __m128 sub(__m128 x, __m128 y) { return _mm_sub_ps(x, y); }
+inline __m128 mul(__m128 x, __m128 y) { return _mm_mul_ps(x, y); }
+#endif  // __SSE__
+
+#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
+inline __m256 add(__m256 x, __m256 y) { return _mm256_add_ps(x, y); }
+inline __m256 sub(__m256 x, __m256 y) { return _mm256_sub_ps(x, y); }
+inline __m256 mul(__m256 x, __m256 y) { return _mm256_mul_ps(x, y); }
+#endif // __AVX__
+
+#if defined(__AVX512F__)
+inline __m512 add(__m512 x, __m512 y) { return _mm512_add_ps(x, y); }
+inline __m512 sub(__m512 x, __m512 y) { return _mm512_sub_ps(x, y); }
+inline __m512 mul(__m512 x, __m512 y) { return _mm512_mul_ps(x, y); }
+#endif // __AVX512F__
+
+#if defined(__ARM_NEON)
+inline float32x4_t add(float32x4_t x, float32x4_t y) { return vaddq_f32(x, y); }
+inline float32x4_t sub(float32x4_t x, float32x4_t y) { return vsubq_f32(x, y); }
+inline float32x4_t mul(float32x4_t x, float32x4_t y) { return vmulq_f32(x, y); }
+#endif // __ARM_NEON
+
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+inline float16x8_t add(float16x8_t x, float16x8_t y) { return vaddq_f16(x, y); }
+inline float16x8_t sub(float16x8_t x, float16x8_t y) { return vsubq_f16(x, y); }
+inline float16x8_t mul(float16x8_t x, float16x8_t y) { return vmulq_f16(x, y); }
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+
+////////////////////////////////////////////////////////////////////////////////////////////////////
+// VECTORIZED FUSED MULTIPLY ADD
+
+/**
+ * Computes a * b + c.
+ */
+template <typename T, typename U>
+inline U madd(T a, T b, U c) {
+    return add(mul(a, b), c);
+}
+
+#if defined(__FMA__)
+#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
+template <>
+inline __m256 madd(__m256 a, __m256 b, __m256 c) {
+    return _mm256_fmadd_ps(a, b, c);
+}
+#endif
+#if defined(__AVX512F__)
+template <>
+inline __m512 madd(__m512 a, __m512 b, __m512 c) {
+    return _mm512_fmadd_ps(a, b, c);
+}
+#endif
+#endif
+
+#if defined(__ARM_FEATURE_FMA)
+template <>
+inline float32x4_t madd(float32x4_t a, float32x4_t b, float32x4_t c) {
+    return vfmaq_f32(c, b, a);
+}
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(_MSC_VER)
+template <>
+inline float16x8_t madd(float16x8_t a, float16x8_t b, float16x8_t c) {
+    return vfmaq_f16(c, b, a);
+}
+#endif
+#endif
+
+////////////////////////////////////////////////////////////////////////////////////////////////////
+// VECTORIZED HORIZONTAL SUM
+
+#if defined(__ARM_NEON)
+inline float hsum(float32x4_t x) {
+    return vaddvq_f32(x);
+}
+#endif // __ARM_NEON
+
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(_MSC_VER)
+inline float hsum(float16x8_t x) {
+    return vaddvq_f32(vaddq_f32(vcvt_f32_f16(vget_low_f16(x)),
+                                vcvt_f32_f16(vget_high_f16(x))));
+}
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+
+#if defined(__SSE__) || defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
+inline float hsum(__m128 x) {
+#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
+    x = _mm_add_ps(x, _mm_movehl_ps(x, x));
+    x = _mm_add_ss(x, _mm_movehdup_ps(x));
+#else
+    __m128 t;
+    t = _mm_shuffle_ps(x, x, _MM_SHUFFLE(2, 3, 0, 1));
+    x = _mm_add_ps(x, t);
+    t = _mm_movehl_ps(t, x);
+    x = _mm_add_ss(x, t);
+#endif
+    return _mm_cvtss_f32(x);
+}
+#endif
+
+#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
+inline float hsum(__m256 x) {
+    return hsum(_mm_add_ps(_mm256_extractf128_ps(x, 1),
+                           _mm256_castps256_ps128(x)));
+}
+#endif // __AVX__
+
+#if defined(__AVX512F__)
+inline float hsum(__m512 x) {
+    return _mm512_reduce_add_ps(x);
+}
+#endif // __AVX512F__
+
+////////////////////////////////////////////////////////////////////////////////////////////////////
+// VECTORIZED MEMORY LOADING
+
+template <typename T, typename U> T load(const U *);
+
+#if defined(__ARM_NEON)
+template <> inline float32x4_t load(const float *p) {
+    return vld1q_f32(p);
+}
+#if !defined(_MSC_VER)
+template <> inline float16x8_t load(const ggml_fp16_t *p) {
+    return vld1q_f16((const float16_t *)p);
+}
+template <> inline float32x4_t load(const ggml_fp16_t *p) {
+    return vcvt_f32_f16(vld1_f16((const float16_t *)p));
+}
+#endif // _MSC_VER
+#endif // __ARM_NEON
+
+#if defined(__SSE__) || defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
+template <> inline __m128 load(const float *p) {
+    return _mm_loadu_ps(p);
+}
+#endif  // __SSE__
+
+#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
+template <> inline __m256 load(const float *p) {
+    return _mm256_loadu_ps(p);
+}
+#endif // __AVX__
+
+#if defined(__F16C__)
+template <> inline __m256 load(const ggml_fp16_t *p) {
+    return _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)p));
+}
+#endif // __F16C__
+
+#if defined(__AVX512F__)
+template <> inline __m512 load(const float *p) {
+    return _mm512_loadu_ps(p);
+}
+template <> inline __m512 load(const ggml_fp16_t *p) {
+    return _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)p));
+}
+#endif // __AVX512F__
+
+////////////////////////////////////////////////////////////////////////////////////////////////////
+// FLOATING POINT MATRIX MULTIPLICATION
+
+template <int KN, typename D, typename V, typename TA, typename TB, typename TC>
+class tinyBLAS {
+  public:
+    tinyBLAS(int64_t k,
+             const TA *A, int64_t lda,
+             const TB *B, int64_t ldb,
+             TC *C, int64_t ldc,
+             int ith, int nth)
+        : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) {
+    }
+
+    void matmul(int64_t m, int64_t n, int task) {
+        if (task == GGML_TASK_TYPE_COMPUTE)
+            mnpack(0, m, 0, n);
+    }
+
+  private:
+    NOINLINE void mnpack(int64_t m0, int64_t m, int64_t n0, int64_t n) {
+        int64_t mc, nc, mp, np;
+        switch ((MIN(m - m0, 5) << 4) | MIN(n - n0, 5)) {
+#if VECTOR_REGISTERS == 32
+        case 0x55:
+            mc = 5;
+            nc = 5;
+            gemm<5, 5>(m0, m, n0, n);
+            break;
+        case 0x45:
+            mc = 4;
+            nc = 5;
+            gemm<4, 5>(m0, m, n0, n);
+            break;
+        case 0x54:
+            mc = 5;
+            nc = 4;
+            gemm<5, 4>(m0, m, n0, n);
+            break;
+        case 0x44:
+            mc = 4;
+            nc = 4;
+            gemm<4, 4>(m0, m, n0, n);
+            break;
+        case 0x53:
+            mc = 5;
+            nc = 3;
+            gemm<5, 3>(m0, m, n0, n);
+            break;
+        case 0x35:
+            mc = 3;
+            nc = 5;
+            gemm<3, 5>(m0, m, n0, n);
+            break;
+        case 0x43:
+            mc = 4;
+            nc = 3;
+            gemm<4, 3>(m0, m, n0, n);
+            break;
+#else
+        case 0x55:
+        case 0x54:
+        case 0x53:
+        case 0x45:
+        case 0x44:
+        case 0x43:
+            mc = 4;
+            nc = 3;
+            gemm<4, 3>(m0, m, n0, n);
+            break;
+        case 0x35:
+#endif
+        case 0x34:
+            mc = 3;
+            nc = 4;
+            gemm<3, 4>(m0, m, n0, n);
+            break;
+        case 0x52:
+            mc = 5;
+            nc = 2;
+            gemm<5, 2>(m0, m, n0, n);
+            break;
+        case 0x33:
+            mc = 3;
+            nc = 3;
+            gemm<3, 3>(m0, m, n0, n);
+            break;
+        case 0x25:
+            mc = 2;
+            nc = 5;
+            gemm<2, 5>(m0, m, n0, n);
+            break;
+        case 0x42:
+            mc = 4;
+            nc = 2;
+            gemm<4, 2>(m0, m, n0, n);
+            break;
+        case 0x24:
+            mc = 2;
+            nc = 4;
+            gemm<2, 4>(m0, m, n0, n);
+            break;
+        case 0x32:
+            mc = 3;
+            nc = 2;
+            gemm<3, 2>(m0, m, n0, n);
+            break;
+        case 0x23:
+            mc = 2;
+            nc = 3;
+            gemm<2, 3>(m0, m, n0, n);
+            break;
+        case 0x51:
+            mc = 5;
+            nc = 1;
+            gemm<5, 1>(m0, m, n0, n);
+            break;
+        case 0x41:
+            mc = 4;
+            nc = 1;
+            gemm<4, 1>(m0, m, n0, n);
+            break;
+        case 0x22:
+            mc = 2;
+            nc = 2;
+            gemm<2, 2>(m0, m, n0, n);
+            break;
+        case 0x15:
+            mc = 1;
+            nc = 5;
+            gemm<1, 5>(m0, m, n0, n);
+            break;
+        case 0x14:
+            mc = 1;
+            nc = 4;
+            gemm<1, 4>(m0, m, n0, n);
+            break;
+        case 0x31:
+            mc = 3;
+            nc = 1;
+            gemm<3, 1>(m0, m, n0, n);
+            break;
+        case 0x13:
+            mc = 1;
+            nc = 3;
+            gemm<1, 3>(m0, m, n0, n);
+            break;
+        case 0x21:
+            mc = 2;
+            nc = 1;
+            gemm<2, 1>(m0, m, n0, n);
+            break;
+        case 0x12:
+            mc = 1;
+            nc = 2;
+            gemm<1, 2>(m0, m, n0, n);
+            break;
+        case 0x11:
+            mc = 1;
+            nc = 1;
+            gemm<1, 1>(m0, m, n0, n);
+            break;
+        default:
+            return;
+        }
+        mp = m0 + (m - m0) / mc * mc;
+        np = n0 + (n - n0) / nc * nc;
+        mnpack(mp, m, n0, np);
+        mnpack(m0, m, np, n);
+    }
+
+    template <int RM, int RN>
+    NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) {
+        int64_t ytiles = (m - m0) / RM;
+        int64_t xtiles = (n - n0) / RN;
+        int64_t tiles = xtiles * ytiles;
+        int64_t duty = (tiles + nth - 1) / nth;
+        int64_t start = duty * ith;
+        int64_t end = start + duty;
+        if (end > tiles)
+            end = tiles;
+        for (int64_t job = start; job < end; ++job) {
+            int64_t ii = m0 + job / xtiles * RM;
+            int64_t jj = n0 + job % xtiles * RN;
+            D Cv[RN][RM] = {};
+            for (int64_t l = 0; l < k; l += KN)
+                for (int64_t j = 0; j < RN; ++j)
+                    for (int64_t i = 0; i < RM; ++i)
+                        Cv[j][i] = madd(load<V>(A + lda * (ii + i) + l),
+                                        load<V>(B + ldb * (jj + j) + l),
+                                        Cv[j][i]);
+            for (int64_t j = 0; j < RN; ++j)
+                for (int64_t i = 0; i < RM; ++i)
+                    C[ldc * (jj + j) + (ii + i)] = hsum(Cv[j][i]);
+        }
+    }
+
+    const TA *const A;
+    const TB *const B;
+    TC *const C;
+    const int64_t k;
+    const int64_t lda;
+    const int64_t ldb;
+    const int64_t ldc;
+    const int ith;
+    const int nth;
+};
+
+//////////////////////////////////////////////////////////////////////////////////////////
+// QUANT ZERO MATRIX MULTIPLICATION
+
+#if defined(__ARM_FEATURE_DOTPROD)
+template <typename TA>
+class tinyBLAS_Q0_ARM {
+  public:
+    tinyBLAS_Q0_ARM(int64_t k,
+                    const TA *A, int64_t lda,
+                    const block_q8_0 *B, int64_t ldb,
+                    float *C, int64_t ldc,
+                    int ith, int nth)
+        : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) {
+    }
+
+    void matmul(int64_t m, int64_t n, int task) {
+        if (task == GGML_TASK_TYPE_COMPUTE)
+            mnpack(0, m, 0, n);
+    }
+
+  private:
+    NOINLINE void mnpack(int64_t m0, int64_t m, int64_t n0, int64_t n) {
+        int64_t mc, nc, mp, np;
+        switch ((MIN(m - m0, 3) << 4) | MIN(n - n0, 3ll)) {
+        case 0x33:
+            mc = 3;
+            nc = 3;
+            gemm<3, 3>(m0, m, n0, n);
+            break;
+        case 0x32:
+            mc = 3;
+            nc = 2;
+            gemm<3, 2>(m0, m, n0, n);
+            break;
+        case 0x23:
+            mc = 2;
+            nc = 3;
+            gemm<2, 3>(m0, m, n0, n);
+            break;
+        case 0x22:
+            mc = 2;
+            nc = 2;
+            gemm<2, 2>(m0, m, n0, n);
+            break;
+        case 0x31:
+            mc = 3;
+            nc = 1;
+            gemm<3, 1>(m0, m, n0, n);
+            break;
+        case 0x13:
+            mc = 1;
+            nc = 3;
+            gemm<1, 3>(m0, m, n0, n);
+            break;
+        case 0x21:
+            mc = 2;
+            nc = 1;
+            gemm<2, 1>(m0, m, n0, n);
+            break;
+        case 0x12:
+            mc = 1;
+            nc = 2;
+            gemm<1, 2>(m0, m, n0, n);
+            break;
+        case 0x11:
+            mc = 1;
+            nc = 1;
+            gemm<1, 1>(m0, m, n0, n);
+            break;
+        default:
+            return;
+        }
+        mp = m0 + (m - m0) / mc * mc;
+        np = n0 + (n - n0) / nc * nc;
+        mnpack(mp, m, n0, np);
+        mnpack(m0, m, np, n);
+    }
+
+    template <int RM, int RN>
+    NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) {
+        int64_t ytiles = (m - m0) / RM;
+        int64_t xtiles = (n - n0) / RN;
+        int64_t tiles = xtiles * ytiles;
+        int64_t duty = (tiles + nth - 1) / nth;
+        int64_t start = duty * ith;
+        int64_t end = start + duty;
+        if (end > tiles)
+            end = tiles;
+        for (int64_t job = start; job < end; ++job) {
+            int64_t ii = m0 + job / xtiles * RM;
+            int64_t jj = n0 + job % xtiles * RN;
+            float32x4_t Cv[RN][RM] = {};
+            for (int64_t l = 0; l < k; ++l)
+                for (int64_t j = 0; j < RN; ++j)
+                    for (int64_t i = 0; i < RM; ++i)
+                        Cv[j][i] = vmlaq_n_f32(Cv[j][i],
+                                               vcvtq_f32_s32(vdotq_s32(
+                                                   vdotq_s32(vdupq_n_s32(0),
+                                                             load_lo(A + lda * (ii + i) + l),
+                                                             load_lo(B + ldb * (jj + j) + l)),
+                                                   load_hi(A + lda * (ii + i) + l),
+                                                   load_hi(B + ldb * (jj + j) + l))),
+                                               unhalf(A[lda * (ii + i) + l].d) *
+                                               unhalf(B[ldb * (jj + j) + l].d));
+            for (int64_t j = 0; j < RN; ++j)
+                for (int64_t i = 0; i < RM; ++i)
+                    C[ldc * (jj + j) + (ii + i)] = hsum(Cv[j][i]);
+        }
+    }
+
+    inline int8x16_t load_lo(const block_q8_0 *b) {
+        return vld1q_s8(b->qs);
+    }
+
+    inline int8x16_t load_hi(const block_q8_0 *b) {
+        return vld1q_s8(b->qs + 16);
+    }
+
+    inline int8x16_t load_lo(const block_q4_0 *b) {
+        return vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vld1q_u8(b->qs),
+                                                     vdupq_n_u8(0x0f))),
+                        vdupq_n_s8(0x8));
+    }
+
+    inline int8x16_t load_hi(const block_q4_0 *b) {
+        return vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(vld1q_u8(b->qs), 4)),
+                        vdupq_n_s8(0x8));
+    }
+
+    const TA *const A;
+    const block_q8_0 *const B;
+    float *const C;
+    const int64_t k;
+    const int64_t lda;
+    const int64_t ldb;
+    const int64_t ldc;
+    const int ith;
+    const int nth;
+};
+#endif // __ARM_FEATURE_DOTPROD
+
+#if defined(__AVX2__) || defined(__AVX512F__) || defined(__AVX__)
+template <typename TA, typename TB, typename TC>
+class tinyBLAS_Q0_AVX {
+  public:
+    tinyBLAS_Q0_AVX(int64_t k,
+                    const TA *A, int64_t lda,
+                    const TB *B, int64_t ldb,
+                    TC *C, int64_t ldc,
+                    int ith, int nth)
+        : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) {
+    }
+
+    void matmul(int64_t m, int64_t n, int task) {
+        if (task == GGML_TASK_TYPE_COMPUTE)
+            mnpack(0, m, 0, n);
+    }
+
+  private:
+    void mnpack(int64_t m0, int64_t m, int64_t n0, int64_t n) {
+        int64_t mc, nc, mp, np;
+        switch ((MIN(m - m0, 4) << 4) | MIN(n - n0, 4)) {
+#if VECTOR_REGISTERS == 32
+        case 0x44:
+            mc = 4;
+            nc = 4;
+            gemm<4, 4>(m0, m, n0, n);
+            break;
+        case 0x43:
+            mc = 4;
+            nc = 3;
+            gemm<4, 3>(m0, m, n0, n);
+            break;
+        case 0x34:
+            mc = 3;
+            nc = 4;
+            gemm<3, 4>(m0, m, n0, n);
+            break;
+        case 0x33:
+            mc = 3;
+            nc = 3;
+            gemm<3, 3>(m0, m, n0, n);
+            break;
+        case 0x42:
+            mc = 4;
+            nc = 2;
+            gemm<4, 2>(m0, m, n0, n);
+            break;
+        case 0x24:
+            mc = 2;
+            nc = 4;
+            gemm<2, 4>(m0, m, n0, n);
+            break;
+#else
+        case 0x44:
+        case 0x43:
+        case 0x42:
+            mc = 4;
+            nc = 2;
+            gemm<4, 2>(m0, m, n0, n);
+            break;
+        case 0x34:
+        case 0x24:
+            mc = 2;
+            nc = 4;
+            gemm<2, 4>(m0, m, n0, n);
+            break;
+        case 0x33:
+#endif
+        case 0x32:
+            mc = 3;
+            nc = 2;
+            gemm<3, 2>(m0, m, n0, n);
+            break;
+        case 0x23:
+            mc = 2;
+            nc = 3;
+            gemm<2, 3>(m0, m, n0, n);
+            break;
+        case 0x41:
+            mc = 4;
+            nc = 1;
+            gemm<4, 1>(m0, m, n0, n);
+            break;
+        case 0x22:
+            mc = 2;
+            nc = 2;
+            gemm<2, 2>(m0, m, n0, n);
+            break;
+        case 0x14:
+            mc = 1;
+            nc = 4;
+            gemm<1, 4>(m0, m, n0, n);
+            break;
+        case 0x31:
+            mc = 3;
+            nc = 1;
+            gemm<3, 1>(m0, m, n0, n);
+            break;
+        case 0x13:
+            mc = 1;
+            nc = 3;
+            gemm<1, 3>(m0, m, n0, n);
+            break;
+        case 0x21:
+            mc = 2;
+            nc = 1;
+            gemm<2, 1>(m0, m, n0, n);
+            break;
+        case 0x12:
+            mc = 1;
+            nc = 2;
+            gemm<1, 2>(m0, m, n0, n);
+            break;
+        case 0x11:
+            mc = 1;
+            nc = 1;
+            gemm<1, 1>(m0, m, n0, n);
+            break;
+        default:
+            return;
+        }
+        mp = m0 + (m - m0) / mc * mc;
+        np = n0 + (n - n0) / nc * nc;
+        mnpack(mp, m, n0, np);
+        mnpack(m0, m, np, n);
+    }
+
+    template <int RM, int RN>
+    NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) {
+        int64_t ytiles = (m - m0) / RM;
+        int64_t xtiles = (n - n0) / RN;
+        int64_t tiles = xtiles * ytiles;
+        int64_t duty = (tiles + nth - 1) / nth;
+        int64_t start = duty * ith;
+        int64_t end = start + duty;
+        if (end > tiles)
+            end = tiles;
+        for (int64_t job = start; job < end; ++job) {
+            int64_t ii = m0 + job / xtiles * RM;
+            int64_t jj = n0 + job % xtiles * RN;
+            __m256 Cv[RN][RM] = {};
+            for (int64_t l = 0; l < k; ++l)
+                for (int64_t j = 0; j < RN; ++j)
+                    for (int64_t i = 0; i < RM; ++i) {
+#if defined(__AVX2__)
+                        __m256 udTmp = updot(_mm256_sign_epi8(load(A + lda * (ii + i) + l),
+                                                              load(A + lda * (ii + i) + l)),
+                                             _mm256_sign_epi8(load(B + ldb * (jj + j) + l),
+                                                              load(A + lda * (ii + i) + l)));
+#else
+                        __m128i ali0 = load0(A + lda * (ii + i) + l);
+                        __m128i ali1 = load1(A + lda * (ii + i) + l);
+                        __m128i blj0 = load0(B + ldb * (jj + j) + l);
+                        __m128i blj1 = load1(B + ldb * (jj + j) + l);
+
+                        __m128i sepAA0 = _mm_sign_epi8(ali0, ali0);
+                        __m128i sepAA1 = _mm_sign_epi8(ali1, ali1);
+                        __m128i sepBA0 = _mm_sign_epi8(blj0, ali0);
+                        __m128i sepBA1 = _mm_sign_epi8(blj1, ali1);
+
+                        // updot
+                        const __m128i oneFill = _mm_set1_epi16(1);
+                        __m128i mad0 = _mm_maddubs_epi16(sepAA0, sepBA0);
+                        __m128i mad1 = _mm_maddubs_epi16(sepAA1, sepBA1);
+                        __m256 udTmp = _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_madd_epi16(oneFill, mad1), _mm_madd_epi16(oneFill, mad0)));
+#endif
+                        Cv[j][i] = madd(_mm256_set1_ps(unhalf(A[lda * (ii + i) + l].d) *
+                                                       unhalf(B[ldb * (jj + j) + l].d)),
+                                                       udTmp,
+                                                       Cv[j][i]);
+                    }
+            for (int64_t j = 0; j < RN; ++j)
+                for (int64_t i = 0; i < RM; ++i)
+                    C[ldc * (jj + j) + (ii + i)] = hsum(Cv[j][i]);
+        }
+    }
+
+    inline __m256i load(const block_q8_0 *b) {
+        return _mm256_loadu_si256((const __m256i *)b->qs);
+    }
+
+    inline __m128i load0(const block_q8_0 *b) {
+        return _mm_loadu_si128((const __m128i *)b->qs);
+    }
+
+    inline __m128i load1(const block_q8_0 *b) {
+        return _mm_loadu_si128(((const __m128i *)b->qs) + 1);
+    }
+
+    inline __m256i load(const block_q4_0 *b) {
+        return _mm256_sub_epi8(denibble(b->qs), _mm256_set1_epi8(8));
+    }
+
+    inline __m128i load0(const block_q4_0 *b) {
+        const __m128i x = _mm_loadu_si128((const __m128i *)(b->qs));
+        return _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), x), _mm_set1_epi8(8));
+    }
+
+    inline __m128i load1(const block_q4_0 *b) {
+        const __m128i x = _mm_loadu_si128((const __m128i *)(b->qs));
+        return _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), _mm_srli_epi16(x, 4)), _mm_set1_epi8(8));
+    }
+
+    inline __m256 updot(__m256i u, __m256i s) {
+        __m256i res;
+#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__))
+        res = _mm256_dpbusd_epi32(_mm256_setzero_si256(), u, s);
+#else
+        res = _mm256_madd_epi16(_mm256_set1_epi16(1), _mm256_maddubs_epi16(u, s));
+#endif
+        return _mm256_cvtepi32_ps(res);
+    }
+
+    static inline __m256i denibble(const uint8_t *p) {
+        __m128i x = _mm_loadu_si128((const __m128i *)p);
+        return _mm256_and_si256(_mm256_set1_epi8(15),
+                                _mm256_insertf128_si256(_mm256_castsi128_si256(x),
+                                                        _mm_srli_epi16(x, 4), 1));
+    }
+
+    const TA *const A;
+    const TB *const B;
+    TC *const C;
+    const int64_t k;
+    const int64_t lda;
+    const int64_t ldb;
+    const int64_t ldc;
+    const int ith;
+    const int nth;
+};
+#endif // __AVX__
+
+} // namespace
+
+/**
+ * Performs optimized matrix multiplication on CPU.
+ *
+ * This subroutine may compute C = Aᵀ * B with column major ordering.
+ * Despite its name, this isn't a generalized implementation. Work is
+ * only performed when a handwritten kernel is written and available.
+ * Otherwise the caller should fall back to a general matmul routine.
+ *
+ * For example, for single-threaded single-precision GEMM you can say
+ *
+ *     llamafile_sgemm(m, n, k, A, lda, B, ldb, C, ldc,
+ *                     0, 1, GGML_TASK_TYPE_COMPUTE,
+ *                     GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32);
+ *
+ * @param m is rows in `A` and `C`
+ * @param n is cols in `B` and `C`
+ * @param k is cols in `A` and rows in `B`
+ * @param A is first input matrix (always transposed)
+ * @param lda is row stride of `A`
+ * @param B is second input matrix (never transposed)
+ * @param ldb is row stride of `B`
+ * @param C is input/output array of output matrices
+ * @param ldc is row stride of `C`
+ * @param ith is thread id (must be less than `nth`)
+ * @param nth is number of threads (must be greater than zero)
+ * @param task is GGML task type
+ * @param Atype is GGML data type of `A`
+ * @param Btype is GGML data type of `B`
+ * @param Ctype is GGML data type of `C`
+ * @return true if this function was able to service the matmul request
+ */
+bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda, const void *B, int64_t ldb, void *C,
+                     int64_t ldc, int ith, int nth, int task, int Atype, int Btype, int Ctype) {
+
+    assert(m >= 0);
+    assert(n >= 0);
+    assert(k >= 0);
+    assert(lda >= k);
+    assert(ldb >= k);
+    assert(ldc >= m);
+    assert(nth > 0);
+    assert(ith < nth);
+
+    if (Ctype != GGML_TYPE_F32)
+        return false;
+
+    switch (Atype) {
+
+    case GGML_TYPE_F32: {
+        if (Btype != GGML_TYPE_F32)
+            return false;
+#if defined(__AVX512F__)
+        if (k % 16)
+            return false;
+        tinyBLAS<16, __m512, __m512, float, float, float> tb{
+            k, (const float *)A, lda,
+            (const float *)B, ldb,
+            (float *)C, ldc,
+            ith, nth};
+        tb.matmul(m, n, task);
+        return true;
+#elif defined(__AVX__) || defined(__AVX2__)
+        if (k % 8)
+            return false;
+        tinyBLAS<8, __m256, __m256, float, float, float> tb{
+            k, (const float *)A, lda,
+            (const float *)B, ldb,
+            (float *)C, ldc,
+            ith, nth};
+        tb.matmul(m, n, task);
+        return true;
+#elif defined(__ARM_NEON)
+        if (n < 4)
+            return false;
+        if (k % 4)
+            return false;
+        tinyBLAS<4, float32x4_t, float32x4_t, float, float, float> tb{
+            k, (const float *)A, lda,
+            (const float *)B, ldb,
+            (float *)C, ldc,
+            ith, nth};
+        tb.matmul(m, n, task);
+        return true;
+#else
+        return false;
+#endif
+    }
+
+    case GGML_TYPE_F16: {
+#if defined(__AVX512F__)
+        if (k % 16)
+            return false;
+        if (Btype != GGML_TYPE_F32)
+            return false;
+        tinyBLAS<16, __m512, __m512, ggml_fp16_t, float, float> tb{
+            k, (const ggml_fp16_t *)A, lda,
+            (const float *)B, ldb,
+            (float *)C, ldc,
+            ith, nth};
+        tb.matmul(m, n, task);
+        return true;
+#elif (defined(__AVX__) || defined(__AVX2__)) && defined(__F16C__)
+        if (k % 8)
+            return false;
+        if (Btype != GGML_TYPE_F32)
+            return false;
+        tinyBLAS<8, __m256, __m256, ggml_fp16_t, float, float> tb{
+            k, (const ggml_fp16_t *)A, lda,
+            (const float *)B, ldb,
+            (float *)C, ldc,
+            ith, nth};
+        tb.matmul(m, n, task);
+        return true;
+#elif defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(_MSC_VER)
+        if (n < 8)
+            return false;
+        if (k % 8)
+            return false;
+        if (Btype != GGML_TYPE_F16)
+            return false;
+        tinyBLAS<8, float16x8_t, float16x8_t, ggml_fp16_t, ggml_fp16_t, float> tb{
+            k, (const ggml_fp16_t *)A, lda,
+            (const ggml_fp16_t *)B, ldb,
+            (float *)C, ldc,
+            ith, nth};
+        tb.matmul(m, n, task);
+        return true;
+#elif defined(__ARM_NEON) && !defined(_MSC_VER)
+        if (k % 4)
+            return false;
+        if (Btype != GGML_TYPE_F32)
+            return false;
+        tinyBLAS<4, float32x4_t, float32x4_t, ggml_fp16_t, float, float> tb{
+            k, (const ggml_fp16_t *)A, lda,
+            (const float *)B, ldb,
+            (float *)C, ldc,
+            ith, nth};
+        tb.matmul(m, n, task);
+        return true;
+#else
+        return false;
+#endif
+    }
+
+    case GGML_TYPE_Q8_0: {
+        if (Btype != GGML_TYPE_Q8_0)
+           return false;
+#if defined(__AVX2__) || defined(__AVX512F__) || defined(__AVX__)
+        tinyBLAS_Q0_AVX<block_q8_0, block_q8_0, float> tb{
+            k, (const block_q8_0 *)A, lda,
+            (const block_q8_0 *)B, ldb,
+            (float *)C, ldc,
+            ith, nth};
+        tb.matmul(m, n, task);
+        return true;
+#elif defined(__ARM_FEATURE_DOTPROD)
+        tinyBLAS_Q0_ARM<block_q8_0> tb{
+            k, (const block_q8_0 *)A, lda,
+            (const block_q8_0 *)B, ldb,
+            (float *)C, ldc,
+            ith, nth};
+        tb.matmul(m, n, task);
+        return true;
+#else
+        return false;
+#endif
+    }
+
+    case GGML_TYPE_Q4_0: {
+        if (Btype != GGML_TYPE_Q8_0)
+            return false;
+#if defined(__AVX2__) || defined(__AVX512F__) || defined(__AVX__)
+        tinyBLAS_Q0_AVX<block_q4_0, block_q8_0, float> tb{
+            k, (const block_q4_0 *)A, lda,
+            (const block_q8_0 *)B, ldb,
+            (float *)C, ldc,
+            ith, nth};
+        tb.matmul(m, n, task);
+        return true;
+#elif defined(__ARM_FEATURE_DOTPROD)
+        tinyBLAS_Q0_ARM<block_q4_0> tb{
+            k, (const block_q4_0 *)A, lda,
+            (const block_q8_0 *)B, ldb,
+            (float *)C, ldc,
+            ith, nth};
+        tb.matmul(m, n, task);
+        return true;
+#else
+        return false;
+#endif
+    }
+
+    default:
+        return false;
+    }
+
+    (void)m;
+    (void)n;
+    (void)k;
+    (void)A;
+    (void)lda;
+    (void)B;
+    (void)ldb;
+    (void)C;
+    (void)ldc;
+    (void)ith;
+    (void)nth;
+    (void)task;
+    (void)Atype;
+    (void)Btype;
+    (void)Ctype;
+}
diff --git a/sgemm.h b/sgemm.h
new file mode 100644
index 000000000..f29747d0a
--- /dev/null
+++ b/sgemm.h
@@ -0,0 +1,14 @@
+#pragma once
+#include <stdint.h>
+#include <stdbool.h>
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+bool llamafile_sgemm(int64_t, int64_t, int64_t, const void *, int64_t,
+                     const void *, int64_t, void *, int64_t, int, int,
+                     int, int, int, int);
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/tests/.gitignore b/tests/.gitignore
index 9427cf13d..620a48ee4 100644
--- a/tests/.gitignore
+++ b/tests/.gitignore
@@ -1,3 +1,4 @@
 *
 !*.*
 *.o
+ggml-common.h
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 10326d531..766a01752 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -1,66 +1,136 @@
-function(llama_build_executable source)
-    get_filename_component(TEST_TARGET ${source} NAME_WE)
+function(llama_test target)
+    include(CMakeParseArguments)
+    set(options)
+    set(oneValueArgs NAME LABEL WORKING_DIRECTORY)
+    set(multiValueArgs ARGS)
+    cmake_parse_arguments(LLAMA_TEST "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+
+    if (NOT DEFINED LLAMA_TEST_LABEL)
+        set(LLAMA_TEST_LABEL "main")
+    endif()
+    if (NOT DEFINED LLAMA_TEST_WORKING_DIRECTORY)
+        set(LLAMA_TEST_WORKING_DIRECTORY .)
+    endif()
+    if (DEFINED LLAMA_TEST_NAME)
+        set(TEST_NAME ${LLAMA_TEST_NAME})
+    else()
+        set(TEST_NAME ${target})
+    endif()
+
+    set(TEST_TARGET ${target})
+
+    add_test(
+        NAME ${TEST_NAME}
+        WORKING_DIRECTORY ${LLAMA_TEST_WORKING_DIRECTORY}
+        COMMAND $<TARGET_FILE:${TEST_TARGET}>
+        ${LLAMA_TEST_ARGS})
+
+    set_property(TEST ${TEST_NAME} PROPERTY LABELS ${LLAMA_TEST_LABEL})
+endfunction()
+
+# Builds and runs a test source file.
+# Optional args:
+# - NAME: name of the executable & test target (defaults to the source file name without extension)
+# - LABEL: label for the test (defaults to main)
+# - ARGS: arguments to pass to the test executable
+# - WORKING_DIRECTORY
+function(llama_target_and_test source)
+    include(CMakeParseArguments)
+    set(options)
+    set(oneValueArgs NAME LABEL WORKING_DIRECTORY)
+    set(multiValueArgs ARGS)
+    cmake_parse_arguments(LLAMA_TEST "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+
+    if (NOT DEFINED LLAMA_TEST_LABEL)
+        set(LLAMA_TEST_LABEL "main")
+    endif()
+    if (NOT DEFINED LLAMA_TEST_WORKING_DIRECTORY)
+        set(LLAMA_TEST_WORKING_DIRECTORY .)
+    endif()
+    if (DEFINED LLAMA_TEST_NAME)
+        set(TEST_TARGET ${LLAMA_TEST_NAME})
+    else()
+        get_filename_component(TEST_TARGET ${source} NAME_WE)
+    endif()
+
     add_executable(${TEST_TARGET} ${source} get-model.cpp)
     install(TARGETS ${TEST_TARGET} RUNTIME)
     target_link_libraries(${TEST_TARGET} PRIVATE common)
+    add_test(
+        NAME ${TEST_TARGET}
+        WORKING_DIRECTORY ${LLAMA_TEST_WORKING_DIRECTORY}
+        COMMAND $<TARGET_FILE:${TEST_TARGET}>
+        ${LLAMA_TEST_ARGS})
+
+    set_property(TEST ${TEST_TARGET} PROPERTY LABELS ${LLAMA_TEST_LABEL})
 endfunction()
 
-function(llama_test_executable name source)
-    get_filename_component(TEST_TARGET ${source} NAME_WE)
-    add_test(NAME ${name} COMMAND $<TARGET_FILE:${TEST_TARGET}> ${ARGN})
-    set_property(TEST ${name} PROPERTY LABELS "main")
-endfunction()
+# build test-tokenizer-0 target once and add many tests
+add_executable(test-tokenizer-0 test-tokenizer-0.cpp)
+target_link_libraries(test-tokenizer-0 PRIVATE common)
+install(TARGETS test-tokenizer-0 RUNTIME)
 
-function(llama_build_and_test_executable source)
-    llama_build_and_test_executable_with_label(${source} "main")
-endfunction()
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-spm         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-spm.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-bpe         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-phi-3             ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-phi-3.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-falcon            ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-bert-bge          ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bert-bge.gguf)
+# TODO: enable when fixed
+#       https://github.com/ggerganov/llama.cpp/pull/7036
+#llama_test(test-tokenizer-0 NAME test-tokenizer-0-mpt               ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
+#llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-llm      ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-deepseek-llm.gguf)
+#llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-coder    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-deepseek-coder.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-starcoder         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-gpt-2             ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-2.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-refact            ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-command-r         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-command-r.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-qwen2             ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-qwen2.gguf)
 
-function(llama_build_and_test_executable_with_label source label)
-    get_filename_component(TEST_TARGET ${source} NAME_WE)
-    add_executable(${TEST_TARGET} ${source} get-model.cpp)
-    install(TARGETS ${TEST_TARGET} RUNTIME)
-    target_link_libraries(${TEST_TARGET} PRIVATE common)
-    add_test(NAME ${TEST_TARGET} COMMAND $<TARGET_FILE:${TEST_TARGET}> ${ARGN})
-    set_property(TEST ${TEST_TARGET} PROPERTY LABELS ${label})
-endfunction()
+# build test-tokenizer-1-bpe target once and add many tests
+add_executable(test-tokenizer-1-bpe test-tokenizer-1-bpe.cpp)
+target_link_libraries(test-tokenizer-1-bpe PRIVATE common)
+install(TARGETS test-tokenizer-1-bpe RUNTIME)
 
-# llama_build_and_test_executable(test-double-float.cpp) # SLOW
-llama_build_and_test_executable(test-quantize-fns.cpp)
-llama_build_and_test_executable(test-quantize-perf.cpp)
-llama_build_and_test_executable(test-sampling.cpp)
-llama_build_and_test_executable(test-chat-template.cpp)
+# TODO: disabled due to slowness
+#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-llama-bpe ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf --ignore-merges)
+#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-falcon    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
+#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-aquila    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
+#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-mpt       ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
+#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-stablelm  ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-stablelm.gguf)
+#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-gpt-neox  ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-neox.gguf)
+#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-refact    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
+#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-starcoder ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
+#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-gpt2      ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt2.gguf)
+#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-bloom     ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bloom.gguf)
 
-llama_build_executable(test-tokenizer-0-llama.cpp)
-llama_test_executable (test-tokenizer-0-llama test-tokenizer-0-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
+# build test-tokenizer-1-spm target once and add many tests
+add_executable(test-tokenizer-1-spm test-tokenizer-1-spm.cpp)
+target_link_libraries(test-tokenizer-1-spm PRIVATE common)
+install(TARGETS test-tokenizer-1-spm RUNTIME)
 
-llama_build_executable(test-tokenizer-0-falcon.cpp)
-llama_test_executable (test-tokenizer-0-falcon test-tokenizer-0-falcon.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
+llama_test(test-tokenizer-1-spm  NAME test-tokenizer-1-llama-spm ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-spm.gguf)
+#llama_test(test-tokenizer-1-spm  NAME test-tokenizer-1-baichuan  ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-baichuan.gguf)
 
-llama_build_executable(test-tokenizer-1-llama.cpp)
-llama_test_executable (test-tokenizer-1-llama    test-tokenizer-1-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
-llama_test_executable (test-tokenizer-1-baichuan test-tokenizer-1-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-baichuan.gguf)
+# llama_target_and_test(test-double-float.cpp) # SLOW
+llama_target_and_test(test-quantize-fns.cpp)
+llama_target_and_test(test-quantize-perf.cpp)
+llama_target_and_test(test-sampling.cpp)
+llama_target_and_test(test-chat-template.cpp)
 
-llama_build_executable(test-tokenizer-1-bpe.cpp)
-llama_test_executable (test-tokenizer-1-falcon           test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
-llama_test_executable (test-tokenizer-1-aquila           test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
-llama_test_executable (test-tokenizer-1-mpt              test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
-llama_test_executable (test-tokenizer-1-stablelm-3b-4e1t test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-stablelm-3b-4e1t.gguf)
-llama_test_executable (test-tokenizer-1-gpt-neox         test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-neox.gguf)
-llama_test_executable (test-tokenizer-1-refact           test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
-llama_test_executable (test-tokenizer-1-starcoder        test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
-llama_test_executable (test-tokenizer-1-gpt2             test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt2.gguf)
-# llama_test_executable (test-tokenizer-1-bloom test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bloom.gguf) # BIG
+llama_target_and_test(test-grammar-parser.cpp)
+llama_target_and_test(test-llama-grammar.cpp)
+llama_target_and_test(test-grammar-integration.cpp)
+llama_target_and_test(test-grad0.cpp)
+# llama_target_and_test(test-opt.cpp) # SLOW
+llama_target_and_test(test-backend-ops.cpp)
 
-llama_build_and_test_executable(test-grammar-parser.cpp)
-llama_build_and_test_executable(test-llama-grammar.cpp)
-llama_build_and_test_executable(test-grad0.cpp)
-# llama_build_and_test_executable(test-opt.cpp) # SLOW
-llama_build_and_test_executable(test-backend-ops.cpp)
+llama_target_and_test(test-rope.cpp)
 
-llama_build_and_test_executable(test-rope.cpp)
+llama_target_and_test(test-model-load-cancel.cpp  LABEL "model")
+llama_target_and_test(test-autorelease.cpp        LABEL "model")
 
-llama_build_and_test_executable_with_label(test-model-load-cancel.cpp "model")
-llama_build_and_test_executable_with_label(test-autorelease.cpp "model")
+llama_target_and_test(test-json-schema-to-grammar.cpp   WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..)
+target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../examples/server)
 
 # dummy executable - not installed
 get_filename_component(TEST_TARGET test-c.c NAME_WE)
diff --git a/tests/run-json-schema-to-grammar.mjs b/tests/run-json-schema-to-grammar.mjs
new file mode 100644
index 000000000..71bf62ed3
--- /dev/null
+++ b/tests/run-json-schema-to-grammar.mjs
@@ -0,0 +1,10 @@
+import { readFileSync } from "fs"
+import { SchemaConverter } from "../examples/server/public/json-schema-to-grammar.mjs"
+
+const [, , file] = process.argv
+const url = `file://${file}`
+let schema = JSON.parse(readFileSync(file, "utf8"));
+const converter = new SchemaConverter({})
+schema = await converter.resolveRefs(schema, url)
+converter.visit(schema, '')
+console.log(converter.formatGrammar())
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index ef37c5af2..45a2cb85a 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -2,6 +2,7 @@
 #include <ggml-alloc.h>
 #include <ggml-backend.h>
 #include <ggml-backend-impl.h>
+
 #include <algorithm>
 #include <array>
 #include <cfloat>
@@ -50,10 +51,9 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
 
     if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_I32) {
         ggml_backend_tensor_set(tensor, data.data(), 0, size * sizeof(float));
-    } else if (ggml_is_quantized(tensor->type) || tensor->type == GGML_TYPE_F16) {
+    } else if (ggml_is_quantized(tensor->type) || tensor->type == GGML_TYPE_F16 || tensor->type == GGML_TYPE_BF16) {
         GGML_ASSERT(size % ggml_blck_size(tensor->type) == 0);
         std::vector<uint8_t> dataq(ggml_row_size(tensor->type, size));
-        int64_t hist[16];
         std::vector<float> imatrix(tensor->ne[0], 1.0f); // dummy importance matrix
         const float * im = imatrix.data();
         if (!ggml_quantize_requires_imatrix(tensor->type)) {
@@ -63,7 +63,7 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
                 im = nullptr;
             }
         }
-        ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], hist, im);
+        ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], im);
         ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size());
     } else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) {
         // This is going to create some weird integers though.
@@ -93,6 +93,8 @@ static std::vector<float> tensor_to_float(const ggml_tensor * t) {
                     size_t i = i3*t->nb[3] + i2*t->nb[2] + i1*t->nb[1] + i0/bs*t->nb[0];
                     if (t->type == GGML_TYPE_F16) {
                         tv.push_back(ggml_fp16_to_fp32(*(ggml_fp16_t*)&buf[i]));
+                    } else if (t->type == GGML_TYPE_BF16) {
+                        tv.push_back(ggml_bf16_to_fp32(*(ggml_bf16_t*)&buf[i]));
                     } else if (t->type == GGML_TYPE_F32) {
                         tv.push_back(*(float *) &buf[i]);
                     } else if (t->type == GGML_TYPE_I32) {
@@ -102,7 +104,7 @@ static std::vector<float> tensor_to_float(const ggml_tensor * t) {
                     } else if (t->type == GGML_TYPE_I8) {
                         tv.push_back((float)*(int8_t *) &buf[i]);
                     } else if (quantized) {
-                        tt.to_float(&buf[i], vq.data(), ggml_blck_size(t->type));
+                        tt.to_float(&buf[i], vq.data(), bs);
                         tv.insert(tv.end(), vq.begin(), vq.end());
                     } else {
                         GGML_ASSERT(false);
@@ -949,14 +951,14 @@ struct test_mul_mat_id : public test_case {
     const ggml_type type_a;
     const ggml_type type_b;
     const int n_mats;
-    const int id;
+    const int n_used;
+    const bool b; // brodcast b matrix
     const int64_t m;
     const int64_t n;
     const int64_t k;
-    const bool v; // view (non-contiguous ids)
 
     std::string vars() override {
-        return VARS_TO_STR8(type_a, type_b, n_mats, id, m, n, k, v);
+        return VARS_TO_STR8(type_a, type_b, n_mats, n_used, b, m, n, k);
     }
 
     double max_nmse_err() override {
@@ -973,24 +975,22 @@ struct test_mul_mat_id : public test_case {
     }
 
     test_mul_mat_id(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32,
-            int n_mats = 2, int id = 0,
-            int64_t m = 32, int64_t n = 32, int64_t k = 32, bool v = false)
-        : type_a(type_a), type_b(type_b), n_mats(n_mats), id(id),
-            m(m), n(n), k(k), v(v) {}
+            int n_mats = 8, int n_used = 2, bool b = false,
+            int64_t m = 32, int64_t n = 32, int64_t k = 32)
+        : type_a(type_a), type_b(type_b), n_mats(n_mats), n_used(n_used), b(b),
+            m(m), n(n), k(k) {
+            GGML_ASSERT(n_used <= n_mats);
+        }
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         // C^T = A * B^T: (k, m) * (k, n) => (m, n)
-        std::vector<ggml_tensor *> mats;
-        for (int i = 0; i < n_mats; i++) {
-            ggml_tensor * a = ggml_new_tensor_2d(ctx, type_a, k, m);
-            mats.push_back(a);
-        }
+        ggml_tensor * as = ggml_new_tensor_3d(ctx, type_a, k, m, n_mats);
         ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_mats, n);
-        if (v) {
-            ids = ggml_view_2d(ctx, ids, n_mats/2, ids->ne[1], ids->nb[1], 0);
+        if (n_used != n_mats) {
+            ids = ggml_view_2d(ctx, ids, n_used, n, ids->nb[1], 0);
         }
-        ggml_tensor * b = ggml_new_tensor_2d(ctx, type_b, k, n);
-        ggml_tensor * out = ggml_mul_mat_id(ctx, mats.data(), n_mats, ids, v ? id/2 : id, b);
+        ggml_tensor * b = ggml_new_tensor_3d(ctx, type_b, k, this->b ? 1 : n_used, n);
+        ggml_tensor * out = ggml_mul_mat_id(ctx, as, b, ids);
         return out;
     }
 
@@ -1093,6 +1093,12 @@ struct test_soft_max : public test_case {
         return VARS_TO_STR5(type, ne, mask, scale, max_bias);
     }
 
+    // the 1024 test with bias occasionally fails:
+    // SOFT_MAX(type=f32,ne=[1024,16,1,1],mask=1,scale=1.000000,max_bias=8.000000): [SOFT_MAX] NMSE = 0.000000103 > 0.000000100 FAIL
+    virtual double max_nmse_err() override {
+        return 1e-6;
+    }
+
     test_soft_max(ggml_type type = GGML_TYPE_F32,
             std::array<int64_t, 4> ne = {10, 10, 10, 10},
             bool mask = false,
@@ -1104,13 +1110,9 @@ struct test_soft_max : public test_case {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
         ggml_tensor * mask = nullptr;
         if (this->mask) {
-            mask = ggml_new_tensor_2d(ctx, type, ne[0], ne[1]);
+            mask = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, ne[0], ne[1]);
         }
-        ggml_tensor * pos = nullptr;
-        if (max_bias > 0.0f) {
-            pos = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, ne[0]);
-        }
-        ggml_tensor * out = ggml_soft_max_ext(ctx, a, mask, pos, scale, max_bias);
+        ggml_tensor * out = ggml_soft_max_ext(ctx, a, mask, scale, max_bias);
         return out;
     }
 };
@@ -1264,7 +1266,7 @@ struct test_argsort : public test_case {
 
     test_argsort(ggml_type type = GGML_TYPE_F32,
             std::array<int64_t, 4> ne = {16, 10, 10, 10},
-            ggml_sort_order order = GGML_SORT_ASC)
+            ggml_sort_order order = GGML_SORT_ORDER_ASC)
         : type(type), ne(ne), order(order) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
@@ -1412,6 +1414,50 @@ struct test_pad : public test_case {
     }
 };
 
+// GGML_OP_ARANGE
+struct test_arange : public test_case {
+    const ggml_type type;
+    const float start;
+    const float stop;
+    const float step;
+
+    std::string vars() override {
+        return VARS_TO_STR4(type, start, stop, step);
+    }
+
+    test_arange(ggml_type type = GGML_TYPE_F32,
+            float start = 0.f, float stop = 10.f, float step = 1.f)
+        : type(type), start(start), stop(stop), step(step)  {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * out = ggml_arange(ctx, start, stop, step);
+        return out;
+    }
+};
+
+// GGML_OP_TIMESTEP_EMBEDDING
+struct test_timestep_embedding : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 4> ne_a;
+    const int dim;
+    const int max_period;
+
+    std::string vars() override {
+        return VARS_TO_STR4(type, ne_a, dim, max_period);
+    }
+
+    test_timestep_embedding(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne_a = {2, 1, 1, 1},
+            int dim = 320, int max_period=10000)
+        : type(type), ne_a(ne_a), dim(dim), max_period(max_period)  {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
+        ggml_tensor * out = ggml_timestep_embedding(ctx, a, dim, max_period);
+        return out;
+    }
+};
+
 // GGML_OP_LEAKY_RELU
 struct test_leaky_relu : public test_case {
     const ggml_type type;
@@ -1434,91 +1480,36 @@ struct test_leaky_relu : public test_case {
     }
 };
 
-// Mixtral MOE
-struct test_moe : public test_case {
-    const int n_experts;
-    const int n_experts_per_tok;
-    const int n_tokens;
-    const int n_embd;
-    const int n_ff;
+// GGML_OP_FLASH_ATTN_EXT
+struct test_flash_attn_ext : public test_case {
+    const int64_t hs; // head size
+    const int64_t nh; // num heads
+    const int64_t kv; // kv size
+    const int64_t nb; // batch size
 
-    std::string op_desc(ggml_tensor * t) override {
-        return "MOE";
-
-        GGML_UNUSED(t);
-    }
+    const float max_bias; // ALiBi
 
     std::string vars() override {
-        return VARS_TO_STR5(n_experts, n_experts_per_tok, n_tokens, n_embd, n_ff);
+        return VARS_TO_STR5(hs, nh, kv, nb, max_bias);
     }
 
-    test_moe(int n_experts = 8, int n_experts_per_tok = 2, int n_tokens = 1, int n_embd = 4096, int n_ff = 14336)
-        : n_experts(n_experts), n_experts_per_tok(n_experts_per_tok), n_tokens(n_tokens), n_embd(n_embd), n_ff(n_ff) {
+    double max_nmse_err() override {
+        return 5e-4;
     }
 
+    test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8, float max_bias = 0.0f)
+        : hs(hs), nh(nh), kv(kv), nb(nb), max_bias(max_bias) {}
+
     ggml_tensor * build_graph(ggml_context * ctx) override {
-        ggml_tensor * ffn_gate_inp = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_experts);
-
-        std::vector<ggml_tensor *> ffn_up_exp(n_experts);
-        std::vector<ggml_tensor *> ffn_gate_exp(n_experts);
-        std::vector<ggml_tensor *> ffn_down_exp(n_experts);
-
-        for (int i = 0; i < n_experts; ++i) {
-            ffn_up_exp[i] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
-            ffn_gate_exp[i] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
-            ffn_down_exp[i] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd);
-        }
-
-        ggml_tensor * cur = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_tokens);
-
-        ggml_tensor * logits = ggml_mul_mat(ctx, ffn_gate_inp, cur);
-        ggml_tensor * probs = ggml_soft_max_ext(ctx, logits, nullptr, nullptr, 1.0f/sqrtf(n_embd), 0.0f);
-
-        // select experts
-        ggml_tensor * selected_experts = ggml_top_k(ctx, probs, n_experts_per_tok);
-
-        ggml_tensor * weights = ggml_get_rows(ctx,
-                ggml_reshape_3d(ctx, probs, 1, n_experts, n_tokens), selected_experts);
-
-        weights = ggml_reshape_2d(ctx, weights, n_experts_per_tok, n_tokens);
-
-        ggml_tensor * weights_sum = ggml_sum_rows(ctx, weights);
-
-        weights = ggml_div(ctx, weights, weights_sum);
-
-        // compute expert outputs
-        ggml_tensor * moe_out = nullptr;
-
-        for (int i = 0; i < n_experts_per_tok; ++i) {
-            ggml_tensor * cur_expert;
-
-            ggml_tensor * cur_up = ggml_mul_mat_id(ctx, ffn_up_exp.data(), n_experts, selected_experts, i, cur);
-
-            ggml_tensor * cur_gate = ggml_mul_mat_id(ctx, ffn_gate_exp.data(), n_experts, selected_experts, i, cur);
-
-            cur_gate = ggml_silu(ctx, cur_gate);
-
-            cur_expert = ggml_mul(ctx, cur_up, cur_gate);
-
-            cur_expert = ggml_mul_mat_id(ctx, ffn_down_exp.data(), n_experts, selected_experts, i, cur_expert);
-
-            cur_expert = ggml_mul(ctx, cur_expert,
-                    ggml_view_2d(ctx, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
-
-            if (i == 0) {
-                moe_out = cur_expert;
-            } else {
-                moe_out = ggml_add(ctx, moe_out, cur_expert);
-            }
-        }
-
-        cur = moe_out;
-
-        return cur;
+        ggml_tensor * q = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, hs, nb, nh, 1);
+        ggml_tensor * k = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, hs, kv, nh, 1);
+        ggml_tensor * v = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, hs, kv, nh, 1);
+        ggml_tensor * mask = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kv, GGML_PAD(nb, GGML_KQ_MASK_PAD), 1, 1);
+        ggml_tensor * out = ggml_flash_attn_ext(ctx, q, k, v, mask, 1.0f/sqrtf(hs), max_bias);
+        return out;
     }
 };
 
-
 enum llm_norm_type {
     LLM_NORM,
     LLM_NORM_RMS,
@@ -1619,7 +1610,7 @@ public:
 
         struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
 
-        kq = ggml_soft_max_ext(ctx, kq, kq_mask, nullptr, kq_scale, 0.0f);
+        kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_scale, 0.0f);
 
         // split cached v into n_head heads
         struct ggml_tensor * v =
@@ -1657,7 +1648,6 @@ public:
     }
 };
 
-
 // Llama
 struct test_llama : public test_llm {
     static constexpr float freq_base = 10000.0f;
@@ -1706,7 +1696,7 @@ struct test_llama : public test_llm {
         struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, hp.n_tokens);
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hp.n_kv, hp.n_tokens, 1);
+        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx, GGML_TYPE_F16, hp.n_kv, hp.n_tokens, 1);
 
         ggml_tensor * k_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400);
         ggml_tensor * v_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400);
@@ -1828,7 +1818,7 @@ struct test_falcon : public test_llm {
         struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, hp.n_tokens);
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hp.n_kv, hp.n_tokens, 1);
+        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx, GGML_TYPE_F16, hp.n_kv, hp.n_tokens, 1);
 
         ggml_tensor * k_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400);
         ggml_tensor * v_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400);
@@ -1909,20 +1899,41 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
     std::default_random_engine rng(0);
 
     const ggml_type all_types[] = {
-        GGML_TYPE_F32, GGML_TYPE_F16,
+        GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_BF16,
         GGML_TYPE_Q4_0, GGML_TYPE_Q4_1,
         GGML_TYPE_Q5_0, GGML_TYPE_Q5_1,
         GGML_TYPE_Q8_0,
         GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
         GGML_TYPE_Q4_K, GGML_TYPE_Q5_K,
         GGML_TYPE_Q6_K,
-        GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS,
-        GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S,
+        GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S,
+        GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M,
+        GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS,
+    };
+
+    const ggml_type base_types[] = {
+        GGML_TYPE_F32, GGML_TYPE_F16,
+        GGML_TYPE_Q4_0,
+        GGML_TYPE_Q4_K,
+        GGML_TYPE_IQ2_XXS
+    };
+
+    const ggml_type other_types[] = {
+        GGML_TYPE_Q4_1,
+        GGML_TYPE_Q5_0, GGML_TYPE_Q5_1,
+        GGML_TYPE_Q8_0,
+        GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
+        GGML_TYPE_Q5_K,
+        GGML_TYPE_Q6_K,
+        GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S,
+        GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M,
+        GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS,
     };
 
     // unary ops
     for (int op = 0; op < GGML_UNARY_OP_COUNT; op++) {
         test_cases.emplace_back(new test_unary((ggml_unary_op) op));
+        test_cases.emplace_back(new test_unary((ggml_unary_op) op, GGML_TYPE_F32, { 7, 13, 19, 23 }));
     }
 
     test_cases.emplace_back(new test_get_rows(GGML_TYPE_F32, 1, 8, 2, 1, false));
@@ -2027,7 +2038,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
         test_cases.emplace_back(new test_rms_norm(GGML_TYPE_F32, {64, 10, 10, 10}, eps));
     }
 
-    for (ggml_type type_a : all_types) {
+    for (ggml_type type_a : base_types) {
         for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) {
             test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1,  1}, {1, 1}));
             test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10,  1}, {1, 1}));
@@ -2047,12 +2058,45 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
         }
     }
 
-    for (ggml_type type_a : all_types) {
+    for (ggml_type type_a : other_types) {
+        for (ggml_type type_b : {GGML_TYPE_F32}) {
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1,  1}, {1, 1}));
+        }
+    }
+
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32,  64, 2,  128, { 8,  1}, {1, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32,  83, 2,  128, { 8,  1}, {4, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32,  64, 2,   64, { 8,  1}, {4, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32,  83, 2,   64, { 8,  1}, {4, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32,  64, 45, 128, { 8,  1}, {4, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 45,  64, { 8,  1}, {4, 1}));
+
+    for (ggml_type type_a : base_types) {
         for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) {
-            for (int n_mats : {2, 4, 8}) {
-                for (int id = 0; id < n_mats; id++) {
-                    for (bool v : {false, true}) {
-                        test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, n_mats, id, 16, 16, 256, v));
+            for (int n_mats : {4, 8}) {
+                for (int n_used : {1, 2, 4}) {
+                    for (bool b : {false, true}) {
+                        for (int n : {1, 32}) {
+                            int m = 512;
+                            int k = 256;
+                            test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, n_mats, n_used, b, m, n, k));
+                        }
+                    }
+                }
+            }
+        }
+    }
+
+    for (ggml_type type_a : other_types) {
+        for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) {
+            for (int n_mats : {4}) {
+                for (int n_used : {2}) {
+                    for (bool b : {false}) {
+                        for (int n : {1}) {
+                            int m = 512;
+                            int k = 256;
+                            test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, n_mats, n_used, b, m, n, k));
+                        }
                     }
                 }
             }
@@ -2083,10 +2127,11 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
 #endif
     for (bool mask : {false, true}) {
         for (float max_bias : {0.0f, 8.0f}) {
+            if (!mask && max_bias > 0.0f) continue;
             for (float scale : {1.0f, 0.1f}) {
                 for (int64_t ne0 : {16, 1024}) {
                     for (int64_t ne1 : {16, 1024}) {
-                        test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0, ne1, 1, 1}, mask, scale, max_bias));
+                        test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0,   ne1,   1, 1}, mask, scale, max_bias));
                         test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0-1, ne1-1, 1, 1}, mask, scale, max_bias));
                     }
                 }
@@ -2096,7 +2141,6 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
 
     test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, false, 0.1f, 0.0f));
     test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true,  0.1f, 0.0f));
-    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, false, 0.1f, 8.0f));
     test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true,  0.1f, 8.0f));
 
     for (ggml_type type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
@@ -2115,9 +2159,10 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
     test_cases.emplace_back(new test_concat(GGML_TYPE_F32));
     test_cases.emplace_back(new test_concat(GGML_TYPE_I32));
 
-    for (ggml_sort_order order : {GGML_SORT_ASC, GGML_SORT_DESC}) {
+    for (ggml_sort_order order : {GGML_SORT_ORDER_ASC, GGML_SORT_ORDER_DESC}) {
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order));
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {16, 10, 10, 10}, order));
+        test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {60, 10, 10, 10}, order)); // qwen
     }
 
     test_cases.emplace_back(new test_sum_rows());
@@ -2125,15 +2170,24 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
     test_cases.emplace_back(new test_group_norm());
     test_cases.emplace_back(new test_acc());
     test_cases.emplace_back(new test_pad());
+    test_cases.emplace_back(new test_arange());
+    test_cases.emplace_back(new test_timestep_embedding());
     test_cases.emplace_back(new test_leaky_relu());
 
+    for (int hs : { 64, 80, 128, 256, }) {
+        for (float max_bias : {0.0f, 8.0f}) {
+            for (int nh : { 32, }) {
+                for (int kv : { 512, 1024, }) {
+                    for (int nb : { 1, 2, 4, 8, }) {
+                        test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, max_bias));
+                    }
+                }
+            }
+        }
+    }
+
     // these tests are disabled to save execution time, but they can be handy for debugging
 #if 0
-#if !defined(__SANITIZE_THREAD__)
-    // FIXME: these tests use too much memory with thread sanitizer
-    test_cases.emplace_back(new test_moe(8, 2, 1, 4096, 8*1024));
-    //test_cases.emplace_back(new test_moe(8, 2, 8, 4096, 14336));
-#endif
     test_cases.emplace_back(new test_llama(1));
     test_cases.emplace_back(new test_llama(2));
     test_cases.emplace_back(new test_falcon(1));
@@ -2176,8 +2230,8 @@ static void usage(char ** argv) {
 
 int main(int argc, char ** argv) {
     test_mode mode = MODE_TEST;
-    const char * op_name = NULL;
-    const char * backend = NULL;
+    const char * op_name_filter = NULL;
+    const char * backend_filter = NULL;
 
     for (int i = 1; i < argc; i++) {
         if (strcmp(argv[i], "test") == 0) {
@@ -2186,14 +2240,14 @@ int main(int argc, char ** argv) {
             mode = MODE_PERF;
         } else if (strcmp(argv[i], "-o") == 0) {
             if (i + 1 < argc) {
-                op_name = argv[++i];
+                op_name_filter = argv[++i];
             } else {
                 usage(argv);
                 return 1;
             }
         } else if (strcmp(argv[i], "-b") == 0) {
             if (i + 1 < argc) {
-                backend = argv[++i];
+                backend_filter = argv[++i];
             } else {
                 usage(argv);
                 return 1;
@@ -2212,7 +2266,7 @@ int main(int argc, char ** argv) {
     for (size_t i = 0; i < ggml_backend_reg_get_count(); i++) {
         printf("Backend %zu/%zu (%s)\n", i + 1, ggml_backend_reg_get_count(), ggml_backend_reg_get_name(i));
 
-        if (backend != NULL && strcmp(backend, ggml_backend_reg_get_name(i)) != 0) {
+        if (backend_filter != NULL && strcmp(backend_filter, ggml_backend_reg_get_name(i)) != 0) {
             printf("  Skipping\n");
             n_ok++;
             continue;
@@ -2220,9 +2274,17 @@ int main(int argc, char ** argv) {
 
         ggml_backend_t backend = ggml_backend_reg_init_backend(i, NULL);
         GGML_ASSERT(backend != NULL);
+
+        if (backend_filter == NULL && ggml_backend_is_cpu(backend)) {
+            printf("  Skipping CPU backend\n");
+            ggml_backend_free(backend);
+            n_ok++;
+            continue;
+        }
+
         printf("  Backend name: %s\n", ggml_backend_name(backend));
 
-        bool ok = test_backend(backend, mode, op_name);
+        bool ok = test_backend(backend, mode, op_name_filter);
 
         printf("  Backend %s: ", ggml_backend_name(backend));
         if (ok) {
diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp
index 9830650d4..4fe9183b9 100644
--- a/tests/test-chat-template.cpp
+++ b/tests/test-chat-template.cpp
@@ -27,12 +27,60 @@ int main(void) {
         "{%- for idx in range(0, messages|length) -%}\\n{%- if messages[idx]['role'] == 'user' -%}\\n{%- if idx > 1 -%}\\n{{- bos_token + '[INST] ' + messages[idx]['content'] + ' [/INST]' -}}\\n{%- else -%}\\n{{- messages[idx]['content'] + ' [/INST]' -}}\\n{%- endif -%}\\n{% elif messages[idx]['role'] == 'system' %}\\n{{- '[INST] <<SYS>>\\\\n' + messages[idx]['content'] + '\\\\n<</SYS>>\\\\n\\\\n' -}}\\n{%- elif messages[idx]['role'] == 'assistant' -%}\\n{{- ' '  + messages[idx]['content'] + ' ' + eos_token -}}\\n{% endif %}\\n{% endfor %}",
         // bofenghuang/vigogne-2-70b-chat
         "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif true == true and not '<<SYS>>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez autant que vous le pouvez.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\\\n' + system_message + '\\\\n<</SYS>>\\\\n\\\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<<SYS>>\\\\n' + content.strip() + '\\\\n<</SYS>>\\\\n\\\\n' }}{% elif message['role'] == 'assistant' %}{{ ' '  + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}",
+        // mlabonne/AlphaMonarch-7B
+        "{% for message in messages %}{{bos_token + message['role'] + '\\n' + message['content'] + eos_token + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ bos_token + 'assistant\\n' }}{% endif %}",
+        // google/gemma-7b-it
+        "{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\\n' + message['content'] | trim + '<end_of_turn>\\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\\n'}}{% endif %}",
+        // OrionStarAI/Orion-14B-Chat
+        "{% for message in messages %}{% if loop.first %}{{ bos_token }}{% endif %}{% if message['role'] == 'user' %}{{ 'Human: ' + message['content'] + '\\n\\nAssistant: ' + eos_token }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token }}{% endif %}{% endfor %}",
+        // openchat/openchat-3.5-0106
+        // The included chat_template differs from the author's suggestions here: https://huggingface.co/openchat/openchat_3.5/discussions/5#65448109b4a3f3a2f486fd9d
+        // So we match against the included template but implement the suggested version.
+        "{{ bos_token }}{% for message in messages %}{{ 'GPT4 Correct ' + message['role'].title() + ': ' + message['content'] + '<|end_of_turn|>'}}{% endfor %}{% if add_generation_prompt %}{{ 'GPT4 Correct Assistant:' }}{% endif %}",
+        // deepseek-ai/deepseek-coder-33b-instruct
+        "{% if not add_generation_prompt is defined %}\n{% set add_generation_prompt = false %}\n{% endif %}\n{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n    {%- if message['role'] == 'system' -%}\n        {%- set ns.found = true -%}\n    {%- endif -%}\n{%- endfor -%}\n{{bos_token}}{%- if not ns.found -%}\n{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\\n'}}\n{%- endif %}\n{%- for message in messages %}\n    {%- if message['role'] == 'system' %}\n{{ message['content'] }}\n    {%- else %}\n        {%- if message['role'] == 'user' %}\n{{'### Instruction:\\n' + message['content'] + '\\n'}}\n        {%- else %}\n{{'### Response:\\n' + message['content'] + '\\n<|EOT|>\\n'}}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{% if add_generation_prompt %}\n{{'### Response:'}}\n{% endif %}",
+        // eachadea/vicuna-13b-1.1
+        // No template included in tokenizer_config.json, so this template likely needs to be manually set.
+        "{%- for message in messages %}{%- if message['role'] == 'system' -%}{{- '' + message['content'] + '\n\n' -}}{%- else -%}{%- if message['role'] == 'user' -%}{{-'USER: ' + message['content'] + '\n'-}}{%- else -%}{{-'ASSISTANT: ' + message['content'] + '</s>\n' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{-'ASSISTANT:'-}}{%- endif -%}",
+        // Orca-Vicuna
+        // No template included in tokenizer_config.json, so this template likely needs to be manually set.
+        "{%- for message in messages %}{%- if message['role'] == 'system' -%}{{-'SYSTEM: ' + message['content'] + '\n' -}}{%- else -%}{%- if message['role'] == 'user' -%}{{-'USER: ' + message['content'] + '\n'-}}{%- else -%}{{-'ASSISTANT: ' + message['content'] + '</s>\n' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{-'ASSISTANT:'-}}{%- endif -%}",
+        // CohereForAI/c4ai-command-r-plus
+        "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% if system_message != false %}{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% elif message['role'] == 'assistant' %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>'  + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}{% endif %}",
+        // Llama-3
+        "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}",
+        // Phi-3
+        "{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + ' ' + message['content'] + '<|end|> ' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|> ' }}{% else %}{{ eos_token }}{% endif %}"
     };
-    std::vector<std::string> expected_substr = {
-        "<|im_start|>assistant\n   I am an assistant   <|im_end|>\n<|im_start|>user\nAnother question<|im_end|>\n<|im_start|>assistant",
-        "[/INST]Hi there</s>[INST] Who are you [/INST]   I am an assistant   </s>[INST] Another question [/INST]",
-        "</s><s>[INST] Who are you [/INST]    I am an assistant    </s><s>[INST] Another question [/INST]",
-        "[/INST] Hi there </s>[INST] Who are you [/INST] I am an assistant </s>[INST] Another question [/INST]",
+    std::vector<std::string> expected_output = {
+        // teknium/OpenHermes-2.5-Mistral-7B
+        "<|im_start|>system\nYou are a helpful assistant<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\nHi there<|im_end|>\n<|im_start|>user\nWho are you<|im_end|>\n<|im_start|>assistant\n   I am an assistant   <|im_end|>\n<|im_start|>user\nAnother question<|im_end|>\n<|im_start|>assistant\n",
+        // mistralai/Mistral-7B-Instruct-v0.2
+        "[INST] You are a helpful assistant\nHello [/INST]Hi there</s>[INST] Who are you [/INST]   I am an assistant   </s>[INST] Another question [/INST]",
+        // TheBloke/FusionNet_34Bx2_MoE-AWQ
+        "[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST] Hi there </s><s>[INST] Who are you [/INST]    I am an assistant    </s><s>[INST] Another question [/INST]",
+        // bofenghuang/vigogne-2-70b-chat
+        "[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST] Hi there </s>[INST] Who are you [/INST] I am an assistant </s>[INST] Another question [/INST]",
+        // mlabonne/AlphaMonarch-7B
+        "system\nYou are a helpful assistant</s>\n<s>user\nHello</s>\n<s>assistant\nHi there</s>\n<s>user\nWho are you</s>\n<s>assistant\n   I am an assistant   </s>\n<s>user\nAnother question</s>\n<s>assistant\n",
+        // google/gemma-7b-it
+        "<start_of_turn>user\nYou are a helpful assistant\n\nHello<end_of_turn>\n<start_of_turn>model\nHi there<end_of_turn>\n<start_of_turn>user\nWho are you<end_of_turn>\n<start_of_turn>model\nI am an assistant<end_of_turn>\n<start_of_turn>user\nAnother question<end_of_turn>\n<start_of_turn>model\n",
+        // OrionStarAI/Orion-14B-Chat
+        "Human: You are a helpful assistant\n\nHello\n\nAssistant: </s>Hi there</s>Human: Who are you\n\nAssistant: </s>   I am an assistant   </s>Human: Another question\n\nAssistant: </s>",
+        // openchat/openchat-3.5-0106
+        "You are a helpful assistant<|end_of_turn|>GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi there<|end_of_turn|>GPT4 Correct User: Who are you<|end_of_turn|>GPT4 Correct Assistant:    I am an assistant   <|end_of_turn|>GPT4 Correct User: Another question<|end_of_turn|>GPT4 Correct Assistant:",
+        // deepseek-ai/deepseek-coder-33b-instruct
+        "You are a helpful assistant### Instruction:\nHello\n### Response:\nHi there\n<|EOT|>\n### Instruction:\nWho are you\n### Response:\n   I am an assistant   \n<|EOT|>\n### Instruction:\nAnother question\n### Response:\n",
+        // eachadea/vicuna-13b-1.1
+        "You are a helpful assistant\n\nUSER: Hello\nASSISTANT: Hi there</s>\nUSER: Who are you\nASSISTANT:    I am an assistant   </s>\nUSER: Another question\nASSISTANT:",
+        // Orca-Vicuna
+        "SYSTEM: You are a helpful assistant\nUSER: Hello\nASSISTANT: Hi there</s>\nUSER: Who are you\nASSISTANT:    I am an assistant   </s>\nUSER: Another question\nASSISTANT:",
+        // CohereForAI/c4ai-command-r-plus
+        "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a helpful assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>Hi there<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Who are you<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>I am an assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Another question<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>",
+        // Llama 3
+        "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHi there<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI am an assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nAnother question<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
+        // Phi 3
+        "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\nI am an assistant<|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
     };
     std::vector<char> formatted_chat(1024);
     int32_t res;
@@ -43,7 +91,7 @@ int main(void) {
 
     for (size_t i = 0; i < templates.size(); i++) {
         std::string custom_template = templates[i];
-        std::string substr = expected_substr[i];
+        std::string expected = expected_output[i];
         formatted_chat.resize(1024);
         res = llama_chat_apply_template(
             nullptr,
@@ -57,8 +105,7 @@ int main(void) {
         formatted_chat.resize(res);
         std::string output(formatted_chat.data(), formatted_chat.size());
         std::cout << output << "\n-------------------------\n";
-        // expect the "formatted_chat" to contain pre-defined strings
-        assert(output.find(substr) != std::string::npos);
+        assert(output == expected);
     }
     return 0;
 }
diff --git a/tests/test-grammar-integration.cpp b/tests/test-grammar-integration.cpp
new file mode 100644
index 000000000..1a4004e2a
--- /dev/null
+++ b/tests/test-grammar-integration.cpp
@@ -0,0 +1,332 @@
+#ifdef NDEBUG
+#undef NDEBUG
+#endif
+
+#define LLAMA_API_INTERNAL
+
+#include "ggml.h"
+#include "llama.h"
+#include "grammar-parser.h"
+#include "unicode.h"
+#include <cassert>
+#include <string>
+#include <vector>
+
+static llama_grammar* build_grammar(const std::string & grammar_str) {
+    auto parsed_grammar = grammar_parser::parse(grammar_str.c_str());
+
+    // Ensure we parsed correctly
+    assert(!parsed_grammar.rules.empty());
+
+    // Ensure we have a root node
+    assert(!(parsed_grammar.symbol_ids.find("root") == parsed_grammar.symbol_ids.end()));
+
+    std::vector<const llama_grammar_element*> grammar_rules(parsed_grammar.c_rules());
+    llama_grammar* grammar = llama_grammar_init(
+        grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
+
+    return grammar;
+}
+
+static bool match_string(const std::string & input, llama_grammar* grammar) {
+    auto decoded = decode_utf8(input, {});
+
+    const auto & code_points = decoded.first;
+
+    for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
+        auto prev_stacks = grammar->stacks;
+        llama_grammar_accept(grammar->rules, prev_stacks, *it, grammar->stacks);
+        if (grammar->stacks.empty()) {
+            // no stacks means that the grammar failed to match at this point
+            return false;
+        }
+    }
+
+    for (const auto & stack : grammar->stacks) {
+        if (stack.empty()) {
+            // An empty stack means that the grammar has been completed
+            return true;
+        }
+    }
+
+    return false;
+}
+
+static void test_grammar(const std::string & test_desc, const std::string & grammar_str, const std::vector<std::string> & passing_strings, const std::vector<std::string> & failing_strings) {
+    fprintf(stderr, "⚫ Testing %s. Grammar: %s\n", test_desc.c_str(), grammar_str.c_str());
+    fflush(stderr);
+
+    auto grammar = build_grammar(grammar_str);
+
+    // Save the original grammar stacks so that we can reset after every new string we want to test
+    auto original_stacks = grammar->stacks;
+
+    fprintf(stderr, "  🔵 Valid strings:\n");
+
+    // Passing strings
+    for (const auto & test_string : passing_strings) {
+        fprintf(stderr, "    \"%s\" ", test_string.c_str());
+        fflush(stderr);
+
+        bool matched = match_string(test_string, grammar);
+
+        if (!matched) {
+            fprintf(stderr, "❌ (failed to match)\n");
+        } else {
+            fprintf(stdout, "✅︎\n");
+        }
+
+        assert(matched);
+
+        // Reset the grammar stacks
+        grammar->stacks = original_stacks;
+    }
+
+    fprintf(stderr, "  🟠 Invalid strings:\n");
+
+    // Failing strings
+    for (const auto & test_string : failing_strings) {
+        fprintf(stderr, "    \"%s\" ", test_string.c_str());
+        fflush(stderr);
+
+        bool matched = match_string(test_string, grammar);
+
+        if (matched) {
+            fprintf(stderr, "❌ (incorrectly matched)\n");
+        } else {
+            fprintf(stdout, "✅︎\n");
+        }
+        assert(!matched);
+
+        // Reset the grammar stacks
+        grammar->stacks = original_stacks;
+    }
+
+    // Clean up allocated memory
+    llama_grammar_free(grammar);
+}
+
+static void test_simple_grammar() {
+    // Test case for a simple grammar
+    test_grammar(
+        "simple grammar",
+        R"""(
+            root ::= expr
+            expr ::= term ("+" term)*
+            term ::= number
+            number ::= [0-9]+)""",
+        // Passing strings
+        {
+            "42",
+            "1+2+3+4+5",
+            "123+456",
+        },
+        // Failing strings
+        {
+            "+",
+            "/ 3",
+            "1+2+3+4+5+",
+            "12a45",
+        }
+    );
+}
+
+static void test_complex_grammar() {
+    // Test case for a more complex grammar, with both failure strings and success strings
+    test_grammar(
+        "medium complexity grammar",
+        // Grammar
+        R"""(
+            root ::= expression
+            expression ::= term ws (("+"|"-") ws term)*
+            term ::= factor ws (("*"|"/") ws factor)*
+            factor ::= number | variable | "(" expression ")" | function-call
+            number ::= [0-9]+
+            variable ::= [a-zA-Z_][a-zA-Z0-9_]*
+            function-call ::= variable ws "(" (expression ("," ws expression)*)? ")"
+            ws ::= [ \t\n\r]?)""",
+        // Passing strings
+        {
+            "42",
+            "1*2*3*4*5",
+            "x",
+            "x+10",
+            "x1+y2",
+            "(a+b)*(c-d)",
+            "func()",
+            "func(x,y+2)",
+            "a*(b+c)-d/e",
+            "f(g(x),h(y,z))",
+            "x + 10",
+            "x1 + y2",
+            "(a + b) * (c - d)",
+            "func()",
+            "func(x, y + 2)",
+            "a * (b + c) - d / e",
+            "f(g(x), h(y, z))",
+            "123+456",
+            "123*456*789-123/456+789*123",
+            "123+456*789-123/456+789*123-456/789+123*456-789/123+456*789-123/456+789*123-456"
+        },
+        // Failing strings
+        {
+            "+",
+            "/ 3x",
+            "x + + y",
+            "a * / b",
+            "func(,)",
+            "func(x y)",
+            "(a + b",
+            "x + y)",
+            "a + b * (c - d",
+            "42 +",
+            "x +",
+            "x + 10 +",
+            "(a + b) * (c - d",
+            "func(",
+            "func(x, y + 2",
+            "a * (b + c) - d /",
+            "f(g(x), h(y, z)",
+            "123+456*789-123/456+789*123-456/789+123*456-789/123+456*789-123/456+789*123-456/",
+        }
+    );
+}
+
+static void test_quantifiers() {
+    // A collection of tests to exercise * + and ? quantifiers
+
+    test_grammar(
+        "* quantifier",
+        // Grammar
+        R"""(root ::= "a"*)""",
+        // Passing strings
+        {
+            "",
+            "a",
+            "aaaaa",
+            "aaaaaaaaaaaaaaaaaa",
+            "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
+        },
+        // Failing strings
+        {
+            "b",
+            "ab",
+            "aab",
+            "ba",
+            "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaab"
+        }
+    );
+    test_grammar(
+        "+ quantifier",
+        // Grammar
+        R"""(root ::= "a"+)""",
+        // Passing strings
+        {
+            "a",
+            "aaaaa",
+            "aaaaaaaaaaaaaaaaaa",
+            "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
+        },
+        // Failing strings
+        {
+            "",
+            "b",
+            "ab",
+            "aab",
+            "ba",
+            "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaab"
+        }
+    );
+    test_grammar(
+        "? quantifier",
+        // Grammar
+        R"""(root ::= "a"?)""",
+        // Passing strings
+        {
+            "",
+            "a"
+        },
+        // Failing strings
+        {
+            "b",
+            "ab",
+            "aa",
+            "ba",
+        }
+    );
+    test_grammar(
+        "mixed quantifiers",
+        // Grammar
+        R"""(
+            root ::= cons+ vowel* cons? (vowel cons)*
+            vowel ::= [aeiouy]
+            cons ::= [bcdfghjklmnpqrstvwxyz]
+            )""",
+        // Passing strings
+        {
+            "yes",
+            "no",
+            "noyes",
+            "crwth",
+            "four",
+            "bryyyy",
+        },
+        // Failing strings
+        {
+            "yess",
+            "yesno",
+            "forty",
+            "catyyy",
+        }
+    );
+}
+
+static void test_failure_missing_root() {
+    fprintf(stderr, "⚫ Testing missing root node:\n");
+    // Test case for a grammar that is missing a root rule
+    const std::string grammar_str = R"""(rot ::= expr
+expr ::= term ("+" term)*
+term ::= number
+number ::= [0-9]+)""";
+
+    grammar_parser::parse_state parsed_grammar = grammar_parser::parse(grammar_str.c_str());
+
+    // Ensure we parsed correctly
+    assert(!parsed_grammar.rules.empty());
+
+    // Ensure we do NOT have a root node
+    assert(parsed_grammar.symbol_ids.find("root") == parsed_grammar.symbol_ids.end());
+    fprintf(stderr, "  ✅︎ Passed\n");
+}
+
+static void test_failure_missing_reference() {
+    fprintf(stderr, "⚫ Testing missing reference node:\n");
+
+    // Test case for a grammar that is missing a referenced rule
+    const std::string grammar_str =
+R"""(root ::= expr
+expr ::= term ("+" term)*
+term ::= numero
+number ::= [0-9]+)""";
+
+    fprintf(stderr, "    Expected error:  ");
+
+    grammar_parser::parse_state parsed_grammar = grammar_parser::parse(grammar_str.c_str());
+
+    // Ensure we did NOT parsed correctly
+    assert(parsed_grammar.rules.empty());
+
+    fprintf(stderr, "    End of expected error.\n");
+    fprintf(stderr, "  ✅︎ Passed\n");
+}
+
+int main() {
+    fprintf(stdout, "Running grammar integration tests...\n");
+    test_simple_grammar();
+    test_complex_grammar();
+    test_quantifiers();
+    test_failure_missing_root();
+    test_failure_missing_reference();
+    fprintf(stdout, "All tests passed.\n");
+    return 0;
+}
diff --git a/tests/test-json-schema-to-grammar.cpp b/tests/test-json-schema-to-grammar.cpp
new file mode 100755
index 000000000..c5361b5b8
--- /dev/null
+++ b/tests/test-json-schema-to-grammar.cpp
@@ -0,0 +1,900 @@
+#ifdef NDEBUG
+#undef NDEBUG
+#endif
+
+#include <cassert>
+#include <fstream>
+#include <sstream>
+#include <regex>
+
+#include "json-schema-to-grammar.h"
+#include "grammar-parser.h"
+
+static std::string trim(const std::string & source) {
+    std::string s(source);
+    s.erase(0,s.find_first_not_of(" \n\r\t"));
+    s.erase(s.find_last_not_of(" \n\r\t")+1);
+    return std::regex_replace(s, std::regex("(^|\n)[ \t]+"), "$1");
+}
+
+enum TestCaseStatus {
+    SUCCESS,
+    FAILURE
+};
+
+struct TestCase {
+    TestCaseStatus expected_status;
+    std::string name;
+    std::string schema;
+    std::string expected_grammar;
+
+    void _print_failure_header() const {
+        fprintf(stderr, "#\n# Test '%s' failed.\n#\n%s\n", name.c_str(), schema.c_str());
+    }
+    void verify(const std::string & actual_grammar) const {
+        if (trim(actual_grammar) != trim(expected_grammar)) {
+        _print_failure_header();
+        fprintf(stderr, "# EXPECTED:\n%s\n# ACTUAL:\n%s\n", expected_grammar.c_str(), actual_grammar.c_str());
+        assert(false);
+        }
+    }
+    void verify_expectation_parseable() const {
+        try {
+            auto state = grammar_parser::parse(expected_grammar.c_str());
+            if (state.symbol_ids.find("root") == state.symbol_ids.end()) {
+                throw std::runtime_error("Grammar failed to parse:\n" + expected_grammar);
+            }
+        } catch (const std::runtime_error & ex) {
+            _print_failure_header();
+            fprintf(stderr, "# GRAMMAR ERROR: %s\n", ex.what());
+            assert(false);
+        }
+    }
+    void verify_status(TestCaseStatus status) const {
+        if (status != expected_status) {
+            _print_failure_header();
+            fprintf(stderr, "# EXPECTED STATUS: %s\n", expected_status == SUCCESS ? "SUCCESS" : "FAILURE");
+            fprintf(stderr, "# ACTUAL STATUS: %s\n", status == SUCCESS ? "SUCCESS" : "FAILURE");
+            assert(false);
+        }
+    }
+};
+
+static void write(const std::string & file, const std::string & content) {
+    std::ofstream f;
+    f.open(file.c_str());
+    f << content.c_str();
+    f.close();
+}
+
+static std::string read(const std::string & file) {
+    std::ostringstream actuals;
+    actuals << std::ifstream(file.c_str()).rdbuf();
+    return actuals.str();
+}
+
+static void test_all(const std::string & lang, std::function<void(const TestCase &)> runner) {
+    fprintf(stderr, "#\n# Testing JSON schema conversion (%s)\n#\n", lang.c_str());
+    auto test = [&](const TestCase & tc) {
+        fprintf(stderr, "- %s%s\n", tc.name.c_str(), tc.expected_status == FAILURE ? " (failure expected)" : "");
+        runner(tc);
+    };
+
+    test({
+        FAILURE,
+        "unknown type",
+        R"""({
+            "type": "kaboom"
+        })""",
+        ""
+    });
+
+    test({
+        FAILURE,
+        "invalid type",
+        R"""({
+            "type": 123
+        })""",
+        ""
+    });
+
+    test({
+        SUCCESS,
+        "empty schema (object)",
+        "{}",
+        R"""(
+            array ::= "[" space ( value ("," space value)* )? "]" space
+            boolean ::= ("true" | "false") space
+            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            null ::= "null" space
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
+            object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space
+            root ::= object
+            space ::= " "?
+            string ::= "\"" char* "\"" space
+            value ::= object | array | string | number | boolean | null
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "exotic formats",
+        R"""({
+            "items": [
+                { "format": "date" },
+                { "format": "uuid" },
+                { "format": "time" },
+                { "format": "date-time" }
+            ]
+        })""",
+        R"""(
+            date ::= [0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( "0" [1-9] | [1-2] [0-9] | "3" [0-1] )
+            date-string ::= "\"" date "\"" space
+            date-time ::= date "T" time
+            date-time-string ::= "\"" date-time "\"" space
+            root ::= "[" space tuple-0 "," space uuid "," space tuple-2 "," space tuple-3 "]" space
+            space ::= " "?
+            time ::= ([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )
+            time-string ::= "\"" time "\"" space
+            tuple-0 ::= date-string
+            tuple-2 ::= time-string
+            tuple-3 ::= date-time-string
+            uuid ::= "\"" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "-" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "-" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "-" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "-" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "string",
+        R"""({
+            "type": "string"
+        })""",
+        R"""(
+            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            root ::= "\"" char* "\"" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "string w/ min length 1",
+        R"""({
+            "type": "string",
+            "minLength": 1
+        })""",
+        R"""(
+            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            root ::= "\"" char+ "\"" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "string w/ min length 3",
+        R"""({
+            "type": "string",
+            "minLength": 3
+        })""",
+        R"""(
+            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            root ::= "\"" char char char (char)* "\"" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "string w/ max length",
+        R"""({
+            "type": "string",
+            "maxLength": 3
+        })""",
+        R"""(
+            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            root ::= "\"" (char (char (char)?)?)? "\"" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "string w/ min & max length",
+        R"""({
+            "type": "string",
+            "minLength": 1,
+            "maxLength": 4
+        })""",
+        R"""(
+            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            root ::= "\"" char (char (char (char)?)?)? "\"" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "boolean",
+        R"""({
+            "type": "boolean"
+        })""",
+        R"""(
+            root ::= ("true" | "false") space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "integer",
+        R"""({
+            "type": "integer"
+        })""",
+        R"""(
+            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            root ::= ("-"? integral-part) space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "string const",
+        R"""({
+            "const": "foo"
+        })""",
+        R"""(
+            root ::= "\"foo\""
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "non-string const",
+        R"""({
+            "const": 123
+        })""",
+        R"""(
+            root ::= "123"
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "non-string enum",
+        R"""({
+            "enum": ["red", "amber", "green", null, 42, ["foo"]]
+        })""",
+        R"""(
+            root ::= "\"red\"" | "\"amber\"" | "\"green\"" | "null" | "42" | "[\"foo\"]"
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "tuple1",
+        R"""({
+            "prefixItems": [{ "type": "string" }]
+        })""",
+        R"""(
+            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            root ::= "[" space string "]" space
+            space ::= " "?
+            string ::= "\"" char* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "tuple2",
+        R"""({
+            "prefixItems": [{ "type": "string" }, { "type": "number" }]
+        })""",
+        R"""(
+            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
+            root ::= "[" space string "," space number "]" space
+            space ::= " "?
+            string ::= "\"" char* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "number",
+        R"""({
+            "type": "number"
+        })""",
+        R"""(
+            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            root ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "minItems",
+        R"""({
+            "items": {
+                "type": "boolean"
+            },
+            "minItems": 2
+        })""",
+        R"""(
+            boolean ::= ("true" | "false") space
+            root ::= "[" space boolean "," space boolean ("," space boolean)* "]" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "maxItems 1",
+        R"""({
+            "items": {
+                "type": "boolean"
+            },
+            "maxItems": 1
+        })""",
+        R"""(
+            boolean ::= ("true" | "false") space
+            root ::= "[" space (boolean)? "]" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "maxItems 2",
+        R"""({
+            "items": {
+                "type": "boolean"
+            },
+            "maxItems": 2
+        })""",
+        R"""(
+            boolean ::= ("true" | "false") space
+            root ::= "[" space (boolean ("," space boolean)?)? "]" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "min + maxItems",
+        R"""({
+            "items": {
+                "type": ["number", "integer"]
+            },
+            "minItems": 3,
+            "maxItems": 5
+        })""",
+        R"""(
+            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            integer ::= ("-"? integral-part) space
+            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            item ::= number | integer
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
+            root ::= "[" space item "," space item "," space item ("," space item ("," space item)?)? "]" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "simple regexp",
+        R"""({
+            "type": "string",
+            "pattern": "^abc?d*efg+(hij)?kl$"
+        })""",
+        R"""(
+            root ::= "\"" "ab" "c"? "d"* "ef" "g"+ ("hij")? "kl" "\"" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "regexp escapes",
+        R"""({
+            "type": "string",
+            "pattern": "^\\[\\]\\{\\}\\(\\)\\|\\+\\*\\?$"
+        })""",
+        R"""(
+            root ::= "\"" "[]{}()|+*?" "\"" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "regexp quote",
+        R"""({
+            "type": "string",
+            "pattern": "^\"$"
+        })""",
+        R"""(
+            root ::= "\"" "\"" "\"" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "regexp",
+        R"""({
+            "type": "string",
+            "pattern": "^(\\([0-9]{1,3}\\))?[0-9]{3}-[0-9]{4} a{3,5}nd...$"
+        })""",
+        R"""(
+            dot ::= [^\x0A\x0D]
+            root ::= "\"" ("(" root-1 (root-1 (root-1)?)? ")")? root-1 root-1 root-1 "-" root-1 root-1 root-1 root-1 " " "aaa" ("a" ("a")?)? "nd" dot dot dot "\"" space
+            root-1 ::= [0-9]
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "required props in original order",
+        R"""({
+            "type": "object",
+            "properties": {
+                "b": {"type": "string"},
+                "c": {"type": "string"},
+                "a": {"type": "string"}
+            },
+            "required": [
+                "a",
+                "b",
+                "c"
+            ],
+            "additionalProperties": false,
+            "definitions": {}
+        })""",
+        R"""(
+            a-kv ::= "\"a\"" space ":" space string
+            b-kv ::= "\"b\"" space ":" space string
+            c-kv ::= "\"c\"" space ":" space string
+            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            root ::= "{" space b-kv "," space c-kv "," space a-kv "}" space
+            space ::= " "?
+            string ::= "\"" char* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "1 optional prop",
+        R"""({
+            "properties": {
+                "a": {
+                "type": "string"
+                }
+            },
+            "additionalProperties": false
+        })""",
+        R"""(
+            a-kv ::= "\"a\"" space ":" space string
+            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            root ::= "{" space  (a-kv )? "}" space
+            space ::= " "?
+            string ::= "\"" char* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "N optional props",
+        R"""({
+            "properties": {
+                "a": {"type": "string"},
+                "b": {"type": "string"},
+                "c": {"type": "string"}
+            },
+            "additionalProperties": false
+        })""",
+        R"""(
+            a-kv ::= "\"a\"" space ":" space string
+            a-rest ::= ( "," space b-kv )? b-rest
+            b-kv ::= "\"b\"" space ":" space string
+            b-rest ::= ( "," space c-kv )?
+            c-kv ::= "\"c\"" space ":" space string
+            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            root ::= "{" space  (a-kv a-rest | b-kv b-rest | c-kv )? "}" space
+            space ::= " "?
+            string ::= "\"" char* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "required + optional props each in original order",
+        R"""({
+            "properties": {
+                "b": {"type": "string"},
+                "a": {"type": "string"},
+                "d": {"type": "string"},
+                "c": {"type": "string"}
+            },
+            "required": ["a", "b"],
+            "additionalProperties": false
+        })""",
+        R"""(
+            a-kv ::= "\"a\"" space ":" space string
+            b-kv ::= "\"b\"" space ":" space string
+            c-kv ::= "\"c\"" space ":" space string
+            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            d-kv ::= "\"d\"" space ":" space string
+            d-rest ::= ( "," space c-kv )?
+            root ::= "{" space b-kv "," space a-kv ( "," space ( d-kv d-rest | c-kv ) )? "}" space
+            space ::= " "?
+            string ::= "\"" char* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "additional props",
+        R"""({
+            "type": "object",
+            "additionalProperties": {"type": "array", "items": {"type": "number"}}
+        })""",
+        R"""(
+            additional-kv ::= string ":" space additional-value
+            additional-kvs ::= additional-kv ( "," space additional-kv )*
+            additional-value ::= "[" space (number ("," space number)*)? "]" space
+            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
+            root ::= "{" space  (additional-kvs )? "}" space
+            space ::= " "?
+            string ::= "\"" char* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "additional props (true)",
+        R"""({
+            "type": "object",
+            "additionalProperties": true
+        })""",
+        R"""(
+            array ::= "[" space ( value ("," space value)* )? "]" space
+            boolean ::= ("true" | "false") space
+            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            null ::= "null" space
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
+            object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space
+            root ::= object
+            space ::= " "?
+            string ::= "\"" char* "\"" space
+            value ::= object | array | string | number | boolean | null
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "additional props (implicit)",
+        R"""({
+            "type": "object"
+        })""",
+        R"""(
+            array ::= "[" space ( value ("," space value)* )? "]" space
+            boolean ::= ("true" | "false") space
+            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            null ::= "null" space
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
+            object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space
+            root ::= object
+            space ::= " "?
+            string ::= "\"" char* "\"" space
+            value ::= object | array | string | number | boolean | null
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "empty w/o additional props",
+        R"""({
+            "type": "object",
+            "additionalProperties": false
+        })""",
+        R"""(
+            root ::= "{" space  "}" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "required + additional props",
+        R"""({
+            "type": "object",
+            "properties": {
+                "a": {"type": "number"}
+            },
+            "required": ["a"],
+            "additionalProperties": {"type": "string"}
+        })""",
+        R"""(
+            a-kv ::= "\"a\"" space ":" space number
+            additional-kv ::= string ":" space string
+            additional-kvs ::= additional-kv ( "," space additional-kv )*
+            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
+            root ::= "{" space a-kv ( "," space ( additional-kvs ) )? "}" space
+            space ::= " "?
+            string ::= "\"" char* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "optional + additional props",
+        R"""({
+            "type": "object",
+            "properties": {
+                "a": {"type": "number"}
+            },
+            "additionalProperties": {"type": "number"}
+        })""",
+        R"""(
+            a-kv ::= "\"a\"" space ":" space number
+            a-rest ::= additional-kvs
+            additional-kv ::= string ":" space number
+            additional-kvs ::= additional-kv ( "," space additional-kv )*
+            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
+            root ::= "{" space  (a-kv a-rest | additional-kvs )? "}" space
+            space ::= " "?
+            string ::= "\"" char* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "required + optional + additional props",
+        R"""({
+            "type": "object",
+            "properties": {
+                "a": {"type": "number"},
+                "b": {"type": "number"}
+            },
+            "required": ["a"],
+            "additionalProperties": {"type": "number"}
+        })""",
+        R"""(
+            a-kv ::= "\"a\"" space ":" space number
+            additional-kv ::= string ":" space number
+            additional-kvs ::= additional-kv ( "," space additional-kv )*
+            b-kv ::= "\"b\"" space ":" space number
+            b-rest ::= additional-kvs
+            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
+            root ::= "{" space a-kv ( "," space ( b-kv b-rest | additional-kvs ) )? "}" space
+            space ::= " "?
+            string ::= "\"" char* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "top-level $ref",
+        R"""({
+            "$ref": "#/definitions/foo",
+            "definitions": {
+                "foo": {
+                    "type": "object",
+                    "properties": {
+                        "a": {
+                            "type": "string"
+                        }
+                    },
+                    "required": [
+                        "a"
+                    ],
+                    "additionalProperties": false
+                }
+            }
+        })""",
+        R"""(
+            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            foo ::= "{" space foo-a-kv "}" space
+            foo-a-kv ::= "\"a\"" space ":" space string
+            root ::= foo
+            space ::= " "?
+            string ::= "\"" char* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "anyOf",
+        R"""({
+            "anyOf": [
+                {"$ref": "#/definitions/foo"},
+                {"$ref": "#/definitions/bar"}
+            ],
+            "definitions": {
+                "foo": {
+                    "properties": {"a": {"type": "number"}}
+                },
+                "bar": {
+                    "properties": {"b": {"type": "number"}}
+                }
+            },
+            "type": "object"
+        })""",
+        R"""(
+            alternative-0 ::= foo
+            alternative-1 ::= bar
+            bar ::= "{" space  (bar-b-kv )? "}" space
+            bar-b-kv ::= "\"b\"" space ":" space number
+            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            foo ::= "{" space  (foo-a-kv )? "}" space
+            foo-a-kv ::= "\"a\"" space ":" space number
+            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
+            root ::= alternative-0 | alternative-1
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "mix of allOf, anyOf and $ref (similar to https://json.schemastore.org/tsconfig.json)",
+        R"""({
+            "allOf": [
+                {"$ref": "#/definitions/foo"},
+                {"$ref": "#/definitions/bar"},
+                {
+                "anyOf": [
+                    {"$ref": "#/definitions/baz"},
+                    {"$ref": "#/definitions/bam"}
+                ]
+                }
+            ],
+            "definitions": {
+                "foo": {
+                    "properties": {"a": {"type": "number"}}
+                },
+                "bar": {
+                    "properties": {"b": {"type": "number"}}
+                },
+                "bam": {
+                    "properties": {"c": {"type": "number"}}
+                },
+                "baz": {
+                    "properties": {"d": {"type": "number"}}
+                }
+            },
+            "type": "object"
+        })""",
+        R"""(
+            a-kv ::= "\"a\"" space ":" space number
+            b-kv ::= "\"b\"" space ":" space number
+            c-kv ::= "\"c\"" space ":" space number
+            d-kv ::= "\"d\"" space ":" space number
+            d-rest ::= ( "," space c-kv )?
+            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
+            root ::= "{" space a-kv "," space b-kv ( "," space ( d-kv d-rest | c-kv ) )? "}" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "conflicting names",
+        R"""({
+            "type": "object",
+            "properties": {
+                "number": {
+                "type": "object",
+                "properties": {
+                    "number": {
+                    "type": "object",
+                        "properties": {
+                            "root": {
+                                "type": "number"
+                            }
+                        },
+                        "required": [
+                            "root"
+                        ],
+                        "additionalProperties": false
+                    }
+                },
+                "required": [
+                    "number"
+                ],
+                "additionalProperties": false
+                }
+            },
+            "required": [
+                "number"
+            ],
+            "additionalProperties": false,
+            "definitions": {}
+        })""",
+        R"""(
+            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
+            number- ::= "{" space number-number-kv "}" space
+            number-kv ::= "\"number\"" space ":" space number-
+            number-number ::= "{" space number-number-root-kv "}" space
+            number-number-kv ::= "\"number\"" space ":" space number-number
+            number-number-root-kv ::= "\"root\"" space ":" space number
+            root ::= "{" space number-kv "}" space
+            space ::= " "?
+        )"""
+    });
+}
+
+int main() {
+    fprintf(stderr, "LLAMA_NODE_AVAILABLE = %s\n", getenv("LLAMA_NODE_AVAILABLE") ? "true" : "false");
+    fprintf(stderr, "LLAMA_PYTHON_AVAILABLE = %s\n", getenv("LLAMA_PYTHON_AVAILABLE") ? "true" : "false");
+
+    test_all("C++", [](const TestCase & tc) {
+        try {
+            tc.verify(json_schema_to_grammar(nlohmann::ordered_json::parse(tc.schema)));
+            tc.verify_status(SUCCESS);
+        } catch (const std::runtime_error & ex) {
+            fprintf(stderr, "Error: %s\n", ex.what());
+            tc.verify_status(FAILURE);
+        }
+    });
+
+    if (getenv("LLAMA_PYTHON_AVAILABLE") || (std::system("python --version") == 0)) {
+        test_all("Python", [](const TestCase & tc) {
+            write("test-json-schema-input.tmp", tc.schema);
+            tc.verify_status(std::system(
+                "python ./examples/json_schema_to_grammar.py test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE);
+            tc.verify(read("test-grammar-output.tmp"));
+        });
+    } else {
+        fprintf(stderr, "\033[33mWARNING: Python not found, skipping Python JSON schema -> grammar tests.\n\033[0m");
+    }
+
+    if (getenv("LLAMA_NODE_AVAILABLE") || (std::system("node --version") == 0)) {
+        test_all("JavaScript", [](const TestCase & tc) {
+            write("test-json-schema-input.tmp", tc.schema);
+            tc.verify_status(std::system(
+                "node ./tests/run-json-schema-to-grammar.mjs test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE);
+            tc.verify(read("test-grammar-output.tmp"));
+        });
+    } else {
+        fprintf(stderr, "\033[33mWARNING: Node not found, skipping JavaScript JSON schema -> grammar tests.\n\033[0m");
+    }
+
+    test_all("Check Expectations Validity", [](const TestCase & tc) {
+        if (tc.expected_status == SUCCESS) {
+            tc.verify_expectation_parseable();
+        }
+    });
+}
diff --git a/tests/test-opt.cpp b/tests/test-opt.cpp
index 2c9997fca..546ca230b 100644
--- a/tests/test-opt.cpp
+++ b/tests/test-opt.cpp
@@ -118,7 +118,7 @@ int main(void) {
     const float fe = ggml_get_f32_1d(e, 0);
     printf("%s: e = %.4f\n", __func__, fe);
 
-    struct ggml_opt_params opt_params = ggml_opt_default_params(GGML_OPT_ADAM);
+    struct ggml_opt_params opt_params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
 
     ggml_opt(ctx, opt_params, e);
 
diff --git a/tests/test-quantize-fns.cpp b/tests/test-quantize-fns.cpp
index 5e92d5742..e690ac6c8 100644
--- a/tests/test-quantize-fns.cpp
+++ b/tests/test-quantize-fns.cpp
@@ -138,11 +138,6 @@ int main(int argc, char * argv[]) {
 
         const ggml_type ei = (ggml_type)i;
 
-        if (ei == GGML_TYPE_IQ2_XXS || ei == GGML_TYPE_IQ2_XS) {
-            printf("Skip %s due to missing quantization functionality\n", ggml_type_name(ei));
-            continue;
-        }
-
         printf("Testing %s\n", ggml_type_name((ggml_type) i));
         ggml_quantize_init(ei);
 
@@ -150,7 +145,9 @@ int main(int argc, char * argv[]) {
             const float total_error = total_quantization_error(qfns, test_size, test_data.data());
             const float max_quantization_error =
                 type == GGML_TYPE_Q2_K    ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
+                type == GGML_TYPE_IQ2_S   ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
                 type == GGML_TYPE_Q3_K    ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS :
+                type == GGML_TYPE_IQ3_S   ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS :
                 type == GGML_TYPE_IQ3_XXS ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS : MAX_QUANTIZATION_TOTAL_ERROR;
             failed = !(total_error < max_quantization_error);
             num_failed += failed;
@@ -167,7 +164,9 @@ int main(int argc, char * argv[]) {
 
             const float vec_dot_error = dot_product_error(qfns, test_size, test_data.data(), test_data2.data());
             const float max_allowed_error = type == GGML_TYPE_Q2_K || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ2_XXS ||
-                                            type == GGML_TYPE_IQ3_XXS ? MAX_DOT_PRODUCT_ERROR_LOWBIT : MAX_DOT_PRODUCT_ERROR;
+                                            type == GGML_TYPE_IQ3_XXS || type == GGML_TYPE_IQ3_S || type == GGML_TYPE_IQ2_S
+                                          ? MAX_DOT_PRODUCT_ERROR_LOWBIT
+                                          : MAX_DOT_PRODUCT_ERROR;
             failed = !(vec_dot_error < max_allowed_error);
             num_failed += failed;
             if (failed || verbose) {
diff --git a/tests/test-tokenizer-0-falcon.cpp b/tests/test-tokenizer-0-falcon.cpp
deleted file mode 100644
index 472b0b3a8..000000000
--- a/tests/test-tokenizer-0-falcon.cpp
+++ /dev/null
@@ -1,187 +0,0 @@
-#include "llama.h"
-#include "common.h"
-#include "console.h"
-
-#include <cstdio>
-#include <string>
-#include <map>
-#include <vector>
-#include <fstream>
-
-// generate using test-tokenizer-0-falcon.py
-static const std::map<std::string, std::vector<llama_token>> & k_tests() {
-    static std::map<std::string, std::vector<llama_token>> _k_tests = {
-        { ""                      , {  }, },
-        { " "                     , {     204, }, },
-        { "  "                    , {     258, }, },
-        { "   "                   , {     466, }, },
-        { "\t"                    , {     192, }, },
-        { "\n"                    , {     193, }, },
-        { "\t\n"                  , {   19125, }, },
-        { "Hello world"           , {    9856,   1079, }, },
-        { " Hello world"          , {   23090,   1079, }, },
-        { "Hello World"           , {    9856,   2889, }, },
-        { " Hello World"          , {   23090,   2889, }, },
-        { " Hello World!"         , {   23090,   2889,     12, }, },
-        { "Hello, world!"         , {    9856,     23,   1079,     12, }, },
-        { " Hello, world!"        , {   23090,     23,   1079,     12, }, },
-        { " this is 🦙.cpp"        , {     414,    304,   3346,    111,    231,     25,  29247, }, },
-        { "w048 7tuijk dsdfhu"    , {      98,  55866,    204,     34,  16682,   7149,  36190,   6869,  11481, }, },
-        { "нещо на Български"     , {     150,    133,   6207,    151,    215,    150,    134,   5052,    133,   6279,   5052,    223,    151,    216,  49679,    123,  53110,  47043,   7795, }, },
-        { "កាន់តែពិសេសអាចខលចេញ"   , {   38154,    206,  38154,    126,  38154,    225,    167,    237,    217,  38154,    221,    167,    237,    208,  38154,    228,  38154,    127,  38154,    237,    167,    237,    207,  38154,    237,  38154,    107,  38154,    126,  38154,    211,  38154,    207,  38154,    233,  38154,    211,    167,    237,    207,  38154,    215, }, },
-        { "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", {    2571,    232,    206,    204,     19,  11003,     20,   8196,    126,    283,    219,  48778,    116,  13392,    204,     19,  51831,    732,  63209,   1741,   7955,    522,     20,  22438,    211,    204,     19,   7927,  53360,    325,    504,    701,    946,  10930,     20, }, },
-        { "Hello"                 , {    9856, }, },
-        { " Hello"                , {   23090, }, },
-        { "  Hello"               , {     204,  23090, }, },
-        { "   Hello"              , {     258,  23090, }, },
-        { "    Hello"             , {     466,  23090, }, },
-        { "    Hello\n    Hello"  , {     466,  23090,    742,  23090, }, },
-        { "\n ="                  , {    1212,     40, }, },
-        { "' era"                 , {      18,   4932, }, },
-    };
-
-    return _k_tests;
-}
-
-int main(int argc, char **argv) {
-    if (argc < 2) {
-        fprintf(stderr, "Usage: %s vocab-file [text-file]\n", argv[0]);
-        return 1;
-    }
-
-    const std::string fname = argv[1];
-
-    std::string fname_text;
-    if (argc > 2) {
-        fname_text = argv[2];
-    }
-
-    fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
-
-    llama_model * model;
-    llama_context * ctx;
-
-    llama_backend_init();
-
-    // load the vocab
-    {
-        auto mparams = llama_model_default_params();
-
-        mparams.vocab_only = true;
-
-        model = llama_load_model_from_file(fname.c_str(), mparams);
-
-        if (model == NULL) {
-            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
-            return 1;
-        }
-
-        auto cparams = llama_context_default_params();
-
-        ctx = llama_new_context_with_model(model, cparams);
-
-        if (ctx == NULL) {
-            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
-            llama_free_model(model);
-            return 1;
-        }
-    }
-
-    if (llama_vocab_type(model) != LLAMA_VOCAB_TYPE_BPE) {
-        fprintf(stderr, "%s : error: vocab type is not BPE\n", __func__);
-        llama_free_model(model);
-        llama_free(ctx);
-        return 2;
-    }
-
-#ifdef _WIN32
-    // We need this for unicode console support
-    console::init(false, false);
-    atexit([]() { console::cleanup(); });
-#endif
-
-    bool success = true;
-
-    for (const auto & test_kv : k_tests()) {
-        const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, false);
-
-        printf("\n");
-        printf("src: '%s'\n", test_kv.first.c_str());
-        printf("res: '%s'\n", llama_detokenize_bpe(ctx, res).c_str());
-        printf("tok: ");
-        for (const auto & tok : res) {
-            printf("%d ", tok);
-        }
-        printf("\n");
-
-        bool correct = res.size() == test_kv.second.size();
-
-        for (int i = 0; i < (int) res.size() && correct; ++i) {
-            if (test_kv.second[i] != res[i]) {
-                correct = false;
-            }
-        }
-
-        if (!correct) {
-            fprintf(stderr, "%s : failed test:    '%s'\n", __func__, test_kv.first.c_str());
-            fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
-                llama_detokenize_bpe(ctx, res).c_str(),
-                llama_detokenize_bpe(ctx, test_kv.second).c_str());
-            fprintf(stderr, "%s : expected tokens: ", __func__);
-            for (const auto & t : test_kv.second) {
-                fprintf(stderr, "%6d, ", t);
-            }
-            fprintf(stderr, "\n");
-            fprintf(stderr, "%s : got tokens:      ", __func__);
-            for (const auto & t : res) {
-                fprintf(stderr, "%6d, ", t);
-            }
-            fprintf(stderr, "\n");
-
-            success = false;
-        }
-    }
-
-    if (!fname_text.empty()) {
-        fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str());
-
-        std::string text;
-        {
-            std::ifstream ifs(fname_text);
-            if (!ifs) {
-                fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_text.c_str());
-                return 1;
-            }
-            text = std::string(std::istreambuf_iterator<char>(ifs), std::istreambuf_iterator<char>());
-        }
-
-        fprintf(stderr, "%s : text size: %zu\n", __func__, text.size());
-
-        const std::vector<llama_token> res = llama_tokenize(ctx, text, false);
-
-        fprintf(stderr, "%s : tokens: %zu\n", __func__, res.size());
-
-        {
-            const std::string fname_out = fname_text + ".tokcpp";
-
-            std::ofstream ofs(fname_out);
-            if (!ofs) {
-                fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str());
-                return 1;
-            }
-
-            for (const auto & tok : res) {
-                ofs << tok << " '" << llama_detokenize_bpe(ctx, std::vector<int>{tok}) << "'" << std::endl;
-            }
-        }
-
-        fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str());
-    }
-
-    llama_free_model(model);
-    llama_free(ctx);
-
-    llama_backend_free();
-
-    return success ? 0 : 3;
-}
diff --git a/tests/test-tokenizer-0-falcon.py b/tests/test-tokenizer-0-falcon.py
deleted file mode 100644
index 4f06ec9bb..000000000
--- a/tests/test-tokenizer-0-falcon.py
+++ /dev/null
@@ -1,82 +0,0 @@
-# tests with BPE tokenizer
-
-import argparse
-
-from transformers import AutoTokenizer
-
-parser = argparse.ArgumentParser()
-parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file")
-parser.add_argument("--fname-tok",   help="path to a text file to tokenize")
-args = parser.parse_args()
-
-dir_tokenizer = args.dir_tokenizer
-
-tokenizer = AutoTokenizer.from_pretrained(dir_tokenizer)
-
-tests = [
-    "",
-    " ",
-    "  ",
-    "   ",
-    "\t",
-    "\n",
-    "\t\n",
-    "Hello world",
-    " Hello world",
-    "Hello World",
-    " Hello World",
-    " Hello World!",
-    "Hello, world!",
-    " Hello, world!",
-    " this is 🦙.cpp",
-    "w048 7tuijk dsdfhu",
-    "нещо на Български",
-    "កាន់តែពិសេសអាចខលចេញ",
-    "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
-    "Hello",
-    " Hello",
-    "  Hello",
-    "   Hello",
-    "    Hello",
-    "    Hello\n    Hello",
-    "\n =",
-    "' era",
-]
-
-for text in tests:
-    print('text: ', text)
-    print(tokenizer.encode(text))
-    print(tokenizer.decode(tokenizer.encode(text)))
-
-print("\n\ntests for C++:\n")
-for text in tests:
-    res = tokenizer.encode(text)
-
-    k = text.replace('\n', '\\n')
-    k = k.replace('\t', '\\t')
-    k = '"' + k + '"'
-    print("{ %-24s, { " % k, end='')
-    for x in res:
-        print("%7d," % x, end='')
-    print(" }, },")
-
-print(tokenizer.encode('hello'))
-print(tokenizer.encode('world'))
-print(tokenizer.encode(' world'))
-print(tokenizer.encode('hello world'))
-
-fname_tok = args.fname_tok
-if fname_tok:
-    print('tokenizing file: ', fname_tok)
-    fname_out = fname_tok + '.tok'
-    with open(fname_tok, 'r', encoding='utf-8') as f:
-        lines = f.readlines()
-        s = ''.join(lines)
-        res = tokenizer.encode(s)
-        # write to file
-        with open(fname_out, 'w', encoding='utf-8') as f:
-            for x in res:
-                f.write(str(x) + ' \'' + tokenizer.decode(x) + '\'\n')
-        print('len(res): ', len(res))
-        print('len(lines): ', len(lines))
-    print('results written to: ', fname_out)
diff --git a/tests/test-tokenizer-0-llama.cpp b/tests/test-tokenizer-0-llama.cpp
deleted file mode 100644
index 0a16cd7eb..000000000
--- a/tests/test-tokenizer-0-llama.cpp
+++ /dev/null
@@ -1,190 +0,0 @@
-#include "llama.h"
-#include "common.h"
-#include "console.h"
-
-#include <cstdio>
-#include <string>
-#include <map>
-#include <vector>
-#include <fstream>
-
-// generate using test-tokenizer-0-llama.py
-static const std::map<std::string, std::vector<llama_token>> & k_tests() {
-    static std::map<std::string, std::vector<llama_token>> _k_tests = {
-        { ""                      , {  }, },
-        { " "                     , {     259, }, },
-        { "  "                    , {    1678, }, },
-        { "   "                   , {     268, }, },
-        { "\t"                    , {   29871,     12, }, },
-        { "\n"                    , {   29871,     13, }, },
-        { "\t\n"                  , {   29871,     12,     13, }, },
-        { "Hello world"           , {   15043,   3186, }, },
-        { " Hello world"          , {   29871,  15043,   3186, }, },
-        { "Hello World"           , {   15043,   2787, }, },
-        { " Hello World"          , {   29871,  15043,   2787, }, },
-        { " Hello World!"         , {   29871,  15043,   2787,  29991, }, },
-        { "Hello, world!"         , {   15043,  29892,   3186,  29991, }, },
-        { " Hello, world!"        , {   29871,  15043,  29892,   3186,  29991, }, },
-        { " this is 🦙.cpp"        , {   29871,    445,    338,  29871,    243,    162,    169,    156,  29889,   8223, }, },
-        { "w048 7tuijk dsdfhu"    , {     281,  29900,  29946,  29947,  29871,  29955,   9161,  13535,  18031,   2176,   6905, }, },
-        { "нещо на Български"     , {    1538,   4851,    665,   1386,  29713,   1305, }, },
-        { "កាន់តែពិសេសអាចខលចេញ"   , {   29871,  31849,  31324,  31934,    228,    162,    142,    228,    161,    146,    228,    162,    133,    228,    161,    153,    228,    161,    186,  31708,    228,    162,    132,  31708,    228,    161,    165,  31324,    228,    161,    136,    228,    161,    132,    228,    161,    158,    228,    161,    136,    228,    162,    132,    228,    161,    140, }, },
-        { "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", {   29871,    243,    162,    157,    131,    313,   8945,  29897,  29871,    243,    162,    155,    185,  30722,    243,    162,    143,    174,  30598,    313,  20787,    953,   3848,    275,  16125,    630,  29897,  29871,  31681,    313,   6194,    953,  29877,   2397,    393,    756,    967,   1914,   5993,  29897, }, },
-        { "Hello"                 , {   15043, }, },
-        { " Hello"                , {   29871,  15043, }, },
-        { "  Hello"               , {     259,  15043, }, },
-        { "   Hello"              , {    1678,  15043, }, },
-        { "    Hello"             , {     268,  15043, }, },
-        { "    Hello\n    Hello"  , {     268,  15043,     13,   1678,  15043, }, },
-        { " ("                    , {   29871,  313, }, },
-    };
-
-    return _k_tests;
-}
-
-int main(int argc, char **argv) {
-    if (argc < 2) {
-        fprintf(stderr, "Usage: %s vocab-file [text-file]\n", argv[0]);
-        return 1;
-    }
-
-    const std::string fname = argv[1];
-
-    std::string fname_text;
-    if (argc > 2) {
-        fname_text = argv[2];
-    }
-
-    fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
-
-    llama_model * model;
-    llama_context * ctx;
-
-    llama_backend_init();
-
-    // load the vocab
-    {
-        auto mparams = llama_model_default_params();
-
-        mparams.vocab_only = true;
-
-        model = llama_load_model_from_file(fname.c_str(), mparams);
-
-        if (model == NULL) {
-            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
-            return 1;
-        }
-
-        auto cparams = llama_context_default_params();
-
-        ctx = llama_new_context_with_model(model, cparams);
-
-        if (ctx == NULL) {
-            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
-            llama_free_model(model);
-            return 1;
-        }
-    }
-
-    if (llama_vocab_type(model) != LLAMA_VOCAB_TYPE_SPM) {
-        fprintf(stderr, "%s : error: vocab type is not SPM\n", __func__);
-        llama_free_model(model);
-        llama_free(ctx);
-        return 2;
-    }
-
-#ifdef _WIN32
-    // We need this for unicode console support
-    console::init(false, false);
-    atexit([]() { console::cleanup(); });
-#endif
-
-    bool success = true;
-
-    for (const auto & test_kv : k_tests()) {
-        const std::vector<llama_token> res_bos   = llama_tokenize(ctx, test_kv.first, true);
-        const std::vector<llama_token> res_nobos = llama_tokenize(ctx, test_kv.first, false);
-
-        printf("\n");
-        printf("src: '%s'\n", test_kv.first.c_str());
-        printf("res: '%s'\n", llama_detokenize_spm(ctx, res_bos).c_str());
-        printf("tok: ");
-        for (const auto & tok : res_bos) {
-            printf("%d ", tok);
-        }
-        printf("\n");
-
-        bool correct = res_nobos.size() == test_kv.second.size() && res_bos.size() == res_nobos.size() + 1 && res_bos[0] == 1;
-
-        for (int i = 0; i < (int) res_nobos.size() && correct; ++i) {
-            if (test_kv.second[i] != res_bos[i + 1]) {
-                correct = false;
-            }
-            if (test_kv.second[i] != res_nobos[i]) {
-                correct = false;
-            }
-        }
-
-        if (!correct) {
-            fprintf(stderr, "%s : failed test:    '%s'\n", __func__, test_kv.first.c_str());
-            fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
-                llama_detokenize_spm(ctx, res_nobos).c_str(),
-                llama_detokenize_spm(ctx, test_kv.second).c_str());
-            fprintf(stderr, "%s : expected tokens: ", __func__);
-            for (const auto & t : test_kv.second) {
-                fprintf(stderr, "%6d, ", t);
-            }
-            fprintf(stderr, "\n");
-            fprintf(stderr, "%s : got tokens:      ", __func__);
-            for (const auto & t : res_nobos) {
-                fprintf(stderr, "%6d, ", t);
-            }
-            fprintf(stderr, "\n");
-
-            success = false;
-        }
-    }
-
-    if (!fname_text.empty()) {
-        fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str());
-
-        std::string text;
-        {
-            std::ifstream ifs(fname_text);
-            if (!ifs) {
-                fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_text.c_str());
-                return 1;
-            }
-            text = std::string(std::istreambuf_iterator<char>(ifs), std::istreambuf_iterator<char>());
-        }
-
-        fprintf(stderr, "%s : text size: %zu\n", __func__, text.size());
-
-        const std::vector<llama_token> res = llama_tokenize(ctx, text, true);
-
-        fprintf(stderr, "%s : tokens: %zu\n", __func__, res.size());
-
-        {
-            const std::string fname_out = fname_text + ".tokcpp";
-
-            std::ofstream ofs(fname_out);
-            if (!ofs) {
-                fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str());
-                return 1;
-            }
-
-            for (const auto & tok : res) {
-                ofs << tok << " '" << llama_detokenize_spm(ctx, std::vector<int>{tok}) << "'" << std::endl;
-            }
-        }
-
-        fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str());
-    }
-
-    llama_free_model(model);
-    llama_free(ctx);
-
-    llama_backend_free();
-
-    return success ? 0 : 3;
-}
diff --git a/tests/test-tokenizer-0-llama.py b/tests/test-tokenizer-0-llama.py
deleted file mode 100644
index f3d4d7e3d..000000000
--- a/tests/test-tokenizer-0-llama.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# tests with SPM tokenizer
-
-import argparse
-
-from sentencepiece import SentencePieceProcessor
-
-parser = argparse.ArgumentParser()
-parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file")
-parser.add_argument("--fname-tok",   help="path to a text file to tokenize")
-args = parser.parse_args()
-
-dir_tokenizer = args.dir_tokenizer
-
-tokenizer = SentencePieceProcessor(dir_tokenizer + '/tokenizer.model')
-
-tests = [
-    "",
-    " ",
-    "  ",
-    "   ",
-    "\t",
-    "\n",
-    "\t\n",
-    "Hello world",
-    " Hello world",
-    "Hello World",
-    " Hello World",
-    " Hello World!",
-    "Hello, world!",
-    " Hello, world!",
-    " this is 🦙.cpp",
-    "w048 7tuijk dsdfhu",
-    "нещо на Български",
-    "កាន់តែពិសេសអាចខលចេញ",
-    "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
-    "Hello",
-    " Hello",
-    "  Hello",
-    "   Hello",
-    "    Hello",
-    "    Hello\n    Hello",
-]
-
-
-for text in tests:
-    print('text: ', text)
-    print('\nwith bos:')
-    print(tokenizer.encode(text, add_bos=True))
-    print(tokenizer.decode(tokenizer.encode(text, add_bos=True)))
-    print('\nwithout bos:')
-    print(tokenizer.encode(text, add_bos=False))
-    print(tokenizer.decode(tokenizer.encode(text, add_bos=False)))
-
-print("'" + tokenizer.id_to_piece(15043) + "'") # '_Hello'
-print("'" + tokenizer.id_to_piece(29871) + "'") # '_'
-print("'" + tokenizer.decode([15043]) + "'")        # 'Hello'
-print("'" + tokenizer.decode([15043, 15043]) + "'") # 'Hello Hello'
-print("'" + tokenizer.decode([29871, 15043]) + "'")               # ' Hello'
-print("'" + tokenizer.decode([29871, 15043, 29871, 15043]) + "'") # ' Hello  Hello'
-
-print("\n\ntests for C++:\n")
-for text in tests:
-    res = tokenizer.encode(text, add_bos=False)
-
-    k = text.replace('\n', '\\n')
-    k = k.replace('\t', '\\t')
-    k = '"' + k + '"'
-    print("{ %-24s, { " % k, end='')
-    for x in res:
-        print("%7d," % x, end='')
-    print(" }, },")
-
-print(tokenizer.encode('hello'))
-print(tokenizer.encode('world'))
-print(tokenizer.encode(' world'))
-print(tokenizer.encode('hello world'))
-
-fname_tok = args.fname_tok
-if fname_tok:
-    print('tokenizing file: ', fname_tok)
-    fname_out = fname_tok + '.tok'
-    with open(fname_tok, 'r', encoding='utf-8') as f:
-        lines = f.readlines()
-        s = ''.join(lines)
-        res = tokenizer.encode(s, add_bos=True)
-        # write to file
-        with open(fname_out, 'w', encoding='utf-8') as f:
-            for x in res:
-                f.write(str(x) + ' \'' + tokenizer.decode(x) + '\'\n')
-        print('len(res): ', len(res))
-        print('len(lines): ', len(lines))
-    print('results written to: ', fname_out)
diff --git a/tests/test-tokenizer-0.cpp b/tests/test-tokenizer-0.cpp
new file mode 100644
index 000000000..d478f1041
--- /dev/null
+++ b/tests/test-tokenizer-0.cpp
@@ -0,0 +1,292 @@
+#include "llama.h"
+#include "common.h"
+#include "console.h"
+
+#include <cstdio>
+#include <string>
+#include <map>
+#include <vector>
+#include <fstream>
+
+//static const std::map<std::string, std::vector<llama_token>> & k_tests() {
+//    static std::map<std::string, std::vector<llama_token>> _k_tests = {
+//        { ""                      , {  }, },
+//        { " "                     , {     220, }, },
+//        { "  "                    , {     256, }, },
+//        { "   "                   , {     262, }, },
+//        { "\t"                    , {     197, }, },
+//        { "\n"                    , {     198, }, },
+//        { "\n\n"                  , {     271, }, },
+//        { "\n\n\n"                , {    1432, }, },
+//        { "\t\n"                  , {    1602, }, },
+//        { "Hello world"           , {    9906,   1917, }, },
+//        { " Hello world"          , {   22691,   1917, }, },
+//        { "Hello World"           , {    9906,   4435, }, },
+//        { " Hello World"          , {   22691,   4435, }, },
+//        { " Hello World!"         , {   22691,   4435,      0, }, },
+//        { "Hello, world!"         , {    9906,     11,   1917,      0, }, },
+//        { " Hello, world!"        , {   22691,     11,   1917,      0, }, },
+//        { " this is 🦙.cpp"        , {     420,    374,  11410,     99,    247,     13,  11055, }, },
+//        { "w048 7tuijk dsdfhu"    , {      86,  23904,    220,     22,     83,   2005,  42908,  11729,   3013,  17156, }, },
+//        { "нещо на Български"     , {   79862, 102118,  13373,  64571,  34694,   3114, 112203,  80112, }, },
+//        { "កាន់តែពិសេសអាចខលចេញ"   , {   21549,    222,  98629,    241,  45358,    233,  21549,    237,  45358,    224,  21549,    244,  21549,    115,  21549,    253,  45358,    223,  21549,    253,  21549,     95,  98629,    227,  21549,    223,  21549,    249,  21549,    227,  45358,    223,  21549,    231, }, },
+//        { "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", {    9468,    248,    222,    320,   8416,      8,  27623,    114, 102470,   9468,    234,    104,  31643,    320,  36773, 100166,  98634,      8,  26602,    227,    320,   3323,  43465,    430,    706,   1202,   1866,   4037,      8, }, },
+//        { "Hello"                 , {    9906, }, },
+//        { " Hello"                , {   22691, }, },
+//        { "  Hello"               , {     220,  22691, }, },
+//        { "   Hello"              , {     256,  22691, }, },
+//        { "    Hello"             , {     262,  22691, }, },
+//        { "    Hello\n    Hello"  , {     262,  22691,    198,    262,  22691, }, },
+//        { " ("                    , {     320, }, },
+//        { "\n ="                  , {     198,    284, }, },
+//        { "' era"                 , {       6,  11639, }, },
+//        { "Hello, y'all! How are you 😁 ?我想在apple工作1314151天～", {    9906,     11,    379,  65948,      0,   2650,    527,    499,  27623,    223,    949,  37046, 101067,  19000,  23182, 102301,   9263,  18136,     16,  36827,  21909, }, },
+//        { "3"                     , {      18, }, },
+//        { "33"                    , {    1644, }, },
+//        { "333"                   , {    8765, }, },
+//        { "3333"                  , {    8765,     18, }, },
+//        { "33333"                 , {    8765,   1644, }, },
+//        { "333333"                , {    8765,   8765, }, },
+//        { "3333333"               , {    8765,   8765,     18, }, },
+//        { "33333333"              , {    8765,   8765,   1644, }, },
+//        { "333333333"             , {    8765,   8765,   8765, }, },
+//    };
+//
+//    return _k_tests;
+//}
+
+using llama_tests = std::map<std::string, std::vector<llama_token>>;
+
+static llama_tests read_tests(const std::string & fname_inp, const std::string & fname_out) {
+    llama_tests tests;
+
+    std::ifstream ifs_inp(fname_inp);
+    if (!ifs_inp) {
+        fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_inp.c_str());
+        return tests;
+    }
+
+    std::string sraw((std::istreambuf_iterator<char>(ifs_inp)), std::istreambuf_iterator<char>());
+
+    std::ifstream ifs_out(fname_out);
+    if (!ifs_out) {
+        fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str());
+        return tests;
+    }
+
+    std::vector<std::string> sout;
+    for (std::string line; std::getline(ifs_out, line);) {
+        sout.push_back(line);
+    }
+
+    const std::string sep = "\n__ggml_vocab_test__\n";
+
+    std::vector<std::string> sinp;
+
+    size_t pos = 0;
+    while (pos < sraw.size()) {
+        const size_t next = sraw.find(sep, pos);
+        if (next == std::string::npos) {
+            sinp.push_back(sraw.substr(pos));
+            break;
+        }
+        sinp.push_back(sraw.substr(pos, next - pos));
+        pos = next + sep.size();
+    }
+
+    if (sinp.size() != sout.size()) {
+        fprintf(stderr, "%s : error: input and output files have different number of tests\n", __func__);
+        return tests;
+    }
+
+    for (size_t i = 0; i < sinp.size(); ++i) {
+        const std::string & s = sinp[i];
+        const std::string & o = string_strip(sout[i]);
+
+        std::vector<llama_token> toks;
+
+        size_t pos = 0;
+        while (pos < o.size()) {
+            size_t next = o.find(' ', pos);
+            if (next == std::string::npos) {
+                next = o.size();
+            }
+            const std::string stok = o.substr(pos, next - pos);
+            toks.push_back(std::stoi(stok));
+            pos = next + 1;
+        }
+
+        tests[s] = toks;
+    }
+
+    return tests;
+}
+
+int main(int argc, char **argv) {
+    if (argc < 2) {
+        fprintf(stderr, "Usage: %s vocab-file [text-file]\n", argv[0]);
+        return 1;
+    }
+
+    const std::string fname = argv[1];
+
+    const std::string fname_inp = fname + ".inp";
+    const std::string fname_out = fname + ".out";
+
+    std::string fname_text;
+    if (argc > 2) {
+        fname_text = argv[2];
+    }
+
+    fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
+
+    llama_model * model;
+    llama_context * ctx;
+
+    llama_backend_init();
+
+    // load the vocab
+    {
+        auto mparams = llama_model_default_params();
+
+        mparams.vocab_only = true;
+
+        model = llama_load_model_from_file(fname.c_str(), mparams);
+
+        if (model == NULL) {
+            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
+            return 1;
+        }
+
+        auto cparams = llama_context_default_params();
+
+        ctx = llama_new_context_with_model(model, cparams);
+
+        if (ctx == NULL) {
+            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
+            llama_free_model(model);
+            return 1;
+        }
+    }
+
+#ifdef _WIN32
+    // We need this for unicode console support
+    console::init(false, false);
+    atexit([]() { console::cleanup(); });
+#endif
+
+    bool success = true;
+
+    const auto k_tests = [&]() -> llama_tests {
+        if (!fname_text.empty()) {
+            return {};
+        }
+
+        const auto res = read_tests(fname_inp, fname_out);
+
+        if (res.empty()) {
+            fprintf(stderr, "%s : error: no tests found\n", __func__);
+            exit(1);
+        }
+
+        return res;
+    }();
+
+    const bool add_special = false;
+
+    for (const auto & test_kv : k_tests) {
+        const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, add_special);
+
+        printf("\n");
+        printf("src: '%s'\n", test_kv.first.c_str());
+        printf("res: '%s'\n", llama_detokenize_bpe(ctx, res).c_str());
+        printf("tok: ");
+        for (const auto & tok : res) {
+            printf("%d ", tok);
+        }
+        printf("\n");
+
+        bool correct = res.size() == test_kv.second.size();
+        for (int i = 0; i < (int) res.size() && correct; ++i) {
+            if (test_kv.second[i] != res[i]) {
+                correct = false;
+            }
+        }
+
+        if (!correct) {
+            fprintf(stderr, "%s : failed test:    '%s'\n", __func__, test_kv.first.c_str());
+            fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
+                llama_detokenize_bpe(ctx, res).c_str(),
+                llama_detokenize_bpe(ctx, test_kv.second).c_str());
+            fprintf(stderr, "%s : expected tokens: ", __func__);
+            for (const auto & t : test_kv.second) {
+                fprintf(stderr, "%6d '%s', ", t, llama_token_to_piece(ctx, t).c_str());
+            }
+            fprintf(stderr, "\n");
+            fprintf(stderr, "%s : got tokens:      ", __func__);
+            for (const auto & t : res) {
+                fprintf(stderr, "%6d '%s', ", t, llama_token_to_piece(ctx, t).c_str());
+            }
+            fprintf(stderr, "\n");
+
+            success = false;
+        }
+    }
+
+    if (!fname_text.empty()) {
+        fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str());
+
+        std::string text;
+        {
+            std::ifstream ifs(fname_text);
+            if (!ifs) {
+                fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_text.c_str());
+                return 1;
+            }
+            text = std::string(std::istreambuf_iterator<char>(ifs), std::istreambuf_iterator<char>());
+        }
+
+        fprintf(stderr, "%s : text size: %zu\n", __func__, text.size());
+
+        std::vector<llama_token> res;
+
+        {
+            const auto t_start = ggml_time_us();
+
+            res = llama_tokenize(ctx, text, add_special);
+
+            const auto t_end = ggml_time_us();
+
+            fprintf(stderr, "%s : tokenized in %.3f ms (cpp)\n", __func__, (t_end - t_start) / 1000.0);
+        }
+
+        fprintf(stderr, "%s : tokens: %zu\n", __func__, res.size());
+
+        {
+            const std::string fname_out = fname_text + ".tokcpp";
+
+            std::ofstream ofs(fname_out);
+            if (!ofs) {
+                fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str());
+                return 1;
+            }
+
+            for (const auto & tok : res) {
+                //ofs << tok << " '" << string_strip(llama_detokenize_bpe(ctx, std::vector<int>{tok})) << "'" << std::endl;
+                ofs << tok << "\n";
+            }
+        }
+
+        fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str());
+    }
+
+    llama_free_model(model);
+    llama_free(ctx);
+
+    llama_backend_free();
+
+    printf("\n");
+    printf("Tests %s\n", success ? "passed" : "failed");
+
+    return success ? 0 : 3;
+}
diff --git a/tests/test-tokenizer-0.py b/tests/test-tokenizer-0.py
new file mode 100644
index 000000000..cd760d1ce
--- /dev/null
+++ b/tests/test-tokenizer-0.py
@@ -0,0 +1,46 @@
+import time
+import argparse
+
+from transformers import AutoTokenizer
+
+parser = argparse.ArgumentParser()
+parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file")
+parser.add_argument("--fname-tok",   help="path to a text file to tokenize", required=True)
+args = parser.parse_args()
+
+dir_tokenizer = args.dir_tokenizer
+fname_tok = args.fname_tok
+
+tokenizer = AutoTokenizer.from_pretrained(dir_tokenizer)
+
+print('tokenizing file: ', fname_tok) # noqa: NP100
+fname_out = fname_tok + '.tok'
+with open(fname_tok, 'r', encoding='utf-8') as f:
+    lines = f.readlines()
+    s = ''.join(lines)
+    t_start = time.time()
+    res = tokenizer.encode(s, add_special_tokens=False)
+    t_end = time.time()
+    print('\nmain : tokenized in', "{:.3f}".format(1000.0 * (t_end - t_start)), 'ms (py)') # noqa: NP100
+    with open(fname_out, 'w', encoding='utf-8') as f:
+        for x in res:
+            # LLaMA v3 for some reason strips the space for these tokens (and others)
+            # if x == 662:
+            #     f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n')
+            # elif x == 1174:
+            #     f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n')
+            # elif x == 2564:
+            #     f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n')
+            # elif x == 758:
+            #     f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n')
+            # elif x == 949:
+            #     f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n')
+            # elif x == 5354:
+            #     f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n')
+            # else:
+            #     f.write(str(x) + ' \'' + tokenizer.decode(x) + '\'\n')
+            # f.write(str(x) + ' \'' + tokenizer.decode(x).strip() + '\'\n')
+            f.write(str(x) + '\n')
+    print('len(res): ', len(res)) # noqa: NP100
+    print('len(lines): ', len(lines)) # noqa: NP100
+print('results written to: ', fname_out) # noqa: NP100
diff --git a/tests/test-tokenizer-0.sh b/tests/test-tokenizer-0.sh
new file mode 100755
index 000000000..2fb8632d8
--- /dev/null
+++ b/tests/test-tokenizer-0.sh
@@ -0,0 +1,34 @@
+#!/bin/bash
+#
+# Usage:
+#
+#   test-tokenizer-0.sh <name> <input>
+#
+
+if [ $# -ne 2 ]; then
+    printf "Usage: $0 <name> <input>\n"
+    exit 1
+fi
+
+name=$1
+input=$2
+
+make -j tests/test-tokenizer-0
+
+printf "Testing %s on %s ...\n" $name $input
+
+python3 ./tests/test-tokenizer-0.py ./models/tokenizers/$name --fname-tok $input > /tmp/test-tokenizer-0-$name-py.log 2>&1
+cat /tmp/test-tokenizer-0-$name-py.log | grep "tokenized in"
+
+./tests/test-tokenizer-0 ./models/ggml-vocab-$name.gguf $input > /tmp/test-tokenizer-0-$name-cpp.log 2>&1
+cat /tmp/test-tokenizer-0-$name-cpp.log | grep "tokenized in"
+
+diff $input.tok $input.tokcpp > /dev/null 2>&1
+
+if [ $? -eq 0 ]; then
+    printf "Tokenization is correct!\n"
+else
+    diff $input.tok $input.tokcpp | head -n 32
+
+    printf "Tokenization differs!\n"
+fi
diff --git a/tests/test-tokenizer-1-bpe.cpp b/tests/test-tokenizer-1-bpe.cpp
index 3596ce55a..209a04ad6 100644
--- a/tests/test-tokenizer-1-bpe.cpp
+++ b/tests/test-tokenizer-1-bpe.cpp
@@ -13,15 +13,27 @@
 #include <vector>
 
 int main(int argc, char **argv) {
-    if (argc < 2) {
-        fprintf(stderr, "Usage: %s <vocab-file>\n", argv[0]);
+    if (argc < 2 || argc > 3) {
+        fprintf(stderr, "Usage: %s <vocab-file> [--ignore-merges]\n", argv[0]);
         return 1;
     }
 
     const std::string fname = argv[1];
+    bool ignore_merges = false;
+    if (argc == 3) {
+        if (std::strcmp(argv[2], "--ignore-merges") != 0) {
+            fprintf(stderr, "Usage: %s <vocab-file> [--ignore-merges]\n", argv[0]);
+            return 1;
+        }
+        ignore_merges = true;
+    }
 
     fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
 
+    if (ignore_merges) {
+        fprintf(stderr, "%s : ignoring merges for tokens inside vocab\n", __func__);
+    }
+
     llama_model * model;
     llama_context * ctx;
 
@@ -64,8 +76,20 @@ int main(int argc, char **argv) {
     for (int i = 0; i < n_vocab; ++i) {
         std::string str = llama_detokenize_bpe(ctx, std::vector<int>(1, i));
         try {
-            auto cps = codepoints_from_utf8(str);
-            std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
+            auto cps = unicode_cpts_from_utf8(str);
+            std::vector<llama_token> tokens = llama_tokenize(ctx, str, false, true);
+            if (ignore_merges && tokens.size() > 1) {
+                fprintf(stderr,
+                        "%s : error: token %d detokenizes to '%s'(%zu) but "
+                        "tokenization of this to multiple tokens: [",
+                        __func__, i, str.c_str(), str.length());
+                fprintf(stderr, "%d", tokens[0]);
+                for (size_t i = 1; i < tokens.size(); i++) {
+                    fprintf(stderr, ", %d", tokens[i]);
+                }
+                fprintf(stderr, "]\n");
+                return 2;
+            }
             std::string check = llama_detokenize_bpe(ctx, tokens);
             if (check != str) {
                 fprintf(stderr, "%s : error: token %d detokenizes to '%s'(%zu) but tokenization of this detokenizes to '%s'(%zu)\n",
@@ -97,7 +121,7 @@ int main(int argc, char **argv) {
                         continue;
                     }
 
-                    std::string str = codepoint_to_utf8(cp);
+                    std::string str = unicode_cpt_to_utf8(cp);
                     std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
                     std::string check = llama_detokenize_bpe(ctx, tokens);
                     if (cp != 9601 && str != check) {
diff --git a/tests/test-tokenizer-1-llama.cpp b/tests/test-tokenizer-1-spm.cpp
similarity index 96%
rename from tests/test-tokenizer-1-llama.cpp
rename to tests/test-tokenizer-1-spm.cpp
index 9333f8686..ac2333dda 100644
--- a/tests/test-tokenizer-1-llama.cpp
+++ b/tests/test-tokenizer-1-spm.cpp
@@ -12,7 +12,7 @@
 #include <thread>
 #include <vector>
 
-int main(int argc, char **argv) {
+int main(int argc, char ** argv) {
     if (argc < 2) {
         fprintf(stderr, "Usage: %s <vocab-file>\n", argv[0]);
         return 1;
@@ -85,7 +85,7 @@ int main(int argc, char **argv) {
                         continue;
                     }
 
-                    std::string str = codepoint_to_utf8(cp);
+                    std::string str = unicode_cpt_to_utf8(cp);
                     std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
                     std::string check = llama_detokenize_spm(ctx, tokens);
                     if (cp != 9601 && str != check) {
diff --git a/tests/test-tokenizer-random.py b/tests/test-tokenizer-random.py
new file mode 100644
index 000000000..5b2ab8ef7
--- /dev/null
+++ b/tests/test-tokenizer-random.py
@@ -0,0 +1,295 @@
+# Test libllama tokenizer == AutoTokenizer.
+# Brute force random tokens/text generation.
+#
+# Sample usage:
+#
+#   python3 tests/test-tokenizer-random.py ./models/ggml-vocab-llama-bpe.gguf ./models/tokenizers/llama-bpe
+#
+
+import time
+import logging
+import argparse
+import subprocess
+import random
+
+from typing import Iterator
+
+import cffi
+from transformers import AutoTokenizer, PreTrainedTokenizerBase
+
+logger = logging.getLogger("test-tokenizer-random-bpe")
+
+
+class LibLlama:
+
+    DEFAULT_PATH_LLAMA_H = "./llama.h"
+    DEFAULT_PATH_LIBLLAMA = "./build/libllama.so"  # CMakeLists.txt: BUILD_SHARED_LIBS ON
+
+    def __init__(self, path_llama_h: str = None, path_libllama: str = None):
+        path_llama_h = path_llama_h or self.DEFAULT_PATH_LLAMA_H
+        path_libllama = path_libllama or self.DEFAULT_PATH_LIBLLAMA
+        (self.ffi, self.lib) = self._load_libllama_cffi(path_llama_h, path_libllama)
+        self.lib.llama_backend_init()
+
+    def _load_libllama_cffi(self, path_llama_h: str, path_libllama: str):
+        cmd = ["gcc", "-E", "-P", "-D__restrict=", "-D__attribute__(x)=", "-D__asm__(x)=", path_llama_h]
+        res = subprocess.run(cmd, stdout=subprocess.PIPE)
+        assert (res.returncode == 0)
+        source = res.stdout.decode()
+        ffi = cffi.FFI()
+        if True:  # workarounds for pycparser
+            source = "typedef struct { } __builtin_va_list;" + "\n" + source
+            source = source.replace("sizeof (int)",    str(ffi.sizeof("int")))
+            source = source.replace("sizeof (void *)", str(ffi.sizeof("void*")))
+            source = source.replace("sizeof (size_t)", str(ffi.sizeof("size_t")))
+            source = source.replace("sizeof(int32_t)", str(ffi.sizeof("int32_t")))
+        ffi.cdef(source, override=True)
+        lib = ffi.dlopen(path_libllama)
+        return (ffi, lib)
+
+    def model_default_params(self, **kwargs):
+        mparams = self.lib.llama_model_default_params()
+        for k, v in kwargs.items():
+            setattr(mparams, k, v)
+        return mparams
+
+    def context_default_params(self, **kwargs):
+        cparams = self.lib.llama_context_default_params()
+        for k, v in kwargs.items():
+            setattr(cparams, k, v)
+        return cparams
+
+
+class LibLlamaModel:
+
+    def __init__(self, libllama: LibLlama, path_model: str, mparams={}, cparams={}):
+        self.lib = libllama.lib
+        self.ffi = libllama.ffi
+        if isinstance(mparams, dict):
+            mparams = libllama.model_default_params(**mparams)
+        self.model = self.lib.llama_load_model_from_file(path_model.encode(), mparams)
+        if not self.model:
+            raise RuntimeError("error: failed to load model '%s'" % path_model)
+        if isinstance(cparams, dict):
+            cparams = libllama.context_default_params(**cparams)
+        self.ctx = self.lib.llama_new_context_with_model(self.model, cparams)
+        if not self.ctx:
+            raise RuntimeError("error: failed to create context for model '%s'" % path_model)
+        n_tokens_max = self.lib.llama_n_ctx(self.ctx)
+        self.token_ids = self.ffi.new("llama_token[]", n_tokens_max)
+
+    def free(self):
+        if self.ctx:
+            self.lib.llama_free(self.ctx)
+        if self.model:
+            self.lib.llama_free_model(self.model)
+        self.ctx = None
+        self.model = None
+        self.lib = None
+
+    def tokenize(self, text: str, n_tokens_max: int = 0, add_special: bool = False, parse_special: bool = False) -> list[int]:
+        n_tokens_max = n_tokens_max if n_tokens_max > 0 else len(self.token_ids)
+        text = text.encode("utf-8")
+        num = self.lib.llama_tokenize(self.model, text, len(text), self.token_ids, n_tokens_max, add_special, parse_special)
+        if num < 0:
+            return []
+        return list(self.token_ids[0:num])
+
+
+def generator_custom_text() -> Iterator[str]:
+    """General tests"""
+    yield from [
+        "",
+        " ",
+        "  ",
+        "   ",
+        "\t",
+        "\n",
+        "\n\n",
+        "\n\n\n",
+        "\t\n",
+        "Hello world",
+        " Hello world",
+        "Hello World",
+        " Hello World",
+        " Hello World!",
+        "Hello, world!",
+        " Hello, world!",
+        " this is 🦙.cpp",
+        "w048 7tuijk dsdfhu",
+        "нещо на Български",
+        "កាន់តែពិសេសអាចខលចេញ",
+        "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
+        "Hello",
+        " Hello",
+        "  Hello",
+        "   Hello",
+        "    Hello",
+        "    Hello\n    Hello",
+        " (",
+        "\n =",
+        "' era",
+        "Hello, y'all! How are you 😁 ?我想在apple工作1314151天～",
+        "3",
+        "33",
+        "333",
+        "3333",
+        "33333",
+        "333333",
+        "3333333",
+        "33333333",
+        "333333333",
+    ]
+
+
+def generator_custom_text_edge_cases() -> Iterator[str]:
+    """Edge cases found while debugging"""
+    yield from [
+        '\x1f-a',   # unicode_ranges_control, {0x00001C, 0x00001F}
+        '¼-a',      # unicode_ranges_digit, 0x00BC
+        '½-a',      # unicode_ranges_digit, 0x00BD
+        '¾-a',      # unicode_ranges_digit, 0x00BE
+        'a 〇b',    # unicode_ranges_digit, 0x3007
+        'Ⅵ-a',     # unicode_ranges_digit, {0x00002150, 0x0000218F} // Number Forms
+        '\uFEFF//', # unicode_ranges_control, 0xFEFF (BOM)
+        '<s>a'      # TODO: Phi-3 fail
+    ]
+
+
+def generator_random_chars(iterations = 100) -> Iterator[str]:
+    """Brute force random text with simple characters"""
+
+    WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5)
+    CHARS = list(set("""
+        ABCDEFGHIJKLMNOPQRSTUVWXYZ
+        abcdefghijklmnopqrstuvwxyz
+        ÁÉÍÓÚÀÈÌÒÙÂÊÎÔÛÄËÏÖÜ
+        áéíóúàèìòùâêîôûäëïöü
+        .-,*/-+ª!"·$%&/()=?¿[]{}<>\\|@#~½¬~;:_
+    """))
+
+    rand = random.Random()
+    for m in range(iterations):
+        rand.seed(m)
+        text = []
+        num_words = rand.randint(300, 400)
+        for i in range(num_words):
+            k = rand.randint(1, 7)
+            word = rand.choices(CHARS, k=k)
+            space = rand.choice(WHITESPACES)
+            text.append("".join(word) + space)
+        yield "".join(text)
+
+
+def generator_random_vocab_chars(tokenizer: PreTrainedTokenizerBase, iterations = 100) -> Iterator[str]:
+    """Brute force random text with vocab characters"""
+
+    vocab_ids = list(tokenizer.vocab.values())
+    vocab_text = tokenizer.decode(vocab_ids, skip_special_tokens=True)
+    vocab_chars = list(set(vocab_text))
+    del vocab_ids, vocab_text
+
+    rand = random.Random()
+    for m in range(iterations):
+        rand.seed(m)
+        text = rand.choices(vocab_chars, k=1024)
+        yield "".join(text)
+
+
+def generator_random_vocab_tokens(tokenizer: PreTrainedTokenizerBase, iterations = 100) -> Iterator[str]:
+    """Brute force random text from vocab tokens"""
+
+    space_id = tokenizer.encode(" ", add_special_tokens=False)[0]
+    vocab_ids = list(tokenizer.vocab.values())
+    vocab_ids = list(sorted(vocab_ids + vocab_ids))
+    for i in range(1, len(vocab_ids), 2):
+        vocab_ids[i] = space_id
+    vocab_tokens = tokenizer.decode(vocab_ids, skip_special_tokens=True)
+    vocab_tokens = vocab_tokens.split(" ")
+    del vocab_ids
+
+    yield from vocab_tokens
+
+    rand = random.Random()
+    for m in range(iterations):
+        rand.seed(m)
+        text = []
+        num_words = rand.randint(300, 400)
+        for i in range(num_words):
+            k = rand.randint(1, 3)
+            tokens = rand.choices(vocab_tokens, k=k)
+            tokens = [t.strip(" \n\r\t") for t in tokens]
+            sep = rand.choice("     \n\r\t")
+            text.append("".join(tokens) + sep)
+        yield "".join(text)
+
+
+def generator_random_bytes(iterations = 100) -> Iterator[str]:
+    """Brute force random bytes"""
+
+    WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5)
+
+    rand = random.Random()
+    for m in range(iterations):
+        rand.seed(m)
+        text = []
+        num_words = rand.randint(300, 400)
+        for i in range(num_words):
+            k = rand.randint(1, 8)
+            word = [chr(r) for r in rand.randbytes(k) if r]
+            word.append(rand.choice(WHITESPACES))
+            text.append("".join(word))
+        yield "".join(text)
+
+
+def test_compare_tokenizer(model: LibLlamaModel, tokenizer: PreTrainedTokenizerBase, generator: Iterator[str]):
+
+    def find_first_mismatch(ids1: list[int], ids2: list[int]):
+        for i, (a,b) in enumerate(zip(ids1, ids2)):
+            if a != b:
+                return i
+        if len(ids1) == len(ids2):
+            return -1
+        return min(len(ids1), len(ids2))
+
+    t0 = time.perf_counter()
+    logger.info("%s: %s" % (generator.__name__, "ini"))
+    for text in generator:
+        ids1 = model.tokenize(text, add_special=False, parse_special=False)
+        ids2 = tokenizer.encode(text, add_special_tokens=False)
+        if ids1 != ids2:
+            i = find_first_mismatch(ids1, ids2)
+            ids1 = list(ids1)[max(0, i - 2) : i + 2 + 1]
+            ids2 = list(ids2)[max(0, i - 2) : i + 2 + 1]
+            text2 = tokenizer.decode(ids2, skip_special_tokens=True)
+            assert (text2 in text)
+            logger.info(" Text:     " + repr(text2))
+            logger.info(" TokenIDs: " + str(ids1))
+            logger.info(" Expected: " + str(ids2))
+            raise Exception()
+    t1 = time.perf_counter()
+    logger.info("%s: end, time: %.3f secs" % (generator.__name__, t1 - t0))
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("vocab_file", help="path to vocab 'gguf' file")
+    parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file")
+    parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
+    args = parser.parse_args()
+
+    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
+
+    model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=2048))
+
+    tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer)
+
+    test_compare_tokenizer(model, tokenizer, generator_custom_text())
+    test_compare_tokenizer(model, tokenizer, generator_custom_text_edge_cases())
+    test_compare_tokenizer(model, tokenizer, generator_random_chars(10_000))
+    test_compare_tokenizer(model, tokenizer, generator_random_vocab_chars(tokenizer, 10_000))
+    test_compare_tokenizer(model, tokenizer, generator_random_vocab_tokens(tokenizer, 10_000))
+    # test_compare_tokenizer(model, tokenizer, generator_random_bytes(10_000)) # FAIL
+
+    model.free()
diff --git a/unicode-data.cpp b/unicode-data.cpp
new file mode 100644
index 000000000..c54175fc3
--- /dev/null
+++ b/unicode-data.cpp
@@ -0,0 +1,2183 @@
+#include "unicode-data.h"
+
+#include <cstdint>
+#include <map>
+#include <utility>
+#include <vector>
+
+// generated with scripts/gen-unicode-data.py
+//
+// TODO: generate unicode_map_nfd
+
+const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_number = {
+{0x00000030, 0x00000039}, {0x000000B2, 0x000000B3}, {0x000000B9, 0x000000B9}, {0x000000BC, 0x000000BE},
+{0x00000660, 0x00000669}, {0x000006F0, 0x000006F9}, {0x000007C0, 0x000007C9}, {0x00000966, 0x0000096F},
+{0x000009E6, 0x000009EF}, {0x000009F4, 0x000009F9}, {0x00000A66, 0x00000A6F}, {0x00000AE6, 0x00000AEF},
+{0x00000B66, 0x00000B6F}, {0x00000B72, 0x00000B77}, {0x00000BE6, 0x00000BF2}, {0x00000C66, 0x00000C6F},
+{0x00000C78, 0x00000C7E}, {0x00000CE6, 0x00000CEF}, {0x00000D58, 0x00000D5E}, {0x00000D66, 0x00000D78},
+{0x00000DE6, 0x00000DEF}, {0x00000E50, 0x00000E59}, {0x00000ED0, 0x00000ED9}, {0x00000F20, 0x00000F33},
+{0x00001040, 0x00001049}, {0x00001090, 0x00001099}, {0x00001369, 0x0000137C}, {0x000016EE, 0x000016F0},
+{0x000017E0, 0x000017E9}, {0x000017F0, 0x000017F9}, {0x00001810, 0x00001819}, {0x00001946, 0x0000194F},
+{0x000019D0, 0x000019DA}, {0x00001A80, 0x00001A89}, {0x00001A90, 0x00001A99}, {0x00001B50, 0x00001B59},
+{0x00001BB0, 0x00001BB9}, {0x00001C40, 0x00001C49}, {0x00001C50, 0x00001C59}, {0x00002070, 0x00002070},
+{0x00002074, 0x00002079}, {0x00002080, 0x00002089}, {0x00002150, 0x00002182}, {0x00002185, 0x00002189},
+{0x00002460, 0x0000249B}, {0x000024EA, 0x000024FF}, {0x00002776, 0x00002793}, {0x00002CFD, 0x00002CFD},
+{0x00003007, 0x00003007}, {0x00003021, 0x00003029}, {0x00003038, 0x0000303A}, {0x00003192, 0x00003195},
+{0x00003220, 0x00003229}, {0x00003248, 0x0000324F}, {0x00003251, 0x0000325F}, {0x00003280, 0x00003289},
+{0x000032B1, 0x000032BF}, {0x0000A620, 0x0000A629}, {0x0000A6E6, 0x0000A6EF}, {0x0000A830, 0x0000A835},
+{0x0000A8D0, 0x0000A8D9}, {0x0000A900, 0x0000A909}, {0x0000A9D0, 0x0000A9D9}, {0x0000A9F0, 0x0000A9F9},
+{0x0000AA50, 0x0000AA59}, {0x0000ABF0, 0x0000ABF9}, {0x0000FF10, 0x0000FF19}, {0x00010107, 0x00010133},
+{0x00010140, 0x00010178}, {0x0001018A, 0x0001018B}, {0x000102E1, 0x000102FB}, {0x00010320, 0x00010323},
+{0x00010341, 0x00010341}, {0x0001034A, 0x0001034A}, {0x000103D1, 0x000103D5}, {0x000104A0, 0x000104A9},
+{0x00010858, 0x0001085F}, {0x00010879, 0x0001087F}, {0x000108A7, 0x000108AF}, {0x000108FB, 0x000108FF},
+{0x00010916, 0x0001091B}, {0x000109BC, 0x000109BD}, {0x000109C0, 0x000109CF}, {0x000109D2, 0x000109FF},
+{0x00010A40, 0x00010A48}, {0x00010A7D, 0x00010A7E}, {0x00010A9D, 0x00010A9F}, {0x00010AEB, 0x00010AEF},
+{0x00010B58, 0x00010B5F}, {0x00010B78, 0x00010B7F}, {0x00010BA9, 0x00010BAF}, {0x00010CFA, 0x00010CFF},
+{0x00010D30, 0x00010D39}, {0x00010E60, 0x00010E7E}, {0x00010F1D, 0x00010F26}, {0x00010F51, 0x00010F54},
+{0x00010FC5, 0x00010FCB}, {0x00011052, 0x0001106F}, {0x000110F0, 0x000110F9}, {0x00011136, 0x0001113F},
+{0x000111D0, 0x000111D9}, {0x000111E1, 0x000111F4}, {0x000112F0, 0x000112F9}, {0x00011450, 0x00011459},
+{0x000114D0, 0x000114D9}, {0x00011650, 0x00011659}, {0x000116C0, 0x000116C9}, {0x00011730, 0x0001173B},
+{0x000118E0, 0x000118F2}, {0x00011950, 0x00011959}, {0x00011C50, 0x00011C6C}, {0x00011D50, 0x00011D59},
+{0x00011DA0, 0x00011DA9}, {0x00011F50, 0x00011F59}, {0x00011FC0, 0x00011FD4}, {0x00012400, 0x0001246E},
+{0x00016A60, 0x00016A69}, {0x00016AC0, 0x00016AC9}, {0x00016B50, 0x00016B59}, {0x00016B5B, 0x00016B61},
+{0x00016E80, 0x00016E96}, {0x0001D2C0, 0x0001D2D3}, {0x0001D2E0, 0x0001D2F3}, {0x0001D360, 0x0001D378},
+{0x0001D7CE, 0x0001D7FF}, {0x0001E140, 0x0001E149}, {0x0001E2F0, 0x0001E2F9}, {0x0001E4F0, 0x0001E4F9},
+{0x0001E8C7, 0x0001E8CF}, {0x0001E950, 0x0001E959}, {0x0001EC71, 0x0001ECAB}, {0x0001ECAD, 0x0001ECAF},
+{0x0001ECB1, 0x0001ECB4}, {0x0001ED01, 0x0001ED2D}, {0x0001ED2F, 0x0001ED3D}, {0x0001F100, 0x0001F10C},
+{0x0001FBF0, 0x0001FBF9},
+};
+
+const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_letter = {
+{0x00000041, 0x0000005A}, {0x00000061, 0x0000007A}, {0x000000AA, 0x000000AA}, {0x000000B5, 0x000000B5},
+{0x000000BA, 0x000000BA}, {0x000000C0, 0x000000D6}, {0x000000D8, 0x000000F6}, {0x000000F8, 0x000002C1},
+{0x000002C6, 0x000002D1}, {0x000002E0, 0x000002E4}, {0x000002EC, 0x000002EC}, {0x000002EE, 0x000002EE},
+{0x00000370, 0x00000374}, {0x00000376, 0x00000377}, {0x0000037A, 0x0000037D}, {0x0000037F, 0x0000037F},
+{0x00000386, 0x00000386}, {0x00000388, 0x0000038A}, {0x0000038C, 0x0000038C}, {0x0000038E, 0x000003A1},
+{0x000003A3, 0x000003F5}, {0x000003F7, 0x00000481}, {0x0000048A, 0x0000052F}, {0x00000531, 0x00000556},
+{0x00000559, 0x00000559}, {0x00000560, 0x00000588}, {0x000005D0, 0x000005EA}, {0x000005EF, 0x000005F2},
+{0x00000620, 0x0000064A}, {0x0000066E, 0x0000066F}, {0x00000671, 0x000006D3}, {0x000006D5, 0x000006D5},
+{0x000006E5, 0x000006E6}, {0x000006EE, 0x000006EF}, {0x000006FA, 0x000006FC}, {0x000006FF, 0x000006FF},
+{0x00000710, 0x00000710}, {0x00000712, 0x0000072F}, {0x0000074D, 0x000007A5}, {0x000007B1, 0x000007B1},
+{0x000007CA, 0x000007EA}, {0x000007F4, 0x000007F5}, {0x000007FA, 0x000007FA}, {0x00000800, 0x00000815},
+{0x0000081A, 0x0000081A}, {0x00000824, 0x00000824}, {0x00000828, 0x00000828}, {0x00000840, 0x00000858},
+{0x00000860, 0x0000086A}, {0x00000870, 0x00000887}, {0x00000889, 0x0000088E}, {0x000008A0, 0x000008C9},
+{0x00000904, 0x00000939}, {0x0000093D, 0x0000093D}, {0x00000950, 0x00000950}, {0x00000958, 0x00000961},
+{0x00000971, 0x00000980}, {0x00000985, 0x0000098C}, {0x0000098F, 0x00000990}, {0x00000993, 0x000009A8},
+{0x000009AA, 0x000009B0}, {0x000009B2, 0x000009B2}, {0x000009B6, 0x000009B9}, {0x000009BD, 0x000009BD},
+{0x000009CE, 0x000009CE}, {0x000009DC, 0x000009DD}, {0x000009DF, 0x000009E1}, {0x000009F0, 0x000009F1},
+{0x000009FC, 0x000009FC}, {0x00000A05, 0x00000A0A}, {0x00000A0F, 0x00000A10}, {0x00000A13, 0x00000A28},
+{0x00000A2A, 0x00000A30}, {0x00000A32, 0x00000A33}, {0x00000A35, 0x00000A36}, {0x00000A38, 0x00000A39},
+{0x00000A59, 0x00000A5C}, {0x00000A5E, 0x00000A5E}, {0x00000A72, 0x00000A74}, {0x00000A85, 0x00000A8D},
+{0x00000A8F, 0x00000A91}, {0x00000A93, 0x00000AA8}, {0x00000AAA, 0x00000AB0}, {0x00000AB2, 0x00000AB3},
+{0x00000AB5, 0x00000AB9}, {0x00000ABD, 0x00000ABD}, {0x00000AD0, 0x00000AD0}, {0x00000AE0, 0x00000AE1},
+{0x00000AF9, 0x00000AF9}, {0x00000B05, 0x00000B0C}, {0x00000B0F, 0x00000B10}, {0x00000B13, 0x00000B28},
+{0x00000B2A, 0x00000B30}, {0x00000B32, 0x00000B33}, {0x00000B35, 0x00000B39}, {0x00000B3D, 0x00000B3D},
+{0x00000B5C, 0x00000B5D}, {0x00000B5F, 0x00000B61}, {0x00000B71, 0x00000B71}, {0x00000B83, 0x00000B83},
+{0x00000B85, 0x00000B8A}, {0x00000B8E, 0x00000B90}, {0x00000B92, 0x00000B95}, {0x00000B99, 0x00000B9A},
+{0x00000B9C, 0x00000B9C}, {0x00000B9E, 0x00000B9F}, {0x00000BA3, 0x00000BA4}, {0x00000BA8, 0x00000BAA},
+{0x00000BAE, 0x00000BB9}, {0x00000BD0, 0x00000BD0}, {0x00000C05, 0x00000C0C}, {0x00000C0E, 0x00000C10},
+{0x00000C12, 0x00000C28}, {0x00000C2A, 0x00000C39}, {0x00000C3D, 0x00000C3D}, {0x00000C58, 0x00000C5A},
+{0x00000C5D, 0x00000C5D}, {0x00000C60, 0x00000C61}, {0x00000C80, 0x00000C80}, {0x00000C85, 0x00000C8C},
+{0x00000C8E, 0x00000C90}, {0x00000C92, 0x00000CA8}, {0x00000CAA, 0x00000CB3}, {0x00000CB5, 0x00000CB9},
+{0x00000CBD, 0x00000CBD}, {0x00000CDD, 0x00000CDE}, {0x00000CE0, 0x00000CE1}, {0x00000CF1, 0x00000CF2},
+{0x00000D04, 0x00000D0C}, {0x00000D0E, 0x00000D10}, {0x00000D12, 0x00000D3A}, {0x00000D3D, 0x00000D3D},
+{0x00000D4E, 0x00000D4E}, {0x00000D54, 0x00000D56}, {0x00000D5F, 0x00000D61}, {0x00000D7A, 0x00000D7F},
+{0x00000D85, 0x00000D96}, {0x00000D9A, 0x00000DB1}, {0x00000DB3, 0x00000DBB}, {0x00000DBD, 0x00000DBD},
+{0x00000DC0, 0x00000DC6}, {0x00000E01, 0x00000E30}, {0x00000E32, 0x00000E33}, {0x00000E40, 0x00000E46},
+{0x00000E81, 0x00000E82}, {0x00000E84, 0x00000E84}, {0x00000E86, 0x00000E8A}, {0x00000E8C, 0x00000EA3},
+{0x00000EA5, 0x00000EA5}, {0x00000EA7, 0x00000EB0}, {0x00000EB2, 0x00000EB3}, {0x00000EBD, 0x00000EBD},
+{0x00000EC0, 0x00000EC4}, {0x00000EC6, 0x00000EC6}, {0x00000EDC, 0x00000EDF}, {0x00000F00, 0x00000F00},
+{0x00000F40, 0x00000F47}, {0x00000F49, 0x00000F6C}, {0x00000F88, 0x00000F8C}, {0x00001000, 0x0000102A},
+{0x0000103F, 0x0000103F}, {0x00001050, 0x00001055}, {0x0000105A, 0x0000105D}, {0x00001061, 0x00001061},
+{0x00001065, 0x00001066}, {0x0000106E, 0x00001070}, {0x00001075, 0x00001081}, {0x0000108E, 0x0000108E},
+{0x000010A0, 0x000010C5}, {0x000010C7, 0x000010C7}, {0x000010CD, 0x000010CD}, {0x000010D0, 0x000010FA},
+{0x000010FC, 0x00001248}, {0x0000124A, 0x0000124D}, {0x00001250, 0x00001256}, {0x00001258, 0x00001258},
+{0x0000125A, 0x0000125D}, {0x00001260, 0x00001288}, {0x0000128A, 0x0000128D}, {0x00001290, 0x000012B0},
+{0x000012B2, 0x000012B5}, {0x000012B8, 0x000012BE}, {0x000012C0, 0x000012C0}, {0x000012C2, 0x000012C5},
+{0x000012C8, 0x000012D6}, {0x000012D8, 0x00001310}, {0x00001312, 0x00001315}, {0x00001318, 0x0000135A},
+{0x00001380, 0x0000138F}, {0x000013A0, 0x000013F5}, {0x000013F8, 0x000013FD}, {0x00001401, 0x0000166C},
+{0x0000166F, 0x0000167F}, {0x00001681, 0x0000169A}, {0x000016A0, 0x000016EA}, {0x000016F1, 0x000016F8},
+{0x00001700, 0x00001711}, {0x0000171F, 0x00001731}, {0x00001740, 0x00001751}, {0x00001760, 0x0000176C},
+{0x0000176E, 0x00001770}, {0x00001780, 0x000017B3}, {0x000017D7, 0x000017D7}, {0x000017DC, 0x000017DC},
+{0x00001820, 0x00001878}, {0x00001880, 0x00001884}, {0x00001887, 0x000018A8}, {0x000018AA, 0x000018AA},
+{0x000018B0, 0x000018F5}, {0x00001900, 0x0000191E}, {0x00001950, 0x0000196D}, {0x00001970, 0x00001974},
+{0x00001980, 0x000019AB}, {0x000019B0, 0x000019C9}, {0x00001A00, 0x00001A16}, {0x00001A20, 0x00001A54},
+{0x00001AA7, 0x00001AA7}, {0x00001B05, 0x00001B33}, {0x00001B45, 0x00001B4C}, {0x00001B83, 0x00001BA0},
+{0x00001BAE, 0x00001BAF}, {0x00001BBA, 0x00001BE5}, {0x00001C00, 0x00001C23}, {0x00001C4D, 0x00001C4F},
+{0x00001C5A, 0x00001C7D}, {0x00001C80, 0x00001C88}, {0x00001C90, 0x00001CBA}, {0x00001CBD, 0x00001CBF},
+{0x00001CE9, 0x00001CEC}, {0x00001CEE, 0x00001CF3}, {0x00001CF5, 0x00001CF6}, {0x00001CFA, 0x00001CFA},
+{0x00001D00, 0x00001DBF}, {0x00001E00, 0x00001F15}, {0x00001F18, 0x00001F1D}, {0x00001F20, 0x00001F45},
+{0x00001F48, 0x00001F4D}, {0x00001F50, 0x00001F57}, {0x00001F59, 0x00001F59}, {0x00001F5B, 0x00001F5B},
+{0x00001F5D, 0x00001F5D}, {0x00001F5F, 0x00001F7D}, {0x00001F80, 0x00001FB4}, {0x00001FB6, 0x00001FBC},
+{0x00001FBE, 0x00001FBE}, {0x00001FC2, 0x00001FC4}, {0x00001FC6, 0x00001FCC}, {0x00001FD0, 0x00001FD3},
+{0x00001FD6, 0x00001FDB}, {0x00001FE0, 0x00001FEC}, {0x00001FF2, 0x00001FF4}, {0x00001FF6, 0x00001FFC},
+{0x00002071, 0x00002071}, {0x0000207F, 0x0000207F}, {0x00002090, 0x0000209C}, {0x00002102, 0x00002102},
+{0x00002107, 0x00002107}, {0x0000210A, 0x00002113}, {0x00002115, 0x00002115}, {0x00002119, 0x0000211D},
+{0x00002124, 0x00002124}, {0x00002126, 0x00002126}, {0x00002128, 0x00002128}, {0x0000212A, 0x0000212D},
+{0x0000212F, 0x00002139}, {0x0000213C, 0x0000213F}, {0x00002145, 0x00002149}, {0x0000214E, 0x0000214E},
+{0x00002183, 0x00002184}, {0x00002C00, 0x00002CE4}, {0x00002CEB, 0x00002CEE}, {0x00002CF2, 0x00002CF3},
+{0x00002D00, 0x00002D25}, {0x00002D27, 0x00002D27}, {0x00002D2D, 0x00002D2D}, {0x00002D30, 0x00002D67},
+{0x00002D6F, 0x00002D6F}, {0x00002D80, 0x00002D96}, {0x00002DA0, 0x00002DA6}, {0x00002DA8, 0x00002DAE},
+{0x00002DB0, 0x00002DB6}, {0x00002DB8, 0x00002DBE}, {0x00002DC0, 0x00002DC6}, {0x00002DC8, 0x00002DCE},
+{0x00002DD0, 0x00002DD6}, {0x00002DD8, 0x00002DDE}, {0x00002E2F, 0x00002E2F}, {0x00003005, 0x00003006},
+{0x00003031, 0x00003035}, {0x0000303B, 0x0000303C}, {0x00003041, 0x00003096}, {0x0000309D, 0x0000309F},
+{0x000030A1, 0x000030FA}, {0x000030FC, 0x000030FF}, {0x00003105, 0x0000312F}, {0x00003131, 0x0000318E},
+{0x000031A0, 0x000031BF}, {0x000031F0, 0x000031FF}, {0x00003400, 0x00004DBF}, {0x00004E00, 0x0000A48C},
+{0x0000A4D0, 0x0000A4FD}, {0x0000A500, 0x0000A60C}, {0x0000A610, 0x0000A61F}, {0x0000A62A, 0x0000A62B},
+{0x0000A640, 0x0000A66E}, {0x0000A67F, 0x0000A69D}, {0x0000A6A0, 0x0000A6E5}, {0x0000A717, 0x0000A71F},
+{0x0000A722, 0x0000A788}, {0x0000A78B, 0x0000A7CA}, {0x0000A7D0, 0x0000A7D1}, {0x0000A7D3, 0x0000A7D3},
+{0x0000A7D5, 0x0000A7D9}, {0x0000A7F2, 0x0000A801}, {0x0000A803, 0x0000A805}, {0x0000A807, 0x0000A80A},
+{0x0000A80C, 0x0000A822}, {0x0000A840, 0x0000A873}, {0x0000A882, 0x0000A8B3}, {0x0000A8F2, 0x0000A8F7},
+{0x0000A8FB, 0x0000A8FB}, {0x0000A8FD, 0x0000A8FE}, {0x0000A90A, 0x0000A925}, {0x0000A930, 0x0000A946},
+{0x0000A960, 0x0000A97C}, {0x0000A984, 0x0000A9B2}, {0x0000A9CF, 0x0000A9CF}, {0x0000A9E0, 0x0000A9E4},
+{0x0000A9E6, 0x0000A9EF}, {0x0000A9FA, 0x0000A9FE}, {0x0000AA00, 0x0000AA28}, {0x0000AA40, 0x0000AA42},
+{0x0000AA44, 0x0000AA4B}, {0x0000AA60, 0x0000AA76}, {0x0000AA7A, 0x0000AA7A}, {0x0000AA7E, 0x0000AAAF},
+{0x0000AAB1, 0x0000AAB1}, {0x0000AAB5, 0x0000AAB6}, {0x0000AAB9, 0x0000AABD}, {0x0000AAC0, 0x0000AAC0},
+{0x0000AAC2, 0x0000AAC2}, {0x0000AADB, 0x0000AADD}, {0x0000AAE0, 0x0000AAEA}, {0x0000AAF2, 0x0000AAF4},
+{0x0000AB01, 0x0000AB06}, {0x0000AB09, 0x0000AB0E}, {0x0000AB11, 0x0000AB16}, {0x0000AB20, 0x0000AB26},
+{0x0000AB28, 0x0000AB2E}, {0x0000AB30, 0x0000AB5A}, {0x0000AB5C, 0x0000AB69}, {0x0000AB70, 0x0000ABE2},
+{0x0000AC00, 0x0000D7A3}, {0x0000D7B0, 0x0000D7C6}, {0x0000D7CB, 0x0000D7FB}, {0x0000F900, 0x0000FA6D},
+{0x0000FA70, 0x0000FAD9}, {0x0000FB00, 0x0000FB06}, {0x0000FB13, 0x0000FB17}, {0x0000FB1D, 0x0000FB1D},
+{0x0000FB1F, 0x0000FB28}, {0x0000FB2A, 0x0000FB36}, {0x0000FB38, 0x0000FB3C}, {0x0000FB3E, 0x0000FB3E},
+{0x0000FB40, 0x0000FB41}, {0x0000FB43, 0x0000FB44}, {0x0000FB46, 0x0000FBB1}, {0x0000FBD3, 0x0000FD3D},
+{0x0000FD50, 0x0000FD8F}, {0x0000FD92, 0x0000FDC7}, {0x0000FDF0, 0x0000FDFB}, {0x0000FE70, 0x0000FE74},
+{0x0000FE76, 0x0000FEFC}, {0x0000FF21, 0x0000FF3A}, {0x0000FF41, 0x0000FF5A}, {0x0000FF66, 0x0000FFBE},
+{0x0000FFC2, 0x0000FFC7}, {0x0000FFCA, 0x0000FFCF}, {0x0000FFD2, 0x0000FFD7}, {0x0000FFDA, 0x0000FFDC},
+{0x00010000, 0x0001000B}, {0x0001000D, 0x00010026}, {0x00010028, 0x0001003A}, {0x0001003C, 0x0001003D},
+{0x0001003F, 0x0001004D}, {0x00010050, 0x0001005D}, {0x00010080, 0x000100FA}, {0x00010280, 0x0001029C},
+{0x000102A0, 0x000102D0}, {0x00010300, 0x0001031F}, {0x0001032D, 0x00010340}, {0x00010342, 0x00010349},
+{0x00010350, 0x00010375}, {0x00010380, 0x0001039D}, {0x000103A0, 0x000103C3}, {0x000103C8, 0x000103CF},
+{0x00010400, 0x0001049D}, {0x000104B0, 0x000104D3}, {0x000104D8, 0x000104FB}, {0x00010500, 0x00010527},
+{0x00010530, 0x00010563}, {0x00010570, 0x0001057A}, {0x0001057C, 0x0001058A}, {0x0001058C, 0x00010592},
+{0x00010594, 0x00010595}, {0x00010597, 0x000105A1}, {0x000105A3, 0x000105B1}, {0x000105B3, 0x000105B9},
+{0x000105BB, 0x000105BC}, {0x00010600, 0x00010736}, {0x00010740, 0x00010755}, {0x00010760, 0x00010767},
+{0x00010780, 0x00010785}, {0x00010787, 0x000107B0}, {0x000107B2, 0x000107BA}, {0x00010800, 0x00010805},
+{0x00010808, 0x00010808}, {0x0001080A, 0x00010835}, {0x00010837, 0x00010838}, {0x0001083C, 0x0001083C},
+{0x0001083F, 0x00010855}, {0x00010860, 0x00010876}, {0x00010880, 0x0001089E}, {0x000108E0, 0x000108F2},
+{0x000108F4, 0x000108F5}, {0x00010900, 0x00010915}, {0x00010920, 0x00010939}, {0x00010980, 0x000109B7},
+{0x000109BE, 0x000109BF}, {0x00010A00, 0x00010A00}, {0x00010A10, 0x00010A13}, {0x00010A15, 0x00010A17},
+{0x00010A19, 0x00010A35}, {0x00010A60, 0x00010A7C}, {0x00010A80, 0x00010A9C}, {0x00010AC0, 0x00010AC7},
+{0x00010AC9, 0x00010AE4}, {0x00010B00, 0x00010B35}, {0x00010B40, 0x00010B55}, {0x00010B60, 0x00010B72},
+{0x00010B80, 0x00010B91}, {0x00010C00, 0x00010C48}, {0x00010C80, 0x00010CB2}, {0x00010CC0, 0x00010CF2},
+{0x00010D00, 0x00010D23}, {0x00010E80, 0x00010EA9}, {0x00010EB0, 0x00010EB1}, {0x00010F00, 0x00010F1C},
+{0x00010F27, 0x00010F27}, {0x00010F30, 0x00010F45}, {0x00010F70, 0x00010F81}, {0x00010FB0, 0x00010FC4},
+{0x00010FE0, 0x00010FF6}, {0x00011003, 0x00011037}, {0x00011071, 0x00011072}, {0x00011075, 0x00011075},
+{0x00011083, 0x000110AF}, {0x000110D0, 0x000110E8}, {0x00011103, 0x00011126}, {0x00011144, 0x00011144},
+{0x00011147, 0x00011147}, {0x00011150, 0x00011172}, {0x00011176, 0x00011176}, {0x00011183, 0x000111B2},
+{0x000111C1, 0x000111C4}, {0x000111DA, 0x000111DA}, {0x000111DC, 0x000111DC}, {0x00011200, 0x00011211},
+{0x00011213, 0x0001122B}, {0x0001123F, 0x00011240}, {0x00011280, 0x00011286}, {0x00011288, 0x00011288},
+{0x0001128A, 0x0001128D}, {0x0001128F, 0x0001129D}, {0x0001129F, 0x000112A8}, {0x000112B0, 0x000112DE},
+{0x00011305, 0x0001130C}, {0x0001130F, 0x00011310}, {0x00011313, 0x00011328}, {0x0001132A, 0x00011330},
+{0x00011332, 0x00011333}, {0x00011335, 0x00011339}, {0x0001133D, 0x0001133D}, {0x00011350, 0x00011350},
+{0x0001135D, 0x00011361}, {0x00011400, 0x00011434}, {0x00011447, 0x0001144A}, {0x0001145F, 0x00011461},
+{0x00011480, 0x000114AF}, {0x000114C4, 0x000114C5}, {0x000114C7, 0x000114C7}, {0x00011580, 0x000115AE},
+{0x000115D8, 0x000115DB}, {0x00011600, 0x0001162F}, {0x00011644, 0x00011644}, {0x00011680, 0x000116AA},
+{0x000116B8, 0x000116B8}, {0x00011700, 0x0001171A}, {0x00011740, 0x00011746}, {0x00011800, 0x0001182B},
+{0x000118A0, 0x000118DF}, {0x000118FF, 0x00011906}, {0x00011909, 0x00011909}, {0x0001190C, 0x00011913},
+{0x00011915, 0x00011916}, {0x00011918, 0x0001192F}, {0x0001193F, 0x0001193F}, {0x00011941, 0x00011941},
+{0x000119A0, 0x000119A7}, {0x000119AA, 0x000119D0}, {0x000119E1, 0x000119E1}, {0x000119E3, 0x000119E3},
+{0x00011A00, 0x00011A00}, {0x00011A0B, 0x00011A32}, {0x00011A3A, 0x00011A3A}, {0x00011A50, 0x00011A50},
+{0x00011A5C, 0x00011A89}, {0x00011A9D, 0x00011A9D}, {0x00011AB0, 0x00011AF8}, {0x00011C00, 0x00011C08},
+{0x00011C0A, 0x00011C2E}, {0x00011C40, 0x00011C40}, {0x00011C72, 0x00011C8F}, {0x00011D00, 0x00011D06},
+{0x00011D08, 0x00011D09}, {0x00011D0B, 0x00011D30}, {0x00011D46, 0x00011D46}, {0x00011D60, 0x00011D65},
+{0x00011D67, 0x00011D68}, {0x00011D6A, 0x00011D89}, {0x00011D98, 0x00011D98}, {0x00011EE0, 0x00011EF2},
+{0x00011F02, 0x00011F02}, {0x00011F04, 0x00011F10}, {0x00011F12, 0x00011F33}, {0x00011FB0, 0x00011FB0},
+{0x00012000, 0x00012399}, {0x00012480, 0x00012543}, {0x00012F90, 0x00012FF0}, {0x00013000, 0x0001342F},
+{0x00013441, 0x00013446}, {0x00014400, 0x00014646}, {0x00016800, 0x00016A38}, {0x00016A40, 0x00016A5E},
+{0x00016A70, 0x00016ABE}, {0x00016AD0, 0x00016AED}, {0x00016B00, 0x00016B2F}, {0x00016B40, 0x00016B43},
+{0x00016B63, 0x00016B77}, {0x00016B7D, 0x00016B8F}, {0x00016E40, 0x00016E7F}, {0x00016F00, 0x00016F4A},
+{0x00016F50, 0x00016F50}, {0x00016F93, 0x00016F9F}, {0x00016FE0, 0x00016FE1}, {0x00016FE3, 0x00016FE3},
+{0x00017000, 0x000187F7}, {0x00018800, 0x00018CD5}, {0x00018D00, 0x00018D08}, {0x0001AFF0, 0x0001AFF3},
+{0x0001AFF5, 0x0001AFFB}, {0x0001AFFD, 0x0001AFFE}, {0x0001B000, 0x0001B122}, {0x0001B132, 0x0001B132},
+{0x0001B150, 0x0001B152}, {0x0001B155, 0x0001B155}, {0x0001B164, 0x0001B167}, {0x0001B170, 0x0001B2FB},
+{0x0001BC00, 0x0001BC6A}, {0x0001BC70, 0x0001BC7C}, {0x0001BC80, 0x0001BC88}, {0x0001BC90, 0x0001BC99},
+{0x0001D400, 0x0001D454}, {0x0001D456, 0x0001D49C}, {0x0001D49E, 0x0001D49F}, {0x0001D4A2, 0x0001D4A2},
+{0x0001D4A5, 0x0001D4A6}, {0x0001D4A9, 0x0001D4AC}, {0x0001D4AE, 0x0001D4B9}, {0x0001D4BB, 0x0001D4BB},
+{0x0001D4BD, 0x0001D4C3}, {0x0001D4C5, 0x0001D505}, {0x0001D507, 0x0001D50A}, {0x0001D50D, 0x0001D514},
+{0x0001D516, 0x0001D51C}, {0x0001D51E, 0x0001D539}, {0x0001D53B, 0x0001D53E}, {0x0001D540, 0x0001D544},
+{0x0001D546, 0x0001D546}, {0x0001D54A, 0x0001D550}, {0x0001D552, 0x0001D6A5}, {0x0001D6A8, 0x0001D6C0},
+{0x0001D6C2, 0x0001D6DA}, {0x0001D6DC, 0x0001D6FA}, {0x0001D6FC, 0x0001D714}, {0x0001D716, 0x0001D734},
+{0x0001D736, 0x0001D74E}, {0x0001D750, 0x0001D76E}, {0x0001D770, 0x0001D788}, {0x0001D78A, 0x0001D7A8},
+{0x0001D7AA, 0x0001D7C2}, {0x0001D7C4, 0x0001D7CB}, {0x0001DF00, 0x0001DF1E}, {0x0001DF25, 0x0001DF2A},
+{0x0001E030, 0x0001E06D}, {0x0001E100, 0x0001E12C}, {0x0001E137, 0x0001E13D}, {0x0001E14E, 0x0001E14E},
+{0x0001E290, 0x0001E2AD}, {0x0001E2C0, 0x0001E2EB}, {0x0001E4D0, 0x0001E4EB}, {0x0001E7E0, 0x0001E7E6},
+{0x0001E7E8, 0x0001E7EB}, {0x0001E7ED, 0x0001E7EE}, {0x0001E7F0, 0x0001E7FE}, {0x0001E800, 0x0001E8C4},
+{0x0001E900, 0x0001E943}, {0x0001E94B, 0x0001E94B}, {0x0001EE00, 0x0001EE03}, {0x0001EE05, 0x0001EE1F},
+{0x0001EE21, 0x0001EE22}, {0x0001EE24, 0x0001EE24}, {0x0001EE27, 0x0001EE27}, {0x0001EE29, 0x0001EE32},
+{0x0001EE34, 0x0001EE37}, {0x0001EE39, 0x0001EE39}, {0x0001EE3B, 0x0001EE3B}, {0x0001EE42, 0x0001EE42},
+{0x0001EE47, 0x0001EE47}, {0x0001EE49, 0x0001EE49}, {0x0001EE4B, 0x0001EE4B}, {0x0001EE4D, 0x0001EE4F},
+{0x0001EE51, 0x0001EE52}, {0x0001EE54, 0x0001EE54}, {0x0001EE57, 0x0001EE57}, {0x0001EE59, 0x0001EE59},
+{0x0001EE5B, 0x0001EE5B}, {0x0001EE5D, 0x0001EE5D}, {0x0001EE5F, 0x0001EE5F}, {0x0001EE61, 0x0001EE62},
+{0x0001EE64, 0x0001EE64}, {0x0001EE67, 0x0001EE6A}, {0x0001EE6C, 0x0001EE72}, {0x0001EE74, 0x0001EE77},
+{0x0001EE79, 0x0001EE7C}, {0x0001EE7E, 0x0001EE7E}, {0x0001EE80, 0x0001EE89}, {0x0001EE8B, 0x0001EE9B},
+{0x0001EEA1, 0x0001EEA3}, {0x0001EEA5, 0x0001EEA9}, {0x0001EEAB, 0x0001EEBB}, {0x00020000, 0x0002A6DF},
+{0x0002A700, 0x0002B739}, {0x0002B740, 0x0002B81D}, {0x0002B820, 0x0002CEA1}, {0x0002CEB0, 0x0002EBE0},
+{0x0002EBF0, 0x0002EE5D}, {0x0002F800, 0x0002FA1D}, {0x00030000, 0x0003134A}, {0x00031350, 0x000323AF},
+};
+
+const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_separator = {
+{0x00000020, 0x00000020}, {0x000000A0, 0x000000A0}, {0x00001680, 0x00001680}, {0x00002000, 0x0000200A},
+{0x00002028, 0x00002029}, {0x0000202F, 0x0000202F}, {0x0000205F, 0x0000205F}, {0x00003000, 0x00003000},
+};
+
+const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_whitespace = {
+{0x00000009, 0x0000000D}, {0x00000020, 0x00000020}, {0x00000085, 0x00000085}, {0x000000A0, 0x000000A0},
+{0x00001680, 0x00001680}, {0x00002000, 0x0000200A}, {0x00002028, 0x00002029}, {0x0000202F, 0x0000202F},
+{0x0000205F, 0x0000205F}, {0x00003000, 0x00003000},
+};
+
+const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_accent_mark = {
+{0x00000300, 0x0000036F}, {0x00000483, 0x00000489}, {0x00000591, 0x000005BD}, {0x000005BF, 0x000005BF},
+{0x000005C1, 0x000005C2}, {0x000005C4, 0x000005C5}, {0x000005C7, 0x000005C7}, {0x00000610, 0x0000061A},
+{0x0000064B, 0x0000065F}, {0x00000670, 0x00000670}, {0x000006D6, 0x000006DC}, {0x000006DF, 0x000006E4},
+{0x000006E7, 0x000006E8}, {0x000006EA, 0x000006ED}, {0x00000711, 0x00000711}, {0x00000730, 0x0000074A},
+{0x000007A6, 0x000007B0}, {0x000007EB, 0x000007F3}, {0x000007FD, 0x000007FD}, {0x00000816, 0x00000819},
+{0x0000081B, 0x00000823}, {0x00000825, 0x00000827}, {0x00000829, 0x0000082D}, {0x00000859, 0x0000085B},
+{0x00000898, 0x0000089F}, {0x000008CA, 0x000008E1}, {0x000008E3, 0x00000903}, {0x0000093A, 0x0000093C},
+{0x0000093E, 0x0000094F}, {0x00000951, 0x00000957}, {0x00000962, 0x00000963}, {0x00000981, 0x00000983},
+{0x000009BC, 0x000009BC}, {0x000009BE, 0x000009C4}, {0x000009C7, 0x000009C8}, {0x000009CB, 0x000009CD},
+{0x000009D7, 0x000009D7}, {0x000009E2, 0x000009E3}, {0x000009FE, 0x000009FE}, {0x00000A01, 0x00000A03},
+{0x00000A3C, 0x00000A3C}, {0x00000A3E, 0x00000A42}, {0x00000A47, 0x00000A48}, {0x00000A4B, 0x00000A4D},
+{0x00000A51, 0x00000A51}, {0x00000A70, 0x00000A71}, {0x00000A75, 0x00000A75}, {0x00000A81, 0x00000A83},
+{0x00000ABC, 0x00000ABC}, {0x00000ABE, 0x00000AC5}, {0x00000AC7, 0x00000AC9}, {0x00000ACB, 0x00000ACD},
+{0x00000AE2, 0x00000AE3}, {0x00000AFA, 0x00000AFF}, {0x00000B01, 0x00000B03}, {0x00000B3C, 0x00000B3C},
+{0x00000B3E, 0x00000B44}, {0x00000B47, 0x00000B48}, {0x00000B4B, 0x00000B4D}, {0x00000B55, 0x00000B57},
+{0x00000B62, 0x00000B63}, {0x00000B82, 0x00000B82}, {0x00000BBE, 0x00000BC2}, {0x00000BC6, 0x00000BC8},
+{0x00000BCA, 0x00000BCD}, {0x00000BD7, 0x00000BD7}, {0x00000C00, 0x00000C04}, {0x00000C3C, 0x00000C3C},
+{0x00000C3E, 0x00000C44}, {0x00000C46, 0x00000C48}, {0x00000C4A, 0x00000C4D}, {0x00000C55, 0x00000C56},
+{0x00000C62, 0x00000C63}, {0x00000C81, 0x00000C83}, {0x00000CBC, 0x00000CBC}, {0x00000CBE, 0x00000CC4},
+{0x00000CC6, 0x00000CC8}, {0x00000CCA, 0x00000CCD}, {0x00000CD5, 0x00000CD6}, {0x00000CE2, 0x00000CE3},
+{0x00000CF3, 0x00000CF3}, {0x00000D00, 0x00000D03}, {0x00000D3B, 0x00000D3C}, {0x00000D3E, 0x00000D44},
+{0x00000D46, 0x00000D48}, {0x00000D4A, 0x00000D4D}, {0x00000D57, 0x00000D57}, {0x00000D62, 0x00000D63},
+{0x00000D81, 0x00000D83}, {0x00000DCA, 0x00000DCA}, {0x00000DCF, 0x00000DD4}, {0x00000DD6, 0x00000DD6},
+{0x00000DD8, 0x00000DDF}, {0x00000DF2, 0x00000DF3}, {0x00000E31, 0x00000E31}, {0x00000E34, 0x00000E3A},
+{0x00000E47, 0x00000E4E}, {0x00000EB1, 0x00000EB1}, {0x00000EB4, 0x00000EBC}, {0x00000EC8, 0x00000ECE},
+{0x00000F18, 0x00000F19}, {0x00000F35, 0x00000F35}, {0x00000F37, 0x00000F37}, {0x00000F39, 0x00000F39},
+{0x00000F3E, 0x00000F3F}, {0x00000F71, 0x00000F84}, {0x00000F86, 0x00000F87}, {0x00000F8D, 0x00000F97},
+{0x00000F99, 0x00000FBC}, {0x00000FC6, 0x00000FC6}, {0x0000102B, 0x0000103E}, {0x00001056, 0x00001059},
+{0x0000105E, 0x00001060}, {0x00001062, 0x00001064}, {0x00001067, 0x0000106D}, {0x00001071, 0x00001074},
+{0x00001082, 0x0000108D}, {0x0000108F, 0x0000108F}, {0x0000109A, 0x0000109D}, {0x0000135D, 0x0000135F},
+{0x00001712, 0x00001715}, {0x00001732, 0x00001734}, {0x00001752, 0x00001753}, {0x00001772, 0x00001773},
+{0x000017B4, 0x000017D3}, {0x000017DD, 0x000017DD}, {0x0000180B, 0x0000180D}, {0x0000180F, 0x0000180F},
+{0x00001885, 0x00001886}, {0x000018A9, 0x000018A9}, {0x00001920, 0x0000192B}, {0x00001930, 0x0000193B},
+{0x00001A17, 0x00001A1B}, {0x00001A55, 0x00001A5E}, {0x00001A60, 0x00001A7C}, {0x00001A7F, 0x00001A7F},
+{0x00001AB0, 0x00001ACE}, {0x00001B00, 0x00001B04}, {0x00001B34, 0x00001B44}, {0x00001B6B, 0x00001B73},
+{0x00001B80, 0x00001B82}, {0x00001BA1, 0x00001BAD}, {0x00001BE6, 0x00001BF3}, {0x00001C24, 0x00001C37},
+{0x00001CD0, 0x00001CD2}, {0x00001CD4, 0x00001CE8}, {0x00001CED, 0x00001CED}, {0x00001CF4, 0x00001CF4},
+{0x00001CF7, 0x00001CF9}, {0x00001DC0, 0x00001DFF}, {0x000020D0, 0x000020F0}, {0x00002CEF, 0x00002CF1},
+{0x00002D7F, 0x00002D7F}, {0x00002DE0, 0x00002DFF}, {0x0000302A, 0x0000302F}, {0x00003099, 0x0000309A},
+{0x0000A66F, 0x0000A672}, {0x0000A674, 0x0000A67D}, {0x0000A69E, 0x0000A69F}, {0x0000A6F0, 0x0000A6F1},
+{0x0000A802, 0x0000A802}, {0x0000A806, 0x0000A806}, {0x0000A80B, 0x0000A80B}, {0x0000A823, 0x0000A827},
+{0x0000A82C, 0x0000A82C}, {0x0000A880, 0x0000A881}, {0x0000A8B4, 0x0000A8C5}, {0x0000A8E0, 0x0000A8F1},
+{0x0000A8FF, 0x0000A8FF}, {0x0000A926, 0x0000A92D}, {0x0000A947, 0x0000A953}, {0x0000A980, 0x0000A983},
+{0x0000A9B3, 0x0000A9C0}, {0x0000A9E5, 0x0000A9E5}, {0x0000AA29, 0x0000AA36}, {0x0000AA43, 0x0000AA43},
+{0x0000AA4C, 0x0000AA4D}, {0x0000AA7B, 0x0000AA7D}, {0x0000AAB0, 0x0000AAB0}, {0x0000AAB2, 0x0000AAB4},
+{0x0000AAB7, 0x0000AAB8}, {0x0000AABE, 0x0000AABF}, {0x0000AAC1, 0x0000AAC1}, {0x0000AAEB, 0x0000AAEF},
+{0x0000AAF5, 0x0000AAF6}, {0x0000ABE3, 0x0000ABEA}, {0x0000ABEC, 0x0000ABED}, {0x0000FB1E, 0x0000FB1E},
+{0x0000FE00, 0x0000FE0F}, {0x0000FE20, 0x0000FE2F}, {0x000101FD, 0x000101FD}, {0x000102E0, 0x000102E0},
+{0x00010376, 0x0001037A}, {0x00010A01, 0x00010A03}, {0x00010A05, 0x00010A06}, {0x00010A0C, 0x00010A0F},
+{0x00010A38, 0x00010A3A}, {0x00010A3F, 0x00010A3F}, {0x00010AE5, 0x00010AE6}, {0x00010D24, 0x00010D27},
+{0x00010EAB, 0x00010EAC}, {0x00010EFD, 0x00010EFF}, {0x00010F46, 0x00010F50}, {0x00010F82, 0x00010F85},
+{0x00011000, 0x00011002}, {0x00011038, 0x00011046}, {0x00011070, 0x00011070}, {0x00011073, 0x00011074},
+{0x0001107F, 0x00011082}, {0x000110B0, 0x000110BA}, {0x000110C2, 0x000110C2}, {0x00011100, 0x00011102},
+{0x00011127, 0x00011134}, {0x00011145, 0x00011146}, {0x00011173, 0x00011173}, {0x00011180, 0x00011182},
+{0x000111B3, 0x000111C0}, {0x000111C9, 0x000111CC}, {0x000111CE, 0x000111CF}, {0x0001122C, 0x00011237},
+{0x0001123E, 0x0001123E}, {0x00011241, 0x00011241}, {0x000112DF, 0x000112EA}, {0x00011300, 0x00011303},
+{0x0001133B, 0x0001133C}, {0x0001133E, 0x00011344}, {0x00011347, 0x00011348}, {0x0001134B, 0x0001134D},
+{0x00011357, 0x00011357}, {0x00011362, 0x00011363}, {0x00011366, 0x0001136C}, {0x00011370, 0x00011374},
+{0x00011435, 0x00011446}, {0x0001145E, 0x0001145E}, {0x000114B0, 0x000114C3}, {0x000115AF, 0x000115B5},
+{0x000115B8, 0x000115C0}, {0x000115DC, 0x000115DD}, {0x00011630, 0x00011640}, {0x000116AB, 0x000116B7},
+{0x0001171D, 0x0001172B}, {0x0001182C, 0x0001183A}, {0x00011930, 0x00011935}, {0x00011937, 0x00011938},
+{0x0001193B, 0x0001193E}, {0x00011940, 0x00011940}, {0x00011942, 0x00011943}, {0x000119D1, 0x000119D7},
+{0x000119DA, 0x000119E0}, {0x000119E4, 0x000119E4}, {0x00011A01, 0x00011A0A}, {0x00011A33, 0x00011A39},
+{0x00011A3B, 0x00011A3E}, {0x00011A47, 0x00011A47}, {0x00011A51, 0x00011A5B}, {0x00011A8A, 0x00011A99},
+{0x00011C2F, 0x00011C36}, {0x00011C38, 0x00011C3F}, {0x00011C92, 0x00011CA7}, {0x00011CA9, 0x00011CB6},
+{0x00011D31, 0x00011D36}, {0x00011D3A, 0x00011D3A}, {0x00011D3C, 0x00011D3D}, {0x00011D3F, 0x00011D45},
+{0x00011D47, 0x00011D47}, {0x00011D8A, 0x00011D8E}, {0x00011D90, 0x00011D91}, {0x00011D93, 0x00011D97},
+{0x00011EF3, 0x00011EF6}, {0x00011F00, 0x00011F01}, {0x00011F03, 0x00011F03}, {0x00011F34, 0x00011F3A},
+{0x00011F3E, 0x00011F42}, {0x00013440, 0x00013440}, {0x00013447, 0x00013455}, {0x00016AF0, 0x00016AF4},
+{0x00016B30, 0x00016B36}, {0x00016F4F, 0x00016F4F}, {0x00016F51, 0x00016F87}, {0x00016F8F, 0x00016F92},
+{0x00016FE4, 0x00016FE4}, {0x00016FF0, 0x00016FF1}, {0x0001BC9D, 0x0001BC9E}, {0x0001CF00, 0x0001CF2D},
+{0x0001CF30, 0x0001CF46}, {0x0001D165, 0x0001D169}, {0x0001D16D, 0x0001D172}, {0x0001D17B, 0x0001D182},
+{0x0001D185, 0x0001D18B}, {0x0001D1AA, 0x0001D1AD}, {0x0001D242, 0x0001D244}, {0x0001DA00, 0x0001DA36},
+{0x0001DA3B, 0x0001DA6C}, {0x0001DA75, 0x0001DA75}, {0x0001DA84, 0x0001DA84}, {0x0001DA9B, 0x0001DA9F},
+{0x0001DAA1, 0x0001DAAF}, {0x0001E000, 0x0001E006}, {0x0001E008, 0x0001E018}, {0x0001E01B, 0x0001E021},
+{0x0001E023, 0x0001E024}, {0x0001E026, 0x0001E02A}, {0x0001E08F, 0x0001E08F}, {0x0001E130, 0x0001E136},
+{0x0001E2AE, 0x0001E2AE}, {0x0001E2EC, 0x0001E2EF}, {0x0001E4EC, 0x0001E4EF}, {0x0001E8D0, 0x0001E8D6},
+{0x0001E944, 0x0001E94A}, {0x000E0100, 0x000E01EF},
+};
+
+const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_punctuation = {
+{0x00000021, 0x00000023}, {0x00000025, 0x0000002A}, {0x0000002C, 0x0000002F}, {0x0000003A, 0x0000003B},
+{0x0000003F, 0x00000040}, {0x0000005B, 0x0000005D}, {0x0000005F, 0x0000005F}, {0x0000007B, 0x0000007B},
+{0x0000007D, 0x0000007D}, {0x000000A1, 0x000000A1}, {0x000000A7, 0x000000A7}, {0x000000AB, 0x000000AB},
+{0x000000B6, 0x000000B7}, {0x000000BB, 0x000000BB}, {0x000000BF, 0x000000BF}, {0x0000037E, 0x0000037E},
+{0x00000387, 0x00000387}, {0x0000055A, 0x0000055F}, {0x00000589, 0x0000058A}, {0x000005BE, 0x000005BE},
+{0x000005C0, 0x000005C0}, {0x000005C3, 0x000005C3}, {0x000005C6, 0x000005C6}, {0x000005F3, 0x000005F4},
+{0x00000609, 0x0000060A}, {0x0000060C, 0x0000060D}, {0x0000061B, 0x0000061B}, {0x0000061D, 0x0000061F},
+{0x0000066A, 0x0000066D}, {0x000006D4, 0x000006D4}, {0x00000700, 0x0000070D}, {0x000007F7, 0x000007F9},
+{0x00000830, 0x0000083E}, {0x0000085E, 0x0000085E}, {0x00000964, 0x00000965}, {0x00000970, 0x00000970},
+{0x000009FD, 0x000009FD}, {0x00000A76, 0x00000A76}, {0x00000AF0, 0x00000AF0}, {0x00000C77, 0x00000C77},
+{0x00000C84, 0x00000C84}, {0x00000DF4, 0x00000DF4}, {0x00000E4F, 0x00000E4F}, {0x00000E5A, 0x00000E5B},
+{0x00000F04, 0x00000F12}, {0x00000F14, 0x00000F14}, {0x00000F3A, 0x00000F3D}, {0x00000F85, 0x00000F85},
+{0x00000FD0, 0x00000FD4}, {0x00000FD9, 0x00000FDA}, {0x0000104A, 0x0000104F}, {0x000010FB, 0x000010FB},
+{0x00001360, 0x00001368}, {0x00001400, 0x00001400}, {0x0000166E, 0x0000166E}, {0x0000169B, 0x0000169C},
+{0x000016EB, 0x000016ED}, {0x00001735, 0x00001736}, {0x000017D4, 0x000017D6}, {0x000017D8, 0x000017DA},
+{0x00001800, 0x0000180A}, {0x00001944, 0x00001945}, {0x00001A1E, 0x00001A1F}, {0x00001AA0, 0x00001AA6},
+{0x00001AA8, 0x00001AAD}, {0x00001B5A, 0x00001B60}, {0x00001B7D, 0x00001B7E}, {0x00001BFC, 0x00001BFF},
+{0x00001C3B, 0x00001C3F}, {0x00001C7E, 0x00001C7F}, {0x00001CC0, 0x00001CC7}, {0x00001CD3, 0x00001CD3},
+{0x00002010, 0x00002027}, {0x00002030, 0x00002043}, {0x00002045, 0x00002051}, {0x00002053, 0x0000205E},
+{0x0000207D, 0x0000207E}, {0x0000208D, 0x0000208E}, {0x00002308, 0x0000230B}, {0x00002329, 0x0000232A},
+{0x00002768, 0x00002775}, {0x000027C5, 0x000027C6}, {0x000027E6, 0x000027EF}, {0x00002983, 0x00002998},
+{0x000029D8, 0x000029DB}, {0x000029FC, 0x000029FD}, {0x00002CF9, 0x00002CFC}, {0x00002CFE, 0x00002CFF},
+{0x00002D70, 0x00002D70}, {0x00002E00, 0x00002E2E}, {0x00002E30, 0x00002E4F}, {0x00002E52, 0x00002E5D},
+{0x00003001, 0x00003003}, {0x00003008, 0x00003011}, {0x00003014, 0x0000301F}, {0x00003030, 0x00003030},
+{0x0000303D, 0x0000303D}, {0x000030A0, 0x000030A0}, {0x000030FB, 0x000030FB}, {0x0000A4FE, 0x0000A4FF},
+{0x0000A60D, 0x0000A60F}, {0x0000A673, 0x0000A673}, {0x0000A67E, 0x0000A67E}, {0x0000A6F2, 0x0000A6F7},
+{0x0000A874, 0x0000A877}, {0x0000A8CE, 0x0000A8CF}, {0x0000A8F8, 0x0000A8FA}, {0x0000A8FC, 0x0000A8FC},
+{0x0000A92E, 0x0000A92F}, {0x0000A95F, 0x0000A95F}, {0x0000A9C1, 0x0000A9CD}, {0x0000A9DE, 0x0000A9DF},
+{0x0000AA5C, 0x0000AA5F}, {0x0000AADE, 0x0000AADF}, {0x0000AAF0, 0x0000AAF1}, {0x0000ABEB, 0x0000ABEB},
+{0x0000FD3E, 0x0000FD3F}, {0x0000FE10, 0x0000FE19}, {0x0000FE30, 0x0000FE52}, {0x0000FE54, 0x0000FE61},
+{0x0000FE63, 0x0000FE63}, {0x0000FE68, 0x0000FE68}, {0x0000FE6A, 0x0000FE6B}, {0x0000FF01, 0x0000FF03},
+{0x0000FF05, 0x0000FF0A}, {0x0000FF0C, 0x0000FF0F}, {0x0000FF1A, 0x0000FF1B}, {0x0000FF1F, 0x0000FF20},
+{0x0000FF3B, 0x0000FF3D}, {0x0000FF3F, 0x0000FF3F}, {0x0000FF5B, 0x0000FF5B}, {0x0000FF5D, 0x0000FF5D},
+{0x0000FF5F, 0x0000FF65}, {0x00010100, 0x00010102}, {0x0001039F, 0x0001039F}, {0x000103D0, 0x000103D0},
+{0x0001056F, 0x0001056F}, {0x00010857, 0x00010857}, {0x0001091F, 0x0001091F}, {0x0001093F, 0x0001093F},
+{0x00010A50, 0x00010A58}, {0x00010A7F, 0x00010A7F}, {0x00010AF0, 0x00010AF6}, {0x00010B39, 0x00010B3F},
+{0x00010B99, 0x00010B9C}, {0x00010EAD, 0x00010EAD}, {0x00010F55, 0x00010F59}, {0x00010F86, 0x00010F89},
+{0x00011047, 0x0001104D}, {0x000110BB, 0x000110BC}, {0x000110BE, 0x000110C1}, {0x00011140, 0x00011143},
+{0x00011174, 0x00011175}, {0x000111C5, 0x000111C8}, {0x000111CD, 0x000111CD}, {0x000111DB, 0x000111DB},
+{0x000111DD, 0x000111DF}, {0x00011238, 0x0001123D}, {0x000112A9, 0x000112A9}, {0x0001144B, 0x0001144F},
+{0x0001145A, 0x0001145B}, {0x0001145D, 0x0001145D}, {0x000114C6, 0x000114C6}, {0x000115C1, 0x000115D7},
+{0x00011641, 0x00011643}, {0x00011660, 0x0001166C}, {0x000116B9, 0x000116B9}, {0x0001173C, 0x0001173E},
+{0x0001183B, 0x0001183B}, {0x00011944, 0x00011946}, {0x000119E2, 0x000119E2}, {0x00011A3F, 0x00011A46},
+{0x00011A9A, 0x00011A9C}, {0x00011A9E, 0x00011AA2}, {0x00011B00, 0x00011B09}, {0x00011C41, 0x00011C45},
+{0x00011C70, 0x00011C71}, {0x00011EF7, 0x00011EF8}, {0x00011F43, 0x00011F4F}, {0x00011FFF, 0x00011FFF},
+{0x00012470, 0x00012474}, {0x00012FF1, 0x00012FF2}, {0x00016A6E, 0x00016A6F}, {0x00016AF5, 0x00016AF5},
+{0x00016B37, 0x00016B3B}, {0x00016B44, 0x00016B44}, {0x00016E97, 0x00016E9A}, {0x00016FE2, 0x00016FE2},
+{0x0001BC9F, 0x0001BC9F}, {0x0001DA87, 0x0001DA8B}, {0x0001E95E, 0x0001E95F},
+};
+
+const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_symbol = {
+{0x00000024, 0x00000024}, {0x0000002B, 0x0000002B}, {0x0000003C, 0x0000003E}, {0x0000005E, 0x0000005E},
+{0x00000060, 0x00000060}, {0x0000007C, 0x0000007C}, {0x0000007E, 0x0000007E}, {0x000000A2, 0x000000A6},
+{0x000000A8, 0x000000A9}, {0x000000AC, 0x000000AC}, {0x000000AE, 0x000000B1}, {0x000000B4, 0x000000B4},
+{0x000000B8, 0x000000B8}, {0x000000D7, 0x000000D7}, {0x000000F7, 0x000000F7}, {0x000002C2, 0x000002C5},
+{0x000002D2, 0x000002DF}, {0x000002E5, 0x000002EB}, {0x000002ED, 0x000002ED}, {0x000002EF, 0x000002FF},
+{0x00000375, 0x00000375}, {0x00000384, 0x00000385}, {0x000003F6, 0x000003F6}, {0x00000482, 0x00000482},
+{0x0000058D, 0x0000058F}, {0x00000606, 0x00000608}, {0x0000060B, 0x0000060B}, {0x0000060E, 0x0000060F},
+{0x000006DE, 0x000006DE}, {0x000006E9, 0x000006E9}, {0x000006FD, 0x000006FE}, {0x000007F6, 0x000007F6},
+{0x000007FE, 0x000007FF}, {0x00000888, 0x00000888}, {0x000009F2, 0x000009F3}, {0x000009FA, 0x000009FB},
+{0x00000AF1, 0x00000AF1}, {0x00000B70, 0x00000B70}, {0x00000BF3, 0x00000BFA}, {0x00000C7F, 0x00000C7F},
+{0x00000D4F, 0x00000D4F}, {0x00000D79, 0x00000D79}, {0x00000E3F, 0x00000E3F}, {0x00000F01, 0x00000F03},
+{0x00000F13, 0x00000F13}, {0x00000F15, 0x00000F17}, {0x00000F1A, 0x00000F1F}, {0x00000F34, 0x00000F34},
+{0x00000F36, 0x00000F36}, {0x00000F38, 0x00000F38}, {0x00000FBE, 0x00000FC5}, {0x00000FC7, 0x00000FCC},
+{0x00000FCE, 0x00000FCF}, {0x00000FD5, 0x00000FD8}, {0x0000109E, 0x0000109F}, {0x00001390, 0x00001399},
+{0x0000166D, 0x0000166D}, {0x000017DB, 0x000017DB}, {0x00001940, 0x00001940}, {0x000019DE, 0x000019FF},
+{0x00001B61, 0x00001B6A}, {0x00001B74, 0x00001B7C}, {0x00001FBD, 0x00001FBD}, {0x00001FBF, 0x00001FC1},
+{0x00001FCD, 0x00001FCF}, {0x00001FDD, 0x00001FDF}, {0x00001FED, 0x00001FEF}, {0x00001FFD, 0x00001FFE},
+{0x00002044, 0x00002044}, {0x00002052, 0x00002052}, {0x0000207A, 0x0000207C}, {0x0000208A, 0x0000208C},
+{0x000020A0, 0x000020C0}, {0x00002100, 0x00002101}, {0x00002103, 0x00002106}, {0x00002108, 0x00002109},
+{0x00002114, 0x00002114}, {0x00002116, 0x00002118}, {0x0000211E, 0x00002123}, {0x00002125, 0x00002125},
+{0x00002127, 0x00002127}, {0x00002129, 0x00002129}, {0x0000212E, 0x0000212E}, {0x0000213A, 0x0000213B},
+{0x00002140, 0x00002144}, {0x0000214A, 0x0000214D}, {0x0000214F, 0x0000214F}, {0x0000218A, 0x0000218B},
+{0x00002190, 0x00002307}, {0x0000230C, 0x00002328}, {0x0000232B, 0x00002426}, {0x00002440, 0x0000244A},
+{0x0000249C, 0x000024E9}, {0x00002500, 0x00002767}, {0x00002794, 0x000027C4}, {0x000027C7, 0x000027E5},
+{0x000027F0, 0x00002982}, {0x00002999, 0x000029D7}, {0x000029DC, 0x000029FB}, {0x000029FE, 0x00002B73},
+{0x00002B76, 0x00002B95}, {0x00002B97, 0x00002BFF}, {0x00002CE5, 0x00002CEA}, {0x00002E50, 0x00002E51},
+{0x00002E80, 0x00002E99}, {0x00002E9B, 0x00002EF3}, {0x00002F00, 0x00002FD5}, {0x00002FF0, 0x00002FFF},
+{0x00003004, 0x00003004}, {0x00003012, 0x00003013}, {0x00003020, 0x00003020}, {0x00003036, 0x00003037},
+{0x0000303E, 0x0000303F}, {0x0000309B, 0x0000309C}, {0x00003190, 0x00003191}, {0x00003196, 0x0000319F},
+{0x000031C0, 0x000031E3}, {0x000031EF, 0x000031EF}, {0x00003200, 0x0000321E}, {0x0000322A, 0x00003247},
+{0x00003250, 0x00003250}, {0x00003260, 0x0000327F}, {0x0000328A, 0x000032B0}, {0x000032C0, 0x000033FF},
+{0x00004DC0, 0x00004DFF}, {0x0000A490, 0x0000A4C6}, {0x0000A700, 0x0000A716}, {0x0000A720, 0x0000A721},
+{0x0000A789, 0x0000A78A}, {0x0000A828, 0x0000A82B}, {0x0000A836, 0x0000A839}, {0x0000AA77, 0x0000AA79},
+{0x0000AB5B, 0x0000AB5B}, {0x0000AB6A, 0x0000AB6B}, {0x0000FB29, 0x0000FB29}, {0x0000FBB2, 0x0000FBC2},
+{0x0000FD40, 0x0000FD4F}, {0x0000FDCF, 0x0000FDCF}, {0x0000FDFC, 0x0000FDFF}, {0x0000FE62, 0x0000FE62},
+{0x0000FE64, 0x0000FE66}, {0x0000FE69, 0x0000FE69}, {0x0000FF04, 0x0000FF04}, {0x0000FF0B, 0x0000FF0B},
+{0x0000FF1C, 0x0000FF1E}, {0x0000FF3E, 0x0000FF3E}, {0x0000FF40, 0x0000FF40}, {0x0000FF5C, 0x0000FF5C},
+{0x0000FF5E, 0x0000FF5E}, {0x0000FFE0, 0x0000FFE6}, {0x0000FFE8, 0x0000FFEE}, {0x0000FFFC, 0x0000FFFD},
+{0x00010137, 0x0001013F}, {0x00010179, 0x00010189}, {0x0001018C, 0x0001018E}, {0x00010190, 0x0001019C},
+{0x000101A0, 0x000101A0}, {0x000101D0, 0x000101FC}, {0x00010877, 0x00010878}, {0x00010AC8, 0x00010AC8},
+{0x0001173F, 0x0001173F}, {0x00011FD5, 0x00011FF1}, {0x00016B3C, 0x00016B3F}, {0x00016B45, 0x00016B45},
+{0x0001BC9C, 0x0001BC9C}, {0x0001CF50, 0x0001CFC3}, {0x0001D000, 0x0001D0F5}, {0x0001D100, 0x0001D126},
+{0x0001D129, 0x0001D164}, {0x0001D16A, 0x0001D16C}, {0x0001D183, 0x0001D184}, {0x0001D18C, 0x0001D1A9},
+{0x0001D1AE, 0x0001D1EA}, {0x0001D200, 0x0001D241}, {0x0001D245, 0x0001D245}, {0x0001D300, 0x0001D356},
+{0x0001D6C1, 0x0001D6C1}, {0x0001D6DB, 0x0001D6DB}, {0x0001D6FB, 0x0001D6FB}, {0x0001D715, 0x0001D715},
+{0x0001D735, 0x0001D735}, {0x0001D74F, 0x0001D74F}, {0x0001D76F, 0x0001D76F}, {0x0001D789, 0x0001D789},
+{0x0001D7A9, 0x0001D7A9}, {0x0001D7C3, 0x0001D7C3}, {0x0001D800, 0x0001D9FF}, {0x0001DA37, 0x0001DA3A},
+{0x0001DA6D, 0x0001DA74}, {0x0001DA76, 0x0001DA83}, {0x0001DA85, 0x0001DA86}, {0x0001E14F, 0x0001E14F},
+{0x0001E2FF, 0x0001E2FF}, {0x0001ECAC, 0x0001ECAC}, {0x0001ECB0, 0x0001ECB0}, {0x0001ED2E, 0x0001ED2E},
+{0x0001EEF0, 0x0001EEF1}, {0x0001F000, 0x0001F02B}, {0x0001F030, 0x0001F093}, {0x0001F0A0, 0x0001F0AE},
+{0x0001F0B1, 0x0001F0BF}, {0x0001F0C1, 0x0001F0CF}, {0x0001F0D1, 0x0001F0F5}, {0x0001F10D, 0x0001F1AD},
+{0x0001F1E6, 0x0001F202}, {0x0001F210, 0x0001F23B}, {0x0001F240, 0x0001F248}, {0x0001F250, 0x0001F251},
+{0x0001F260, 0x0001F265}, {0x0001F300, 0x0001F6D7}, {0x0001F6DC, 0x0001F6EC}, {0x0001F6F0, 0x0001F6FC},
+{0x0001F700, 0x0001F776}, {0x0001F77B, 0x0001F7D9}, {0x0001F7E0, 0x0001F7EB}, {0x0001F7F0, 0x0001F7F0},
+{0x0001F800, 0x0001F80B}, {0x0001F810, 0x0001F847}, {0x0001F850, 0x0001F859}, {0x0001F860, 0x0001F887},
+{0x0001F890, 0x0001F8AD}, {0x0001F8B0, 0x0001F8B1}, {0x0001F900, 0x0001FA53}, {0x0001FA60, 0x0001FA6D},
+{0x0001FA70, 0x0001FA7C}, {0x0001FA80, 0x0001FA88}, {0x0001FA90, 0x0001FABD}, {0x0001FABF, 0x0001FAC5},
+{0x0001FACE, 0x0001FADB}, {0x0001FAE0, 0x0001FAE8}, {0x0001FAF0, 0x0001FAF8}, {0x0001FB00, 0x0001FB92},
+{0x0001FB94, 0x0001FBCA},
+};
+
+const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_control = {
+{0x00000000, 0x0000001F}, {0x0000007F, 0x0000009F}, {0x000000AD, 0x000000AD}, {0x00000378, 0x00000379},
+{0x00000380, 0x00000383}, {0x0000038B, 0x0000038B}, {0x0000038D, 0x0000038D}, {0x000003A2, 0x000003A2},
+{0x00000530, 0x00000530}, {0x00000557, 0x00000558}, {0x0000058B, 0x0000058C}, {0x00000590, 0x00000590},
+{0x000005C8, 0x000005CF}, {0x000005EB, 0x000005EE}, {0x000005F5, 0x00000605}, {0x0000061C, 0x0000061C},
+{0x000006DD, 0x000006DD}, {0x0000070E, 0x0000070F}, {0x0000074B, 0x0000074C}, {0x000007B2, 0x000007BF},
+{0x000007FB, 0x000007FC}, {0x0000082E, 0x0000082F}, {0x0000083F, 0x0000083F}, {0x0000085C, 0x0000085D},
+{0x0000085F, 0x0000085F}, {0x0000086B, 0x0000086F}, {0x0000088F, 0x00000897}, {0x000008E2, 0x000008E2},
+{0x00000984, 0x00000984}, {0x0000098D, 0x0000098E}, {0x00000991, 0x00000992}, {0x000009A9, 0x000009A9},
+{0x000009B1, 0x000009B1}, {0x000009B3, 0x000009B5}, {0x000009BA, 0x000009BB}, {0x000009C5, 0x000009C6},
+{0x000009C9, 0x000009CA}, {0x000009CF, 0x000009D6}, {0x000009D8, 0x000009DB}, {0x000009DE, 0x000009DE},
+{0x000009E4, 0x000009E5}, {0x000009FF, 0x00000A00}, {0x00000A04, 0x00000A04}, {0x00000A0B, 0x00000A0E},
+{0x00000A11, 0x00000A12}, {0x00000A29, 0x00000A29}, {0x00000A31, 0x00000A31}, {0x00000A34, 0x00000A34},
+{0x00000A37, 0x00000A37}, {0x00000A3A, 0x00000A3B}, {0x00000A3D, 0x00000A3D}, {0x00000A43, 0x00000A46},
+{0x00000A49, 0x00000A4A}, {0x00000A4E, 0x00000A50}, {0x00000A52, 0x00000A58}, {0x00000A5D, 0x00000A5D},
+{0x00000A5F, 0x00000A65}, {0x00000A77, 0x00000A80}, {0x00000A84, 0x00000A84}, {0x00000A8E, 0x00000A8E},
+{0x00000A92, 0x00000A92}, {0x00000AA9, 0x00000AA9}, {0x00000AB1, 0x00000AB1}, {0x00000AB4, 0x00000AB4},
+{0x00000ABA, 0x00000ABB}, {0x00000AC6, 0x00000AC6}, {0x00000ACA, 0x00000ACA}, {0x00000ACE, 0x00000ACF},
+{0x00000AD1, 0x00000ADF}, {0x00000AE4, 0x00000AE5}, {0x00000AF2, 0x00000AF8}, {0x00000B00, 0x00000B00},
+{0x00000B04, 0x00000B04}, {0x00000B0D, 0x00000B0E}, {0x00000B11, 0x00000B12}, {0x00000B29, 0x00000B29},
+{0x00000B31, 0x00000B31}, {0x00000B34, 0x00000B34}, {0x00000B3A, 0x00000B3B}, {0x00000B45, 0x00000B46},
+{0x00000B49, 0x00000B4A}, {0x00000B4E, 0x00000B54}, {0x00000B58, 0x00000B5B}, {0x00000B5E, 0x00000B5E},
+{0x00000B64, 0x00000B65}, {0x00000B78, 0x00000B81}, {0x00000B84, 0x00000B84}, {0x00000B8B, 0x00000B8D},
+{0x00000B91, 0x00000B91}, {0x00000B96, 0x00000B98}, {0x00000B9B, 0x00000B9B}, {0x00000B9D, 0x00000B9D},
+{0x00000BA0, 0x00000BA2}, {0x00000BA5, 0x00000BA7}, {0x00000BAB, 0x00000BAD}, {0x00000BBA, 0x00000BBD},
+{0x00000BC3, 0x00000BC5}, {0x00000BC9, 0x00000BC9}, {0x00000BCE, 0x00000BCF}, {0x00000BD1, 0x00000BD6},
+{0x00000BD8, 0x00000BE5}, {0x00000BFB, 0x00000BFF}, {0x00000C0D, 0x00000C0D}, {0x00000C11, 0x00000C11},
+{0x00000C29, 0x00000C29}, {0x00000C3A, 0x00000C3B}, {0x00000C45, 0x00000C45}, {0x00000C49, 0x00000C49},
+{0x00000C4E, 0x00000C54}, {0x00000C57, 0x00000C57}, {0x00000C5B, 0x00000C5C}, {0x00000C5E, 0x00000C5F},
+{0x00000C64, 0x00000C65}, {0x00000C70, 0x00000C76}, {0x00000C8D, 0x00000C8D}, {0x00000C91, 0x00000C91},
+{0x00000CA9, 0x00000CA9}, {0x00000CB4, 0x00000CB4}, {0x00000CBA, 0x00000CBB}, {0x00000CC5, 0x00000CC5},
+{0x00000CC9, 0x00000CC9}, {0x00000CCE, 0x00000CD4}, {0x00000CD7, 0x00000CDC}, {0x00000CDF, 0x00000CDF},
+{0x00000CE4, 0x00000CE5}, {0x00000CF0, 0x00000CF0}, {0x00000CF4, 0x00000CFF}, {0x00000D0D, 0x00000D0D},
+{0x00000D11, 0x00000D11}, {0x00000D45, 0x00000D45}, {0x00000D49, 0x00000D49}, {0x00000D50, 0x00000D53},
+{0x00000D64, 0x00000D65}, {0x00000D80, 0x00000D80}, {0x00000D84, 0x00000D84}, {0x00000D97, 0x00000D99},
+{0x00000DB2, 0x00000DB2}, {0x00000DBC, 0x00000DBC}, {0x00000DBE, 0x00000DBF}, {0x00000DC7, 0x00000DC9},
+{0x00000DCB, 0x00000DCE}, {0x00000DD5, 0x00000DD5}, {0x00000DD7, 0x00000DD7}, {0x00000DE0, 0x00000DE5},
+{0x00000DF0, 0x00000DF1}, {0x00000DF5, 0x00000E00}, {0x00000E3B, 0x00000E3E}, {0x00000E5C, 0x00000E80},
+{0x00000E83, 0x00000E83}, {0x00000E85, 0x00000E85}, {0x00000E8B, 0x00000E8B}, {0x00000EA4, 0x00000EA4},
+{0x00000EA6, 0x00000EA6}, {0x00000EBE, 0x00000EBF}, {0x00000EC5, 0x00000EC5}, {0x00000EC7, 0x00000EC7},
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+{0x0001F8AE, 0x0001F8AF}, {0x0001F8B2, 0x0001F8FF}, {0x0001FA54, 0x0001FA5F}, {0x0001FA6E, 0x0001FA6F},
+{0x0001FA7D, 0x0001FA7F}, {0x0001FA89, 0x0001FA8F}, {0x0001FABE, 0x0001FABE}, {0x0001FAC6, 0x0001FACD},
+{0x0001FADC, 0x0001FADF}, {0x0001FAE9, 0x0001FAEF}, {0x0001FAF9, 0x0001FAFF}, {0x0001FB93, 0x0001FB93},
+{0x0001FBCB, 0x0001FBEF}, {0x0001FBFA, 0x0001FFFF}, {0x0002A6E0, 0x0002A6FF}, {0x0002B73A, 0x0002B73F},
+{0x0002B81E, 0x0002B81F}, {0x0002CEA2, 0x0002CEAF}, {0x0002EBE1, 0x0002EBEF}, {0x0002EE5E, 0x0002F7FF},
+{0x0002FA1E, 0x0002FFFF}, {0x0003134B, 0x0003134F}, {0x000323B0, 0x000E00FF}, {0x000E01F0, 0x0010FFFF},
+};
+
+const std::map<char32_t, char32_t> unicode_map_lowercase = {
+{0x00000041, 0x00000061}, {0x00000042, 0x00000062}, {0x00000043, 0x00000063}, {0x00000044, 0x00000064},
+{0x00000045, 0x00000065}, {0x00000046, 0x00000066}, {0x00000047, 0x00000067}, {0x00000048, 0x00000068},
+{0x00000049, 0x00000069}, {0x0000004A, 0x0000006A}, {0x0000004B, 0x0000006B}, {0x0000004C, 0x0000006C},
+{0x0000004D, 0x0000006D}, {0x0000004E, 0x0000006E}, {0x0000004F, 0x0000006F}, {0x00000050, 0x00000070},
+{0x00000051, 0x00000071}, {0x00000052, 0x00000072}, {0x00000053, 0x00000073}, {0x00000054, 0x00000074},
+{0x00000055, 0x00000075}, {0x00000056, 0x00000076}, {0x00000057, 0x00000077}, {0x00000058, 0x00000078},
+{0x00000059, 0x00000079}, {0x0000005A, 0x0000007A}, {0x000000C0, 0x000000E0}, {0x000000C1, 0x000000E1},
+{0x000000C2, 0x000000E2}, {0x000000C3, 0x000000E3}, {0x000000C4, 0x000000E4}, {0x000000C5, 0x000000E5},
+{0x000000C6, 0x000000E6}, {0x000000C7, 0x000000E7}, {0x000000C8, 0x000000E8}, {0x000000C9, 0x000000E9},
+{0x000000CA, 0x000000EA}, {0x000000CB, 0x000000EB}, {0x000000CC, 0x000000EC}, {0x000000CD, 0x000000ED},
+{0x000000CE, 0x000000EE}, {0x000000CF, 0x000000EF}, {0x000000D0, 0x000000F0}, {0x000000D1, 0x000000F1},
+{0x000000D2, 0x000000F2}, {0x000000D3, 0x000000F3}, {0x000000D4, 0x000000F4}, {0x000000D5, 0x000000F5},
+{0x000000D6, 0x000000F6}, {0x000000D8, 0x000000F8}, {0x000000D9, 0x000000F9}, {0x000000DA, 0x000000FA},
+{0x000000DB, 0x000000FB}, {0x000000DC, 0x000000FC}, {0x000000DD, 0x000000FD}, {0x000000DE, 0x000000FE},
+{0x00000100, 0x00000101}, {0x00000102, 0x00000103}, {0x00000104, 0x00000105}, {0x00000106, 0x00000107},
+{0x00000108, 0x00000109}, {0x0000010A, 0x0000010B}, {0x0000010C, 0x0000010D}, {0x0000010E, 0x0000010F},
+{0x00000110, 0x00000111}, {0x00000112, 0x00000113}, {0x00000114, 0x00000115}, {0x00000116, 0x00000117},
+{0x00000118, 0x00000119}, {0x0000011A, 0x0000011B}, {0x0000011C, 0x0000011D}, {0x0000011E, 0x0000011F},
+{0x00000120, 0x00000121}, {0x00000122, 0x00000123}, {0x00000124, 0x00000125}, {0x00000126, 0x00000127},
+{0x00000128, 0x00000129}, {0x0000012A, 0x0000012B}, {0x0000012C, 0x0000012D}, {0x0000012E, 0x0000012F},
+{0x00000130, 0x00000069}, {0x00000132, 0x00000133}, {0x00000134, 0x00000135}, {0x00000136, 0x00000137},
+{0x00000139, 0x0000013A}, {0x0000013B, 0x0000013C}, {0x0000013D, 0x0000013E}, {0x0000013F, 0x00000140},
+{0x00000141, 0x00000142}, {0x00000143, 0x00000144}, {0x00000145, 0x00000146}, {0x00000147, 0x00000148},
+{0x0000014A, 0x0000014B}, {0x0000014C, 0x0000014D}, {0x0000014E, 0x0000014F}, {0x00000150, 0x00000151},
+{0x00000152, 0x00000153}, {0x00000154, 0x00000155}, {0x00000156, 0x00000157}, {0x00000158, 0x00000159},
+{0x0000015A, 0x0000015B}, {0x0000015C, 0x0000015D}, {0x0000015E, 0x0000015F}, {0x00000160, 0x00000161},
+{0x00000162, 0x00000163}, {0x00000164, 0x00000165}, {0x00000166, 0x00000167}, {0x00000168, 0x00000169},
+{0x0000016A, 0x0000016B}, {0x0000016C, 0x0000016D}, {0x0000016E, 0x0000016F}, {0x00000170, 0x00000171},
+{0x00000172, 0x00000173}, {0x00000174, 0x00000175}, {0x00000176, 0x00000177}, {0x00000178, 0x000000FF},
+{0x00000179, 0x0000017A}, {0x0000017B, 0x0000017C}, {0x0000017D, 0x0000017E}, {0x00000181, 0x00000253},
+{0x00000182, 0x00000183}, {0x00000184, 0x00000185}, {0x00000186, 0x00000254}, {0x00000187, 0x00000188},
+{0x00000189, 0x00000256}, {0x0000018A, 0x00000257}, {0x0000018B, 0x0000018C}, {0x0000018E, 0x000001DD},
+{0x0000018F, 0x00000259}, {0x00000190, 0x0000025B}, {0x00000191, 0x00000192}, {0x00000193, 0x00000260},
+{0x00000194, 0x00000263}, {0x00000196, 0x00000269}, {0x00000197, 0x00000268}, {0x00000198, 0x00000199},
+{0x0000019C, 0x0000026F}, {0x0000019D, 0x00000272}, {0x0000019F, 0x00000275}, {0x000001A0, 0x000001A1},
+{0x000001A2, 0x000001A3}, {0x000001A4, 0x000001A5}, {0x000001A6, 0x00000280}, {0x000001A7, 0x000001A8},
+{0x000001A9, 0x00000283}, {0x000001AC, 0x000001AD}, {0x000001AE, 0x00000288}, {0x000001AF, 0x000001B0},
+{0x000001B1, 0x0000028A}, {0x000001B2, 0x0000028B}, {0x000001B3, 0x000001B4}, {0x000001B5, 0x000001B6},
+{0x000001B7, 0x00000292}, {0x000001B8, 0x000001B9}, {0x000001BC, 0x000001BD}, {0x000001C4, 0x000001C6},
+{0x000001C5, 0x000001C6}, {0x000001C7, 0x000001C9}, {0x000001C8, 0x000001C9}, {0x000001CA, 0x000001CC},
+{0x000001CB, 0x000001CC}, {0x000001CD, 0x000001CE}, {0x000001CF, 0x000001D0}, {0x000001D1, 0x000001D2},
+{0x000001D3, 0x000001D4}, {0x000001D5, 0x000001D6}, {0x000001D7, 0x000001D8}, {0x000001D9, 0x000001DA},
+{0x000001DB, 0x000001DC}, {0x000001DE, 0x000001DF}, {0x000001E0, 0x000001E1}, {0x000001E2, 0x000001E3},
+{0x000001E4, 0x000001E5}, {0x000001E6, 0x000001E7}, {0x000001E8, 0x000001E9}, {0x000001EA, 0x000001EB},
+{0x000001EC, 0x000001ED}, {0x000001EE, 0x000001EF}, {0x000001F1, 0x000001F3}, {0x000001F2, 0x000001F3},
+{0x000001F4, 0x000001F5}, {0x000001F6, 0x00000195}, {0x000001F7, 0x000001BF}, {0x000001F8, 0x000001F9},
+{0x000001FA, 0x000001FB}, {0x000001FC, 0x000001FD}, {0x000001FE, 0x000001FF}, {0x00000200, 0x00000201},
+{0x00000202, 0x00000203}, {0x00000204, 0x00000205}, {0x00000206, 0x00000207}, {0x00000208, 0x00000209},
+{0x0000020A, 0x0000020B}, {0x0000020C, 0x0000020D}, {0x0000020E, 0x0000020F}, {0x00000210, 0x00000211},
+{0x00000212, 0x00000213}, {0x00000214, 0x00000215}, {0x00000216, 0x00000217}, {0x00000218, 0x00000219},
+{0x0000021A, 0x0000021B}, {0x0000021C, 0x0000021D}, {0x0000021E, 0x0000021F}, {0x00000220, 0x0000019E},
+{0x00000222, 0x00000223}, {0x00000224, 0x00000225}, {0x00000226, 0x00000227}, {0x00000228, 0x00000229},
+{0x0000022A, 0x0000022B}, {0x0000022C, 0x0000022D}, {0x0000022E, 0x0000022F}, {0x00000230, 0x00000231},
+{0x00000232, 0x00000233}, {0x0000023A, 0x00002C65}, {0x0000023B, 0x0000023C}, {0x0000023D, 0x0000019A},
+{0x0000023E, 0x00002C66}, {0x00000241, 0x00000242}, {0x00000243, 0x00000180}, {0x00000244, 0x00000289},
+{0x00000245, 0x0000028C}, {0x00000246, 0x00000247}, {0x00000248, 0x00000249}, {0x0000024A, 0x0000024B},
+{0x0000024C, 0x0000024D}, {0x0000024E, 0x0000024F}, {0x00000370, 0x00000371}, {0x00000372, 0x00000373},
+{0x00000376, 0x00000377}, {0x0000037F, 0x000003F3}, {0x00000386, 0x000003AC}, {0x00000388, 0x000003AD},
+{0x00000389, 0x000003AE}, {0x0000038A, 0x000003AF}, {0x0000038C, 0x000003CC}, {0x0000038E, 0x000003CD},
+{0x0000038F, 0x000003CE}, {0x00000391, 0x000003B1}, {0x00000392, 0x000003B2}, {0x00000393, 0x000003B3},
+{0x00000394, 0x000003B4}, {0x00000395, 0x000003B5}, {0x00000396, 0x000003B6}, {0x00000397, 0x000003B7},
+{0x00000398, 0x000003B8}, {0x00000399, 0x000003B9}, {0x0000039A, 0x000003BA}, {0x0000039B, 0x000003BB},
+{0x0000039C, 0x000003BC}, {0x0000039D, 0x000003BD}, {0x0000039E, 0x000003BE}, {0x0000039F, 0x000003BF},
+{0x000003A0, 0x000003C0}, {0x000003A1, 0x000003C1}, {0x000003A3, 0x000003C3}, {0x000003A4, 0x000003C4},
+{0x000003A5, 0x000003C5}, {0x000003A6, 0x000003C6}, {0x000003A7, 0x000003C7}, {0x000003A8, 0x000003C8},
+{0x000003A9, 0x000003C9}, {0x000003AA, 0x000003CA}, {0x000003AB, 0x000003CB}, {0x000003CF, 0x000003D7},
+{0x000003D8, 0x000003D9}, {0x000003DA, 0x000003DB}, {0x000003DC, 0x000003DD}, {0x000003DE, 0x000003DF},
+{0x000003E0, 0x000003E1}, {0x000003E2, 0x000003E3}, {0x000003E4, 0x000003E5}, {0x000003E6, 0x000003E7},
+{0x000003E8, 0x000003E9}, {0x000003EA, 0x000003EB}, {0x000003EC, 0x000003ED}, {0x000003EE, 0x000003EF},
+{0x000003F4, 0x000003B8}, {0x000003F7, 0x000003F8}, {0x000003F9, 0x000003F2}, {0x000003FA, 0x000003FB},
+{0x000003FD, 0x0000037B}, {0x000003FE, 0x0000037C}, {0x000003FF, 0x0000037D}, {0x00000400, 0x00000450},
+{0x00000401, 0x00000451}, {0x00000402, 0x00000452}, {0x00000403, 0x00000453}, {0x00000404, 0x00000454},
+{0x00000405, 0x00000455}, {0x00000406, 0x00000456}, {0x00000407, 0x00000457}, {0x00000408, 0x00000458},
+{0x00000409, 0x00000459}, {0x0000040A, 0x0000045A}, {0x0000040B, 0x0000045B}, {0x0000040C, 0x0000045C},
+{0x0000040D, 0x0000045D}, {0x0000040E, 0x0000045E}, {0x0000040F, 0x0000045F}, {0x00000410, 0x00000430},
+{0x00000411, 0x00000431}, {0x00000412, 0x00000432}, {0x00000413, 0x00000433}, {0x00000414, 0x00000434},
+{0x00000415, 0x00000435}, {0x00000416, 0x00000436}, {0x00000417, 0x00000437}, {0x00000418, 0x00000438},
+{0x00000419, 0x00000439}, {0x0000041A, 0x0000043A}, {0x0000041B, 0x0000043B}, {0x0000041C, 0x0000043C},
+{0x0000041D, 0x0000043D}, {0x0000041E, 0x0000043E}, {0x0000041F, 0x0000043F}, {0x00000420, 0x00000440},
+{0x00000421, 0x00000441}, {0x00000422, 0x00000442}, {0x00000423, 0x00000443}, {0x00000424, 0x00000444},
+{0x00000425, 0x00000445}, {0x00000426, 0x00000446}, {0x00000427, 0x00000447}, {0x00000428, 0x00000448},
+{0x00000429, 0x00000449}, {0x0000042A, 0x0000044A}, {0x0000042B, 0x0000044B}, {0x0000042C, 0x0000044C},
+{0x0000042D, 0x0000044D}, {0x0000042E, 0x0000044E}, {0x0000042F, 0x0000044F}, {0x00000460, 0x00000461},
+{0x00000462, 0x00000463}, {0x00000464, 0x00000465}, {0x00000466, 0x00000467}, {0x00000468, 0x00000469},
+{0x0000046A, 0x0000046B}, {0x0000046C, 0x0000046D}, {0x0000046E, 0x0000046F}, {0x00000470, 0x00000471},
+{0x00000472, 0x00000473}, {0x00000474, 0x00000475}, {0x00000476, 0x00000477}, {0x00000478, 0x00000479},
+{0x0000047A, 0x0000047B}, {0x0000047C, 0x0000047D}, {0x0000047E, 0x0000047F}, {0x00000480, 0x00000481},
+{0x0000048A, 0x0000048B}, {0x0000048C, 0x0000048D}, {0x0000048E, 0x0000048F}, {0x00000490, 0x00000491},
+{0x00000492, 0x00000493}, {0x00000494, 0x00000495}, {0x00000496, 0x00000497}, {0x00000498, 0x00000499},
+{0x0000049A, 0x0000049B}, {0x0000049C, 0x0000049D}, {0x0000049E, 0x0000049F}, {0x000004A0, 0x000004A1},
+{0x000004A2, 0x000004A3}, {0x000004A4, 0x000004A5}, {0x000004A6, 0x000004A7}, {0x000004A8, 0x000004A9},
+{0x000004AA, 0x000004AB}, {0x000004AC, 0x000004AD}, {0x000004AE, 0x000004AF}, {0x000004B0, 0x000004B1},
+{0x000004B2, 0x000004B3}, {0x000004B4, 0x000004B5}, {0x000004B6, 0x000004B7}, {0x000004B8, 0x000004B9},
+{0x000004BA, 0x000004BB}, {0x000004BC, 0x000004BD}, {0x000004BE, 0x000004BF}, {0x000004C0, 0x000004CF},
+{0x000004C1, 0x000004C2}, {0x000004C3, 0x000004C4}, {0x000004C5, 0x000004C6}, {0x000004C7, 0x000004C8},
+{0x000004C9, 0x000004CA}, {0x000004CB, 0x000004CC}, {0x000004CD, 0x000004CE}, {0x000004D0, 0x000004D1},
+{0x000004D2, 0x000004D3}, {0x000004D4, 0x000004D5}, {0x000004D6, 0x000004D7}, {0x000004D8, 0x000004D9},
+{0x000004DA, 0x000004DB}, {0x000004DC, 0x000004DD}, {0x000004DE, 0x000004DF}, {0x000004E0, 0x000004E1},
+{0x000004E2, 0x000004E3}, {0x000004E4, 0x000004E5}, {0x000004E6, 0x000004E7}, {0x000004E8, 0x000004E9},
+{0x000004EA, 0x000004EB}, {0x000004EC, 0x000004ED}, {0x000004EE, 0x000004EF}, {0x000004F0, 0x000004F1},
+{0x000004F2, 0x000004F3}, {0x000004F4, 0x000004F5}, {0x000004F6, 0x000004F7}, {0x000004F8, 0x000004F9},
+{0x000004FA, 0x000004FB}, {0x000004FC, 0x000004FD}, {0x000004FE, 0x000004FF}, {0x00000500, 0x00000501},
+{0x00000502, 0x00000503}, {0x00000504, 0x00000505}, {0x00000506, 0x00000507}, {0x00000508, 0x00000509},
+{0x0000050A, 0x0000050B}, {0x0000050C, 0x0000050D}, {0x0000050E, 0x0000050F}, {0x00000510, 0x00000511},
+{0x00000512, 0x00000513}, {0x00000514, 0x00000515}, {0x00000516, 0x00000517}, {0x00000518, 0x00000519},
+{0x0000051A, 0x0000051B}, {0x0000051C, 0x0000051D}, {0x0000051E, 0x0000051F}, {0x00000520, 0x00000521},
+{0x00000522, 0x00000523}, {0x00000524, 0x00000525}, {0x00000526, 0x00000527}, {0x00000528, 0x00000529},
+{0x0000052A, 0x0000052B}, {0x0000052C, 0x0000052D}, {0x0000052E, 0x0000052F}, {0x00000531, 0x00000561},
+{0x00000532, 0x00000562}, {0x00000533, 0x00000563}, {0x00000534, 0x00000564}, {0x00000535, 0x00000565},
+{0x00000536, 0x00000566}, {0x00000537, 0x00000567}, {0x00000538, 0x00000568}, {0x00000539, 0x00000569},
+{0x0000053A, 0x0000056A}, {0x0000053B, 0x0000056B}, {0x0000053C, 0x0000056C}, {0x0000053D, 0x0000056D},
+{0x0000053E, 0x0000056E}, {0x0000053F, 0x0000056F}, {0x00000540, 0x00000570}, {0x00000541, 0x00000571},
+{0x00000542, 0x00000572}, {0x00000543, 0x00000573}, {0x00000544, 0x00000574}, {0x00000545, 0x00000575},
+{0x00000546, 0x00000576}, {0x00000547, 0x00000577}, {0x00000548, 0x00000578}, {0x00000549, 0x00000579},
+{0x0000054A, 0x0000057A}, {0x0000054B, 0x0000057B}, {0x0000054C, 0x0000057C}, {0x0000054D, 0x0000057D},
+{0x0000054E, 0x0000057E}, {0x0000054F, 0x0000057F}, {0x00000550, 0x00000580}, {0x00000551, 0x00000581},
+{0x00000552, 0x00000582}, {0x00000553, 0x00000583}, {0x00000554, 0x00000584}, {0x00000555, 0x00000585},
+{0x00000556, 0x00000586}, {0x000010A0, 0x00002D00}, {0x000010A1, 0x00002D01}, {0x000010A2, 0x00002D02},
+{0x000010A3, 0x00002D03}, {0x000010A4, 0x00002D04}, {0x000010A5, 0x00002D05}, {0x000010A6, 0x00002D06},
+{0x000010A7, 0x00002D07}, {0x000010A8, 0x00002D08}, {0x000010A9, 0x00002D09}, {0x000010AA, 0x00002D0A},
+{0x000010AB, 0x00002D0B}, {0x000010AC, 0x00002D0C}, {0x000010AD, 0x00002D0D}, {0x000010AE, 0x00002D0E},
+{0x000010AF, 0x00002D0F}, {0x000010B0, 0x00002D10}, {0x000010B1, 0x00002D11}, {0x000010B2, 0x00002D12},
+{0x000010B3, 0x00002D13}, {0x000010B4, 0x00002D14}, {0x000010B5, 0x00002D15}, {0x000010B6, 0x00002D16},
+{0x000010B7, 0x00002D17}, {0x000010B8, 0x00002D18}, {0x000010B9, 0x00002D19}, {0x000010BA, 0x00002D1A},
+{0x000010BB, 0x00002D1B}, {0x000010BC, 0x00002D1C}, {0x000010BD, 0x00002D1D}, {0x000010BE, 0x00002D1E},
+{0x000010BF, 0x00002D1F}, {0x000010C0, 0x00002D20}, {0x000010C1, 0x00002D21}, {0x000010C2, 0x00002D22},
+{0x000010C3, 0x00002D23}, {0x000010C4, 0x00002D24}, {0x000010C5, 0x00002D25}, {0x000010C7, 0x00002D27},
+{0x000010CD, 0x00002D2D}, {0x000013A0, 0x0000AB70}, {0x000013A1, 0x0000AB71}, {0x000013A2, 0x0000AB72},
+{0x000013A3, 0x0000AB73}, {0x000013A4, 0x0000AB74}, {0x000013A5, 0x0000AB75}, {0x000013A6, 0x0000AB76},
+{0x000013A7, 0x0000AB77}, {0x000013A8, 0x0000AB78}, {0x000013A9, 0x0000AB79}, {0x000013AA, 0x0000AB7A},
+{0x000013AB, 0x0000AB7B}, {0x000013AC, 0x0000AB7C}, {0x000013AD, 0x0000AB7D}, {0x000013AE, 0x0000AB7E},
+{0x000013AF, 0x0000AB7F}, {0x000013B0, 0x0000AB80}, {0x000013B1, 0x0000AB81}, {0x000013B2, 0x0000AB82},
+{0x000013B3, 0x0000AB83}, {0x000013B4, 0x0000AB84}, {0x000013B5, 0x0000AB85}, {0x000013B6, 0x0000AB86},
+{0x000013B7, 0x0000AB87}, {0x000013B8, 0x0000AB88}, {0x000013B9, 0x0000AB89}, {0x000013BA, 0x0000AB8A},
+{0x000013BB, 0x0000AB8B}, {0x000013BC, 0x0000AB8C}, {0x000013BD, 0x0000AB8D}, {0x000013BE, 0x0000AB8E},
+{0x000013BF, 0x0000AB8F}, {0x000013C0, 0x0000AB90}, {0x000013C1, 0x0000AB91}, {0x000013C2, 0x0000AB92},
+{0x000013C3, 0x0000AB93}, {0x000013C4, 0x0000AB94}, {0x000013C5, 0x0000AB95}, {0x000013C6, 0x0000AB96},
+{0x000013C7, 0x0000AB97}, {0x000013C8, 0x0000AB98}, {0x000013C9, 0x0000AB99}, {0x000013CA, 0x0000AB9A},
+{0x000013CB, 0x0000AB9B}, {0x000013CC, 0x0000AB9C}, {0x000013CD, 0x0000AB9D}, {0x000013CE, 0x0000AB9E},
+{0x000013CF, 0x0000AB9F}, {0x000013D0, 0x0000ABA0}, {0x000013D1, 0x0000ABA1}, {0x000013D2, 0x0000ABA2},
+{0x000013D3, 0x0000ABA3}, {0x000013D4, 0x0000ABA4}, {0x000013D5, 0x0000ABA5}, {0x000013D6, 0x0000ABA6},
+{0x000013D7, 0x0000ABA7}, {0x000013D8, 0x0000ABA8}, {0x000013D9, 0x0000ABA9}, {0x000013DA, 0x0000ABAA},
+{0x000013DB, 0x0000ABAB}, {0x000013DC, 0x0000ABAC}, {0x000013DD, 0x0000ABAD}, {0x000013DE, 0x0000ABAE},
+{0x000013DF, 0x0000ABAF}, {0x000013E0, 0x0000ABB0}, {0x000013E1, 0x0000ABB1}, {0x000013E2, 0x0000ABB2},
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+{0x0000A79A, 0x0000A79B}, {0x0000A79C, 0x0000A79D}, {0x0000A79E, 0x0000A79F}, {0x0000A7A0, 0x0000A7A1},
+{0x0000A7A2, 0x0000A7A3}, {0x0000A7A4, 0x0000A7A5}, {0x0000A7A6, 0x0000A7A7}, {0x0000A7A8, 0x0000A7A9},
+{0x0000A7AA, 0x00000266}, {0x0000A7AB, 0x0000025C}, {0x0000A7AC, 0x00000261}, {0x0000A7AD, 0x0000026C},
+{0x0000A7AE, 0x0000026A}, {0x0000A7B0, 0x0000029E}, {0x0000A7B1, 0x00000287}, {0x0000A7B2, 0x0000029D},
+{0x0000A7B3, 0x0000AB53}, {0x0000A7B4, 0x0000A7B5}, {0x0000A7B6, 0x0000A7B7}, {0x0000A7B8, 0x0000A7B9},
+{0x0000A7BA, 0x0000A7BB}, {0x0000A7BC, 0x0000A7BD}, {0x0000A7BE, 0x0000A7BF}, {0x0000A7C2, 0x0000A7C3},
+{0x0000A7C4, 0x0000A794}, {0x0000A7C5, 0x00000282}, {0x0000A7C6, 0x00001D8E}, {0x0000A7C7, 0x0000A7C8},
+{0x0000A7C9, 0x0000A7CA}, {0x0000A7F5, 0x0000A7F6}, {0x0000FF21, 0x0000FF41}, {0x0000FF22, 0x0000FF42},
+{0x0000FF23, 0x0000FF43}, {0x0000FF24, 0x0000FF44}, {0x0000FF25, 0x0000FF45}, {0x0000FF26, 0x0000FF46},
+{0x0000FF27, 0x0000FF47}, {0x0000FF28, 0x0000FF48}, {0x0000FF29, 0x0000FF49}, {0x0000FF2A, 0x0000FF4A},
+{0x0000FF2B, 0x0000FF4B}, {0x0000FF2C, 0x0000FF4C}, {0x0000FF2D, 0x0000FF4D}, {0x0000FF2E, 0x0000FF4E},
+{0x0000FF2F, 0x0000FF4F}, {0x0000FF30, 0x0000FF50}, {0x0000FF31, 0x0000FF51}, {0x0000FF32, 0x0000FF52},
+{0x0000FF33, 0x0000FF53}, {0x0000FF34, 0x0000FF54}, {0x0000FF35, 0x0000FF55}, {0x0000FF36, 0x0000FF56},
+{0x0000FF37, 0x0000FF57}, {0x0000FF38, 0x0000FF58}, {0x0000FF39, 0x0000FF59}, {0x0000FF3A, 0x0000FF5A},
+{0x00010400, 0x00010428}, {0x00010401, 0x00010429}, {0x00010402, 0x0001042A}, {0x00010403, 0x0001042B},
+{0x00010404, 0x0001042C}, {0x00010405, 0x0001042D}, {0x00010406, 0x0001042E}, {0x00010407, 0x0001042F},
+{0x00010408, 0x00010430}, {0x00010409, 0x00010431}, {0x0001040A, 0x00010432}, {0x0001040B, 0x00010433},
+{0x0001040C, 0x00010434}, {0x0001040D, 0x00010435}, {0x0001040E, 0x00010436}, {0x0001040F, 0x00010437},
+{0x00010410, 0x00010438}, {0x00010411, 0x00010439}, {0x00010412, 0x0001043A}, {0x00010413, 0x0001043B},
+{0x00010414, 0x0001043C}, {0x00010415, 0x0001043D}, {0x00010416, 0x0001043E}, {0x00010417, 0x0001043F},
+{0x00010418, 0x00010440}, {0x00010419, 0x00010441}, {0x0001041A, 0x00010442}, {0x0001041B, 0x00010443},
+{0x0001041C, 0x00010444}, {0x0001041D, 0x00010445}, {0x0001041E, 0x00010446}, {0x0001041F, 0x00010447},
+{0x00010420, 0x00010448}, {0x00010421, 0x00010449}, {0x00010422, 0x0001044A}, {0x00010423, 0x0001044B},
+{0x00010424, 0x0001044C}, {0x00010425, 0x0001044D}, {0x00010426, 0x0001044E}, {0x00010427, 0x0001044F},
+{0x000104B0, 0x000104D8}, {0x000104B1, 0x000104D9}, {0x000104B2, 0x000104DA}, {0x000104B3, 0x000104DB},
+{0x000104B4, 0x000104DC}, {0x000104B5, 0x000104DD}, {0x000104B6, 0x000104DE}, {0x000104B7, 0x000104DF},
+{0x000104B8, 0x000104E0}, {0x000104B9, 0x000104E1}, {0x000104BA, 0x000104E2}, {0x000104BB, 0x000104E3},
+{0x000104BC, 0x000104E4}, {0x000104BD, 0x000104E5}, {0x000104BE, 0x000104E6}, {0x000104BF, 0x000104E7},
+{0x000104C0, 0x000104E8}, {0x000104C1, 0x000104E9}, {0x000104C2, 0x000104EA}, {0x000104C3, 0x000104EB},
+{0x000104C4, 0x000104EC}, {0x000104C5, 0x000104ED}, {0x000104C6, 0x000104EE}, {0x000104C7, 0x000104EF},
+{0x000104C8, 0x000104F0}, {0x000104C9, 0x000104F1}, {0x000104CA, 0x000104F2}, {0x000104CB, 0x000104F3},
+{0x000104CC, 0x000104F4}, {0x000104CD, 0x000104F5}, {0x000104CE, 0x000104F6}, {0x000104CF, 0x000104F7},
+{0x000104D0, 0x000104F8}, {0x000104D1, 0x000104F9}, {0x000104D2, 0x000104FA}, {0x000104D3, 0x000104FB},
+{0x00010C80, 0x00010CC0}, {0x00010C81, 0x00010CC1}, {0x00010C82, 0x00010CC2}, {0x00010C83, 0x00010CC3},
+{0x00010C84, 0x00010CC4}, {0x00010C85, 0x00010CC5}, {0x00010C86, 0x00010CC6}, {0x00010C87, 0x00010CC7},
+{0x00010C88, 0x00010CC8}, {0x00010C89, 0x00010CC9}, {0x00010C8A, 0x00010CCA}, {0x00010C8B, 0x00010CCB},
+{0x00010C8C, 0x00010CCC}, {0x00010C8D, 0x00010CCD}, {0x00010C8E, 0x00010CCE}, {0x00010C8F, 0x00010CCF},
+{0x00010C90, 0x00010CD0}, {0x00010C91, 0x00010CD1}, {0x00010C92, 0x00010CD2}, {0x00010C93, 0x00010CD3},
+{0x00010C94, 0x00010CD4}, {0x00010C95, 0x00010CD5}, {0x00010C96, 0x00010CD6}, {0x00010C97, 0x00010CD7},
+{0x00010C98, 0x00010CD8}, {0x00010C99, 0x00010CD9}, {0x00010C9A, 0x00010CDA}, {0x00010C9B, 0x00010CDB},
+{0x00010C9C, 0x00010CDC}, {0x00010C9D, 0x00010CDD}, {0x00010C9E, 0x00010CDE}, {0x00010C9F, 0x00010CDF},
+{0x00010CA0, 0x00010CE0}, {0x00010CA1, 0x00010CE1}, {0x00010CA2, 0x00010CE2}, {0x00010CA3, 0x00010CE3},
+{0x00010CA4, 0x00010CE4}, {0x00010CA5, 0x00010CE5}, {0x00010CA6, 0x00010CE6}, {0x00010CA7, 0x00010CE7},
+{0x00010CA8, 0x00010CE8}, {0x00010CA9, 0x00010CE9}, {0x00010CAA, 0x00010CEA}, {0x00010CAB, 0x00010CEB},
+{0x00010CAC, 0x00010CEC}, {0x00010CAD, 0x00010CED}, {0x00010CAE, 0x00010CEE}, {0x00010CAF, 0x00010CEF},
+{0x00010CB0, 0x00010CF0}, {0x00010CB1, 0x00010CF1}, {0x00010CB2, 0x00010CF2}, {0x000118A0, 0x000118C0},
+{0x000118A1, 0x000118C1}, {0x000118A2, 0x000118C2}, {0x000118A3, 0x000118C3}, {0x000118A4, 0x000118C4},
+{0x000118A5, 0x000118C5}, {0x000118A6, 0x000118C6}, {0x000118A7, 0x000118C7}, {0x000118A8, 0x000118C8},
+{0x000118A9, 0x000118C9}, {0x000118AA, 0x000118CA}, {0x000118AB, 0x000118CB}, {0x000118AC, 0x000118CC},
+{0x000118AD, 0x000118CD}, {0x000118AE, 0x000118CE}, {0x000118AF, 0x000118CF}, {0x000118B0, 0x000118D0},
+{0x000118B1, 0x000118D1}, {0x000118B2, 0x000118D2}, {0x000118B3, 0x000118D3}, {0x000118B4, 0x000118D4},
+{0x000118B5, 0x000118D5}, {0x000118B6, 0x000118D6}, {0x000118B7, 0x000118D7}, {0x000118B8, 0x000118D8},
+{0x000118B9, 0x000118D9}, {0x000118BA, 0x000118DA}, {0x000118BB, 0x000118DB}, {0x000118BC, 0x000118DC},
+{0x000118BD, 0x000118DD}, {0x000118BE, 0x000118DE}, {0x000118BF, 0x000118DF}, {0x00016E40, 0x00016E60},
+{0x00016E41, 0x00016E61}, {0x00016E42, 0x00016E62}, {0x00016E43, 0x00016E63}, {0x00016E44, 0x00016E64},
+{0x00016E45, 0x00016E65}, {0x00016E46, 0x00016E66}, {0x00016E47, 0x00016E67}, {0x00016E48, 0x00016E68},
+{0x00016E49, 0x00016E69}, {0x00016E4A, 0x00016E6A}, {0x00016E4B, 0x00016E6B}, {0x00016E4C, 0x00016E6C},
+{0x00016E4D, 0x00016E6D}, {0x00016E4E, 0x00016E6E}, {0x00016E4F, 0x00016E6F}, {0x00016E50, 0x00016E70},
+{0x00016E51, 0x00016E71}, {0x00016E52, 0x00016E72}, {0x00016E53, 0x00016E73}, {0x00016E54, 0x00016E74},
+{0x00016E55, 0x00016E75}, {0x00016E56, 0x00016E76}, {0x00016E57, 0x00016E77}, {0x00016E58, 0x00016E78},
+{0x00016E59, 0x00016E79}, {0x00016E5A, 0x00016E7A}, {0x00016E5B, 0x00016E7B}, {0x00016E5C, 0x00016E7C},
+{0x00016E5D, 0x00016E7D}, {0x00016E5E, 0x00016E7E}, {0x00016E5F, 0x00016E7F}, {0x0001E900, 0x0001E922},
+{0x0001E901, 0x0001E923}, {0x0001E902, 0x0001E924}, {0x0001E903, 0x0001E925}, {0x0001E904, 0x0001E926},
+{0x0001E905, 0x0001E927}, {0x0001E906, 0x0001E928}, {0x0001E907, 0x0001E929}, {0x0001E908, 0x0001E92A},
+{0x0001E909, 0x0001E92B}, {0x0001E90A, 0x0001E92C}, {0x0001E90B, 0x0001E92D}, {0x0001E90C, 0x0001E92E},
+{0x0001E90D, 0x0001E92F}, {0x0001E90E, 0x0001E930}, {0x0001E90F, 0x0001E931}, {0x0001E910, 0x0001E932},
+{0x0001E911, 0x0001E933}, {0x0001E912, 0x0001E934}, {0x0001E913, 0x0001E935}, {0x0001E914, 0x0001E936},
+{0x0001E915, 0x0001E937}, {0x0001E916, 0x0001E938}, {0x0001E917, 0x0001E939}, {0x0001E918, 0x0001E93A},
+{0x0001E919, 0x0001E93B}, {0x0001E91A, 0x0001E93C}, {0x0001E91B, 0x0001E93D}, {0x0001E91C, 0x0001E93E},
+{0x0001E91D, 0x0001E93F}, {0x0001E91E, 0x0001E940}, {0x0001E91F, 0x0001E941}, {0x0001E920, 0x0001E942},
+{0x0001E921, 0x0001E943},
+};
+
+const std::map<char32_t, char32_t> unicode_map_uppercase = {
+{0x00000061, 0x00000041}, {0x00000062, 0x00000042}, {0x00000063, 0x00000043}, {0x00000064, 0x00000044},
+{0x00000065, 0x00000045}, {0x00000066, 0x00000046}, {0x00000067, 0x00000047}, {0x00000068, 0x00000048},
+{0x00000069, 0x00000049}, {0x0000006A, 0x0000004A}, {0x0000006B, 0x0000004B}, {0x0000006C, 0x0000004C},
+{0x0000006D, 0x0000004D}, {0x0000006E, 0x0000004E}, {0x0000006F, 0x0000004F}, {0x00000070, 0x00000050},
+{0x00000071, 0x00000051}, {0x00000072, 0x00000052}, {0x00000073, 0x00000053}, {0x00000074, 0x00000054},
+{0x00000075, 0x00000055}, {0x00000076, 0x00000056}, {0x00000077, 0x00000057}, {0x00000078, 0x00000058},
+{0x00000079, 0x00000059}, {0x0000007A, 0x0000005A}, {0x000000B5, 0x0000039C}, {0x000000DF, 0x00000053},
+{0x000000E0, 0x000000C0}, {0x000000E1, 0x000000C1}, {0x000000E2, 0x000000C2}, {0x000000E3, 0x000000C3},
+{0x000000E4, 0x000000C4}, {0x000000E5, 0x000000C5}, {0x000000E6, 0x000000C6}, {0x000000E7, 0x000000C7},
+{0x000000E8, 0x000000C8}, {0x000000E9, 0x000000C9}, {0x000000EA, 0x000000CA}, {0x000000EB, 0x000000CB},
+{0x000000EC, 0x000000CC}, {0x000000ED, 0x000000CD}, {0x000000EE, 0x000000CE}, {0x000000EF, 0x000000CF},
+{0x000000F0, 0x000000D0}, {0x000000F1, 0x000000D1}, {0x000000F2, 0x000000D2}, {0x000000F3, 0x000000D3},
+{0x000000F4, 0x000000D4}, {0x000000F5, 0x000000D5}, {0x000000F6, 0x000000D6}, {0x000000F8, 0x000000D8},
+{0x000000F9, 0x000000D9}, {0x000000FA, 0x000000DA}, {0x000000FB, 0x000000DB}, {0x000000FC, 0x000000DC},
+{0x000000FD, 0x000000DD}, {0x000000FE, 0x000000DE}, {0x000000FF, 0x00000178}, {0x00000101, 0x00000100},
+{0x00000103, 0x00000102}, {0x00000105, 0x00000104}, {0x00000107, 0x00000106}, {0x00000109, 0x00000108},
+{0x0000010B, 0x0000010A}, {0x0000010D, 0x0000010C}, {0x0000010F, 0x0000010E}, {0x00000111, 0x00000110},
+{0x00000113, 0x00000112}, {0x00000115, 0x00000114}, {0x00000117, 0x00000116}, {0x00000119, 0x00000118},
+{0x0000011B, 0x0000011A}, {0x0000011D, 0x0000011C}, {0x0000011F, 0x0000011E}, {0x00000121, 0x00000120},
+{0x00000123, 0x00000122}, {0x00000125, 0x00000124}, {0x00000127, 0x00000126}, {0x00000129, 0x00000128},
+{0x0000012B, 0x0000012A}, {0x0000012D, 0x0000012C}, {0x0000012F, 0x0000012E}, {0x00000131, 0x00000049},
+{0x00000133, 0x00000132}, {0x00000135, 0x00000134}, {0x00000137, 0x00000136}, {0x0000013A, 0x00000139},
+{0x0000013C, 0x0000013B}, {0x0000013E, 0x0000013D}, {0x00000140, 0x0000013F}, {0x00000142, 0x00000141},
+{0x00000144, 0x00000143}, {0x00000146, 0x00000145}, {0x00000148, 0x00000147}, {0x00000149, 0x000002BC},
+{0x0000014B, 0x0000014A}, {0x0000014D, 0x0000014C}, {0x0000014F, 0x0000014E}, {0x00000151, 0x00000150},
+{0x00000153, 0x00000152}, {0x00000155, 0x00000154}, {0x00000157, 0x00000156}, {0x00000159, 0x00000158},
+{0x0000015B, 0x0000015A}, {0x0000015D, 0x0000015C}, {0x0000015F, 0x0000015E}, {0x00000161, 0x00000160},
+{0x00000163, 0x00000162}, {0x00000165, 0x00000164}, {0x00000167, 0x00000166}, {0x00000169, 0x00000168},
+{0x0000016B, 0x0000016A}, {0x0000016D, 0x0000016C}, {0x0000016F, 0x0000016E}, {0x00000171, 0x00000170},
+{0x00000173, 0x00000172}, {0x00000175, 0x00000174}, {0x00000177, 0x00000176}, {0x0000017A, 0x00000179},
+{0x0000017C, 0x0000017B}, {0x0000017E, 0x0000017D}, {0x0000017F, 0x00000053}, {0x00000180, 0x00000243},
+{0x00000183, 0x00000182}, {0x00000185, 0x00000184}, {0x00000188, 0x00000187}, {0x0000018C, 0x0000018B},
+{0x00000192, 0x00000191}, {0x00000195, 0x000001F6}, {0x00000199, 0x00000198}, {0x0000019A, 0x0000023D},
+{0x0000019E, 0x00000220}, {0x000001A1, 0x000001A0}, {0x000001A3, 0x000001A2}, {0x000001A5, 0x000001A4},
+{0x000001A8, 0x000001A7}, {0x000001AD, 0x000001AC}, {0x000001B0, 0x000001AF}, {0x000001B4, 0x000001B3},
+{0x000001B6, 0x000001B5}, {0x000001B9, 0x000001B8}, {0x000001BD, 0x000001BC}, {0x000001BF, 0x000001F7},
+{0x000001C5, 0x000001C4}, {0x000001C6, 0x000001C4}, {0x000001C8, 0x000001C7}, {0x000001C9, 0x000001C7},
+{0x000001CB, 0x000001CA}, {0x000001CC, 0x000001CA}, {0x000001CE, 0x000001CD}, {0x000001D0, 0x000001CF},
+{0x000001D2, 0x000001D1}, {0x000001D4, 0x000001D3}, {0x000001D6, 0x000001D5}, {0x000001D8, 0x000001D7},
+{0x000001DA, 0x000001D9}, {0x000001DC, 0x000001DB}, {0x000001DD, 0x0000018E}, {0x000001DF, 0x000001DE},
+{0x000001E1, 0x000001E0}, {0x000001E3, 0x000001E2}, {0x000001E5, 0x000001E4}, {0x000001E7, 0x000001E6},
+{0x000001E9, 0x000001E8}, {0x000001EB, 0x000001EA}, {0x000001ED, 0x000001EC}, {0x000001EF, 0x000001EE},
+{0x000001F0, 0x0000004A}, {0x000001F2, 0x000001F1}, {0x000001F3, 0x000001F1}, {0x000001F5, 0x000001F4},
+{0x000001F9, 0x000001F8}, {0x000001FB, 0x000001FA}, {0x000001FD, 0x000001FC}, {0x000001FF, 0x000001FE},
+{0x00000201, 0x00000200}, {0x00000203, 0x00000202}, {0x00000205, 0x00000204}, {0x00000207, 0x00000206},
+{0x00000209, 0x00000208}, {0x0000020B, 0x0000020A}, {0x0000020D, 0x0000020C}, {0x0000020F, 0x0000020E},
+{0x00000211, 0x00000210}, {0x00000213, 0x00000212}, {0x00000215, 0x00000214}, {0x00000217, 0x00000216},
+{0x00000219, 0x00000218}, {0x0000021B, 0x0000021A}, {0x0000021D, 0x0000021C}, {0x0000021F, 0x0000021E},
+{0x00000223, 0x00000222}, {0x00000225, 0x00000224}, {0x00000227, 0x00000226}, {0x00000229, 0x00000228},
+{0x0000022B, 0x0000022A}, {0x0000022D, 0x0000022C}, {0x0000022F, 0x0000022E}, {0x00000231, 0x00000230},
+{0x00000233, 0x00000232}, {0x0000023C, 0x0000023B}, {0x0000023F, 0x00002C7E}, {0x00000240, 0x00002C7F},
+{0x00000242, 0x00000241}, {0x00000247, 0x00000246}, {0x00000249, 0x00000248}, {0x0000024B, 0x0000024A},
+{0x0000024D, 0x0000024C}, {0x0000024F, 0x0000024E}, {0x00000250, 0x00002C6F}, {0x00000251, 0x00002C6D},
+{0x00000252, 0x00002C70}, {0x00000253, 0x00000181}, {0x00000254, 0x00000186}, {0x00000256, 0x00000189},
+{0x00000257, 0x0000018A}, {0x00000259, 0x0000018F}, {0x0000025B, 0x00000190}, {0x0000025C, 0x0000A7AB},
+{0x00000260, 0x00000193}, {0x00000261, 0x0000A7AC}, {0x00000263, 0x00000194}, {0x00000265, 0x0000A78D},
+{0x00000266, 0x0000A7AA}, {0x00000268, 0x00000197}, {0x00000269, 0x00000196}, {0x0000026A, 0x0000A7AE},
+{0x0000026B, 0x00002C62}, {0x0000026C, 0x0000A7AD}, {0x0000026F, 0x0000019C}, {0x00000271, 0x00002C6E},
+{0x00000272, 0x0000019D}, {0x00000275, 0x0000019F}, {0x0000027D, 0x00002C64}, {0x00000280, 0x000001A6},
+{0x00000282, 0x0000A7C5}, {0x00000283, 0x000001A9}, {0x00000287, 0x0000A7B1}, {0x00000288, 0x000001AE},
+{0x00000289, 0x00000244}, {0x0000028A, 0x000001B1}, {0x0000028B, 0x000001B2}, {0x0000028C, 0x00000245},
+{0x00000292, 0x000001B7}, {0x0000029D, 0x0000A7B2}, {0x0000029E, 0x0000A7B0}, {0x00000345, 0x00000399},
+{0x00000371, 0x00000370}, {0x00000373, 0x00000372}, {0x00000377, 0x00000376}, {0x0000037B, 0x000003FD},
+{0x0000037C, 0x000003FE}, {0x0000037D, 0x000003FF}, {0x00000390, 0x00000399}, {0x000003AC, 0x00000386},
+{0x000003AD, 0x00000388}, {0x000003AE, 0x00000389}, {0x000003AF, 0x0000038A}, {0x000003B0, 0x000003A5},
+{0x000003B1, 0x00000391}, {0x000003B2, 0x00000392}, {0x000003B3, 0x00000393}, {0x000003B4, 0x00000394},
+{0x000003B5, 0x00000395}, {0x000003B6, 0x00000396}, {0x000003B7, 0x00000397}, {0x000003B8, 0x00000398},
+{0x000003B9, 0x00000399}, {0x000003BA, 0x0000039A}, {0x000003BB, 0x0000039B}, {0x000003BC, 0x0000039C},
+{0x000003BD, 0x0000039D}, {0x000003BE, 0x0000039E}, {0x000003BF, 0x0000039F}, {0x000003C0, 0x000003A0},
+{0x000003C1, 0x000003A1}, {0x000003C2, 0x000003A3}, {0x000003C3, 0x000003A3}, {0x000003C4, 0x000003A4},
+{0x000003C5, 0x000003A5}, {0x000003C6, 0x000003A6}, {0x000003C7, 0x000003A7}, {0x000003C8, 0x000003A8},
+{0x000003C9, 0x000003A9}, {0x000003CA, 0x000003AA}, {0x000003CB, 0x000003AB}, {0x000003CC, 0x0000038C},
+{0x000003CD, 0x0000038E}, {0x000003CE, 0x0000038F}, {0x000003D0, 0x00000392}, {0x000003D1, 0x00000398},
+{0x000003D5, 0x000003A6}, {0x000003D6, 0x000003A0}, {0x000003D7, 0x000003CF}, {0x000003D9, 0x000003D8},
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+{0x000118C5, 0x000118A5}, {0x000118C6, 0x000118A6}, {0x000118C7, 0x000118A7}, {0x000118C8, 0x000118A8},
+{0x000118C9, 0x000118A9}, {0x000118CA, 0x000118AA}, {0x000118CB, 0x000118AB}, {0x000118CC, 0x000118AC},
+{0x000118CD, 0x000118AD}, {0x000118CE, 0x000118AE}, {0x000118CF, 0x000118AF}, {0x000118D0, 0x000118B0},
+{0x000118D1, 0x000118B1}, {0x000118D2, 0x000118B2}, {0x000118D3, 0x000118B3}, {0x000118D4, 0x000118B4},
+{0x000118D5, 0x000118B5}, {0x000118D6, 0x000118B6}, {0x000118D7, 0x000118B7}, {0x000118D8, 0x000118B8},
+{0x000118D9, 0x000118B9}, {0x000118DA, 0x000118BA}, {0x000118DB, 0x000118BB}, {0x000118DC, 0x000118BC},
+{0x000118DD, 0x000118BD}, {0x000118DE, 0x000118BE}, {0x000118DF, 0x000118BF}, {0x00016E60, 0x00016E40},
+{0x00016E61, 0x00016E41}, {0x00016E62, 0x00016E42}, {0x00016E63, 0x00016E43}, {0x00016E64, 0x00016E44},
+{0x00016E65, 0x00016E45}, {0x00016E66, 0x00016E46}, {0x00016E67, 0x00016E47}, {0x00016E68, 0x00016E48},
+{0x00016E69, 0x00016E49}, {0x00016E6A, 0x00016E4A}, {0x00016E6B, 0x00016E4B}, {0x00016E6C, 0x00016E4C},
+{0x00016E6D, 0x00016E4D}, {0x00016E6E, 0x00016E4E}, {0x00016E6F, 0x00016E4F}, {0x00016E70, 0x00016E50},
+{0x00016E71, 0x00016E51}, {0x00016E72, 0x00016E52}, {0x00016E73, 0x00016E53}, {0x00016E74, 0x00016E54},
+{0x00016E75, 0x00016E55}, {0x00016E76, 0x00016E56}, {0x00016E77, 0x00016E57}, {0x00016E78, 0x00016E58},
+{0x00016E79, 0x00016E59}, {0x00016E7A, 0x00016E5A}, {0x00016E7B, 0x00016E5B}, {0x00016E7C, 0x00016E5C},
+{0x00016E7D, 0x00016E5D}, {0x00016E7E, 0x00016E5E}, {0x00016E7F, 0x00016E5F}, {0x0001E922, 0x0001E900},
+{0x0001E923, 0x0001E901}, {0x0001E924, 0x0001E902}, {0x0001E925, 0x0001E903}, {0x0001E926, 0x0001E904},
+{0x0001E927, 0x0001E905}, {0x0001E928, 0x0001E906}, {0x0001E929, 0x0001E907}, {0x0001E92A, 0x0001E908},
+{0x0001E92B, 0x0001E909}, {0x0001E92C, 0x0001E90A}, {0x0001E92D, 0x0001E90B}, {0x0001E92E, 0x0001E90C},
+{0x0001E92F, 0x0001E90D}, {0x0001E930, 0x0001E90E}, {0x0001E931, 0x0001E90F}, {0x0001E932, 0x0001E910},
+{0x0001E933, 0x0001E911}, {0x0001E934, 0x0001E912}, {0x0001E935, 0x0001E913}, {0x0001E936, 0x0001E914},
+{0x0001E937, 0x0001E915}, {0x0001E938, 0x0001E916}, {0x0001E939, 0x0001E917}, {0x0001E93A, 0x0001E918},
+{0x0001E93B, 0x0001E919}, {0x0001E93C, 0x0001E91A}, {0x0001E93D, 0x0001E91B}, {0x0001E93E, 0x0001E91C},
+{0x0001E93F, 0x0001E91D}, {0x0001E940, 0x0001E91E}, {0x0001E941, 0x0001E91F}, {0x0001E942, 0x0001E920},
+{0x0001E943, 0x0001E921},
+};
+
+const std::multimap<uint32_t, uint32_t> unicode_map_nfd = {
+{0x000000C0, 0x00000041}, {0x000000C0, 0x00000300}, {0x000000C1, 0x00000041}, {0x000000C1, 0x00000301},
+{0x000000C2, 0x00000041}, {0x000000C2, 0x00000302}, {0x000000C3, 0x00000041}, {0x000000C3, 0x00000303},
+{0x000000C4, 0x00000041}, {0x000000C4, 0x00000308}, {0x000000C5, 0x00000041}, {0x000000C5, 0x0000030A},
+{0x000000C7, 0x00000043}, {0x000000C7, 0x00000327}, {0x000000C8, 0x00000045}, {0x000000C8, 0x00000300},
+{0x000000C9, 0x00000045}, {0x000000C9, 0x00000301}, {0x000000CA, 0x00000045}, {0x000000CA, 0x00000302},
+{0x000000CB, 0x00000045}, {0x000000CB, 0x00000308}, {0x000000CC, 0x00000049}, {0x000000CC, 0x00000300},
+{0x000000CD, 0x00000049}, {0x000000CD, 0x00000301}, {0x000000CE, 0x00000049}, {0x000000CE, 0x00000302},
+{0x000000CF, 0x00000049}, {0x000000CF, 0x00000308}, {0x000000D1, 0x0000004E}, {0x000000D1, 0x00000303},
+{0x000000D2, 0x0000004F}, {0x000000D2, 0x00000300}, {0x000000D3, 0x0000004F}, {0x000000D3, 0x00000301},
+{0x000000D4, 0x0000004F}, {0x000000D4, 0x00000302}, {0x000000D5, 0x0000004F}, {0x000000D5, 0x00000303},
+{0x000000D6, 0x0000004F}, {0x000000D6, 0x00000308}, {0x000000D9, 0x00000055}, {0x000000D9, 0x00000300},
+{0x000000DA, 0x00000055}, {0x000000DA, 0x00000301}, {0x000000DB, 0x00000055}, {0x000000DB, 0x00000302},
+{0x000000DC, 0x00000055}, {0x000000DC, 0x00000308}, {0x000000DD, 0x00000059}, {0x000000DD, 0x00000301},
+{0x000000E0, 0x00000061}, {0x000000E0, 0x00000300}, {0x000000E1, 0x00000061}, {0x000000E1, 0x00000301},
+{0x000000E2, 0x00000061}, {0x000000E2, 0x00000302}, {0x000000E3, 0x00000061}, {0x000000E3, 0x00000303},
+{0x000000E4, 0x00000061}, {0x000000E4, 0x00000308}, {0x000000E5, 0x00000061}, {0x000000E5, 0x0000030A},
+{0x000000E7, 0x00000063}, {0x000000E7, 0x00000327}, {0x000000E8, 0x00000065}, {0x000000E8, 0x00000300},
+{0x000000E9, 0x00000065}, {0x000000E9, 0x00000301}, {0x000000EA, 0x00000065}, {0x000000EA, 0x00000302},
+{0x000000EB, 0x00000065}, {0x000000EB, 0x00000308}, {0x000000EC, 0x00000069}, {0x000000EC, 0x00000300},
+{0x000000ED, 0x00000069}, {0x000000ED, 0x00000301}, {0x000000EE, 0x00000069}, {0x000000EE, 0x00000302},
+{0x000000EF, 0x00000069}, {0x000000EF, 0x00000308}, {0x000000F1, 0x0000006E}, {0x000000F1, 0x00000303},
+{0x000000F2, 0x0000006F}, {0x000000F2, 0x00000300}, {0x000000F3, 0x0000006F}, {0x000000F3, 0x00000301},
+{0x000000F4, 0x0000006F}, {0x000000F4, 0x00000302}, {0x000000F5, 0x0000006F}, {0x000000F5, 0x00000303},
+{0x000000F6, 0x0000006F}, {0x000000F6, 0x00000308}, {0x000000F9, 0x00000075}, {0x000000F9, 0x00000300},
+{0x000000FA, 0x00000075}, {0x000000FA, 0x00000301}, {0x000000FB, 0x00000075}, {0x000000FB, 0x00000302},
+{0x000000FC, 0x00000075}, {0x000000FC, 0x00000308}, {0x000000FD, 0x00000079}, {0x000000FD, 0x00000301},
+{0x000000FF, 0x00000079}, {0x000000FF, 0x00000308}, {0x00000100, 0x00000041}, {0x00000100, 0x00000304},
+{0x00000101, 0x00000061}, {0x00000101, 0x00000304}, {0x00000102, 0x00000041}, {0x00000102, 0x00000306},
+{0x00000103, 0x00000061}, {0x00000103, 0x00000306}, {0x00000104, 0x00000041}, {0x00000104, 0x00000328},
+{0x00000105, 0x00000061}, {0x00000105, 0x00000328}, {0x00000106, 0x00000043}, {0x00000106, 0x00000301},
+{0x00000107, 0x00000063}, {0x00000107, 0x00000301}, {0x00000108, 0x00000043}, {0x00000108, 0x00000302},
+{0x00000109, 0x00000063}, {0x00000109, 0x00000302}, {0x0000010A, 0x00000043}, {0x0000010A, 0x00000307},
+{0x0000010B, 0x00000063}, {0x0000010B, 0x00000307}, {0x0000010C, 0x00000043}, {0x0000010C, 0x0000030C},
+{0x0000010D, 0x00000063}, {0x0000010D, 0x0000030C}, {0x0000010E, 0x00000044}, {0x0000010E, 0x0000030C},
+{0x0000010F, 0x00000064}, {0x0000010F, 0x0000030C}, {0x00000112, 0x00000045}, {0x00000112, 0x00000304},
+{0x00000113, 0x00000065}, {0x00000113, 0x00000304}, {0x00000114, 0x00000045}, {0x00000114, 0x00000306},
+{0x00000115, 0x00000065}, {0x00000115, 0x00000306}, {0x00000116, 0x00000045}, {0x00000116, 0x00000307},
+{0x00000117, 0x00000065}, {0x00000117, 0x00000307}, {0x00000118, 0x00000045}, {0x00000118, 0x00000328},
+{0x00000119, 0x00000065}, {0x00000119, 0x00000328}, {0x0000011A, 0x00000045}, {0x0000011A, 0x0000030C},
+{0x0000011B, 0x00000065}, {0x0000011B, 0x0000030C}, {0x0000011C, 0x00000047}, {0x0000011C, 0x00000302},
+{0x0000011D, 0x00000067}, {0x0000011D, 0x00000302}, {0x0000011E, 0x00000047}, {0x0000011E, 0x00000306},
+{0x0000011F, 0x00000067}, {0x0000011F, 0x00000306}, {0x00000120, 0x00000047}, {0x00000120, 0x00000307},
+{0x00000121, 0x00000067}, {0x00000121, 0x00000307}, {0x00000122, 0x00000047}, {0x00000122, 0x00000327},
+{0x00000123, 0x00000067}, {0x00000123, 0x00000327}, {0x00000124, 0x00000048}, {0x00000124, 0x00000302},
+{0x00000125, 0x00000068}, {0x00000125, 0x00000302}, {0x00000128, 0x00000049}, {0x00000128, 0x00000303},
+{0x00000129, 0x00000069}, {0x00000129, 0x00000303}, {0x0000012A, 0x00000049}, {0x0000012A, 0x00000304},
+{0x0000012B, 0x00000069}, {0x0000012B, 0x00000304}, {0x0000012C, 0x00000049}, {0x0000012C, 0x00000306},
+{0x0000012D, 0x00000069}, {0x0000012D, 0x00000306}, {0x0000012E, 0x00000049}, {0x0000012E, 0x00000328},
+{0x0000012F, 0x00000069}, {0x0000012F, 0x00000328}, {0x00000130, 0x00000049}, {0x00000130, 0x00000307},
+{0x00000134, 0x0000004A}, {0x00000134, 0x00000302}, {0x00000135, 0x0000006A}, {0x00000135, 0x00000302},
+{0x00000136, 0x0000004B}, {0x00000136, 0x00000327}, {0x00000137, 0x0000006B}, {0x00000137, 0x00000327},
+{0x00000139, 0x0000004C}, {0x00000139, 0x00000301}, {0x0000013A, 0x0000006C}, {0x0000013A, 0x00000301},
+{0x0000013B, 0x0000004C}, {0x0000013B, 0x00000327}, {0x0000013C, 0x0000006C}, {0x0000013C, 0x00000327},
+{0x0000013D, 0x0000004C}, {0x0000013D, 0x0000030C}, {0x0000013E, 0x0000006C}, {0x0000013E, 0x0000030C},
+{0x00000143, 0x0000004E}, {0x00000143, 0x00000301}, {0x00000144, 0x0000006E}, {0x00000144, 0x00000301},
+{0x00000145, 0x0000004E}, {0x00000145, 0x00000327}, {0x00000146, 0x0000006E}, {0x00000146, 0x00000327},
+{0x00000147, 0x0000004E}, {0x00000147, 0x0000030C}, {0x00000148, 0x0000006E}, {0x00000148, 0x0000030C},
+{0x0000014C, 0x0000004F}, {0x0000014C, 0x00000304}, {0x0000014D, 0x0000006F}, {0x0000014D, 0x00000304},
+{0x0000014E, 0x0000004F}, {0x0000014E, 0x00000306}, {0x0000014F, 0x0000006F}, {0x0000014F, 0x00000306},
+{0x00000150, 0x0000004F}, {0x00000150, 0x0000030B}, {0x00000151, 0x0000006F}, {0x00000151, 0x0000030B},
+{0x00000154, 0x00000052}, {0x00000154, 0x00000301}, {0x00000155, 0x00000072}, {0x00000155, 0x00000301},
+{0x00000156, 0x00000052}, {0x00000156, 0x00000327}, {0x00000157, 0x00000072}, {0x00000157, 0x00000327},
+{0x00000158, 0x00000052}, {0x00000158, 0x0000030C}, {0x00000159, 0x00000072}, {0x00000159, 0x0000030C},
+{0x0000015A, 0x00000053}, {0x0000015A, 0x00000301}, {0x0000015B, 0x00000073}, {0x0000015B, 0x00000301},
+{0x0000015C, 0x00000053}, {0x0000015C, 0x00000302}, {0x0000015D, 0x00000073}, {0x0000015D, 0x00000302},
+{0x0000015E, 0x00000053}, {0x0000015E, 0x00000327}, {0x0000015F, 0x00000073}, {0x0000015F, 0x00000327},
+{0x00000160, 0x00000053}, {0x00000160, 0x0000030C}, {0x00000161, 0x00000073}, {0x00000161, 0x0000030C},
+{0x00000162, 0x00000054}, {0x00000162, 0x00000327}, {0x00000163, 0x00000074}, {0x00000163, 0x00000327},
+{0x00000164, 0x00000054}, {0x00000164, 0x0000030C}, {0x00000165, 0x00000074}, {0x00000165, 0x0000030C},
+{0x00000168, 0x00000055}, {0x00000168, 0x00000303}, {0x00000169, 0x00000075}, {0x00000169, 0x00000303},
+{0x0000016A, 0x00000055}, {0x0000016A, 0x00000304}, {0x0000016B, 0x00000075}, {0x0000016B, 0x00000304},
+{0x0000016C, 0x00000055}, {0x0000016C, 0x00000306}, {0x0000016D, 0x00000075}, {0x0000016D, 0x00000306},
+{0x0000016E, 0x00000055}, {0x0000016E, 0x0000030A}, {0x0000016F, 0x00000075}, {0x0000016F, 0x0000030A},
+{0x00000170, 0x00000055}, {0x00000170, 0x0000030B}, {0x00000171, 0x00000075}, {0x00000171, 0x0000030B},
+{0x00000172, 0x00000055}, {0x00000172, 0x00000328}, {0x00000173, 0x00000075}, {0x00000173, 0x00000328},
+{0x00000174, 0x00000057}, {0x00000174, 0x00000302}, {0x00000175, 0x00000077}, {0x00000175, 0x00000302},
+{0x00000176, 0x00000059}, {0x00000176, 0x00000302}, {0x00000177, 0x00000079}, {0x00000177, 0x00000302},
+{0x00000178, 0x00000059}, {0x00000178, 0x00000308}, {0x00000179, 0x0000005A}, {0x00000179, 0x00000301},
+{0x0000017A, 0x0000007A}, {0x0000017A, 0x00000301}, {0x0000017B, 0x0000005A}, {0x0000017B, 0x00000307},
+{0x0000017C, 0x0000007A}, {0x0000017C, 0x00000307}, {0x0000017D, 0x0000005A}, {0x0000017D, 0x0000030C},
+{0x0000017E, 0x0000007A}, {0x0000017E, 0x0000030C}, {0x000001A0, 0x0000004F}, {0x000001A0, 0x0000031B},
+{0x000001A1, 0x0000006F}, {0x000001A1, 0x0000031B}, {0x000001AF, 0x00000055}, {0x000001AF, 0x0000031B},
+{0x000001B0, 0x00000075}, {0x000001B0, 0x0000031B}, {0x000001CD, 0x00000041}, {0x000001CD, 0x0000030C},
+{0x000001CE, 0x00000061}, {0x000001CE, 0x0000030C}, {0x000001CF, 0x00000049}, {0x000001CF, 0x0000030C},
+{0x000001D0, 0x00000069}, {0x000001D0, 0x0000030C}, {0x000001D1, 0x0000004F}, {0x000001D1, 0x0000030C},
+{0x000001D2, 0x0000006F}, {0x000001D2, 0x0000030C}, {0x000001D3, 0x00000055}, {0x000001D3, 0x0000030C},
+{0x000001D4, 0x00000075}, {0x000001D4, 0x0000030C}, {0x000001D5, 0x00000055}, {0x000001D5, 0x00000308},
+{0x000001D5, 0x00000304}, {0x000001D6, 0x00000075}, {0x000001D6, 0x00000308}, {0x000001D6, 0x00000304},
+{0x000001D7, 0x00000055}, {0x000001D7, 0x00000308}, {0x000001D7, 0x00000301}, {0x000001D8, 0x00000075},
+{0x000001D8, 0x00000308}, {0x000001D8, 0x00000301}, {0x000001D9, 0x00000055}, {0x000001D9, 0x00000308},
+{0x000001D9, 0x0000030C}, {0x000001DA, 0x00000075}, {0x000001DA, 0x00000308}, {0x000001DA, 0x0000030C},
+{0x000001DB, 0x00000055}, {0x000001DB, 0x00000308}, {0x000001DB, 0x00000300}, {0x000001DC, 0x00000075},
+{0x000001DC, 0x00000308}, {0x000001DC, 0x00000300}, {0x000001DE, 0x00000041}, {0x000001DE, 0x00000308},
+{0x000001DE, 0x00000304}, {0x000001DF, 0x00000061}, {0x000001DF, 0x00000308}, {0x000001DF, 0x00000304},
+{0x000001E0, 0x00000041}, {0x000001E0, 0x00000307}, {0x000001E0, 0x00000304}, {0x000001E1, 0x00000061},
+{0x000001E1, 0x00000307}, {0x000001E1, 0x00000304}, {0x000001E2, 0x000000C6}, {0x000001E2, 0x00000304},
+{0x000001E3, 0x000000E6}, {0x000001E3, 0x00000304}, {0x000001E6, 0x00000047}, {0x000001E6, 0x0000030C},
+{0x000001E7, 0x00000067}, {0x000001E7, 0x0000030C}, {0x000001E8, 0x0000004B}, {0x000001E8, 0x0000030C},
+{0x000001E9, 0x0000006B}, {0x000001E9, 0x0000030C}, {0x000001EA, 0x0000004F}, {0x000001EA, 0x00000328},
+{0x000001EB, 0x0000006F}, {0x000001EB, 0x00000328}, {0x000001EC, 0x0000004F}, {0x000001EC, 0x00000328},
+{0x000001EC, 0x00000304}, {0x000001ED, 0x0000006F}, {0x000001ED, 0x00000328}, {0x000001ED, 0x00000304},
+{0x000001EE, 0x000001B7}, {0x000001EE, 0x0000030C}, {0x000001EF, 0x00000292}, {0x000001EF, 0x0000030C},
+{0x000001F0, 0x0000006A}, {0x000001F0, 0x0000030C}, {0x000001F4, 0x00000047}, {0x000001F4, 0x00000301},
+{0x000001F5, 0x00000067}, {0x000001F5, 0x00000301}, {0x000001F8, 0x0000004E}, {0x000001F8, 0x00000300},
+{0x000001F9, 0x0000006E}, {0x000001F9, 0x00000300}, {0x000001FA, 0x00000041}, {0x000001FA, 0x0000030A},
+{0x000001FA, 0x00000301}, {0x000001FB, 0x00000061}, {0x000001FB, 0x0000030A}, {0x000001FB, 0x00000301},
+{0x000001FC, 0x000000C6}, {0x000001FC, 0x00000301}, {0x000001FD, 0x000000E6}, {0x000001FD, 0x00000301},
+{0x000001FE, 0x000000D8}, {0x000001FE, 0x00000301}, {0x000001FF, 0x000000F8}, {0x000001FF, 0x00000301},
+{0x00000200, 0x00000041}, {0x00000200, 0x0000030F}, {0x00000201, 0x00000061}, {0x00000201, 0x0000030F},
+{0x00000202, 0x00000041}, {0x00000202, 0x00000311}, {0x00000203, 0x00000061}, {0x00000203, 0x00000311},
+{0x00000204, 0x00000045}, {0x00000204, 0x0000030F}, {0x00000205, 0x00000065}, {0x00000205, 0x0000030F},
+{0x00000206, 0x00000045}, {0x00000206, 0x00000311}, {0x00000207, 0x00000065}, {0x00000207, 0x00000311},
+{0x00000208, 0x00000049}, {0x00000208, 0x0000030F}, {0x00000209, 0x00000069}, {0x00000209, 0x0000030F},
+{0x0000020A, 0x00000049}, {0x0000020A, 0x00000311}, {0x0000020B, 0x00000069}, {0x0000020B, 0x00000311},
+{0x0000020C, 0x0000004F}, {0x0000020C, 0x0000030F}, {0x0000020D, 0x0000006F}, {0x0000020D, 0x0000030F},
+{0x0000020E, 0x0000004F}, {0x0000020E, 0x00000311}, {0x0000020F, 0x0000006F}, {0x0000020F, 0x00000311},
+{0x00000210, 0x00000052}, {0x00000210, 0x0000030F}, {0x00000211, 0x00000072}, {0x00000211, 0x0000030F},
+{0x00000212, 0x00000052}, {0x00000212, 0x00000311}, {0x00000213, 0x00000072}, {0x00000213, 0x00000311},
+{0x00000214, 0x00000055}, {0x00000214, 0x0000030F}, {0x00000215, 0x00000075}, {0x00000215, 0x0000030F},
+{0x00000216, 0x00000055}, {0x00000216, 0x00000311}, {0x00000217, 0x00000075}, {0x00000217, 0x00000311},
+{0x00000218, 0x00000053}, {0x00000218, 0x00000326}, {0x00000219, 0x00000073}, {0x00000219, 0x00000326},
+{0x0000021A, 0x00000054}, {0x0000021A, 0x00000326}, {0x0000021B, 0x00000074}, {0x0000021B, 0x00000326},
+{0x0000021E, 0x00000048}, {0x0000021E, 0x0000030C}, {0x0000021F, 0x00000068}, {0x0000021F, 0x0000030C},
+{0x00000226, 0x00000041}, {0x00000226, 0x00000307}, {0x00000227, 0x00000061}, {0x00000227, 0x00000307},
+{0x00000228, 0x00000045}, {0x00000228, 0x00000327}, {0x00000229, 0x00000065}, {0x00000229, 0x00000327},
+{0x0000022A, 0x0000004F}, {0x0000022A, 0x00000308}, {0x0000022A, 0x00000304}, {0x0000022B, 0x0000006F},
+{0x0000022B, 0x00000308}, {0x0000022B, 0x00000304}, {0x0000022C, 0x0000004F}, {0x0000022C, 0x00000303},
+{0x0000022C, 0x00000304}, {0x0000022D, 0x0000006F}, {0x0000022D, 0x00000303}, {0x0000022D, 0x00000304},
+{0x0000022E, 0x0000004F}, {0x0000022E, 0x00000307}, {0x0000022F, 0x0000006F}, {0x0000022F, 0x00000307},
+{0x00000230, 0x0000004F}, {0x00000230, 0x00000307}, {0x00000230, 0x00000304}, {0x00000231, 0x0000006F},
+{0x00000231, 0x00000307}, {0x00000231, 0x00000304}, {0x00000232, 0x00000059}, {0x00000232, 0x00000304},
+{0x00000233, 0x00000079}, {0x00000233, 0x00000304}, {0x00000340, 0x00000300}, {0x00000341, 0x00000301},
+{0x00000343, 0x00000313}, {0x00000344, 0x00000308}, {0x00000344, 0x00000301}, {0x00000374, 0x000002B9},
+{0x0000037E, 0x0000003B}, {0x00000385, 0x000000A8}, {0x00000385, 0x00000301}, {0x00000386, 0x00000391},
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+{0x0002FA06, 0x000099C2}, {0x0002FA07, 0x000099FE}, {0x0002FA08, 0x00004BCE}, {0x0002FA09, 0x00029B30},
+{0x0002FA0A, 0x00009B12}, {0x0002FA0B, 0x00009C40}, {0x0002FA0C, 0x00009CFD}, {0x0002FA0D, 0x00004CCE},
+{0x0002FA0E, 0x00004CED}, {0x0002FA0F, 0x00009D67}, {0x0002FA10, 0x0002A0CE}, {0x0002FA11, 0x00004CF8},
+{0x0002FA12, 0x0002A105}, {0x0002FA13, 0x0002A20E}, {0x0002FA14, 0x0002A291}, {0x0002FA15, 0x00009EBB},
+{0x0002FA16, 0x00004D56}, {0x0002FA17, 0x00009EF9}, {0x0002FA18, 0x00009EFE}, {0x0002FA19, 0x00009F05},
+{0x0002FA1A, 0x00009F0F}, {0x0002FA1B, 0x00009F16}, {0x0002FA1D, 0x0002A600},
+};
diff --git a/unicode-data.h b/unicode-data.h
new file mode 100644
index 000000000..3cccf2068
--- /dev/null
+++ b/unicode-data.h
@@ -0,0 +1,17 @@
+#pragma once
+
+#include <cstdint>
+#include <map>
+#include <utility>
+#include <vector>
+
+extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_number;
+extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_letter;
+extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_separator;
+extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_whitespace;
+extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_accent_mark;
+extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_punctuation;
+extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_symbol;
+extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_control;
+extern const std::multimap<uint32_t, uint32_t>          unicode_map_nfd;
+extern const std::map<char32_t, char32_t>               unicode_map_lowercase;
diff --git a/unicode.cpp b/unicode.cpp
new file mode 100644
index 000000000..ca03c49d3
--- /dev/null
+++ b/unicode.cpp
@@ -0,0 +1,818 @@
+#include "unicode.h"
+#include "unicode-data.h"
+
+#include <cassert>
+#include <cstddef>
+#include <cstdint>
+#include <map>
+#include <regex>
+#include <stdexcept>
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <utility>
+#include <vector>
+#include <locale>
+#include <codecvt>
+
+static std::string unicode_cpts_to_utf8(const std::vector<uint32_t> & cps) {
+    std::string result;
+    for (size_t i = 0; i < cps.size(); ++i) {
+        result.append(unicode_cpt_to_utf8(cps[i]));
+    }
+    return result;
+}
+
+static uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset) {
+    assert(offset < utf8.size());
+    if (!(utf8[offset + 0] & 0x80)) {
+        auto result = utf8[offset + 0];
+        offset += 1;
+        return result;
+    }
+    if (!(utf8[offset + 0] & 0x40)) {
+        throw std::invalid_argument("invalid character");
+    }
+    if (!(utf8[offset + 0] & 0x20)) {
+        if (offset + 1 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80)) {
+            throw std::invalid_argument("invalid character");
+        }
+        auto result = ((utf8[offset + 0] & 0x1f) << 6) | (utf8[offset + 1] & 0x3f);
+        offset += 2;
+        return result;
+    }
+    if (!(utf8[offset + 0] & 0x10)) {
+        if (offset + 2 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80)) {
+            throw std::invalid_argument("invalid character");
+        }
+        auto result = ((utf8[offset + 0] & 0x0f) << 12) | ((utf8[offset + 1] & 0x3f) << 6) | (utf8[offset + 2] & 0x3f);
+        offset += 3;
+        return result;
+    }
+    if (!(utf8[offset + 0] & 0x08)) {
+        if (offset + 3 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80) || !((utf8[offset + 3] & 0xc0) == 0x80)) {
+            throw std::invalid_argument("invalid character");
+        }
+        auto result = ((utf8[offset + 0] & 0x07) << 18) | ((utf8[offset + 1] & 0x3f) << 12) | ((utf8[offset + 2] & 0x3f) << 6) | (utf8[offset + 3] & 0x3f);
+        offset += 4;
+        return result;
+    }
+    throw std::invalid_argument("failed to convert utf8 to codepoint");
+}
+
+//static std::vector<uint16_t> unicode_cpt_to_utf16(uint32_t cp) {
+//    std::vector<uint16_t> result;
+//    if (/* 0x0000 <= cp && */ cp <= 0xffff) {
+//        result.emplace_back(cp);
+//        return result;
+//    }
+//    if (0x10000 <= cp && cp <= 0x10ffff) {
+//        result.emplace_back(0xd800 | ((cp - 0x10000) >> 10));
+//        result.emplace_back(0xdc00 | ((cp - 0x10000) & 0x03ff));
+//        return result;
+//    }
+//    throw std::invalid_argument("failed to convert codepoint to utf16");
+//}
+
+//static std::vector<uint16_t> unicode_cpts_to_utf16(const std::vector<uint32_t> & cps) {
+//    std::vector<uint16_t> result;
+//    for (size_t i = 0; i < cps.size(); ++i) {
+//        auto temp = unicode_cpt_to_utf16(cps[i]);
+//        result.insert(result.end(), temp.begin(), temp.end());
+//    }
+//    return result;
+//}
+
+//static uint32_t unicode_cpt_from_utf16(const std::vector<uint16_t> & utf16, size_t & offset) {
+//    assert(offset < utf16.size());
+//    if (((utf16[0] >> 10) << 10) != 0xd800) {
+//        auto result = utf16[offset + 0];
+//        offset += 1;
+//        return result;
+//    }
+//
+//    if (offset + 1 >= utf16.size() || !((utf16[1] & 0xdc00) == 0xdc00)) {
+//        throw std::invalid_argument("invalid character");
+//    }
+//
+//    auto result = 0x10000 + (((utf16[0] & 0x03ff) << 10) | (utf16[1] & 0x03ff));
+//    offset += 2;
+//    return result;
+//}
+
+//static std::vector<uint32_t> unicode_cpts_from_utf16(const std::vector<uint16_t> & utf16) {
+//    std::vector<uint32_t> result;
+//    size_t offset = 0;
+//    while (offset < utf16.size()) {
+//        result.push_back(unicode_cpt_from_utf16(utf16, offset));
+//    }
+//    return result;
+//}
+
+static std::unordered_map<uint32_t, int> unicode_cpt_type_map() {
+    std::unordered_map<uint32_t, int> cpt_types;
+    for (auto p : unicode_ranges_number) {
+        for (auto i = p.first; i <= p.second; ++i) {
+            cpt_types[i] = CODEPOINT_TYPE_NUMBER;
+        }
+    }
+    for (auto p : unicode_ranges_letter) {
+        for (auto i = p.first; i <= p.second; ++i) {
+            cpt_types[i] = CODEPOINT_TYPE_LETTER;
+        }
+    }
+    for (auto p : unicode_ranges_separator) {
+        for (auto i = p.first; i <= p.second; ++i) {
+            cpt_types[i] = CODEPOINT_TYPE_SEPARATOR;
+        }
+    }
+    for (auto p : unicode_ranges_accent_mark) {
+        for (auto i = p.first; i <= p.second; ++i) {
+            cpt_types[i] = CODEPOINT_TYPE_ACCENT_MARK;
+        }
+    }
+    for (auto p : unicode_ranges_punctuation) {
+        for (auto i = p.first; i <= p.second; ++i) {
+            cpt_types[i] = CODEPOINT_TYPE_PUNCTUATION;
+        }
+    }
+    for  (auto p : unicode_ranges_symbol) {
+        for (auto i = p.first; i <= p.second; ++i) {
+            cpt_types[i] = CODEPOINT_TYPE_SYMBOL;
+        }
+    }
+    for (auto p : unicode_ranges_control) {
+        for (auto i = p.first; i <= p.second; ++i) {
+            cpt_types[i] = CODEPOINT_TYPE_CONTROL;
+        }
+    }
+    return cpt_types;
+}
+
+static std::unordered_map<uint8_t, std::string> unicode_byte_to_utf8_map() {
+    std::unordered_map<uint8_t, std::string> map;
+    for (int ch = u'!'; ch <= u'~'; ++ch) {
+        assert(0 <= ch && ch < 256);
+        map[ch] = unicode_cpt_to_utf8(ch);
+    }
+    for (int ch = u'¡'; ch <= u'¬'; ++ch) {
+        assert(0 <= ch && ch < 256);
+        map[ch] = unicode_cpt_to_utf8(ch);
+    }
+    for (int ch = u'®'; ch <= u'ÿ'; ++ch) {
+        assert(0 <= ch && ch < 256);
+        map[ch] = unicode_cpt_to_utf8(ch);
+    }
+    auto n = 0;
+    for (int ch = 0; ch < 256; ++ch) {
+        if (map.find(ch) == map.end()) {
+            map[ch] = unicode_cpt_to_utf8(256 + n);
+            ++n;
+        }
+    }
+    return map;
+}
+
+static std::unordered_map<std::string, uint8_t> unicode_utf8_to_byte_map() {
+    std::unordered_map<std::string, uint8_t> map;
+    for (int ch = u'!'; ch <= u'~'; ++ch) {
+        assert(0 <= ch && ch < 256);
+        map[unicode_cpt_to_utf8(ch)] = ch;
+    }
+    for (int ch = u'¡'; ch <= u'¬'; ++ch) {
+        assert(0 <= ch && ch < 256);
+        map[unicode_cpt_to_utf8(ch)] = ch;
+    }
+    for (int ch = u'®'; ch <= u'ÿ'; ++ch) {
+        assert(0 <= ch && ch < 256);
+        map[unicode_cpt_to_utf8(ch)] = ch;
+    }
+    auto n = 0;
+    for (int ch = 0; ch < 256; ++ch) {
+        if (map.find(unicode_cpt_to_utf8(ch)) == map.end()) {
+            map[unicode_cpt_to_utf8(256 + n)] = ch;
+            ++n;
+        }
+    }
+    return map;
+}
+
+static inline std::wstring unicode_wstring_from_utf8(const std::string & s) {
+    std::wstring_convert<std::codecvt_utf8<wchar_t>> conv;
+    return conv.from_bytes(s);
+}
+
+static std::vector<std::string> unicode_byte_encoding_process(const std::vector<std::string> & bpe_words) {
+    std::vector<std::string> bpe_encoded_words;
+    for (const auto & word : bpe_words) {
+        std::string text_utf;
+        auto utf_word =  unicode_cpts_from_utf8(word);
+        for (size_t i = 0; i < utf_word.size(); ++i) {
+            text_utf += unicode_cpt_to_utf8(utf_word[i]);
+        }
+
+        std::string encoded_token;
+        for (char & c : text_utf) {
+            encoded_token += unicode_byte_to_utf8(c);
+        }
+        bpe_encoded_words.emplace_back(encoded_token);
+    }
+    return bpe_encoded_words;
+}
+
+// GPT2 system regex:  's|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+
+static std::vector<size_t> unicode_regex_split_custom_gpt2(const std::string & text, const std::vector<size_t> & offsets) {
+    std::vector<size_t> bpe_offsets; // store the offset of each word
+    bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size
+
+    const auto cpts = unicode_cpts_from_utf8(text);
+
+    size_t start = 0;
+    for (auto offset : offsets) {
+        const size_t offset_ini = start;
+        const size_t offset_end = start + offset;
+        assert(offset_end <= cpts.size());
+        start = offset_end;
+
+        auto _get_cpt = [&] (const size_t pos) -> char32_t {
+            return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : 0;
+        };
+
+        auto _get_cpt_type = [&] (const size_t pos) -> int {
+            return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_type(cpts[pos]) : CODEPOINT_TYPE_UNIDENTIFIED;
+        };
+
+        size_t _prev_end = offset_ini;
+        auto _add_token = [&] (const size_t end) -> size_t {
+            assert(_prev_end <= end && end <= offset_end);
+            size_t len = end - _prev_end;
+            if (len > 0) {
+                bpe_offsets.push_back(len);
+            }
+            _prev_end = end;
+            //if (len > 0) {
+            //    std::string s = "";
+            //    for(size_t p = end-len; p < end; p++)
+            //        s += unicode_cpt_to_utf8(cpts[p]);
+            //    printf(">>> '%s'\n", s.c_str());
+            //}
+            return len;
+        };
+
+        for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) {
+            const char32_t cpt = _get_cpt(pos);
+            const int cpt_type = _get_cpt_type(pos);
+
+            // regex: 's|'t|'re|'ve|'m|'ll|'d
+            if (cpt == '\'' && pos+1 < offset_end) {
+                char32_t cpt_next = _get_cpt(pos+1);
+                if (cpt_next == 's' || cpt_next == 't' || cpt_next == 'm' || cpt_next == 'd') {
+                    pos += _add_token(pos+2);
+                    continue;
+                }
+                if (pos+2 < offset_end) {
+                    char32_t cpt_next_next = _get_cpt(pos+2);
+                    if ((cpt_next == 'r' && cpt_next_next == 'e') ||
+                        (cpt_next == 'v' && cpt_next_next == 'e') ||
+                        (cpt_next == 'l' && cpt_next_next == 'l')) {
+                        pos += _add_token(pos+3);
+                        continue;
+                    }
+                }
+            }
+
+            char32_t cpt2 = (cpt == ' ' ? _get_cpt(pos+1) : cpt);
+            int cpt2_type = (cpt == ' ' ? _get_cpt_type(pos+1) : cpt_type);
+            // regex: <space>?\p{L}+
+            if (cpt2_type == CODEPOINT_TYPE_LETTER) {
+                pos += (cpt == ' ');
+                while (cpt2_type == CODEPOINT_TYPE_LETTER) {
+                    cpt2_type = _get_cpt_type(++pos);
+                }
+                _add_token(pos);
+                continue;
+            }
+            // regex: <space>?\p{N}+
+            if (cpt2_type == CODEPOINT_TYPE_NUMBER) {
+                pos += (cpt == ' ');
+                while (cpt2_type == CODEPOINT_TYPE_NUMBER) {
+                    cpt2_type = _get_cpt_type(++pos);
+                }
+                _add_token(pos);
+                continue;
+            }
+            // regex: <space>?[^\s\p{L}\p{N}]+
+            if (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) {
+                pos += (cpt == ' ');
+                while (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) {
+                    cpt2_type = _get_cpt_type(++pos);
+                    cpt2 = _get_cpt(pos);
+                }
+                _add_token(pos);
+                continue;
+            }
+
+            size_t num_whitespaces = 0;
+            while (unicode_cpt_is_whitespace(_get_cpt(pos+num_whitespaces))) {
+                num_whitespaces++;
+            }
+
+            // regex: \s+(?!\S)
+            if (num_whitespaces > 1 && _get_cpt(pos+num_whitespaces) != 0) {
+                pos += num_whitespaces - 1;
+                _add_token(pos);
+                continue;
+            }
+
+            // regex: \s+
+            if (num_whitespaces > 0) {
+                pos += num_whitespaces;
+                _add_token(pos);
+                continue;
+            }
+
+            // no matches
+            _add_token(++pos);
+        }
+    }
+
+    return bpe_offsets;
+}
+
+// LLAMA3 system regex: "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"
+static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string & text, const std::vector<size_t> & offsets) {
+    std::vector<size_t> bpe_offsets; // store the offset of each word
+    bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size
+
+    const auto cpts = unicode_cpts_from_utf8(text);
+
+    size_t start = 0;
+    for (auto offset : offsets) {
+        const size_t offset_ini = start;
+        const size_t offset_end = start + offset;
+        assert(offset_end <= cpts.size());
+        start = offset_end;
+
+        auto _get_cpt = [&] (const size_t pos) -> char32_t {
+            return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : 0;
+        };
+
+        auto _get_cpt_type = [&] (const size_t pos) -> int {
+            return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_type(cpts[pos]) : CODEPOINT_TYPE_UNIDENTIFIED;
+        };
+
+        size_t _prev_end = offset_ini;
+        auto _add_token = [&] (const size_t end) -> size_t {
+            assert(_prev_end <= end && end <= offset_end);
+            size_t len = end - _prev_end;
+            if (len > 0) {
+                bpe_offsets.push_back(len);
+            }
+            _prev_end = end;
+            //if (len > 0) {
+            //    std::string s = "";
+            //    for(size_t p = end-len; p < end; p++)
+            //        s += unicode_cpt_to_utf8(cpts[p]);
+            //    printf(">>> '%s'\n", s.c_str());
+            //}
+            return len;
+        };
+
+        for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) {
+            const char32_t cpt = _get_cpt(pos);
+            const int cpt_type = _get_cpt_type(pos);
+
+            // regex: (?i:'s|'t|'re|'ve|'m|'ll|'d) // case insensitive
+            if (cpt == '\'' && pos+1 < offset_end) {
+                char32_t cpt_next = unicode_tolower(_get_cpt(pos+1));
+                if (cpt_next == 's' || cpt_next == 't' || cpt_next == 'm' || cpt_next == 'd') {
+                    pos += _add_token(pos+2);
+                    continue;
+                }
+                if (pos+2 < offset_end) {
+                    char32_t cpt_next_next = unicode_tolower(_get_cpt(pos+2));
+                    if ((cpt_next == 'r' && cpt_next_next == 'e') ||
+                        (cpt_next == 'v' && cpt_next_next == 'e') ||
+                        (cpt_next == 'l' && cpt_next_next == 'l')) {
+                        pos += _add_token(pos+3);
+                        continue;
+                    }
+                }
+            }
+
+            // regex: [^\r\n\p{L}\p{N}]?\p{L}+  //####FIXME: the first \p{L} is correct?
+            if (cpt != '\r' && cpt != '\n' && /*cpt_type != CODEPOINT_TYPE_LETTER &&*/ cpt_type != CODEPOINT_TYPE_NUMBER) {
+                if (cpt_type == CODEPOINT_TYPE_LETTER || _get_cpt_type(pos+1) == CODEPOINT_TYPE_LETTER) {  // one or more letters
+                    pos++;
+                    while (_get_cpt_type(pos) == CODEPOINT_TYPE_LETTER) {
+                        pos++;
+                    }
+                    _add_token(pos);
+                    continue;
+                }
+            }
+
+            // regex: \p{N}{1,3}
+            if (cpt_type == CODEPOINT_TYPE_NUMBER) {
+                size_t ini = pos;
+                while (_get_cpt_type(pos) == CODEPOINT_TYPE_NUMBER) {
+                    if (++pos - ini >= 3 ) {
+                        _add_token(pos);
+                        ini = pos;
+                    }
+                }
+                _add_token(pos);
+                continue;
+            }
+
+            // regex: <space>?[^\s\p{L}\p{N}]+[\r\n]*
+            char32_t cpt2 = (cpt == ' ' ? _get_cpt(pos+1) : cpt);
+            int cpt2_type = (cpt == ' ' ? _get_cpt_type(pos+1) : cpt_type);
+            if (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) {
+                pos += (cpt == ' ');
+                while (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) {
+                    cpt2_type = _get_cpt_type(++pos);
+                    cpt2 = _get_cpt(pos);
+                }
+                while (cpt2 == '\r' || cpt2 == '\n') {
+                    cpt2 = _get_cpt(++pos);
+                }
+                _add_token(pos);
+                continue;
+            }
+
+            size_t num_whitespaces = 0;
+            size_t last_end_r_or_n = 0;
+            while (unicode_cpt_is_whitespace(_get_cpt(pos+num_whitespaces))) {
+                char32_t cpt2 = _get_cpt(pos+num_whitespaces);
+                if (cpt2 == '\r' || cpt2 == '\n') {
+                    last_end_r_or_n = pos + num_whitespaces + 1;
+                }
+                num_whitespaces++;
+            }
+
+            // regex: \s*[\r\n]+
+            if (last_end_r_or_n > 0) {
+                pos = last_end_r_or_n;
+                _add_token(pos);
+                continue;
+            }
+
+            // regex: \s+(?!\S)
+            if (num_whitespaces > 1 && _get_cpt(pos+num_whitespaces) != 0) {
+                pos += num_whitespaces - 1;
+                _add_token(pos);
+                continue;
+            }
+
+            // regex: \s+
+            if (num_whitespaces > 0) {
+                pos += num_whitespaces;
+                _add_token(pos);
+                continue;
+            }
+
+            // no matches
+            _add_token(++pos);
+        }
+    }
+
+    return bpe_offsets;
+}
+
+// use std::wregex to split the text
+static std::vector<size_t> unicode_regex_split_stl(const std::wstring & wtext, const std::wstring & regex_expr, const std::vector<size_t> & offsets) {
+    std::wregex expr(regex_expr);
+    std::vector<size_t> bpe_offsets; // store the offset of each word
+    bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size
+    size_t start = 0;
+    for (auto offset : offsets) {
+        std::wcregex_iterator it(wtext.data() + start, wtext.data() + start + offset, expr);
+        std::wcregex_iterator end;
+
+        int64_t start_idx = 0;
+        while (it != end) {
+            std::wcmatch match = *it;
+            if (match.position() > start_idx) {
+                bpe_offsets.emplace_back(match.position() - start_idx);
+            }
+            bpe_offsets.emplace_back(match.length());
+            start_idx = match.position() + match.length();
+            ++it;
+        }
+
+        if (start_idx < (int64_t) offset) {
+            bpe_offsets.emplace_back(offset - start_idx);
+        }
+        start += offset;
+    }
+
+    return bpe_offsets;
+}
+
+// use std::regex to split the text
+static std::vector<size_t> unicode_regex_split_stl(const std::string & text, const std::string & regex_expr, const std::vector<size_t> & offsets) {
+    std::regex expr(regex_expr);
+    std::vector<size_t> bpe_offsets; // store the offset of each word
+    bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size
+    size_t start = 0;
+    for (auto offset : offsets) {
+        std::cregex_iterator it(text.data() + start, text.data() + start + offset, expr);
+        std::cregex_iterator end;
+
+        int64_t start_idx = 0;
+        while (it != end) {
+            std::cmatch match = *it;
+            if (match.position() > start_idx) {
+                bpe_offsets.emplace_back(match.position() - start_idx);
+            }
+            bpe_offsets.emplace_back(match.length());
+            start_idx = match.position() + match.length();
+            ++it;
+        }
+
+        if (start_idx < (int64_t) offset) {
+            bpe_offsets.emplace_back(offset - start_idx);
+        }
+        start += offset;
+    }
+
+    return bpe_offsets;
+}
+
+static std::vector<size_t> unicode_regex_split_custom(const std::string & text, const std::string & regex_expr, const std::vector<size_t> & offsets) {
+    std::vector<size_t> bpe_offsets;
+
+    if (regex_expr == "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)") {
+        bpe_offsets = unicode_regex_split_custom_gpt2(text, offsets);
+    } else if (
+            regex_expr == "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+" ||
+            regex_expr == "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+") {
+
+        bpe_offsets = unicode_regex_split_custom_llama3(text, offsets);
+    }
+
+    return bpe_offsets;
+}
+
+//
+// interface
+//
+
+std::string unicode_cpt_to_utf8(uint32_t cp) {
+    std::string result;
+
+    if (/* 0x00 <= cp && */ cp <= 0x7f) {
+        result.push_back(cp);
+        return result;
+    }
+    if (0x80 <= cp && cp <= 0x7ff) {
+        result.push_back(0xc0 | ((cp >> 6) & 0x1f));
+        result.push_back(0x80 | (cp & 0x3f));
+        return result;
+    }
+    if (0x800 <= cp && cp <= 0xffff) {
+        result.push_back(0xe0 | ((cp >> 12) & 0x0f));
+        result.push_back(0x80 | ((cp >> 6) & 0x3f));
+        result.push_back(0x80 | (cp & 0x3f));
+        return result;
+    }
+    if (0x10000 <= cp && cp <= 0x10ffff) {
+        result.push_back(0xf0 | ((cp >> 18) & 0x07));
+        result.push_back(0x80 | ((cp >> 12) & 0x3f));
+        result.push_back(0x80 | ((cp >> 6) & 0x3f));
+        result.push_back(0x80 | (cp & 0x3f));
+        return result;
+    }
+
+    throw std::invalid_argument("invalid codepoint");
+}
+
+std::vector<uint32_t> unicode_cpts_normalize_nfd(const std::vector<uint32_t> & cpts) {
+    std::vector<uint32_t> result;
+    result.reserve(cpts.size());
+    for (size_t i = 0; i < cpts.size(); ++i) {
+        auto it = unicode_map_nfd.find(cpts[i]);
+        if (it == unicode_map_nfd.end()) {
+            result.push_back(cpts[i]);
+        } else {
+            result.push_back(it->second);
+        }
+    }
+    return result;
+}
+
+std::vector<uint32_t> unicode_cpts_from_utf8(const std::string & utf8) {
+    std::vector<uint32_t> result;
+    size_t offset = 0;
+    while (offset < utf8.size()) {
+        result.push_back(unicode_cpt_from_utf8(utf8, offset));
+    }
+    return result;
+}
+
+int unicode_cpt_type(uint32_t cp) {
+    static std::unordered_map<uint32_t, int> cpt_types = unicode_cpt_type_map();
+    const auto it = cpt_types.find(cp);
+    return it == cpt_types.end() ? CODEPOINT_TYPE_UNIDENTIFIED : it->second;
+}
+
+int unicode_cpt_type(const std::string & utf8) {
+    if (utf8.length() == 0) {
+        return CODEPOINT_TYPE_UNIDENTIFIED;
+    }
+    size_t offset = 0;
+    return unicode_cpt_type(unicode_cpt_from_utf8(utf8, offset));
+}
+
+bool unicode_cpt_is_whitespace(uint32_t cp) {
+    static const std::unordered_set<uint32_t> is_whitespace = [] {
+        std::unordered_set<uint32_t> is_whitespace;
+        for (auto p : unicode_ranges_whitespace) {
+            for (auto i = p.first; i <= p.second; ++i) {
+                is_whitespace.insert(i);
+            }
+        }
+        return is_whitespace;
+    }();
+    return (bool)is_whitespace.count(cp);
+}
+
+std::string unicode_byte_to_utf8(uint8_t byte) {
+    static std::unordered_map<uint8_t, std::string> map = unicode_byte_to_utf8_map();
+    return map.at(byte);
+}
+
+uint8_t unicode_utf8_to_byte(const std::string & utf8) {
+    static std::unordered_map<std::string, uint8_t> map = unicode_utf8_to_byte_map();
+    return map.at(utf8);
+}
+
+char32_t unicode_tolower(char32_t cp) {
+    auto it = unicode_map_lowercase.find(cp);
+    return it == unicode_map_lowercase.end() ? cp : it->second;
+}
+
+std::vector<std::string> unicode_regex_split(const std::string & text, const std::vector<std::string> & regex_exprs) {
+    // unicode categories
+    static const std::map<std::string, int> k_ucat_enum = {
+        { "\\p{N}", CODEPOINT_TYPE_NUMBER },
+        { "\\p{L}", CODEPOINT_TYPE_LETTER },
+        { "\\p{P}", CODEPOINT_TYPE_PUNCTUATION },
+    };
+
+    static const std::map<int, int> k_ucat_cpt = {
+        { CODEPOINT_TYPE_NUMBER,        0xD1 },
+        { CODEPOINT_TYPE_LETTER,        0xD2 },
+        { CODEPOINT_TYPE_PUNCTUATION,   0xD3 },
+    };
+
+    static const std::map<int, std::string> k_ucat_map = {
+        { CODEPOINT_TYPE_NUMBER,        "\x30-\x39" }, // 0-9
+        { CODEPOINT_TYPE_LETTER,        "\x41-\x5A\x61-\x7A" }, // A-Za-z
+        { CODEPOINT_TYPE_PUNCTUATION,   "\x21-\x23\x25-\x2A\x2C-\x2F\x3A-\x3B\x3F-\x40\\\x5B-\\\x5D\x5F\\\x7B\\\x7D" }, // !-#%-*,-/:-;?-@\[-\]_\{\}
+    };
+
+    // compute collapsed codepoints only if needed by at least one regex
+    bool need_collapse = false;
+    for (auto & regex_expr : regex_exprs) {
+        // search for unicode categories
+        for (const auto & ucat : k_ucat_enum) {
+            if (std::string::npos != regex_expr.find(ucat.first)) {
+                need_collapse = true;
+                break;
+            }
+        }
+    }
+
+    const auto cpts = unicode_cpts_from_utf8(text);
+
+    // generate a "collapsed" representation of the text, where all codepoints are replaced by a single byte
+    // ref: https://github.com/ggerganov/llama.cpp/pull/6920#issuecomment-2081479935
+    std::string text_collapsed;
+    if (need_collapse) {
+        // collapse all unicode categories
+        text_collapsed.resize(cpts.size());
+
+        for (size_t i = 0; i < cpts.size(); ++i) {
+            // keep single-byte codepoints as is
+            if (cpts[i] < 128) {
+                text_collapsed[i] = cpts[i];
+                continue;
+            }
+
+            const int cpt_type = unicode_cpt_type(cpts[i]);
+
+            if (k_ucat_cpt.find(cpt_type) != k_ucat_cpt.end()) {
+                text_collapsed[i] = k_ucat_cpt.at(cpt_type);
+            } else {
+                text_collapsed[i] = (char) 0xD0; // fallback
+            }
+        }
+    }
+
+    std::vector<size_t> bpe_offsets = { cpts.size() };
+
+    for (auto & regex_expr : regex_exprs) {
+        // first, see if we have an efficient custom regex implementation
+        auto tmp = unicode_regex_split_custom(text, regex_expr, bpe_offsets);
+
+        if (!tmp.empty()) {
+            bpe_offsets = std::move(tmp);
+            continue;
+        }
+
+        // fallback to general-purpose std::regex / std::wregex
+        try {
+            // if a unicode category is used in the regex, we use the collapsed text and replace the unicode category
+            // with the corresponding collapsed representation
+            bool use_collapsed = false;
+            for (auto & ucat : k_ucat_enum) {
+                if (std::string::npos != regex_expr.find(ucat.first)) {
+                    use_collapsed = true;
+                    break;
+                }
+            }
+
+            if (use_collapsed) {
+                // sanity-check that the original regex does not contain any non-ASCII characters
+                const auto cpts_regex = unicode_cpts_from_utf8(regex_expr);
+                for (size_t i = 0; i < cpts_regex.size(); ++i) {
+                    if (cpts_regex[i] >= 128) {
+                        throw std::runtime_error("Regex includes both unicode categories and non-ASCII characters - not supported");
+                    }
+                }
+
+                // generate a collapsed representation of the regex
+                std::string regex_expr_collapsed;
+
+                // track if we are inside [], because nested [] are not allowed
+                bool inside = false;
+                for (size_t i = 0; i < regex_expr.size(); ++i) {
+                    if (regex_expr[i] == '[' && (i == 0 || regex_expr[i - 1] != '\\')) {
+                        regex_expr_collapsed += '[';
+                        inside = true;
+                        continue;
+                    }
+
+                    if (inside && regex_expr[i] == ']' && regex_expr[i - 1] != '\\') {
+                        regex_expr_collapsed += ']';
+                        inside = false;
+                        continue;
+                    }
+
+                    if (regex_expr[i + 0] == '\\' && i + 4 < regex_expr.size() &&
+                        regex_expr[i + 1] == 'p' &&
+                        regex_expr[i + 2] == '{' &&
+                        regex_expr[i + 4] == '}') {
+                        const std::string pat = regex_expr.substr(i, 5);
+                        if (k_ucat_enum.find(pat) != k_ucat_enum.end()) {
+                            if (!inside) {
+                                regex_expr_collapsed += '[';
+                            }
+                            regex_expr_collapsed += k_ucat_cpt.at(k_ucat_enum.at(pat));
+                            regex_expr_collapsed += k_ucat_map.at(k_ucat_enum.at(pat));
+                            if (!inside) {
+                                regex_expr_collapsed += ']';
+                            }
+                            i += 4;
+                            continue;
+                        }
+                    }
+
+                    regex_expr_collapsed += regex_expr[i];
+                }
+
+                //printf("text_collapsed: %s\n", text_collapsed.c_str());
+                //printf("regex_expr_collapsed: %s\n", regex_expr_collapsed.c_str());
+                bpe_offsets = unicode_regex_split_stl(text_collapsed, regex_expr_collapsed, bpe_offsets);
+            } else {
+                // no unicode category used, we can use std::wregex directly
+                const std::wstring wtext       = unicode_wstring_from_utf8(text);
+                const std::wstring wregex_expr = unicode_wstring_from_utf8(regex_expr);
+
+                //printf("text: %s\n", text.c_str());
+                //printf("regex_expr: %s\n", regex_expr.c_str());
+                bpe_offsets = unicode_regex_split_stl(wtext, wregex_expr, bpe_offsets);
+            }
+        } catch (std::regex_error & e) {
+            fprintf(stderr, "Failed to process regex: '%s'\n", regex_expr.c_str());
+            fprintf(stderr, "Regex error: %s\n", e.what());
+            throw std::runtime_error("Failed to process regex");
+        }
+    }
+
+    std::vector<std::string> bpe_words;
+    bpe_words.reserve(bpe_offsets.size()); // reserve memory for the approximate size
+
+    size_t start = 0;
+    for (size_t & offset : bpe_offsets) {
+        bpe_words.emplace_back();
+        for (size_t i = start; i < start + offset; ++i) {
+            bpe_words.back() += unicode_cpt_to_utf8(cpts[i]);
+        }
+        start += offset;
+    }
+
+    return unicode_byte_encoding_process(bpe_words);
+}
diff --git a/unicode.h b/unicode.h
index 263260702..d6a14d470 100644
--- a/unicode.h
+++ b/unicode.h
@@ -1,475 +1,31 @@
-#pragma once
+#pragma once
 
-#include <cassert>
-#include <stdexcept>
+#include <cstdint>
 #include <string>
-#include <unordered_map>
 #include <vector>
 
-static const std::vector<std::pair<uint32_t, uint32_t>> digit_ranges = {
-{0x30, 0x39}, {0xB2, 0xB3}, {0xB9, 0xB9}, {0x660, 0x669}, {0x6F0, 0x6F9}, {0x7C0, 0x7C9}, {0x966, 0x96F}, {0x9E6, 0x9EF}, {0xA66, 0xA6F}, {0xAE6, 0xAEF}, {0xB66, 0xB6F}, {0xBE6, 0xBEF}, {0xC66, 0xC6F},
-{0xCE6, 0xCEF}, {0xD66, 0xD6F}, {0xDE6, 0xDEF}, {0xE50, 0xE59}, {0xED0, 0xED9}, {0xF20, 0xF29}, {0x1040, 0x1049}, {0x1090, 0x1099}, {0x1369, 0x1371}, {0x17E0, 0x17E9}, {0x1810, 0x1819}, {0x1946, 0x194F},
-{0x19D0, 0x19DA}, {0x1A80, 0x1A89}, {0x1A90, 0x1A99}, {0x1B50, 0x1B59}, {0x1BB0, 0x1BB9}, {0x1C40, 0x1C49}, {0x1C50, 0x1C59}, {0x2070, 0x2070}, {0x2074, 0x2079}, {0x2080, 0x2089}, {0x2460, 0x2468},
-{0x2474, 0x247C}, {0x2488, 0x2490}, {0x24EA, 0x24EA}, {0x24F5, 0x24FD}, {0x24FF, 0x24FF}, {0x2776, 0x277E}, {0x2780, 0x2788}, {0x278A, 0x2792}, {0xA620, 0xA629}, {0xA8D0, 0xA8D9}, {0xA900, 0xA909},
-{0xA9D0, 0xA9D9}, {0xA9F0, 0xA9F9}, {0xAA50, 0xAA59}, {0xABF0, 0xABF9}, {0xFF10, 0xFF19}, {0x104A0, 0x104A9}, {0x10A40, 0x10A43}, {0x10D30, 0x10D39}, {0x10E60, 0x10E68}, {0x11052, 0x1105A},
-{0x11066, 0x1106F}, {0x110F0, 0x110F9}, {0x11136, 0x1113F}, {0x111D0, 0x111D9}, {0x112F0, 0x112F9}, {0x11450, 0x11459}, {0x114D0, 0x114D9}, {0x11650, 0x11659}, {0x116C0, 0x116C9}, {0x11730, 0x11739},
-{0x118E0, 0x118E9}, {0x11950, 0x11959}, {0x11C50, 0x11C59}, {0x11D50, 0x11D59}, {0x11DA0, 0x11DA9}, {0x16A60, 0x16A69}, {0x16B50, 0x16B59}, {0x1D7CE, 0x1D7FF}, {0x1E140, 0x1E149}, {0x1E2F0, 0x1E2F9},
-{0x1E950, 0x1E959}, {0x1F100, 0x1F10A}, {0x1FBF0, 0x1FBF9},
-};
-
-static const std::vector<std::pair<uint32_t, uint32_t>> letter_ranges = {
-{0x41, 0x5A}, {0x61, 0x7A}, {0xAA, 0xAA}, {0xB5, 0xB5}, {0xBA, 0xBA}, {0xC0, 0xD6}, {0xD8, 0xF6}, {0xF8, 0x2C1}, {0x2C6, 0x2D1}, {0x2E0, 0x2E4}, {0x2EC, 0x2EC}, {0x2EE, 0x2EE}, {0x370, 0x374},
-{0x376, 0x377}, {0x37A, 0x37D}, {0x37F, 0x37F}, {0x386, 0x386}, {0x388, 0x38A}, {0x38C, 0x38C}, {0x38E, 0x3A1}, {0x3A3, 0x3F5}, {0x3F7, 0x481}, {0x48A, 0x52F}, {0x531, 0x556}, {0x559, 0x559},
-{0x560, 0x588}, {0x5D0, 0x5EA}, {0x5EF, 0x5F2}, {0x620, 0x64A}, {0x66E, 0x66F}, {0x671, 0x6D3}, {0x6D5, 0x6D5}, {0x6E5, 0x6E6}, {0x6EE, 0x6EF}, {0x6FA, 0x6FC}, {0x6FF, 0x6FF}, {0x710, 0x710},
-{0x712, 0x72F}, {0x74D, 0x7A5}, {0x7B1, 0x7B1}, {0x7CA, 0x7EA}, {0x7F4, 0x7F5}, {0x7FA, 0x7FA}, {0x800, 0x815}, {0x81A, 0x81A}, {0x824, 0x824}, {0x828, 0x828}, {0x840, 0x858}, {0x860, 0x86A},
-{0x8A0, 0x8B4}, {0x8B6, 0x8C7}, {0x904, 0x939}, {0x93D, 0x93D}, {0x950, 0x950}, {0x958, 0x961}, {0x971, 0x980}, {0x985, 0x98C}, {0x98F, 0x990}, {0x993, 0x9A8}, {0x9AA, 0x9B0}, {0x9B2, 0x9B2},
-{0x9B6, 0x9B9}, {0x9BD, 0x9BD}, {0x9CE, 0x9CE}, {0x9DC, 0x9DD}, {0x9DF, 0x9E1}, {0x9F0, 0x9F1}, {0x9FC, 0x9FC}, {0xA05, 0xA0A}, {0xA0F, 0xA10}, {0xA13, 0xA28}, {0xA2A, 0xA30}, {0xA32, 0xA33},
-{0xA35, 0xA36}, {0xA38, 0xA39}, {0xA59, 0xA5C}, {0xA5E, 0xA5E}, {0xA72, 0xA74}, {0xA85, 0xA8D}, {0xA8F, 0xA91}, {0xA93, 0xAA8}, {0xAAA, 0xAB0}, {0xAB2, 0xAB3}, {0xAB5, 0xAB9}, {0xABD, 0xABD},
-{0xAD0, 0xAD0}, {0xAE0, 0xAE1}, {0xAF9, 0xAF9}, {0xB05, 0xB0C}, {0xB0F, 0xB10}, {0xB13, 0xB28}, {0xB2A, 0xB30}, {0xB32, 0xB33}, {0xB35, 0xB39}, {0xB3D, 0xB3D}, {0xB5C, 0xB5D}, {0xB5F, 0xB61},
-{0xB71, 0xB71}, {0xB83, 0xB83}, {0xB85, 0xB8A}, {0xB8E, 0xB90}, {0xB92, 0xB95}, {0xB99, 0xB9A}, {0xB9C, 0xB9C}, {0xB9E, 0xB9F}, {0xBA3, 0xBA4}, {0xBA8, 0xBAA}, {0xBAE, 0xBB9}, {0xBD0, 0xBD0},
-{0xC05, 0xC0C}, {0xC0E, 0xC10}, {0xC12, 0xC28}, {0xC2A, 0xC39}, {0xC3D, 0xC3D}, {0xC58, 0xC5A}, {0xC60, 0xC61}, {0xC80, 0xC80}, {0xC85, 0xC8C}, {0xC8E, 0xC90}, {0xC92, 0xCA8}, {0xCAA, 0xCB3},
-{0xCB5, 0xCB9}, {0xCBD, 0xCBD}, {0xCDE, 0xCDE}, {0xCE0, 0xCE1}, {0xCF1, 0xCF2}, {0xD04, 0xD0C}, {0xD0E, 0xD10}, {0xD12, 0xD3A}, {0xD3D, 0xD3D}, {0xD4E, 0xD4E}, {0xD54, 0xD56}, {0xD5F, 0xD61},
-{0xD7A, 0xD7F}, {0xD85, 0xD96}, {0xD9A, 0xDB1}, {0xDB3, 0xDBB}, {0xDBD, 0xDBD}, {0xDC0, 0xDC6}, {0xE01, 0xE30}, {0xE32, 0xE33}, {0xE40, 0xE46}, {0xE81, 0xE82}, {0xE84, 0xE84}, {0xE86, 0xE8A},
-{0xE8C, 0xEA3}, {0xEA5, 0xEA5}, {0xEA7, 0xEB0}, {0xEB2, 0xEB3}, {0xEBD, 0xEBD}, {0xEC0, 0xEC4}, {0xEC6, 0xEC6}, {0xEDC, 0xEDF}, {0xF00, 0xF00}, {0xF40, 0xF47}, {0xF49, 0xF6C}, {0xF88, 0xF8C},
-{0x1000, 0x102A}, {0x103F, 0x103F}, {0x1050, 0x1055}, {0x105A, 0x105D}, {0x1061, 0x1061}, {0x1065, 0x1066}, {0x106E, 0x1070}, {0x1075, 0x1081}, {0x108E, 0x108E}, {0x10A0, 0x10C5}, {0x10C7, 0x10C7},
-{0x10CD, 0x10CD}, {0x10D0, 0x10FA}, {0x10FC, 0x1248}, {0x124A, 0x124D}, {0x1250, 0x1256}, {0x1258, 0x1258}, {0x125A, 0x125D}, {0x1260, 0x1288}, {0x128A, 0x128D}, {0x1290, 0x12B0}, {0x12B2, 0x12B5},
-{0x12B8, 0x12BE}, {0x12C0, 0x12C0}, {0x12C2, 0x12C5}, {0x12C8, 0x12D6}, {0x12D8, 0x1310}, {0x1312, 0x1315}, {0x1318, 0x135A}, {0x1380, 0x138F}, {0x13A0, 0x13F5}, {0x13F8, 0x13FD}, {0x1401, 0x166C},
-{0x166F, 0x167F}, {0x1681, 0x169A}, {0x16A0, 0x16EA}, {0x16F1, 0x16F8}, {0x1700, 0x170C}, {0x170E, 0x1711}, {0x1720, 0x1731}, {0x1740, 0x1751}, {0x1760, 0x176C}, {0x176E, 0x1770}, {0x1780, 0x17B3},
-{0x17D7, 0x17D7}, {0x17DC, 0x17DC}, {0x1820, 0x1878}, {0x1880, 0x1884}, {0x1887, 0x18A8}, {0x18AA, 0x18AA}, {0x18B0, 0x18F5}, {0x1900, 0x191E}, {0x1950, 0x196D}, {0x1970, 0x1974}, {0x1980, 0x19AB},
-{0x19B0, 0x19C9}, {0x1A00, 0x1A16}, {0x1A20, 0x1A54}, {0x1AA7, 0x1AA7}, {0x1B05, 0x1B33}, {0x1B45, 0x1B4B}, {0x1B83, 0x1BA0}, {0x1BAE, 0x1BAF}, {0x1BBA, 0x1BE5}, {0x1C00, 0x1C23}, {0x1C4D, 0x1C4F},
-{0x1C5A, 0x1C7D}, {0x1C80, 0x1C88}, {0x1C90, 0x1CBA}, {0x1CBD, 0x1CBF}, {0x1CE9, 0x1CEC}, {0x1CEE, 0x1CF3}, {0x1CF5, 0x1CF6}, {0x1CFA, 0x1CFA}, {0x1D00, 0x1DBF}, {0x1E00, 0x1F15}, {0x1F18, 0x1F1D},
-{0x1F20, 0x1F45}, {0x1F48, 0x1F4D}, {0x1F50, 0x1F57}, {0x1F59, 0x1F59}, {0x1F5B, 0x1F5B}, {0x1F5D, 0x1F5D}, {0x1F5F, 0x1F7D}, {0x1F80, 0x1FB4}, {0x1FB6, 0x1FBC}, {0x1FBE, 0x1FBE}, {0x1FC2, 0x1FC4},
-{0x1FC6, 0x1FCC}, {0x1FD0, 0x1FD3}, {0x1FD6, 0x1FDB}, {0x1FE0, 0x1FEC}, {0x1FF2, 0x1FF4}, {0x1FF6, 0x1FFC}, {0x2071, 0x2071}, {0x207F, 0x207F}, {0x2090, 0x209C}, {0x2102, 0x2102}, {0x2107, 0x2107},
-{0x210A, 0x2113}, {0x2115, 0x2115}, {0x2119, 0x211D}, {0x2124, 0x2124}, {0x2126, 0x2126}, {0x2128, 0x2128}, {0x212A, 0x212D}, {0x212F, 0x2139}, {0x213C, 0x213F}, {0x2145, 0x2149}, {0x214E, 0x214E},
-{0x2183, 0x2184}, {0x2C00, 0x2C2E}, {0x2C30, 0x2C5E}, {0x2C60, 0x2CE4}, {0x2CEB, 0x2CEE}, {0x2CF2, 0x2CF3}, {0x2D00, 0x2D25}, {0x2D27, 0x2D27}, {0x2D2D, 0x2D2D}, {0x2D30, 0x2D67}, {0x2D6F, 0x2D6F},
-{0x2D80, 0x2D96}, {0x2DA0, 0x2DA6}, {0x2DA8, 0x2DAE}, {0x2DB0, 0x2DB6}, {0x2DB8, 0x2DBE}, {0x2DC0, 0x2DC6}, {0x2DC8, 0x2DCE}, {0x2DD0, 0x2DD6}, {0x2DD8, 0x2DDE}, {0x2E2F, 0x2E2F}, {0x3005, 0x3006},
-{0x3031, 0x3035}, {0x303B, 0x303C}, {0x3041, 0x3096}, {0x309D, 0x309F}, {0x30A1, 0x30FA}, {0x30FC, 0x30FF}, {0x3105, 0x312F}, {0x3131, 0x318E}, {0x31A0, 0x31BF}, {0x31F0, 0x31FF}, {0x3400, 0x4DBF},
-{0x4E00, 0x9FFC}, {0xA000, 0xA48C}, {0xA4D0, 0xA4FD}, {0xA500, 0xA60C}, {0xA610, 0xA61F}, {0xA62A, 0xA62B}, {0xA640, 0xA66E}, {0xA67F, 0xA69D}, {0xA6A0, 0xA6E5}, {0xA717, 0xA71F}, {0xA722, 0xA788},
-{0xA78B, 0xA7BF}, {0xA7C2, 0xA7CA}, {0xA7F5, 0xA801}, {0xA803, 0xA805}, {0xA807, 0xA80A}, {0xA80C, 0xA822}, {0xA840, 0xA873}, {0xA882, 0xA8B3}, {0xA8F2, 0xA8F7}, {0xA8FB, 0xA8FB}, {0xA8FD, 0xA8FE},
-{0xA90A, 0xA925}, {0xA930, 0xA946}, {0xA960, 0xA97C}, {0xA984, 0xA9B2}, {0xA9CF, 0xA9CF}, {0xA9E0, 0xA9E4}, {0xA9E6, 0xA9EF}, {0xA9FA, 0xA9FE}, {0xAA00, 0xAA28}, {0xAA40, 0xAA42}, {0xAA44, 0xAA4B},
-{0xAA60, 0xAA76}, {0xAA7A, 0xAA7A}, {0xAA7E, 0xAAAF}, {0xAAB1, 0xAAB1}, {0xAAB5, 0xAAB6}, {0xAAB9, 0xAABD}, {0xAAC0, 0xAAC0}, {0xAAC2, 0xAAC2}, {0xAADB, 0xAADD}, {0xAAE0, 0xAAEA}, {0xAAF2, 0xAAF4},
-{0xAB01, 0xAB06}, {0xAB09, 0xAB0E}, {0xAB11, 0xAB16}, {0xAB20, 0xAB26}, {0xAB28, 0xAB2E}, {0xAB30, 0xAB5A}, {0xAB5C, 0xAB69}, {0xAB70, 0xABE2}, {0xAC00, 0xD7A3}, {0xD7B0, 0xD7C6}, {0xD7CB, 0xD7FB},
-{0xF900, 0xFA6D}, {0xFA70, 0xFAD9}, {0xFB00, 0xFB06}, {0xFB13, 0xFB17}, {0xFB1D, 0xFB1D}, {0xFB1F, 0xFB28}, {0xFB2A, 0xFB36}, {0xFB38, 0xFB3C}, {0xFB3E, 0xFB3E}, {0xFB40, 0xFB41}, {0xFB43, 0xFB44},
-{0xFB46, 0xFBB1}, {0xFBD3, 0xFD3D}, {0xFD50, 0xFD8F}, {0xFD92, 0xFDC7}, {0xFDF0, 0xFDFB}, {0xFE70, 0xFE74}, {0xFE76, 0xFEFC}, {0xFF21, 0xFF3A}, {0xFF41, 0xFF5A}, {0xFF66, 0xFFBE}, {0xFFC2, 0xFFC7},
-{0xFFCA, 0xFFCF}, {0xFFD2, 0xFFD7}, {0xFFDA, 0xFFDC}, {0x10000, 0x1000B}, {0x1000D, 0x10026}, {0x10028, 0x1003A}, {0x1003C, 0x1003D}, {0x1003F, 0x1004D}, {0x10050, 0x1005D}, {0x10080, 0x100FA},
-{0x10280, 0x1029C}, {0x102A0, 0x102D0}, {0x10300, 0x1031F}, {0x1032D, 0x10340}, {0x10342, 0x10349}, {0x10350, 0x10375}, {0x10380, 0x1039D}, {0x103A0, 0x103C3}, {0x103C8, 0x103CF}, {0x10400, 0x1049D},
-{0x104B0, 0x104D3}, {0x104D8, 0x104FB}, {0x10500, 0x10527}, {0x10530, 0x10563}, {0x10600, 0x10736}, {0x10740, 0x10755}, {0x10760, 0x10767}, {0x10800, 0x10805}, {0x10808, 0x10808}, {0x1080A, 0x10835},
-{0x10837, 0x10838}, {0x1083C, 0x1083C}, {0x1083F, 0x10855}, {0x10860, 0x10876}, {0x10880, 0x1089E}, {0x108E0, 0x108F2}, {0x108F4, 0x108F5}, {0x10900, 0x10915}, {0x10920, 0x10939}, {0x10980, 0x109B7},
-{0x109BE, 0x109BF}, {0x10A00, 0x10A00}, {0x10A10, 0x10A13}, {0x10A15, 0x10A17}, {0x10A19, 0x10A35}, {0x10A60, 0x10A7C}, {0x10A80, 0x10A9C}, {0x10AC0, 0x10AC7}, {0x10AC9, 0x10AE4}, {0x10B00, 0x10B35},
-{0x10B40, 0x10B55}, {0x10B60, 0x10B72}, {0x10B80, 0x10B91}, {0x10C00, 0x10C48}, {0x10C80, 0x10CB2}, {0x10CC0, 0x10CF2}, {0x10D00, 0x10D23}, {0x10E80, 0x10EA9}, {0x10EB0, 0x10EB1}, {0x10F00, 0x10F1C},
-{0x10F27, 0x10F27}, {0x10F30, 0x10F45}, {0x10FB0, 0x10FC4}, {0x10FE0, 0x10FF6}, {0x11003, 0x11037}, {0x11083, 0x110AF}, {0x110D0, 0x110E8}, {0x11103, 0x11126}, {0x11144, 0x11144}, {0x11147, 0x11147},
-{0x11150, 0x11172}, {0x11176, 0x11176}, {0x11183, 0x111B2}, {0x111C1, 0x111C4}, {0x111DA, 0x111DA}, {0x111DC, 0x111DC}, {0x11200, 0x11211}, {0x11213, 0x1122B}, {0x11280, 0x11286}, {0x11288, 0x11288},
-{0x1128A, 0x1128D}, {0x1128F, 0x1129D}, {0x1129F, 0x112A8}, {0x112B0, 0x112DE}, {0x11305, 0x1130C}, {0x1130F, 0x11310}, {0x11313, 0x11328}, {0x1132A, 0x11330}, {0x11332, 0x11333}, {0x11335, 0x11339},
-{0x1133D, 0x1133D}, {0x11350, 0x11350}, {0x1135D, 0x11361}, {0x11400, 0x11434}, {0x11447, 0x1144A}, {0x1145F, 0x11461}, {0x11480, 0x114AF}, {0x114C4, 0x114C5}, {0x114C7, 0x114C7}, {0x11580, 0x115AE},
-{0x115D8, 0x115DB}, {0x11600, 0x1162F}, {0x11644, 0x11644}, {0x11680, 0x116AA}, {0x116B8, 0x116B8}, {0x11700, 0x1171A}, {0x11800, 0x1182B}, {0x118A0, 0x118DF}, {0x118FF, 0x11906}, {0x11909, 0x11909},
-{0x1190C, 0x11913}, {0x11915, 0x11916}, {0x11918, 0x1192F}, {0x1193F, 0x1193F}, {0x11941, 0x11941}, {0x119A0, 0x119A7}, {0x119AA, 0x119D0}, {0x119E1, 0x119E1}, {0x119E3, 0x119E3}, {0x11A00, 0x11A00},
-{0x11A0B, 0x11A32}, {0x11A3A, 0x11A3A}, {0x11A50, 0x11A50}, {0x11A5C, 0x11A89}, {0x11A9D, 0x11A9D}, {0x11AC0, 0x11AF8}, {0x11C00, 0x11C08}, {0x11C0A, 0x11C2E}, {0x11C40, 0x11C40}, {0x11C72, 0x11C8F},
-{0x11D00, 0x11D06}, {0x11D08, 0x11D09}, {0x11D0B, 0x11D30}, {0x11D46, 0x11D46}, {0x11D60, 0x11D65}, {0x11D67, 0x11D68}, {0x11D6A, 0x11D89}, {0x11D98, 0x11D98}, {0x11EE0, 0x11EF2}, {0x11FB0, 0x11FB0},
-{0x12000, 0x12399}, {0x12480, 0x12543}, {0x13000, 0x1342E}, {0x14400, 0x14646}, {0x16800, 0x16A38}, {0x16A40, 0x16A5E}, {0x16AD0, 0x16AED}, {0x16B00, 0x16B2F}, {0x16B40, 0x16B43}, {0x16B63, 0x16B77},
-{0x16B7D, 0x16B8F}, {0x16E40, 0x16E7F}, {0x16F00, 0x16F4A}, {0x16F50, 0x16F50}, {0x16F93, 0x16F9F}, {0x16FE0, 0x16FE1}, {0x16FE3, 0x16FE3}, {0x17000, 0x187F7}, {0x18800, 0x18CD5}, {0x18D00, 0x18D08},
-{0x1B000, 0x1B11E}, {0x1B150, 0x1B152}, {0x1B164, 0x1B167}, {0x1B170, 0x1B2FB}, {0x1BC00, 0x1BC6A}, {0x1BC70, 0x1BC7C}, {0x1BC80, 0x1BC88}, {0x1BC90, 0x1BC99}, {0x1D400, 0x1D454}, {0x1D456, 0x1D49C},
-{0x1D49E, 0x1D49F}, {0x1D4A2, 0x1D4A2}, {0x1D4A5, 0x1D4A6}, {0x1D4A9, 0x1D4AC}, {0x1D4AE, 0x1D4B9}, {0x1D4BB, 0x1D4BB}, {0x1D4BD, 0x1D4C3}, {0x1D4C5, 0x1D505}, {0x1D507, 0x1D50A}, {0x1D50D, 0x1D514},
-{0x1D516, 0x1D51C}, {0x1D51E, 0x1D539}, {0x1D53B, 0x1D53E}, {0x1D540, 0x1D544}, {0x1D546, 0x1D546}, {0x1D54A, 0x1D550}, {0x1D552, 0x1D6A5}, {0x1D6A8, 0x1D6C0}, {0x1D6C2, 0x1D6DA}, {0x1D6DC, 0x1D6FA},
-{0x1D6FC, 0x1D714}, {0x1D716, 0x1D734}, {0x1D736, 0x1D74E}, {0x1D750, 0x1D76E}, {0x1D770, 0x1D788}, {0x1D78A, 0x1D7A8}, {0x1D7AA, 0x1D7C2}, {0x1D7C4, 0x1D7CB}, {0x1E100, 0x1E12C}, {0x1E137, 0x1E13D},
-{0x1E14E, 0x1E14E}, {0x1E2C0, 0x1E2EB}, {0x1E800, 0x1E8C4}, {0x1E900, 0x1E943}, {0x1E94B, 0x1E94B}, {0x1EE00, 0x1EE03}, {0x1EE05, 0x1EE1F}, {0x1EE21, 0x1EE22}, {0x1EE24, 0x1EE24}, {0x1EE27, 0x1EE27},
-{0x1EE29, 0x1EE32}, {0x1EE34, 0x1EE37}, {0x1EE39, 0x1EE39}, {0x1EE3B, 0x1EE3B}, {0x1EE42, 0x1EE42}, {0x1EE47, 0x1EE47}, {0x1EE49, 0x1EE49}, {0x1EE4B, 0x1EE4B}, {0x1EE4D, 0x1EE4F}, {0x1EE51, 0x1EE52},
-{0x1EE54, 0x1EE54}, {0x1EE57, 0x1EE57}, {0x1EE59, 0x1EE59}, {0x1EE5B, 0x1EE5B}, {0x1EE5D, 0x1EE5D}, {0x1EE5F, 0x1EE5F}, {0x1EE61, 0x1EE62}, {0x1EE64, 0x1EE64}, {0x1EE67, 0x1EE6A}, {0x1EE6C, 0x1EE72},
-{0x1EE74, 0x1EE77}, {0x1EE79, 0x1EE7C}, {0x1EE7E, 0x1EE7E}, {0x1EE80, 0x1EE89}, {0x1EE8B, 0x1EE9B}, {0x1EEA1, 0x1EEA3}, {0x1EEA5, 0x1EEA9}, {0x1EEAB, 0x1EEBB}, {0x20000, 0x2A6DD}, {0x2A700, 0x2B734},
-{0x2B740, 0x2B81D}, {0x2B820, 0x2CEA1}, {0x2CEB0, 0x2EBE0}, {0x2F800, 0x2FA1D}, {0x30000, 0x3134A},
-};
-
-static const std::vector<std::pair<uint32_t, uint32_t>> whitespace_ranges = {
-{0x9, 0xD}, {0x1C, 0x20}, {0x85, 0x85}, {0xA0, 0xA0}, {0x1680, 0x1680}, {0x2000, 0x200A}, {0x2028, 0x2029}, {0x202F, 0x202F}, {0x205F, 0x205F}, {0x3000, 0x3000},
-};
-
-static const std::vector<std::pair<uint32_t, uint32_t>> accent_mark_ranges = {
-{0x300, 0x36F}, {0x483, 0x489}, {0x591, 0x5BD}, {0x5BF, 0x5BF}, {0x5C1, 0x5C2}, {0x5C4, 0x5C5}, {0x5C7, 0x5C7}, {0x610, 0x61A}, {0x64B, 0x65F}, {0x670, 0x670}, {0x6D6, 0x6DC}, {0x6DF, 0x6E4},
-{0x6E7, 0x6E8}, {0x6EA, 0x6ED}, {0x711, 0x711}, {0x730, 0x74A}, {0x7A6, 0x7B0}, {0x7EB, 0x7F3}, {0x7FD, 0x7FD}, {0x816, 0x819}, {0x81B, 0x823}, {0x825, 0x827}, {0x829, 0x82D}, {0x859, 0x85B},
-{0x8D3, 0x8E1}, {0x8E3, 0x903}, {0x93A, 0x93C}, {0x93E, 0x94F}, {0x951, 0x957}, {0x962, 0x963}, {0x981, 0x983}, {0x9BC, 0x9BC}, {0x9BE, 0x9C4}, {0x9C7, 0x9C8}, {0x9CB, 0x9CD}, {0x9D7, 0x9D7},
-{0x9E2, 0x9E3}, {0x9FE, 0x9FE}, {0xA01, 0xA03}, {0xA3C, 0xA3C}, {0xA3E, 0xA42}, {0xA47, 0xA48}, {0xA4B, 0xA4D}, {0xA51, 0xA51}, {0xA70, 0xA71}, {0xA75, 0xA75}, {0xA81, 0xA83}, {0xABC, 0xABC},
-{0xABE, 0xAC5}, {0xAC7, 0xAC9}, {0xACB, 0xACD}, {0xAE2, 0xAE3}, {0xAFA, 0xAFF}, {0xB01, 0xB03}, {0xB3C, 0xB3C}, {0xB3E, 0xB44}, {0xB47, 0xB48}, {0xB4B, 0xB4D}, {0xB55, 0xB57}, {0xB62, 0xB63},
-{0xB82, 0xB82}, {0xBBE, 0xBC2}, {0xBC6, 0xBC8}, {0xBCA, 0xBCD}, {0xBD7, 0xBD7}, {0xC00, 0xC04}, {0xC3E, 0xC44}, {0xC46, 0xC48}, {0xC4A, 0xC4D}, {0xC55, 0xC56}, {0xC62, 0xC63}, {0xC81, 0xC83},
-{0xCBC, 0xCBC}, {0xCBE, 0xCC4}, {0xCC6, 0xCC8}, {0xCCA, 0xCCD}, {0xCD5, 0xCD6}, {0xCE2, 0xCE3}, {0xD00, 0xD03}, {0xD3B, 0xD3C}, {0xD3E, 0xD44}, {0xD46, 0xD48}, {0xD4A, 0xD4D}, {0xD57, 0xD57},
-{0xD62, 0xD63}, {0xD81, 0xD83}, {0xDCA, 0xDCA}, {0xDCF, 0xDD4}, {0xDD6, 0xDD6}, {0xDD8, 0xDDF}, {0xDF2, 0xDF3}, {0xE31, 0xE31}, {0xE34, 0xE3A}, {0xE47, 0xE4E}, {0xEB1, 0xEB1}, {0xEB4, 0xEBC},
-{0xEC8, 0xECD}, {0xF18, 0xF19}, {0xF35, 0xF35}, {0xF37, 0xF37}, {0xF39, 0xF39}, {0xF3E, 0xF3F}, {0xF71, 0xF84}, {0xF86, 0xF87}, {0xF8D, 0xF97}, {0xF99, 0xFBC}, {0xFC6, 0xFC6}, {0x102B, 0x103E},
-{0x1056, 0x1059}, {0x105E, 0x1060}, {0x1062, 0x1064}, {0x1067, 0x106D}, {0x1071, 0x1074}, {0x1082, 0x108D}, {0x108F, 0x108F}, {0x109A, 0x109D}, {0x135D, 0x135F}, {0x1712, 0x1714}, {0x1732, 0x1734},
-{0x1752, 0x1753}, {0x1772, 0x1773}, {0x17B4, 0x17D3}, {0x17DD, 0x17DD}, {0x180B, 0x180D}, {0x1885, 0x1886}, {0x18A9, 0x18A9}, {0x1920, 0x192B}, {0x1930, 0x193B}, {0x1A17, 0x1A1B}, {0x1A55, 0x1A5E},
-{0x1A60, 0x1A7C}, {0x1A7F, 0x1A7F}, {0x1AB0, 0x1AC0}, {0x1B00, 0x1B04}, {0x1B34, 0x1B44}, {0x1B6B, 0x1B73}, {0x1B80, 0x1B82}, {0x1BA1, 0x1BAD}, {0x1BE6, 0x1BF3}, {0x1C24, 0x1C37}, {0x1CD0, 0x1CD2},
-{0x1CD4, 0x1CE8}, {0x1CED, 0x1CED}, {0x1CF4, 0x1CF4}, {0x1CF7, 0x1CF9}, {0x1DC0, 0x1DF9}, {0x1DFB, 0x1DFF}, {0x20D0, 0x20F0}, {0x2CEF, 0x2CF1}, {0x2D7F, 0x2D7F}, {0x2DE0, 0x2DFF}, {0x302A, 0x302F},
-{0x3099, 0x309A}, {0xA66F, 0xA672}, {0xA674, 0xA67D}, {0xA69E, 0xA69F}, {0xA6F0, 0xA6F1}, {0xA802, 0xA802}, {0xA806, 0xA806}, {0xA80B, 0xA80B}, {0xA823, 0xA827}, {0xA82C, 0xA82C}, {0xA880, 0xA881},
-{0xA8B4, 0xA8C5}, {0xA8E0, 0xA8F1}, {0xA8FF, 0xA8FF}, {0xA926, 0xA92D}, {0xA947, 0xA953}, {0xA980, 0xA983}, {0xA9B3, 0xA9C0}, {0xA9E5, 0xA9E5}, {0xAA29, 0xAA36}, {0xAA43, 0xAA43}, {0xAA4C, 0xAA4D},
-{0xAA7B, 0xAA7D}, {0xAAB0, 0xAAB0}, {0xAAB2, 0xAAB4}, {0xAAB7, 0xAAB8}, {0xAABE, 0xAABF}, {0xAAC1, 0xAAC1}, {0xAAEB, 0xAAEF}, {0xAAF5, 0xAAF6}, {0xABE3, 0xABEA}, {0xABEC, 0xABED}, {0xFB1E, 0xFB1E},
-{0xFE00, 0xFE0F}, {0xFE20, 0xFE2F}, {0x101FD, 0x101FD}, {0x102E0, 0x102E0}, {0x10376, 0x1037A}, {0x10A01, 0x10A03}, {0x10A05, 0x10A06}, {0x10A0C, 0x10A0F}, {0x10A38, 0x10A3A}, {0x10A3F, 0x10A3F},
-{0x10AE5, 0x10AE6}, {0x10D24, 0x10D27}, {0x10EAB, 0x10EAC}, {0x10F46, 0x10F50}, {0x11000, 0x11002}, {0x11038, 0x11046}, {0x1107F, 0x11082}, {0x110B0, 0x110BA}, {0x11100, 0x11102}, {0x11127, 0x11134},
-{0x11145, 0x11146}, {0x11173, 0x11173}, {0x11180, 0x11182}, {0x111B3, 0x111C0}, {0x111C9, 0x111CC}, {0x111CE, 0x111CF}, {0x1122C, 0x11237}, {0x1123E, 0x1123E}, {0x112DF, 0x112EA}, {0x11300, 0x11303},
-{0x1133B, 0x1133C}, {0x1133E, 0x11344}, {0x11347, 0x11348}, {0x1134B, 0x1134D}, {0x11357, 0x11357}, {0x11362, 0x11363}, {0x11366, 0x1136C}, {0x11370, 0x11374}, {0x11435, 0x11446}, {0x1145E, 0x1145E},
-{0x114B0, 0x114C3}, {0x115AF, 0x115B5}, {0x115B8, 0x115C0}, {0x115DC, 0x115DD}, {0x11630, 0x11640}, {0x116AB, 0x116B7}, {0x1171D, 0x1172B}, {0x1182C, 0x1183A}, {0x11930, 0x11935}, {0x11937, 0x11938},
-{0x1193B, 0x1193E}, {0x11940, 0x11940}, {0x11942, 0x11943}, {0x119D1, 0x119D7}, {0x119DA, 0x119E0}, {0x119E4, 0x119E4}, {0x11A01, 0x11A0A}, {0x11A33, 0x11A39}, {0x11A3B, 0x11A3E}, {0x11A47, 0x11A47},
-{0x11A51, 0x11A5B}, {0x11A8A, 0x11A99}, {0x11C2F, 0x11C36}, {0x11C38, 0x11C3F}, {0x11C92, 0x11CA7}, {0x11CA9, 0x11CB6}, {0x11D31, 0x11D36}, {0x11D3A, 0x11D3A}, {0x11D3C, 0x11D3D}, {0x11D3F, 0x11D45},
-{0x11D47, 0x11D47}, {0x11D8A, 0x11D8E}, {0x11D90, 0x11D91}, {0x11D93, 0x11D97}, {0x11EF3, 0x11EF6}, {0x16AF0, 0x16AF4}, {0x16B30, 0x16B36}, {0x16F4F, 0x16F4F}, {0x16F51, 0x16F87}, {0x16F8F, 0x16F92},
-{0x16FE4, 0x16FE4}, {0x16FF0, 0x16FF1}, {0x1BC9D, 0x1BC9E}, {0x1D165, 0x1D169}, {0x1D16D, 0x1D172}, {0x1D17B, 0x1D182}, {0x1D185, 0x1D18B}, {0x1D1AA, 0x1D1AD}, {0x1D242, 0x1D244}, {0x1DA00, 0x1DA36},
-{0x1DA3B, 0x1DA6C}, {0x1DA75, 0x1DA75}, {0x1DA84, 0x1DA84}, {0x1DA9B, 0x1DA9F}, {0x1DAA1, 0x1DAAF}, {0x1E000, 0x1E006}, {0x1E008, 0x1E018}, {0x1E01B, 0x1E021}, {0x1E023, 0x1E024}, {0x1E026, 0x1E02A},
-{0x1E130, 0x1E136}, {0x1E2EC, 0x1E2EF}, {0x1E8D0, 0x1E8D6}, {0x1E944, 0x1E94A}, {0xE0100, 0xE01EF},
-};
-
-static const std::vector<std::pair<uint32_t, uint32_t>> punctuation_ranges = {
-{0x21, 0x23}, {0x25, 0x2A}, {0x2C, 0x2F}, {0x3A, 0x3B}, {0x3F, 0x40}, {0x5B, 0x5D}, {0x5F, 0x5F}, {0x7B, 0x7B}, {0x7D, 0x7D}, {0xA1, 0xA1}, {0xA7, 0xA7}, {0xAB, 0xAB}, {0xB6, 0xB7}, {0xBB, 0xBB},
-{0xBF, 0xBF}, {0x37E, 0x37E}, {0x387, 0x387}, {0x55A, 0x55F}, {0x589, 0x58A}, {0x5BE, 0x5BE}, {0x5C0, 0x5C0}, {0x5C3, 0x5C3}, {0x5C6, 0x5C6}, {0x5F3, 0x5F4}, {0x609, 0x60A}, {0x60C, 0x60D},
-{0x61B, 0x61B}, {0x61E, 0x61F}, {0x66A, 0x66D}, {0x6D4, 0x6D4}, {0x700, 0x70D}, {0x7F7, 0x7F9}, {0x830, 0x83E}, {0x85E, 0x85E}, {0x964, 0x965}, {0x970, 0x970}, {0x9FD, 0x9FD}, {0xA76, 0xA76},
-{0xAF0, 0xAF0}, {0xC77, 0xC77}, {0xC84, 0xC84}, {0xDF4, 0xDF4}, {0xE4F, 0xE4F}, {0xE5A, 0xE5B}, {0xF04, 0xF12}, {0xF14, 0xF14}, {0xF3A, 0xF3D}, {0xF85, 0xF85}, {0xFD0, 0xFD4}, {0xFD9, 0xFDA},
-{0x104A, 0x104F}, {0x10FB, 0x10FB}, {0x1360, 0x1368}, {0x1400, 0x1400}, {0x166E, 0x166E}, {0x169B, 0x169C}, {0x16EB, 0x16ED}, {0x1735, 0x1736}, {0x17D4, 0x17D6}, {0x17D8, 0x17DA}, {0x1800, 0x180A},
-{0x1944, 0x1945}, {0x1A1E, 0x1A1F}, {0x1AA0, 0x1AA6}, {0x1AA8, 0x1AAD}, {0x1B5A, 0x1B60}, {0x1BFC, 0x1BFF}, {0x1C3B, 0x1C3F}, {0x1C7E, 0x1C7F}, {0x1CC0, 0x1CC7}, {0x1CD3, 0x1CD3}, {0x2010, 0x2027},
-{0x2030, 0x2043}, {0x2045, 0x2051}, {0x2053, 0x205E}, {0x207D, 0x207E}, {0x208D, 0x208E}, {0x2308, 0x230B}, {0x2329, 0x232A}, {0x2768, 0x2775}, {0x27C5, 0x27C6}, {0x27E6, 0x27EF}, {0x2983, 0x2998},
-{0x29D8, 0x29DB}, {0x29FC, 0x29FD}, {0x2CF9, 0x2CFC}, {0x2CFE, 0x2CFF}, {0x2D70, 0x2D70}, {0x2E00, 0x2E2E}, {0x2E30, 0x2E4F}, {0x2E52, 0x2E52}, {0x3001, 0x3003}, {0x3008, 0x3011}, {0x3014, 0x301F},
-{0x3030, 0x3030}, {0x303D, 0x303D}, {0x30A0, 0x30A0}, {0x30FB, 0x30FB}, {0xA4FE, 0xA4FF}, {0xA60D, 0xA60F}, {0xA673, 0xA673}, {0xA67E, 0xA67E}, {0xA6F2, 0xA6F7}, {0xA874, 0xA877}, {0xA8CE, 0xA8CF},
-{0xA8F8, 0xA8FA}, {0xA8FC, 0xA8FC}, {0xA92E, 0xA92F}, {0xA95F, 0xA95F}, {0xA9C1, 0xA9CD}, {0xA9DE, 0xA9DF}, {0xAA5C, 0xAA5F}, {0xAADE, 0xAADF}, {0xAAF0, 0xAAF1}, {0xABEB, 0xABEB}, {0xFD3E, 0xFD3F},
-{0xFE10, 0xFE19}, {0xFE30, 0xFE52}, {0xFE54, 0xFE61}, {0xFE63, 0xFE63}, {0xFE68, 0xFE68}, {0xFE6A, 0xFE6B}, {0xFF01, 0xFF03}, {0xFF05, 0xFF0A}, {0xFF0C, 0xFF0F}, {0xFF1A, 0xFF1B}, {0xFF1F, 0xFF20},
-{0xFF3B, 0xFF3D}, {0xFF3F, 0xFF3F}, {0xFF5B, 0xFF5B}, {0xFF5D, 0xFF5D}, {0xFF5F, 0xFF65}, {0x10100, 0x10102}, {0x1039F, 0x1039F}, {0x103D0, 0x103D0}, {0x1056F, 0x1056F}, {0x10857, 0x10857},
-{0x1091F, 0x1091F}, {0x1093F, 0x1093F}, {0x10A50, 0x10A58}, {0x10A7F, 0x10A7F}, {0x10AF0, 0x10AF6}, {0x10B39, 0x10B3F}, {0x10B99, 0x10B9C}, {0x10EAD, 0x10EAD}, {0x10F55, 0x10F59}, {0x11047, 0x1104D},
-{0x110BB, 0x110BC}, {0x110BE, 0x110C1}, {0x11140, 0x11143}, {0x11174, 0x11175}, {0x111C5, 0x111C8}, {0x111CD, 0x111CD}, {0x111DB, 0x111DB}, {0x111DD, 0x111DF}, {0x11238, 0x1123D}, {0x112A9, 0x112A9},
-{0x1144B, 0x1144F}, {0x1145A, 0x1145B}, {0x1145D, 0x1145D}, {0x114C6, 0x114C6}, {0x115C1, 0x115D7}, {0x11641, 0x11643}, {0x11660, 0x1166C}, {0x1173C, 0x1173E}, {0x1183B, 0x1183B}, {0x11944, 0x11946},
-{0x119E2, 0x119E2}, {0x11A3F, 0x11A46}, {0x11A9A, 0x11A9C}, {0x11A9E, 0x11AA2}, {0x11C41, 0x11C45}, {0x11C70, 0x11C71}, {0x11EF7, 0x11EF8}, {0x11FFF, 0x11FFF}, {0x12470, 0x12474}, {0x16A6E, 0x16A6F},
-{0x16AF5, 0x16AF5}, {0x16B37, 0x16B3B}, {0x16B44, 0x16B44}, {0x16E97, 0x16E9A}, {0x16FE2, 0x16FE2}, {0x1BC9F, 0x1BC9F}, {0x1DA87, 0x1DA8B}, {0x1E95E, 0x1E95F},
-};
-
-static const std::vector<std::pair<uint32_t, uint32_t>> symbol_ranges = {
-{0x24, 0x24}, {0x2B, 0x2B}, {0x3C, 0x3E}, {0x5E, 0x5E}, {0x60, 0x60}, {0x7C, 0x7C}, {0x7E, 0x7E}, {0xA2, 0xA6}, {0xA8, 0xA9}, {0xAC, 0xAC}, {0xAE, 0xB1}, {0xB4, 0xB4}, {0xB8, 0xB8}, {0xD7, 0xD7},
-{0xF7, 0xF7}, {0x2C2, 0x2C5}, {0x2D2, 0x2DF}, {0x2E5, 0x2EB}, {0x2ED, 0x2ED}, {0x2EF, 0x2FF}, {0x375, 0x375}, {0x384, 0x385}, {0x3F6, 0x3F6}, {0x482, 0x482}, {0x58D, 0x58F}, {0x606, 0x608},
-{0x60B, 0x60B}, {0x60E, 0x60F}, {0x6DE, 0x6DE}, {0x6E9, 0x6E9}, {0x6FD, 0x6FE}, {0x7F6, 0x7F6}, {0x7FE, 0x7FF}, {0x9F2, 0x9F3}, {0x9FA, 0x9FB}, {0xAF1, 0xAF1}, {0xB70, 0xB70}, {0xBF3, 0xBFA},
-{0xC7F, 0xC7F}, {0xD4F, 0xD4F}, {0xD79, 0xD79}, {0xE3F, 0xE3F}, {0xF01, 0xF03}, {0xF13, 0xF13}, {0xF15, 0xF17}, {0xF1A, 0xF1F}, {0xF34, 0xF34}, {0xF36, 0xF36}, {0xF38, 0xF38}, {0xFBE, 0xFC5},
-{0xFC7, 0xFCC}, {0xFCE, 0xFCF}, {0xFD5, 0xFD8}, {0x109E, 0x109F}, {0x1390, 0x1399}, {0x166D, 0x166D}, {0x17DB, 0x17DB}, {0x1940, 0x1940}, {0x19DE, 0x19FF}, {0x1B61, 0x1B6A}, {0x1B74, 0x1B7C},
-{0x1FBD, 0x1FBD}, {0x1FBF, 0x1FC1}, {0x1FCD, 0x1FCF}, {0x1FDD, 0x1FDF}, {0x1FED, 0x1FEF}, {0x1FFD, 0x1FFE}, {0x2044, 0x2044}, {0x2052, 0x2052}, {0x207A, 0x207C}, {0x208A, 0x208C}, {0x20A0, 0x20BF},
-{0x2100, 0x2101}, {0x2103, 0x2106}, {0x2108, 0x2109}, {0x2114, 0x2114}, {0x2116, 0x2118}, {0x211E, 0x2123}, {0x2125, 0x2125}, {0x2127, 0x2127}, {0x2129, 0x2129}, {0x212E, 0x212E}, {0x213A, 0x213B},
-{0x2140, 0x2144}, {0x214A, 0x214D}, {0x214F, 0x214F}, {0x218A, 0x218B}, {0x2190, 0x2307}, {0x230C, 0x2328}, {0x232B, 0x2426}, {0x2440, 0x244A}, {0x249C, 0x24E9}, {0x2500, 0x2767}, {0x2794, 0x27C4},
-{0x27C7, 0x27E5}, {0x27F0, 0x2982}, {0x2999, 0x29D7}, {0x29DC, 0x29FB}, {0x29FE, 0x2B73}, {0x2B76, 0x2B95}, {0x2B97, 0x2BFF}, {0x2CE5, 0x2CEA}, {0x2E50, 0x2E51}, {0x2E80, 0x2E99}, {0x2E9B, 0x2EF3},
-{0x2F00, 0x2FD5}, {0x2FF0, 0x2FFB}, {0x3004, 0x3004}, {0x3012, 0x3013}, {0x3020, 0x3020}, {0x3036, 0x3037}, {0x303E, 0x303F}, {0x309B, 0x309C}, {0x3190, 0x3191}, {0x3196, 0x319F}, {0x31C0, 0x31E3},
-{0x3200, 0x321E}, {0x322A, 0x3247}, {0x3250, 0x3250}, {0x3260, 0x327F}, {0x328A, 0x32B0}, {0x32C0, 0x33FF}, {0x4DC0, 0x4DFF}, {0xA490, 0xA4C6}, {0xA700, 0xA716}, {0xA720, 0xA721}, {0xA789, 0xA78A},
-{0xA828, 0xA82B}, {0xA836, 0xA839}, {0xAA77, 0xAA79}, {0xAB5B, 0xAB5B}, {0xAB6A, 0xAB6B}, {0xFB29, 0xFB29}, {0xFBB2, 0xFBC1}, {0xFDFC, 0xFDFD}, {0xFE62, 0xFE62}, {0xFE64, 0xFE66}, {0xFE69, 0xFE69},
-{0xFF04, 0xFF04}, {0xFF0B, 0xFF0B}, {0xFF1C, 0xFF1E}, {0xFF3E, 0xFF3E}, {0xFF40, 0xFF40}, {0xFF5C, 0xFF5C}, {0xFF5E, 0xFF5E}, {0xFFE0, 0xFFE6}, {0xFFE8, 0xFFEE}, {0xFFFC, 0xFFFD}, {0x10137, 0x1013F},
-{0x10179, 0x10189}, {0x1018C, 0x1018E}, {0x10190, 0x1019C}, {0x101A0, 0x101A0}, {0x101D0, 0x101FC}, {0x10877, 0x10878}, {0x10AC8, 0x10AC8}, {0x1173F, 0x1173F}, {0x11FD5, 0x11FF1}, {0x16B3C, 0x16B3F},
-{0x16B45, 0x16B45}, {0x1BC9C, 0x1BC9C}, {0x1D000, 0x1D0F5}, {0x1D100, 0x1D126}, {0x1D129, 0x1D164}, {0x1D16A, 0x1D16C}, {0x1D183, 0x1D184}, {0x1D18C, 0x1D1A9}, {0x1D1AE, 0x1D1E8}, {0x1D200, 0x1D241},
-{0x1D245, 0x1D245}, {0x1D300, 0x1D356}, {0x1D6C1, 0x1D6C1}, {0x1D6DB, 0x1D6DB}, {0x1D6FB, 0x1D6FB}, {0x1D715, 0x1D715}, {0x1D735, 0x1D735}, {0x1D74F, 0x1D74F}, {0x1D76F, 0x1D76F}, {0x1D789, 0x1D789},
-{0x1D7A9, 0x1D7A9}, {0x1D7C3, 0x1D7C3}, {0x1D800, 0x1D9FF}, {0x1DA37, 0x1DA3A}, {0x1DA6D, 0x1DA74}, {0x1DA76, 0x1DA83}, {0x1DA85, 0x1DA86}, {0x1E14F, 0x1E14F}, {0x1E2FF, 0x1E2FF}, {0x1ECAC, 0x1ECAC},
-{0x1ECB0, 0x1ECB0}, {0x1ED2E, 0x1ED2E}, {0x1EEF0, 0x1EEF1}, {0x1F000, 0x1F02B}, {0x1F030, 0x1F093}, {0x1F0A0, 0x1F0AE}, {0x1F0B1, 0x1F0BF}, {0x1F0C1, 0x1F0CF}, {0x1F0D1, 0x1F0F5}, {0x1F10D, 0x1F1AD},
-{0x1F1E6, 0x1F202}, {0x1F210, 0x1F23B}, {0x1F240, 0x1F248}, {0x1F250, 0x1F251}, {0x1F260, 0x1F265}, {0x1F300, 0x1F6D7}, {0x1F6E0, 0x1F6EC}, {0x1F6F0, 0x1F6FC}, {0x1F700, 0x1F773}, {0x1F780, 0x1F7D8},
-{0x1F7E0, 0x1F7EB}, {0x1F800, 0x1F80B}, {0x1F810, 0x1F847}, {0x1F850, 0x1F859}, {0x1F860, 0x1F887}, {0x1F890, 0x1F8AD}, {0x1F8B0, 0x1F8B1}, {0x1F900, 0x1F978}, {0x1F97A, 0x1F9CB}, {0x1F9CD, 0x1FA53},
-{0x1FA60, 0x1FA6D}, {0x1FA70, 0x1FA74}, {0x1FA78, 0x1FA7A}, {0x1FA80, 0x1FA86}, {0x1FA90, 0x1FAA8}, {0x1FAB0, 0x1FAB6}, {0x1FAC0, 0x1FAC2}, {0x1FAD0, 0x1FAD6}, {0x1FB00, 0x1FB92}, {0x1FB94, 0x1FBCA},
-};
-
-static const std::vector<std::pair<uint32_t, uint32_t>> control_ranges = {
-{0x0, 0x8}, {0xE, 0x1B}, {0x7F, 0x84}, {0x86, 0x9F}, {0xAD, 0xAD}, {0x378, 0x379}, {0x380, 0x383}, {0x38B, 0x38B}, {0x38D, 0x38D}, {0x3A2, 0x3A2}, {0x530, 0x530}, {0x557, 0x558}, {0x58B, 0x58C},
-{0x590, 0x590}, {0x5C8, 0x5CF}, {0x5EB, 0x5EE}, {0x5F5, 0x605}, {0x61C, 0x61D}, {0x6DD, 0x6DD}, {0x70E, 0x70F}, {0x74B, 0x74C}, {0x7B2, 0x7BF}, {0x7FB, 0x7FC}, {0x82E, 0x82F}, {0x83F, 0x83F},
-{0x85C, 0x85D}, {0x85F, 0x85F}, {0x86B, 0x89F}, {0x8B5, 0x8B5}, {0x8C8, 0x8D2}, {0x8E2, 0x8E2}, {0x984, 0x984}, {0x98D, 0x98E}, {0x991, 0x992}, {0x9A9, 0x9A9}, {0x9B1, 0x9B1}, {0x9B3, 0x9B5},
-{0x9BA, 0x9BB}, {0x9C5, 0x9C6}, {0x9C9, 0x9CA}, {0x9CF, 0x9D6}, {0x9D8, 0x9DB}, {0x9DE, 0x9DE}, {0x9E4, 0x9E5}, {0x9FF, 0xA00}, {0xA04, 0xA04}, {0xA0B, 0xA0E}, {0xA11, 0xA12}, {0xA29, 0xA29},
-{0xA31, 0xA31}, {0xA34, 0xA34}, {0xA37, 0xA37}, {0xA3A, 0xA3B}, {0xA3D, 0xA3D}, {0xA43, 0xA46}, {0xA49, 0xA4A}, {0xA4E, 0xA50}, {0xA52, 0xA58}, {0xA5D, 0xA5D}, {0xA5F, 0xA65}, {0xA77, 0xA80},
-{0xA84, 0xA84}, {0xA8E, 0xA8E}, {0xA92, 0xA92}, {0xAA9, 0xAA9}, {0xAB1, 0xAB1}, {0xAB4, 0xAB4}, {0xABA, 0xABB}, {0xAC6, 0xAC6}, {0xACA, 0xACA}, {0xACE, 0xACF}, {0xAD1, 0xADF}, {0xAE4, 0xAE5},
-{0xAF2, 0xAF8}, {0xB00, 0xB00}, {0xB04, 0xB04}, {0xB0D, 0xB0E}, {0xB11, 0xB12}, {0xB29, 0xB29}, {0xB31, 0xB31}, {0xB34, 0xB34}, {0xB3A, 0xB3B}, {0xB45, 0xB46}, {0xB49, 0xB4A}, {0xB4E, 0xB54},
-{0xB58, 0xB5B}, {0xB5E, 0xB5E}, {0xB64, 0xB65}, {0xB78, 0xB81}, {0xB84, 0xB84}, {0xB8B, 0xB8D}, {0xB91, 0xB91}, {0xB96, 0xB98}, {0xB9B, 0xB9B}, {0xB9D, 0xB9D}, {0xBA0, 0xBA2}, {0xBA5, 0xBA7},
-{0xBAB, 0xBAD}, {0xBBA, 0xBBD}, {0xBC3, 0xBC5}, {0xBC9, 0xBC9}, {0xBCE, 0xBCF}, {0xBD1, 0xBD6}, {0xBD8, 0xBE5}, {0xBFB, 0xBFF}, {0xC0D, 0xC0D}, {0xC11, 0xC11}, {0xC29, 0xC29}, {0xC3A, 0xC3C},
-{0xC45, 0xC45}, {0xC49, 0xC49}, {0xC4E, 0xC54}, {0xC57, 0xC57}, {0xC5B, 0xC5F}, {0xC64, 0xC65}, {0xC70, 0xC76}, {0xC8D, 0xC8D}, {0xC91, 0xC91}, {0xCA9, 0xCA9}, {0xCB4, 0xCB4}, {0xCBA, 0xCBB},
-{0xCC5, 0xCC5}, {0xCC9, 0xCC9}, {0xCCE, 0xCD4}, {0xCD7, 0xCDD}, {0xCDF, 0xCDF}, {0xCE4, 0xCE5}, {0xCF0, 0xCF0}, {0xCF3, 0xCFF}, {0xD0D, 0xD0D}, {0xD11, 0xD11}, {0xD45, 0xD45}, {0xD49, 0xD49},
-{0xD50, 0xD53}, {0xD64, 0xD65}, {0xD80, 0xD80}, {0xD84, 0xD84}, {0xD97, 0xD99}, {0xDB2, 0xDB2}, {0xDBC, 0xDBC}, {0xDBE, 0xDBF}, {0xDC7, 0xDC9}, {0xDCB, 0xDCE}, {0xDD5, 0xDD5}, {0xDD7, 0xDD7},
-{0xDE0, 0xDE5}, {0xDF0, 0xDF1}, {0xDF5, 0xE00}, {0xE3B, 0xE3E}, {0xE5C, 0xE80}, {0xE83, 0xE83}, {0xE85, 0xE85}, {0xE8B, 0xE8B}, {0xEA4, 0xEA4}, {0xEA6, 0xEA6}, {0xEBE, 0xEBF}, {0xEC5, 0xEC5},
-{0xEC7, 0xEC7}, {0xECE, 0xECF}, {0xEDA, 0xEDB}, {0xEE0, 0xEFF}, {0xF48, 0xF48}, {0xF6D, 0xF70}, {0xF98, 0xF98}, {0xFBD, 0xFBD}, {0xFCD, 0xFCD}, {0xFDB, 0xFFF}, {0x10C6, 0x10C6}, {0x10C8, 0x10CC},
-{0x10CE, 0x10CF}, {0x1249, 0x1249}, {0x124E, 0x124F}, {0x1257, 0x1257}, {0x1259, 0x1259}, {0x125E, 0x125F}, {0x1289, 0x1289}, {0x128E, 0x128F}, {0x12B1, 0x12B1}, {0x12B6, 0x12B7}, {0x12BF, 0x12BF},
-{0x12C1, 0x12C1}, {0x12C6, 0x12C7}, {0x12D7, 0x12D7}, {0x1311, 0x1311}, {0x1316, 0x1317}, {0x135B, 0x135C}, {0x137D, 0x137F}, {0x139A, 0x139F}, {0x13F6, 0x13F7}, {0x13FE, 0x13FF}, {0x169D, 0x169F},
-{0x16F9, 0x16FF}, {0x170D, 0x170D}, {0x1715, 0x171F}, {0x1737, 0x173F}, {0x1754, 0x175F}, {0x176D, 0x176D}, {0x1771, 0x1771}, {0x1774, 0x177F}, {0x17DE, 0x17DF}, {0x17EA, 0x17EF}, {0x17FA, 0x17FF},
-{0x180E, 0x180F}, {0x181A, 0x181F}, {0x1879, 0x187F}, {0x18AB, 0x18AF}, {0x18F6, 0x18FF}, {0x191F, 0x191F}, {0x192C, 0x192F}, {0x193C, 0x193F}, {0x1941, 0x1943}, {0x196E, 0x196F}, {0x1975, 0x197F},
-{0x19AC, 0x19AF}, {0x19CA, 0x19CF}, {0x19DB, 0x19DD}, {0x1A1C, 0x1A1D}, {0x1A5F, 0x1A5F}, {0x1A7D, 0x1A7E}, {0x1A8A, 0x1A8F}, {0x1A9A, 0x1A9F}, {0x1AAE, 0x1AAF}, {0x1AC1, 0x1AFF}, {0x1B4C, 0x1B4F},
-{0x1B7D, 0x1B7F}, {0x1BF4, 0x1BFB}, {0x1C38, 0x1C3A}, {0x1C4A, 0x1C4C}, {0x1C89, 0x1C8F}, {0x1CBB, 0x1CBC}, {0x1CC8, 0x1CCF}, {0x1CFB, 0x1CFF}, {0x1DFA, 0x1DFA}, {0x1F16, 0x1F17}, {0x1F1E, 0x1F1F},
-{0x1F46, 0x1F47}, {0x1F4E, 0x1F4F}, {0x1F58, 0x1F58}, {0x1F5A, 0x1F5A}, {0x1F5C, 0x1F5C}, {0x1F5E, 0x1F5E}, {0x1F7E, 0x1F7F}, {0x1FB5, 0x1FB5}, {0x1FC5, 0x1FC5}, {0x1FD4, 0x1FD5}, {0x1FDC, 0x1FDC},
-{0x1FF0, 0x1FF1}, {0x1FF5, 0x1FF5}, {0x1FFF, 0x1FFF}, {0x200B, 0x200F}, {0x202A, 0x202E}, {0x2060, 0x206F}, {0x2072, 0x2073}, {0x208F, 0x208F}, {0x209D, 0x209F}, {0x20C0, 0x20CF}, {0x20F1, 0x20FF},
-{0x218C, 0x218F}, {0x2427, 0x243F}, {0x244B, 0x245F}, {0x2B74, 0x2B75}, {0x2B96, 0x2B96}, {0x2C2F, 0x2C2F}, {0x2C5F, 0x2C5F}, {0x2CF4, 0x2CF8}, {0x2D26, 0x2D26}, {0x2D28, 0x2D2C}, {0x2D2E, 0x2D2F},
-{0x2D68, 0x2D6E}, {0x2D71, 0x2D7E}, {0x2D97, 0x2D9F}, {0x2DA7, 0x2DA7}, {0x2DAF, 0x2DAF}, {0x2DB7, 0x2DB7}, {0x2DBF, 0x2DBF}, {0x2DC7, 0x2DC7}, {0x2DCF, 0x2DCF}, {0x2DD7, 0x2DD7}, {0x2DDF, 0x2DDF},
-{0x2E53, 0x2E7F}, {0x2E9A, 0x2E9A}, {0x2EF4, 0x2EFF}, {0x2FD6, 0x2FEF}, {0x2FFC, 0x2FFF}, {0x3040, 0x3040}, {0x3097, 0x3098}, {0x3100, 0x3104}, {0x3130, 0x3130}, {0x318F, 0x318F}, {0x31E4, 0x31EF},
-{0x321F, 0x321F}, {0x9FFD, 0x9FFF}, {0xA48D, 0xA48F}, {0xA4C7, 0xA4CF}, {0xA62C, 0xA63F}, {0xA6F8, 0xA6FF}, {0xA7C0, 0xA7C1}, {0xA7CB, 0xA7F4}, {0xA82D, 0xA82F}, {0xA83A, 0xA83F}, {0xA878, 0xA87F},
-{0xA8C6, 0xA8CD}, {0xA8DA, 0xA8DF}, {0xA954, 0xA95E}, {0xA97D, 0xA97F}, {0xA9CE, 0xA9CE}, {0xA9DA, 0xA9DD}, {0xA9FF, 0xA9FF}, {0xAA37, 0xAA3F}, {0xAA4E, 0xAA4F}, {0xAA5A, 0xAA5B}, {0xAAC3, 0xAADA},
-{0xAAF7, 0xAB00}, {0xAB07, 0xAB08}, {0xAB0F, 0xAB10}, {0xAB17, 0xAB1F}, {0xAB27, 0xAB27}, {0xAB2F, 0xAB2F}, {0xAB6C, 0xAB6F}, {0xABEE, 0xABEF}, {0xABFA, 0xABFF}, {0xD7A4, 0xD7AF}, {0xD7C7, 0xD7CA},
-{0xD7FC, 0xF8FF}, {0xFA6E, 0xFA6F}, {0xFADA, 0xFAFF}, {0xFB07, 0xFB12}, {0xFB18, 0xFB1C}, {0xFB37, 0xFB37}, {0xFB3D, 0xFB3D}, {0xFB3F, 0xFB3F}, {0xFB42, 0xFB42}, {0xFB45, 0xFB45}, {0xFBC2, 0xFBD2},
-{0xFD40, 0xFD4F}, {0xFD90, 0xFD91}, {0xFDC8, 0xFDEF}, {0xFDFE, 0xFDFF}, {0xFE1A, 0xFE1F}, {0xFE53, 0xFE53}, {0xFE67, 0xFE67}, {0xFE6C, 0xFE6F}, {0xFE75, 0xFE75}, {0xFEFD, 0xFF00}, {0xFFBF, 0xFFC1},
-{0xFFC8, 0xFFC9}, {0xFFD0, 0xFFD1}, {0xFFD8, 0xFFD9}, {0xFFDD, 0xFFDF}, {0xFFE7, 0xFFE7}, {0xFFEF, 0xFFFB}, {0xFFFE, 0xFFFF}, {0x1000C, 0x1000C}, {0x10027, 0x10027}, {0x1003B, 0x1003B},
-{0x1003E, 0x1003E}, {0x1004E, 0x1004F}, {0x1005E, 0x1007F}, {0x100FB, 0x100FF}, {0x10103, 0x10106}, {0x10134, 0x10136}, {0x1018F, 0x1018F}, {0x1019D, 0x1019F}, {0x101A1, 0x101CF}, {0x101FE, 0x1027F},
-{0x1029D, 0x1029F}, {0x102D1, 0x102DF}, {0x102FC, 0x102FF}, {0x10324, 0x1032C}, {0x1034B, 0x1034F}, {0x1037B, 0x1037F}, {0x1039E, 0x1039E}, {0x103C4, 0x103C7}, {0x103D6, 0x103FF}, {0x1049E, 0x1049F},
-{0x104AA, 0x104AF}, {0x104D4, 0x104D7}, {0x104FC, 0x104FF}, {0x10528, 0x1052F}, {0x10564, 0x1056E}, {0x10570, 0x105FF}, {0x10737, 0x1073F}, {0x10756, 0x1075F}, {0x10768, 0x107FF}, {0x10806, 0x10807},
-{0x10809, 0x10809}, {0x10836, 0x10836}, {0x10839, 0x1083B}, {0x1083D, 0x1083E}, {0x10856, 0x10856}, {0x1089F, 0x108A6}, {0x108B0, 0x108DF}, {0x108F3, 0x108F3}, {0x108F6, 0x108FA}, {0x1091C, 0x1091E},
-{0x1093A, 0x1093E}, {0x10940, 0x1097F}, {0x109B8, 0x109BB}, {0x109D0, 0x109D1}, {0x10A04, 0x10A04}, {0x10A07, 0x10A0B}, {0x10A14, 0x10A14}, {0x10A18, 0x10A18}, {0x10A36, 0x10A37}, {0x10A3B, 0x10A3E},
-{0x10A49, 0x10A4F}, {0x10A59, 0x10A5F}, {0x10AA0, 0x10ABF}, {0x10AE7, 0x10AEA}, {0x10AF7, 0x10AFF}, {0x10B36, 0x10B38}, {0x10B56, 0x10B57}, {0x10B73, 0x10B77}, {0x10B92, 0x10B98}, {0x10B9D, 0x10BA8},
-{0x10BB0, 0x10BFF}, {0x10C49, 0x10C7F}, {0x10CB3, 0x10CBF}, {0x10CF3, 0x10CF9}, {0x10D28, 0x10D2F}, {0x10D3A, 0x10E5F}, {0x10E7F, 0x10E7F}, {0x10EAA, 0x10EAA}, {0x10EAE, 0x10EAF}, {0x10EB2, 0x10EFF},
-{0x10F28, 0x10F2F}, {0x10F5A, 0x10FAF}, {0x10FCC, 0x10FDF}, {0x10FF7, 0x10FFF}, {0x1104E, 0x11051}, {0x11070, 0x1107E}, {0x110BD, 0x110BD}, {0x110C2, 0x110CF}, {0x110E9, 0x110EF}, {0x110FA, 0x110FF},
-{0x11135, 0x11135}, {0x11148, 0x1114F}, {0x11177, 0x1117F}, {0x111E0, 0x111E0}, {0x111F5, 0x111FF}, {0x11212, 0x11212}, {0x1123F, 0x1127F}, {0x11287, 0x11287}, {0x11289, 0x11289}, {0x1128E, 0x1128E},
-{0x1129E, 0x1129E}, {0x112AA, 0x112AF}, {0x112EB, 0x112EF}, {0x112FA, 0x112FF}, {0x11304, 0x11304}, {0x1130D, 0x1130E}, {0x11311, 0x11312}, {0x11329, 0x11329}, {0x11331, 0x11331}, {0x11334, 0x11334},
-{0x1133A, 0x1133A}, {0x11345, 0x11346}, {0x11349, 0x1134A}, {0x1134E, 0x1134F}, {0x11351, 0x11356}, {0x11358, 0x1135C}, {0x11364, 0x11365}, {0x1136D, 0x1136F}, {0x11375, 0x113FF}, {0x1145C, 0x1145C},
-{0x11462, 0x1147F}, {0x114C8, 0x114CF}, {0x114DA, 0x1157F}, {0x115B6, 0x115B7}, {0x115DE, 0x115FF}, {0x11645, 0x1164F}, {0x1165A, 0x1165F}, {0x1166D, 0x1167F}, {0x116B9, 0x116BF}, {0x116CA, 0x116FF},
-{0x1171B, 0x1171C}, {0x1172C, 0x1172F}, {0x11740, 0x117FF}, {0x1183C, 0x1189F}, {0x118F3, 0x118FE}, {0x11907, 0x11908}, {0x1190A, 0x1190B}, {0x11914, 0x11914}, {0x11917, 0x11917}, {0x11936, 0x11936},
-{0x11939, 0x1193A}, {0x11947, 0x1194F}, {0x1195A, 0x1199F}, {0x119A8, 0x119A9}, {0x119D8, 0x119D9}, {0x119E5, 0x119FF}, {0x11A48, 0x11A4F}, {0x11AA3, 0x11ABF}, {0x11AF9, 0x11BFF}, {0x11C09, 0x11C09},
-{0x11C37, 0x11C37}, {0x11C46, 0x11C4F}, {0x11C6D, 0x11C6F}, {0x11C90, 0x11C91}, {0x11CA8, 0x11CA8}, {0x11CB7, 0x11CFF}, {0x11D07, 0x11D07}, {0x11D0A, 0x11D0A}, {0x11D37, 0x11D39}, {0x11D3B, 0x11D3B},
-{0x11D3E, 0x11D3E}, {0x11D48, 0x11D4F}, {0x11D5A, 0x11D5F}, {0x11D66, 0x11D66}, {0x11D69, 0x11D69}, {0x11D8F, 0x11D8F}, {0x11D92, 0x11D92}, {0x11D99, 0x11D9F}, {0x11DAA, 0x11EDF}, {0x11EF9, 0x11FAF},
-{0x11FB1, 0x11FBF}, {0x11FF2, 0x11FFE}, {0x1239A, 0x123FF}, {0x1246F, 0x1246F}, {0x12475, 0x1247F}, {0x12544, 0x12FFF}, {0x1342F, 0x143FF}, {0x14647, 0x167FF}, {0x16A39, 0x16A3F}, {0x16A5F, 0x16A5F},
-{0x16A6A, 0x16A6D}, {0x16A70, 0x16ACF}, {0x16AEE, 0x16AEF}, {0x16AF6, 0x16AFF}, {0x16B46, 0x16B4F}, {0x16B5A, 0x16B5A}, {0x16B62, 0x16B62}, {0x16B78, 0x16B7C}, {0x16B90, 0x16E3F}, {0x16E9B, 0x16EFF},
-{0x16F4B, 0x16F4E}, {0x16F88, 0x16F8E}, {0x16FA0, 0x16FDF}, {0x16FE5, 0x16FEF}, {0x16FF2, 0x16FFF}, {0x187F8, 0x187FF}, {0x18CD6, 0x18CFF}, {0x18D09, 0x1AFFF}, {0x1B11F, 0x1B14F}, {0x1B153, 0x1B163},
-{0x1B168, 0x1B16F}, {0x1B2FC, 0x1BBFF}, {0x1BC6B, 0x1BC6F}, {0x1BC7D, 0x1BC7F}, {0x1BC89, 0x1BC8F}, {0x1BC9A, 0x1BC9B}, {0x1BCA0, 0x1CFFF}, {0x1D0F6, 0x1D0FF}, {0x1D127, 0x1D128}, {0x1D173, 0x1D17A},
-{0x1D1E9, 0x1D1FF}, {0x1D246, 0x1D2DF}, {0x1D2F4, 0x1D2FF}, {0x1D357, 0x1D35F}, {0x1D379, 0x1D3FF}, {0x1D455, 0x1D455}, {0x1D49D, 0x1D49D}, {0x1D4A0, 0x1D4A1}, {0x1D4A3, 0x1D4A4}, {0x1D4A7, 0x1D4A8},
-{0x1D4AD, 0x1D4AD}, {0x1D4BA, 0x1D4BA}, {0x1D4BC, 0x1D4BC}, {0x1D4C4, 0x1D4C4}, {0x1D506, 0x1D506}, {0x1D50B, 0x1D50C}, {0x1D515, 0x1D515}, {0x1D51D, 0x1D51D}, {0x1D53A, 0x1D53A}, {0x1D53F, 0x1D53F},
-{0x1D545, 0x1D545}, {0x1D547, 0x1D549}, {0x1D551, 0x1D551}, {0x1D6A6, 0x1D6A7}, {0x1D7CC, 0x1D7CD}, {0x1DA8C, 0x1DA9A}, {0x1DAA0, 0x1DAA0}, {0x1DAB0, 0x1DFFF}, {0x1E007, 0x1E007}, {0x1E019, 0x1E01A},
-{0x1E022, 0x1E022}, {0x1E025, 0x1E025}, {0x1E02B, 0x1E0FF}, {0x1E12D, 0x1E12F}, {0x1E13E, 0x1E13F}, {0x1E14A, 0x1E14D}, {0x1E150, 0x1E2BF}, {0x1E2FA, 0x1E2FE}, {0x1E300, 0x1E7FF}, {0x1E8C5, 0x1E8C6},
-{0x1E8D7, 0x1E8FF}, {0x1E94C, 0x1E94F}, {0x1E95A, 0x1E95D}, {0x1E960, 0x1EC70}, {0x1ECB5, 0x1ED00}, {0x1ED3E, 0x1EDFF}, {0x1EE04, 0x1EE04}, {0x1EE20, 0x1EE20}, {0x1EE23, 0x1EE23}, {0x1EE25, 0x1EE26},
-{0x1EE28, 0x1EE28}, {0x1EE33, 0x1EE33}, {0x1EE38, 0x1EE38}, {0x1EE3A, 0x1EE3A}, {0x1EE3C, 0x1EE41}, {0x1EE43, 0x1EE46}, {0x1EE48, 0x1EE48}, {0x1EE4A, 0x1EE4A}, {0x1EE4C, 0x1EE4C}, {0x1EE50, 0x1EE50},
-{0x1EE53, 0x1EE53}, {0x1EE55, 0x1EE56}, {0x1EE58, 0x1EE58}, {0x1EE5A, 0x1EE5A}, {0x1EE5C, 0x1EE5C}, {0x1EE5E, 0x1EE5E}, {0x1EE60, 0x1EE60}, {0x1EE63, 0x1EE63}, {0x1EE65, 0x1EE66}, {0x1EE6B, 0x1EE6B},
-{0x1EE73, 0x1EE73}, {0x1EE78, 0x1EE78}, {0x1EE7D, 0x1EE7D}, {0x1EE7F, 0x1EE7F}, {0x1EE8A, 0x1EE8A}, {0x1EE9C, 0x1EEA0}, {0x1EEA4, 0x1EEA4}, {0x1EEAA, 0x1EEAA}, {0x1EEBC, 0x1EEEF}, {0x1EEF2, 0x1EFFF},
-{0x1F02C, 0x1F02F}, {0x1F094, 0x1F09F}, {0x1F0AF, 0x1F0B0}, {0x1F0C0, 0x1F0C0}, {0x1F0D0, 0x1F0D0}, {0x1F0F6, 0x1F0FF}, {0x1F1AE, 0x1F1E5}, {0x1F203, 0x1F20F}, {0x1F23C, 0x1F23F}, {0x1F249, 0x1F24F},
-{0x1F252, 0x1F25F}, {0x1F266, 0x1F2FF}, {0x1F6D8, 0x1F6DF}, {0x1F6ED, 0x1F6EF}, {0x1F6FD, 0x1F6FF}, {0x1F774, 0x1F77F}, {0x1F7D9, 0x1F7DF}, {0x1F7EC, 0x1F7FF}, {0x1F80C, 0x1F80F}, {0x1F848, 0x1F84F},
-{0x1F85A, 0x1F85F}, {0x1F888, 0x1F88F}, {0x1F8AE, 0x1F8AF}, {0x1F8B2, 0x1F8FF}, {0x1F979, 0x1F979}, {0x1F9CC, 0x1F9CC}, {0x1FA54, 0x1FA5F}, {0x1FA6E, 0x1FA6F}, {0x1FA75, 0x1FA77}, {0x1FA7B, 0x1FA7F},
-{0x1FA87, 0x1FA8F}, {0x1FAA9, 0x1FAAF}, {0x1FAB7, 0x1FABF}, {0x1FAC3, 0x1FACF}, {0x1FAD7, 0x1FAFF}, {0x1FB93, 0x1FB93}, {0x1FBCB, 0x1FBEF}, {0x1FBFA, 0x1FFFF}, {0x2A6DE, 0x2A6FF}, {0x2B735, 0x2B73F},
-{0x2B81E, 0x2B81F}, {0x2CEA2, 0x2CEAF}, {0x2EBE1, 0x2F7FF}, {0x2FA1E, 0x2FFFF}, {0x3134B, 0xE00FF}, {0xE01F0, 0x10FFFF},
-};
-
-static std::string codepoint_to_utf8(uint32_t cp) {
-    std::string result;
-    if (/* 0x00 <= cp && */ cp <= 0x7f) {
-        result.push_back(cp);
-    }
-    else if (0x80 <= cp && cp <= 0x7ff) {
-        result.push_back(0xc0 | ((cp >> 6) & 0x1f));
-        result.push_back(0x80 | (cp & 0x3f));
-    }
-    else if (0x800 <= cp && cp <= 0xffff) {
-        result.push_back(0xe0 | ((cp >> 12) & 0x0f));
-        result.push_back(0x80 | ((cp >> 6) & 0x3f));
-        result.push_back(0x80 | (cp & 0x3f));
-    }
-    else if (0x10000 <= cp && cp <= 0x10ffff) {
-        result.push_back(0xf0 | ((cp >> 18) & 0x07));
-        result.push_back(0x80 | ((cp >> 12) & 0x3f));
-        result.push_back(0x80 | ((cp >> 6) & 0x3f));
-        result.push_back(0x80 | (cp & 0x3f));
-    }
-    else {
-        throw std::invalid_argument("invalid codepoint");
-    }
-    return result;
-}
-
-static std::string codepoints_to_utf8(const std::vector<uint32_t> & cps) {
-    std::string result;
-    for (size_t i = 0; i < cps.size(); ++i) {
-        result.append(codepoint_to_utf8(cps[i]));
-    }
-    return result;
-}
-
-static uint32_t codepoint_from_utf8(const std::string & utf8, size_t & offset) {
-    assert(offset < utf8.size());
-    if (!(utf8[offset + 0] & 0x80)) {
-        auto result = utf8[offset + 0];
-        offset += 1;
-        return result;
-    }
-    if (!(utf8[offset + 0] & 0x40)) {
-        throw std::invalid_argument("invalid character");
-    }
-    if (!(utf8[offset + 0] & 0x20)) {
-        if (offset + 1 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80)) {
-            throw std::invalid_argument("invalid character");
-        }
-        auto result = ((utf8[offset + 0] & 0x1f) << 6) | (utf8[offset + 1] & 0x3f);
-        offset += 2;
-        return result;
-    }
-    if (!(utf8[offset + 0] & 0x10)) {
-        if (offset + 2 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80)) {
-            throw std::invalid_argument("invalid character");
-        }
-        auto result = ((utf8[offset + 0] & 0x0f) << 12) | ((utf8[offset + 1] & 0x3f) << 6) | (utf8[offset + 2] & 0x3f);
-        offset += 3;
-        return result;
-    }
-    if (!(utf8[offset + 0] & 0x08)) {
-        if (offset + 3 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80) || !((utf8[offset + 3] & 0xc0) == 0x80)) {
-            throw std::invalid_argument("invalid character");
-        }
-        auto result = ((utf8[offset + 0] & 0x07) << 18) | ((utf8[offset + 1] & 0x3f) << 12) | ((utf8[offset + 2] & 0x3f) << 6) | (utf8[offset + 3] & 0x3f);
-        offset += 4;
-        return result;
-    }
-    throw std::invalid_argument("invalid string");
-}
-
-static std::vector<uint32_t> codepoints_from_utf8(const std::string & utf8) {
-    std::vector<uint32_t> result;
-    size_t offset = 0;
-    while (offset < utf8.size()) {
-        result.push_back(codepoint_from_utf8(utf8, offset));
-    }
-    return result;
-}
-
-static std::vector<uint16_t> codepoint_to_utf16(uint32_t cp) {
-    std::vector<uint16_t> result;
-    if (/* 0x0000 <= cp && */ cp <= 0xffff) {
-        result.emplace_back(cp);
-    }
-    else if (0x10000 <= cp && cp <= 0x10ffff) {
-        result.emplace_back(0xd800 | ((cp - 0x10000) >> 10));
-        result.emplace_back(0xdc00 | ((cp - 0x10000) & 0x03ff));
-    }
-    else {
-        throw std::invalid_argument("invalid codepoint");
-    }
-    return result;
-}
-
-static std::vector<uint16_t> codepoints_to_utf16(const std::vector<uint32_t> & cps) {
-    std::vector<uint16_t> result;
-    for (size_t i = 0; i < cps.size(); ++i) {
-        auto temp = codepoint_to_utf16(cps[i]);
-        result.insert(result.end(), temp.begin(), temp.end());
-    }
-    return result;
-}
-
-static uint32_t codepoint_from_utf16(const std::vector<uint16_t> & utf16, size_t & offset) {
-    assert(offset < utf16.size());
-    if (((utf16[0] >> 10) << 10) != 0xd800) {
-        auto result = utf16[offset + 0];
-        offset += 1;
-        return result;
-    }
-
-    if (offset + 1 >= utf16.size() || !((utf16[1] & 0xdc00) == 0xdc00)) {
-        throw std::invalid_argument("invalid character");
-    }
-
-    auto result = 0x10000 + (((utf16[0] & 0x03ff) << 10) | (utf16[1] & 0x03ff));
-    offset += 2;
-    return result;
-}
-
-static std::vector<uint32_t> codepoints_from_utf16(const std::vector<uint16_t> & utf16) {
-    std::vector<uint32_t> result;
-    size_t offset = 0;
-    while (offset < utf16.size()) {
-        result.push_back(codepoint_from_utf16(utf16, offset));
-    }
-    return result;
-}
-
 #define CODEPOINT_TYPE_UNIDENTIFIED 0
-#define CODEPOINT_TYPE_DIGIT 1
-#define CODEPOINT_TYPE_LETTER 2
-#define CODEPOINT_TYPE_WHITESPACE 3
-#define CODEPOINT_TYPE_ACCENT_MARK 4
-#define CODEPOINT_TYPE_PUNCTUATION 5
-#define CODEPOINT_TYPE_SYMBOL 6
-#define CODEPOINT_TYPE_CONTROL 7
+#define CODEPOINT_TYPE_NUMBER       1
+#define CODEPOINT_TYPE_LETTER       2
+#define CODEPOINT_TYPE_SEPARATOR    3
+#define CODEPOINT_TYPE_ACCENT_MARK  4
+#define CODEPOINT_TYPE_PUNCTUATION  5
+#define CODEPOINT_TYPE_SYMBOL       6
+#define CODEPOINT_TYPE_CONTROL      7
 
-static std::unordered_map<uint32_t, int> codepoint_type_map() {
-    std::unordered_map<uint32_t, int> codepoint_types;
-    for (auto p : digit_ranges) {
-        for (auto i = p.first; i <= p.second; ++ i) {
-            codepoint_types[i] = CODEPOINT_TYPE_DIGIT;
-        }
-    }
-    for (auto p : letter_ranges) {
-        for (auto i = p.first; i <= p.second; ++ i) {
-            codepoint_types[i] = CODEPOINT_TYPE_LETTER;
-        }
-    }
-    for (auto p : whitespace_ranges) {
-        for (auto i = p.first; i <= p.second; ++ i) {
-            codepoint_types[i] = CODEPOINT_TYPE_WHITESPACE;
-        }
-    }
-    for (auto p : accent_mark_ranges) {
-        for (auto i = p.first; i <= p.second; ++ i) {
-            codepoint_types[i] = CODEPOINT_TYPE_ACCENT_MARK;
-        }
-    }
-    for (auto p : punctuation_ranges) {
-        for (auto i = p.first; i <= p.second; ++ i) {
-            codepoint_types[i] = CODEPOINT_TYPE_PUNCTUATION;
-        }
-    }
-    for  (auto p : symbol_ranges) {
-        for (auto i = p.first; i <= p.second; ++i) {
-            codepoint_types[i] = CODEPOINT_TYPE_SYMBOL;
-        }
-    }
-    for (auto p : control_ranges) {
-        for (auto i = p.first; i <= p.second; ++ i) {
-            codepoint_types[i] = CODEPOINT_TYPE_CONTROL;
-        }
-    }
-    return codepoint_types;
-}
+std::string unicode_cpt_to_utf8(uint32_t cp);
+std::vector<uint32_t> unicode_cpts_from_utf8(const std::string & utf8);
 
-static int codepoint_type(uint32_t cp) {
-    static std::unordered_map<uint32_t, int> codepoint_types = codepoint_type_map();
-    return codepoint_types.find(cp) == codepoint_types.end() ? CODEPOINT_TYPE_UNIDENTIFIED : codepoint_types.at(cp);
-}
+std::vector<uint32_t> unicode_cpts_normalize_nfd(const std::vector<uint32_t> & cpts);
 
-static int codepoint_type(const std::string & utf8) {
-    if (utf8.length() == 0) {
-        return CODEPOINT_TYPE_UNIDENTIFIED;
-    }
-    size_t offset = 0;
-    return codepoint_type(codepoint_from_utf8(utf8, offset));
-}
+int unicode_cpt_type(uint32_t cp);
+int unicode_cpt_type(const std::string & utf8);
 
-static std::unordered_map<uint8_t, std::string> bytes_to_unicode_map_bpe() {
-    std::unordered_map<uint8_t, std::string> map;
-    for (int ch = u'!'; ch <= u'~'; ++ch) {
-        assert(0 <= ch && ch < 256);
-        map[ch] = codepoint_to_utf8(ch);
-    }
-    for (int ch = u'¡'; ch <= u'¬'; ++ch) {
-        assert(0 <= ch && ch < 256);
-        map[ch] = codepoint_to_utf8(ch);
-    }
-    for (int ch = u'®'; ch <= u'ÿ'; ++ch) {
-        assert(0 <= ch && ch < 256);
-        map[ch] = codepoint_to_utf8(ch);
-    }
-    auto n = 0;
-    for (int ch = 0; ch < 256; ++ch) {
-        if (map.find(ch) == map.end()) {
-            map[ch] = codepoint_to_utf8(256 + n);
-            ++n;
-        }
-    }
-    return map;
-}
+bool unicode_cpt_is_whitespace(uint32_t cp);
 
-static std::string bytes_to_unicode_bpe(uint8_t byte) {
-    static std::unordered_map<uint8_t, std::string> map = bytes_to_unicode_map_bpe();
-    return map.at(byte);
-}
+std::string unicode_byte_to_utf8(uint8_t byte);
+uint8_t unicode_utf8_to_byte(const std::string & utf8);
 
-static std::unordered_map<std::string, uint8_t> unicode_to_bytes_map_bpe() {
-    std::unordered_map<std::string, uint8_t> map;
-    for (int ch = u'!'; ch <= u'~'; ++ch) {
-        assert(0 <= ch && ch < 256);
-        map[codepoint_to_utf8(ch)] = ch;
-    }
-    for (int ch = u'¡'; ch <= u'¬'; ++ch) {
-        assert(0 <= ch && ch < 256);
-        map[codepoint_to_utf8(ch)] = ch;
-    }
-    for (int ch = u'®'; ch <= u'ÿ'; ++ch) {
-        assert(0 <= ch && ch < 256);
-        map[codepoint_to_utf8(ch)] = ch;
-    }
-    auto n = 0;
-    for (int ch = 0; ch < 256; ++ch) {
-        if (map.find(codepoint_to_utf8(ch)) == map.end()) {
-            map[codepoint_to_utf8(256 + n)] = ch;
-            ++n;
-        }
-    }
-    return map;
-}
-
-static uint8_t unicode_to_bytes_bpe(const std::string & utf8) {
-    static std::unordered_map<std::string, uint8_t> map = unicode_to_bytes_map_bpe();
-    return map.at(utf8);
-}
+char32_t unicode_tolower(char32_t cp);
 
+std::vector<std::string> unicode_regex_split(const std::string & text, const std::vector<std::string> & regex_exprs);